prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
#GiG
import numpy as np
import pandas as pd
from pathlib import Path
from deep_blocker import DeepBlocker
from tuple_embedding_models import AutoEncoderTupleEmbedding, CTTTupleEmbedding, HybridTupleEmbedding, SIFEmbedding
from vector_pairing_models import ExactTopKVectorPairing
import blocking_utils
from configurati... | pd.DataFrame(lines,columns=['full']) | pandas.DataFrame |
import os
import pandas as pd
import copy
import jieba
import numpy as np
import time
def readExcel(url):
df=pd.read_excel(url,na_values='')
return df
def writeExcel(df,url=''):
write = pd.ExcelWriter(url)
df.to_excel(write, sheet_name='Sheet1')
write.save()
def genDF(df_Base,df_IPC):
column... | pd.DataFrame(None,columns=['Company','Cat']) | pandas.DataFrame |
# Standard library imports
import pandas as pd
import numpy as np
from math import pi
# Local application/library specific imports
from .water_demand import (
set_cropland_share,
get_ky_list,
get_kc_list,
get_evap_i,
get_eto,
get_eff_rainfall_i,
get_effective_rainfall,
get_season_days,
... | pd.DataFrame() | pandas.DataFrame |
import os
import json
import random
import numpy as np
import pandas as pd
from copy import deepcopy
from string import ascii_uppercase, digits
from shutil import copyfile, rmtree, copytree
from datetime import datetime
#from ai.bot import Agent
from ai.bot import Agent
from tasks.games.chess.chess import Chess
from s... | pd.DataFrame() | pandas.DataFrame |
import numpy as np
import pytest
from pandas.errors import UnsupportedFunctionCall
from pandas import DataFrame, DatetimeIndex, Series
import pandas._testing as tm
from pandas.core.window import Expanding
def test_doc_string():
df = DataFrame({"B": [0, 1, 2, np.nan, 4]})
df
df.expanding(2... | Series([1, 2]) | pandas.Series |
import io
import json
import os
import re
import pickle
import subprocess
import pandas as pd
import numpy as np
from textblob import TextBlob, Blobber
from textblob_de import TextBlobDE as TextBlobDE
from textblob_fr import PatternTagger as PatternTaggerFR, PatternAnalyzer as PatternAnalyzerFR
import nltk
nltk.down... | pd.concat(df_list) | pandas.concat |
"""
Transfer applications.
|pic1|
.. |pic1| image:: ../images_source/transfer_tools/transfer.png
:width: 30%
"""
import os
import sys
from subprocess import Popen, PIPE
from pathlib import Path
import pandas as pd
import pexpect
import requests
import zipfile
from selenium.webdriver.chrome import webdriv... | pd.concat([dat_all, dat]) | pandas.concat |
import sys
import pandas as pd
import numpy as np
from scipy import stats
from itertools import compress
import statsmodels.stats.multitest as smt
import scikits.bootstrap as bootstrap
from sklearn.decomposition import PCA
from .scaler import scaler
from .imputeData import imputeData
class statistics:
usage = """G... | pd.DataFrame({mean_fold_change_name: [meanFoldChange], mean_fold_change_name_CIlower: CIs[0], mean_fold_change_name_CIupper: CIs[1], mean_fold_change_name_sig: [sigMeanFold]}) | pandas.DataFrame |
# coding=utf-8
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
from numpy import nan
import numpy as np
import pandas as pd
from pandas.types.common import is_integer, is_scalar
from pandas import Index, Series, DataFrame, isnull, date_range
from pandas.core.index import MultiIndex
from pa... | pd.Series(['foo'], index=[0]) | pandas.Series |
from datetime import date
import unittest
import dolphindb as ddb
import pandas as pd
import numpy as np
from pandas.testing import assert_frame_equal
from setup import HOST, PORT, WORK_DIR, DATA_DIR
from numpy.testing import assert_array_equal, assert_array_almost_equal
import dolphindb.settings as keys
impor... | assert_frame_equal(re, expected) | pandas.testing.assert_frame_equal |
from itertools import product
import pandas as pd
from pandas.testing import assert_series_equal, assert_frame_equal
import pytest
from solarforecastarbiter.validation import quality_mapping
def test_ok_user_flagged():
assert quality_mapping.DESCRIPTION_MASK_MAPPING['OK'] == 0
assert quality_mapping.DESCR... | pd.Series([0, 0, 0, 1, 1]) | pandas.Series |
# -*- coding: utf-8 -*-
from __future__ import print_function
import pytest
import operator
from collections import OrderedDict
from datetime import datetime
from itertools import chain
import warnings
import numpy as np
from pandas import (notna, DataFrame, Series, MultiIndex, date_range,
Time... | pd.Timestamp('20130101') | pandas.Timestamp |
#!/usr/bin/env python
import pandas as pd
import argparse
import datetime
import time
import sys
import investpy
scrap_delay = 2
def main():
parser = argparse.ArgumentParser(description='scrap investing.com daily close')
parser.add_argument('-input_file', type=str, default='data_tickers/investing_stock_info.... | pd.concat(info_list) | pandas.concat |
##############################################################
# Author: <NAME>
##############################################################
'''
Module : create_kallisto_ec_count_matrix
Description : Create equivalence class matrix from kallisto.
