prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
#%%
import os
import sys
try:
os.chdir('/Volumes/GoogleDrive/My Drive/python_code/connectome_tools/')
print(os.getcwd())
except:
pass
# %%
import sys
sys.path.append('/Volumes/GoogleDrive/My Drive/python_code/maggot_models/')
sys.path.append('/Volumes/GoogleDrive/My Drive/python_code/connectome_tools/')
fr... | pd.DataFrame(ff_fb_character_ad_output, columns = ['neuron', 'feedforward', 'feedback', 'p_ff', 'p_fb']) | pandas.DataFrame |
'''
@ Author : <NAME>
@ E-mail : <EMAIL>
@ Github : https://github.com/WooilJeong/PublicDataReader
@ Blog : https://wooiljeong.github.io
'''
import pandas as pd
import numpy as np
import datetime
import requests
from bs4 import BeautifulSoup
from PublicDataReader.PublicDataPortal.__init__ import *
class AptTradeRead... | pd.date_range(start=start_date, end=end_date, freq='m') | pandas.date_range |
"""
>>> from blaze.expr import Symbol
>>> from blaze.compute.pandas import compute
>>> accounts = Symbol('accounts', 'var * {name: string, amount: int}')
>>> deadbeats = accounts[accounts['amount'] < 0]['name']
>>> from pandas import DataFrame
>>> data = [['Alice', 100], ['Bob', -50], ['Charlie', -20]]
>>> df = Data... | pd.concat([a, b[[c for c in b.columns if c != a.name]]], axis=1) | pandas.concat |
import csv
from io import StringIO
import os
import numpy as np
import pytest
from pandas.errors import ParserError
import pandas as pd
from pandas import (
DataFrame,
Index,
MultiIndex,
NaT,
Series,
Timestamp,
date_range,
read_csv,
to_datetime,
)
import pandas._testing as tm
impo... | tm.ensure_clean("csv_date_format_with_dst") | pandas._testing.ensure_clean |
import pandas as pd
import numpy as np
import os
pd.options.mode.chained_assignment = None
sp_dir = '/Users/rwang/RMI/Climate Action Engine - Documents/OCI Phase 2'
opem_dir = '/Users/rwang/Documents/OCI+/Downstream/opem'
print('Merging upstream and midstream results...')
# import sqlite3
# connection = sqlite3.conn... | pd.read_csv('./opem_output.csv',header=1) | pandas.read_csv |
"""
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(response) | pandas.DataFrame |
import numpy as np
import pandas as pd
from PyQuantum.Common.Matrix import *
from PyQuantum.TC.FullBase import *
class HamiltonianL:
def set_base(self, base):
self.base = base
def __init__(self, capacity, cavity, RWA=True, reduced=True):
self.capacity = capacity
self.cavity = cavity
... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/python3
# -*- coding: utf-8 -*-
## jupyter console
import numpy as np
import pandas as pd
from pandas import Series, DataFrame
## merge
data1 = pd.DataFrame({'key':['b','b','a','c','a','a','b'], 'data1':range(7)})
data1
data2 = pd.DataFrame({'key':['a','b','d'], 'data2':range(3)})
data2
pd.merge(data1, da... | pd.merge(data1, data2, on='key') | pandas.merge |
import re
import os
import subprocess
from functools import lru_cache
from typing import Dict, List
import pandas as pd
from helpers import (
NOW,
RemoteCommand,
Settings,
create_settings,
nix_build,
spawn,
flamegraph_env,
read_stats,
write_stats,
scone_env
)
from network import... | pd.DataFrame(stats) | pandas.DataFrame |
import pandas as pd
import os
from elab_queries import import_elab_pull
# sort columns in elab queries for PPBC blocks/frozens or DLP (edit line 9 & 11)
# edit sorted_filename - eLab_<storage/PPBC>_samples.csv
def sort_columns_PPBC(pull_filename, sorted_filename):
elab_pull = import_elab_pull(pull_filename)
#... | pd.read_excel(submitted_doc) | pandas.read_excel |
#%%
import os
import sys
try:
os.chdir('/Volumes/GoogleDrive/My Drive/python_code/connectome_tools/')
sys.path.append('/Volumes/GoogleDrive/My Drive/python_code/maggot_models/')
sys.path.append('/Volumes/GoogleDrive/My Drive/python_code/connectome_tools/')
except:
pass
from pymaid_creds import url, nam... | pd.read_csv('VNC_interaction/data/brA1_axon-dendrite.csv', header = 0, index_col = 0) | pandas.read_csv |
import os
import pandas as pd
import config as cfg
from src.utils.data_processing import medea_path
directory = medea_path('data', 'raw', 'AggregatedGenerationPerType')
df_ror = pd.DataFrame()
for file in os.listdir(directory):
filename = os.fsdecode(file)
print(filename)
if filename.endswith('.csv'):
... | pd.DataFrame() | pandas.DataFrame |
"""Tests suite for Period handling.
