prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import pytest
import pandas as pd
from pandas import compat
import pandas.util.testing as tm
import pandas.util._test_decorators as td
from pandas.util.testing import assert_frame_equal, assert_raises_regex
COMPRESSION_TYPES = [None, 'bz2', 'gzip',
pytest.param('xz', marks=td.skip_if_no_lzma)]
... | assert_frame_equal(df, roundtripped_df) | pandas.util.testing.assert_frame_equal |
from datetime import datetime, timedelta
from io import StringIO
import re
import sys
import numpy as np
import pytest
from pandas._libs.tslib import iNaT
from pandas.compat import PYPY
from pandas.compat.numpy import np_array_datetime64_compat
from pandas.core.dtypes.common import (
is_datetime64_dtype,
is_... | tm.assert_numpy_array_equal(codes, exp_arr) | pandas.util.testing.assert_numpy_array_equal |
'''
Script to convert txt results to csv
'''
import csv
import glob
import os
import sys
import pandas as pd
def parse_gpt2_file(filename: str):
'''
Parses a GPT2 file to return all the results
'''
results = []
curr_output = ''
with open(filename, 'r') as f:
for line in f.readlines... | pd.DataFrame(results, columns=['output']) | pandas.DataFrame |
"""
Training script. Should be pretty adaptable to whatever.
"""
import argparse
import os
import shutil
import json
from copy import deepcopy
import multiprocessing
import numpy as np
import pandas as pd
import torch
from allennlp.common.params import Params
from allennlp.training.learning_rate_schedule... | pd.DataFrame(train_results[-ARGS_RESET_EVERY:]) | pandas.DataFrame |
from selenium import webdriver as wd
from selenium.webdriver.chrome.options import Options
import time
import csv
import os
import random
import json
import shutil
import pandas as pd
from modules.checker import Checker
from modules.basic_scraping_module import get_response #, get_soup
from modules.supplier_utils.unifo... | pd.read_csv(csv_path) | pandas.read_csv |
#!/usr/bin/python
# _____________________________________________________________________________
# ----------------
# import libraries
# ----------------
# standard libraries
# -----
import torch
import numpy as np
import os
import pandas as pd
import matplotlib.pyplot as plt
from torch.utils.data import Dataset, D... | pd.concat(streambits, ignore_index=True) | pandas.concat |
import vectorbt as vbt
import numpy as np
import pandas as pd
from numba import njit
from datetime import datetime
import pytest
from vectorbt.generic import nb as generic_nb
from vectorbt.generic.enums import range_dt
from tests.utils import record_arrays_close
seed = 42
day_dt = np.timedelta64(86400000000000)
ma... | pd.Timedelta('0 days 00:00:00') | pandas.Timedelta |
from flowsa.common import WITHDRAWN_KEYWORD
from flowsa.flowbyfunctions import assign_fips_location_system
from flowsa.location import US_FIPS
import math
import pandas as pd
import io
from flowsa.settings import log
from string import digits
YEARS_COVERED = {
"asbestos": "2014-2018",
"barite": "2014-2018",
... | pd.DataFrame(df_raw_data.loc[4:9]) | pandas.DataFrame |
from __future__ import print_function
import unittest
from unittest import mock
from io import BytesIO, StringIO
import random
import six
import os
import re
import logging
import numpy as np
import pandas as pd
from . import utils as test_utils
import dataprofiler as dp
from dataprofiler.profilers.profile_builder ... | pd.DataFrame([[1, 2, 3]], index=["hello"]) | pandas.DataFrame |
import pandas as pd
