hexsha
string | size
int64 | ext
string | lang
string | max_stars_repo_path
string | max_stars_repo_name
string | max_stars_repo_head_hexsha
string | max_stars_repo_licenses
list | max_stars_count
int64 | max_stars_repo_stars_event_min_datetime
string | max_stars_repo_stars_event_max_datetime
string | max_issues_repo_path
string | max_issues_repo_name
string | max_issues_repo_head_hexsha
string | max_issues_repo_licenses
list | max_issues_count
int64 | max_issues_repo_issues_event_min_datetime
string | max_issues_repo_issues_event_max_datetime
string | max_forks_repo_path
string | max_forks_repo_name
string | max_forks_repo_head_hexsha
string | max_forks_repo_licenses
list | max_forks_count
int64 | max_forks_repo_forks_event_min_datetime
string | max_forks_repo_forks_event_max_datetime
string | content
string | avg_line_length
float64 | max_line_length
int64 | alphanum_fraction
float64 | qsc_code_num_words_quality_signal
int64 | qsc_code_num_chars_quality_signal
float64 | qsc_code_mean_word_length_quality_signal
float64 | qsc_code_frac_words_unique_quality_signal
float64 | qsc_code_frac_chars_top_2grams_quality_signal
float64 | qsc_code_frac_chars_top_3grams_quality_signal
float64 | qsc_code_frac_chars_top_4grams_quality_signal
float64 | qsc_code_frac_chars_dupe_5grams_quality_signal
float64 | qsc_code_frac_chars_dupe_6grams_quality_signal
float64 | qsc_code_frac_chars_dupe_7grams_quality_signal
float64 | qsc_code_frac_chars_dupe_8grams_quality_signal
float64 | qsc_code_frac_chars_dupe_9grams_quality_signal
float64 | qsc_code_frac_chars_dupe_10grams_quality_signal
float64 | qsc_code_frac_chars_replacement_symbols_quality_signal
float64 | qsc_code_frac_chars_digital_quality_signal
float64 | qsc_code_frac_chars_whitespace_quality_signal
float64 | qsc_code_size_file_byte_quality_signal
float64 | qsc_code_num_lines_quality_signal
float64 | qsc_code_num_chars_line_max_quality_signal
float64 | qsc_code_num_chars_line_mean_quality_signal
float64 | qsc_code_frac_chars_alphabet_quality_signal
float64 | qsc_code_frac_chars_comments_quality_signal
float64 | qsc_code_cate_xml_start_quality_signal
float64 | qsc_code_frac_lines_dupe_lines_quality_signal
float64 | qsc_code_cate_autogen_quality_signal
float64 | qsc_code_frac_lines_long_string_quality_signal
float64 | qsc_code_frac_chars_string_length_quality_signal
float64 | qsc_code_frac_chars_long_word_length_quality_signal
float64 | qsc_code_frac_lines_string_concat_quality_signal
float64 | qsc_code_cate_encoded_data_quality_signal
float64 | qsc_code_frac_chars_hex_words_quality_signal
float64 | qsc_code_frac_lines_prompt_comments_quality_signal
float64 | qsc_code_frac_lines_assert_quality_signal
float64 | qsc_codepython_cate_ast_quality_signal
float64 | qsc_codepython_frac_lines_func_ratio_quality_signal
float64 | qsc_codepython_cate_var_zero_quality_signal
bool | qsc_codepython_frac_lines_pass_quality_signal
float64 | qsc_codepython_frac_lines_import_quality_signal
float64 | qsc_codepython_frac_lines_simplefunc_quality_signal
float64 | qsc_codepython_score_lines_no_logic_quality_signal
float64 | qsc_codepython_frac_lines_print_quality_signal
float64 | qsc_code_num_words
int64 | qsc_code_num_chars
int64 | qsc_code_mean_word_length
int64 | qsc_code_frac_words_unique
null | qsc_code_frac_chars_top_2grams
int64 | qsc_code_frac_chars_top_3grams
int64 | qsc_code_frac_chars_top_4grams
int64 | qsc_code_frac_chars_dupe_5grams
int64 | qsc_code_frac_chars_dupe_6grams
int64 | qsc_code_frac_chars_dupe_7grams
int64 | qsc_code_frac_chars_dupe_8grams
int64 | qsc_code_frac_chars_dupe_9grams
int64 | qsc_code_frac_chars_dupe_10grams
int64 | qsc_code_frac_chars_replacement_symbols
int64 | qsc_code_frac_chars_digital
int64 | qsc_code_frac_chars_whitespace
int64 | qsc_code_size_file_byte
int64 | qsc_code_num_lines
int64 | qsc_code_num_chars_line_max
int64 | qsc_code_num_chars_line_mean
int64 | qsc_code_frac_chars_alphabet
int64 | qsc_code_frac_chars_comments
int64 | qsc_code_cate_xml_start
int64 | qsc_code_frac_lines_dupe_lines
int64 | qsc_code_cate_autogen
int64 | qsc_code_frac_lines_long_string
int64 | qsc_code_frac_chars_string_length
int64 | qsc_code_frac_chars_long_word_length
int64 | qsc_code_frac_lines_string_concat
null | qsc_code_cate_encoded_data
int64 | qsc_code_frac_chars_hex_words
int64 | qsc_code_frac_lines_prompt_comments
int64 | qsc_code_frac_lines_assert
int64 | qsc_codepython_cate_ast
int64 | qsc_codepython_frac_lines_func_ratio
int64 | qsc_codepython_cate_var_zero
int64 | qsc_codepython_frac_lines_pass
int64 | qsc_codepython_frac_lines_import
int64 | qsc_codepython_frac_lines_simplefunc
int64 | qsc_codepython_score_lines_no_logic
int64 | qsc_codepython_frac_lines_print
int64 | effective
string | hits
int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
f6ab4cd55330704676a9aec66d388ca08c20c740
| 1,289
|
py
|
Python
|
2021_05_19/dojo_test.py
|
devppjr/dojo
|
de0bf116153bd1251dd948c18fc73ac41def0fa5
|
[
"MIT"
] | 114
|
2015-03-10T22:17:42.000Z
|
2022-03-09T17:49:48.000Z
|
2021_05_19/dojo_test.py
|
devppjr/dojo
|
de0bf116153bd1251dd948c18fc73ac41def0fa5
|
[
"MIT"
] | 9
|
2018-09-04T12:49:59.000Z
|
2019-11-17T21:29:51.000Z
|
2021_05_19/dojo_test.py
|
devppjr/dojo
|
de0bf116153bd1251dd948c18fc73ac41def0fa5
|
[
"MIT"
] | 39
|
2015-01-29T01:20:56.000Z
|
2022-02-17T16:26:25.000Z
|
import unittest
from dojo import main, function_right, function_in
class DojoTest(unittest.TestCase):
def test_function_right1(self):
self.assertEqual(function_right("1492", "2013"), 0)
def test_function_right2(self):
self.assertEqual(function_right("1234", "1234"), 4)
def test_function_right3(self):
self.assertEqual(function_right("1235", "1342"), 1)
def test_function_wrongs1(self):
self.assertEqual(function_in("1492", "2013"), 2)
def test_function_wrongs2(self):
self.assertEqual(function_in("1492", "1865"), 1)
def test_function_wrongs3(self):
self.assertEqual(function_in("1492", "1234"), 3)
def test_function_main1(self):
self.assertEqual(main("1492", "2013"), "0-2")
def test_function_main2(self):
self.assertEqual(main("1492", "1865"), "1-0")
def test_function_main3(self):
self.assertEqual(main("1492", "1234"), "1-2")
def test_function_main4(self):
self.assertEqual(main("1492", "4321"), "0-3")
def test_function_main2(self):
self.assertEqual(main("1492", "1492"), "4-0")
# 2013 1865 1234 4321 7491
if __name__ == '__main__':
unittest.main()
# Lara -> Carreira -> Joao -> Ildefonso
# Duas funçoes
| 26.306122
| 59
| 0.644686
| 162
| 1,289
| 4.895062
| 0.283951
| 0.0971
| 0.208071
| 0.204288
| 0.466583
| 0.24338
| 0.118537
| 0.118537
| 0.118537
| 0
| 0
| 0.131965
| 0.206362
| 1,289
| 48
| 60
| 26.854167
| 0.643206
| 0.058185
| 0
| 0.074074
| 0
| 0
| 0.091887
| 0
| 0
| 0
| 0
| 0
| 0.407407
| 1
| 0.407407
| false
| 0
| 0.074074
| 0
| 0.518519
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 5
|
f6c767116efed68f35683ab89afc20d337369776
| 57
|
py
|
Python
|
ingestion/Blocktrace/Utils/__init__.py
|
mharrisb1/blocktrace
|
3c54286d4f28c3b0610f577dfdbbf643953475fa
|
[
"MIT"
] | null | null | null |
ingestion/Blocktrace/Utils/__init__.py
|
mharrisb1/blocktrace
|
3c54286d4f28c3b0610f577dfdbbf643953475fa
|
[
"MIT"
] | null | null | null |
ingestion/Blocktrace/Utils/__init__.py
|
mharrisb1/blocktrace
|
3c54286d4f28c3b0610f577dfdbbf643953475fa
|
[
"MIT"
] | null | null | null |
from Blocktrace.Utils.BlocktraceLog import BlocktraceLog
| 28.5
| 56
| 0.894737
| 6
| 57
| 8.5
| 0.833333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.070175
| 57
| 1
| 57
| 57
| 0.962264
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
1008a42d80a9d96fc462dd16f302a76153e65a73
| 50
|
py
|
Python
|
layers/__init__.py
|
Eralaf/ssd.pytorch
|
acad53fd801f32120ecb3ff57950556e35db3d1c
|
[
"MIT"
] | null | null | null |
layers/__init__.py
|
Eralaf/ssd.pytorch
|
acad53fd801f32120ecb3ff57950556e35db3d1c
|
[
"MIT"
] | null | null | null |
layers/__init__.py
|
Eralaf/ssd.pytorch
|
acad53fd801f32120ecb3ff57950556e35db3d1c
|
[
"MIT"
] | null | null | null |
from .functions import *
from .modules import *
| 16.666667
| 24
| 0.72
| 6
| 50
| 6
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.2
| 50
| 2
| 25
| 25
| 0.9
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
120b5cff3daf1978ed8b9f7ebd4c97384e6d3730
| 76
|
py
|
Python
|
workspace/module/python-2.7/LxPreset/prsMethods/__init__.py
|
no7hings/Lynxi
|
43c745198a714c2e5aca86c6d7a014adeeb9abf7
|
[
"MIT"
] | 2
|
2018-03-06T03:33:55.000Z
|
2019-03-26T03:25:11.000Z
|
workspace/module/python-2.7/LxPreset/prsMethods/__init__.py
|
no7hings/lynxi
|
43c745198a714c2e5aca86c6d7a014adeeb9abf7
|
[
"MIT"
] | null | null | null |
workspace/module/python-2.7/LxPreset/prsMethods/__init__.py
|
no7hings/lynxi
|
43c745198a714c2e5aca86c6d7a014adeeb9abf7
|
[
"MIT"
] | null | null | null |
# coding:utf-8
from ._prsMtdProduct import *
from ._prsMtdUtility import *
| 15.2
| 29
| 0.763158
| 9
| 76
| 6.222222
| 0.777778
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.015385
| 0.144737
| 76
| 4
| 30
| 19
| 0.846154
| 0.157895
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
121d8d31c3088d7d42fcd997dc692350325d116b
| 88
|
py
|
Python
|
heuslertools/xps/__init__.py
|
LukeSkywalker92/heuslertools
|
58108511eec4a027f7d42888e66b50b2dc8d7612
|
[
"MIT"
] | null | null | null |
heuslertools/xps/__init__.py
|
LukeSkywalker92/heuslertools
|
58108511eec4a027f7d42888e66b50b2dc8d7612
|
[
"MIT"
] | null | null | null |
heuslertools/xps/__init__.py
|
LukeSkywalker92/heuslertools
|
58108511eec4a027f7d42888e66b50b2dc8d7612
|
[
"MIT"
] | null | null | null |
"""
Tools for handling XPS measurements
"""
from .xps_measurement import XPSMeasurement
| 17.6
| 43
| 0.795455
| 10
| 88
| 6.9
| 0.9
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.125
| 88
| 4
| 44
| 22
| 0.896104
| 0.397727
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
89e7971d7c3bd4bd0fa247b729f9a895a0fab833
| 11,923
|
py
|
Python
|
bb2cogs/server_log.py
|
Team-EG/j-bot
|
2e160707d13cc4988f370713fc9f57c7cff3f5bb
|
[
"MIT"
] | 2
|
2020-07-07T01:15:15.000Z
|
2021-08-15T19:49:32.000Z
|
bb2cogs/server_log.py
|
Team-EG/j-bot
|
2e160707d13cc4988f370713fc9f57c7cff3f5bb
|
[
"MIT"
] | null | null | null |
bb2cogs/server_log.py
|
Team-EG/j-bot
|
2e160707d13cc4988f370713fc9f57c7cff3f5bb
|
[
"MIT"
] | 1
|
2020-04-08T04:23:10.000Z
|
2020-04-08T04:23:10.000Z
|
import discord
import json
import time
import os
import shutil
from time import localtime, strftime
from discord.ext import commands
class Server_Log(commands.Cog):
def __init__(self, client):
self.client = client
# embed 탬플릿 (앞에 #을 지우고 사용하세요)
# embed.add_field(name='', value='', inline=False)
@commands.Cog.listener()
async def on_message_delete(self, message):
embed = discord.Embed(title='메시지 삭제됨', colour=discord.Color.red())
embed.set_author(name=message.author.display_name, icon_url=message.author.avatar_url)
embed.add_field(name=f'#{message.channel}', value=f'{message.content}')
try:
with open("data/guildsetup.json", "r") as f:
data = json.load(f)
channel = discord.utils.get(message.guild.text_channels, name=data[str(message.guild.id)]['log_channel'])
await channel.send(embed=embed)
except:
pass
@commands.Cog.listener()
async def on_raw_bulk_message_delete(self, payload):
if len(payload.message_ids) == 1:
return
embed = discord.Embed(title='메시지 대량 삭제됨', colour=discord.Color.red())
embed.add_field(name='삭제된 메시지 개수', value=str(len(payload.message_ids)), inline=False)
embed.add_field(name='메시지가 삭제된 채널', value=f"<#{payload.channel_id}>", inline=False)
try:
with open("data/guildsetup.json", "r") as f:
data = json.load(f)
channel = discord.utils.get(self.client.get_guild(payload.guild_id).text_channels, name=data[str(payload.guild_id)]['log_channel'])
await channel.send(embed=embed)
except:
pass
@commands.Cog.listener()
async def on_message_edit(self, before, after):
if before.content == after.content:
return
embed = discord.Embed(title='메시지 수정됨', colour=discord.Color.lighter_grey())
embed.set_author(name=before.author.display_name, icon_url=before.author.avatar_url)
embed.add_field(name='기존 내용', value=f'{before.content}')
embed.add_field(name='수정된 내용', value=f'{after.content}', inline=False)
try:
with open("data/guildsetup.json", "r") as f:
data = json.load(f)
channel = discord.utils.get(after.guild.text_channels, name=data[str(after.guild.id)]['log_channel'])
await channel.send(embed=embed)
except:
pass
@commands.Cog.listener()
async def on_guild_channel_delete(self, channel):
embed = discord.Embed(title='채널 삭제됨', colour=discord.Color.red())
embed.set_author(name=channel.guild.name, icon_url=channel.guild.icon_url)
embed.add_field(name='채널 이름', value=f'{channel.name}', inline=False)
try:
with open("data/guildsetup.json", "r") as f:
data = json.load(f)
channel = discord.utils.get(channel.guild.text_channels, name=data[str(channel.guild.id)]['log_channel'])
await channel.send(embed=embed)
except:
pass
@commands.Cog.listener()
async def on_guild_channel_create(self, channel):
embed = discord.Embed(title='채널 생성됨', colour=discord.Color.green())
embed.set_author(name=channel.guild.name, icon_url=channel.guild.icon_url)
embed.add_field(name='채널 이름', value=f'{channel.name}', inline=False)
try:
with open("data/guildsetup.json", "r") as f:
data = json.load(f)
channel = discord.utils.get(channel.guild.text_channels, name=data[str(channel.guild.id)]['log_channel'])
await channel.send(embed=embed)
except:
pass
@commands.Cog.listener()
async def on_guild_channel_update(self, before, after):
num = 0
embed = discord.Embed(title='채널 업데이트됨', colour=discord.Color.lighter_grey())
embed.set_author(name=after.name)
if not before.name == after.name:
embed.add_field(name='채널 이름', value=f'{before.name} -> {after.name}', inline=False)
num += 1
if not before.changed_roles == after.changed_roles:
before_role = before.changed_roles
after_role = after.changed_roles
br_list = []
ar_list = []
for i in before_role:
br_mention = i.mention
br_list.append(br_mention)
for i in after_role:
ar_mention = i.mention
ar_list.append(ar_mention)
if len(br_list) == len(ar_list):
return
embed.add_field(name='역할 변경', value=f'{br_list} -> {ar_list}', inline=False)
num += 1
if not before.category == after.category:
embed.add_field(name='카테고리 변경', value=f'{before.category} -> {after.category}', inline=False)
num += 1
try:
if num == 0:
return
with open("data/guildsetup.json", "r") as f:
data = json.load(f)
channel = discord.utils.get(after.guild.text_channels, name=data[str(after.guild.id)]['log_channel'])
await channel.send(embed=embed)
except:
pass
@commands.Cog.listener()
async def on_member_update(self, before, after):
num = 0
embed = discord.Embed(title='유저 업데이트됨', colour=discord.Color.lighter_grey())
embed.set_author(name=after.display_name, icon_url=after.avatar_url)
if not before.display_name == after.display_name:
embed.add_field(name='닉네임 변경 전', value=f'{before.display_name}', inline=False)
embed.add_field(name='닉네임 변경 후', value=f'{after.display_name}', inline=False)
num += 1
if not before.roles == after.roles:
before_role = before.roles
after_role = after.roles
br_list = []
ar_list = []
for i in before_role:
br_mention = i.mention
br_list.append(br_mention)
for i in after_role:
ar_mention = i.mention
ar_list.append(ar_mention)
if len(br_list) == len(ar_list):
return
embed.add_field(name='역할 변경 전', value=f'{br_list}', inline=False)
embed.add_field(name='역할 변경 후', value=f'{ar_list}', inline=False)
num += 1
try:
if num == 0:
return
with open("data/guildsetup.json", "r") as f:
data = json.load(f)
channel = discord.utils.get(after.guild.text_channels, name=data[str(after.guild.id)]['log_channel'])
await channel.send(embed=embed)
except:
pass
@commands.Cog.listener()
async def on_guild_update(self, before, after):
num = 0
embed = discord.Embed(title='서버 업데이트됨', colour=discord.Color.lighter_grey())
embed.set_author(name=after.name, icon_url=after.icon_url)
if not before.name == after.name:
embed.add_field(name='서버 이름', value=f'{before.name} -> {after.name}', inline=False)
num += 1
if not before.region == after.region:
embed.add_field(name='서버 지역', value=f'{before.region} -> {after.region}', inline=False)
num += 1
if not before.verification_level == after.verification_level:
embed.add_field(name='서버 보안 수준', value=f'{before.verification_level} -> {after.verification_level}', inline=False)
num += 1
if not before.owner_id == after.owner_id:
embed.add_field(name='서버 소유자', value=f'{before.owner.display_name} -> {after.owner.mention}', inline=False)
num += 1
try:
if num == 0:
return
with open("data/guildsetup.json", "r") as f:
data = json.load(f)
channel = discord.utils.get(after.text_channels, name=data[str(after.id)]['log_channel'])
await channel.send(embed=embed)
except:
pass
@commands.Cog.listener()
async def on_guild_role_create(self, role):
embed = discord.Embed(title='역할 생성됨', colour=discord.Color.green())
embed.set_author(name=role.guild.name, icon_url=role.guild.icon_url)
embed.add_field(name='역할', value=f'{role.mention}', inline=False)
try:
with open("data/guildsetup.json", "r") as f:
data = json.load(f)
channel = discord.utils.get(role.guild.text_channels, name=data[str(role.guild.id)]['log_channel'])
await channel.send(embed=embed)
except:
pass
@commands.Cog.listener()
async def on_guild_role_delete(self, role):
embed = discord.Embed(title='역할 삭제됨', colour=discord.Color.red())
embed.set_author(name=role.guild.name, icon_url=role.guild.icon_url)
embed.add_field(name='역할 이름', value=f'{role.name}', inline=False)
try:
with open("data/guildsetup.json", "r") as f:
data = json.load(f)
channel = discord.utils.get(role.guild.text_channels, name=data[str(role.guild.id)]['log_channel'])
await channel.send(embed=embed)
except:
pass
@commands.Cog.listener()
async def on_member_ban(self, guild, user):
embed = discord.Embed(title='맴버 차단됨', colour=discord.Color.red())
embed.set_author(name=guild.name, icon_url=guild.icon_url)
embed.add_field(name='맴버 이름', value=f'{user.name}', inline=False)
embed.add_field(name='맴버 서버 닉네임', value=f'{user.display_name}', inline=False)
try:
with open("data/guildsetup.json", "r") as f:
data = json.load(f)
channel = discord.utils.get(guild.text_channels, name=data[str(guild.id)]['log_channel'])
await channel.send(embed=embed)
except:
pass
@commands.Cog.listener()
async def on_member_unban(self, guild, user):
embed = discord.Embed(title='맴버 차단 해제됨', colour=discord.Color.green())
embed.set_author(name=guild.name, icon_url=guild.icon_url)
embed.add_field(name='맴버 이름', value=f'{user.name}', inline=False)
try:
with open("data/guildsetup.json", "r") as f:
data = json.load(f)
channel = discord.utils.get(guild.text_channels, name=data[str(guild.id)]['log_channel'])
await channel.send(embed=embed)
except:
pass
@commands.Cog.listener()
async def on_guild_role_update(self, before, after):
num = 0
embed = discord.Embed(title='역할 업데이트됨', colour=discord.Color.lighter_grey())
embed.set_author(name=after.guild.name, icon_url=after.guild.icon_url)
if not before.name == after.name:
embed.add_field(name='역할 이름', value=f'{before.name} -> {after.mention}', inline=False)
num += 1
if not before.colour == after.colour:
embed.add_field(name='역할 색깔', value=f'{before.colour} -> {after.colour}', inline=False)
num += 1
try:
if num == 0:
return
with open("data/guildsetup.json", "r") as f:
data = json.load(f)
channel = discord.utils.get(after.guild.text_channels, name=data[str(after.guild.id)]['log_channel'])
await channel.send(embed=embed)
except:
pass
# embed 탬플릿 (앞에 #을 지우고 사용하세요)
# embed.add_field(name='', value=f'{}', inline=False)
def setup(client):
client.add_cog(Server_Log(client))
| 39.480132
| 144
| 0.579552
| 1,525
| 11,923
| 4.406557
| 0.091148
| 0.032143
| 0.052232
| 0.068304
| 0.814732
| 0.785268
| 0.733929
| 0.675298
| 0.650298
| 0.622173
| 0
| 0.002366
| 0.291034
| 11,923
| 301
| 145
| 39.611296
| 0.792618
| 0.012916
| 0
| 0.659751
| 0
| 0
| 0.106971
| 0.012652
| 0
| 0
| 0
| 0
| 0
| 1
| 0.008299
| false
| 0.053942
| 0.029046
| 0
| 0.074689
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
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| 0
| 1
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| null | 0
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| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 5
|
d61719e2cae28c54567aaaf041ba2a915c5d1f7f
| 159
|
py
|
Python
|
tests/__init__.py
|
typoctf/pytxdata
|
36989500b1583161efd2253f66204d97a1c81f29
|
[
"MIT"
] | null | null | null |
tests/__init__.py
|
typoctf/pytxdata
|
36989500b1583161efd2253f66204d97a1c81f29
|
[
"MIT"
] | null | null | null |
tests/__init__.py
|
typoctf/pytxdata
|
36989500b1583161efd2253f66204d97a1c81f29
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
from pytxdata import set_directory
def setup_module(module):
set_directory()
def teardown_module(module):
set_directory()
| 13.25
| 34
| 0.704403
| 20
| 159
| 5.35
| 0.6
| 0.336449
| 0.280374
| 0.448598
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.007634
| 0.176101
| 159
| 11
| 35
| 14.454545
| 0.80916
| 0.132075
| 0
| 0.4
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.4
| false
| 0
| 0.2
| 0
| 0.6
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
7f1b30301a3de59ffe806cb4a987f4371c4fabd2
| 187
|
py
|
Python
|
environments/gym_pycolab/envs/__init__.py
|
braemt/attentive-multi-task-deep-reinforcement-learning
|
921feefce98076f88c892f0b7e6db8572f596763
|
[
"MIT"
] | 12
|
2019-04-07T02:04:48.000Z
|
2022-03-22T12:57:47.000Z
|
environments/gym_pycolab/envs/__init__.py
|
braemt/attentive-multi-task-deep-reinforcement-learning
|
921feefce98076f88c892f0b7e6db8572f596763
|
[
"MIT"
] | null | null | null |
environments/gym_pycolab/envs/__init__.py
|
braemt/attentive-multi-task-deep-reinforcement-learning
|
921feefce98076f88c892f0b7e6db8572f596763
|
[
"MIT"
] | 7
|
2019-04-07T02:04:49.000Z
|
2020-12-28T10:30:27.000Z
|
# THIS FILE IS NEW OR MODIFIED COMPARED TO https://github.com/deepmind/pycolab
from gym_pycolab.envs.pycolab_env import PycolabEnv
from gym_pycolab.envs.pycolab_grid_worlds_env import *
| 37.4
| 78
| 0.834225
| 30
| 187
| 5
| 0.7
| 0.093333
| 0.186667
| 0.24
| 0.333333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.101604
| 187
| 4
| 79
| 46.75
| 0.892857
| 0.406417
| 0
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| 0
| 1
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| true
| 0
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| 1
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| null | 0
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| 1
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| 0
| 0
| 0
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| 1
| 0
| 0
| 0
| 0
| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
612f56f7c268214366f505c80cc4b6be3d3d1ca4
| 36
|
py
|
Python
|
tests/__init__.py
|
ChromaticIsobar/chromatictools
|
746bb68547c383812a5996503328fc1ab40dacb4
|
[
"MIT"
] | null | null | null |
tests/__init__.py
|
ChromaticIsobar/chromatictools
|
746bb68547c383812a5996503328fc1ab40dacb4
|
[
"MIT"
] | 1
|
2022-03-10T14:43:55.000Z
|
2022-03-10T14:43:55.000Z
|
tests/__init__.py
|
ChromaticIsobar/chromatictools
|
746bb68547c383812a5996503328fc1ab40dacb4
|
[
"MIT"
] | null | null | null |
"""Unit tests for chromatictools"""
| 18
| 35
| 0.722222
| 4
| 36
| 6.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.111111
| 36
| 1
| 36
| 36
| 0.8125
| 0.805556
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
614093ac65193448e0967d14d56afb529662f27f
| 6,993
|
py
|
Python
|
ofspy/test/test_contract.py
|
ehsanfar/ofspy_v2
|
6eedfec4bb36c48473abfd473941c5d3b34590b6
|
[
"Apache-2.0"
] | null | null | null |
ofspy/test/test_contract.py
|
ehsanfar/ofspy_v2
|
6eedfec4bb36c48473abfd473941c5d3b34590b6
|
[
"Apache-2.0"
] | null | null | null |
ofspy/test/test_contract.py
|
ehsanfar/ofspy_v2
|
6eedfec4bb36c48473abfd473941c5d3b34590b6
|
[
"Apache-2.0"
] | null | null | null |
"""
Copyright 2015 Paul T. Grogan, Massachusetts Institute of Technology
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
"""
Test cases for L{ofspy.contract}.