Parts derived from scikits.timeseries code, original authors:
- <NAME> & <NAME>
- pierregm_at_uga_dot_edu - mattknow_ca_at_hotmail_dot_com
"""
from unittest import TestCase
from datetime import datetime, timedelta
from numpy.ma.testutils import assert_equal
from pandas.tseries.p... | Period(freq='D', year=2007, month=1, day=1) | pandas.tseries.period.Period |
import tweepy
from pandas import DataFrame
import time
# Twitter Credentials
consumer_key = 'riiUgzG0nHSkUGt5c521LgcnD'
consumer_key_secret = '<KEY>'
access_token = '<KEY>'
access_token_secret = '<KEY>'
bearer_token = '<KEY>'
# For Request
tweetsPerQuery = 100
maxTweets = 1000
users = ['realDonaldTrump', 'shanedawso... | DataFrame(outtweets, columns=["id", "created_at", "text"]) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Thu May 17 10:37:45 2018
@author: l_cry
"""
import pandas as pd
'''
pfo_path:ๆ็ป็ๆไปถ่ทฏๅพ๏ผ
code๏ผ่ก็ฅจไปฃ็ ๏ผ
name๏ผ่ก็ฅจๅ็งฐ๏ผ
num๏ผ่ก็ฅจๆฐ้๏ผ
ref_path:ๅ็
งๆๆฐ็ๆไปถ่ทฏๅพ๏ผ้ๅ
ๅซๆฅๆdateใๅฝๆฅๆๆฐๆถ็ไปทclose
-------------
return๏ผ
pfo:df
ref:df
'''
def pre_s... | pd.read_excel('C:/Users/l_cry/Desktop/ๆๆฐ่กๆ
ๅบๅhs300.xls',sheet_name='Sheet2',index_col=0) | pandas.read_excel |
"""
The `star' class is the core of PBjam and refers to a single target that is to
be peakbagged. Each `star' instance is assigned an ID and physical input
parameters, as well as a time series or power spectrum.
The different steps in the peakbagging process are then passed the `star'
instance, updating it with t... | pd.DataFrame(self.asy_fit.samples, columns=self.asy_fit.par_names) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
import html
import os
import re
import pandas as pd
import requests
target_url = {"jp": "http://scp-jp.wikidot.com/foundation-tales-jp",
"en": "http://scp-jp.wikidot.com/foundation-tales",
"ru": "http://scp-jp.wikidot.com/foundation-tales-ru",
... | pd.DataFrame(columns=['url', 'title', 'author', 'branches']) | pandas.DataFrame |
from __future__ import print_function, absolute_import
import sys, gzip, time, datetime, random, os, logging, gc,\
scipy, sklearn, sklearn.model_selection,\
sklearn.utils, sklearn.externals.joblib, inspect, bcolz, pickle
import numpy as np
import pandas as pd
from pandas import Series, DataFrame
def ... | pd.read_csv(reader, nrows=nrows, sep=sep) | pandas.read_csv |
import sys, os
sys.path.append("../ern/")
sys.path.append("../..dies/dies/")
sys.path.append(os.path.expanduser("~/workspace/prophesy_code/"))
import pandas as pd
import numpy as np
import glob, argparse, copy, tqdm
from ern.shift_features import ShiftFeatures
from ern.utils import to_short_name
import pathlib
from er... | pd.read_csv(file, sep=";") | 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, ... | OrderedDict([['a', 1.5], ['b', 3.0], ['c', 4.0]]) | pandas.compat.OrderedDict |
# -*- coding: utf-8 -*-
# @Author: <NAME>
# @Date: 2016-11-16 16:23:55
# @Last Modified by: <NAME>
# @Last Modified time: 2017-01-17 15:11:17
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import glob
import os
pd.set_option("display.width", None)
# v4
# including br... | pd.DataFrame(columns=mpfe_eval.columns) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Fri Mar 23 11:21:16 2018
@author: Chathuranga_08290
"""
# Importing the libraries
import tensorflow as tf # module for deep learning
import numpy as np # module for numerical calculations + linear algebra
import pandas as pd # module for data processing
import matplotlib.pyplot ... | pd.merge(result_df, submission_frequency_df, on='ForecastId') | pandas.merge |
# -*- coding: utf-8 -*-
"""Demo39_Pandas.ipynb
# MUNG - FU PANDA
Welcome to the Pandas tutorial. Pandas is an excellent tool for data wrangling also known as data munging.
It refers to the cleaning and preperation of data from Raw format to a usable and suitable format for our use.