Copyright : (c) <NAME>, Dec 2018
License : MIT
Maintainer :... | pd.merge(counts, tx_stack, left_on='ec_names', right_on='ec_names') | pandas.merge |
from datetime import timedelta
import pytest
import pandas.util._test_decorators as td
from pandas import (
DataFrame,
to_datetime,
)
@pytest.fixture(params=[True, False])
def raw(request):
"""raw keyword argument for rolling.apply"""
return request.param
@pytest.fixture(
params=[
"tr... | DataFrame([[2.0, 4.0], [1.0, 2.0], [5.0, 2.0], [8.0, 1.0]], columns=[1, 0.0]) | pandas.DataFrame |
from __future__ import division
import pytest
import numpy as np
from datetime import timedelta
from pandas import (
Interval, IntervalIndex, Index, isna, notna, interval_range, Timestamp,
Timedelta, compat, date_range, timedelta_range, DateOffset)
from pandas.compat import lzip
from pandas.tseries.offsets imp... | tm.assert_raises_regex(ValueError, msg) | pandas.util.testing.assert_raises_regex |
import submodels_module as modelbank
import numpy as np
from itertools import combinations
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
import pandas as pd
import load_format_data
#Determine the most generalizable model from the top CV models
def get_loss_list(model_list):
model_los... | pd.DataFrame([model_name_list,model_loss_list,model_loss_std_list]) | pandas.DataFrame |
from datetime import (
datetime,
timedelta,
)
import re
import numpy as np
import pytest
from pandas._libs import iNaT
from pandas.errors import InvalidIndexError
import pandas.util._test_decorators as td
from pandas.core.dtypes.common import is_integer
import pandas as pd
from pandas import (
Categoric... | is_integer(result) | pandas.core.dtypes.common.is_integer |
import unittest
from context import grama as gr
from context import data
from numpy import NaN
from pandas import DataFrame, RangeIndex
from pandas.testing import assert_frame_equal
class TestPivotLonger(unittest.TestCase):
"""Test implementation of pivot_longer
"""
def test_pivot_longer(self):
"... | assert_frame_equal(long, expected) | pandas.testing.assert_frame_equal |
# -*- coding: utf-8 -*-
"""Supports Kp index values. Downloads data from ftp.gfz-potsdam.de or SWPC.
Parameters
----------
platform
'sw'
name
'kp'
tag
- '' : Standard Kp data
- 'forecast' : Grab forecast data from SWPC (next 3 days)
- 'recent' : Grab last 30 days of Kp data from SWPC
Note
----
Sta... | pds.date_range(forecast_date, periods=24, freq='3H') | pandas.date_range |
# Version 2.0 of the t-SNE Stock Market Example: with triggers and better defined functions.
# Try with simulated data so we know that they are clustered
#___________________________________________________________________________________________________ Import packages
import numpy as np
import pandas as pd
... | pd.read_csv('all_stocks_5yr.csv') | pandas.read_csv |
from nose.tools import with_setup
import pandas as pd
from ..widget import utils as utils
from ..widget.encoding import Encoding
df = None
encoding = None
def _setup():
global df, encoding
records = [
{
"buildingID": 0,
"date": "6/1/13",
"temp_diff": 12,
... | pd.date_range("2012", periods=3, freq="A") | pandas.date_range |
import pandas as pd
df = pd.read_csv('D:/5674-833_4th/part5/stock-data.csv')
#문자열인 날짜 데이터를 판다스 Timestamp로 변환
df['new_date']=pd.to_datetime(df['Date'])
df.set_index('new_date',inplace= True)
# print(df.loc['2018'].head())
# df_ym = df.loc['2018-07']
# print(df_ym)
#
today = | pd.to_datetime('2018-12-25') | pandas.to_datetime |
import pandas as pd
from src.features import build_features
from sklearn.model_selection import train_test_split
def make_dataset():
"""
This function laods the raw data, builds some features and saves the df.
It is not meant to be called but once to produce the dataset.