"""
import unittest
from ..data import Data
from ..demand import Demand
from ..valueSchedule import ValueSchedule
from ..surface import Surface
from ..orbit import Orbit
from ..simulator import Simulator
from ..contract import Contract
class ContractTestCase(unittest.TestCase):
def setUp(self):
self.contract1 = Contract(Demand(0, 'SAR', 1, ValueSchedule([(0,1)],-1)))
self.contract2 = Contract(Demand(1, 'SAR', 1, ValueSchedule([(1,4), (2,2)],-1)))
self.sim = Simulator(entities=set([self.contract1, self.contract2]),
initTime=0, timeStep=1, maxTime=3)
self.surface = Surface(0)
self.orbit = Orbit(0, 'LEO')
def tearDown(self):
self.contract1 = None
self.contract2 = None
self.sim = None
self.surface = None
self.orbit = None
def test_getValue(self):
self.sim.init()
self.assertEqual(self.contract1.getValue(), 1)
self.assertEqual(self.contract2.getValue(), 4)
self.sim.advance()
self.assertEqual(self.contract1.getValue(), -1)
self.assertEqual(self.contract2.getValue(), 4)
self.sim.advance()
self.assertEqual(self.contract1.getValue(), -1)
self.assertEqual(self.contract2.getValue(), 2)
self.sim.advance()
self.assertEqual(self.contract1.getValue(), -1)
self.assertEqual(self.contract2.getValue(), -1)
def test_isDefaulted(self):
self.sim.init()
self.assertFalse(self.contract1.isDefaulted(self.orbit))
self.assertFalse(self.contract2.isDefaulted(self.orbit))
self.assertFalse(self.contract1.isDefaulted(self.surface))
self.assertFalse(self.contract2.isDefaulted(self.surface))
self.assertTrue(self.contract1.isDefaulted(None))
self.assertTrue(self.contract2.isDefaulted(None))
self.sim.advance()
self.assertTrue(self.contract1.isDefaulted(self.orbit))
self.assertFalse(self.contract2.isDefaulted(self.orbit))
self.assertTrue(self.contract1.isDefaulted(self.surface))
self.assertFalse(self.contract2.isDefaulted(self.surface))
self.assertTrue(self.contract1.isDefaulted(None))
self.assertTrue(self.contract2.isDefaulted(None))
self.sim.advance()
self.assertTrue(self.contract1.isDefaulted(self.orbit))
self.assertFalse(self.contract2.isDefaulted(self.orbit))
self.assertTrue(self.contract1.isDefaulted(self.surface))
self.assertFalse(self.contract2.isDefaulted(self.surface))
self.assertTrue(self.contract1.isDefaulted(None))
self.assertTrue(self.contract2.isDefaulted(None))
self.sim.advance()
self.assertTrue(self.contract1.isDefaulted(self.orbit))
self.assertTrue(self.contract2.isDefaulted(self.orbit))
self.assertTrue(self.contract1.isDefaulted(self.surface))
self.assertTrue(self.contract2.isDefaulted(self.surface))
self.assertTrue(self.contract1.isDefaulted(None))
self.assertTrue(self.contract2.isDefaulted(None))
def test_isCompleted(self):
self.sim.init()
self.assertFalse(self.contract1.isCompleted(self.orbit))
self.assertFalse(self.contract2.isCompleted(self.orbit))
self.assertTrue(self.contract1.isCompleted(self.surface))
self.assertTrue(self.contract2.isCompleted(self.surface))
self.assertFalse(self.contract1.isCompleted(None))
self.assertFalse(self.contract2.isCompleted(None))
self.sim.advance()
self.assertFalse(self.contract1.isCompleted(self.orbit))
self.assertFalse(self.contract2.isCompleted(self.orbit))
self.assertFalse(self.contract1.isCompleted(self.surface))
self.assertTrue(self.contract2.isCompleted(self.surface))
self.assertFalse(self.contract1.isCompleted(None))
self.assertFalse(self.contract2.isCompleted(None))
self.sim.advance()
self.assertFalse(self.contract1.isCompleted(self.orbit))
self.assertFalse(self.contract2.isCompleted(self.orbit))
self.assertFalse(self.contract1.isCompleted(self.surface))
self.assertTrue(self.contract2.isCompleted(self.surface))
self.assertFalse(self.contract1.isCompleted(None))
self.assertFalse(self.contract2.isCompleted(None))
self.sim.advance()
self.assertFalse(self.contract1.isCompleted(self.orbit))
self.assertFalse(self.contract2.isCompleted(self.orbit))
self.assertFalse(self.contract1.isCompleted(self.surface))
self.assertFalse(self.contract2.isCompleted(self.surface))
self.assertFalse(self.contract1.isCompleted(None))
self.assertFalse(self.contract2.isCompleted(None))
def test_init(self):
self.contract1.init(self.sim)
self.assertEqual(self.contract1.elapsedTime, self.contract1._initElapsedTime)
self.contract1._initElapsedTime = 1
self.contract1.init(self.sim)
self.assertEqual(self.contract1.elapsedTime, 1)
def test_tick(self):
self.contract1.init(self.sim)
self.contract2.init(self.sim)
self.contract1.tick(self.sim)
self.contract2.tick(self.sim)
self.assertEqual(self.contract1.elapsedTime, self.sim.initTime)
self.assertEqual(self.contract2.elapsedTime, self.sim.initTime)
self.contract1.tock()
self.contract2.tock()
self.contract1.tick(self.sim)
self.contract2.tick(self.sim)
self.assertEqual(self.contract1.elapsedTime, self.sim.timeStep)
self.assertEqual(self.contract2.elapsedTime, self.sim.timeStep)
def test_tock(self):
self.contract1.init(self.sim)
self.contract2.init(self.sim)
self.contract1.tick(self.sim)
self.contract2.tick(self.sim)
self.contract1.tock()
self.contract2.tock()
self.assertEqual(self.contract1.elapsedTime, self.sim.timeStep)
self.assertEqual(self.contract2.elapsedTime, self.sim.timeStep)
self.contract1.tick(self.sim)
self.contract2.tick(self.sim)
self.contract1.tock()
self.contract2.tock()
self.assertEqual(self.contract1.elapsedTime, 2*self.sim.timeStep)
self.assertEqual(self.contract2.elapsedTime, 2*self.sim.timeStep)
| 44.541401
| 88
| 0.694123
| 804
| 6,993
| 6.027363
| 0.141791
| 0.134131
| 0.109781
| 0.086669
| 0.756707
| 0.746801
| 0.736071
| 0.718737
| 0.697482
| 0.697482
| 0
| 0.023055
| 0.187473
| 6,993
| 157
| 89
| 44.541401
| 0.829813
| 0.084799
| 0
| 0.653543
| 0
| 0
| 0.001417
| 0
| 0
| 0
| 0
| 0
| 0.519685
| 1
| 0.062992
| false
| 0
| 0.062992
| 0
| 0.133858
| 0
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| 0
| null | 0
| 0
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| 1
| 1
| 1
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| 1
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| 0
| 0
| 0
|
0
| 5
|
617aad89503d97714477f7cf2f26c05390d91b5b
| 119
|
py
|
Python
|
heuslertools/magnetism/__init__.py
|
LukeSkywalker92/heuslertools
|
58108511eec4a027f7d42888e66b50b2dc8d7612
|
[
"MIT"
] | null | null | null |
heuslertools/magnetism/__init__.py
|
LukeSkywalker92/heuslertools
|
58108511eec4a027f7d42888e66b50b2dc8d7612
|
[
"MIT"
] | null | null | null |
heuslertools/magnetism/__init__.py
|
LukeSkywalker92/heuslertools
|
58108511eec4a027f7d42888e66b50b2dc8d7612
|
[
"MIT"
] | null | null | null |
"""
Tools for handling magnetic conversions and calculations
"""
from .layer import Layer
from .crystal import Crystal
| 19.833333
| 56
| 0.789916
| 15
| 119
| 6.266667
| 0.733333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.142857
| 119
| 5
| 57
| 23.8
| 0.921569
| 0.470588
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
618bc96f684dccae541012862da9fde6ed20280c
| 37
|
py
|
Python
|
tests/__init__.py
|
DigitalBiomarkerDiscoveryPipeline/fasting
|
85e0e05ff3279a1503413fd33a1d785281d9a940
|
[
"MIT"
] | 2
|
2021-02-24T14:56:19.000Z
|
2021-08-21T08:59:51.000Z
|
tests/__init__.py
|
DigitalBiomarkerDiscoveryPipeline/fasting
|
85e0e05ff3279a1503413fd33a1d785281d9a940
|
[
"MIT"
] | null | null | null |
tests/__init__.py
|
DigitalBiomarkerDiscoveryPipeline/fasting
|
85e0e05ff3279a1503413fd33a1d785281d9a940
|
[
"MIT"
] | 1
|
2021-03-01T16:10:57.000Z
|
2021-03-01T16:10:57.000Z
|
"""Unit test package for fasting."""
| 18.5
| 36
| 0.675676
| 5
| 37
| 5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.135135
| 37
| 1
| 37
| 37
| 0.78125
| 0.810811
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
619aa212575066a9c49e10ad2bf1defd273ada82
| 170
|
py
|
Python
|
src/main.py
|
focom/proto-memoire
|
af4b01fe502509b0a987290f2c11c3b0829a3e59
|
[
"MIT"
] | null | null | null |
src/main.py
|
focom/proto-memoire
|
af4b01fe502509b0a987290f2c11c3b0829a3e59
|
[
"MIT"
] | null | null | null |
src/main.py
|
focom/proto-memoire
|
af4b01fe502509b0a987290f2c11c3b0829a3e59
|
[
"MIT"
] | null | null | null |
# from classes.game import *
from tkinter import *
from classes.personality import Personality
from classes.main import *
if ( __name__ == '__main__'):
main = Main()
| 24.285714
| 43
| 0.729412
| 21
| 170
| 5.52381
| 0.428571
| 0.284483
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.170588
| 170
| 6
| 44
| 28.333333
| 0.822695
| 0.152941
| 0
| 0
| 0
| 0
| 0.056338
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.6
| 0
| 0.6
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
61a8e72cbc532c6676092b6a4b9a5a2a210ce8ee
| 32
|
py
|
Python
|
evaluation.py
|
AndreSoble/PerformerDualEncoder
|
6394133c788a3a6571119d62486356a2f16211f5
|
[
"MIT"
] | 1
|
2021-05-05T21:00:59.000Z
|
2021-05-05T21:00:59.000Z
|
evaluation.py
|
AndreSoble/PerformerDualEncoder
|
6394133c788a3a6571119d62486356a2f16211f5
|
[
"MIT"
] | null | null | null |
evaluation.py
|
AndreSoble/PerformerDualEncoder
|
6394133c788a3a6571119d62486356a2f16211f5
|
[
"MIT"
] | 1
|
2021-02-03T16:30:14.000Z
|
2021-02-03T16:30:14.000Z
|
def run_xnli(model):
pass
| 6.4
| 20
| 0.625
| 5
| 32
| 3.8
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.28125
| 32
| 5
| 21
| 6.4
| 0.826087
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| false
| 0.5
| 0
| 0
| 0.5
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 5
|
61b5243a2b658de71e307f2ebf301b95b418d28b
| 198
|
py
|
Python
|
python/basic-tutorials/demo/002/arithmetic-sum.py
|
capricorncd/my-notes
|
a2899a99bbb9680bf3a77652a4b154afd53964f5
|
[
"MIT"
] | 5
|
2017-10-23T01:30:14.000Z
|
2021-04-05T16:51:02.000Z
|
python/basic-tutorials/demo/002/arithmetic-sum.py
|
capricorncd/my-notes
|
a2899a99bbb9680bf3a77652a4b154afd53964f5
|
[
"MIT"
] | 3
|
2017-08-03T02:50:30.000Z
|
2017-11-22T03:35:16.000Z
|
python/basic-tutorials/demo/002/arithmetic-sum.py
|
capricorncd/my-notes
|
a2899a99bbb9680bf3a77652a4b154afd53964f5
|
[
"MIT"
] | 10
|
2017-10-08T11:38:08.000Z
|
2021-04-09T09:00:31.000Z
|
x1 = 1
d = 3
n = 100
x100 = x1 + (100 - 1) * d
s = 0
while (n > 0):
s += x1 + (n - 1) * d
n -= 1
'''
# or
while (n > 0):
n -= 1
s += x1 + n * d
'''
print 'x100 = ', x100
print 'sum = ', s
| 10.421053
| 25
| 0.378788
| 39
| 198
| 1.923077
| 0.333333
| 0.08
| 0.186667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.227642
| 0.378788
| 198
| 18
| 26
| 11
| 0.382114
| 0
| 0
| 0
| 0
| 0
| 0.090278
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 0.2
| 0
| 0
| 1
| null | 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
61ba33b39ffda24136b0c8348dd9c4eb37a9c3d4
| 224
|
py
|
Python
|
pyblis/__init__.py
|
jcrist/pyblis
|
d9c67d40a15c656a4681ba1b9ca0c52eff40163c
|
[
"BSD-3-Clause"
] | 2
|
2020-03-07T14:02:51.000Z
|
2021-02-03T05:18:11.000Z
|
pyblis/__init__.py
|
jcrist/pyblis
|
d9c67d40a15c656a4681ba1b9ca0c52eff40163c
|
[
"BSD-3-Clause"
] | null | null | null |
pyblis/__init__.py
|
jcrist/pyblis
|
d9c67d40a15c656a4681ba1b9ca0c52eff40163c
|
[
"BSD-3-Clause"
] | null | null | null |
from . import lib
from ._wrappers import dot
def _init_numba():
"""Initialize the numba extension"""
from . import _numba
from ._version import get_versions
__version__ = get_versions()['version']
del get_versions
| 20.363636
| 40
| 0.745536
| 29
| 224
| 5.344828
| 0.517241
| 0.212903
| 0.232258
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.165179
| 224
| 10
| 41
| 22.4
| 0.828877
| 0.133929
| 0
| 0
| 0
| 0
| 0.037234
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.142857
| false
| 0
| 0.571429
| 0
| 0.714286
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
61bf6f612dc50ff5fce3dd4b3ef93ff78b65a5e7
| 5,717
|
py
|
Python
|
reversible2/distribution.py
|
robintibor/reversible2
|
e6fea33ba41c7f76ee50295329b4ef27b879a7fa
|
[
"MIT"
] | null | null | null |
reversible2/distribution.py
|
robintibor/reversible2
|
e6fea33ba41c7f76ee50295329b4ef27b879a7fa
|
[
"MIT"
] | null | null | null |
reversible2/distribution.py
|
robintibor/reversible2
|
e6fea33ba41c7f76ee50295329b4ef27b879a7fa
|
[
"MIT"
] | null | null | null |
import torch as th
from reversible2.gaussian import get_gauss_samples
import torch.nn.functional as F
class TwoClassDist(object):
def __init__(self, n_class_dims, n_non_class_dims, i_class_inds, truncate_to=3):
super(TwoClassDist, self).__init__()
self.class_means = th.zeros(n_class_dims, requires_grad=True)
self.non_class_means = th.zeros(
n_class_dims + n_non_class_dims, requires_grad=True
)
self.class_log_stds = th.zeros(n_class_dims, requires_grad=True)
self.non_class_log_stds = th.zeros(
n_class_dims + n_non_class_dims, requires_grad=True
)
self.truncate_to = truncate_to
self.i_class_inds = i_class_inds
def get_mean_std(self, i_class):
i_i_class = self.i_class_inds[i_class]
cur_mean = th.cat(
(
self.non_class_means[:i_i_class],
self.class_means[i_class : i_class + 1],
self.non_class_means[i_i_class + 1 :],
)
)
cur_log_std = th.cat(
(
self.non_class_log_stds[:i_i_class],
self.class_log_stds[i_class : i_class + 1],
self.non_class_log_stds[i_i_class + 1 :],
)
)
return cur_mean, th.exp(cur_log_std)
def get_samples(self, i_class, n_samples):
cur_mean, cur_std = self.get_mean_std(i_class)
samples = get_gauss_samples(
n_samples, cur_mean, cur_std, truncate_to=self.truncate_to
)
return samples
def cuda(self):
self.class_means.data = self.class_means.data.cuda()
self.non_class_means.data = self.non_class_means.data.cuda()
self.class_log_stds.data = self.class_log_stds.data.cuda()
self.non_class_log_stds.data = self.non_class_log_stds.data.cuda()
return self
def parameters(self):
return [
self.class_means,
self.non_class_means,
self.class_log_stds,
self.non_class_log_stds,
]
def change_to_other_class(self, outs, i_class_from, i_class_to, eps=1e-6):
mean_from, std_from = self.get_mean_std(i_class_from)
mean_to, std_to = self.get_mean_std(i_class_to)
normed = (outs - mean_from.unsqueeze(0)) / (std_from.unsqueeze(0) + eps)
transformed = (normed * std_to.unsqueeze(0)) + mean_to.unsqueeze(0)
return transformed
def get_class_log_prob(self, i_class, out):
mean, std = self.get_mean_std(i_class)
cls_dist = th.distributions.MultivariateNormal(
mean[self.i_class_inds],
covariance_matrix=th.diag(std[self.i_class_inds] ** 2),
)
return cls_dist.log_prob(out[:, self.i_class_inds])
def get_total_log_prob(self, i_class, out):
mean, std = self.get_mean_std(i_class)
cls_dist = th.distributions.MultivariateNormal(
mean, covariance_matrix=th.diag(std ** 2)
)
return cls_dist.log_prob(out)
def set_mean_std(self, i_class, mean, std):
i_i_class = self.i_class_inds[i_class]
self.class_means.data[i_class] = mean.data[i_i_class]
self.class_log_stds.data[i_class] = th.log(std).data[i_i_class]
self.non_class_means.data[:i_i_class] = mean.data[:i_i_class]
self.non_class_means.data[i_i_class + 1 :] = mean.data[i_i_class + 1 :]
self.non_class_log_stds.data[:i_i_class] = th.log(std).data[:i_i_class]
self.non_class_log_stds.data[i_i_class + 1 :] = th.log(std).data[i_i_class + 1 :]
class TwoClassIndependentDist(object):
def __init__(self, n_dims, truncate_to=3):
super(TwoClassIndependentDist, self).__init__()
self.class_means = th.zeros(2, n_dims, requires_grad=True)
self.class_log_stds = th.zeros(2, n_dims, requires_grad=True)
self.truncate_to = truncate_to
def get_mean_std(self, i_class):
cur_mean = self.class_means[i_class]
cur_log_std = self.class_log_stds[i_class]
return cur_mean, th.exp(cur_log_std)
def get_samples(self, i_class, n_samples):
cur_mean, cur_std = self.get_mean_std(i_class)
samples = get_gauss_samples(
n_samples, cur_mean, cur_std, truncate_to=self.truncate_to
)
return samples
def cuda(self):
self.class_means.data = self.class_means.data.cuda()
self.class_log_stds.data = self.class_log_stds.data.cuda()
return self
def parameters(self):
return [self.class_means, self.class_log_stds]
def change_to_other_class(self, outs, i_class_from, i_class_to, eps=1e-6):
mean_from, std_from = self.get_mean_std(i_class_from)
mean_to, std_to = self.get_mean_std(i_class_to)
normed = (outs - mean_from.unsqueeze(0)) / (std_from.unsqueeze(0) + eps)
transformed = (normed * std_to.unsqueeze(0)) + mean_to.unsqueeze(0)
return transformed
def get_class_log_prob(self, i_class, out):
return self.get_total_log_prob(i_class, out)
def get_total_log_prob(self, i_class, out):
mean, std = self.get_mean_std(i_class)
cls_dist = th.distributions.MultivariateNormal(
mean, covariance_matrix=th.diag(std ** 2)
)
return cls_dist.log_prob(out)
def set_mean_std(self, i_class, mean, std):
self.class_means.data[i_class] = mean.data
self.class_log_stds.data[i_class] = th.log(std).data
def log_softmax(self, outs):
log_probs = th.stack([self.get_total_log_prob(i_class, outs)
for i_class in range(2)], dim=-1)
log_softmaxed = F.log_softmax(log_probs, dim=1)
return log_softmaxed
| 39.427586
| 89
| 0.650341
| 866
| 5,717
| 3.900693
| 0.08776
| 0.110124
| 0.071048
| 0.056838
| 0.880699
| 0.833037
| 0.804026
| 0.726169
| 0.673179
| 0.622558
| 0
| 0.007211
| 0.248032
| 5,717
| 144
| 90
| 39.701389
| 0.778553
| 0
| 0
| 0.471545
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.154472
| false
| 0
| 0.02439
| 0.02439
| 0.317073
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
f606a9f839b318a8bb089bf46410cb9dd7e84ecb
| 155
|
py
|
Python
|
onwebchange/__init__.py
|
ClericPy/OnWebChange
|
af12ec80582f7368aa2f4e707b49e7430632af42
|
[
"MIT"
] | null | null | null |
onwebchange/__init__.py
|
ClericPy/OnWebChange
|
af12ec80582f7368aa2f4e707b49e7430632af42
|
[
"MIT"
] | 5
|
2019-09-08T16:47:37.000Z
|
2020-02-06T15:34:18.000Z
|
onwebchange/__init__.py
|
ClericPy/OnWebChange
|
af12ec80582f7368aa2f4e707b49e7430632af42
|
[
"MIT"
] | null | null | null |
#! coding: utf-8
from .core import WatchdogCage, WatchdogTask, WebHandler
__version__ = '0.3.4'
__all__ = ['WatchdogCage', 'WatchdogTask', 'WebHandler']
| 22.142857
| 56
| 0.729032
| 17
| 155
| 6.176471
| 0.823529
| 0.457143
| 0.647619
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.029412
| 0.122581
| 155
| 6
| 57
| 25.833333
| 0.742647
| 0.096774
| 0
| 0
| 0
| 0
| 0.280576
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.333333
| 0
| 0.333333
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
f644537b6e546a2020aa4b7f9b60d43cbb0d0df1
| 632
|
py
|
Python
|
OnshoreBattlebot2018/Controllers/__init__.py
|
roy-love/BattleCode2018-ReferenceBot
|
c017cf804728dd1555ab6a60acdd2a6f7a929281
|
[
"MIT"
] | null | null | null |
OnshoreBattlebot2018/Controllers/__init__.py
|
roy-love/BattleCode2018-ReferenceBot
|
c017cf804728dd1555ab6a60acdd2a6f7a929281
|
[
"MIT"
] | null | null | null |
OnshoreBattlebot2018/Controllers/__init__.py
|
roy-love/BattleCode2018-ReferenceBot
|
c017cf804728dd1555ab6a60acdd2a6f7a929281
|
[
"MIT"
] | null | null | null |
from Controllers.BuildController import BuildController
from Controllers.CommunicationController import CommunicationController
from Controllers.EnemyTrackingController import EnemyTrackingController
from Controllers.MapController import MapController
from Controllers.PathfindingController import PathfindingController
from Controllers.ResearchTreeController import ResearchTreeController
from Controllers.StrategyController import StrategyController
from Controllers.TargettingController import TargettingController
from Controllers.UnitController import UnitController
from Controllers.MissionController import MissionController
| 57.454545
| 71
| 0.920886
| 50
| 632
| 11.64
| 0.26
| 0.257732
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.063291
| 632
| 10
| 72
| 63.2
| 0.983108
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 1
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
f65d41308aed99f15b52247bd4e97b9eb05b11c2
| 122
|
py
|
Python
|
xl_tensorflow/metrics/detection.py
|
Lannister-Xiaolin/xl_tensorflow
|
99e0f458769ee1e45ebf55c789961e40f7d2eeac
|
[
"Apache-2.0"
] | null | null | null |
xl_tensorflow/metrics/detection.py
|
Lannister-Xiaolin/xl_tensorflow
|
99e0f458769ee1e45ebf55c789961e40f7d2eeac
|
[
"Apache-2.0"
] | 1
|
2020-11-13T18:52:23.000Z
|
2020-11-13T18:52:23.000Z
|
xl_tensorflow/metrics/detection.py
|
Lannister-Xiaolin/xl_tensorflow
|
99e0f458769ee1e45ebf55c789961e40f7d2eeac
|
[
"Apache-2.0"
] | null | null | null |
#!usr/bin/env python3
# -*- coding: UTF-8 -*-
from .rafaelpadilla.Evaluator import Evaluator,BoundingBoxes,BoundingBox
| 17.428571
| 72
| 0.745902
| 14
| 122
| 6.5
| 0.928571
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.018519
| 0.114754
| 122
| 6
| 73
| 20.333333
| 0.824074
| 0.344262
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
f65e47ab78876208a7ef3fc2fbd0ef3aa071fd57
| 19,177
|
py
|
Python
|
BAUM.py
|
praveenbaim/B-AIM
|
5269cfc9a690fb7d9c5609f27c1ce67b0c886aac
|
[
"Unlicense"
] | 1
|
2021-01-29T07:14:34.000Z
|
2021-01-29T07:14:34.000Z
|
BAUM.py
|
praveenbaim/B-AIM
|
5269cfc9a690fb7d9c5609f27c1ce67b0c886aac
|
[
"Unlicense"
] | null | null | null |
BAUM.py
|
praveenbaim/B-AIM
|
5269cfc9a690fb7d9c5609f27c1ce67b0c886aac
|
[
"Unlicense"
] | null | null | null |
# mantra
print("mantra")
#BAUMBAUMBHOLE
print("BAUMBAUMBHOLE")
#start
print("start")
import math
import facebook
# TIME(creation+preservance+destruction+love+wealth+knowledge=0) code to write its own code repeats here when the individual stages with the 1.xls phases of 198 steps to learn itself the next step
# example of only time has been mentioned to make things easy to
# NOTE KEEP UPDATING THIS CODE PAGE WITH ANY CHANGES IN THE REMAINING 9 FILES
# mantra
print("mantra")
# AUMAIMHREEMKLEEMCHAMUNDAYAVICCHE
print("AUMAIMHREEMKLEEMCHAMUNDAYAVICCHE")
#THE DEFINITION OF INTELLIGENCE CONSTANTLY KEEPS CHANGING
print("THE DEFINITION OF INTELLIGENCE CONSTANTLY KEEPS CHANGING")
#start
print("start")
#todo import functions
#todo add colors based on distance
import math
import facebook
kali_x=0
kali_y=0
kali_z=0
#KALI(TIME= (0,0,0)) gave birth to BRAHMA(creation=(0,0,10))
brahma_x=0
brahma_y=0
brahma_z=10
#Distance between KALI and BRAHMA
kali_brahma=((kali_x-brahma_x)**2)+((kali_y-brahma_y)**2)+((kali_z-brahma_z)**2)
dist_kali_brahma=math.sqrt(kali_brahma)
print("\n\nDistance between KALI and BRAHMA:",dist_kali_brahma)
print("CREATION INITIATED")
#Creation could not sustain through which VISHNU(PRESERVANCE=(0,10,0)) was born
vishnu_x=0
vishnu_y=10
vishnu_z=0
#Distance between BRAHMA and VISHNU
brahma_vishnu=((brahma_x-vishnu_x)**2)+((brahma_y-vishnu_y)**2)+((brahma_z-vishnu_z)**2)
dist_brahma_vishnu=math.sqrt(brahma_vishnu)
print("\n\nDistance between BRAHMA and VISHNU:",dist_brahma_vishnu)
print("PRESERVANCE INITIATED")
#Preservance not ending so SHIVA WAS BORN (Destruction=(0,0,-10))
shiva_x=0
shiva_y=0
shiva_z=-10
#Distance between VISHNU and SHIVA
vishnu_shiva=((vishnu_x-shiva_x)**2)+((vishnu_y-shiva_y)**2)+((vishnu_z-shiva_z)**2)
dist_vishnu_shiva=math.sqrt(vishnu_shiva)
print("\n\nDistance between VISHNU and SHIVA:",dist_vishnu_shiva)
print("DESTRUCTION INITIATED")
#Destruction made dark matter=(0,0,0)
darkmatter_x=0
darkmatter_y=0
darkmatter_z=0
#Distance between SHIVA and DARK MATTER
shiva_darkmatter=((shiva_x-darkmatter_x)**2)+((shiva_y-darkmatter_y)**2)+((shiva_z-darkmatter_z)**2)
dist_shiva_darkmatter=math.sqrt(shiva_darkmatter)
print("\n\nDistance between SHIVA AND DARKMATTER:",dist_shiva_darkmatter)
print("DESTRUCTION made DARKMATTER")
#RETAINANCE DONE BY DARMATTER INTO SHIVANI(LOVE=(-10,0,0))
parvathy_x=-10
parvathy_y=0
parvathy_z=0
#Distance between DARKMATTER and PARVATHY
darkmatter_parvathy=((darkmatter_x-parvathy_x)**2)+((darkmatter_y-parvathy_y)**2)+((darkmatter_z-parvathy_z)**2)
dist_darkmatter_parvathy=math.sqrt(darkmatter_parvathy)
print("\n\nDistance between DARKMATTER and PARVATHY:",dist_darkmatter_parvathy)
print("RETAINANCE INITIATED")
#RETAINANCE REQUIRED LAKSHMI SUPPORT(WEALTH=(0,-10,0))
lakshmi_x=0
lakshmi_y=-10
lakshmi_z=0
#Distance between PARVATHY and LAKSHMI
parvathy_lakshmi=((parvathy_x-lakshmi_x)**2)+((parvathy_y-lakshmi_y)**2)+((parvathy_z-lakshmi_z)**2)
dist_parvathy_lakshmi=math.sqrt(parvathy_lakshmi)
print("\n\nDistance between PARVATHY and LAKSHMI:",dist_parvathy_lakshmi)
print("RESOURCES INITIATED")
#RESOURCES ABSORB KNOWLEDGE(SARASWATHI=(10,0,0)
saraswathi_x=10
saraswathi_y=0
saraswathi_z=0
#Distance between LAKSHMI and SARASWATHI
lakshmi_saraswathi=((lakshmi_x-saraswathi_x)**2)+((lakshmi_y-saraswathi_y)**2)+((lakshmi_z-saraswathi_z)**2)
dist_lakshmi_saraswathi=math.sqrt(lakshmi_saraswathi)
print("\n\nDistance between LAKSHMI and SARASWATHI:",dist_lakshmi_saraswathi)
print("KNOWLEDGE ACQUIRED")
#DEEP LEARNING CYCLES AN ITERATION DARKMATTER(TIME=(0,0,0)
darkmatter_x=0
darkmatter_y=0
darkmatter_z=0
#Distance between SARASWATHI and DARKMATTER
saraswathi_darkmatter=((saraswathi_x-darkmatter_x)**2)+((saraswathi_y-darkmatter_y)**2)+((saraswathi_z-darkmatter_z)**2)
dist_saraswathi_darkmatter=math.sqrt(saraswathi_darkmatter)
print("\n\nDistance between SARASWATHI and DARKMATTER:",dist_saraswathi_darkmatter)
print("CYCLE ITERATION COMPLETED")
BAUM1 = [["MY","TIPS"],
["DEDICATION","ADDED","ON","DATE","AUM"],
["BLOG","BOOK"],
["SRI","LAKSHMI","KUBERA","YANTRAM",
"MODEL","FORMING","IN","SOMETIME",
"AS","PREDICTED","USING","BAIM",
"DATED","19:49","-","SUNDAY",
"06/08/2017"],
["TIME","FRAMES",
"BAUMBUMBHOLE","VIDEO",
"BAIM","CALL","SCRIPT",
"ENTREPRENEUR",
"DIGITAL","MARKETING"],
["1","12/11/16","BUSINESS","MODEL","CREATION",
"SWOT","ANALYSIS","USING",
"ASSUMPTION","THEORY"],
["CONCLUSION",
"COMMUNICATE","WITH",
"CERN","DATA",
"USING","MY","AI"],
["AND","CONTINOUS","PULLING","DATA",
"AND","TRAIN","THE","AI","SYSTEM"],
["6/8/2017","-","PROJECT","VISHNU"],
["NEED","MONEY","FOREVER","ACCORDING",
"TO","MY","PLAN","AND","APPROVAL"],
["PROJECT","WISDOM"]]
BAUM2 =["7:53","PM","6/8/2017",
"ENTER","NEW","PROFILE","FOR","OPERA","BROWSER",
"PRIVACY","CHECKUP","FOR","GMAIL","PRAVEEN.KADIRI",
"PROFILE","WITH","CHANGED","PASSWORD","TO",
"Heartbreak!123",
"RESOURCES","IN","YOUTUBE","PLAYLIST","NEEDS","TO","BE","ADDED",
"CATEGORIZED","INTO","4",
"1","AUM",
"2","PINEAL","GLAND",
"3","FAV",
"4","FAVOURITES",
"PICASA","HAS","ALL","THE","PHOTOS","THAT","ARE","TO","BE",
"CONSIDERED","FOR","THE","PROJECT","FINAL",
"PRESENTATION",
"PROJECT","MANAGEMENT","FOLDER","COPY",
"WILL","BE","EDITED","AND","MADE","CHANGES","WITHIN"]
BAUM3 = ["NEED","TECHIES","HUB","PPT","TO","SEND","ACROSS",
"WITH","HALF","MADE","PPTS","OF","MINE","AMAZON","AND",
"B-AIM","->","AMAZON","24","SLIDES",
"B-AIM","-","26","SLIDES",