- Python Basics
- Object Oriented... | pd.read_excel('../Datasets/Churn-Modelling.xlsx') | pandas.read_excel |
import pandas as pd
from sqlalchemy import create_engine
from library import cf
import talib.abstract as ta
import pymysql.cursors
import numpy as np
from library.logging_pack import *
logger.debug("subindex์์!!!!")
pymysql.install_as_MySQLdb()
daily_craw_engine=create_engine(
"mysql+mysql... | pd.DataFrame(th_ma112, columns=['ma112']) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
่ฐ็จwsetๅฝๆฐ็้จๅ
ไธ่ฝฝๆฐๆฎ็ๆนๆณ
1.ๅจๆถ้ดไธไฝฟ็จๆๅๅฏไปฅๆๅฐ็ไธ่ฝฝๆฐๆฎ๏ผไฝๅทฒ็ปไธไบไธ้จๅ๏ผ่ฆ่กฅไธๆถๅฆๆๆชไบไธไฝ๏ผๅๅพๅ
จ้ไธ
2.ๅจๆไปถไธ๏ผไธไธชๆไปถไธ็ป๏ผไธ็ปไธๆ ท๏ผๅ ไธญ้ดไธไธช๏ผ็ดๅฐไธ่ฝๅ ไบ๏ผ้ๅบ
"""
import os
import pandas as pd
from .utils import asDateTime
def download_sectorconstituent(w, date, sector, windcode, field='wind_code'):
"""
ๆฟๅๆไปฝ
ไธญไฟก่ฏ... | pd.to_datetime(df['ex_dividend_date']) | pandas.to_datetime |
# pylint: disable-msg=W0612,E1101,W0141
import nose
from numpy.random import randn
import numpy as np
from pandas.core.index import Index, MultiIndex
from pandas import Panel, DataFrame, Series, notnull, isnull
from pandas.util.testing import (assert_almost_equal,
assert_series_equal... | pd.PeriodIndex(['2013-01', '2013-02', '2013-03'], freq='M', name='period1') | pandas.PeriodIndex |
# 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(pd_data, columns=cols, index=index) | pandas.DataFrame |
import datetime
from unittest import TestCase
import numpy as np
import pandas as pd
from mlnext import pipeline
class TestColumnSelector(TestCase):
def setUp(self):
data = np.arange(8).reshape(-1, 2)
cols = ['a', 'b']
self.df = pd.DataFrame(data, columns=cols)
def test_select_col... | pd.DataFrame([[0.1, 0.4, 0.6, 0.8, 1.2, 1.5]]) | pandas.DataFrame |
def is_sha1(maybe_sha):
if len(maybe_sha) != 40:
return False
try:
sha_int = int(maybe_sha, 16)
except ValueError:
return False
return True
def validate(date_text):
try:
datetime.datetime.strptime(date_text, '%d-%m-%Y:%S-%M-%H')
return True
except ValueEr... | pd.read_csv(GLOBAL_LIST) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 14 11:05:32 2021
Data Source: https://www.kaggle.com/nandalald/turkey-price
@author: Ashish
"""
import pandas as pd
import matplotlib.pyplot as plt
# load data
df1 = pd.read_csv('../../data/kaggle_turkey_foodprice_train.csv')
df2 = pd.read_csv('../../data/kaggle_... | pd.Categorical(df['ProductName']) | pandas.Categorical |
import time, glob, os
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
def load_csv(exp_dir):
res_l = []
tim_l = []
for file in sorted(glob.glob(exp_dir + '/res/res*.csv')):
res = pd.read_csv(file, index_col=[0, 1], header=[0, 1])
res = res.grou... | pd.concat(res_l) | pandas.concat |
import datetime
import re
from itertools import islice
import numpy as np
import pandas as pd
from bs4 import BeautifulSoup
from dateutil.parser import parse as d
from utils_pandas import daterange
from utils_pandas import export
from utils_scraping import any_in
from utils_scraping import camelot_cache
from utils_sc... | pd.isna(recovered) | pandas.isna |
"""Module with tests realted adding and managing metadata."""
import os
import json
import io
import unittest
import pandas as pd
from pandas.testing import assert_frame_equal
from hicognition.test_helpers import LoginTestCase, TempDirTestCase
# import sys
# add path to import app
# sys.path.append("./")
from app impo... | pd.read_csv(metadata.file_path) | pandas.read_csv |
import numpy as np
import scipy.stats as stats
import pandas as pd
import loter.pipeline as lt
import loter.initparam as initparam
import loter.initdata as initdata
import loter.opti as opti
import loter.estimatea as esta
import loter.estimateh as esth
import loter.graph as ests
######################################... | pd.Series(arr[i]) | pandas.Series |
from pandas.testing import assert_frame_equal
import pandas as pd
import pytest
from speed_daemon import data
@pytest.fixture
def default_input():
return {
"download": 1000000,
"ping": 1000000,
"timestamp": "2020-10-12T03:09:18.231187Z",
"upload": 1000000,
}
@pytest.fixture
... | pd.to_datetime("2020-10-12") | pandas.to_datetime |
# -*- coding: utf-8 -*-
# %%
# LightGBM install: use conda: https://anaconda.org/conda-forge/lightgbm
# StratifiedKFold: This cross-validation object is a variation of KFold that returns stratified folds. The folds are made by preserving the percentage of samples for each class.