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
import os
import argparse
from sklearn import preprocessing
from matplotlib.ticker import EngFormatter
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-f1', '--logFolder1', help=... | pd.read_csv(path_base2+"/ddpg/results_seed_exp.csv") | pandas.read_csv |
import pytest
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
from sklearn.linear_model import LinearRegression, LogisticRegression
from sklearn.neural_network import MLPClassifier, MLPRegressor
from sklearn.svm import LinearSVC, LinearSVR
from foreshadow.console import generate_model
from f... | pd.DataFrame(cancer.data, columns=cancer.feature_names) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
Date: 2022/2/24 15:02
Desc: 东方财富网-数据中心-新股数据-打新收益率
东方财富网-数据中心-新股数据-打新收益率
http://data.eastmoney.com/xg/xg/dxsyl.html
东方财富网-数据中心-新股数据-新股申购与中签查询
http://data.eastmoney.com/xg/xg/default_2.html
"""
import pandas as pd
import requests
from tqdm import tqdm
from akshare.utils i... | me(big_df['中签缴款日期']) | pandas.to_datetime |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import abc
import sys
import copy
import time
import datetime
import importlib
from pathlib import Path
from concurrent.futures import ThreadPoolExecutor, as_completed
import fire
import requests
import numpy as np
import pandas as pd
from tqdm ... | pd.Timedelta(days=1) | pandas.Timedelta |
import numpy as np
import pytest
import sklearn
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.utils.validation import check_is_fitted
from sklearn.exceptions import NotFittedError
from distutils.version import LooseVersion
from dirty_cat import SuperVectorizer
from dirty_cat impor... | pd.Series([5.2, 2.4, 6.2, 10.45, 9.], dtype='float') | pandas.Series |
"""
Prepare training and testing datasets as CSV dictionaries 2.0 (Further modification required for GBM)
Created on 04/26/2019
@author: RH
"""
import os
import pandas as pd
import sklearn.utils as sku
import numpy as np
import re
# get all full paths of images
def image_ids_in(root_dir, ignore=['.DS_Store','dict.c... | pd.concat(trlist) | pandas.concat |
import pandas as pd
from databalancer.paraphraseGeneratorClient import paraPharaseGenerator
from databalancer.paraphraseGeneratorClient import modelAndTokenizerInitializer
from databalancer.paraphraseInputGeneratorClient impo... | pd.concat([dataOriginal, each_df], ignore_index=True) | pandas.concat |
# -*- coding: utf-8 -*-
"""
Created on Fri Jun 02 16:27:16 2017
@author: daniel
"""
import Tomography as tom
import quPy as qp
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import os
import json
import io
dataNN=np.loadtxt("foersterdefect_n_n.tsv")
dataNN_2=np.loadtxt("foersterdefect_n_n-2.... | pd.DataFrame(index=dataNN[:,0],data=dataNN[:,4]/1e9) | pandas.DataFrame |
import re
import pandas as pd
# import matplotlib
# matplotlib.use('Agg')
import matplotlib.pyplot as plt
from matplotlib.figure import Figure
import matplotlib.ticker as ticker
import matplotlib.dates as mdates
import numpy as np
import seaborn as sns; sns.set()
from scipy.spatial.distance import squareform
from scip... | pd.concat([df_events_owd, df_events_sr], sort=True) | pandas.concat |
"""
Combines medication statistics for various sublocalizations.
"""
import pandas as pd
from click import *
from logging import *
from typing import *
def load_data(path: str, sublocalization: str) -> pd.DataFrame:
"""
Loads data from the given path and with the given sublocalization.