"""
raw_data = pd.read_csv(... | pd.DataFrame(df[mask]) | pandas.DataFrame |
import pandas as pd
from pandas._testing import assert_frame_equal
import pytest
import numpy as np
from scripts.normalize_data import (
remove_whitespace_from_column_names,
normalize_expedition_section_cols,
remove_bracket_text,
remove_whitespace,
ddm2dec,
remove_empty_unnamed_columns,
nor... | pd.DataFrame(data) | pandas.DataFrame |
"""
Script to processes basic data from all query
files to notebooks1.csv. After notebooks1.csv is created, files
can be downloaded with download.py.
"""
import time
import os
import datetime
import json
import sys
import argparse
import requests
import pandas as pd
from consts import (
URL,
COUNT_TRIGGER,... | pd.concat([repos_df, repos_done]) | pandas.concat |
"""Mock data for bwaw.insights tests."""
import pandas as pd
ACTIVE_BUSES = pd.DataFrame([
['213', 21.0921481, '1001', '2021-02-09 15:45:27', 52.224536, '2'],
['213', 21.0911025, '1001', '2021-02-09 15:46:22', 52.2223788, '2'],
['138', 21.0921481, '1001', '2021-02-09 15:45:27', 52.224536, '05'],
['138'... | pd.to_datetime(SPEED_INCIDENT['Time']) | pandas.to_datetime |
'''
MIT License
Copyright (c) 2020 <NAME>
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distri... | pd.merge(df, df_localidades, left_on='origen', right_on='Localidad') | pandas.merge |
"""Store the data in a nice big dataframe"""
import sys
from datetime import datetime, timedelta
import pandas as pd
import geopandas as gpd
import numpy as np
class Combine:
"""Combine defined countries together"""
THE_EU = [ 'Austria', 'Italy', 'Belgium', 'Latvia',
'Bulgaria', 'L... | pd.read_csv('data/belgium.csv', delimiter=',') | pandas.read_csv |
'''
Functions for the neural network
'''
import os
import numpy as np
import numba as nb
import pandas as pd
# Project imports
from utils import tickers, strategy, dates, fundamentals, io
# Other imports and type-hinting
from pandas import DataFrame as pandasDF
def main(nn_config: dict,
strat_config: dict)... | pd.read_csv(f'data/{ticker}.csv') | pandas.read_csv |
import pytest
import datetime
from pymapd._loaders import _build_input_rows
from pymapd import _pandas_loaders
from omnisci.mapd.MapD import TStringRow, TStringValue, TColumn, TColumnData
import pandas as pd
import numpy as np
from omnisci.mapd.ttypes import TColumnType
from omnisci.common.ttypes import TTypeInfo
def... | pd.Timestamp("2017") | pandas.Timestamp |
"""
Copyright 2019 <NAME>.
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 distribut... | pd.DataFrame(df) | pandas.DataFrame |
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from IPython.core.display import display
from scipy.stats import chi2_contingency
import glob
import os
import warnings
warnings.filterwarnings("ignore")
pd.options.display.float_format = '{:.4f}'.format
class DataExplorer()... | pd.to_datetime(date_df[colname]) | pandas.to_datetime |
import os.path
import json
import zipfile
import numpy as np
import pandas as pd
import requests
from openpyxl import load_workbook
import ukcensusapi.Nomisweb as Api
import ukpopulation.utils as utils
class SNPPData:
"""
Functionality for downloading and collating UK Subnational Population Projection (NPP) dat... | pd.DataFrame(data=females[1:, 1:], index=females[1:, 0], columns=females[0, 1:]) | pandas.DataFrame |
import unittest
import logging
import os
import numpy as np
import pandas as pd
import cmapPy.pandasGEXpress as GCToo
import cmapPy.pandasGEXpress.parse as parse
import broadinstitute_psp.utils.setup_logger as setup_logger
import broadinstitute_psp.tear.continuous_renormalization as renorm
# Setup logger
... | pd.Series([1.47, 1.42, 1.37, 1.31]) | pandas.Series |
import logging
import os
import unittest
import pandas as pd
import moneytrack as mt
logging.basicConfig(level=logging.DEBUG)
field_names = mt.Config.FieldNames
class DatasetTests(unittest.TestCase):
def test_a(self):
sim = mt.simulation.AccountSimulatorFixedRate(date=pd.to_datetime("2021-01-01"), ayr... | pd.to_datetime("2021-01-01") | pandas.to_datetime |
#!/usr/bin/env python
# -*- coding:utf-8 _*-
"""
@author: <NAME>
@file: grow_path.py
@time: 2021/01/19/13:42
"""
import os
import time
import argparse
import numpy as np
import pandas as pd
from rdkit import Chem
from rdkit.Chem import Descriptors
from rdkit.Chem.rdMolDescriptors import CalcExactMolWt
import subpro... | pd.read_csv(merged_df_path) | pandas.read_csv |
import pymysql
import pandas as pd
import numpy as np
import tushare as ts
from tqdm import tqdm
from sqlalchemy import create_engine
from getdata import read_mysql_and_insert
from datetime import date,timedelta,datetime
pro = ts.pro_api(token='？？？')
def strategy():
"""
auto strategy
"""
... | pd.DataFrame(index=all_stock_info.ts_code) | pandas.DataFrame |
"""
Functions and methods to extract statistics concerning the most used emojis in twitter datasets.