# KFold: Provides train/test indices t... | pd.DataFrame(X_t) | pandas.DataFrame |
from decimal import Decimal
import unittest, sys
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
from unittest.mock import patch
from forex_predictor.data_extraction.process_raw_data import create_relevant_data_row, create_row, find_start_date_index, get_dataframe_from_dates, get_dates, ... | pd.read_csv('tests/resources/dataframe_data.csv') | pandas.read_csv |
# There are several ways to create a DataFrame.
# One way way is to use a dictionary. For example:
dict = {"country": ["Brazil", "Russia", "India", "China", "South Africa"],
"capital": ["Brasilia", "Moscow", "New Dehli", "Beijing", "Pretoria"],
"area": [8.516, 17.10, 3.286, 9.597, 1.221],
"populati... | pd.read_csv('countries.csv') | pandas.read_csv |
"""Author: <NAME>
This contains the main Spomato class to be used to access the Spotify API and create new playlists based on the user's
defined criteria.
"""
import os
import pandas as pd
import spotipy
class Spomato():
"""Object used to access spotify API through spotipy and generate playlists.
This can ... | pd.Series([x['name'], x['id']], index=index) | pandas.Series |
# coding: utf-8
import numpy as np
import pandas as pd
import umap
from bokeh.resources import INLINE, CDN
from bokeh.embed import file_html
#https://umap-learn.readthedocs.io/en/latest/basic_usage.html
def embeddable_image(image_path):
from io import BytesIO
from PIL import Image
import base64
i... | pd.DataFrame(embedding, columns=('x','y'), index=bn_feat.index) | pandas.DataFrame |
import json
from operator import itemgetter
from pathlib import Path
import geopandas as gpd
import pandas as pd
from shapely import wkt
from dtcv.pt_lib import *
p = Path('/Users/eric/Google Sync/sandiegodata.org/Projects/Downtown Partnership Homeless'
'/Annotations/complete/gcp')
intersections_file = '... | pd.DataFrame(rows, columns=['source', 'image'] + cols + ['intersection']) | pandas.DataFrame |
"""SQL io tests
The SQL tests are broken down in different classes:
- `PandasSQLTest`: base class with common methods for all test classes
- Tests for the public API (only tests with sqlite3)
- `_TestSQLApi` base class
- `TestSQLApi`: test the public API with sqlalchemy engine
- `TestSQLiteFallbackApi`: t... | Series([1.1, None]) | pandas.Series |
from flask import Flask, render_template, request, flash
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import base64
from scipy.signal import medfilt
from scipy.integrate import trapz
import xml.etree.ElementTree as et
from datetime import date
today = date.toda... | pd.pivot_table(df, columns=test.columns) | pandas.pivot_table |
from typing import TYPE_CHECKING
import numpy as np
import pandas as pd
from vivarium.framework.randomness import get_hash
from vivarium_csu_sanofi_multiple_myeloma.constants import models
from vivarium_csu_sanofi_multiple_myeloma.constants.metadata import SCENARIOS, HAZARD_RATE_SOURCES
from vivarium_csu_sanofi_mult... | pd.Timestamp(f'{upper_year}-01-01') | pandas.Timestamp |
#%%
# Let's import some packages
import numpy as np
import pandas as pd
from sklearn.model_selection import ShuffleSplit
from sklearn.datasets import load_boston
from sklearn.linear_model import LinearRegression
import scipy.stats as stats
import matplotlib.pyplot as plt
import sklearn
from sklearn.metrics import r2_... | pd.DataFrame(boston.data) | pandas.DataFrame |
import matplotlib
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['ps.fonttype'] = 42
from matplotlib.widgets import Slider, Button
import numpy as np
import matplotlib.pyplot as plt
from cell_cycle_gating import cellcycle_phases as cc
from cell_cycle_gating import dead_cell_filter as dcf
from cell_cycle_g... | pd.read_csv(results_file) | pandas.read_csv |
import requests
import json
import numpy as np
import pandas as pd
import sqlalchemy as sql
import time
from scripts.config import *
from sqlalchemy import create_engine
from utils.logger import logger
from typing import *
SEARCH = "https://api.twitter.com/2/tweets/search/all"
class DB:
@property
def locati... | pd.DataFrame({'username': ''}, index=[0]) | pandas.DataFrame |
from logging import root
import os
import pandas as pd
import mysql.connector
from query import Query
class findDifferenceBetweenSalaries:
def __init__(self):
self.mydb = mysql.connector.connect(
host="xxxxxxxxxx",
user="xxxxx",
password="<PASSWORD>",
port=... | pd.read_sql(Query.DEPARTMENTS_TABLE, self.mydb) | pandas.read_sql |
"""Main module."""