Args:
pa... | pd.concat(data) | pandas.concat |
# -*- coding: utf-8 -*-
# pylint: disable=E1101
# flake8: noqa
from datetime import datetime
import csv
import os
import sys
import re
import nose
import platform
from multiprocessing.pool import ThreadPool
from numpy import nan
import numpy as np
from pandas.io.common import DtypeWarning
from pandas import DataFr... | StringIO(data) | pandas.compat.StringIO |
import operator
import warnings
import numpy as np
import pandas as pd
from pandas import DataFrame, Series, Timestamp, date_range, to_timedelta
import pandas._testing as tm
from pandas.core.algorithms import checked_add_with_arr
from .pandas_vb_common import numeric_dtypes
try:
import pandas.core.computation.e... | pd.offsets.YearBegin() | pandas.offsets.YearBegin |
import unittest
import numpy as np
import pandas as pd
from pyalink.alink import *
class TestDataFrame(unittest.TestCase):
def setUp(self):
data_null = np.array([
["007", 1, 1, 2.0, True],
[None, 2, 2, None, True],
["12", None, 4, 2.0, False],
["1312", 0,... | pd.Int64Dtype() | pandas.Int64Dtype |
import sys
sys.path.insert(0, './')
try:
import wandb
except:
pass
from rlf.exp_mgr import config_mgr
from rlf.rl.utils import CacheHelper
import yaml
import argparse
from collections import defaultdict
import pickle
import os
import os.path as osp
import pandas as pd
import hashlib
import json
def get_arg_p... | pd.concat([all_df, df]) | pandas.concat |
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 30 18:07:56 2020
@author: Fabio
"""
import pandas as pd
import matplotlib.pyplot as plt
def df_filterbydate(df, dataLB, dataUB):
df['Data_Registrazione'] = pd.to_datetime(df['Data_Registrazione'], infer_datetime_format=True).dt.date
df = df[(df['Data_Registrazi... | pd.isna(pie) | pandas.isna |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import os
import pandas as pd
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-f', '--format', default='forex', choices=['forex', 'stock'], help="c... | pd.DataFrame(columns=columns) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
Date: 2022/2/2 23:26
Desc: 东方财富网-行情首页-沪深京 A 股
"""
import requests
import pandas as pd
def stock_zh_a_spot_em() -> pd.DataFrame:
"""
东方财富网-沪深京 A 股-实时行情
http://quote.eastmoney.com/center/gridlist.html#hs_a_board
:return: 实时行情
:rtype: pandas.DataFrame
... | numeric(temp_df["总市值"], errors="coerce") | pandas.to_numeric |
import pyspark
from pyspark.sql import SQLContext
import pandas as pd
import csv
import os
def load_states():
# read US states
f = open('states.txt', 'r')
states = set()
for line in f.readlines():
l = line.strip('\n')
if l != '':
states.add(l)
return states
def vali... | pd.DataFrame.from_dict(dict_train_busi_review_count, orient='index') | pandas.DataFrame.from_dict |
#
# Copyright (c) nexB Inc. and others. All rights reserved.
# http://nexb.com and https://github.com/nexB/scancode-toolkit/
# The ScanCode software is licensed under the Apache License version 2.0.
# Data generated with ScanCode require an acknowledgment.
# ScanCode is a trademark of nexB Inc.
#
#
# Copyright (c) nexB... | pd.read_hdf(path_or_buf=file_path, key=df_key) | pandas.read_hdf |
import pytest
import numpy as np
import pandas as pd
from pandas.testing import assert_frame_equal
import dask.dataframe as dd
from dask_sql.utils import ParsingException
def test_select(c, df):
result_df = c.sql("SELECT * FROM df")
result_df = result_df.compute()
assert_frame_equal(result_df, df)
de... | assert_frame_equal(result_df, expected_df) | pandas.testing.assert_frame_equal |
# 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... | tm.equalContents(union, everything) | pandas.util.testing.equalContents |
# clean SG weather data
import os.path
import sys
import pandas as pd
import logging
INPUT_DIR = '../../Data/raw/weather_SG'
OUTPUT_DIR = '../../Data/interim/weather_SG'
OUTPUT_FILE = "weekly-weather.csv"
DICT_RENAME={'Station':'location',
'Year':'year', 'Month':'month', 'Day':'day',
'Dail... | pd.DataFrame(columns=COLS_RENAMED) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# author：zhengk
import pandas as pd
from pandas.plotting import register_matplotlib_converters