"YOUTUBE",":","ACTIVATE","QI","FLOW","WITH","OM",
"MANTRA","AND","POWERFUL","DRUMS",
"BUDDHA","FACE"]
#green
print("green")
#Pr
print("Pr")
#black
print("black")
#pivot table
print("pivot table")
#rows
print("rows")
#against
print("against")
#columns
print("columns")
#match
print("match")
#colors
print("colors")
#5+2(black+white)
print("5+2(black+white)")
#Project shiva
print("Project shiva")
#baum baum bole
print("baum baum bole")
#business 2 sides baum
print("business 2 sides baum")
#1 task i can do 2 tasks i am not able to link
print("1 task i can do 2 tasks i am not able to link")
#3=2+(1)
print("3=2+(1)")
#333.3.33 last excel calculation
print("333.3.33 last excel calculation")
#yellow
print("yellow")
#PK
print("PK")
#THE RESTORATION
print("THE RESTORATION")
#opposite
print("opposite")
#white
print("white")
#phone contacts
print("phone contacts")
#contact colors last updated
print("contact colors last updated")
#calls
print("calls")
#lappy profiles
print("lappy profiles")
#10 projects
print("10 projects")
#666+1+333
print("666+1+333")
#blue
print("blue")
#graphs
print("graphs")
#geographical locations
print("geographical locations")
#calculation
print("calculation")
#excel file
print("excel file")
#* and 1 max compatibility
print("* and 1 max compatibility")
#tab apps
print("tab apps")
#resources
print("resources")
#programming
print("programming")
#contacts
print("contacts")
#syncing
print("syncing")
#ads on profiles should change
print("ads on profiles should change")
#emails
print("emails")
#start clean
print("start clean")
#small zero
print("small zero")
#big zero
print("big zero")
#cern
print("cern")
#transaction
print("transaction")
#to cern
print("to cern")
#should give access to cern info
print("should give access to cern info")
#using ARN
print("using ARN")
#opposite
print("opposite")
#wiccan
print("wiccan")
#calendar
print("calendar")
#black
print("black")
#do what you can the game project starts
print("do what you can the game project starts")
#tab apps
print("tab apps")
#pyramids
print("pyramids")
#secret
print("secret")
#the game
print("he game")
#link
print("link")
#research symbols
print("research symbols")
#8
print("8")
#2d
print("2d")
#3d
print("3d")
#3d+2d
print("3d+2d")
#4
print("4")
#8
print("8")
#swastik
print("swastik")
#baum
print("baum")
#1
print("1")
#green
print("green")
#Pr
print("Pr")
#weigh
print("weigh")
#Project B-AUM
print("Project B-AUM")
#photos
print("photos")
#last updated
print("last updated")
#18-06-2016 18:05
print("18-06-2016 18:05")
#news
print("news")
#translate
print("translate")
#1
print("1")
#opposite
print("opposite")
#vishnu promise
print("vishnu promise")
#white
print("white")
#tab app
print("tab app")
#0
print("0")
#numbers
print("numbers")
#folders
print("folders")
#project
print("project")
#B-AUM
print("B-AUM")
#Sarpam
print("Sarpam")
#chess
print("chess")
#chessboard
print("chessboard")
#using
print("using")
#tic tac toe
print("tic tac toe")
#and
print("and")
#PSO
print("PSO")
#7
print("7")
#USA
print("USA")
#Restoration
print("restoration")
#the restoration of the gospel of jesus christ
print("the restoration of the gospel of jesus christ")
#last updated
print("last updated")
#moves
print("moves")
#opposite
print("opposite")
#shiva promise
print("shiva promise")
#university
print("university")
#blame
print("blame")
#enter the web
print("enter the web")
#herbalife
print("herbalife")
#ARN
print("ARN")
#disputes barclays
print("disputes barclays")
#globalize
print("globalize")
#reverse
print("reverse")
#engineering
print("engineering")
#project shiva vishnu
print("project shiva vishnu")
#last updated
print("last updated")
#moves
print("moves")
#opposite
print("opposite")
#university
print("university")
#meedha pettala
print("meedha pettala")
#yellow
print("yellow")
#PK
print("PK")
#shiva vishnu promise
print("shiva vishnu promise")
#0
print("0")
#shiva vishnu vishnu promise
print("shiva vishnu vishnu promise")
#4
print("4")
#refer opposite SWOT
print("refer opposite SWOT")
#white
print("white")
#blue
print("blue")
#e
print("e")
#tablet
print("tablet")
#movies
print("movies")
#songs
print("songs")
#boolean algebra
print("boolean algebra")
#blue
print("blue")
#white
print("white")
#kalyan lok
print("kalyan lok")
#blue
print("blue")
#opposite
print("opposite")
#OR
print("OR")
#vishnu promise
print("vishnu promise")
#0=1+1
print("0=1+1")
#miracle
print("miracle")
#god
print("god")
#particle
print("particle")
#UK
print("UK")
#CERN
print("CERN")
#pick phone
print("pick phone")
#phone apps
print("phone apps")
#0
print("0")
#1 transaction should change the world
print("1 transaction should change the world")
#shiva promise
print("#shiva promise")
#PSO
print("PSO")
#2
print("2")
#raelian movement
print("raelian movement")
#swastik
print("swastik")
#baum
print("baum")
#colors
print("colors")
#5
print("5")
#red
print("red")
#white
print("white")
#blue
print("blue")
#yellow
print("yellow")
#white
print("white")
#blue
print("blue")
#green
print("green")
#white
print("white")
#blue
print("blue")
#AUM
print("AUM")
#white
print("white")
#baum
print("baum")
#use excel to filter raelian symbol
print("use excel to filter raelian symbol")
#01-Jul
print("01-Jul")
#red
print("red")
#PR
print("PR")
#6
print("6")
#colors
print("colors")
#SWOT
print("SWOT")
#vishnu vishnu promise
print("vishnu vishnu promise")
#game ends
print("ame ends")
#swastik
print("swastik")
#LVUGKE
print("LVUGKE")
#instead of living die/50-50 live using baum and let die
print("instead of living die/50-50 live using baum and let die")
#ADGRACE
print("ADGRACE")
#sell world
print("sell world")
#1+ give donation
print("1+ give donation")
#calculation refer to notes
print("calculation refer to notes")
#michelle calculation 200 200
print("michelle calculation 200 200")
#Journalist calculation
print("Journalist calculation")
#credability came into exitence
print("credability came into exitence")
#04:03
print("04:03")
#make email sarpam to hackers
print("make email sarpam to hackers")
#all fields
print("all fields")
#using boolean algebra
print("using boolean algebra")
#9
print("9")
#blue
print("blue")
#white
print("white")
#kalyan lok
print("kalyan lok")
#blue
print("blue")
# CREATION
# PRESERVANCE
# DESTRUCTION
# DARK MATTER
# LOVE
# WEALTH
# KNOWLEDGE
#green
print("green")
#Pr
print("Pr")
#black
print("black")
#pivot table
print("pivot table")
#rows
print("rows")
#against
print("against")
#columns
print("columns")
#match
print("match")
#colors
print("colors")
#5+2(black+white)
print("5+2(black+white)")
#Project shiva
print("Project shiva")
#baum baum bole
print("baum baum bole")
#business 2 sides baum
print("business 2 sides baum")
#1 task i can do 2 tasks i am not able to link
print("1 task i can do 2 tasks i am not able to link")
#3=2+(1)
print("3=2+(1)")
#333.3.33 last excel calculation
print("333.3.33 last excel calculation")
#yellow
print("yellow")
#PK
print("PK")
#THE RESTORATION
print("THE RESTORATION")
#opposite
print("opposite")
#white
print("white")
#phone contacts
print("phone contacts")
#contact colors last updated
print("contact colors last updated")
#calls
print("calls")
#lappy profiles
print("lappy profiles")
#10 projects
print("10 projects")
#666+1+333
print("666+1+333")
#blue
print("blue")
#graphs
print("graphs")
#geographical locations
print("geographical locations")
#calculation
print("calculation")
#excel file
print("excel file")
#* and 1 max compatibility
print("* and 1 max compatibility")
#tab apps
print("tab apps")
#resources
print("resources")
#programming
print("programming")
#contacts
print("contacts")
#syncing
print("syncing")
#ads on profiles should change
print("ads on profiles should change")
#emails
print("emails")
#start clean
print("start clean")
#small zero
print("small zero")
#big zero
print("big zero")
#cern
print("cern")
#transaction
print("transaction")
#to cern
print("to cern")
#should give access to cern info
print("should give access to cern info")
#using ARN
print("using ARN")
#opposite
print("opposite")
#wiccan
print("wiccan")
#calendar
print("calendar")
#black
print("black")
#do what you can the game project starts
print("do what you can the game project starts")
#tab apps
print("tab apps")
#pyramids
print("pyramids")
#secret
print("secret")
#the game
print("he game")
#link
print("link")
#research symbols
print("research symbols")
#8
print("8")
#2d
print("2d")
#3d
print("3d")
#3d+2d
print("3d+2d")
#4
print("4")
#8
print("8")
#swastik
print("swastik")
#baum
print("baum")
#1
print("1")
#green
print("green")
#Pr
print("Pr")
#weigh
print("weigh")
#Project B-AUM
print("Project B-AUM")
#photos
print("photos")
#last updated
print("last updated")
#18-06-2016 18:05
print("18-06-2016 18:05")
#news
print("news")
#translate
print("translate")
#1
print("1")
#opposite
print("opposite")
#vishnu promise
print("vishnu promise")
#white
print("white")
#tab app
print("tab app")
#0
print("0")
#numbers
print("numbers")
#folders
print("folders")
#project
print("project")
#B-AUM
print("B-AUM")
#Sarpam
print("Sarpam")
#chess
print("chess")
#chessboard
print("chessboard")
#using
print("using")
#tic tac toe
print("tic tac toe")
#and
print("and")
#PSO
print("PSO")
#7
print("7")
#USA
print("USA")
#Restoration
print("restoration")
#the restoration of the gospel of jesus christ
print("the restoration of the gospel of jesus christ")
#last updated
print("last updated")
#moves
print("moves")
#opposite
print("opposite")
#shiva promise
print("shiva promise")
#university
print("university")
#blame
print("blame")
#enter the web
print("enter the web")
#herbalife
print("herbalife")
#ARN
print("ARN")
#disputes barclays
print("disputes barclays")
#globalize
print("globalize")
#reverse
print("reverse")
#engineering
print("engineering")
#project shiva vishnu
print("project shiva vishnu")
#last updated
print("last updated")
#moves
print("moves")
#opposite
print("opposite")
#university
print("university")
#meedha pettala
print("meedha pettala")
#yellow
print("yellow")
#PK
print("PK")
#shiva vishnu promise
print("shiva vishnu promise")
#0
print("0")
#shiva vishnu vishnu promise
print("shiva vishnu vishnu promise")
#4
print("4")
#refer opposite SWOT
print("refer opposite SWOT")
#white
print("white")
#blue
print("blue")
#e
print("e")
#tablet
print("tablet")
#movies
print("movies")
#songs
print("songs")
#boolean algebra
print("boolean algebra")
#blue
print("blue")
#white
print("white")
#kalyan lok
print("kalyan lok")
#blue
print("blue")
#opposite
print("opposite")
#OR
print("OR")
#vishnu promise
print("vishnu promise")
#0=1+1
print("0=1+1")
#miracle
print("miracle")
#god
print("god")
#particle
print("particle")
#UK
print("UK")
#CERN
print("CERN")
#pick phone
print("pick phone")
#phone apps
print("phone apps")
#0
print("0")
#1 transaction should change the world
print("1 transaction should change the world")
#shiva promise
print("#shiva promise")
#PSO
print("PSO")
#2
print("2")
#raelian movement
print("raelian movement")
#swastik
print("swastik")
#baum
print("baum")
#colors
print("colors")
#5
print("5")
#red
print("red")
#white
print("white")
#blue
print("blue")
#yellow
print("yellow")
#white
print("white")
#blue
print("blue")
#green
print("green")
#white
print("white")
#blue
print("blue")
#AUM
print("AUM")
#white
print("white")
#baum
print("baum")
#use excel to filter raelian symbol
print("use excel to filter raelian symbol")
#01-Jul
print("01-Jul")
#red
print("red")
#PR
print("PR")
#6
print("6")
#colors
print("colors")
#SWOT
print("SWOT")
#vishnu vishnu promise
print("vishnu vishnu promise")
#game ends
print("ame ends")
#swastik
print("swastik")
#LVUGKE
print("LVUGKE")
#instead of living die/50-50 live using baum and let die
print("instead of living die/50-50 live using baum and let die")
#ADGRACE
print("ADGRACE")
#sell world
print("sell world")
#1+ give donation
print("1+ give donation")
#calculation refer to notes
print("calculation refer to notes")
#michelle calculation 200 200
print("michelle calculation 200 200")
#Journalist calculation
print("Journalist calculation")
#credability came into exitence
print("credability came into exitence")
#04:03
print("04:03")
#make email sarpam to hackers
print("make email sarpam to hackers")
#all fields
print("all fields")
#using boolean algebra
print("using boolean algebra")
#9
print("9")
#blue
print("blue")
#white
print("white")
#kalyan lok
print("kalyan lok")
#blue
print("blue")
| 19.488821
| 197
| 0.673046
| 2,626
| 19,177
| 4.868241
| 0.155369
| 0.015645
| 0.02112
| 0.013767
| 0.709168
| 0.701502
| 0.701502
| 0.69368
| 0.69368
| 0.69368
| 0
| 0.027729
| 0.163164
| 19,177
| 983
| 198
| 19.508647
| 0.768881
| 0.263806
| 0
| 0.794521
| 0
| 0
| 0.415787
| 0.002521
| 0
| 0
| 0
| 0.001017
| 0
| 1
| 0
| false
| 0.001957
| 0.007828
| 0
| 0.007828
| 0.812133
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 5
|
9c85978b13d95120915d356ea0dfd1e037f89dec
| 100
|
py
|
Python
|
positions/examples/ci_settings_postgres.py
|
Elec/django-positions
|
1ee904ccfc82679d630cbab4358c5d6cc763f6df
|
[
"BSD-3-Clause"
] | 118
|
2015-01-13T20:04:00.000Z
|
2022-03-22T17:27:05.000Z
|
positions/examples/ci_settings_postgres.py
|
Elec/django-positions
|
1ee904ccfc82679d630cbab4358c5d6cc763f6df
|
[
"BSD-3-Clause"
] | 28
|
2015-02-11T22:14:52.000Z
|
2021-06-10T17:29:38.000Z
|
positions/examples/ci_settings_postgres.py
|
elec/django-positions
|
1ee904ccfc82679d630cbab4358c5d6cc763f6df
|
[
"BSD-3-Clause"
] | 48
|
2015-01-19T15:54:09.000Z
|
2022-03-07T02:58:55.000Z
|
from .settings_postgres import *
DATABASES['default'].update({'USER': 'postgres', 'PASSWORD': ''})
| 25
| 65
| 0.69
| 10
| 100
| 6.8
| 0.9
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.09
| 100
| 3
| 66
| 33.333333
| 0.747253
| 0
| 0
| 0
| 0
| 0
| 0.27
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.5
| 0.5
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
|
0
| 5
|
9c92d28196a805d20a4e7df03fcc9494d36778ad
| 56
|
py
|
Python
|
panopticon/fifemon-condor-probe/fifemon/__init__.py
|
opensciencegrid/open-pool-display
|
ff78368a1f747076602466a2a22ab15c76d4da43
|
[
"Apache-2.0"
] | 8
|
2016-05-19T20:45:09.000Z
|
2019-05-23T15:31:13.000Z
|
panopticon/fifemon-condor-probe/fifemon/__init__.py
|
opensciencegrid/open-pool-display
|
ff78368a1f747076602466a2a22ab15c76d4da43
|
[
"Apache-2.0"
] | 12
|
2022-01-19T22:39:32.000Z
|
2022-03-22T18:47:20.000Z
|
panopticon/fifemon-condor-probe/fifemon/__init__.py
|
opensciencegrid/open-pool-display
|
ff78368a1f747076602466a2a22ab15c76d4da43
|
[
"Apache-2.0"
] | 8
|
2022-01-18T23:27:43.000Z
|
2022-02-28T21:56:26.000Z
|
from .probe import Probe
from .graphite import Graphite
| 18.666667
| 30
| 0.821429
| 8
| 56
| 5.75
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.142857
| 56
| 2
| 31
| 28
| 0.958333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
9c96d125c0e26df6795bfc9a37b310c85a40704f
| 625
|
py
|
Python
|
saopy/DUL/__init__.py
|
CityPulse/CP_Resourcemanagement
|
aa670fa89d5e086a98ade3ccc152518be55abf2e
|
[
"MIT"
] | 2
|
2016-11-03T14:57:45.000Z
|
2019-05-13T13:21:08.000Z
|
saopy/DUL/__init__.py
|
CityPulse/CP_Resourcemanagement
|
aa670fa89d5e086a98ade3ccc152518be55abf2e
|
[
"MIT"
] | null | null | null |
saopy/DUL/__init__.py
|
CityPulse/CP_Resourcemanagement
|
aa670fa89d5e086a98ade3ccc152518be55abf2e
|
[
"MIT"
] | 1
|
2020-07-23T11:27:15.000Z
|
2020-07-23T11:27:15.000Z
|
import saopy.model
from saopy.model import DUL___DesignedArtifact as DesignedArtifact
from saopy.model import DUL___Event as Event
from saopy.model import DUL___InformationEntity as InformationEntity
from saopy.model import DUL___InformationObject as InformationObject
from saopy.model import DUL___Method as Method
from saopy.model import DUL___Object as Object
from saopy.model import DUL___PhysicalObject as PhysicalObject
from saopy.model import DUL___Process as Process
from saopy.model import DUL___Quality as Quality
from saopy.model import DUL___Region as Region
from saopy.model import DUL___Situation as Situation
| 44.642857
| 68
| 0.8704
| 91
| 625
| 5.615385
| 0.186813
| 0.234834
| 0.30137
| 0.430528
| 0.495108
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.1104
| 625
| 13
| 69
| 48.076923
| 0.919065
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
9c9d989cde84aba6f75c0afd56f61aaaca182ba2
| 133
|
py
|
Python
|
mmdet/ops/nms/__init__.py
|
morkovka1337/mmdetection
|
5187d94b6c96084b17817249622d6e4520213ae6
|
[
"Apache-2.0"
] | 58
|
2020-09-21T08:17:26.000Z
|
2022-03-31T19:38:14.000Z
|
mmdet/ops/nms/__init__.py
|
morkovka1337/mmdetection
|
5187d94b6c96084b17817249622d6e4520213ae6
|
[
"Apache-2.0"
] | 170
|
2020-09-08T12:29:06.000Z
|
2022-03-31T18:28:09.000Z
|
mmdet/ops/nms/__init__.py
|
morkovka1337/mmdetection
|
5187d94b6c96084b17817249622d6e4520213ae6
|
[
"Apache-2.0"
] | 21
|
2020-10-06T13:49:41.000Z
|
2022-03-30T14:52:45.000Z
|
# Copyright (C) 2020-2021 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
#
from .nms_wrapper import nms, batched_nms, NMSop
| 26.6
| 48
| 0.774436
| 20
| 133
| 5.05
| 0.9
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.08547
| 0.120301
| 133
| 4
| 49
| 33.25
| 0.777778
| 0.578947
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
9cdefab39a05dd0175512bb650e79319b14ae506
| 186
|
py
|
Python
|
awdd/json_writer.py
|
rickmark/awdd_decode
|
d53e0eb52b9b5aabe11b30fd2fa4e403e3c08fd0
|
[
"MIT"
] | 3
|
2021-10-02T17:59:13.000Z
|
2022-01-12T20:25:55.000Z
|
awdd/json_writer.py
|
rickmark/awdd_decode
|
d53e0eb52b9b5aabe11b30fd2fa4e403e3c08fd0
|
[
"MIT"
] | null | null | null |
awdd/json_writer.py
|
rickmark/awdd_decode
|
d53e0eb52b9b5aabe11b30fd2fa4e403e3c08fd0
|
[
"MIT"
] | 1
|
2022-03-25T19:14:05.000Z
|
2022-03-25T19:14:05.000Z
|
from . import WriterBase
from .object import *
from io import IOBase
class JsonWriter(WriterBase):
def write_to(self, value: DiagnosticObject, stream: IOBase) -> None:
pass
| 23.25
| 72
| 0.725806
| 23
| 186
| 5.826087
| 0.73913
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.193548
| 186
| 8
| 73
| 23.25
| 0.893333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.166667
| false
| 0.166667
| 0.5
| 0
| 0.833333
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 0
|
0
| 5
|
9cf99846ffb943106017680308b45e856eebf63f
| 3,777
|
py
|
Python
|
simplechinese/representation.py
|
chenmingxiang110/SimpleChinese2
|
91f90672f25daadbfccd2ab22f026a65889705af
|
[
"MIT"
] | 78
|
2021-06-21T02:28:14.000Z
|
2022-03-18T13:35:16.000Z
|
simplechinese/representation.py
|
chenmingxiang110/SimpleChinese
|
4562fd4bcb0e6922904715f5214f141e92db90e5
|
[
"MIT"
] | 3
|
2021-06-30T11:03:58.000Z
|
2021-09-09T10:39:27.000Z
|
simplechinese/representation.py
|
chenmingxiang110/SimpleChinese2
|
91f90672f25daadbfccd2ab22f026a65889705af
|
[
"MIT"
] | 24
|
2021-06-21T02:30:49.000Z
|
2021-08-23T09:49:03.000Z
|
import numpy as np
import pandas as pd
from .nlp import extract_words
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.decomposition import PCA, NMF
from sklearn.cluster import KMeans, DBSCAN, MeanShift
import jieba
jieba.setLogLevel(60)
def pca(x, n_components=2):
"""
Perform dimension reduction with the principal component analysis algorithm. The input data should be a pandas.Series of vectors.
|
"""
pca = PCA(n_components=n_components)
return pd.Series(pca.fit_transform(list(x)).tolist(), index=x.index)
def nmf(x, n_components=2):
"""
Perform dimension reduction with the non-negative matrix factorization algorithm. The input data should be a pandas.Series of vectors.
|
"""
nmf = NMF(n_components=n_components, init="random", random_state=0)
return pd.Series(nmf.fit_transform(list(x)).tolist(), index=x.index)
def term_frequency(x, mode=0, max_features=None, return_feature_names=False):
"""
Extract the words and vectorize each element in the pandas.Series by the frequency of each word.
Args:
x: The pandas.Series to be parsed.
max_features: The maximum number of features
return_feature_names: Return the token words or not.
mode: 0: No single character words. The words may be overlapped.
1: Have single character words. The words may be overlapped.
2: No single character words. The words are not overlapped.
3: Have single character words. The words are not overlapped.
4: Only single characters.
Returns:
The vectorization result.
|
"""
if mode not in [0,1,2,3,4]:
raise ValueError("The mode should be chosen from 0-4.")
if not isinstance(x, pd.Series):
raise ValueError("The type of the input variable should be pandas.Series.")
vectorizer = CountVectorizer(max_features=max_features,
lowercase=False,
token_pattern="\S+")
y = extract_words(x, isList=False, mode=mode, token=" ")
y = pd.Series(vectorizer.fit_transform(y).toarray().tolist(), index=x.index)
if return_feature_names:
return (y, tf.get_feature_names())
else:
return y
def tfidf(x, mode=0, max_features=None, min_df=1, return_feature_names=False):
"""
Extract the words and vectorize each element in the pandas.Series by the tfidf scores.
Args:
x: The pandas.Series to be parsed.
max_features: The maximum number of features
return_feature_names: Return the token words or not.
mode: 0: No single character words. The words may be overlapped.
1: Have single character words. The words may be overlapped.
2: No single character words. The words are not overlapped.
3: Have single character words. The words are not overlapped.
4: Only single characters.
Returns:
The vectorization result.
|
"""
if mode not in [0,1,2,3,4]:
raise ValueError("The mode should be chosen from 0-4.")
if not isinstance(x, pd.Series):
raise ValueError("The type of the input variable should be pandas.Series.")
vectorizer = TfidfVectorizer(use_idf=True,
max_features=max_features,
min_df=min_df,
token_pattern="\S+",
lowercase=False,)
y = extract_words(x, isList=False, mode=mode, token=" ")
y = pd.Series(vectorizer.fit_transform(y).toarray().tolist(), index=y.index)
if return_feature_names:
return (y, tf.get_feature_names())
else:
return y
| 34.336364
| 138
| 0.647074
| 508
| 3,777
| 4.720472
| 0.244094
| 0.033361
| 0.066722
| 0.076731
| 0.742285
| 0.742285
| 0.724771
| 0.724771
| 0.724771
| 0.65638
| 0
| 0.011586
| 0.268732
| 3,777
| 109
| 139
| 34.651376
| 0.856626
| 0.393963
| 0
| 0.409091
| 0
| 0
| 0.091337
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.090909
| false
| 0
| 0.159091
| 0
| 0.386364
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
1426ddaa4bbd36738aacef6f4f0ee634b6cc8737
| 419
|
py
|
Python
|
challenges/quicksort/test_quicksort.py
|
jayadams011/data-structures-and-algorithms
|
b9a49c65ca769c82b2a34d840bd1e4dd626be025
|
[
"MIT"
] | null | null | null |
challenges/quicksort/test_quicksort.py
|
jayadams011/data-structures-and-algorithms
|
b9a49c65ca769c82b2a34d840bd1e4dd626be025
|
[
"MIT"
] | 4
|
2018-03-22T16:56:06.000Z
|
2018-03-28T23:30:29.000Z
|
challenges/quicksort/test_quicksort.py
|
jayadams011/data-structures-and-algorithms
|
b9a49c65ca769c82b2a34d840bd1e4dd626be025
|
[
"MIT"
] | null | null | null |
"""Test and test imports."""
from .quicksort import quicksort
import pytest
def test_empty_quick_sort():
"""Test empty quick sort."""
assert quicksort([]) == ([])
def test_small_quick_sort():
"""Test small quick sort."""
assert quicksort([2, 3, 1]) == ([1, 2, 3])
def test_large_quick_sort():
"""Test large quick sort."""
assert quicksort([910, 78, 56, 34, 12]) == ([12, 34, 56, 78, 910])
| 22.052632
| 70
| 0.613365
| 59
| 419
| 4.20339
| 0.372881
| 0.217742
| 0.157258
| 0.290323
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.083086
| 0.195704
| 419
| 18
| 71
| 23.277778
| 0.652819
| 0.217184
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.375
| 1
| 0.375
| true
| 0
| 0.25
| 0
| 0.625
| 0
| 0
| 0
| 0
| null | 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 1
| 0
|
0
| 5
|
145f729a8b36b2cdea29781ceb79dae47b08e7b8
| 149
|
py
|
Python
|
driver_logbook/app/users/__init__.py
|
solloc/driver-logbook
|
466d48c11d9722b042191f4ddcca6c680b5f47fe
|
[
"MIT"
] | null | null | null |
driver_logbook/app/users/__init__.py
|
solloc/driver-logbook
|
466d48c11d9722b042191f4ddcca6c680b5f47fe
|
[
"MIT"
] | 20
|
2019-03-24T17:56:05.000Z
|
2020-06-04T17:50:55.000Z
|
driver_logbook/app/users/__init__.py
|
solloc/driver-logbook
|
466d48c11d9722b042191f4ddcca6c680b5f47fe
|
[
"MIT"
] | null | null | null |
from flask import Blueprint
bp = Blueprint('users', __name__, url_prefix='/users')
from driver_logbook.app.users import routes # noqa: E402, F401
| 24.833333
| 63
| 0.758389
| 21
| 149
| 5.095238
| 0.761905
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| 0
| 0.046512
| 0.134228
| 149
| 5
| 64
| 29.8
| 0.782946
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| 1
| 1
|
0
| 5
|
14674695fff0c74c2eec1a69050ffd5c7041e7ff
| 132
|
py
|
Python
|
BERT/model/__init__.py
|
FDChongLi/TwoWaysToImproveCSC
|
488d8d3e94a57c9f93679370e17cc561131883cc
|
[
"MIT"
] | 50
|
2021-06-04T13:11:23.000Z
|
2022-03-25T09:15:00.000Z
|
BERT/model/__init__.py
|
FDChongLi/TwoWaysToImproveCSC
|
488d8d3e94a57c9f93679370e17cc561131883cc
|
[
"MIT"
] | 8
|
2021-06-17T09:06:38.000Z
|
2022-01-28T07:21:30.000Z
|
BERT/model/__init__.py
|
FDChongLi/TwoWaysToImproveCSC
|
488d8d3e94a57c9f93679370e17cc561131883cc
|
[
"MIT"
] | 11
|
2021-06-11T08:41:50.000Z
|
2022-03-01T06:43:22.000Z
|
from .dataset import BertDataset,construct
from .BertFineTune import BertFineTune
from .AdGen import BFTLogitGen,readAllConfusionSet
| 44
| 50
| 0.878788
| 14
| 132
| 8.285714
| 0.642857
| 0
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| 0
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| 0
| 0
| 0.083333
| 132
| 3
| 50
| 44
| 0.958678
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| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
1496d72d5c3304fda087067b240900a56db3abc5
| 112
|
py
|
Python
|
djstripe/contrib/__init__.py
|
hoopit/dj-stripe
|
726853081cd95be86777492c23fb61de5d35a72a
|
[
"MIT"
] | 2
|
2020-09-01T20:05:28.000Z
|
2021-07-22T08:20:42.000Z
|
djstripe/contrib/__init__.py
|
hoopit/dj-stripe
|
726853081cd95be86777492c23fb61de5d35a72a
|
[
"MIT"
] | 1
|
2021-02-24T15:01:41.000Z
|
2021-03-25T20:44:53.000Z
|
djstripe/contrib/__init__.py
|
hoopit/dj-stripe
|
726853081cd95be86777492c23fb61de5d35a72a
|
[
"MIT"
] | 2
|
2020-01-31T14:26:09.000Z
|
2020-07-14T04:24:15.000Z
|
"""
.. module:: dj-stripe.contrib.
:synopsis: Extra modules not pertaining to core logic of dj-stripe.