import math
import os
import sys
import datetime
import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
# print("matplotlib=", matplotlib.rcParams.keys())
matplotlib.rcParams['text.usetex'] = True
# matplotlib.rcParams['text.latex.unicode'] = True key not available
... | pd.Series(ndiff) | pandas.Series |
import pandas as pd
from pathlib import Path
# import matplotlib.pyplot as plt
# added by Pierre
import matplotlib as mpl
mpl.use('TkAgg') # or whatever other backend that you want
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import os
import argparse
from stable_baseli... | pd.read_csv(filename, index_col=None, header=0) | pandas.read_csv |
# Copyright (c) 2020 Huawei Technologies Co., Ltd.
# <EMAIL>
#
# 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 a... | pd.NamedAgg(column='TotalDownload', aggfunc=calc_total) | pandas.NamedAgg |
#%%
import re
import pandas as pd
#%%
# processing bilayer and monolayer energies
bilayers_filename = './R2/BilayersEnergies'
monolayers_filename = './R2/MonolayersEnergies'
with open(bilayers_filename, 'r') as ofile:
bi_lines = ofile.readlines()
with open(monolayers_filename, 'r') as ofile:
mo_lines = ofi... | pd.read_csv('./R2/areas.csv') | pandas.read_csv |
from typing import List, Tuple
from datetime import datetime
from os import listdir
from os.path import join, isdir
import yaml
import geopy.distance
import pandas as pd
import xarray as xr
import xlrd
import numpy as np
import geopandas as gpd
from epippy.geographics import match_points_to_regions, get_nuts_shapes,... | pd.DataFrame(index=timestamps, columns=ror_capacity_ds.index) | pandas.DataFrame |
"""
Functions specific to preprocess raw extract data from HMIS.
The raw data is provided in the following format:
(king data is divided by year;
for pierce & snohomish all years are in one folder)
data/*county*/*year*/Affiliation.csv
Client.csv
Disabiliti... | pd.read_csv(fname, low_memory=False, encoding=encoding) | pandas.read_csv |
from __future__ import annotations
from contextlib import contextmanager
import os
from pathlib import Path
import random
from shutil import rmtree
import string
import tempfile
from typing import (
IO,
Any,
)
import numpy as np
from pandas import set_option
from pandas.io.common import get_handle
@contex... | set_option("compute.use_numexpr", use) | pandas.set_option |
from collections import defaultdict
import pandas as pd
import re
converters = {}
class AnnotationConverter:
SPEAKER_ID_TO_TYPE = defaultdict(
lambda: "NA",
{
"C1": "OCH",
"C2": "OCH",
"CHI": "CHI",
"CHI*": "CHI",
"FA0": "FEM",
... | pd.read_csv(filename) | pandas.read_csv |
from tinydb import TinyDB, Query
from tinydb.storages import JSONStorage
from tinydb.middlewares import CachingMiddleware
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# https://pypi.org/project/tinydb/
dbECC = TinyDB('../../db/serverdbECC.json',
indent=4, separators=(',', ': '),
def... | pd.DataFrame(data_info) | pandas.DataFrame |
import anndata as ad
import pandas as pd
def load_metadata(adata, metadata_file, path='', separator=';', remove_index_str = None):
"""
Load observational metadata in adata.obs.
Input metadata file as csv/txt and the adata object to annotate.
first raw of the metadata file is considered as a header
... | pd.DataFrame('NA', index=str_index, columns=metadata.columns) | pandas.DataFrame |
import numpy as np
import pandas as pd
def get_eval_df(sequencer):
ids = [ss.identifier for ss in sequencer.get_pairs()]
classes = ["mean"] + ["cls {}".format(i) for i in range(sequencer.n_classes)]
return | pd.DataFrame(columns=ids, index=classes) | pandas.DataFrame |
import os
import pickle
import librosa
import warnings
import numpy as np
import pandas as pd
warnings.filterwarnings('ignore')
from scipy.stats import skew, kurtosis
from pychorus import find_and_output_chorus
from flask import Flask, request, json, render_template
# Create flask app
app = Flask(__name__)
# Load p... | pd.read_csv('Data/bestfeatures.csv') | pandas.read_csv |
import builtins
from io import StringIO
import numpy as np
import pytest
from pandas.errors import UnsupportedFunctionCall
import pandas as pd
from pandas import DataFrame, Index, MultiIndex, Series, Timestamp, date_range, isna
import pandas._testing as tm
import pandas.core.nanops as nanops
from pandas.util import ... | pd.DataFrame({"A": [1, 2, 1], "B": [1, 2, 3]}) | pandas.DataFrame |
import unittest
import pandas as pd
from chemcharts.core.container.chemdata import ChemData
from chemcharts.core.container.fingerprint import *
from chemcharts.core.functions.binning import Binning
class TestBinning(unittest.TestCase):
@classmethod
def setUpClass(cls) -> None:
smiles = Smiles(["COc1c... | pd.DataFrame([1, 3, 4, 5, 2, 1, 6], columns=["test_value"]) | pandas.DataFrame |
"""
Module full of various helpers for creating matplotlib animations.