from matplotlib.font_manager import FontProperties
import matplotlib.pyplot as plt
# 数据分析
def pandas_analysis():
# 读取评论
df = pd.read_csv('comment.csv', sep=';', header=None)
# 整理数据
df... | pd.to_datetime(df['date']) | pandas.to_datetime |
import sys
sys.path.append("../")
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scipy.linalg as ln
from openpyxl import Workbook
import xlsxwriter as xlsx
import pickle
############## Read data and convert to dictionary ###############################################
data_list=['confe... | pd.read_csv('../data/'+data+'.txt', sep='\t', header=None, names=['ID2','ID1','start_time','end_time']) | pandas.read_csv |
import pandas as pd
import numpy as np
import scipy
import os, sys, time, json, math
import matplotlib.pyplot as plt
import seaborn as sns
from functools import reduce
from os.path import join
from datetime import datetime
from scipy.integrate import odeint
from numpy import loadtxt
from scipy.optimize import minimize
... | pd.isnull(Cldl0) | pandas.isnull |
from collections import (
abc,
deque,
)
from decimal import Decimal
from warnings import catch_warnings
import numpy as np
import pytest
import pandas as pd
from pandas import (
DataFrame,
Index,
MultiIndex,
PeriodIndex,
Series,
concat,
date_range,
)
import pandas._testing as tm
fr... | DataFrame(np.r_[df.values, df2.values], index=exp_index) | pandas.DataFrame |
from matplotlib.dates import date2num, num2date
from matplotlib.colors import ListedColormap
from matplotlib import dates as mdates
from matplotlib import pyplot as plt
from matplotlib.patches import Patch
from matplotlib import ticker
from global_config import config
import matplotlib.pyplot as plt
import scipy.io as... | pd.to_datetime(data[data.type=='fitted'].index.values[0]) | pandas.to_datetime |
# Copyright (c) 2018-2022, NVIDIA CORPORATION.
import numpy as np
import pandas as pd
import pytest
from pandas.api import types as ptypes
import cudf
from cudf.api import types as types
@pytest.mark.parametrize(
"obj, expect",
(
# Base Python objects.
(bool(), False),
(int(), False)... | pd.Series(dtype="datetime64[s]") | pandas.Series |
##### file path
### input
# data_set keys and lebels
path_df_part_1_uic_label = "df_part_1_uic_label.csv"
path_df_part_2_uic_label = "df_part_2_uic_label.csv"
path_df_part_3_uic = "df_part_3_uic.csv"
# data_set features
path_df_part_1_U = "df_part_1_U.csv"
path_df_part_1_I = "df_part_1_I.csv"
path_df_part_1_... | pd.merge(df_part_2_uic_label_0, df_part_2_U, how='left', on=['user_id']) | pandas.merge |
# -*- coding: utf-8 -*-
# Arithmetc tests for DataFrame/Series/Index/Array classes that should
# behave identically.
from datetime import timedelta
import operator
import pytest
import numpy as np
import pandas as pd
import pandas.util.testing as tm
from pandas.core import ops
from pandas.errors import NullFrequency... | tm.assert_equal(result, expected) | pandas.util.testing.assert_equal |
__author__ = "<NAME>"
__copyright__ = "Sprace.org.br"
__version__ = "1.0.0"
import os
import numpy as np
import pandas as pd
#from torch.utils.data import Dataset, DataLoader
from sklearn.preprocessing import MinMaxScaler, StandardScaler
from enum import Enum
from pickle import dump, load
class FeatureType(Enum):
... | pd.DataFrame(x_data) | pandas.DataFrame |
import pandas as pd
from scipy import stats
import numpy as np
import math
import os
import sys
import json, csv
import itertools as it
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
import scikit_posthocs
from statsmodels.sandbox.stats.multicomp import multiple... | pd.read_csv("data/ALL/"+input_file, sep="\t", header=0, warn_bad_lines=True, error_bad_lines=False) | pandas.read_csv |
# -*- coding: utf-8 -*-
import numpy as np
import pytest
from pandas._libs.tslib import iNaT
import pandas.compat as compat
from pandas.core.dtypes.dtypes import CategoricalDtype
import pandas as pd
from pandas import (
CategoricalIndex, DatetimeIndex, Float64Index, Index, Int64Index,
IntervalIndex, MultiIn... | Series(array_b) | pandas.Series |
# -*- coding: utf-8 -*-
"""
dopplertext is a program to convert Doppler parameters stored on DCM images of PW Doppler into a readable, useable format.