"""
from collections import Counter
import emoji
import seaborn as sns
import pandas as pd
import sys
import resource
from tqdm import tqdm
from IPython.core.debugger import set_trace
from tqdm import tqdm
import matpl... | pd.read_csv(tweet_path, chunksize=10000) | pandas.read_csv |
from __future__ import absolute_import
from __future__ import print_function
import os
import pandas as pd
import numpy as np
import sys
import shutil
from sklearn.preprocessing import MinMaxScaler
def dataframe_from_csv(path, header=0, index_col=False):
return pd.read_csv(path, header=header, index_col=index_... | pd.factorize(dx_type) | pandas.factorize |
# BSD 2-CLAUSE LICENSE
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
# Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
# Redistributions i... | pd.concat([runtimes_df, split_runtime_df]) | pandas.concat |
import pytz
import pytest
import dateutil
import warnings
import numpy as np
from datetime import timedelta
from itertools import product
import pandas as pd
import pandas._libs.tslib as tslib
import pandas.util.testing as tm
from pandas.errors import PerformanceWarning
from pandas.core.indexes.datetimes import cdate_... | Timestamp('2000-01-31 00:23:00') | pandas.Timestamp |
import pandas as pd
import snowflake.connector
import getpass as pwd
| pd.set_option('display.max_rows', None) | pandas.set_option |
# Copyright 2016 <NAME> and The Novo Nordisk Foundation Center for Biosustainability, DTU.
# 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
# Unle... | DataFrame(columns=["formula", "atoms", "bonds", "tanimoto_similarity", "structural_score"]) | pandas.DataFrame |
import pandas as pd
import os
def get_oneday_data(machine_path, machine_id, day):
data = | pd.read_csv(machine_path, header=None) | pandas.read_csv |
from zipline.api import symbol
from zipline import run_algorithm
import pandas as pd
def validate_single_stock(ticker):
def init(context):
symbol(ticker)
def handle_data(context, data):
pass
start = pd.to_datetime("2017-01-09").tz_localize('US/Eastern')
end = | pd.to_datetime("2017-01-11") | pandas.to_datetime |
import openpyxl
import pandas as pd
from datetime import datetime, timedelta
import xlsxwriter
now = datetime.now()
date_time = now.strftime("%m_%d_%Y %I_%M_%p")
federal_tax_rate_path = "./federaltaxrates.csv"
state_tax_rate_path = "./statetaxrates.csv"
city_tax_rate_path = "./NYCtaxrates.csv"
# calculate social s... | pd.DataFrame(analytics_table) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
import json
import matplotlib.pyplot as plt
from datetime import datetime
from sys import stdout
from sklearn.preprocessing import scale
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import RBF, Constant... | pd.concat([X, bio_data], axis=1) | pandas.concat |
import multiprocessing
import os
from queue import Queue
from typing import List
from injector import inject
import pandas as pd
from pandas import DataFrame
from domain.operation.execution.services.OperationCacheService import OperationCacheService
from infrastructor.connection.adapters.ConnectionAdapter import Conn... | pd.notnull(df) | pandas.notnull |
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
#-------------read csv---------------------
df_2010_2011 = pd.read_csv("/mnt/nadavrap-students/STS/data/data_Shapira_20200911_2010_2011.csv")
df_2012_2013 = pd.read_csv("/mnt/nadavrap-students/STS/data/data_Shapira_20200911_2012_2013.csv")
df_2014_... | pd.merge(df1, df_2012, on='hospid') | pandas.merge |
#!/usr/bin/env python
#-*- coding:utf-8 -*-
"""
*.py: Description of what * does.