"""
import numpy as np
import pandas as pd
import pytweening
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
# In order to get the final points, there's 2 high-level st... | pd.DataFrame(xx_minmax) | pandas.DataFrame |
import argparse
import os
import torch
import numpy as np
import pandas as pd
import torch.nn as nn
import torch.optim as optim
from tqdm import tqdm as tqdm_notebook
from datasets import DataManager
from utils import *
from models import get_model
seed_everything(43)
ap = argparse.ArgumentParser(description='pretra... | pd.DataFrame(df_data, columns = ['train_losses','valid_losses','valid_accuracy']) | pandas.DataFrame |
import pandas as pd
import functools as ft
import numpy as np
import sys
# driver script for applying the refactoring transformation
class Stmt:
def __init__(self, start_line, start_char, end_line, end_char):
self.start_line = start_line
self.start_char = start_char
self.end_line = end_line
self.end_char = e... | pd.concat([recursive_swaps, swap_df]) | pandas.concat |
"""
Last.FM Datasets and Helpers.
References:
- [Last.FM Dataset 1K](http://ocelma.net/MusicRecommendationDataset/lastfm-1K.html)
- [Lenskit datasets](https://github.com/lenskit/lkpy/blob/master/lenskit/datasets.py)
"""
import logging
import os
import pandas as pd
from skipgrammar.datasets.common import (UserItemIte... | pd.read_parquet(self.listens_filepath) | pandas.read_parquet |
import numpy as np
import pandas as pd
import pickle
import time
import random
import os
from sklearn import linear_model, model_selection, ensemble
from sklearn.svm import SVC
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.base import clone
from sklearn import metrics
from sklearn.model_selectio... | pd.read_csv(dir_+'integrated_pgd_y.csv',index_col=0,header=None) | pandas.read_csv |
"""
All rights reserved to cnvrg.io
http://www.cnvrg.io
SKTrainer.py
==============================================================================
"""
import os
import pickle
import numpy as np
import pandas as pd
from cnvrg import Experiment
from cnvrg.charts import Bar, MatrixHeatmap, Scatterplot
from sklea... | pd.concat([self.__y_train, self.__y_test], axis=0) | pandas.concat |
# python gt-gen-vac-fixed-num-cbgs-crossgroup.py args.quick_test
# python gt-gen-vac-fixed-num-cbgs-crossgroup.py False
import setproctitle
setproctitle.setproctitle("gnn-simu-vac@chenlin")
import os
import datetime
import pandas as pd
import numpy as np
import pickle
import random
import argparse
import constants... | pd.DataFrame() | pandas.DataFrame |
import os
import re
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
from urllib.error import URLError
from .util import download_file, get_path, timer
#todo: using flags in ncdc data to filter
#todo: extra filter for bad data (999s etc)
STATION_LIST_PATH = get_path(__file__, 'isd-hist... | pd.DataFrame() | pandas.DataFrame |
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import warnings
warnings.filterwarnings('ignore')
soal = ['EXT1', 'EXT2', 'EXT3', 'EXT4', 'EXT5', 'EXT6', 'EXT7', 'EXT8', 'EXT9', 'EXT10', 'EST1', 'EST2', 'EST3', 'EST4', 'EST5', 'EST6', 'EST7', 'EST8', 'EST9', 'EST10', 'AGR1', 'AGR2', 'AGR3', 'AGR... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
import unittest
import platform
import pandas as pd
import numpy as np
import pyarrow.parquet as pq
import hpat
from hpat.tests.test_utils import (
count_array_REPs, count_parfor_REPs, count_array_OneDs, get_start_end)
from hpat.tests.gen_test_data import ParquetGenerator
from numba import ... | pd.Series([np.nan, 2., 3.]) | pandas.Series |
#SPDX-License-Identifier: MIT
""" Helper methods constant across all workers """
import requests
import datetime
import time
import traceback
import json
import os
import sys
import math
import logging
import numpy
import copy
import concurrent
import multiprocessing
import psycopg2
import psycopg2.extensions
import cs... | pd.notnull(df[column]) | pandas.notnull |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Dec 20 22:28:42 2018
@author: Erkin
"""
#%%
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import seaborn as sns
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
def warn(*args, *... | pd.DataFrame(precisions, columns=['precision_hold','precision_buy']) | pandas.DataFrame |
import pandas as pd
import numpy as np
from datetime import date, timedelta
from ai4good.utils.logger_util import get_logger
from ai4good.models.validate.model_metrics import model_metrics
logger = get_logger(__name__)
def model_validation_metrics(population:int, model:str, age_categories:list, case_cols:list, df_bas... | pd.DataFrame(columns=cols_results) | pandas.DataFrame |
import random
import time
import algorithms # local module
import pandas as pd
from graphics import graphic as simulation_graphic
from process import Process
from pathlib import Path
def get_cpu_time_unit():
"""
a unit of time independent on cpu run this code for simulating time
"""
started_at = ti... | pd.DataFrame(algorithms_data, columns=columns, index=exists_algorithms) | pandas.DataFrame |
# author: <NAME>
# date: 2021-11-27
"""This script imports preprocessed test data and fitted Ridge and RandomForestRegressor models.