Copyright (c) 2018 <NAME>.
This file is part of dopplertext.
dopplertext is free software: you can redistribute it and/or modify
it under the terms of the GNU Lesser ... | pd.DataFrame([]) | pandas.DataFrame |
"""
[Optional] When using db_out.csv check consistency:
should be the same ID column in raw and out.
With this I won't need the ID column at all.
"""
import os.path as p
from typing import NamedTuple, List, Tuple, Callable, Dict, Any
import ast
import pandas as pd
from pandas import DataFrame
import numpy as np
# n... | pd.concat([df_out_ru[out_columns], df_out_en[out_columns]], ignore_index=True) | pandas.concat |
import os
import pandas as pd
from pandas.io.json import json_normalize
import streamlit as st
from typing import List
import streamlit.components.v1 as components
from awesome_table.column import (ColumnDType, Column)
_RELEASE = True
class AwesomeTable():
"""AwesomeTable is a component for Streamlit to build a t... | pd.to_datetime(data[col.name]) | pandas.to_datetime |
"""
Pulsar search analysis
"""
import os, glob
import numpy as np
import pylab as plt
import matplotlib.ticker as ticker
import pandas as pd
from astropy.io import fits
from skymaps import SkyDir, Band
from . import (sourceinfo, associations, _html, fermi_catalog)
from .. import tools
from analysis_base import html_... | pd.read_csv(filename, index_col=0) | pandas.read_csv |
"""
This script contains all necessary code to extract and convert the patients data from the Sciensano hospital survey into parameters usable by the BIOMATH COVID-19 SEIRD model.
You must place the super secret detailed hospitalization dataset `COVID19BE_CLINIC.csv` in the same folder as this script in order to run it... | pd.to_datetime(df['dt_onset']) | pandas.to_datetime |
import os
import pandas
def getProfInfo(ProfFile):
f=open(ProfFile)
lines=f.readlines()
f.close()
return lines
curDirect=os.getcwd()
os.chdir(curDirect+"/Data")
UniFiles=iter(os.listdir(curDirect+"/Data"))
data={'Name':[],'Profile Link':[],'Department Website':[],'E-mail':[],'Interests':[]}
for unifile in UniFiles:... | pandas.DataFrame(data) | pandas.DataFrame |
import pandas as pd
import numpy as np
from ini.ini import *
from constant.constant import *
import time
import pickle
# import keras as ks
class Deep_Learning:
def __init__(self,env):
self.__env = env
self.__model = None
pass
def get_env(self):
return self.__env
def set_en... | pd.DataFrame(columns=[COM_SEC, COM_DATE, Y_HAT]) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
@author: LeeZChuan
"""
import pandas as pd
import numpy as np
import requests
import os
from pandas.core.frame import DataFrame
import json
import datetime
import time
pd.set_option('display.max_columns',1000)
pd.set_option('display.width', 1000)
pd.set_option('display.max_colwidth',100... | pd.DataFrame(list_address) | pandas.DataFrame |
# Fundamental libraries
import os
import re
import sys
import time
import glob
import random
import datetime
import warnings
import itertools
import numpy as np
import pandas as pd
import pickle as cp
import seaborn as sns
import multiprocessing
from scipy import stats
from pathlib import Path
from ast import literal_e... | pd.DataFrame({'RESAMPLE_IDX':compiled_rs_idx,'Accuracy':compiled_accuracy}) | pandas.DataFrame |
import numpy as np
import pandas as pd
from woodwork.logical_types import (
URL,
Age,
AgeNullable,
Boolean,
BooleanNullable,
Categorical,
CountryCode,
Datetime,
Double,
EmailAddress,
Filepath,
Integer,
IntegerNullable,
IPAddress,
LatLong,
NaturalLanguage,... | pd.Series(['2020-01-01', None, '2020-01-02', '2020-01-03']) | pandas.Series |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Copyright 2014-2019 OpenEEmeter contributors
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/LIC... | pd.Timestamp("2016-01-15 00:00:00+0000", tz="UTC") | pandas.Timestamp |