Last Modified:
"""
__author__ = "<NAME>"
__email__ = "<EMAIL>"
__version__ = "0.0.1"
# import gevent
from .dbManager import SQLiteWrapper, MongoDBWrapper
import pandas as pd
from . import GeoPoint, encode, blacklist, loc_defaul... | pd.DataFrame([i.__dict__ for i in ldist]) | pandas.DataFrame |
from netCDF4 import Dataset
import pandas as pd
import numpy as np
ncep_path = '/SubX/forecast/tas2m/daily/full/NCEP-CFSv2/' # the path where the raw data from NCEP-CFSv2 is saved
gmao_path = '/SubX/forecast/tas2m/daily/full/GMAO-GEOS_V2p1/'
for model in ['NCEP', 'GMAO']:
if model == 'NCEP':
path = ncep_... | pd.date_range('2017-07-01', '2019-12-31') | pandas.date_range |
import argparse
import math
import json
from tqdm import tqdm
from nltk.tag import pos_tag
import pandas as pd
import networkx as nx
import torch
import config
def get_relevant_tokens(word_count_path, threshold):
d = pd.read_csv(word_count_path, sep='\t', header=None, quotechar=None, quoting=3)
d.columns = ... | pd.read_csv(Seid_amharic_sentiment_path, header=None) | pandas.read_csv |
#!/usr/bin/python
# -*- coding: utf-8 -*-
"""
Created on Thu Jul 18 04:52:01 2019
@author: jamiesom
"""
import pandas as pd
from electricitylci.globals import data_dir, output_dir
import numpy as np
from electricitylci.eia860_facilities import eia860_generator_info
import re
def generate_power_plant_construction(year... | pd.read_csv(f"{data_dir}/plant_construction_inventory.csv") | pandas.read_csv |
from plotly.offline import plot, iplot, init_notebook_mode
from pandas.plotting import register_matplotlib_converters
import seaborn as sns
import matplotlib.pyplot as plt
from urllib.request import urlopen
from datetime import timedelta
import json
import numpy as np
import pandas as pd
import plotly.express as px
imp... | pd.read_csv('datasets/complete.csv', parse_dates=['Date']) | pandas.read_csv |
import pandas as pd
import datetime
import numpy as np
import icd
def get_age(row):
"""Calculate the age of patient by row
Arg:
row: the row of pandas dataframe.
return the patient age
"""
raw_age = row['DOD'].year - row['DOB'].year
if (row['DOD'].month < row['DOB'].month) or ((row['... | pd.read_csv(mimic_admissions) | pandas.read_csv |
import pandas as pd
# bookings_to_arr
#
# Accepts a pandas dataframe containing bookings data and returns a pandas
# dataframe containing changes in ARR with the following columns:
# - date - the date of the change
# - type - the type of the change (new, upsell, downsell, and churn)
# - customer_id - the id o... | pd.Timestamp(ts_input="10/1/2021", tz="UTC") | pandas.Timestamp |
import pandas as pd
def load_dataset(csv_path):
df_inflacao = | pd.read_csv(csv_path, sep=';', decimal=',') | pandas.read_csv |
""" Compute the accuracies required to compute the overlapping scores.
Namely, for each model m trained with data augmentation on one candidate corruption (trained with corruption_trainings.py), get the accuracy of m for each candidate corruption (using the ImageNet validation set corrupted with the considered corrupt... | pandas.DataFrame(res_array, index=list_models, columns=list_corruptions) | pandas.DataFrame |
"""Run 20newsgroups data experiment."""
import os
import numpy as np
import random
import pickle
import pandas as pd
import methods_20news
from methods_20news import Methods
from prep_20news import *
from utils_20news import *
from statistics import mean
from statistics import median
from statistics import stdev
rand... | pd.get_dummies(subcat_all) | pandas.get_dummies |
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 14 09:27:05 2021
@author: vargh
"""
import numpy as np
import pandas as pd
from sympy import symbols, pi, Eq, integrate, diff, init_printing, solve
from scipy.optimize import curve_fit
from scipy.integrate import cumtrapz
from scipy.interpolate import interp1d, interp2d
... | pd.read_csv(filename) | pandas.read_csv |
# -*- coding: utf-8 -*-
import nose
import numpy as np
from datetime import datetime
from pandas.util import testing as tm
from pandas.core import config as cf
from pandas.compat import u
from pandas.tslib import iNaT
from pandas import (NaT, Float64Index, Series,
DatetimeIndex, TimedeltaIndex, da... | notnull(-np.inf) | pandas.types.missing.notnull |
import os
import gzip
import pandas as pd
import scipy.io as sio
import pathlib
from enum import Enum
import torch
from sklearn.preprocessing import MinMaxScaler, normalize, StandardScaler
class Btype(Enum):
Undefined = 0
Normal = 1
ESSV_aka_PAC = 2
Aberrated = 3
ESV_aka_PVC = 4... | pd.DataFrame(test_labels_subset, columns=['patient', 'segment', 'frame', 'bt_label', 'rt_label']) | pandas.DataFrame |
from kivy.uix.boxlayout import BoxLayout
from kivy.uix.label import Label
from kivy.uix.popup import Popup
import pandas as pd
class Ledger(BoxLayout):
"""
Ledger data structure:
x: first number in equation (float)
y: second number in equation (float)
op: operator ['+', "-", '*', '/'] (str)
z... | pd.DataFrame(columns=['x', 'y', 'op', 'z']) | pandas.DataFrame |
#!/usr/bin/env python3
"""
Author : <NAME>
Date : 2022-02-03
Purpose: Parse tracy JSON files and produce summary .xlsx sheet.