"""
| 18.666667
| 71
| 0.669643
| 15
| 112
| 5
| 0.866667
| 0.213333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.178571
| 112
| 5
| 72
| 22.4
| 0.815217
| 0.919643
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 0
| 0
| null | 1
| 0
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| null | 0
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| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
14b922b57e69d204a3fc4aa954fba22264105fe4
| 182
|
py
|
Python
|
engine/__init__.py
|
KelOdgSmile/ml-cvnets
|
503ec3b4ec187cfa0ed451d0f61de22f669b0081
|
[
"AML"
] | 1
|
2021-12-20T09:25:18.000Z
|
2021-12-20T09:25:18.000Z
|
engine/__init__.py
|
footh/ml-cvnets
|
d9064fe7e7a2d6a7a9817df936432856a0500a25
|
[
"AML"
] | null | null | null |
engine/__init__.py
|
footh/ml-cvnets
|
d9064fe7e7a2d6a7a9817df936432856a0500a25
|
[
"AML"
] | null | null | null |
#
# For licensing see accompanying LICENSE file.
# Copyright (C) 2020 Apple Inc. All Rights Reserved.
#
from .training_engine import Trainer
from .evaluation_engine import Evaluator
| 26
| 52
| 0.796703
| 24
| 182
| 5.958333
| 0.875
| 0.167832
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.025641
| 0.142857
| 182
| 7
| 53
| 26
| 0.891026
| 0.521978
| 0
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| null | 0
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| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
14bf0695cc1566c0ab41862b46716cfaf0878e18
| 42
|
py
|
Python
|
tests/__init__.py
|
afraniofilho/covid_vision
|
b633c255030ff9dcb00523e377c0e1ef6771ca50
|
[
"MIT"
] | 2
|
2020-05-11T14:22:17.000Z
|
2020-06-25T02:12:20.000Z
|
tests/__init__.py
|
afraniofilho/covid_vision
|
b633c255030ff9dcb00523e377c0e1ef6771ca50
|
[
"MIT"
] | 6
|
2020-11-13T18:50:31.000Z
|
2022-03-12T00:31:29.000Z
|
tests/__init__.py
|
afraniofilho/covid_vision
|
b633c255030ff9dcb00523e377c0e1ef6771ca50
|
[
"MIT"
] | 3
|
2020-05-12T21:47:33.000Z
|
2020-07-10T07:30:28.000Z
|
"""Unit test package for covid_vision."""
| 21
| 41
| 0.714286
| 6
| 42
| 4.833333
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.119048
| 42
| 1
| 42
| 42
| 0.783784
| 0.833333
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 1
| 0
| null | 0
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| null | 0
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| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
1af9125d99e3f74dc4339a22b2da726ad6cb8c35
| 183
|
py
|
Python
|
Command.py
|
mucsci-students/2021sp-420-team1
|
b3cedc9bd1ac185d703a3e8a754601239abf91fc
|
[
"MIT"
] | 2
|
2021-02-02T21:53:57.000Z
|
2021-02-09T02:48:38.000Z
|
Command.py
|
mucsci-students/2021sp-420-team1
|
b3cedc9bd1ac185d703a3e8a754601239abf91fc
|
[
"MIT"
] | 94
|
2021-02-02T21:44:00.000Z
|
2021-05-03T03:09:26.000Z
|
Command.py
|
mucsci-students/2021sp-420-team1
|
b3cedc9bd1ac185d703a3e8a754601239abf91fc
|
[
"MIT"
] | null | null | null |
class Command:
def __init__(self, function, *args):
self.function = function
self.args = list(args)
def execute(self):
self.function(*self.args)
| 26.142857
| 41
| 0.595628
| 21
| 183
| 5
| 0.428571
| 0.342857
| 0.304762
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.289617
| 183
| 6
| 42
| 30.5
| 0.807692
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
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| 0.5
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| null | 1
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| null | 0
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| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
21029a0ea7fc54328578c835da55a3fbeb88a820
| 107
|
py
|
Python
|
{{cookiecutter.project_slug}}/src/{{cookiecutter.package_name}}/__init__.py
|
miguelarbesu/cookiecutter-reproducible-science
|
f25f136c9baaa80d0eafdcec97e86f7344f05840
|
[
"BSD-3-Clause"
] | null | null | null |
{{cookiecutter.project_slug}}/src/{{cookiecutter.package_name}}/__init__.py
|
miguelarbesu/cookiecutter-reproducible-science
|
f25f136c9baaa80d0eafdcec97e86f7344f05840
|
[
"BSD-3-Clause"
] | null | null | null |
{{cookiecutter.project_slug}}/src/{{cookiecutter.package_name}}/__init__.py
|
miguelarbesu/cookiecutter-reproducible-science
|
f25f136c9baaa80d0eafdcec97e86f7344f05840
|
[
"BSD-3-Clause"
] | null | null | null |
"""{{cookiecutter.project_name}}
{{cookiecutter.short_description}}
"""
from ._version import __version__
| 17.833333
| 34
| 0.766355
| 10
| 107
| 7.5
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.074766
| 107
| 5
| 35
| 21.4
| 0.757576
| 0.598131
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
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| null | 0
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| 1
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
21699b805fefbcb30a0912dd591950ea7f4257c1
| 97
|
py
|
Python
|
dirutility/walk/__init__.py
|
DeeeFOX/dirutility
|
92317992c8bd2a3feffea7f204e8573a4dea8fd1
|
[
"MIT"
] | 2
|
2018-07-27T18:34:10.000Z
|
2018-10-09T21:40:34.000Z
|
dirutility/walk/__init__.py
|
mrstephenneal/dirutility
|
c51b4c3bd543da8bb69e496d0c3ec8333897042c
|
[
"MIT"
] | 7
|
2018-07-27T17:29:36.000Z
|
2018-10-01T13:29:52.000Z
|
dirutility/walk/__init__.py
|
mrstephenneal/dirutility
|
c51b4c3bd543da8bb69e496d0c3ec8333897042c
|
[
"MIT"
] | 1
|
2019-09-26T13:04:04.000Z
|
2019-09-26T13:04:04.000Z
|
__all__ = ['DirPaths', 'DirTree', 'gui']
from dirutility.walk.walk import DirPaths, DirTree, gui
| 32.333333
| 55
| 0.731959
| 12
| 97
| 5.583333
| 0.666667
| 0.447761
| 0.537313
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.113402
| 97
| 3
| 55
| 32.333333
| 0.77907
| 0
| 0
| 0
| 0
| 0
| 0.183673
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.5
| 0
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| 0
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| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
dcf8cd3439021fd6e4dbcf2b897da09154ac2f30
| 74
|
py
|
Python
|
stonks/__init__.py
|
jlowe77/Eris-Cogs
|
2ade8f82db3477527af3cff3b48ebb281e1a6987
|
[
"Apache-2.0"
] | 6
|
2020-05-13T20:43:53.000Z
|
2021-06-23T16:10:13.000Z
|
stonks/__init__.py
|
jlowe77/Eris-Cogs
|
2ade8f82db3477527af3cff3b48ebb281e1a6987
|
[
"Apache-2.0"
] | 12
|
2019-04-02T13:29:10.000Z
|
2020-03-27T18:07:16.000Z
|
stonks/__init__.py
|
jlowe77/Eris-Cogs
|
2ade8f82db3477527af3cff3b48ebb281e1a6987
|
[
"Apache-2.0"
] | 9
|
2020-06-07T21:46:54.000Z
|
2022-03-01T22:49:02.000Z
|
from .stonks import Stonks
def setup(bot):
bot.add_cog(Stonks(bot))
| 12.333333
| 28
| 0.702703
| 12
| 74
| 4.25
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.175676
| 74
| 5
| 29
| 14.8
| 0.836066
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
b4a870e4e2dfd5b019b036004693a326b91bd6d7
| 48
|
py
|
Python
|
coregen/exceptions.py
|
coregen/coregen
|
afd3e33f9a6d90504768fac2cfcbdf9752f3a125
|
[
"MIT"
] | null | null | null |
coregen/exceptions.py
|
coregen/coregen
|
afd3e33f9a6d90504768fac2cfcbdf9752f3a125
|
[
"MIT"
] | 3
|
2020-03-24T18:11:23.000Z
|
2021-02-02T22:26:31.000Z
|
coregen/exceptions.py
|
coregen/coregen
|
afd3e33f9a6d90504768fac2cfcbdf9752f3a125
|
[
"MIT"
] | null | null | null |
class ReadOnlyModification(Exception):
pass
| 16
| 38
| 0.791667
| 4
| 48
| 9.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.145833
| 48
| 2
| 39
| 24
| 0.926829
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.5
| 0
| 0
| 0.5
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 5
|
b4b797ad81987171badb6ffbf44c41e7bc818f39
| 2,949
|
py
|
Python
|
transformer/source/source_formatter.py
|
SimpleConstructs/fixed-width-parser
|
b9bb4953e66dcea2b6ce3aeb2fed58a3f34a784b
|
[
"Apache-2.0"
] | null | null | null |
transformer/source/source_formatter.py
|
SimpleConstructs/fixed-width-parser
|
b9bb4953e66dcea2b6ce3aeb2fed58a3f34a784b
|
[
"Apache-2.0"
] | 3
|
2021-07-18T14:34:38.000Z
|
2021-07-25T12:49:09.000Z
|
transformer/source/source_formatter.py
|
SimpleConstructs/fixed-width-transformer
|
b9bb4953e66dcea2b6ce3aeb2fed58a3f34a784b
|
[
"Apache-2.0"
] | null | null | null |
from transformer.library import logger
from transformer.library.exceptions import SourceFileError
from transformer.source import SourceFormatterConfig
from io import StringIO
import pandas as pd
log = logger.set_logger(__name__)
class AbstractDataMapper:
def run(self, config: SourceFormatterConfig, file_name: str) -> pd.DataFrame: pass
class HeaderSourceFormatter(AbstractDataMapper):
def run(self, config: SourceFormatterConfig, file_name: str) -> pd.DataFrame:
try:
data = pd.read_fwf(file_name, colspecs=config.specs, names=config.names,
converters={h: str for h in config.names}, delimiter="\n\t", nrows=1)
if len(data.index) == 0:
raise SourceFileError("Invalid Source File, Index is empty", file_name)
return data
except FileNotFoundError as e:
raise SourceFileError(e, file_name)
class BodySourceFormatter(AbstractDataMapper):
def run(self, config: SourceFormatterConfig, file_name: str) -> pd.DataFrame:
try:
data = pd.read_fwf(file_name, colspecs=config.specs, header=0,
names=config.names,
converters={h: str for h in config.names}, skipfooter=1,
delimiter="\n\t")
if len(data.index) == 0:
raise SourceFileError("Invalid Source File, Index is empty", file_name)
return data
except FileNotFoundError as e:
raise SourceFileError(e, file_name)
class FooterSourceFormatter(AbstractDataMapper):
def run(self, config: SourceFormatterConfig, file_name: str) -> pd.DataFrame:
try:
with open(file_name, 'r') as lines:
read = lines.readlines()
if len(read) == 0:
raise SourceFileError("Invalid Source File, Index is empty", file_name)
last_line = read[-1]
data = pd.read_fwf(StringIO(last_line), colspecs=config.specs,
names=config.names,
converters={h: str for h in config.names}, delimiter="\n\t")
return data
except FileNotFoundError as e:
raise SourceFileError(e, file_name)
class BodyOnlySourceFormatter(AbstractDataMapper):
def run(self, config: SourceFormatterConfig, file_name: str) -> pd.DataFrame:
try:
data = pd.read_fwf(file_name, colspecs=config.specs, header=None,
names=config.names,
converters={h: str for h in config.names}, skipfooter=0,
delimiter="\n\t")
if len(data.index) == 0:
raise SourceFileError("Invalid Source File, Index is empty", file_name)
return data
except FileNotFoundError as e:
raise SourceFileError(e, file_name)
| 44.014925
| 100
| 0.60156
| 318
| 2,949
| 5.490566
| 0.216981
| 0.077892
| 0.068729
| 0.080183
| 0.772623
| 0.772623
| 0.772623
| 0.772623
| 0.772623
| 0.772623
| 0
| 0.004442
| 0.312987
| 2,949
| 67
| 101
| 44.014925
| 0.857354
| 0
| 0
| 0.571429
| 0
| 0
| 0.05322
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.089286
| false
| 0.017857
| 0.089286
| 0
| 0.339286
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
b4cdca63b483c0cd06121f8bbc53151a8081b512
| 330
|
py
|
Python
|
winnow/config/__init__.py
|
benetech/Winnow2.0
|
bc428d7f74bd7db71b6d70ab15dc7a5c37786c46
|
[
"MIT"
] | 26
|
2019-12-16T21:22:14.000Z
|
2022-03-25T16:05:32.000Z
|
winnow/config/__init__.py
|
benetech/Winnow2.0
|
bc428d7f74bd7db71b6d70ab15dc7a5c37786c46
|
[
"MIT"
] | 325
|
2019-10-28T16:24:45.000Z
|
2022-03-31T13:12:15.000Z
|
winnow/config/__init__.py
|
benetech/Winnow2.0
|
bc428d7f74bd7db71b6d70ab15dc7a5c37786c46
|
[
"MIT"
] | 9
|
2019-10-09T16:20:38.000Z
|
2021-12-22T18:44:45.000Z
|
from .config import Config, ProcessingConfig, DatabaseConfig, RepresentationConfig, SourcesConfig, TemplatesConfig
# Explicitly reexport api
# See discussion in https://bugs.launchpad.net/pyflakes/+bug/1178905
__all__ = ["Config", "ProcessingConfig", "DatabaseConfig", "RepresentationConfig", "SourcesConfig", "TemplatesConfig"]
| 55
| 118
| 0.806061
| 29
| 330
| 9.034483
| 0.758621
| 0.167939
| 0.274809
| 0.427481
| 0.641221
| 0.641221
| 0
| 0
| 0
| 0
| 0
| 0.023102
| 0.081818
| 330
| 5
| 119
| 66
| 0.841584
| 0.272727
| 0
| 0
| 0
| 0
| 0.35443
| 0
| 0
| 0
| 0
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| 0
| 1
| 0
| false
| 0
| 0.5
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| null | 0
| 1
| 1
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| 0
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| 0
| 0
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
b4da341a0f524cb15d0fcebf5f537593a74ff209
| 152
|
py
|
Python
|
expense_tracker/expense_tracker/views/__init__.py
|
morganelle/expense-tracker
|
b5b0b85d87bff4eb453b6d779f28b84e8ce1ddcc
|
[
"MIT"
] | null | null | null |
expense_tracker/expense_tracker/views/__init__.py
|
morganelle/expense-tracker
|
b5b0b85d87bff4eb453b6d779f28b84e8ce1ddcc
|
[
"MIT"
] | null | null | null |
expense_tracker/expense_tracker/views/__init__.py
|
morganelle/expense-tracker
|
b5b0b85d87bff4eb453b6d779f28b84e8ce1ddcc
|
[
"MIT"
] | null | null | null |
from .default import home_page
def includeme(config):
"""List of views to include for config."""
config.add_view(home_page, route_name='home')
| 25.333333
| 49
| 0.723684
| 23
| 152
| 4.608696
| 0.782609
| 0.150943
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.157895
| 152
| 6
| 49
| 25.333333
| 0.828125
| 0.236842
| 0
| 0
| 0
| 0
| 0.036036
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
2ec3d0f9022701fa40d960da9428dcab2eec08c3
| 1,357
|
py
|
Python
|
src/stronghold/rosalind_seto.py
|
cowboysmall/rosalind
|
021e4392a8fc946b97bbf86bbb8227b28bb5e462
|
[
"MIT"
] | null | null | null |
src/stronghold/rosalind_seto.py
|
cowboysmall/rosalind
|
021e4392a8fc946b97bbf86bbb8227b28bb5e462
|
[
"MIT"
] | null | null | null |
src/stronghold/rosalind_seto.py
|
cowboysmall/rosalind
|
021e4392a8fc946b97bbf86bbb8227b28bb5e462
|
[
"MIT"
] | null | null | null |
import os
import sys
sys.path.append(os.path.join(os.path.dirname(__file__), '../tools'))
'''
alternative implementation:
U = set(i for i in xrange(1, int(file.readline().strip()) + 1))
set1 = set(int(item) for item in file.readline().strip()[1:-1].split(', '))
set2 = set(int(item) for item in file.readline().strip()[1:-1].split(', '))
print '{%s}' % ', '.join(str(element) for element in set1 | set2)
print '{%s}' % ', '.join(str(element) for element in set1 & set2)
print '{%s}' % ', '.join(str(element) for element in set1 - set2)
print '{%s}' % ', '.join(str(element) for element in set2 - set1)
print '{%s}' % ', '.join(str(element) for element in U - set1)
print '{%s}' % ', '.join(str(element) for element in U - set2)
'''
def main(argv):
with open(argv[0]) as file:
U = set(str(i) for i in xrange(1, int(file.readline().strip()) + 1))
set1 = set(file.readline().strip()[1:-1].split(', '))
set2 = set(file.readline().strip()[1:-1].split(', '))
print '{%s}' % ', '.join(set1 | set2)
print '{%s}' % ', '.join(set1 & set2)
print '{%s}' % ', '.join(set1 - set2)
print '{%s}' % ', '.join(set2 - set1)
print '{%s}' % ', '.join(U - set1)
print '{%s}' % ', '.join(U - set2)
if __name__ == "__main__":
main(sys.argv[1:])
| 34.794872
| 79
| 0.53353
| 192
| 1,357
| 3.708333
| 0.197917
| 0.101124
| 0.168539
| 0.151685
| 0.77809
| 0.724719
| 0.724719
| 0.724719
| 0.675562
| 0.641854
| 0
| 0.03626
| 0.227708
| 1,357
| 38
| 80
| 35.710526
| 0.64313
| 0
| 0
| 0
| 0
| 0
| 0.083582
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0.125
| null | null | 0.375
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
2edd365129dd4028fad298f9cdf273cea2a57931
| 1,510
|
py
|
Python
|
tests/classes/simple_code.py
|
Jesse-Yung/jsonclasses
|
d40c52aec42bcb978a80ceb98b93ab38134dc790
|
[
"MIT"
] | 50
|
2021-08-18T08:08:04.000Z
|
2022-03-20T07:23:26.000Z
|
tests/classes/simple_code.py
|
Jesse-Yung/jsonclasses
|
d40c52aec42bcb978a80ceb98b93ab38134dc790
|
[
"MIT"
] | 1
|
2021-11-23T02:12:29.000Z
|
2021-11-23T13:35:26.000Z
|
tests/classes/simple_code.py
|
Jesse-Yung/jsonclasses
|
d40c52aec42bcb978a80ceb98b93ab38134dc790
|
[
"MIT"
] | 8
|
2021-07-01T02:39:15.000Z
|
2021-12-10T02:20:18.000Z
|
from __future__ import annotations
from typing import Optional
from jsonclasses import jsonclass, types
@jsonclass
class SimpleCode:
code: Optional[str] = types.str.length(4)
min_code: Optional[str] = types.str.minlength(4)
max_code: Optional[str] = types.str.maxlength(8)
l_code: Optional[list[int]] = types.listof(int).length(4)
l_min_code: Optional[list[int]] = types.listof(int).minlength(4)
l_max_code: Optional[list[int]] = types.listof(int).maxlength(8)
c_code: Optional[str] = types.str.length(lambda: 4)
c_min_code: Optional[str] = types.str.minlength(lambda: 4)
c_max_code: Optional[str] = types.str.maxlength(lambda: 8)
cl_code: Optional[list[int]] = types.listof(int).length(lambda: 4)
cl_min_code: Optional[list[int]] = types.listof(int).minlength(lambda: 4)
cl_max_code: Optional[list[int]] = types.listof(int).maxlength(lambda: 8)
t_code: Optional[str] = types.str.length(types.default(4))
t_min_code: Optional[str] = types.str.minlength(types.default(4))
t_max_code: Optional[str] = types.str.maxlength(types.default(8))
tl_code: Optional[list[int]] = types.listof(int).length(types.default(4))
tl_min_code: Optional[list[int]] = types.listof(int).minlength(types.default(4))
tl_max_code: Optional[list[int]] = types.listof(int).maxlength(types.default(8))
cd_code: Optional[str] = types.str.length(lambda: 4, lambda: 5)
td_code: Optional[str] = types.str.length(types.default(4), types.default(4).add(1))
| 44.411765
| 88
| 0.715232
| 231
| 1,510
| 4.532468
| 0.151515
| 0.229226
| 0.157593
| 0.210124
| 0.746896
| 0.746896
| 0.719198
| 0.518625
| 0.338109
| 0
| 0
| 0.017477
| 0.128477
| 1,510
| 33
| 89
| 45.757576
| 0.778116
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.12
| 0
| 0.96
| 0
| 0
| 0
| 0
| null | 1
| 0
| 1
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
d316dd34344fda00b5c0b1835717cb2e54ed3cb3
| 71
|
py
|
Python
|
tests/package_with_increments/module.py
|
borzunov/plusplus
|
e862cef91f1d25cba63b0a15159f7a356a6e9291
|
[
"MIT"
] | 88
|
2021-09-12T16:44:15.000Z
|
2022-03-23T08:08:39.000Z
|
tests/package_with_increments/module.py
|
borzunov/plusplus
|
e862cef91f1d25cba63b0a15159f7a356a6e9291
|
[
"MIT"
] | null | null | null |
tests/package_with_increments/module.py
|
borzunov/plusplus
|
e862cef91f1d25cba63b0a15159f7a356a6e9291
|
[
"MIT"
] | 4
|
2021-09-16T05:24:22.000Z
|
2021-09-25T05:54:45.000Z
|
def increment_and_return(x):
return ++x
CONSTANT = 777
++CONSTANT
| 11.833333
| 28
| 0.704225
| 10
| 71
| 4.8
| 0.7
| 0.291667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.051724
| 0.183099
| 71
| 6
| 29
| 11.833333
| 0.775862
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0
| 0.25
| 0.5
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
|
0
| 5
|
d379d3a2ade89ad9c4a1014e0282170eee584917
| 143
|
py
|
Python
|
app/datetime.py
|
tfuji384/atodeyomu
|
8a5e61a593d099726131e060a33b8801056bc286
|
[
"MIT"
] | null | null | null |
app/datetime.py
|
tfuji384/atodeyomu
|
8a5e61a593d099726131e060a33b8801056bc286
|
[
"MIT"
] | null | null | null |
app/datetime.py
|
tfuji384/atodeyomu
|
8a5e61a593d099726131e060a33b8801056bc286
|
[
"MIT"
] | null | null | null |
from datetime import datetime
from pytz import timezone
jst = timezone('Asia/Tokyo')
def now() -> datetime:
return datetime.now(tz=jst)
| 15.888889
| 31
| 0.727273
| 20
| 143
| 5.2
| 0.6
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.167832
| 143
| 8
| 32
| 17.875
| 0.87395
| 0
| 0
| 0
| 0
| 0
| 0.06993
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.2
| false
| 0
| 0.4
| 0.2
| 0.8
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
|
0
| 5
|
d397e39178be6cf6746a0c4cf51b3c96c3a928f9
| 381
|
py
|
Python
|
rlearn/utils/__init__.py
|
joaopfonseca/research-learn
|
7121a5e35c2c422d7c3e4de947123aff1744cdcb
|
[
"MIT"
] | 1
|
2021-06-18T15:22:11.000Z
|
2021-06-18T15:22:11.000Z
|
rlearn/utils/__init__.py
|
joaopfonseca/research-learn
|
7121a5e35c2c422d7c3e4de947123aff1744cdcb
|
[
"MIT"
] | 1
|
2021-03-09T11:28:14.000Z
|
2021-03-09T11:28:14.000Z
|
rlearn/utils/__init__.py
|
joaopfonseca/research-learn
|
7121a5e35c2c422d7c3e4de947123aff1744cdcb
|
[
"MIT"
] | null | null | null |
"""
The :mod:`rlearn.utils` module includes various utilities.
"""
from .validation import (
check_param_grids,
check_datasets,
check_random_states,
check_estimator_type,
check_oversamplers_classifiers,
)
__all__ = [
'check_param_grids',
'check_datasets',
'check_random_states',
'check_estimator_type',
'check_oversamplers_classifiers',
]
| 19.05
| 58
| 0.71916
| 40
| 381
| 6.3
| 0.55
| 0.079365
| 0.119048
| 0.15873
| 0.722222
| 0.722222
| 0.722222
| 0.722222
| 0.722222
| 0.722222
| 0
| 0
| 0.181102
| 381
| 19
| 59
| 20.052632
| 0.807692
| 0.152231
| 0
| 0
| 0
| 0
| 0.31746
| 0.095238
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.071429
| 0
| 0.071429
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
6c947f09d79a6ad32bf77eefb2c36f8dd3f8439a
| 158,076
|
py
|
Python
|
Pandas/Pandas.py
|
jrderek/Data-science-master-resources
|
95adab02dccbf5fbe6333389324a1f8d032d3165
|
[
"MIT"
] | 14
|
2020-09-17T17:04:04.000Z
|
2021-08-19T05:08:49.000Z
|
Pandas/Pandas.py
|
jrderek/Data-science-master-resources
|
95adab02dccbf5fbe6333389324a1f8d032d3165
|
[
"MIT"
] | 85
|
2020-10-01T16:53:21.000Z
|
2021-07-08T17:44:17.000Z
|
Pandas/Pandas.py
|
jrderek/Data-science-master-resources
|
95adab02dccbf5fbe6333389324a1f8d032d3165
|
[
"MIT"
] | 5
|
2020-09-18T08:53:01.000Z
|
2021-08-19T05:12:52.000Z
|
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Part 2"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# missing data"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"d = {\n",
" 'A': [1,2, np.nan],\n",
" 'B':[5, np.nan, np.nan],\n",
" 'C':[1,2,3]\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame(d)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>5.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C\n",
"0 1.0 5.0 1\n",
"1 2.0 NaN 2\n",
"2 NaN NaN 3"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## drop nan method"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>5.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C\n",
"0 1.0 5.0 1"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.dropna()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>5.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C\n",
"0 1.0 5.0 1"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.dropna(axis = 0)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"df.dropna??"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>C</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" C\n",
"0 1\n",
"1 2\n",
"2 3"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.dropna(axis = 1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## filling value"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>5.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C\n",
"0 1.0 5.0 1\n",
"1 2.0 NaN 2\n",
"2 NaN NaN 3"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Filling Value</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>Filling Value</td>\n",
" <td>Filling Value</td>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C\n",
"0 1 5 1\n",
"1 2 Filling Value 2\n",
"2 Filling Value Filling Value 3"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.fillna(value = 'Filling Value')"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame(d)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"a = df['A'].fillna(value = df['A'].mean())"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
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" <th>B</th>\n",
" <th>C</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>5.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C\n",
"0 1.0 5.0 1\n",
"1 2.0 NaN 2\n",
"2 NaN NaN 3"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 1.0\n",
"1 2.0\n",
"2 1.5\n",
"Name: A, dtype: float64"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
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" <tr style=\"text-align: right;\">\n",
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" <th>B</th>\n",
" <th>C</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>5.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C\n",
"0 1.0 5.0 1\n",
"1 2.0 NaN 2\n",
"2 NaN NaN 3"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 1.0\n",
"1 2.0\n",
"2 1.5\n",
"Name: A, dtype: float64"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['A'].fillna(value = df['A'].mean())"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" <th>B</th>\n",
" <th>C</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>5.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C\n",
"0 1.0 5.0 1\n",
"1 2.0 NaN 2\n",
"2 NaN NaN 3"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"df.fillna??"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"df['A'].fillna(value = df['A'].mean(), inplace = True)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
" .dataframe thead th {\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>5.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>1.5</td>\n",
" <td>NaN</td>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C\n",
"0 1.0 5.0 1\n",
"1 2.0 NaN 2\n",
"2 1.5 NaN 3"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Group By"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"data = {\n",
" 'Company':['Google', 'Google', 'MSFT', 'FB','FB','IBM'],\n",
" 'Person': ['Sam','Nihad','Any','Van','Rakib','Ovi'],\n",
" 'Sales': [200, 120, 340, 124, 243, 350]\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame(data)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" <th>Sales</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>Google</td>\n",
" <td>Sam</td>\n",
" <td>200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>Google</td>\n",
" <td>Nihad</td>\n",
" <td>120</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>MSFT</td>\n",
" <td>Any</td>\n",
" <td>340</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>FB</td>\n",
" <td>Van</td>\n",
" <td>124</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>FB</td>\n",
" <td>Rakib</td>\n",
" <td>243</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>IBM</td>\n",
" <td>Ovi</td>\n",
" <td>350</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Company Person Sales\n",
"0 Google Sam 200\n",
"1 Google Nihad 120\n",
"2 MSFT Any 340\n",
"3 FB Van 124\n",
"4 FB Rakib 243\n",
"5 IBM Ovi 350"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"byComp = df.groupby('Company')"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sales</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Company</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>FB</td>\n",
" <td>183.5</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Google</td>\n",
" <td>160.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>IBM</td>\n",
" <td>350.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>MSFT</td>\n",
" <td>340.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sales\n",
"Company \n",
"FB 183.5\n",
"Google 160.0\n",
"IBM 350.0\n",
"MSFT 340.0"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"byComp.mean()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" <th></th>\n",
" <th>Sales</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Company</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>FB</td>\n",
" <td>367</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Google</td>\n",
" <td>320</td>\n",
" </tr>\n",
" <tr>\n",
" <td>IBM</td>\n",
" <td>350</td>\n",
" </tr>\n",
" <tr>\n",
" <td>MSFT</td>\n",
" <td>340</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sales\n",
"Company \n",
"FB 367\n",
"Google 320\n",
"IBM 350\n",
"MSFT 340"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"byComp.sum()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Company</th>\n",
" <th>Person</th>\n",
" <th>Sales</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>Google</td>\n",
" <td>Sam</td>\n",
" <td>200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>Google</td>\n",
" <td>Nihad</td>\n",
" <td>120</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>MSFT</td>\n",
" <td>Any</td>\n",
" <td>340</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>FB</td>\n",
" <td>Van</td>\n",
" <td>124</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>FB</td>\n",
" <td>Rakib</td>\n",
" <td>243</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>IBM</td>\n",
" <td>Ovi</td>\n",
" <td>350</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Company Person Sales\n",
"0 Google Sam 200\n",
"1 Google Nihad 120\n",
"2 MSFT Any 340\n",
"3 FB Van 124\n",
"4 FB Rakib 243\n",
"5 IBM Ovi 350"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <th></th>\n",
" <th>Sales</th>\n",
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" <tr>\n",