# Copyright 1999-2021 Alibaba Group Holding Ltd.
#
# 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 a... | pd.testing.assert_frame_equal(df, mdf2) | pandas.testing.assert_frame_equal |
import numpy as np
import pandas as pd
from scipy import signal
import matplotlib.pyplot as plt
import matplotlib as mpl
import seaborn as sns
from matplotlib.pyplot import cm
from scipy.interpolate import interp1d
from .core import spk_time_to_scv, firing_pos_from_scv, smooth
from ..base import SPKTAG
from ..utils imp... | pd.concat([self.pos_df, self.spike_df], sort=True) | pandas.concat |
"""Tests for Table Schema integration."""
import json
from collections import OrderedDict
import numpy as np
import pandas as pd
import pytest
from pandas import DataFrame
from pandas.core.dtypes.dtypes import (
PeriodDtype, CategoricalDtype, DatetimeTZDtype)
from pandas.io.json.table_schema import (
as_json_... | pd.to_datetime(['2016'], utc=True) | pandas.to_datetime |
from datetime import date
from typing import Dict, List, Optional, Union
try:
from sklearn.base import TransformerMixin # type: ignore
from sklearn.exceptions import NotFittedError # type: ignore
except ImportError:
TransformerMixin = object
NotFittedError = Exception
import itertools
import uuid
... | pd.merge(df, result_features, left_on=SYSTEM_RECORD_ID, right_on=SYSTEM_RECORD_ID, how="left") | pandas.merge |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"Merge meteogram files"
import re
import glob
import functools
import itertools
from collections import OrderedDict, defaultdict, namedtuple
import netCDF4
import numpy as np
import pandas as pd
var_signature = namedtuple('var_signature', 'name dtype dimensions')
time... | pd.Index([]) | pandas.Index |
# -*- coding: utf-8 -*-
import locale
from datetime import date
from os import chdir, path
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from adjustText import adjust_text
from matplotlib.ticker import PercentFormatter
from mpl_toolkits.axes_grid1.inset_locator import ... | pd.DataFrame({"% vaccini": vacc_res, "decessi": dec_res}) | pandas.DataFrame |
import pandas as pd
# A simple script to convert my excel file to be readable by YNAB.
# YNAB wants: Date,Payee,Category,Memo,Outflow,Inflow
__author__ = "<NAME> <<EMAIL>>"
import_csv = 'xacts.csv'
# read csv
df = pd.read_csv(import_csv, encoding = "ISO-8859-1", thousands=',')
# Build YNAB Category
df['Category'] ... | pd.to_numeric(df['Outflow']) | pandas.to_numeric |
####################################################
# IMPORTS (FROM LIBRARY) ###########################
####################################################
from pandas import DataFrame
####################################################
# FUNCTION TO GENERATE THE PARTICIPATION DATA ######
########################... | DataFrame(data) | pandas.DataFrame |
"""Unit tests for the reading functionality in dframeio.parquet"""
# pylint: disable=redefined-outer-name
from pathlib import Path
import pandas as pd
import pandera as pa
import pandera.typing
import pytest
from pandas.testing import assert_frame_equal
import dframeio
class SampleDataSchema(pa.SchemaModel):
""... | pd.DataFrame(df) | pandas.DataFrame |
import requests,json,os,re,argparse
import pandas as pd
from time import sleep
parser=argparse.ArgumentParser()
parser.add_argument('-i','--input_file', required=True, help='Input csv file with user name and orcid id')
parser.add_argument('-o','--output_xml', required=True, help='Output xml file')
args=parser.parse_ar... | pd.read_csv(input_file) | pandas.read_csv |
# -*- coding: utf-8 -*-
from __future__ import print_function
import pytest
import random
import numpy as np
import pandas as pd