It then evaluates them on the test set and outputs evaluation metrics to the output directory.
Usage: fit_model.py --source_data=<filepath> --output_dir=<filepath>
Options:
--source_d... | pd.DataFrame(ridge_feats) | pandas.DataFrame |
import json
import pickle
from datetime import timedelta, datetime
import joblib
import numpy as np
import pandas as pd
class Processor:
def __init__(self, raw_data: dict):
self.df = self.clean(raw_data)
def clean(self, raw_data):
df = pd.DataFrame()
df["Confirmed"] = | pd.DataFrame.from_dict(raw_data, orient="index") | pandas.DataFrame.from_dict |
## Online battery validation
import os
import glob
import pandas as pd
import numpy as np
import pickle
class BESS(object):
def __init__(self, max_energy, max_power, init_soc_proc, efficiency):
self.soc = init_soc_proc
self.max_e_capacity = max_energy
self.efficiency = efficiency
... | pd.read_csv(apath, sep=",") | pandas.read_csv |
'''
ilf core utilities
'''
import sys
import re
import math
import json
import io
import pandas as pd
from itertools import chain
import pytricia as pt
from .numbers import IP4PROTOCOLS, IP4SERVICES
# -- Helpers
def lowest_bit(num):
bit, low = -1, (num & -num)
if not low:
return 0
while(low):
... | pd.read_csv(inp, skipinitialspace=True) | pandas.read_csv |
'''
DEMO UTILITIES
'''
#%% Import Python modules
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy import stats
from matplotlib.patches import Rectangle
from fcgadgets.macgyver import utilities_general as gu
from fcgadgets.cbrunner import cbrun_utilities as cbu
#%%
def G... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
from .constants import PARSING_SCHEME
from ..decorators import float_property_decorator, int_property_decorator
from .nba_utils import _retrieve_all_teams
from .. import utils
from .roster import Roster
from .schedule import Schedule
class Team:
"""
An object containing all of a team's sea... | pd.DataFrame([fields_to_include], index=[self._abbreviation]) | pandas.DataFrame |
import pandas as pd
import pprint
import re
ISO8601YMD = re.compile(r'\d{4}-\d{2}-\d{2}T')
NY = 'America/New_York'
class Entity(object):
'''This helper class provides property access (the "dot notation")
to the json object, backed by the original object stored in the _raw
field.
'''
def __init__... | pd.DataFrame() | pandas.DataFrame |
import tensorflow as tf
from keras.models import Sequential
from keras.callbacks import ModelCheckpoint
from keras.models import load_model
from keras.optimizers import Adam
import numpy as np
import pandas as pd
import time
import warnings
warnings.filterwarnings("ignore")
import glob
import matplotlib.pyplot as plt
i... | pd.DataFrame(data=results_dict) | pandas.DataFrame |
from __future__ import print_function
import os
import pandas as pd
import xgboost as xgb
import time
import shutil
from sklearn import preprocessing
from sklearn.cross_validation import train_test_split
import numpy as np
def archive_results(filename,results,algo,script):
"""
:type algo: basestring
:type... | pd.merge(test,visits, on='patient_id',how='left') | pandas.merge |
# general
import logging
import os
import sys
import time
import configparser
import math
import scipy.optimize as opt
from scipy.spatial import ConvexHull
from copy import deepcopy
from itertools import combinations
# graph
import networkx as nx
import geonetworkx as gnx
# data
import pandas as pd
# optimization
impor... | pd.DataFrame() | pandas.DataFrame |
import numpy as np
import pandas as pd
from tspdb.src.database_module.db_class import Interface
from tspdb.src.pindex.pindex_utils import index_ts_mapper, index_ts_inv_mapper, index_exists, get_bound_time
from scipy.stats import norm
def unnormalize(arr, mean, std):
return arr *std + mean
def get_predict... | pd.to_datetime(t1) | pandas.to_datetime |
import os
import time
import configparser
import joblib
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.optim as optim
from tensorboardX import SummaryWriter
from sklearn.metrics import accuracy_score, classification_report
from sklearn.model_selection import train_test_split
from... | pd.read_csv(label_path, sep="\t", index_col=0) | pandas.read_csv |
import pandas as pd
from tarpan.shared.compare_parameters import (
save_compare_parameters, CompareParametersType)
def run_model():
data1 = {
"x": [1, 2, 3, 4, 5, 6],
"y": [-1, -2, -3, -4, -5, -6],
"z": [40, 21, 32, 41, 11, 31]
}
df1 = pd.DataFrame(data1)
data2 = {
... | pd.DataFrame(data2) | pandas.DataFrame |
import os
import sys
import random
import re
import copy
import matplotlib
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import logging
import datetime as dt
from math import radians, cos, sin, asin, sqrt
from datetime import datetime,timedelta
from objects.objects import Cluster,Order,Vehicle,... | pd.DataFrame(AllNeighborList) | pandas.DataFrame |
################################################################################
# Module: schedule.py
# Description: Functions for handling conversion of EnergyPlus schedule objects
# License: MIT, see full license in LICENSE.txt
# Web: https://github.com/samuelduchesne/archetypal
#####################################... | pd.Series(zeros, index=index) | pandas.Series |
#############################################################
#
# Test 2. regression model for a fixed input string size N, Ternary
#
#############################################################
import sys, os
sys.path.append("../..")