" <th>Company</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>FB</td>\n",
" <td>84.145707</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Google</td>\n",
" <td>56.568542</td>\n",
" </tr>\n",
" <tr>\n",
" <td>IBM</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>MSFT</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sales\n",
"Company \n",
"FB 84.145707\n",
"Google 56.568542\n",
"IBM NaN\n",
"MSFT NaN"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"byComp.std()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" <th>Person</th>\n",
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" </tr>\n",
" <tr>\n",
" <th>Company</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>FB</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Google</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>IBM</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>MSFT</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" Person Sales\n",
"Company \n",
"FB 2 2\n",
"Google 2 2\n",
"IBM 1 1\n",
"MSFT 1 1"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.groupby('Company').count()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" <th></th>\n",
" <th>Company</th>\n",
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" <th>Sales</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>Google</td>\n",
" <td>Sam</td>\n",
" <td>200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>Google</td>\n",
" <td>Nihad</td>\n",
" <td>120</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>MSFT</td>\n",
" <td>Any</td>\n",
" <td>340</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>FB</td>\n",
" <td>Van</td>\n",
" <td>124</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>FB</td>\n",
" <td>Rakib</td>\n",
" <td>243</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>IBM</td>\n",
" <td>Ovi</td>\n",
" <td>350</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Company Person Sales\n",
"0 Google Sam 200\n",
"1 Google Nihad 120\n",
"2 MSFT Any 340\n",
"3 FB Van 124\n",
"4 FB Rakib 243\n",
"5 IBM Ovi 350"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
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" }\n",
"\n",
" .dataframe thead tr:last-of-type th {\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"8\" halign=\"left\">Sales</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Company</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>FB</td>\n",
" <td>2.0</td>\n",
" <td>183.5</td>\n",
" <td>84.145707</td>\n",
" <td>124.0</td>\n",
" <td>153.75</td>\n",
" <td>183.5</td>\n",
" <td>213.25</td>\n",
" <td>243.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Google</td>\n",
" <td>2.0</td>\n",
" <td>160.0</td>\n",
" <td>56.568542</td>\n",
" <td>120.0</td>\n",
" <td>140.00</td>\n",
" <td>160.0</td>\n",
" <td>180.00</td>\n",
" <td>200.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>IBM</td>\n",
" <td>1.0</td>\n",
" <td>350.0</td>\n",
" <td>NaN</td>\n",
" <td>350.0</td>\n",
" <td>350.00</td>\n",
" <td>350.0</td>\n",
" <td>350.00</td>\n",
" <td>350.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>MSFT</td>\n",
" <td>1.0</td>\n",
" <td>340.0</td>\n",
" <td>NaN</td>\n",
" <td>340.0</td>\n",
" <td>340.00</td>\n",
" <td>340.0</td>\n",
" <td>340.00</td>\n",
" <td>340.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sales \n",
" count mean std min 25% 50% 75% max\n",
"Company \n",
"FB 2.0 183.5 84.145707 124.0 153.75 183.5 213.25 243.0\n",
"Google 2.0 160.0 56.568542 120.0 140.00 160.0 180.00 200.0\n",
"IBM 1.0 350.0 NaN 350.0 350.00 350.0 350.00 350.0\n",
"MSFT 1.0 340.0 NaN 340.0 340.00 340.0 340.00 340.0"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.groupby('Company').describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Merging, Joining and Concatenating"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Concatenating"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"df1 = pd.DataFrame(np.random.randint(1, 20, size = (4, 3)), [1,2,3,4], ['W', 'X', 'Y'])"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"df2 = pd.DataFrame(np.random.randint(20,40, size = (4, 3)), [5,6,7,8], ['W', 'X', 'Y'])"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"df3 = pd.DataFrame(np.random.randint(40,60, size = (4, 3)), [9,10,11,12], ['W', 'X', 'Y'])"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
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],
"text/plain": [
" W X Y\n",
"1 9 10 19\n",
"2 14 15 8\n",
"3 6 13 6\n",
"4 4 11 14"
]
},
"execution_count": 53,
"metadata": {},
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],
"source": [
"df1"
]
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{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
" W X Y\n",
"5 32 26 24\n",
"6 25 35 20\n",
"7 29 36 25\n",
"8 23 27 29"
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},
"execution_count": 54,
"metadata": {},
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],
"source": [
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{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
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"text/plain": [
" W X Y\n",
"9 52 50 47\n",
"10 54 53 40\n",
"11 57 53 51\n",
"12 43 58 47"
]
},
"execution_count": 55,
"metadata": {},
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],
"source": [
"df3"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
"farme = [df1, df2, df3]"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
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" <tr>\n",
" <td>8</td>\n",
" <td>23</td>\n",
" <td>27</td>\n",
" <td>29</td>\n",
" </tr>\n",
" <tr>\n",
" <td>9</td>\n",
" <td>52</td>\n",
" <td>50</td>\n",
" <td>47</td>\n",
" </tr>\n",
" <tr>\n",
" <td>10</td>\n",
" <td>54</td>\n",
" <td>53</td>\n",
" <td>40</td>\n",
" </tr>\n",
" <tr>\n",
" <td>11</td>\n",
" <td>57</td>\n",
" <td>53</td>\n",
" <td>51</td>\n",
" </tr>\n",
" <tr>\n",
" <td>12</td>\n",
" <td>43</td>\n",
" <td>58</td>\n",
" <td>47</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" W X Y\n",
"1 9 10 19\n",
"2 14 15 8\n",
"3 6 13 6\n",
"4 4 11 14\n",
"5 32 26 24\n",
"6 25 35 20\n",
"7 29 36 25\n",
"8 23 27 29\n",
"9 52 50 47\n",
"10 54 53 40\n",
"11 57 53 51\n",
"12 43 58 47"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.concat(farme)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" <th>W</th>\n",
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" <th>X</th>\n",
" <th>Y</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>9.0</td>\n",
" <td>10.0</td>\n",
" <td>19.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>14.0</td>\n",
" <td>15.0</td>\n",
" <td>8.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>6.0</td>\n",
" <td>13.0</td>\n",
" <td>6.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>4.0</td>\n",
" <td>11.0</td>\n",
" <td>14.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>32.0</td>\n",
" <td>26.0</td>\n",
" <td>24.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>6</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>25.0</td>\n",
" <td>35.0</td>\n",
" <td>20.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>29.0</td>\n",
" <td>36.0</td>\n",
" <td>25.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>8</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>23.0</td>\n",
" <td>27.0</td>\n",
" <td>29.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>52.0</td>\n",
" <td>50.0</td>\n",
" <td>47.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>10</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>54.0</td>\n",
" <td>53.0</td>\n",
" <td>40.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>11</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>57.0</td>\n",
" <td>53.0</td>\n",
" <td>51.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>12</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>43.0</td>\n",
" <td>58.0</td>\n",
" <td>47.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" W X Y W X Y W X Y\n",
"1 9.0 10.0 19.0 NaN NaN NaN NaN NaN NaN\n",
"2 14.0 15.0 8.0 NaN NaN NaN NaN NaN NaN\n",
"3 6.0 13.0 6.0 NaN NaN NaN NaN NaN NaN\n",
"4 4.0 11.0 14.0 NaN NaN NaN NaN NaN NaN\n",
"5 NaN NaN NaN 32.0 26.0 24.0 NaN NaN NaN\n",
"6 NaN NaN NaN 25.0 35.0 20.0 NaN NaN NaN\n",
"7 NaN NaN NaN 29.0 36.0 25.0 NaN NaN NaN\n",
"8 NaN NaN NaN 23.0 27.0 29.0 NaN NaN NaN\n",
"9 NaN NaN NaN NaN NaN NaN 52.0 50.0 47.0\n",
"10 NaN NaN NaN NaN NaN NaN 54.0 53.0 40.0\n",
"11 NaN NaN NaN NaN NaN NaN 57.0 53.0 51.0\n",
"12 NaN NaN NaN NaN NaN NaN 43.0 58.0 47.0"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.concat(farme, axis = 1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Merging"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
"left = {\n",
" 'key':['k0','k1','k2','k3'],\n",
" 'A':[10,20,30,40],\n",
" 'B':[1,2,3,4]\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [],
"source": [
"right = {\n",
" 'key':['k0','k1','k2','k3'],\n",
" 'C':[100,200,300,400],\n",
" 'D':[11,12,13,14]\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [],
"source": [
"left = pd.DataFrame(left)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
"right = pd.DataFrame(right)"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>key</th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>k0</td>\n",
" <td>10</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>k1</td>\n",
" <td>20</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>k2</td>\n",
" <td>30</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>k3</td>\n",
" <td>40</td>\n",
" <td>4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" key A B\n",
"0 k0 10 1\n",
"1 k1 20 2\n",
"2 k2 30 3\n",
"3 k3 40 4"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"left"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"</style>\n",
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" <th></th>\n",
" <th>key</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>k0</td>\n",
" <td>100</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>k1</td>\n",
" <td>200</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>k2</td>\n",
" <td>300</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>k3</td>\n",
" <td>400</td>\n",
" <td>14</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" key C D\n",
"0 k0 100 11\n",
"1 k1 200 12\n",
"2 k2 300 13\n",
"3 k3 400 14"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"right"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>key</th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>k0</td>\n",
" <td>10</td>\n",
" <td>1</td>\n",
" <td>100</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>k1</td>\n",
" <td>20</td>\n",
" <td>2</td>\n",
" <td>200</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>k2</td>\n",
" <td>30</td>\n",
" <td>3</td>\n",
" <td>300</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>k3</td>\n",
" <td>40</td>\n",
" <td>4</td>\n",
" <td>400</td>\n",
" <td>14</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" key A B C D\n",
"0 k0 10 1 100 11\n",
"1 k1 20 2 200 12\n",
"2 k2 30 3 300 13\n",
"3 k3 40 4 400 14"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.merge(left, right, on='key')"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [],
"source": [
"pd.merge??"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Joining"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [],
"source": [
"left = {\n",
" 'A':['A0','A1','A2'],\n",
" 'B':['B0','B1','B2']\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [],
"source": [
"right = {\n",
" 'C':['C0','C2','C3'],\n",
" 'D':['D0','D2','D3']\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [],
"source": [
"left = pd.DataFrame(left, index = ['k0','k1','k2'])"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [],
"source": [
"right = pd.DataFrame(right, index = ['k0','k2','k3'])"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>k0</td>\n",
" <td>A0</td>\n",
" <td>B0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>k1</td>\n",
" <td>A1</td>\n",
" <td>B1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>k2</td>\n",
" <td>A2</td>\n",
" <td>B2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B\n",
"k0 A0 B0\n",
"k1 A1 B1\n",
"k2 A2 B2"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"left"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>k0</td>\n",
" <td>C0</td>\n",
" <td>D0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>k2</td>\n",
" <td>C2</td>\n",
" <td>D2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>k3</td>\n",
" <td>C3</td>\n",
" <td>D3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" C D\n",
"k0 C0 D0\n",
"k2 C2 D2\n",
"k3 C3 D3"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"right"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>k0</td>\n",
" <td>A0</td>\n",
" <td>B0</td>\n",
" <td>C0</td>\n",
" <td>D0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>k2</td>\n",
" <td>A2</td>\n",
" <td>B2</td>\n",
" <td>C2</td>\n",
" <td>D2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"k0 A0 B0 C0 D0\n",
"k2 A2 B2 C2 D2"
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"left.join(right, how = 'inner')"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [],
"source": [
"left.join??"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Operations"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame({\n",
" 'col1':[444,555,666,444],\n",
" 'col2':[1,2,3,4],\n",
" 'col3':['abc','def','ghi','xyz']\n",
"})"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>col1</th>\n",
" <th>col2</th>\n",
" <th>col3</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>444</td>\n",
" <td>1</td>\n",
" <td>abc</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>555</td>\n",
" <td>2</td>\n",
" <td>def</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>666</td>\n",
" <td>3</td>\n",
" <td>ghi</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>444</td>\n",
" <td>4</td>\n",
" <td>xyz</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" col1 col2 col3\n",
"0 444 1 abc\n",
"1 555 2 def\n",
"2 666 3 ghi\n",
"3 444 4 xyz"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([444, 555, 666], dtype=int64)"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['col1'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['col1'].nunique()"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"444 2\n",
"555 1\n",
"666 1\n",
"Name: col1, dtype: int64"
]
},
"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['col1'].value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Apply Method"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [],
"source": [
"def times2(x):\n",
" return 2*x"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 2\n",
"1 4\n",
"2 6\n",
"3 8\n",
"Name: col2, dtype: int64"
]
},
"execution_count": 85,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['col2'].apply(times2)"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 1\n",
"1 4\n",
"2 9\n",
"3 16\n",
"Name: col2, dtype: int64"
]
},
"execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['col2'].apply(lambda x: x**2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Reading File"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_excel('SampleData.xlsx', sheet_name = 'SalesOrders')"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>OrderDate</th>\n",
" <th>Region</th>\n",
" <th>Rep</th>\n",
" <th>Item</th>\n",
" <th>Units</th>\n",
" <th>Unit Cost</th>\n",
" <th>Total</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>2019-01-06</td>\n",
" <td>East</td>\n",
" <td>Jones</td>\n",
" <td>Pencil</td>\n",
" <td>95</td>\n",
" <td>1.99</td>\n",
" <td>189.05</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>2019-01-23</td>\n",
" <td>Central</td>\n",
" <td>Kivell</td>\n",
" <td>Binder</td>\n",
" <td>50</td>\n",
" <td>19.99</td>\n",
" <td>999.50</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>2019-02-09</td>\n",
" <td>Central</td>\n",
" <td>Jardine</td>\n",
" <td>Pencil</td>\n",
" <td>36</td>\n",
" <td>4.99</td>\n",
" <td>179.64</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>2019-02-26</td>\n",
" <td>Central</td>\n",
" <td>Gill</td>\n",
" <td>Pen</td>\n",
" <td>27</td>\n",
" <td>19.99</td>\n",
" <td>539.73</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>2019-03-15</td>\n",
" <td>West</td>\n",
" <td>Sorvino</td>\n",
" <td>Pencil</td>\n",
" <td>56</td>\n",
" <td>2.99</td>\n",
" <td>167.44</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>2019-04-01</td>\n",
" <td>East</td>\n",
" <td>Jones</td>\n",
" <td>Binder</td>\n",
" <td>60</td>\n",
" <td>4.99</td>\n",
" <td>299.40</td>\n",
" </tr>\n",
" <tr>\n",
" <td>6</td>\n",
" <td>2019-04-18</td>\n",
" <td>Central</td>\n",
" <td>Andrews</td>\n",
" <td>Pencil</td>\n",
" <td>75</td>\n",
" <td>1.99</td>\n",
" <td>149.25</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7</td>\n",
" <td>2019-05-05</td>\n",
" <td>Central</td>\n",
" <td>Jardine</td>\n",
" <td>Pencil</td>\n",
" <td>90</td>\n",
" <td>4.99</td>\n",
" <td>449.10</td>\n",
" </tr>\n",
" <tr>\n",
" <td>8</td>\n",
" <td>2019-05-22</td>\n",
" <td>West</td>\n",
" <td>Thompson</td>\n",
" <td>Pencil</td>\n",
" <td>32</td>\n",
" <td>1.99</td>\n",
" <td>63.68</td>\n",
" </tr>\n",
" <tr>\n",
" <td>9</td>\n",
" <td>2019-06-08</td>\n",
" <td>East</td>\n",
" <td>Jones</td>\n",
" <td>Binder</td>\n",
" <td>60</td>\n",
" <td>8.99</td>\n",
" <td>539.40</td>\n",
" </tr>\n",
" <tr>\n",
" <td>10</td>\n",
" <td>2019-06-25</td>\n",
" <td>Central</td>\n",
" <td>Morgan</td>\n",
" <td>Pencil</td>\n",
" <td>90</td>\n",
" <td>4.99</td>\n",
" <td>449.10</td>\n",
" </tr>\n",
" <tr>\n",
" <td>11</td>\n",
" <td>2019-07-12</td>\n",
" <td>East</td>\n",
" <td>Howard</td>\n",
" <td>Binder</td>\n",
" <td>29</td>\n",
" <td>1.99</td>\n",
" <td>57.71</td>\n",
" </tr>\n",
" <tr>\n",
" <td>12</td>\n",
" <td>2019-07-29</td>\n",
" <td>East</td>\n",
" <td>Parent</td>\n",
" <td>Binder</td>\n",
" <td>81</td>\n",
" <td>19.99</td>\n",
" <td>1619.19</td>\n",
" </tr>\n",
" <tr>\n",
" <td>13</td>\n",
" <td>2019-08-15</td>\n",
" <td>East</td>\n",
" <td>Jones</td>\n",
" <td>Pencil</td>\n",
" <td>35</td>\n",
" <td>4.99</td>\n",
" <td>174.65</td>\n",
" </tr>\n",
" <tr>\n",
" <td>14</td>\n",
" <td>2019-09-01</td>\n",
" <td>Central</td>\n",
" <td>Smith</td>\n",
" <td>Desk</td>\n",
" <td>2</td>\n",
" <td>125.00</td>\n",
" <td>250.00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>15</td>\n",
" <td>2019-09-18</td>\n",
" <td>East</td>\n",
" <td>Jones</td>\n",
" <td>Pen Set</td>\n",
" <td>16</td>\n",
" <td>15.99</td>\n",
" <td>255.84</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16</td>\n",
" <td>2019-10-05</td>\n",
" <td>Central</td>\n",
" <td>Morgan</td>\n",
" <td>Binder</td>\n",
" <td>28</td>\n",
" <td>8.99</td>\n",
" <td>251.72</td>\n",
" </tr>\n",
" <tr>\n",
" <td>17</td>\n",
" <td>2019-10-22</td>\n",
" <td>East</td>\n",
" <td>Jones</td>\n",
" <td>Pen</td>\n",
" <td>64</td>\n",
" <td>8.99</td>\n",
" <td>575.36</td>\n",
" </tr>\n",
" <tr>\n",
" <td>18</td>\n",
" <td>2019-11-08</td>\n",
" <td>East</td>\n",
" <td>Parent</td>\n",
" <td>Pen</td>\n",
" <td>15</td>\n",
" <td>19.99</td>\n",
" <td>299.85</td>\n",
" </tr>\n",
" <tr>\n",
" <td>19</td>\n",
" <td>2019-11-25</td>\n",
" <td>Central</td>\n",
" <td>Kivell</td>\n",
" <td>Pen Set</td>\n",
" <td>96</td>\n",
" <td>4.99</td>\n",
" <td>479.04</td>\n",
" </tr>\n",
" <tr>\n",
" <td>20</td>\n",
" <td>2019-12-12</td>\n",
" <td>Central</td>\n",
" <td>Smith</td>\n",
" <td>Pencil</td>\n",
" <td>67</td>\n",
" <td>1.29</td>\n",
" <td>86.43</td>\n",
" </tr>\n",
" <tr>\n",
" <td>21</td>\n",
" <td>2019-12-29</td>\n",
" <td>East</td>\n",
" <td>Parent</td>\n",
" <td>Pen Set</td>\n",
" <td>74</td>\n",
" <td>15.99</td>\n",
" <td>1183.26</td>\n",
" </tr>\n",
" <tr>\n",
" <td>22</td>\n",
" <td>2020-01-15</td>\n",
" <td>Central</td>\n",
" <td>Gill</td>\n",
" <td>Binder</td>\n",
" <td>46</td>\n",
" <td>8.99</td>\n",
" <td>413.54</td>\n",
" </tr>\n",
" <tr>\n",
" <td>23</td>\n",
" <td>2020-02-01</td>\n",
" <td>Central</td>\n",
" <td>Smith</td>\n",
" <td>Binder</td>\n",
" <td>87</td>\n",
" <td>15.00</td>\n",
" <td>1305.00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>24</td>\n",
" <td>2020-02-18</td>\n",
" <td>East</td>\n",
" <td>Jones</td>\n",
" <td>Binder</td>\n",
" <td>4</td>\n",
" <td>4.99</td>\n",
" <td>19.96</td>\n",
" </tr>\n",
" <tr>\n",
" <td>25</td>\n",
" <td>2020-03-07</td>\n",
" <td>West</td>\n",
" <td>Sorvino</td>\n",
" <td>Binder</td>\n",
" <td>7</td>\n",
" <td>19.99</td>\n",
" <td>139.93</td>\n",
" </tr>\n",
" <tr>\n",
" <td>26</td>\n",
" <td>2020-03-24</td>\n",
" <td>Central</td>\n",
" <td>Jardine</td>\n",
" <td>Pen Set</td>\n",
" <td>50</td>\n",
" <td>4.99</td>\n",
" <td>249.50</td>\n",
" </tr>\n",
" <tr>\n",
" <td>27</td>\n",
" <td>2020-04-10</td>\n",
" <td>Central</td>\n",
" <td>Andrews</td>\n",
" <td>Pencil</td>\n",
" <td>66</td>\n",
" <td>1.99</td>\n",
" <td>131.34</td>\n",
" </tr>\n",
" <tr>\n",
" <td>28</td>\n",
" <td>2020-04-27</td>\n",
" <td>East</td>\n",
" <td>Howard</td>\n",
" <td>Pen</td>\n",
" <td>96</td>\n",
" <td>4.99</td>\n",
" <td>479.04</td>\n",
" </tr>\n",
" <tr>\n",
" <td>29</td>\n",
" <td>2020-05-14</td>\n",
" <td>Central</td>\n",
" <td>Gill</td>\n",
" <td>Pencil</td>\n",
" <td>53</td>\n",
" <td>1.29</td>\n",
" <td>68.37</td>\n",
" </tr>\n",
" <tr>\n",
" <td>30</td>\n",
" <td>2020-05-31</td>\n",
" <td>Central</td>\n",
" <td>Gill</td>\n",
" <td>Binder</td>\n",
" <td>80</td>\n",
" <td>8.99</td>\n",
" <td>719.20</td>\n",
" </tr>\n",
" <tr>\n",
" <td>31</td>\n",
" <td>2020-06-17</td>\n",
" <td>Central</td>\n",
" <td>Kivell</td>\n",
" <td>Desk</td>\n",
" <td>5</td>\n",
" <td>125.00</td>\n",
" <td>625.00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>32</td>\n",
" <td>2020-07-04</td>\n",
" <td>East</td>\n",
" <td>Jones</td>\n",
" <td>Pen Set</td>\n",
" <td>62</td>\n",
" <td>4.99</td>\n",
" <td>309.38</td>\n",
" </tr>\n",
" <tr>\n",
" <td>33</td>\n",
" <td>2020-07-21</td>\n",
" <td>Central</td>\n",
" <td>Morgan</td>\n",
" <td>Pen Set</td>\n",
" <td>55</td>\n",
" <td>12.49</td>\n",
" <td>686.95</td>\n",
" </tr>\n",
" <tr>\n",
" <td>34</td>\n",
" <td>2020-08-07</td>\n",
" <td>Central</td>\n",
" <td>Kivell</td>\n",
" <td>Pen Set</td>\n",
" <td>42</td>\n",
" <td>23.95</td>\n",
" <td>1005.90</td>\n",
" </tr>\n",
" <tr>\n",
" <td>35</td>\n",
" <td>2020-08-24</td>\n",
" <td>West</td>\n",
" <td>Sorvino</td>\n",
" <td>Desk</td>\n",
" <td>3</td>\n",
" <td>275.00</td>\n",
" <td>825.00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>36</td>\n",
" <td>2020-09-10</td>\n",
" <td>Central</td>\n",
" <td>Gill</td>\n",
" <td>Pencil</td>\n",
" <td>7</td>\n",
" <td>1.29</td>\n",
" <td>9.03</td>\n",
" </tr>\n",
" <tr>\n",
" <td>37</td>\n",
" <td>2020-09-27</td>\n",
" <td>West</td>\n",
" <td>Sorvino</td>\n",
" <td>Pen</td>\n",
" <td>76</td>\n",
" <td>1.99</td>\n",
" <td>151.24</td>\n",
" </tr>\n",
" <tr>\n",
" <td>38</td>\n",
" <td>2020-10-14</td>\n",
" <td>West</td>\n",
" <td>Thompson</td>\n",
" <td>Binder</td>\n",
" <td>57</td>\n",
" <td>19.99</td>\n",
" <td>1139.43</td>\n",
" </tr>\n",
" <tr>\n",
" <td>39</td>\n",
" <td>2020-10-31</td>\n",
" <td>Central</td>\n",
" <td>Andrews</td>\n",
" <td>Pencil</td>\n",
" <td>14</td>\n",
" <td>1.29</td>\n",
" <td>18.06</td>\n",
" </tr>\n",
" <tr>\n",
" <td>40</td>\n",
" <td>2020-11-17</td>\n",
" <td>Central</td>\n",
" <td>Jardine</td>\n",
" <td>Binder</td>\n",
" <td>11</td>\n",
" <td>4.99</td>\n",
" <td>54.89</td>\n",
" </tr>\n",
" <tr>\n",
" <td>41</td>\n",
" <td>2020-12-04</td>\n",
" <td>Central</td>\n",
" <td>Jardine</td>\n",
" <td>Binder</td>\n",
" <td>94</td>\n",
" <td>19.99</td>\n",
" <td>1879.06</td>\n",
" </tr>\n",
" <tr>\n",
" <td>42</td>\n",
" <td>2020-12-21</td>\n",
" <td>Central</td>\n",
" <td>Andrews</td>\n",
" <td>Binder</td>\n",
" <td>28</td>\n",
" <td>4.99</td>\n",
" <td>139.72</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" OrderDate Region Rep Item Units Unit Cost Total\n",
"0 2019-01-06 East Jones Pencil 95 1.99 189.05\n",
"1 2019-01-23 Central Kivell Binder 50 19.99 999.50\n",
"2 2019-02-09 Central Jardine Pencil 36 4.99 179.64\n",
"3 2019-02-26 Central Gill Pen 27 19.99 539.73\n",
"4 2019-03-15 West Sorvino Pencil 56 2.99 167.44\n",
"5 2019-04-01 East Jones Binder 60 4.99 299.40\n",
"6 2019-04-18 Central Andrews Pencil 75 1.99 149.25\n",
"7 2019-05-05 Central Jardine Pencil 90 4.99 449.10\n",
"8 2019-05-22 West Thompson Pencil 32 1.99 63.68\n",
"9 2019-06-08 East Jones Binder 60 8.99 539.40\n",
"10 2019-06-25 Central Morgan Pencil 90 4.99 449.10\n",
"11 2019-07-12 East Howard Binder 29 1.99 57.71\n",
"12 2019-07-29 East Parent Binder 81 19.99 1619.19\n",
"13 2019-08-15 East Jones Pencil 35 4.99 174.65\n",
"14 2019-09-01 Central Smith Desk 2 125.00 250.00\n",
"15 2019-09-18 East Jones Pen Set 16 15.99 255.84\n",
"16 2019-10-05 Central Morgan Binder 28 8.99 251.72\n",
"17 2019-10-22 East Jones Pen 64 8.99 575.36\n",
"18 2019-11-08 East Parent Pen 15 19.99 299.85\n",
"19 2019-11-25 Central Kivell Pen Set 96 4.99 479.04\n",
"20 2019-12-12 Central Smith Pencil 67 1.29 86.43\n",
"21 2019-12-29 East Parent Pen Set 74 15.99 1183.26\n",
"22 2020-01-15 Central Gill Binder 46 8.99 413.54\n",
"23 2020-02-01 Central Smith Binder 87 15.00 1305.00\n",
"24 2020-02-18 East Jones Binder 4 4.99 19.96\n",
"25 2020-03-07 West Sorvino Binder 7 19.99 139.93\n",
"26 2020-03-24 Central Jardine Pen Set 50 4.99 249.50\n",
"27 2020-04-10 Central Andrews Pencil 66 1.99 131.34\n",
"28 2020-04-27 East Howard Pen 96 4.99 479.04\n",
"29 2020-05-14 Central Gill Pencil 53 1.29 68.37\n",
"30 2020-05-31 Central Gill Binder 80 8.99 719.20\n",
"31 2020-06-17 Central Kivell Desk 5 125.00 625.00\n",
"32 2020-07-04 East Jones Pen Set 62 4.99 309.38\n",
"33 2020-07-21 Central Morgan Pen Set 55 12.49 686.95\n",
"34 2020-08-07 Central Kivell Pen Set 42 23.95 1005.90\n",
"35 2020-08-24 West Sorvino Desk 3 275.00 825.00\n",
"36 2020-09-10 Central Gill Pencil 7 1.29 9.03\n",
"37 2020-09-27 West Sorvino Pen 76 1.99 151.24\n",
"38 2020-10-14 West Thompson Binder 57 19.99 1139.43\n",
"39 2020-10-31 Central Andrews Pencil 14 1.29 18.06\n",
"40 2020-11-17 Central Jardine Binder 11 4.99 54.89\n",
"41 2020-12-04 Central Jardine Binder 94 19.99 1879.06\n",
"42 2020-12-21 Central Andrews Binder 28 4.99 139.72"
]
},
"execution_count": 89,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [
{
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" <th>Region</th>\n",
" <th>Rep</th>\n",
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" <td>0</td>\n",
" <td>2019-01-06</td>\n",
" <td>East</td>\n",
" <td>Jones</td>\n",
" <td>Pencil</td>\n",
" <td>95</td>\n",
" <td>1.99</td>\n",
" <td>189.05</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>2019-01-23</td>\n",
" <td>Central</td>\n",
" <td>Kivell</td>\n",
" <td>Binder</td>\n",
" <td>50</td>\n",
" <td>19.99</td>\n",
" <td>999.50</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>2019-02-09</td>\n",
" <td>Central</td>\n",
" <td>Jardine</td>\n",
" <td>Pencil</td>\n",
" <td>36</td>\n",
" <td>4.99</td>\n",
" <td>179.64</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>2019-02-26</td>\n",
" <td>Central</td>\n",
" <td>Gill</td>\n",
" <td>Pen</td>\n",
" <td>27</td>\n",
" <td>19.99</td>\n",
" <td>539.73</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>2019-03-15</td>\n",
" <td>West</td>\n",
" <td>Sorvino</td>\n",
" <td>Pencil</td>\n",
" <td>56</td>\n",
" <td>2.99</td>\n",
" <td>167.44</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>2019-04-01</td>\n",
" <td>East</td>\n",
" <td>Jones</td>\n",
" <td>Binder</td>\n",
" <td>60</td>\n",
" <td>4.99</td>\n",
" <td>299.40</td>\n",
" </tr>\n",
" <tr>\n",
" <td>6</td>\n",
" <td>2019-04-18</td>\n",
" <td>Central</td>\n",
" <td>Andrews</td>\n",
" <td>Pencil</td>\n",
" <td>75</td>\n",
" <td>1.99</td>\n",
" <td>149.25</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7</td>\n",
" <td>2019-05-05</td>\n",
" <td>Central</td>\n",
" <td>Jardine</td>\n",
" <td>Pencil</td>\n",
" <td>90</td>\n",
" <td>4.99</td>\n",
" <td>449.10</td>\n",
" </tr>\n",
" <tr>\n",
" <td>8</td>\n",
" <td>2019-05-22</td>\n",
" <td>West</td>\n",
" <td>Thompson</td>\n",
" <td>Pencil</td>\n",
" <td>32</td>\n",
" <td>1.99</td>\n",
" <td>63.68</td>\n",
" </tr>\n",
" <tr>\n",
" <td>9</td>\n",
" <td>2019-06-08</td>\n",
" <td>East</td>\n",
" <td>Jones</td>\n",
" <td>Binder</td>\n",
" <td>60</td>\n",
" <td>8.99</td>\n",
" <td>539.40</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" OrderDate Region Rep Item Units Unit Cost Total\n",
"0 2019-01-06 East Jones Pencil 95 1.99 189.05\n",