"""
import argparse
from typing import NamedTuple
import json, pathlib, time
import pandas as pd
class Args(NamedTuple):
""" Command-line arguments """
json_file_path: pathlib.Path
... | pd.DataFrame.from_dict(SNP_data, orient='index') | pandas.DataFrame.from_dict |
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from sklearn.utils import resample
from scipy.ndimage import gaussian_filter
from scipy import signal
import cv2
## plot the data classes as a circle to view the unbalance between the classes
def plot_num_of_classes(labels):
plt.figure(figsize... | pd.DataFrame(df_4) | pandas.DataFrame |
import importlib
import pandas as pd
from pandas import compat
from .parser import Parser
import logging
class UberModel(object):
"""
Collection of static methods used across all the ubertool models.
"""
def __init__(self):
"""Main utility class for building Ubertool model classes for model e... | pd.Series([], dtype="object") | pandas.Series |
# %%
import pandas as pd
import numpy as np
import requests # http 요청 모듈
from bs4 import BeautifulSoup # 웹 크롤링 모듈
from urllib.request import urlopen # 웹 크롤링 모듈
from urllib.parse import quote_plus, urlencode
from pandas import DataFrame, Series # 시리즈, 데이터프레임 모듈
from pandas import ExcelFile, ExcelWriter # 엑셀 읽기, 쓰기... | pd.set_option("display.max_columns", 15) | pandas.set_option |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#-----------------------------------------------------------------------------
# Copyright (c) 2015, IBM Corp.
# All rights reserved.
#
# Distributed under the terms of the BSD Simplified License.
#
# The full license is in the LICENSE file, distributed with this software.
... | pd.Categorical(dataframe['level_1'], agg_values) | pandas.Categorical |
from collections import abc, deque
from decimal import Decimal
from io import StringIO
from warnings import catch_warnings
import numpy as np
from numpy.random import randn
import pytest
from pandas.core.dtypes.dtypes import CategoricalDtype
import pandas as pd
from pandas import (
Categorical,
DataFrame,
... | Series([4, 5, 6]) | pandas.Series |
# Copyright 2017 Google Inc.
#
# 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, ... | pd.Series(['', '']) | pandas.Series |
import math
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
def clean_portfolio(portfolio):
""" Clean the portfolio dataset.
- It makes columns for the channels
- Changes the name of the id column to offer_id
Input:
- portfolio: original dataset
Returns:
- portfolio_... | pd.merge(trans_prof, portfolio_clean, on='offer_id', how='left') | pandas.merge |
#!/usr/bin/python3
# import the module
import os
import glob
import pandas as pd
import csv
from sqlalchemy import create_engine
import psycopg2
import config #you need to create this config.py file and update the variables with your database, username and password
import subprocess
import sys
#Note: you need to ind... | pd.read_csv("/home/bmain/pesticide/chem_com.csv") | pandas.read_csv |
# -*- coding: utf-8 -*-
from __future__ import print_function
from datetime import datetime, timedelta
import functools
import itertools
import numpy as np
import numpy.ma as ma
import numpy.ma.mrecords as mrecords
from numpy.random import randn
import pytest
from pandas.compat import (
PY3, PY36, OrderedDict, ... | tm.assert_frame_equal(result, expected) | pandas.util.testing.assert_frame_equal |
# encoding: utf-8
import argparse
import os
import sys
import torch
from torch.backends import cudnn
import numpy as np
import random
sys.path.append('.')