sys.path.append("..")
sys.path.append(os.getcwd())
import numpy as np
import pandas... | pd.read_pickle("../../data/nba-hosoi/nba_scores_2103-2018.pkl") | pandas.read_pickle |
import pandas as pd
from pandas import date_range
ave_daily_balance_keyword_lst = ['็ปๆฏ', 'ๅฉๆฏ', 'ๅญฃๆฏ', 'ๅ
ฅๆฏ']
# ็ๆ3ใ6ใ9ใ12ๆ21ๅทๅฐ25ๅท็ๅญ็ฌฆไธฒ
ms = ["%s" % x for x in range(3, 13, 3)]
ds = ["%s" % x for x in range(21, 26)]
ave_days = ["%s%s" % (x, y) for x in ms for y in ds]
def get_month(datetime_str):
month = datetime_st... | pd.to_datetime(df['transDate']) | pandas.to_datetime |
import glob
import matplotlib
matplotlib.use("Agg")
import bokeh.plotting as plt
from bokeh.embed import file_html
from bokeh.resources import CDN
import cherrypy
import pandas as pd
import numpy as np
class Main(object):
@cherrypy.expose
def index(self):
df = pd.concat([pd.read_csv(fname) for fname... | pd.to_datetime(df.dt_local) | pandas.to_datetime |
# -*- coding: UTF-8 -*-
"""
ๆญค่ๆฌ็จไบๅฑ็คบhard marginๅsoft margin
"""
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.svm import SVC
def generate_data(n):
"""
็ๆๆจกๅๆ้ๆฐๆฎ
"""
np.random.seed(2046)
X = np.r_[np.random.randn(n, 2) - [1, 1], np.random.randn(n, 2) + [3, 3]]
... | pd.DataFrame(data, columns=["y", "x1", "x2"]) | pandas.DataFrame |
#! /usr/bin/env python
# -*- coding: utf-8 -*-
"""
@version:
@author: zzh
@file: factor_earning_expectation.py
@time: 2019-9-19
"""
import pandas as pd
class FactorEarningExpectation():
"""
็ๅฉ้ขๆ
"""
def __init__(self):
__str__ = 'factor_earning_expectation'
self.name = '็ๅฉ้ขๆต'
... | pd.merge(factor_earning_expect, earning_expect, on='security_code') | pandas.merge |
# -*- coding: utf-8 -*-
import logging
import os
import xml.etree.ElementTree as ET
from urllib.request import urlretrieve
import pandas as pd
from .constants import TRAIN_DATA_FILE_PATH, TRAIN_DATA_URL, TEST_DATA_FILE_PATH, TEST_DATA_URL, col_names
log = logging.getLogger(__name__)
def download_scai_mirna_corpor... | pd.concat([training_df, test_df]) | pandas.concat |
import argparse
import sys
import os
from pathlib import Path
import logging
from typing import Dict
import numpy as np
import pandas as pd
import scipy.sparse as sp
from joblib import dump
from knodle.trainer.utils import log_section
from examples.data_preprocessing.tac_based_dataset.utils.utils import count_file_li... | pd.DataFrame.from_dict({"sample": samples, "rules": rules, "enc_rules": enc_rules}) | pandas.DataFrame.from_dict |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import os
from scipy import integrate, stats
from numpy import absolute, mean
from pandas import DataFrame
from itertools import islice
import researchpy as rp
import seaborn as sns
import statsmodels.api as sm
from statsmo... | pd.concat([df_accuracy1, group_v]) | pandas.concat |
"""PD hate crimes _jobs file."""
import glob
import os
import csv
import string
import logging
import pandas as pd
from datetime import datetime
from trident.util import general
conf = general.config
prod_file = f"{conf['prod_data_dir']}/hate_crimes_datasd.csv"
def get_data():
"""Download Hate Crimes data from F... | pd.to_datetime(df['date'],errors='coerce') | pandas.to_datetime |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.