"1 2019-01-23 Central Kivell Binder 50 19.99 999.50\n",
"2 2019-02-09 Central Jardine Pencil 36 4.99 179.64\n",
"3 2019-02-26 Central Gill Pen 27 19.99 539.73\n",
"4 2019-03-15 West Sorvino Pencil 56 2.99 167.44\n",
"5 2019-04-01 East Jones Binder 60 4.99 299.40\n",
"6 2019-04-18 Central Andrews Pencil 75 1.99 149.25\n",
"7 2019-05-05 Central Jardine Pencil 90 4.99 449.10\n",
"8 2019-05-22 West Thompson Pencil 32 1.99 63.68\n",
"9 2019-06-08 East Jones Binder 60 8.99 539.40"
]
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" <tr>\n",
" <td>38</td>\n",
" <td>2020-10-14</td>\n",
" <td>West</td>\n",
" <td>Thompson</td>\n",
" <td>Binder</td>\n",
" <td>57</td>\n",
" <td>19.99</td>\n",
" <td>1139.43</td>\n",
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" <tr>\n",
" <td>39</td>\n",
" <td>2020-10-31</td>\n",
" <td>Central</td>\n",
" <td>Andrews</td>\n",
" <td>Pencil</td>\n",
" <td>14</td>\n",
" <td>1.29</td>\n",
" <td>18.06</td>\n",
" </tr>\n",
" <tr>\n",
" <td>40</td>\n",
" <td>2020-11-17</td>\n",
" <td>Central</td>\n",
" <td>Jardine</td>\n",
" <td>Binder</td>\n",
" <td>11</td>\n",
" <td>4.99</td>\n",
" <td>54.89</td>\n",
" </tr>\n",
" <tr>\n",
" <td>41</td>\n",
" <td>2020-12-04</td>\n",
" <td>Central</td>\n",
" <td>Jardine</td>\n",
" <td>Binder</td>\n",
" <td>94</td>\n",
" <td>19.99</td>\n",
" <td>1879.06</td>\n",
" </tr>\n",
" <tr>\n",
" <td>42</td>\n",
" <td>2020-12-21</td>\n",
" <td>Central</td>\n",
" <td>Andrews</td>\n",
" <td>Binder</td>\n",
" <td>28</td>\n",
" <td>4.99</td>\n",
" <td>139.72</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
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],
"text/plain": [
" OrderDate Region Rep Item Units Unit Cost Total\n",
"38 2020-10-14 West Thompson Binder 57 19.99 1139.43\n",
"39 2020-10-31 Central Andrews Pencil 14 1.29 18.06\n",
"40 2020-11-17 Central Jardine Binder 11 4.99 54.89\n",
"41 2020-12-04 Central Jardine Binder 94 19.99 1879.06\n",
"42 2020-12-21 Central Andrews Binder 28 4.99 139.72"
]
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.tail()"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['OrderDate', 'Region', 'Rep', 'Item', 'Units', 'Unit Cost', 'Total'], dtype='object')"
]
},
"execution_count": 93,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['OrderDate', 'Region', 'Rep', 'Item', 'Units', 'Unit Cost', 'Total']"
]
},
"execution_count": 94,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns.to_list()"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 43 entries, 0 to 42\n",
"Data columns (total 7 columns):\n",
"OrderDate 43 non-null datetime64[ns]\n",
"Region 43 non-null object\n",
"Rep 43 non-null object\n",
"Item 43 non-null object\n",
"Units 43 non-null int64\n",
"Unit Cost 43 non-null float64\n",
"Total 43 non-null float64\n",
"dtypes: datetime64[ns](1), float64(2), int64(1), object(3)\n",
"memory usage: 2.5+ KB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"OrderDate 43\n",
"Region 3\n",
"Rep 11\n",
"Item 5\n",
"Units 37\n",
"Unit Cost 12\n",
"Total 41\n",
"dtype: int64"
]
},
"execution_count": 97,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.nunique()"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {},
"outputs": [
{
"data": {
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" <td>0</td>\n",
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" <td>95</td>\n",
" <td>1.99</td>\n",
" <td>189.05</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>2019-01-23</td>\n",
" <td>Central</td>\n",
" <td>Kivell</td>\n",
" <td>Binder</td>\n",
" <td>50</td>\n",
" <td>19.99</td>\n",
" <td>999.50</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>2019-02-09</td>\n",
" <td>Central</td>\n",
" <td>Jardine</td>\n",
" <td>Pencil</td>\n",
" <td>36</td>\n",
" <td>4.99</td>\n",
" <td>179.64</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>2019-02-26</td>\n",
" <td>Central</td>\n",
" <td>Gill</td>\n",
" <td>Pen</td>\n",
" <td>27</td>\n",
" <td>19.99</td>\n",
" <td>539.73</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>2019-03-15</td>\n",
" <td>West</td>\n",
" <td>Sorvino</td>\n",
" <td>Pencil</td>\n",
" <td>56</td>\n",
" <td>2.99</td>\n",
" <td>167.44</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" OrderDate Region Rep Item Units Unit Cost Total\n",
"0 2019-01-06 East Jones Pencil 95 1.99 189.05\n",
"1 2019-01-23 Central Kivell Binder 50 19.99 999.50\n",
"2 2019-02-09 Central Jardine Pencil 36 4.99 179.64\n",
"3 2019-02-26 Central Gill Pen 27 19.99 539.73\n",
"4 2019-03-15 West Sorvino Pencil 56 2.99 167.44"
]
},
"execution_count": 98,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {},
"outputs": [],
"source": [
"df['20% Dis'] = df['Total'].apply(lambda x: x*(20/100))"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {},
"outputs": [
{
"data": {
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" <th>Rep</th>\n",
" <th>Item</th>\n",
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" <th>Unit Cost</th>\n",
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" <th>20% Dis</th>\n",
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" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>2019-01-06</td>\n",
" <td>East</td>\n",
" <td>Jones</td>\n",
" <td>Pencil</td>\n",
" <td>95</td>\n",
" <td>1.99</td>\n",
" <td>189.05</td>\n",
" <td>37.810</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>2019-01-23</td>\n",
" <td>Central</td>\n",
" <td>Kivell</td>\n",
" <td>Binder</td>\n",
" <td>50</td>\n",
" <td>19.99</td>\n",
" <td>999.50</td>\n",
" <td>199.900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>2019-02-09</td>\n",
" <td>Central</td>\n",
" <td>Jardine</td>\n",
" <td>Pencil</td>\n",
" <td>36</td>\n",
" <td>4.99</td>\n",
" <td>179.64</td>\n",
" <td>35.928</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>2019-02-26</td>\n",
" <td>Central</td>\n",
" <td>Gill</td>\n",
" <td>Pen</td>\n",
" <td>27</td>\n",
" <td>19.99</td>\n",
" <td>539.73</td>\n",
" <td>107.946</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>2019-03-15</td>\n",
" <td>West</td>\n",
" <td>Sorvino</td>\n",
" <td>Pencil</td>\n",
" <td>56</td>\n",
" <td>2.99</td>\n",
" <td>167.44</td>\n",
" <td>33.488</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" OrderDate Region Rep Item Units Unit Cost Total 20% Dis\n",
"0 2019-01-06 East Jones Pencil 95 1.99 189.05 37.810\n",
"1 2019-01-23 Central Kivell Binder 50 19.99 999.50 199.900\n",
"2 2019-02-09 Central Jardine Pencil 36 4.99 179.64 35.928\n",
"3 2019-02-26 Central Gill Pen 27 19.99 539.73 107.946\n",
"4 2019-03-15 West Sorvino Pencil 56 2.99 167.44 33.488"
]
},
"execution_count": 100,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {},
"outputs": [],
"source": [
"df['Payment'] = df['Total'] - df['20% Dis']"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
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" <th>Rep</th>\n",
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" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>2019-01-06</td>\n",
" <td>East</td>\n",
" <td>Jones</td>\n",
" <td>Pencil</td>\n",
" <td>95</td>\n",
" <td>1.99</td>\n",
" <td>189.05</td>\n",
" <td>37.810</td>\n",
" <td>151.240</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>2019-01-23</td>\n",
" <td>Central</td>\n",
" <td>Kivell</td>\n",
" <td>Binder</td>\n",
" <td>50</td>\n",
" <td>19.99</td>\n",
" <td>999.50</td>\n",
" <td>199.900</td>\n",
" <td>799.600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>2019-02-09</td>\n",
" <td>Central</td>\n",
" <td>Jardine</td>\n",
" <td>Pencil</td>\n",
" <td>36</td>\n",
" <td>4.99</td>\n",
" <td>179.64</td>\n",
" <td>35.928</td>\n",
" <td>143.712</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>2019-02-26</td>\n",
" <td>Central</td>\n",
" <td>Gill</td>\n",
" <td>Pen</td>\n",
" <td>27</td>\n",
" <td>19.99</td>\n",
" <td>539.73</td>\n",
" <td>107.946</td>\n",
" <td>431.784</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>2019-03-15</td>\n",
" <td>West</td>\n",
" <td>Sorvino</td>\n",
" <td>Pencil</td>\n",
" <td>56</td>\n",
" <td>2.99</td>\n",
" <td>167.44</td>\n",
" <td>33.488</td>\n",
" <td>133.952</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" OrderDate Region Rep Item Units Unit Cost Total 20% Dis \\\n",
"0 2019-01-06 East Jones Pencil 95 1.99 189.05 37.810 \n",
"1 2019-01-23 Central Kivell Binder 50 19.99 999.50 199.900 \n",
"2 2019-02-09 Central Jardine Pencil 36 4.99 179.64 35.928 \n",
"3 2019-02-26 Central Gill Pen 27 19.99 539.73 107.946 \n",
"4 2019-03-15 West Sorvino Pencil 56 2.99 167.44 33.488 \n",
"\n",
" Payment \n",
"0 151.240 \n",
"1 799.600 \n",
"2 143.712 \n",
"3 431.784 \n",
"4 133.952 "
]
},
"execution_count": 102,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (12,6))\n",
"plt.plot(df['OrderDate'], df['Total'])\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| 36.89916
| 46,840
| 0.513563
| 14,383
| 158,076
| 5.621845
| 0.12334
| 0.042927
| 0.059363
| 0.014618
| 0.379117
| 0.355594
| 0.341248
| 0.326222
| 0.310949
| 0.305656
| 0
| 0.110144
| 0.300621
| 158,076
| 4,283
| 46,841
| 36.907775
| 0.621247
| 0
| 0
| 0.787532
| 0
| 0.004903
| 0.715036
| 0.310003
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.0007
| 0
| 0.0007
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
6c9d49b908c5df3035e07d3c483b13e9188d77b7
| 145
|
py
|
Python
|
streamy/analyzer/cross_reference.py
|
wallarelvo/streamy
|
a173bf66da111d176c0a1d37f144374ea0b80e32
|
[
"Apache-2.0"
] | 1
|
2015-08-21T23:26:36.000Z
|
2015-08-21T23:26:36.000Z
|
streamy/analyzer/cross_reference.py
|
wallarelvo/streamy
|
a173bf66da111d176c0a1d37f144374ea0b80e32
|
[
"Apache-2.0"
] | null | null | null |
streamy/analyzer/cross_reference.py
|
wallarelvo/streamy
|
a173bf66da111d176c0a1d37f144374ea0b80e32
|
[
"Apache-2.0"
] | null | null | null |
#!/usr/bin/env python
class CrossReference(object):
def __init__(self, db):
self.db = db
def check(self, tweet):
pass
| 14.5
| 29
| 0.593103
| 19
| 145
| 4.315789
| 0.736842
| 0.146341
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.282759
| 145
| 9
| 30
| 16.111111
| 0.788462
| 0.137931
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.4
| false
| 0.2
| 0
| 0
| 0.6
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 1
| 0
|
0
| 5
|
6cab85835a16a5d370724ff624afcd5138307bec
| 208
|
py
|
Python
|
exercicios/exercicio074.py
|
NicoCassio/cursoemvideo-python
|
2686ff74f4d45bdb0dc194f49f4dd19aae629d52
|
[
"MIT"
] | null | null | null |
exercicios/exercicio074.py
|
NicoCassio/cursoemvideo-python
|
2686ff74f4d45bdb0dc194f49f4dd19aae629d52
|
[
"MIT"
] | null | null | null |
exercicios/exercicio074.py
|
NicoCassio/cursoemvideo-python
|
2686ff74f4d45bdb0dc194f49f4dd19aae629d52
|
[
"MIT"
] | null | null | null |
import random
n = (random.randint(1, 10), random.randint(1, 10), random.randint(1, 10), random.randint(1, 10), random.randint(1, 10))
print(n)
print(f'Maior: {sorted(n)[-1]}')
print(f'Menor: {sorted(n)[0]}')
| 34.666667
| 119
| 0.658654
| 37
| 208
| 3.702703
| 0.324324
| 0.474453
| 0.510949
| 0.583942
| 0.583942
| 0.583942
| 0.583942
| 0.583942
| 0.583942
| 0.583942
| 0
| 0.089947
| 0.091346
| 208
| 5
| 120
| 41.6
| 0.634921
| 0
| 0
| 0
| 0
| 0
| 0.206731
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.2
| 0
| 0.2
| 0.6
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 5
|
6cc208bf4d040b6c5060c4962b16878b3b8edaa0
| 211
|
py
|
Python
|
module/__init__.py
|
Goochaozheng/ChunkFusion
|
7458a8e08886cc76cfeb87881c51e23b1d0674c3
|
[
"MIT"
] | 3
|
2022-03-15T08:34:15.000Z
|
2022-03-15T08:40:06.000Z
|
module/__init__.py
|
Goochaozheng/ChunkFusion
|
7458a8e08886cc76cfeb87881c51e23b1d0674c3
|
[
"MIT"
] | null | null | null |
module/__init__.py
|
Goochaozheng/ChunkFusion
|
7458a8e08886cc76cfeb87881c51e23b1d0674c3
|
[
"MIT"
] | null | null | null |
from .chunk import Chunk
from .chunkManager import ChunkManager, constructChunksFromVolume
from .pipeline import Pipeline
from .tsdfIntegrator import TSDFIntegrator
from .fusionIntegrator import FusionIntegrator
| 42.2
| 65
| 0.876777
| 21
| 211
| 8.809524
| 0.380952
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.094787
| 211
| 5
| 66
| 42.2
| 0.968586
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
9f8da547ea9ccfd1eec5f9d4772695ba8cda36ba
| 38
|
py
|
Python
|
dizoo/minigrid/envs/__init__.py
|
konnase/DI-engine
|
f803499cad191e9277b10e194132d74757bcfc8e
|
[
"Apache-2.0"
] | 2
|
2021-07-30T15:55:45.000Z
|
2021-07-30T16:35:10.000Z
|
dizoo/minigrid/envs/__init__.py
|
konnase/DI-engine
|
f803499cad191e9277b10e194132d74757bcfc8e
|
[
"Apache-2.0"
] | null | null | null |
dizoo/minigrid/envs/__init__.py
|
konnase/DI-engine
|
f803499cad191e9277b10e194132d74757bcfc8e
|
[
"Apache-2.0"
] | null | null | null |
from .minigrid_env import MiniGridEnv
| 19
| 37
| 0.868421
| 5
| 38
| 6.4
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.105263
| 38
| 1
| 38
| 38
| 0.941176
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
4ca434cdd519a4fa3f6ae73f37dd8acf42fa09d8
| 110
|
py
|
Python
|
config.py
|
itszn/led-dashboard
|
e5b0a82cba895f2bb79de3489b7f1a65e1864b95
|
[
"MIT"
] | 1
|
2018-07-31T02:53:52.000Z
|
2018-07-31T02:53:52.000Z
|
config.py
|
itszn/led-dashboard
|
e5b0a82cba895f2bb79de3489b7f1a65e1864b95
|
[
"MIT"
] | null | null | null |
config.py
|
itszn/led-dashboard
|
e5b0a82cba895f2bb79de3489b7f1a65e1864b95
|
[
"MIT"
] | null | null | null |
CONFIG_LOCATION = '/home/rgb_user/.config/rgbd/config.json'
LIGHT_CTL = '/home/rgb_user/.local/bin/lightctl'
| 27.5
| 59
| 0.763636
| 17
| 110
| 4.705882
| 0.705882
| 0.175
| 0.275
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.063636
| 110
| 3
| 60
| 36.666667
| 0.776699
| 0
| 0
| 0
| 0
| 0
| 0.669725
| 0.669725
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| null | 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
4ce55501804ffbf405bcb1d02055ce11b1e3850e
| 2,544
|
py
|
Python
|
app/service/interfaces/i_rest_svc.py
|
mihaid-b/caldera
|
90af73188a9865757c167efd31cbd87a8e6160b1
|
[
"Apache-2.0"
] | 3,385
|
2017-11-29T02:08:31.000Z
|
2022-03-31T13:38:11.000Z
|
app/service/interfaces/i_rest_svc.py
|
mihaid-b/caldera
|
90af73188a9865757c167efd31cbd87a8e6160b1
|
[
"Apache-2.0"
] | 1,283
|
2017-11-29T16:45:31.000Z
|
2022-03-31T20:10:04.000Z
|
app/service/interfaces/i_rest_svc.py
|
mihaid-b/caldera
|
90af73188a9865757c167efd31cbd87a8e6160b1
|
[
"Apache-2.0"
] | 800
|
2017-11-29T17:48:43.000Z
|
2022-03-30T22:39:40.000Z
|
import abc
class RestServiceInterface(abc.ABC):
@abc.abstractmethod
def persist_adversary(self, access, data):
"""
Save a new adversary from either the GUI or REST API. This writes a new YML file into the core data/ directory.
:param access
:param data:
:return: the ID of the created adversary
"""
pass
@abc.abstractmethod
def update_planner(self, data):
"""
Update a new planner from either the GUI or REST API with new stopping conditions.
This overwrites the existing YML file.
:param data:
:return: the ID of the created adversary
"""
pass
@abc.abstractmethod
def persist_ability(self, access, data):
pass
@abc.abstractmethod
def persist_source(self, access, data):
pass
@abc.abstractmethod
def delete_agent(self, data):
pass
@abc.abstractmethod
def delete_ability(self, data):
pass
@abc.abstractmethod
def delete_adversary(self, data):
pass
@abc.abstractmethod
def delete_operation(self, data):
pass
@abc.abstractmethod
def display_objects(self, object_name, data):
pass
@abc.abstractmethod
def display_result(self, data):
pass
@abc.abstractmethod
def display_operation_report(self, data):
pass
@abc.abstractmethod
def download_contact_report(self, contact):
pass
@abc.abstractmethod
def update_agent_data(self, data):
pass
@abc.abstractmethod
def update_chain_data(self, data):
pass
@abc.abstractmethod
def create_operation(self, access, data):
pass
@abc.abstractmethod
def create_schedule(self, access, data):
pass
@abc.abstractmethod
def list_payloads(self):
pass
@abc.abstractmethod
def find_abilities(self, paw):
pass
@abc.abstractmethod
def get_potential_links(self, op_id, paw):
pass
@abc.abstractmethod
def apply_potential_link(self, link):
pass
@abc.abstractmethod
def task_agent_with_ability(self, paw, ability_id, obfuscator, facts):
pass
@abc.abstractmethod
def get_link_pin(self, json_data):
pass
@abc.abstractmethod
def construct_agents_for_group(self, group):
pass
@abc.abstractmethod
def update_config(self, data):
pass
@abc.abstractmethod
def update_operation(self, op_id, state, autonomous):
pass
| 21.74359
| 119
| 0.637972
| 296
| 2,544
| 5.344595
| 0.27027
| 0.268647
| 0.316056
| 0.364096
| 0.579646
| 0.458281
| 0.404551
| 0.082174
| 0.082174
| 0.082174
| 0
| 0
| 0.28695
| 2,544
| 116
| 120
| 21.931034
| 0.872106
| 0.139544
| 0
| 0.649351
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.324675
| false
| 0.324675
| 0.012987
| 0
| 0.350649
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 5
|
4ceb26036819c2bb77eb3521cfbe455bb414498c
| 188
|
py
|
Python
|
Modulo4/scripts/pandas/lectura.py
|
Flor246/PythonFlor
|
6481e258286bdb939e42190363cdd6d3bb70c00d
|
[
"Apache-2.0"
] | null | null | null |
Modulo4/scripts/pandas/lectura.py
|
Flor246/PythonFlor
|
6481e258286bdb939e42190363cdd6d3bb70c00d
|
[
"Apache-2.0"
] | null | null | null |
Modulo4/scripts/pandas/lectura.py
|
Flor246/PythonFlor
|
6481e258286bdb939e42190363cdd6d3bb70c00d
|
[
"Apache-2.0"
] | null | null | null |
import pandas as pd
# 1. Leer fuentes de informacion 'csv','excel', 'sql','json','etc'
# 2. Procesamiento
# 3. Obtengo un resultado -> 'guardo un archivo' 'excel', 'sql', 'csv', 'etc'
| 20.888889
| 77
| 0.643617
| 27
| 188
| 4.481481
| 0.814815
| 0.132231
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.019231
| 0.170213
| 188
| 9
| 77
| 20.888889
| 0.75641
| 0.835106
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
4cfc93c855a879bdfd9109df075297cf07a881c4
| 3,261
|
py
|
Python
|
tests/test_ring_random.py
|
senk8/crypto-math
|
91c7b02a28e91190089b0213065498ce3c6b2e18
|
[
"MIT"
] | 1
|
2022-01-01T07:48:29.000Z
|
2022-01-01T07:48:29.000Z
|
tests/test_ring_random.py
|
senk8/crypto-math
|
91c7b02a28e91190089b0213065498ce3c6b2e18
|
[
"MIT"
] | null | null | null |
tests/test_ring_random.py
|
senk8/crypto-math
|
91c7b02a28e91190089b0213065498ce3c6b2e18
|
[
"MIT"
] | null | null | null |
from crypto_math import GF, poly_ring
import pytest
import numpy as np
import random
def r_setup(p):
F = GF(p)
R = poly_ring(F)
return R
@pytest.mark.parametrize(
"p",
[
2,
3,
5,
7,
11,
101,
257,
65537,
],
)
def test_add_random(p):
R = r_setup(p)
for _ in range(10 ** 2):
x = np.poly1d(
[random.randint(0, 10 ** 2) for _ in range(random.randint(1, 10 ** 2))]
)
y = np.poly1d(
[random.randint(0, 10 ** 2) for _ in range(random.randint(1, 10 ** 2))]
)
actual = R(x.coeffs) + R(y.coeffs)
expect = R((x + y).coeffs)
assert actual == expect
@pytest.mark.parametrize(
"p",
[
2,
3,
5,
7,
11,
101,
257,
65537,
],
)
def test_sub_random(p):
R = r_setup(p)
for _ in range(10 ** 2):
x = np.poly1d(
[random.randint(0, 10 ** 2) for _ in range(random.randint(1, 10 ** 2))]
)
y = np.poly1d(
[random.randint(0, 10 ** 2) for _ in range(random.randint(1, 10 ** 2))]
)
actual = R(x.coeffs) - R(y.coeffs)
expect = R((x - y).coeffs)
assert actual == expect
@pytest.mark.parametrize(
"p",
[
2,
3,
5,
7,
11,
101,
257,
65537,
],
)
def test_mul_random(p):
R = r_setup(p)
for _ in range(10 ** 2):
x = np.poly1d(
[random.randint(0, 10 ** 2) for _ in range(random.randint(1, 10 ** 2))]
)
y = np.poly1d(
[random.randint(0, 10 ** 2) for _ in range(random.randint(1, 10 ** 2))]
)
actual = R(x.coeffs) * R(y.coeffs)
expect = R((x * y).coeffs)
assert actual == expect
@pytest.mark.parametrize(
"p,MOD",
[
(2, (1, 1, 1)),
(3, (1, 0, 2, 1)),
(5, (1, 0, 4, 4, 2)),
(7, (1, 0, 0, 0, 1, 1)),
(11, (1, 0, 3, 4, 6, 7, 2)),
(101, (1, 0, 0, 0, 0, 0, 6, 99)),
(65537, (1, 1, 3, 11, 44, 65484, 153, 53377, 59)),
],
)
def test_divison_random(p, MOD):
R = r_setup(p)
for _ in range(10 ** 2):
n = random.randint(1, (len(MOD) - 1) * 2)
x = np.poly1d([random.randint(0, p) for _ in range(n)])
y = np.poly1d(MOD)
expect_q, expect_r = x / y
actual_q, actual_r = R.division(R(x.coeffs), R(y.coeffs))
assert R(expect_q.coeffs) == actual_q
assert R(expect_r.coeffs) == actual_r
@pytest.mark.parametrize(
"p,MOD",
[
(2, (1, 1, 1)),
(3, (1, 0, 2, 1)),
(5, (1, 0, 4, 4, 2)),
(7, (1, 0, 0, 0, 1, 4)),
(11, (1, 0, 3, 4, 6, 7, 2)),
(101, (1, 0, 0, 0, 0, 0, 6, 99)),
(65537, (1, 1, 3, 11, 44, 65484, 153, 53377, 59)),
],
)
def test_ext_euclid_random(p, MOD):
R = r_setup(p)
y = R(MOD)
for _ in range(10 ** 2):
n = random.randint(1, len(MOD) - 1)
x = R([random.randint(0, p) for _ in range(n)])
if x.is_zero():
continue
gcd, e, _ = R.ext_euclid(x, y)
_, r = R.division(e * x, y)
assert gcd == R.one()
assert r == R.one()
| 21.885906
| 83
| 0.443116
| 487
| 3,261
| 2.874743
| 0.141684
| 0.036429
| 0.092857
| 0.105
| 0.780714
| 0.779286
| 0.767857
| 0.740714
| 0.703571
| 0.697857
| 0
| 0.133168
| 0.380558
| 3,261
| 148
| 84
| 22.033784
| 0.559901
| 0
| 0
| 0.589147
| 0
| 0
| 0.003987
| 0
| 0
| 0
| 0
| 0
| 0.054264
| 1
| 0.046512
| false
| 0
| 0.031008
| 0
| 0.085271
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
98041a99b10b65d86e7c08350dd32f0ec07d5d05
| 133
|
py
|
Python
|
factoriocalc/__main__.py
|
ekimekim/factoriocalc
|
18583ee0ea16a12c061b272db68469edee86606d
|
[
"MIT"
] | 23
|
2017-12-09T05:35:10.000Z
|
2022-02-08T06:05:10.000Z
|
factoriocalc/__main__.py
|
ekimekim/factoriocalc
|
18583ee0ea16a12c061b272db68469edee86606d
|
[
"MIT"
] | 2
|
2021-02-17T09:27:01.000Z
|
2021-02-19T00:39:23.000Z
|
factoriocalc/__main__.py
|
ekimekim/factoriocalc
|
18583ee0ea16a12c061b272db68469edee86606d
|
[
"MIT"
] | 1
|
2021-02-17T01:18:18.000Z
|
2021-02-17T01:18:18.000Z
|
import logging
from factoriocalc.main import main
import argh
logging.basicConfig(level=logging.DEBUG)
argh.dispatch_command(main)
| 16.625
| 40
| 0.842105
| 18
| 133
| 6.166667
| 0.611111
| 0.18018
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.090226
| 133
| 7
| 41
| 19
| 0.917355
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.6
| 0
| 0.6
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
e21c971e3c54dd5e57fee6dda37462e2572720bc
| 181
|
py
|
Python
|
sympy/equation/tests/test_equation.py
|
mcpl-sympy/sympy
|
3cbea35437ebd3d5767ca44f3b1ed6c519970c5b
|
[
"BSD-3-Clause"
] | null | null | null |
sympy/equation/tests/test_equation.py
|
mcpl-sympy/sympy
|
3cbea35437ebd3d5767ca44f3b1ed6c519970c5b
|
[
"BSD-3-Clause"
] | 3
|
2020-05-24T14:09:45.000Z
|
2020-09-27T07:13:08.000Z
|
sympy/equation/tests/test_equation.py
|
mcpl-sympy/sympy
|
3cbea35437ebd3d5767ca44f3b1ed6c519970c5b
|
[
"BSD-3-Clause"
] | 2
|
2020-09-22T13:23:08.000Z
|
2020-09-25T05:12:28.000Z
|
from sympy import Eqn, Equation, symbols
x, y = symbols('x y')
def test_Eqn():
assert Eqn(x, y) == Equation(x, y)
assert Eqn(x, y).lhs == x
assert Eqn(x, y).rhs == y
| 18.1
| 40
| 0.585635
| 33
| 181
| 3.181818
| 0.393939
| 0.114286
| 0.285714
| 0.314286
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.243094
| 181
| 9
| 41
| 20.111111
| 0.766423
| 0
| 0
| 0
| 0
| 0
| 0.016575
| 0
| 0
| 0
| 0
| 0
| 0.5
| 1
| 0.166667
| true
| 0
| 0.166667
| 0
| 0.333333
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
e226ddb55a17e91cfbc94375599200f000759782
| 93
|
py
|
Python
|
gidtools/gidconfig/__init__.py
|
Giddius/gidtools_utils
|
ab0667a0c7b6115df327ebdbd40f290a73f9dbd4
|
[
"MIT"
] | null | null | null |
gidtools/gidconfig/__init__.py
|
Giddius/gidtools_utils
|
ab0667a0c7b6115df327ebdbd40f290a73f9dbd4
|
[
"MIT"
] | null | null | null |
gidtools/gidconfig/__init__.py
|
Giddius/gidtools_utils
|
ab0667a0c7b6115df327ebdbd40f290a73f9dbd4
|
[
"MIT"
] | null | null | null |
from .classes import *
from .data import *
from .functions import *
from .factories import *
| 18.6
| 24
| 0.741935
| 12
| 93
| 5.75
| 0.5
| 0.434783
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.172043
| 93
| 4
| 25
| 23.25
| 0.896104
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
e25a45cdd57c65bc64e0aeb8922ad962c93ba610
| 29
|
py
|
Python
|
api/queue/push/__init__.py
|
sofia008/api-redis-queue
|
8d65665c8a9f44990565baa8c7ba43d7f01425d3
|
[
"Apache-2.0"
] | null | null | null |
api/queue/push/__init__.py
|
sofia008/api-redis-queue
|
8d65665c8a9f44990565baa8c7ba43d7f01425d3
|
[
"Apache-2.0"
] | null | null | null |
api/queue/push/__init__.py
|
sofia008/api-redis-queue
|
8d65665c8a9f44990565baa8c7ba43d7f01425d3
|
[
"Apache-2.0"
] | null | null | null |
# api/queue/push/__init__.py
| 14.5
| 28
| 0.758621
| 5
| 29
| 3.6
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.068966
| 29
| 1
| 29
| 29
| 0.666667
| 0.896552
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
e25e236aa2cfbf2410bc7412d9e8584f0f451440
| 378
|
py
|
Python
|
openapi_genclient/api/__init__.py
|
nicholasid7/tnk_open_api
|
25829e974ef45a925568278c02df701979526a28
|
[
"Unlicense"
] | null | null | null |
openapi_genclient/api/__init__.py
|
nicholasid7/tnk_open_api
|
25829e974ef45a925568278c02df701979526a28
|
[
"Unlicense"
] | null | null | null |
openapi_genclient/api/__init__.py
|
nicholasid7/tnk_open_api
|
25829e974ef45a925568278c02df701979526a28
|
[
"Unlicense"
] | null | null | null |
from __future__ import absolute_import
# flake8: noqa
# import apis into api package
from openapi_genclient.api.market_api import MarketApi
from openapi_genclient.api.operations_api import OperationsApi
from openapi_genclient.api.orders_api import OrdersApi
from openapi_genclient.api.portfolio_api import PortfolioApi
from openapi_genclient.api.sandbox_api import SandboxApi
| 34.363636
| 62
| 0.873016
| 52
| 378
| 6.057692
| 0.423077
| 0.174603
| 0.31746
| 0.365079
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.002915
| 0.092593
| 378
| 10
| 63
| 37.8
| 0.915452
| 0.108466
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
e265ba34fd41c076542bbface9865f8ab134ac92
| 29
|
py
|
Python
|
trafficlight_recognizer/nodes/region_tlr_tensorflow/trafficlight_recognizer/__init__.py
|
astuff/autoware.ai-core_perception
|
bc00df8d8ab6448b4e8ec7a74c0bb42f8e26565e
|
[
"Apache-2.0"
] | null | null | null |
trafficlight_recognizer/nodes/region_tlr_tensorflow/trafficlight_recognizer/__init__.py
|
astuff/autoware.ai-core_perception
|
bc00df8d8ab6448b4e8ec7a74c0bb42f8e26565e
|
[
"Apache-2.0"
] | null | null | null |
trafficlight_recognizer/nodes/region_tlr_tensorflow/trafficlight_recognizer/__init__.py
|
astuff/autoware.ai-core_perception
|
bc00df8d8ab6448b4e8ec7a74c0bb42f8e26565e
|
[
"Apache-2.0"
] | null | null | null |
from tensorflow_tlr import *
| 14.5
| 28
| 0.827586
| 4
| 29
| 5.75
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.137931
| 29
| 1
| 29
| 29
| 0.92
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
e2a2f9d452d53123fb314afdca2e17c258e343c4
| 222
|
py
|
Python
|
stats/cron.py
|
bradeckert/eyebrowse-server
|
0cd89dd65b5a7766a7c98067349fef05ce640a1a
|
[
"Unlicense"
] | null | null | null |
stats/cron.py
|
bradeckert/eyebrowse-server
|
0cd89dd65b5a7766a7c98067349fef05ce640a1a
|
[
"Unlicense"
] | null | null | null |
stats/cron.py
|
bradeckert/eyebrowse-server
|
0cd89dd65b5a7766a7c98067349fef05ce640a1a
|
[
"Unlicense"
] | null | null | null |
import kronos
from stats.cron_tasks.calculate_stats import user_stat_gen
from common.cron_tasks.add_favicons import add_favicons
@kronos.register("0 * * * *")
def hourly_cron():
user_stat_gen()
add_favicons()
| 22.2
| 58
| 0.761261
| 32
| 222
| 4.9375
| 0.53125
| 0.208861
| 0.139241
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.005263
| 0.144144
| 222
| 9
| 59
| 24.666667
| 0.826316
| 0
| 0
| 0
| 0
| 0
| 0.040541
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.142857
| true
| 0
| 0.428571
| 0
| 0.571429
| 0
| 0
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
2c47e05604ebab13ea6b4511ed367ac3f90f8df2
| 401
|
py
|
Python
|
api/exceptions.py
|
jeancochrane/bunny-hook
|
94a12e61b40129d37eebf5a547edbd6661c534ab
|
[
"MIT"
] | null | null | null |
api/exceptions.py
|
jeancochrane/bunny-hook
|
94a12e61b40129d37eebf5a547edbd6661c534ab
|
[
"MIT"
] | 12
|
2018-02-20T15:36:38.000Z
|
2018-02-27T03:07:47.000Z
|
api/exceptions.py
|
jeancochrane/bunny-hook
|
94a12e61b40129d37eebf5a547edbd6661c534ab
|
[
"MIT"
] | null | null | null |
# exceptions.py -- custom exception classes for this module
class PayloadException(Exception):
'''
Something went wrong with the payload from the GitHub API.
'''
pass
class WorkerException(Exception):
'''
Something went wrong in the worker process.
'''
pass
class QueueException(Exception):
'''
Something went wrong in the queue process.