from data import make_data_loader
from model import build_model
from engine.evaluator import do_inference
from config import cfg
from utils.logger import setup... | pd.ExcelWriter(xls_filename, engine="openpyxl", mode='a') | pandas.ExcelWriter |
from .nwb_interface import NWBDataset
from .chop import ChopInterface, chop_data, merge_chops
from itertools import product
import numpy as np
import pandas as pd
import h5py
import sys
import os
import logging
logger = logging.getLogger(__name__)
PARAMS = {
'mc_maze': {
'spk_field': 'spikes',
'h... | pd.concat([dataset.trial_info, align_jit], axis=1) | pandas.concat |
import sys
import itertools
from pathlib import Path
from matplotlib import pyplot as plt
import pandas as pd
import seaborn as sns
from matplotlib import cm
FIGURES_DIR = (
Path(__file__).resolve().parents[2]
/ "figures"
/ "ukbiobank"
/ Path(sys.argv[0]).stem
)
FIGURES_DIR.mkdir(exist_ok=True, paren... | pd.concat(scores, ignore_index=False) | pandas.concat |
"""Module to support machine learning of activity states from acc data"""
from accelerometer import utils
from accelerometer.models import MODELS
from io import BytesIO
import numpy as np
import os
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
import sklearn.ensemble._forest as forest
import ... | pd.DataFrame(data=cnf_matrix, columns=labels, index=labels) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import streamlit as st
import pandas as pd
import numpy as np
import geopandas as gpd
from pathlib import Path
from PIL import Image
import altair as alt
import pydeck as pdk
import numpy as np
from api_key import mapbox_key
import matplotlib.pyplot as plt
import plotly.exp... | pd.merge(filtered_drug,df_geometries[['id','lat','lon']],how = 'left',left_on = 'patientid',right_on = 'id') | pandas.merge |
#!/usr/bin/env python3
import pdb
import pandas as pd
from pylru import lrudecorator
import seaborn as sns
BII_URL = 'http://ipbes.s3.amazonaws.com/weighted/' \
'historical-BIIAb-npp-country-1880-2014.csv'
@lrudecorator(10)
def get_raw_bii_data():
return pd.read_csv(BII_URL)
def findt(ss):
rval = [None] * le... | pd.read_csv(url, encoding='utf-8') | pandas.read_csv |
import os
import pandas as pd
from Lib.get_texts import get_generated_lyrics, get_lyrics_dataset
from Lib.get_structure import get_lyrics_structure
from Lib.get_sentiment import calculate_sentiment_scores
from Lib.get_bagofwords import get_repetition_scores, combine_bag_of_words, lemmatize_lyrics
# This method perfo... | pd.concat([df, df2], axis=1) | pandas.concat |
"""
Download, transform and simulate various datasets.
"""
# Author: <NAME> <<EMAIL>>
# <NAME> <<EMAIL>>
# License: MIT
from os.path import join
from re import sub
from collections import Counter
from itertools import product
from urllib.parse import urljoin
from string import ascii_lowercase
from zipfile imp... | pd.read_csv(FETCH_URLS["volkert"]) | pandas.read_csv |
'''
Created on Sep 10, 2017
@author: twong
'''
import json
import logging
import random
import pandas as pd
import requests
_logger = logging.getLogger(__name__)
def _deserialize_roster_json(roster_json):
roster_cooked = json.loads(roster_json)
try:
players_json = roster_cooked['d'][0]
except ... | pd.concat([t.roster for t in teams], ignore_index=True) | pandas.concat |
import numpy as np
import pandas as pd
from scipy import stats
from ..base import AbstractDensity
class Multinomial:
def __init__(self, probs):
'''
Define a multinomial random variable object
:param probs: The probability of each class, with classes indexed as 0 to len(probs)-1
''... | pd.DataFrame({'bart_simpson': samples}) | pandas.DataFrame |
"""
This script reads all the bootstrap performance result files, plots histograms, and calculates averages.
t-tests are done to compute p-values and confidence intervals are computed
"""
import pandas as pd
import numpy as np
import os
import matplotlib.pyplot as plt
import matplotlib
from scipy import stat... | pd.read_csv(data) | pandas.read_csv |
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler, LabelEncoder
from querying.tracking_query import get_play
def ft_in(x):
if '-' in x:
meas=x.split('-')
#this will be a list ['ft','in']
inches = int(meas[0])*12 + int(meas[1])
return inches
... | pd.merge(play_p, track19, left_on = ['gameId', 'playId'], right_on = ['gameId', 'playId']) | pandas.merge |
# pylint: disable=E1101,E1103,W0232
from datetime import datetime, timedelta
from pandas.compat import range, lrange, lzip, u, zip
import operator
import re
import nose
import warnings
import os
import numpy as np
from numpy.testing import assert_array_equal
from pandas import period_range, date_range
from pandas.c... | MultiIndex.from_tuples(tups) | pandas.core.index.MultiIndex.from_tuples |
from __future__ import division
import pandas as pd
import numpy as np
import scipy.stats
import argparse
import datetime
def fileToList(group_list):
with open(group_list, 'r') as fh:
return [line.strip() for line in fh.readlines()]
def cleanDF(df, sample_names):
'''
Convert string nans to np.nan ... | pd.read_csv(data_input, sep=',') | pandas.read_csv |
import numpy as np
import pandas as pd
from pandas.testing import assert_series_equal
import pytest
from ber_public.deap import dim
from ber_public.deap import fab
from ber_public.deap import vent
def test_calculate_fabric_heat_loss():
"""Output is equivalent to DEAP 4.2.0 example A"""
floor_area = pd.Series... | pd.Series([0.14]) | pandas.Series |
"""This module contains pyspark wrangler utility tests.