'''
pass
| 17.434783
| 62
| 0.660848
| 44
| 401
| 6.022727
| 0.590909
| 0.203774
| 0.249057
| 0.30566
| 0.241509
| 0.241509
| 0
| 0
| 0
| 0
| 0
| 0
| 0.254364
| 401
| 22
| 63
| 18.227273
| 0.886288
| 0.508728
| 0
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.5
| 0
| 0
| 0.5
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 5
|
2cb71fabddcc8735b2f04d305eeff9ac1dd375ec
| 60
|
py
|
Python
|
nextoff/test/testfile.py
|
rakesh4real/nextoff
|
1482af694a24dd4665044f92acc8e5b66c1adbda
|
[
"MIT"
] | null | null | null |
nextoff/test/testfile.py
|
rakesh4real/nextoff
|
1482af694a24dd4665044f92acc8e5b66c1adbda
|
[
"MIT"
] | null | null | null |
nextoff/test/testfile.py
|
rakesh4real/nextoff
|
1482af694a24dd4665044f92acc8e5b66c1adbda
|
[
"MIT"
] | null | null | null |
def printHi():
print('hi')
def print2():
print('2')
| 12
| 15
| 0.533333
| 8
| 60
| 4
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.043478
| 0.233333
| 60
| 5
| 16
| 12
| 0.652174
| 0
| 0
| 0
| 0
| 0
| 0.04918
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| true
| 0
| 0
| 0
| 0.5
| 1
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 5
|
2cb8fc61213da7dda3faeaf5a771eb7684804845
| 388
|
py
|
Python
|
crowdsrc/src/serializers/__init__.py
|
BenEast/crowdsrc-backend
|
d06f0379e60bbac11128b3dd94dbc2d40b5f7a2e
|
[
"MIT"
] | null | null | null |
crowdsrc/src/serializers/__init__.py
|
BenEast/crowdsrc-backend
|
d06f0379e60bbac11128b3dd94dbc2d40b5f7a2e
|
[
"MIT"
] | null | null | null |
crowdsrc/src/serializers/__init__.py
|
BenEast/crowdsrc-backend
|
d06f0379e60bbac11128b3dd94dbc2d40b5f7a2e
|
[
"MIT"
] | null | null | null |
from .project_serializers import *
from .skill_serializers import *
from .task_serializers import *
from .task_submission_serializers import *
from .team_message_serializers import *
from .user_settings_serializers import *
from .user_serializers import *
from .search_serializers import *
from .review_serializers import *
from .crowd_serializers import *
from .auth_serializers import *
| 35.272727
| 42
| 0.832474
| 47
| 388
| 6.574468
| 0.319149
| 0.605178
| 0.679612
| 0.161812
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.110825
| 388
| 11
| 43
| 35.272727
| 0.895652
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
2ccda4815633659af823cfb780f7273a47e4396c
| 77
|
py
|
Python
|
segtypes/common/segment.py
|
paulsapps/splat
|
e312b86f36982dfddc4b00f082d7066f0b259938
|
[
"MIT"
] | 31
|
2021-01-23T01:21:40.000Z
|
2022-03-19T03:56:42.000Z
|
segtypes/common/segment.py
|
paulsapps/splat
|
e312b86f36982dfddc4b00f082d7066f0b259938
|
[
"MIT"
] | 44
|
2021-02-03T15:10:37.000Z
|
2022-03-03T08:29:47.000Z
|
segtypes/common/segment.py
|
paulsapps/splat
|
e312b86f36982dfddc4b00f082d7066f0b259938
|
[
"MIT"
] | 10
|
2021-03-16T19:37:24.000Z
|
2022-03-03T15:09:48.000Z
|
from segtypes.segment import Segment
class CommonSegment(Segment):
pass
| 15.4
| 36
| 0.792208
| 9
| 77
| 6.777778
| 0.777778
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.155844
| 77
| 4
| 37
| 19.25
| 0.938462
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.333333
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
|
0
| 5
|
e2c1c75d937d3ca9e08d088a2cc21dd4522f223d
| 410
|
py
|
Python
|
tests/python/functions/test_parameter_list.py
|
AustinScola/seligimus
|
206654fd8cf5e9b9a9da25439ccde2efe5a6cc7a
|
[
"MIT"
] | 1
|
2021-01-30T15:57:40.000Z
|
2021-01-30T15:57:40.000Z
|
tests/python/functions/test_parameter_list.py
|
AustinScola/seligimus
|
206654fd8cf5e9b9a9da25439ccde2efe5a6cc7a
|
[
"MIT"
] | 100
|
2021-01-30T16:01:46.000Z
|
2021-07-24T14:00:04.000Z
|
tests/python/functions/test_parameter_list.py
|
AustinScola/seligimus
|
206654fd8cf5e9b9a9da25439ccde2efe5a6cc7a
|
[
"MIT"
] | null | null | null |
"""Test seligimus.python.functions.parameter_list."""
from inspect import Parameter
from typing import List
from seligimus.python.functions.parameter_list import ParameterList
def test_parameter_list() -> None:
"""Test seligimus.python.functions.parameter_list.ParameterList."""
assert issubclass(ParameterList, List)
assert ParameterList.__args__ == (Parameter, ) # type: ignore[attr-defined]
| 34.166667
| 80
| 0.780488
| 46
| 410
| 6.76087
| 0.434783
| 0.167203
| 0.231511
| 0.318328
| 0.382637
| 0.263666
| 0
| 0
| 0
| 0
| 0
| 0
| 0.114634
| 410
| 11
| 81
| 37.272727
| 0.856749
| 0.334146
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.333333
| 1
| 0.166667
| true
| 0
| 0.5
| 0
| 0.666667
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
e2d9a25b0f95bfde91eaa1bf9633ecfa3f19a9d4
| 253
|
py
|
Python
|
micro_orm/fields/exceptions.py
|
RooYnnER/micro-orm
|
7ec329072a71565696fe27f44ff08412c708ff29
|
[
"MIT"
] | null | null | null |
micro_orm/fields/exceptions.py
|
RooYnnER/micro-orm
|
7ec329072a71565696fe27f44ff08412c708ff29
|
[
"MIT"
] | null | null | null |
micro_orm/fields/exceptions.py
|
RooYnnER/micro-orm
|
7ec329072a71565696fe27f44ff08412c708ff29
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
class NotNullFieldError(Exception):
pass
class TooLargeContent(Exception):
pass
class NoDateTimeGiven(Exception):
pass
class NoBooleanGiven(Exception):
pass
class NoListOrTupleGiven(Exception):
pass
| 11.5
| 36
| 0.70751
| 23
| 253
| 7.782609
| 0.478261
| 0.363128
| 0.402235
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.004926
| 0.197628
| 253
| 21
| 37
| 12.047619
| 0.876847
| 0.083004
| 0
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.5
| 0
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 5
|
e2f11fc768e2a6277dadb7f00424666aad1c40f0
| 19
|
py
|
Python
|
esp32/tools/fw_version.py
|
rbk1/micropython-bfh
|
2ad86c3084ba5cf2cc14166d318e367e23057095
|
[
"MIT"
] | null | null | null |
esp32/tools/fw_version.py
|
rbk1/micropython-bfh
|
2ad86c3084ba5cf2cc14166d318e367e23057095
|
[
"MIT"
] | null | null | null |
esp32/tools/fw_version.py
|
rbk1/micropython-bfh
|
2ad86c3084ba5cf2cc14166d318e367e23057095
|
[
"MIT"
] | null | null | null |
number = '1.6.7.b1'
| 19
| 19
| 0.578947
| 5
| 19
| 2.2
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.235294
| 0.105263
| 19
| 1
| 19
| 19
| 0.411765
| 0
| 0
| 0
| 0
| 0
| 0.4
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
390fd4af3cc3fd24554ca6e720df720d1d0363b9
| 464
|
py
|
Python
|
tests/unit/chains.py
|
btclib-org/btclib_node
|
3e5b2a55195e60e1d30505b52bc1ddd8d51c74cf
|
[
"MIT"
] | 4
|
2021-01-25T23:39:13.000Z
|
2021-08-08T06:27:53.000Z
|
tests/unit/chains.py
|
btclib-org/btclib_node
|
3e5b2a55195e60e1d30505b52bc1ddd8d51c74cf
|
[
"MIT"
] | null | null | null |
tests/unit/chains.py
|
btclib-org/btclib_node
|
3e5b2a55195e60e1d30505b52bc1ddd8d51c74cf
|
[
"MIT"
] | 1
|
2020-12-18T06:28:18.000Z
|
2020-12-18T06:28:18.000Z
|
from btclib_node.chains import Main, RegTest, SigNet, TestNet
def test_genesis():
assert (
Main().genesis.hash
== "000000000019d6689c085ae165831e934ff763ae46a2a6c172b3f1b60a8ce26f"
)
assert (
TestNet().genesis.hash
== "000000000933ea01ad0ee984209779baaec3ced90fa3f408719526f8d77f4943"
)
assert (
SigNet().genesis.hash
== "00000008819873e925422c1ff0f99f7cc9bbb232af63a077a480a3633bee1ef6"
)
| 27.294118
| 77
| 0.702586
| 27
| 464
| 12
| 0.62963
| 0.101852
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.363636
| 0.217672
| 464
| 16
| 78
| 29
| 0.528926
| 0
| 0
| 0.214286
| 0
| 0
| 0.413793
| 0.413793
| 0
| 0
| 0
| 0
| 0.214286
| 1
| 0.071429
| true
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| 0.071429
| 0
| 0.142857
| 0
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| 1
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| 0
| 0
| 0
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| 1
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| 0
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| 0
| 1
| null | 0
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| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
3917dc151674e0d9469bc7e808800e97ebdaaa90
| 136
|
py
|
Python
|
support/pycad/acad.py
|
vicwjb/Pycad
|
7391cd694b7a91ad9f9964ec95833c1081bc1f84
|
[
"MIT"
] | 1
|
2020-03-25T03:27:24.000Z
|
2020-03-25T03:27:24.000Z
|
support/pycad/acad.py
|
vicwjb/Pycad
|
7391cd694b7a91ad9f9964ec95833c1081bc1f84
|
[
"MIT"
] | null | null | null |
support/pycad/acad.py
|
vicwjb/Pycad
|
7391cd694b7a91ad9f9964ec95833c1081bc1f84
|
[
"MIT"
] | null | null | null |
import clr, pycad
from pycad.system import *
import System
clr.ImportExtensions(System.Linq)
clr.ImportExtensions(acap)
codebase = None
| 22.666667
| 33
| 0.816176
| 19
| 136
| 5.894737
| 0.578947
| 0.339286
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.095588
| 136
| 6
| 34
| 22.666667
| 0.902439
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0.833333
| null | null | 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
1a64d0d2de3dbce053ed1648030790e7ef1ce79c
| 144
|
py
|
Python
|
stylo/testing/domain.py
|
mvinoba/stylo
|
84f3a74cf9cb29c6d24b990dc9a474562114392b
|
[
"MIT"
] | null | null | null |
stylo/testing/domain.py
|
mvinoba/stylo
|
84f3a74cf9cb29c6d24b990dc9a474562114392b
|
[
"MIT"
] | null | null | null |
stylo/testing/domain.py
|
mvinoba/stylo
|
84f3a74cf9cb29c6d24b990dc9a474562114392b
|
[
"MIT"
] | null | null | null |
from stylo.domain import RealDomain
from stylo.testing._factory import define_domain_test
BaseRealDomainTest = define_domain_test(RealDomain)
| 24
| 53
| 0.868056
| 18
| 144
| 6.666667
| 0.555556
| 0.15
| 0.266667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.090278
| 144
| 5
| 54
| 28.8
| 0.916031
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.666667
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
1a6e7d9f348f5a54d9c8c07bb00b38e2f556ce8c
| 1,549
|
py
|
Python
|
example/migrations/0004_alter_body_stream_feild.py
|
fabienheureux/wagtail-wordpress-import
|
3c27330258e24a6b52f3d580060f607706bbc9d0
|
[
"MIT"
] | 22
|
2021-12-06T14:47:40.000Z
|
2022-03-31T18:12:34.000Z
|
example/migrations/0004_alter_body_stream_feild.py
|
fabienheureux/wagtail-wordpress-import
|
3c27330258e24a6b52f3d580060f607706bbc9d0
|
[
"MIT"
] | 87
|
2021-10-01T09:25:16.000Z
|
2022-02-14T10:56:16.000Z
|
example/migrations/0004_alter_body_stream_feild.py
|
fabienheureux/wagtail-wordpress-import
|
3c27330258e24a6b52f3d580060f607706bbc9d0
|
[
"MIT"
] | 5
|
2021-12-11T21:43:26.000Z
|
2022-03-07T06:15:21.000Z
|
# Generated by Django 3.2.7 on 2021-11-15 13:38
from django.db import migrations
import wagtail.core.blocks
import wagtail.core.fields
import wagtail.images.blocks
class Migration(migrations.Migration):
dependencies = [
('example', '0003_add_unique_constraint_name'),
]
operations = [
migrations.AlterField(
model_name='testpage',
name='body',
field=wagtail.core.fields.StreamField([('rich_text', wagtail.core.blocks.RichTextBlock(features=['anchor-identifier', 'h1', 'h2', 'h3', 'h4', 'h5', 'h6', 'bold', 'italic', 'ol', 'ul', 'hr', 'link', 'document-link', 'image', 'embed', 'superscript', 'subscript', 'strikethrough', 'blockquote'])), ('heading', wagtail.core.blocks.StructBlock([('text', wagtail.core.blocks.CharBlock(form_classname='title')), ('importance', wagtail.core.blocks.ChoiceBlock(choices=[('h1', 'H1'), ('h2', 'H2'), ('h3', 'H3'), ('h4', 'H4'), ('h5', 'H5'), ('h6', 'H6')]))])), ('image', wagtail.core.blocks.StructBlock([('image', wagtail.images.blocks.ImageChooserBlock()), ('caption', wagtail.core.blocks.CharBlock(required=False)), ('link', wagtail.core.blocks.URLBlock(required=False)), ('alignment', wagtail.core.blocks.ChoiceBlock(choices=[('left', 'Left'), ('right', 'Right'), ('center', 'Center')]))])), ('block_quote', wagtail.core.blocks.StructBlock([('quote', wagtail.core.blocks.CharBlock(form_classname='title')), ('attribution', wagtail.core.blocks.CharBlock(required=False))])), ('raw_html', wagtail.core.blocks.RawHTMLBlock())]),
),
]
| 70.409091
| 1,130
| 0.664945
| 176
| 1,549
| 5.795455
| 0.488636
| 0.161765
| 0.216667
| 0.101961
| 0.231373
| 0.162745
| 0.086275
| 0
| 0
| 0
| 0
| 0.02689
| 0.111685
| 1,549
| 21
| 1,131
| 73.761905
| 0.71439
| 0.029051
| 0
| 0
| 1
| 0
| 0.215712
| 0.020639
| 0
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| 1
| 0
| false
| 0
| 0.333333
| 0
| 0.533333
| 0
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| null | 0
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| null | 0
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| 0
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| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
1ab76a4654a62c5d7e4aeaf569a25191b51e80e0
| 833
|
py
|
Python
|
test/test_apps_wrapper.py
|
fnproject/fn_python
|
79575fc4867378331602a52422bc808f0f808b50
|
[
"Apache-2.0"
] | 6
|
2017-09-24T16:50:49.000Z
|
2019-10-23T22:14:39.000Z
|
test/test_apps_wrapper.py
|
fnproject/fn_python
|
79575fc4867378331602a52422bc808f0f808b50
|
[
"Apache-2.0"
] | null | null | null |
test/test_apps_wrapper.py
|
fnproject/fn_python
|
79575fc4867378331602a52422bc808f0f808b50
|
[
"Apache-2.0"
] | null | null | null |
# coding: utf-8
"""
fn
The open source serverless platform.
OpenAPI spec version: 0.2.1
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
from __future__ import absolute_import
import os
import sys
import unittest
import swagger_client
from swagger_client.rest import ApiException
from swagger_client.models.apps_wrapper import AppsWrapper
class TestAppsWrapper(unittest.TestCase):
""" AppsWrapper unit test stubs """
def setUp(self):
pass
def tearDown(self):
pass
def testAppsWrapper(self):
"""
Test AppsWrapper
"""
# FIXME: construct object with mandatory attributes with example values
#model = swagger_client.models.apps_wrapper.AppsWrapper()
pass
if __name__ == '__main__':
unittest.main()
| 18.511111
| 79
| 0.683073
| 96
| 833
| 5.729167
| 0.635417
| 0.094545
| 0.061818
| 0.083636
| 0.109091
| 0
| 0
| 0
| 0
| 0
| 0
| 0.006279
| 0.235294
| 833
| 44
| 80
| 18.931818
| 0.857143
| 0.386555
| 0
| 0.1875
| 1
| 0
| 0.017699
| 0
| 0
| 0
| 0
| 0.022727
| 0
| 1
| 0.1875
| false
| 0.1875
| 0.4375
| 0
| 0.6875
| 0
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| 0
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| null | 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 0
|
0
| 5
|
1ac56bde10e31f868f4f2626cec4d3acb37cc5f2
| 26
|
py
|
Python
|
test.py
|
HoolyPanda/VkTUI
|
2a4aa545c5da5d3f7482d01919ac79cd1afced12
|
[
"MIT"
] | null | null | null |
test.py
|
HoolyPanda/VkTUI
|
2a4aa545c5da5d3f7482d01919ac79cd1afced12
|
[
"MIT"
] | null | null | null |
test.py
|
HoolyPanda/VkTUI
|
2a4aa545c5da5d3f7482d01919ac79cd1afced12
|
[
"MIT"
] | null | null | null |
import os
os.system('vim')
| 13
| 16
| 0.730769
| 5
| 26
| 3.8
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.076923
| 26
| 2
| 16
| 13
| 0.791667
| 0
| 0
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| 0
| 0
| 0.111111
| 0
| 0
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| 0
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| 0
| 1
| 0
| true
| 0
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| 0
| 0.5
| 0
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| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
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| 0
| 0
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| 0
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| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
46bef2acece39601dd0203a080f62b6e054c57a6
| 129
|
py
|
Python
|
customerDetails/customerDBOps.py
|
surajsjain/social-media-analytics-app
|
1f310dcf2f79c9f80edee80dd59d8c63f827f04a
|
[
"MIT"
] | null | null | null |
customerDetails/customerDBOps.py
|
surajsjain/social-media-analytics-app
|
1f310dcf2f79c9f80edee80dd59d8c63f827f04a
|
[
"MIT"
] | 8
|
2020-06-05T20:49:10.000Z
|
2022-02-10T00:37:59.000Z
|
customerDetails/customerDBOps.py
|
surajsjain/social-media-analytics-app
|
1f310dcf2f79c9f80edee80dd59d8c63f827f04a
|
[
"MIT"
] | 3
|
2020-01-26T10:48:25.000Z
|
2020-08-25T17:47:54.000Z
|
version https://git-lfs.github.com/spec/v1
oid sha256:33ec7c63416ee6099552660c0b37e950bd763df731340e67b0f9ac6a4f168b4e
size 1010
| 32.25
| 75
| 0.883721
| 13
| 129
| 8.769231
| 1
| 0
| 0
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| 0
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| 0
| 0
| 0
| 0
| 0
| 0.414634
| 0.046512
| 129
| 3
| 76
| 43
| 0.512195
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 1
| 0
| 0
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| 0
| null | null | 0
| 0
| null | null | 0
| 1
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| null | 0
| 0
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| 0
| 1
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| null | 1
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| 0
| 1
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| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
46cb39c582e0d75ed2a2e39d00a156838ac87b62
| 105
|
py
|
Python
|
pydtk/__init__.py
|
dataware-tools/pydtk
|
1da61fb8ca90de6c39a371a9b2b65f4473932991
|
[
"Apache-2.0"
] | 11
|
2020-10-09T01:29:18.000Z
|
2022-01-21T13:21:40.000Z
|
pydtk/__init__.py
|
dataware-tools/pydtk
|
1da61fb8ca90de6c39a371a9b2b65f4473932991
|
[
"Apache-2.0"
] | 64
|
2020-10-20T04:55:22.000Z
|
2022-01-24T15:52:32.000Z
|
pydtk/__init__.py
|
dataware-tools/pydtk
|
1da61fb8ca90de6c39a371a9b2b65f4473932991
|
[
"Apache-2.0"
] | 1
|
2021-07-30T04:52:38.000Z
|
2021-07-30T04:52:38.000Z
|
"""pydtk modules."""
__version__ = "0.0.0-0"
__commit_id__ = "335be874b189127a42620a0475877155cdf0f870"
| 21
| 58
| 0.761905
| 10
| 105
| 7.1
| 0.7
| 0.084507
| 0.084507
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.364583
| 0.085714
| 105
| 4
| 59
| 26.25
| 0.375
| 0.133333
| 0
| 0
| 0
| 0
| 0.552941
| 0.470588
| 0
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| false
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| 1
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
46cd2183259d492ac88bd181f889432f5634582a
| 80
|
py
|
Python
|
reservoirpy/krpy/__init__.py
|
scuervo91/reservoirpy
|
a4db620baf3ff66a85c7f61b1919713a8642e6fc
|
[
"MIT"
] | 16
|
2020-05-07T01:57:04.000Z
|
2021-11-27T12:45:59.000Z
|
reservoirpy/krpy/__init__.py
|
scuervo91/reservoirpy
|
a4db620baf3ff66a85c7f61b1919713a8642e6fc
|
[
"MIT"
] | null | null | null |
reservoirpy/krpy/__init__.py
|
scuervo91/reservoirpy
|
a4db620baf3ff66a85c7f61b1919713a8642e6fc
|
[
"MIT"
] | 5
|
2020-05-12T07:28:24.000Z
|
2021-12-10T21:24:59.000Z
|
from .kr import Kr, KrWaterOil, KrGasOil, kr_curve, sw_denormalize, sw_normalize
| 80
| 80
| 0.825
| 12
| 80
| 5.25
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.1
| 80
| 1
| 80
| 80
| 0.875
| 0
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| true
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| null | 0
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| 1
| 0
| 1
| 0
|
0
| 5
|
46d0d6a76b1b5fc741596f98c0d2cbdf1530a94a
| 52
|
py
|
Python
|
dpiswitchutils/exceptions.py
|
Kidlike/plasma-dpi-switcher
|
8c34eb8131119c382cad265d7a78219568d247c9
|
[
"Unlicense"
] | 47
|
2019-03-24T19:49:32.000Z
|
2021-08-02T19:46:52.000Z
|
dpiswitchutils/exceptions.py
|
Kidlike/plasma-dpi-switcher
|
8c34eb8131119c382cad265d7a78219568d247c9
|
[
"Unlicense"
] | 13
|
2019-03-24T20:05:07.000Z
|
2022-01-01T17:27:16.000Z
|
dpiswitchutils/exceptions.py
|
valueerrorx/plasma-dpi-switcher
|
eb3f5104f743d76cf098c6bf8280cee9928bd927
|
[
"Unlicense"
] | 7
|
2019-03-29T18:47:14.000Z
|
2021-10-14T21:30:16.000Z
|
class ProfileNotFoundException(Exception):
pass
| 17.333333
| 42
| 0.807692
| 4
| 52
| 10.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.134615
| 52
| 2
| 43
| 26
| 0.933333
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.5
| 0
| 0
| 0.5
| 0
| 1
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 5
|
46fbf42104de947c0e77e71c2447ee3c75c2b9df
| 127
|
py
|
Python
|
Parkeringshus/main/admin.py
|
jeppefm1/Parkering
|
9b9522039095cb7e17966487d2a9e4e6889fe40c
|
[
"Apache-2.0"
] | 1
|
2019-03-08T15:09:41.000Z
|
2019-03-08T15:09:41.000Z
|
Parkeringshus/main/admin.py
|
jeppefm1/Parkering
|
9b9522039095cb7e17966487d2a9e4e6889fe40c
|
[
"Apache-2.0"
] | null | null | null |
Parkeringshus/main/admin.py
|
jeppefm1/Parkering
|
9b9522039095cb7e17966487d2a9e4e6889fe40c
|
[
"Apache-2.0"
] | 1
|
2019-03-08T15:08:13.000Z
|
2019-03-08T15:08:13.000Z
|
from django.contrib import admin
from .models import Contact, Plates
from django.db import models
# Register your models here.
| 25.4
| 35
| 0.811024
| 19
| 127
| 5.421053
| 0.631579
| 0.194175
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.141732
| 127
| 4
| 36
| 31.75
| 0.944954
| 0.204724
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
200233a5b713ca830801d9ef8cb1586a8cffeca9
| 386
|
py
|
Python
|
scripts/patches/codecommit.py
|
compose-x/troposphere
|
9a94a8fafd8b4da1cd1f4239be0e7aa0681fd8d4
|
[
"BSD-2-Clause"
] | null | null | null |
scripts/patches/codecommit.py
|
compose-x/troposphere
|
9a94a8fafd8b4da1cd1f4239be0e7aa0681fd8d4
|
[
"BSD-2-Clause"
] | null | null | null |
scripts/patches/codecommit.py
|
compose-x/troposphere
|
9a94a8fafd8b4da1cd1f4239be0e7aa0681fd8d4
|
[
"BSD-2-Clause"
] | null | null | null |
patches = [
# backwards compatibility
{
"op": "move",
"from": "/PropertyTypes/AWS::CodeCommit::Repository.RepositoryTrigger",
"path": "/PropertyTypes/AWS::CodeCommit::Repository.Trigger",
},
{
"op": "replace",
"path": "/ResourceTypes/AWS::CodeCommit::Repository/Properties/Triggers/ItemType",
"value": "Trigger",
},
]
| 27.571429
| 90
| 0.585492
| 29
| 386
| 7.793103
| 0.655172
| 0.172566
| 0.30531
| 0.318584
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.235751
| 386
| 13
| 91
| 29.692308
| 0.766102
| 0.059585
| 0
| 0
| 0
| 0
| 0.609418
| 0.501385
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
201a8046f17dd3c5a0f00a1ac9a291ced49a6097
| 64
|
py
|
Python
|
Python/Tests/TestData/DebuggerProject/Sub/paths.py
|
nanshuiyu/pytools
|
9f9271fe8cf564b4f94e9456d400f4306ea77c23
|
[
"Apache-2.0"
] | null | null | null |
Python/Tests/TestData/DebuggerProject/Sub/paths.py
|
nanshuiyu/pytools
|
9f9271fe8cf564b4f94e9456d400f4306ea77c23
|
[
"Apache-2.0"
] | null | null | null |
Python/Tests/TestData/DebuggerProject/Sub/paths.py
|
nanshuiyu/pytools
|
9f9271fe8cf564b4f94e9456d400f4306ea77c23
|
[
"Apache-2.0"
] | null | null | null |
import os, sys
print(os.curdir)
print('\n'.join(sys.path))
| 12.8
| 27
| 0.640625
| 11
| 64
| 3.727273
| 0.727273
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.15625
| 64
| 4
| 28
| 16
| 0.759259
| 0
| 0
| 0
| 0
| 0
| 0.033333
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.333333
| 0
| 0.333333
| 0.666667
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 1
|
0
| 5
|
2026f416c0600dcd20f5d5688a92a53f72dc578d
| 1,103
|
py
|
Python
|
fsetools/tests/test_lib_pd7974_1_2019.py
|
fsepy/fsetools
|
6b6c647912551680109a84d8640b9cfbe7970970
|
[
"Apache-2.0"
] | 1
|
2020-02-25T21:47:56.000Z
|
2020-02-25T21:47:56.000Z
|
fsetools/tests/test_lib_pd7974_1_2019.py
|
fsepy/fsetools
|
6b6c647912551680109a84d8640b9cfbe7970970
|
[
"Apache-2.0"
] | 12
|
2020-02-24T10:10:57.000Z
|
2020-09-18T11:18:08.000Z
|
fsetools/tests/test_lib_pd7974_1_2019.py
|
fsepy/fsetools
|
6b6c647912551680109a84d8640b9cfbe7970970
|
[
"Apache-2.0"
] | null | null | null |
from fsetools.libstd.pd_7974_1_2019 import _test_eq_10_virtual_origin as test_eq_10_virtual_origin
from fsetools.libstd.pd_7974_1_2019 import _test_eq_14_plume_temperature as test_eq_14_plume_temperature
from fsetools.libstd.pd_7974_1_2019 import _test_eq_15_plume_velocity as test_eq_15_plume_velocity
from fsetools.libstd.pd_7974_1_2019 import _test_eq_22_t_squared_fire_growth as test_eq_22_t_squared_fire_growth
from fsetools.libstd.pd_7974_1_2019 import _test_eq_26_axisymmetric_ceiling_jet_temperature as test_eq_26_axisymmetric_ceiling_jet_temperature
from fsetools.libstd.pd_7974_1_2019 import _test_eq_27_axisymmetric_ceiling_jet_velocity as test_eq_27_axisymmetric_ceiling_jet_velocity
from fsetools.libstd.pd_7974_1_2019 import _test_eq_55_activation_of_heat_detector_device as test_eq_55_activation_of_heat_detector_device
test_eq_10_virtual_origin()
test_eq_14_plume_temperature()
test_eq_15_plume_velocity()
test_eq_22_t_squared_fire_growth()
test_eq_26_axisymmetric_ceiling_jet_temperature()
test_eq_27_axisymmetric_ceiling_jet_velocity()
test_eq_55_activation_of_heat_detector_device()
| 68.9375
| 142
| 0.929284
| 195
| 1,103
| 4.54359
| 0.179487
| 0.142212
| 0.142212
| 0.158014
| 0.984199
| 0.802483
| 0.802483
| 0.488713
| 0.366817
| 0.366817
| 0
| 0.099715
| 0.045331
| 1,103
| 15
| 143
| 73.533333
| 0.74169
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
645235755581f6ffbc646d90c7755fc2678bb4b5
| 146
|
py
|
Python
|
object_detection2/modeling/backbone/__init__.py
|
vghost2008/wml
|
d0c5a1da6c228e321ae59a563e9ac84aa66266ff
|
[
"MIT"
] | 6
|
2019-12-10T17:18:56.000Z
|
2022-03-01T01:00:35.000Z
|
object_detection2/modeling/backbone/__init__.py
|
vghost2008/wml
|
d0c5a1da6c228e321ae59a563e9ac84aa66266ff
|
[
"MIT"
] | 2
|
2021-08-25T16:16:01.000Z
|
2022-02-10T05:21:19.000Z
|
object_detection2/modeling/backbone/__init__.py
|
vghost2008/wml
|
d0c5a1da6c228e321ae59a563e9ac84aa66266ff
|
[
"MIT"
] | 2
|
2019-12-07T09:57:35.000Z
|
2021-09-06T04:58:10.000Z
|
from . import resnet,fpn,shufflenetv2,bifpn,buildin_hooks,mobilenets,efficientnet,twfpn
from . import hrnet,resnetv2,darknet,spp_pan,vgg,fpnv2,dla
| 73
| 87
| 0.849315
| 21
| 146
| 5.809524
| 0.904762
| 0.163934
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.021583
| 0.047945
| 146
| 2
| 88
| 73
| 0.856115
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
647534a58eeb1d21b2a69713160c6bef78363374
| 433
|
py
|
Python
|
argus/exc.py
|
rkargon/image-sorter
|
f9cc5253613d6f476f5a0b0bd5121e955683e32b
|
[
"MIT"
] | null | null | null |
argus/exc.py
|
rkargon/image-sorter
|
f9cc5253613d6f476f5a0b0bd5121e955683e32b
|
[
"MIT"
] | null | null | null |
argus/exc.py
|
rkargon/image-sorter
|
f9cc5253613d6f476f5a0b0bd5121e955683e32b
|
[
"MIT"
] | null | null | null |
"""
File for representing argus-specific exceptions.
"""
class ArgusException(Exception):
"""
Exception class for the Argus application
"""
pass
class ImageLoadException(ArgusException):
"""
Exception raised when an image file could not be loaded
"""
pass
class InvalidQueryException(ArgusException):
"""
Exception raised when Argus is given a query that it can't parse
"""
pass
| 17.32
| 68
| 0.676674
| 47
| 433
| 6.234043
| 0.659574
| 0.235495
| 0.197952
| 0.225256
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.240185
| 433
| 24
| 69
| 18.041667
| 0.890578
| 0.487298
| 0
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.5
| 0
| 0
| 0.5
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 5
|
649f6a649e1401755f5a1eaad8eb7fc06bcf24a9
| 89
|
py
|
Python
|
ex04/salary.py
|
CoderDojo-Karlskrona/python-exercises
|
b22dd8a642b67d1c18790643718f133d7fc470fe
|
[
"Apache-2.0"
] | null | null | null |
ex04/salary.py
|
CoderDojo-Karlskrona/python-exercises
|
b22dd8a642b67d1c18790643718f133d7fc470fe
|
[
"Apache-2.0"
] | null | null | null |
ex04/salary.py
|
CoderDojo-Karlskrona/python-exercises
|
b22dd8a642b67d1c18790643718f133d7fc470fe
|
[
"Apache-2.0"
] | null | null | null |
hrs = float(raw_input("Timmar: "))
rate = float(raw_input('Timpeng: '))
print hrs * rate
| 22.25
| 36
| 0.674157
| 13
| 89
| 4.461538
| 0.615385
| 0.275862
| 0.448276
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.134831
| 89
| 3
| 37
| 29.666667
| 0.753247
| 0
| 0
| 0
| 0
| 0
| 0.191011
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 0.333333
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
b39a4154089b2b8d9a1483281dafb22bbe7091bf
| 16,180
|
py
|
Python
|
kat/tracer/ppparser.py
|
TOT0RoKR/kat
|
5fafe688593a60700f1067ca51ec9d5f148a396e
|
[
"MIT"
] | 3
|
2019-10-27T08:26:15.000Z
|
2019-11-26T12:23:38.000Z
|
kat/tracer/ppparser.py
|
tot0rokr/kat
|
5fafe688593a60700f1067ca51ec9d5f148a396e
|
[
"MIT"
] | null | null | null |
kat/tracer/ppparser.py
|
tot0rokr/kat
|
5fafe688593a60700f1067ca51ec9d5f148a396e
|
[
"MIT"
] | 1
|
2020-01-25T00:14:51.000Z
|
2020-01-25T00:14:51.000Z
|
"""
Author: TOT0Ro (tot0roprog@gmail.com)
tot0rokr.github.io
ppparser.py
preprocess parser
tokens: Result of lexical analysis about preprocesses in "*.c" c code
return: constructed tags
"""
from kat.lib.tag import *
from kat.lib.file import File
from kat.lib.token import Token
from kat.lib.scope import *
from kat.tracer.tokenlib import *
import re
def parse(tokens, file_tag):
scope_stack = []
tags = []
include_files = [] # included libraries
# they'll be mapped by pptracer after
# returning this function
included_scope = []
path = file_tag.path
base = file_tag.scope
scope_stack.append(base)
tokens.append(Token(token_kind['T_LAST'])) # This is only used by last chunk
error_text = None
tokens.reverse()
if tokens.pop().kind != token_kind['T_FIRST']:
raise AssertionError("Error initialized tokens")
def start():
while True:
if tokens[-1].kind == token_kind['T_NEWLINE']:
tokens.pop()
elif tokens[-1].kind == token_kind['T_COMMENT_SINGL_LINE']:
tokens.pop()
comment_single()
elif tokens[-1].kind == token_kind['T_COMMENT_MULTI_LINE_OPEN']:
tokens.pop()
comment_multiline()
elif tokens[-1].kind == token_kind['T_QUOTES_DOUBLE']:
tokens.pop()
string()
elif tokens[-1].kind == token_kind['T_QUOTES_SINGLE']:
tokens.pop()
charactor()
elif tokens[-1].kind == token_kind['T_INCLUDE_STD_H']:
include_files.append(tokens.pop().substance)
elif tokens[-1].kind == token_kind['T_INCLUDE_USR_H']:
include_files.append(tokens.pop().substance)
elif tokens[-1].kind == token_kind['T_INCLUDE_MACRO']:
include_files.append(tokens.pop().substance)
elif tokens[-1].kind == token_kind['T_PREPROCESS']:
tok = tokens.pop()
if tok.substance == preprocess_kind['macro']:
macro()
elif tok.substance == preprocess_kind['macrofunc']:
macrofunc()
elif tok.substance == preprocess_kind['undef']:
macroundef()
elif tok.substance == preprocess_kind['ifdef']:
ppifdef(tok.line_nr)
elif tok.substance == preprocess_kind['ifndef']:
ppifndef(tok.line_nr)
elif tok.substance == preprocess_kind['if']:
ppif(tok.line_nr)
elif tok.substance == preprocess_kind['elif']:
ppelif(tok.line_nr)
elif tok.substance == preprocess_kind['else']:
ppelse(tok.line_nr)
elif tok.substance == preprocess_kind['endif']:
ppendif(tok.line_nr)
# pass preprocess
elif tok.substance == preprocess_kind['pragma']:
expression("pass")
elif tok.substance == preprocess_kind['error']:
expression("pass")
elif tok.substance == preprocess_kind['line']:
expression("pass")
elif tok.substance == preprocess_kind['warning']:
expression("pass")
elif tokens[-1].kind == token_kind['T_LAST']:
return (tags, include_files, included_scope)
else:
error_text = "error_start"
raise AssertionError(error_text + " : "
+ path + " : " + str(tokens[-1].line_nr))
def comment_single():
while True:
if tokens[-1].kind == token_kind['T_NEWLINE']:
tokens.pop()
break
else:
tokens.pop()
def comment_multiline():
while True:
if tokens[-1].kind == token_kind['T_COMMENT_MULTI_LINE_CLOSE']:
tokens.pop()
break
else:
tokens.pop()
def string():
data = ""
while True:
if tokens[-1].kind == token_kind['T_NEWLINE']:
error_text = "string"
raise AssertionError(error_text)
elif tokens[-1].kind == token_kind['T_QUOTES_DOUBLE']:
tokens.pop()
break
else:
tok = tokens.pop()
# if tok is None:
# raise AssertionError(token_kind_index[tok.kind] + " " \
# + path + " ")# + tok.line_nr)
# if tok.substance is None:
# raise AssertionError(token_kind_index[tok.kind] + " " \
# + path + " ")# + tok.line_nr)
data += tok.substance #tokens.pop().substance
return data
def charactor():
data = ""
while True:
if tokens[-1].kind == token_kind['T_NEWLINE']:
error_text = "charactor_start"
raise AssertionError(error_text + " : "
+ path + " : " + str(tokens[-1].line_nr))
elif tokens[-1].kind == token_kind['T_QUOTES_SINGLE']:
tokens.pop()
break
else:
data = tokens.pop().substance
return data
def macro():
if tokens[-1].kind >= token_kind['T_IDENTIFIER'] \
and tokens[-1].kind <= token_kind['T_SIZEOF']: # listing
tok = tokens.pop()
tag = MacroTag(name=tok.substance, line=tok.line_nr, path=path
, scope=scope_stack[-1], type="macro")
expr = expression("macro")
# TODO: put {expr} in {tag}
file_tag.append_defined_tag(tag.name)
tags.append(tag)
else:
error_text = "macro"
raise AssertionError(error_text + " : "
+ path + " : " + str(tokens[-1].line_nr))
def macrofunc():
if tokens[-1].kind >= token_kind['T_IDENTIFIER'] \
and tokens[-1].kind <= token_kind['T_SIZEOF']: # listing
tok = tokens.pop()
tag = MacroTag(name=tok.substance, line=tok.line_nr, path=path
, scope=scope_stack[-1], type="macrofunc")
argu_list = macrofunc_argu()
# TODO: put {argu_list} in {tag}
expr = expression("macro")
# TODO: put {expr} in {tag}
file_tag.append_defined_tag(tag.name)
tags.append(tag)
else:
error_text = "macrofunc"
raise AssertionError(error_text)
def macroundef():
if tokens[-1].kind >= token_kind['T_IDENTIFIER'] \
and tokens[-1].kind <= token_kind['T_SIZEOF']: # listing
tok = tokens.pop()
tag = MacroTag(name=tok.substance, line=tok.line_nr, path=path
, scope=scope_stack[-1], type="undef")
file_tag.append_defined_tag(tag.name)
tags.append(tag)
expression("pass")
else:
error_text = "macroundef"
raise AssertionError(error_text)
def macrofunc_argu():
if tokens[-1].kind == token_kind['T_PARENTHESIS_OPEN']:
tokens.pop()
li = []
if tokens[-1].kind >= token_kind['T_IDENTIFIER'] \
and tokens[-1].kind <= token_kind['T_SIZEOF']: # listing
li.append(tokens.pop())
while tokens[-1].kind == token_kind['T_COMMA']:
tokens.pop()
if tokens[-1].kind >= token_kind['T_IDENTIFIER'] \
and tokens[-1].kind <= token_kind['T_SIZEOF']:
li.append(tokens.pop())
elif tokens[-1].kind == token_kind['T_VARIABLE_ARGUMENTS']:
li.append(tokens.pop())
break
elif tokens[-1].kind == token_kind['T_VARIABLE_ARGUMENTS']:
li.append(tokens.pop())
elif tokens[-1].kind == token_kind['T_PARENTHESIS_CLOSE']: # list end
tokens.pop()
else:
error_text = "macrofunc_argu - listing / line:" + str(tokens[-1].line_nr) \
+ " " + tokens[-1].substance + " " + path
raise AssertionError(error_text)
return li
else:
error_text = "macrofunc_argu"
raise AssertionError(error_text)
def expression(type):
if type == "macro":
expr = []
while True:
if tokens[-1].kind == token_kind['T_NEWLINE']:
tokens.pop()
break
elif tokens[-1].kind == token_kind['T_COMMENT_SINGL_LINE']:
tokens.pop()
comment_single()
elif tokens[-1].kind == token_kind['T_COMMENT_MULTI_LINE_OPEN']:
tokens.pop()
comment_multiline()
elif tokens[-1].kind == token_kind['T_QUOTES_DOUBLE']:
tokens.pop()
string()
elif tokens[-1].kind == token_kind['T_QUOTES_SINGLE']:
tokens.pop()
charactor()
elif tokens[-1].kind == token_kind['T_BACKSLASH']:
tokens.pop()
if tokens[-1].kind == token_kind['T_NEWLINE']:
tokens.pop()
else:
error_text = "expression - macro"
raise AssertionError(error_text + "\n"
+ path + "\n" + str(tokens[-1].line_nr))
break
else:
# TODO: write what is in macro
expr = tokens.pop()
return expr
elif type == "start":
while True:
if tokens[-1].kind == token_kind['T_NEWLINE']:
tokens.pop()
break
elif tokens[-1].kind == token_kind['T_COMMENT_SINGL_LINE']:
tokens.pop()
comment_single()
elif tokens[-1].kind == token_kind['T_COMMENT_MULTI_LINE_OPEN']:
tokens.pop()
comment_multiline()
elif tokens[-1].kind == token_kind['T_QUOTES_DOUBLE']:
tokens.pop()
string()
elif tokens[-1].kind == token_kind['T_QUOTES_SINGLE']:
tokens.pop()
charactor()
elif tokens[-1].kind == token_kind['T_BACKSLASH']:
tokens.pop()
if tokens[-1].kind == token_kind['T_NEWLINE']:
tokens.pop()
else:
error_text = "expression - start"
raise AssertionError(error_text)
else:
tokens.pop()
return None
elif type == "ppif":
expr = []
while True:
if tokens[-1].kind == token_kind['T_NEWLINE']:
tokens.pop()
break
elif tokens[-1].kind == token_kind['T_COMMENT_SINGL_LINE']:
tokens.pop()
comment_single()
elif tokens[-1].kind == token_kind['T_COMMENT_MULTI_LINE_OPEN']:
tokens.pop()
comment_multiline()
elif tokens[-1].kind == token_kind['T_QUOTES_DOUBLE']:
tokens.pop()
string()
elif tokens[-1].kind == token_kind['T_QUOTES_SINGLE']:
tokens.pop()
charactor()
elif tokens[-1].kind == token_kind['T_BACKSLASH']:
tokens.pop()
if tokens[-1].kind == token_kind['T_NEWLINE']:
tokens.pop()
else:
error_text = "expression - ppif"
raise AssertionError(error_text)
else:
tokens.pop()
return expr
elif type == "pass":
while True:
if tokens[-1].kind == token_kind['T_NEWLINE']:
tokens.pop()
break
elif tokens[-1].kind == token_kind['T_COMMENT_SINGL_LINE']:
tokens.pop()
comment_single()
elif tokens[-1].kind == token_kind['T_COMMENT_MULTI_LINE_OPEN']:
tokens.pop()
comment_multiline()
elif tokens[-1].kind == token_kind['T_QUOTES_DOUBLE']:
tokens.pop()
string()
elif tokens[-1].kind == token_kind['T_QUOTES_SINGLE']:
tokens.pop()
charactor()
elif tokens[-1].kind == token_kind['T_BACKSLASH']:
tokens.pop()
if tokens[-1].kind == token_kind['T_NEWLINE']:
tokens.pop()
else:
error_text = "expression - start"
raise AssertionError(error_text)
else:
tokens.pop()
return None
else:
error_text = "expression"
raise AssertionError(error_text)
def ppifdef(line_nr):
if tokens[-1].kind >= token_kind['T_IDENTIFIER'] \
and tokens[-1].kind <= token_kind['T_SIZEOF']: # listing
tok = tokens.pop()
scope = PreprocessScope(path=file_tag, scope=scope_stack[-1], start=line_nr)
scope.condition = [tok.substance, 1, "=="]
scope_stack.append(scope)
expression("pass")
else:
error_text = "ppifdef"
raise AssertionError(error_text + "\n"
+ path + "\n" + str(tokens[-1].line_nr))
def ppifndef(line_nr):
if tokens[-1].kind >= token_kind['T_IDENTIFIER'] \
and tokens[-1].kind <= token_kind['T_SIZEOF']: # listing
tok = tokens.pop()
scope = PreprocessScope(path=file_tag, scope=scope_stack[-1], start=line_nr)
scope.condition = [tok.substance, 1, "!="]
scope_stack.append(scope)
expression("pass")
else:
error_text = "ppifndef"
raise AssertionError(error_text + "\n"
+ path + "\n" + str(tokens[-1].line_nr))
def ppif(line_nr):
expr = expression("ppif")
scope = PreprocessScope(path=file_tag, scope=scope_stack[-1], start=line_nr)
scope.condition = expr
scope_stack.append(scope)
def ppelif(line_nr):
pre_scope = scope_stack.pop()
pre_scope.line[1] = line_nr - 1
expr = expression("ppif") # expression of ppelif is same
scope = PreprocessScope(path=file_tag, scope=pre_scope.contained_by, start=line_nr)
scope.condition = expr
pre_scope.post_associator = scope
scope.pre_associator = pre_scope
scope_stack.append(scope)
included_scope.append(pre_scope)
def ppelse(line_nr):
pre_scope = scope_stack.pop()
pre_scope.line[1] = line_nr - 1
scope = PreprocessScope(path=file_tag, scope=pre_scope.contained_by, start=line_nr)
pre_scope.post_associator = scope
scope.pre_associator = pre_scope
scope_stack.append(scope)
included_scope.append(pre_scope)
expression("pass")
def ppendif(line_nr):
pre_scope = scope_stack.pop()
pre_scope.line[1] = line_nr - 1
included_scope.append(pre_scope)
expression("pass")
return start()
| 37.281106
| 91
| 0.487268
| 1,623
| 16,180
| 4.646334
| 0.091805
| 0.065906
| 0.086195
| 0.118817
| 0.80175
| 0.746851
| 0.727755
| 0.687575
| 0.654555
| 0.646996
| 0
| 0.009053
| 0.399197
| 16,180
| 433
| 92
| 37.367206
| 0.766691
| 0.047837
| 0
| 0.722857
| 0
| 0
| 0.085101
| 0.009824
| 0
| 0
| 0
| 0.002309
| 0.045714
| 1
| 0.048571
| false
| 0.028571
| 0.017143
| 0
| 0.091429
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
b3ca7b2a37dd4f09243302b94ee6adf3aea053dc
| 407
|
py
|
Python
|
model_search/search/__init__.py
|
LinqCod/model_search
|
d90bc39994bc2a5f5028035ac954f796eda03310
|
[
"Apache-2.0"
] | null | null | null |
model_search/search/__init__.py
|
LinqCod/model_search
|
d90bc39994bc2a5f5028035ac954f796eda03310
|
[
"Apache-2.0"
] | null | null | null |
model_search/search/__init__.py
|
LinqCod/model_search
|
d90bc39994bc2a5f5028035ac954f796eda03310
|
[
"Apache-2.0"
] | null | null | null |
"""Phoenix Search Algorithms."""
from model_search.search import categorical_harmonica
from model_search.search import constrained_descent
from model_search.search import coordinate_descent
from model_search.search import harmonica
from model_search.search import identity
from model_search.search import linear_model
from model_search.search import search_algorithm # pylint: disable=g-import-not-at-top
| 40.7
| 87
| 0.859951
| 57
| 407
| 5.929825
| 0.350877
| 0.186391
| 0.310651
| 0.434911
| 0.653846
| 0.414201
| 0
| 0
| 0
| 0
| 0
| 0
| 0.088452
| 407
| 9
| 88
| 45.222222
| 0.911051
| 0.154791
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
b3d48aafe91ec2cf2b2b320e4a9c36c99c2d8a8d
| 116
|
py
|
Python
|
fenics_numpy/__init__.py
|
IvanYashchuk/numpy-fenics-adjoint
|
7707968cee3cd779e474971da02a058cb4f1d07a
|
[
"MIT"
] | 3
|
2020-09-06T20:13:38.000Z
|
2022-01-22T17:14:50.000Z
|
fenics_numpy/__init__.py
|
IvanYashchuk/numpy-fenics-adjoint
|
7707968cee3cd779e474971da02a058cb4f1d07a
|
[
"MIT"
] | 3
|
2020-09-30T14:50:11.000Z
|
2021-01-13T16:37:26.000Z
|
fenics_numpy/__init__.py
|
IvanYashchuk/numpy-fenics-adjoint
|
7707968cee3cd779e474971da02a058cb4f1d07a
|
[
"MIT"
] | null | null | null |
from .helpers import fenics_to_numpy, numpy_to_fenics
from .core import evaluate_primal, evaluate_vjp, evaluate_jvp
| 38.666667
| 61
| 0.862069
| 18
| 116
| 5.166667
| 0.611111
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.094828
| 116
| 2
| 62
| 58
| 0.885714
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
b60de5813b39b6fd95521f8b9a8242435b23d90a
| 174
|
py
|
Python
|
test.py
|
ava6969/rgb_stacking_extend
|
a36f1e35aa796e77201321161056e174966e7707
|
[
"Apache-2.0"
] | null | null | null |
test.py
|
ava6969/rgb_stacking_extend
|
a36f1e35aa796e77201321161056e174966e7707
|
[
"Apache-2.0"
] | null | null | null |
test.py
|
ava6969/rgb_stacking_extend
|
a36f1e35aa796e77201321161056e174966e7707
|
[
"Apache-2.0"
] | null | null | null |
from stable_baselines3.common.vec_env import VecFrameStack, DummyVecEnv
import gym
env = VecFrameStack( DummyVecEnv([ lambda : gym.make("PongNoFrameskip-v4") ]), 4, 'first')
| 43.5
| 90
| 0.781609
| 21
| 174
| 6.380952
| 0.761905
| 0.358209
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.019108
| 0.097701
| 174
| 4
| 90
| 43.5
| 0.834395
| 0
| 0
| 0
| 0
| 0
| 0.131429
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.666667
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
376aed7327caead109d75d85288e0b5166736a12
| 90
|
py
|
Python
|
pastepoints/__init__.py
|
majordookie/paste-cogs
|
b4e9fd919a8c5dde020b6aabac000c27648b835a
|
[
"Apache-2.0"
] | 1
|
2019-07-01T20:19:59.000Z
|
2019-07-01T20:19:59.000Z
|
pastepoints/__init__.py
|
majordookie/paste-cogs
|
b4e9fd919a8c5dde020b6aabac000c27648b835a
|
[
"Apache-2.0"
] | null | null | null |
pastepoints/__init__.py
|
majordookie/paste-cogs
|
b4e9fd919a8c5dde020b6aabac000c27648b835a
|
[
"Apache-2.0"
] | 3
|
2020-10-20T15:02:13.000Z
|
2020-10-28T17:22:41.000Z
|
from .pastepoints import PastePoints
def setup(bot):
bot.add_cog(PastePoints(bot))
| 22.5
| 37
| 0.744444
| 12
| 90
| 5.5
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.155556
| 90
| 4
| 38
| 22.5
| 0.868421
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
37772944621f302d383870fda142b6ebb99b423c
| 122
|
py
|
Python
|
Exercises_2/meal_planner/meals_plans/admin.py
|
WillDutcher/project-3-getting-started-with-django
|
e21cbfeb0e9d1a164a28d9dcca30b69789e2c8ef
|
[
"CC0-1.0"
] | null | null | null |
Exercises_2/meal_planner/meals_plans/admin.py
|
WillDutcher/project-3-getting-started-with-django
|
e21cbfeb0e9d1a164a28d9dcca30b69789e2c8ef
|
[
"CC0-1.0"
] | null | null | null |
Exercises_2/meal_planner/meals_plans/admin.py
|
WillDutcher/project-3-getting-started-with-django
|
e21cbfeb0e9d1a164a28d9dcca30b69789e2c8ef
|
[
"CC0-1.0"
] | null | null | null |
from django.contrib import admin
from .models import Day, Meal
admin.site.register(Day)
admin.site.register(Meal)
| 17.428571
| 33
| 0.754098
| 18
| 122
| 5.111111
| 0.555556
| 0.195652
| 0.369565
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.155738
| 122
| 6
| 34
| 20.333333
| 0.893204
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
37c9bfe60dcdf422ddae7eb933e08f9498eb8efd
| 78
|
py
|
Python
|
tools/portal/__main__.py
|
hoefkensj/BTRWin
|
1432868ad60155f5ae26f33903a890497e089480
|
[
"MIT"
] | null | null | null |
tools/portal/__main__.py
|
hoefkensj/BTRWin
|
1432868ad60155f5ae26f33903a890497e089480
|
[
"MIT"
] | null | null | null |
tools/portal/__main__.py
|
hoefkensj/BTRWin
|
1432868ad60155f5ae26f33903a890497e089480
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python
import ui.cli
if __name__ == '__main__':
ui.cli.cli()
| 13
| 26
| 0.666667
| 13
| 78
| 3.384615
| 0.769231
| 0.227273
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.141026
| 78
| 5
| 27
| 15.6
| 0.656716
| 0.25641
| 0
| 0
| 0
| 0
| 0.140351
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.333333
| 0
| 0.333333
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 5
|
805ebe61b5732b523350b530f6f8bdf8e58468d3
| 299
|
py
|
Python
|
ansible/module_utils/selvpc_utils/limits.py
|
Zeppelinen-DevOps/ansible-selvpc-modules
|
e2b2211ac5144b1bad7f07749da1097ef0826cab
|
[
"Apache-2.0"
] | 15
|
2017-07-04T18:01:11.000Z
|
2022-02-09T13:52:42.000Z
|
ansible/module_utils/selvpc_utils/limits.py
|
Zeppelinen-DevOps/ansible-selvpc-modules
|
e2b2211ac5144b1bad7f07749da1097ef0826cab
|
[
"Apache-2.0"
] | 4
|
2018-11-06T14:10:40.000Z
|
2020-12-14T19:22:17.000Z
|
ansible/module_utils/selvpc_utils/limits.py
|
Zeppelinen-DevOps/ansible-selvpc-modules
|
e2b2211ac5144b1bad7f07749da1097ef0826cab
|
[
"Apache-2.0"
] | 5
|
2018-09-13T22:03:08.000Z
|
2021-05-28T07:15:38.000Z
|
from ansible.module_utils.selvpc_utils import wrappers
@wrappers.get_object('quotas')
def get_domain_quotas(module, client):
return client.quotas.get_domain_quotas()
@wrappers.get_object('quotas')
def get_free_domain_quotas(module, client):
return client.quotas.get_free_domain_quotas()
| 24.916667
| 54
| 0.80602
| 42
| 299
| 5.404762
| 0.357143
| 0.211454
| 0.14978
| 0.202643
| 0.651982
| 0.651982
| 0.396476
| 0.396476
| 0
| 0
| 0
| 0
| 0.093645
| 299
| 11
| 55
| 27.181818
| 0.837638
| 0
| 0
| 0.285714
| 0
| 0
| 0.040134
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.285714
| false
| 0
| 0.142857
| 0.285714
| 0.714286
| 0
| 0
| 0
| 0
| null | 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 5
|
80d0d8f290ede2e31914f3e7f7053cb7a59f2038
| 215
|
py
|
Python
|
src/jyquickhelper/js/visjs/__init__.py
|
sdpython/jyquickhelper
|
b8b106ada7f8b606d4f152e186a3343b6c4ab2bb
|
[
"MIT"
] | 3
|
2020-03-06T23:17:14.000Z
|
2021-10-16T05:51:21.000Z
|
src/jyquickhelper/js/visjs/__init__.py
|
sdpython/jyquickhelper
|
b8b106ada7f8b606d4f152e186a3343b6c4ab2bb
|
[
"MIT"
] | 9
|
2016-12-07T10:28:01.000Z
|
2021-10-16T10:45:43.000Z
|
src/jyquickhelper/js/visjs/__init__.py
|
sdpython/jyquickhelper
|
b8b106ada7f8b606d4f152e186a3343b6c4ab2bb
|
[
"MIT"
] | null | null | null |
"""
@file
@brief `vis.js <https://github.com/almende/vis>`_
The script was obtained from the
`release page <https://github.com/almende/vis/tree/master/dist>`_.
"""
def version():
"version"
return "4.21.1"
| 17.916667
| 66
| 0.665116
| 32
| 215
| 4.40625
| 0.75
| 0.156028
| 0.198582
| 0.297872
| 0.340426
| 0
| 0
| 0
| 0
| 0
| 0
| 0.021622
| 0.139535
| 215
| 11
| 67
| 19.545455
| 0.740541
| 0.75814
| 0
| 0
| 0
| 0
| 0.25
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| true
| 0
| 0
| 0
| 0.666667
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 1
| 0
|
0
| 5
|
80efb86748950814ed8312cec1f5876d47fccb8b
| 584
|
py
|
Python
|
8_things_to_know_exercise/challenge/anyall_solution.py
|
RiddhiDamani/Python
|
06cba66aeafd9dc0fa849ec2112c0786a3e8f001
|
[
"MIT"
] | null | null | null |
8_things_to_know_exercise/challenge/anyall_solution.py
|
RiddhiDamani/Python
|
06cba66aeafd9dc0fa849ec2112c0786a3e8f001
|
[
"MIT"
] | null | null | null |
8_things_to_know_exercise/challenge/anyall_solution.py
|
RiddhiDamani/Python
|
06cba66aeafd9dc0fa849ec2112c0786a3e8f001
|
[
"MIT"
] | null | null | null |
import string
def contains_punctuation(input_str):
return any(char in string.punctuation
for char in input_str
)
assert contains_punctuation('Readability counts.') == True
assert contains_punctuation('Errors should never pass silently') == False
assert contains_punctuation('If the implementation is hard to explain, it\'s a bad idea.') == True
assert contains_punctuation('There should be one-- and preferably only one --obvious way to do it.') == True
assert contains_punctuation('Simple is better than complex') == False
print('Passed all tests ...')
| 44.923077
| 109
| 0.741438
| 79
| 584
| 5.379747
| 0.64557
| 0.268235
| 0.294118
| 0.204706
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.167808
| 584
| 13
| 110
| 44.923077
| 0.874486
| 0
| 0
| 0
| 0
| 0
| 0.375218
| 0
| 0
| 0
| 0
| 0
| 0.454545
| 1
| 0.090909
| false
| 0.181818
| 0.090909
| 0.090909
| 0.272727
| 0.090909
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 5
|
80fbda0063a0bf3e8725b18929bfa0af834eb161
| 2,888
|
py
|
Python
|
16/16.py
|
PaeP3nguin/advent-of-code-2017
|
fb6b42437016f905df156cb6e8f02047ca2e86ae
|
[
"MIT"
] | null | null | null |
16/16.py
|
PaeP3nguin/advent-of-code-2017
|
fb6b42437016f905df156cb6e8f02047ca2e86ae
|
[
"MIT"
] | null | null | null |
16/16.py
|
PaeP3nguin/advent-of-code-2017
|
fb6b42437016f905df156cb6e8f02047ca2e86ae
|
[
"MIT"
] | null | null | null |
#!python3
def main():
with open('16.txt', 'r') as f, open('16_out.txt', 'w') as f_out:
line = f.readline()
dance_moves = line.strip().split(',')
letters = [chr(c) for c in range(97, 113)]
# Part 1
programs = letters.copy()
# programs = ['a', 'b', 'c', 'd', 'e']
# dance_moves = ['s1', 'x3/4', 'pe/b']
for move in dance_moves:
if move[0] == 's':
spin_size = int(move[1:])
programs = programs[-spin_size:] + programs[:len(programs) - spin_size]
elif move[0] == 'x':
a, b = [int(n) for n in move[1:].split('/')]
programs[a], programs[b] = programs[b], programs[a]
elif move[0] == 'p':
swap_a, swap_b = move[1], move[3]
a, b = programs.index(swap_a), programs.index(swap_b)
programs[a], programs[b] = programs[b], programs[a]
print(''.join(programs))
print(''.join(programs), file=f_out)
# Part 2
seen = set()
cycle_len = 0
programs = letters.copy()
for i in range(1000000000):
programs_str = ''.join(programs)
if programs_str in seen:
cycle_len = i
break
seen.add(programs_str)
for move in dance_moves:
if move[0] == 's':
spin_size = int(move[1:])
programs = programs[-spin_size:] + programs[:len(programs) - spin_size]
elif move[0] == 'x':
a, b = [int(n) for n in move[1:].split('/')]
programs[a], programs[b] = programs[b], programs[a]
elif move[0] == 'p':
swap_a, swap_b = move[1], move[3]
a, b = programs.index(swap_a), programs.index(swap_b)
programs[a], programs[b] = programs[b], programs[a]
print(f'Cycle length: {cycle_len}')
# Sure I mean I guess I could do a better job but hey, copy pasta is easy
programs = letters.copy()
for i in range(1000000000 % cycle_len):
for move in dance_moves:
if move[0] == 's':
spin_size = int(move[1:])
programs = programs[-spin_size:] + programs[:len(programs) - spin_size]
elif move[0] == 'x':
a, b = [int(n) for n in move[1:].split('/')]
programs[a], programs[b] = programs[b], programs[a]
elif move[0] == 'p':
swap_a, swap_b = move[1], move[3]
a, b = programs.index(swap_a), programs.index(swap_b)
programs[a], programs[b] = programs[b], programs[a]
print(''.join(programs))
print(''.join(programs), file=f_out)
if __name__ == '__main__':
main()
| 40.676056
| 91
| 0.475069
| 365
| 2,888
| 3.635616
| 0.216438
| 0.12208
| 0.15373
| 0.081387
| 0.728711
| 0.728711
| 0.728711
| 0.728711
| 0.668425
| 0.668425
| 0
| 0.031457
| 0.372576
| 2,888
| 70
| 92
| 41.257143
| 0.700883
| 0.058172
| 0
| 0.701754
| 0
| 0
| 0.023581
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.017544
| false
| 0
| 0
| 0
| 0.017544
| 0.087719
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
03831210086302bcde2dadbee9b58d7417ddc096
| 136
|
py
|
Python
|
scenes/__init__.py
|
zachdj/ultimate-tic-tac-toe-weka
|
70b1451186e0aaf0582a6c474aa6950ae287543b
|
[
"MIT"
] | null | null | null |
scenes/__init__.py
|
zachdj/ultimate-tic-tac-toe-weka
|
70b1451186e0aaf0582a6c474aa6950ae287543b
|
[
"MIT"
] | null | null | null |
scenes/__init__.py
|
zachdj/ultimate-tic-tac-toe-weka
|
70b1451186e0aaf0582a6c474aa6950ae287543b
|
[
"MIT"
] | null | null | null |
from .MainMenu import MainMenu
from .SetupGame import SetupGame
from .PlayGame import PlayGame
from .GameCompleted import GameCompleted
| 27.2
| 40
| 0.852941
| 16
| 136
| 7.25
| 0.375
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.117647
| 136
| 4
| 41
| 34
| 0.966667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
03ecbf6fb0eee71a8973934bf7da01e68bb58672
| 45
|
py
|
Python
|
tests/__init__.py
|
doreenfan/vacation_router
|
638e5ae6fbed4d93cb89d5c801172c439e98f79d
|
[
"Apache-2.0"
] | null | null | null |
tests/__init__.py
|
doreenfan/vacation_router
|
638e5ae6fbed4d93cb89d5c801172c439e98f79d
|
[
"Apache-2.0"
] | 1
|
2021-12-26T01:49:37.000Z
|
2021-12-26T01:49:37.000Z
|
tests/__init__.py
|
doreenfan/vacation_router
|
638e5ae6fbed4d93cb89d5c801172c439e98f79d
|
[
"Apache-2.0"
] | 1
|
2021-12-26T01:30:53.000Z
|
2021-12-26T01:30:53.000Z
|
"""Unit test package for vacation_router."""
| 22.5
| 44
| 0.733333
| 6
| 45
| 5.333333
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.111111
| 45
| 1
| 45
| 45
| 0.8
| 0.844444
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 5
|
ff09c2255a34785e1bdaa1a49ad2a12cf1e69b96
| 71
|
py
|
Python
|
plotly_study/express/data.py
|
lucasiscovici/plotly_py
|
42ab769febb45fbbe0a3c677dc4306a4f59cea36
|
[
"MIT"
] | null | null | null |
plotly_study/express/data.py
|
lucasiscovici/plotly_py
|
42ab769febb45fbbe0a3c677dc4306a4f59cea36
|
[
"MIT"
] | null | null | null |
plotly_study/express/data.py
|
lucasiscovici/plotly_py
|
42ab769febb45fbbe0a3c677dc4306a4f59cea36
|
[
"MIT"
] | null | null | null |
from __future__ import absolute_import
from plotly_study.data import *
| 23.666667
| 38
| 0.859155
| 10
| 71
| 5.5
| 0.7
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.112676
| 71
| 2
| 39
| 35.5
| 0.873016
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 5
|
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