isort:skip_file
"""
import pytest
import pandas as pd
from pywrangler.pyspark.util import ColumnCacher
from pyspark.sql import functions as F
pytestmark = pytest.mark.pyspark # noqa: E402
pyspark = pytest.importorskip("pyspark") # noqa: E402
from pywrangler.... | pd.DataFrame(data) | pandas.DataFrame |
import os,sys
import pandas as pd
import numpy as np
import skbio.io
import gffpandas.gffpandas as gffpd
from statistics import stdev
def find_locs(kmer, blast_df):
"""
Finds the start and stop locations of this k-mer in each genome
"""
locs = []
# filter blast results to just our kmer of interest... | pd.read_csv("data/gene_labels.tsv",sep='\t') | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Sun Sep 1 18:10:18 2019
@author: <NAME>
Code will plot the keypoint coordinates vs time in order to assign the maximum
value from this plot to the real-world distance measurement. This will be
the label.
Coding Improvement Note: Make use of functions for things lik... | pd.DataFrame(data=promsx_6[0][0:]) | pandas.DataFrame |
from collections import Counter
import sys
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer, HashingVectorizer
from sklearn.feature_extraction import DictVectorizer
import sklearn.cluster.k_means_
from sklearn.cluster.k_means_ import KMeans, MiniBatchKMeans
from sklearn.cluster import Spectr... | pd.DataFrame(m) | pandas.DataFrame |
# Copyright (c) 2018-2021, NVIDIA CORPORATION.
import array as arr
import datetime
import io
import operator
import random
import re
import string
import textwrap
from copy import copy
import cupy
import numpy as np
import pandas as pd
import pyarrow as pa
import pytest
from numba import cuda
import cudf
from cudf.c... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import click
import os
@click.command()
@click.argument('input_folder')
@click.argument('output_folder')
def main(input_folder, output_folder):
if not os.path.exists(output_folder):
os.makedirs(output_folder)
files = [[(x[0] + '/' + y, x[0].split('/')[-1].replace('DATA_', '').rep... | pd.read_csv(all_mutations_filtered_mut_type_gene) | pandas.read_csv |
import pytest
import numpy as np
import pandas as pd
from systrade.trading.brokers import PaperBroker
T_START = pd.to_datetime('2019/07/10-09:30:00:000000', format='%Y/%m/%d-%H:%M:%S:%f')
T_END = pd.to_datetime('2019/07/10-10:00:00:000000', format='%Y/%m/%d-%H:%M:%S:%f')
TIMEINDEX = pd.date_range(start=T_START,en... | pd.to_datetime('2019/07/10-09:29:00:000000', format='%Y/%m/%d-%H:%M:%S:%f') | pandas.to_datetime |
import requests
import os
import json
import pandas as pd
import numpy as np
from requests.exceptions import HTTPError
import re
import matplotlib.pyplot as plt
# Part 1 Get Data With API
## Function 1-Get dataset with specific game "platform" and "type"
def api_game(platform = 'pc', type = 'game'):
"""
... | pd.DataFrame(g_list) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import sys
import os
import pandas as pd
PROJECT_ID = "dots-stock" # @param {type:"string"}
REGION = "us-central1" # @param {type:"string"}
USER = "shkim01" # <---CHANGE THIS
BUCKET_NAME = "gs://pipeline-dots-stock" # @param {type:"string"}
PIPELINE_ROOT = f"{BUCKET_NAME}/pipeline_root/{USE... | pd.read_csv(bros_dataset.path, index_col=0) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Fri Jun 26 23:51:40 2020
@author: Narendrakumar
"""
# Part 1 - Data Preprocessing
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Importing the dataset
dataset = | pd.read_csv('Churn_Modelling.csv') | pandas.read_csv |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
scenario_filenames = ["OUTPUT_110011_20201117123025"]
scenario_labels =["Lockdown enabled,Self Isolation,Mask Compliance (0.5)"]
MAX_DAY = 250#250#120
POPULATION = 10000.0
FIGSIZE = [20,10]
plt.rcParams.update({'font.size': 22})
#### compari... | pd.to_datetime(df1["Date_Time"]) | pandas.to_datetime |
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