from pandas.compat import lrange
from pandas.api.types import CategoricalDtype
from pandas import (DataFrame, Series, MultiIndex, Timestamp,
date_range, NaT, IntervalIn... | assert_frame_equal(result, expected) | pandas.util.testing.assert_frame_equal |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import zipfile
import os
import geopy.distance
import random
import pandas as pd
import numpy as np
import csv
from enum import Enum
from yaml import safe_load
from maro.cli.data_pipeline.utils import download_file, StaticParameter
from maro.u... | pd.concat(used_stations, ignore_index=True) | pandas.concat |
import boto3
import json
import matplotlib
import matplotlib.pyplot as plt
import pandas as pd
from l4ecwcw import *
from io import StringIO
from matplotlib import gridspec
# Mandatory to ensure text is rendered in SVG plots:
matplotlib.rcParams['svg.fonttype'] = 'none'
client = boto3.client('lookoutequipment')
dpi =... | pd.to_datetime(model_response['EvaluationDataEndTime']) | pandas.to_datetime |
import string
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
###############
# for first time use uncomment this
#
# read in data
data=pd.read_csv('VS_Extensions_1week_correct.csv')
data=data.drop(['MacAddressHash1'], axis=1)
# now we need to parse out Extensions Used
df=data.groupby('MacAddr... | pd.concat([df, dfpre], axis=1) | pandas.concat |
import xlrd
import os
import pandas as pd
os.chdir('/Users/zhengzhiheng/PycharmProjects/untitled3')
wordbook = xlrd.open_workbook('test.xlsx')
sheet_name = wordbook.sheet_names()
print(sheet_name)
lst = pd.read_excel('test.xlsx', sheet_name=0)
lst2 = pd.read_excel('test.xlsx', sheet_name=1)
print(lst.head(5))
# ignore... | pd.concat([lst, lst2], axis=0, ignore_index=True) | pandas.concat |
import re
import numpy as np
import numpy.testing as npt
import pandas as pd
import pandas.testing as pdt
import pytest
from aneris.convenience import harmonise_all
from aneris.errors import (
AmbiguousHarmonisationMethod,
MissingHarmonisationYear,
MissingHistoricalError,
)
pytest.importorskip("pint")
im... | pd.concat([hist_df, co2_afolu_hist, bc_afolu_hist]) | pandas.concat |
from __future__ import division
import pandas as pd
def merge_subunits(genes):
""" Merge list of protein subunit genes into complex
Args:
genes (pandas.Series): list of genes
Returns:
str: boolean rule
"""
genes = genes.dropna()
if len(genes) == 0:
return None
e... | pd.merge(gene2gene, gprs, how='right') | pandas.merge |
"""
Test our groupby support based on the pandas groupby tests.
"""
#
# This file is licensed under the Pandas 3 clause BSD license.
#
from sparklingpandas.test.sp_test_case import \
SparklingPandasTestCase
from pandas import bdate_range
from pandas.core.index import Index, MultiIndex
from pandas.core.api import D... | Index(['bar', 'foo'], name='A') | pandas.core.index.Index |
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from matplotlib.colors import LogNorm
import matplotlib
import os
import sys
import appaloosa
import pandas as pd
import datetime
import warnings
from scipy.optimize import curve_fit, minimize
from astropy.stats import funcs
import emcee
impo... | pd.read_csv(kicfile, delimiter='|') | pandas.read_csv |
import pandas as pd
def merge_data(left_df, right_df, date_col="date"):
# get clean copies of data with date format
left_df = left_df.copy()
left_df[date_col] = | pd.to_datetime(left_df[date_col]) | pandas.to_datetime |
import sys
import numpy
import pandas as pd
import constants as kk
from pyswarm import pso
import os
import input
import datetime as dt
def preparation():
project_path = 'C:\\Users\\FrancescoBaldi\\switchdrive\\Work in progress\\Paper 0\\Ecos2015PaperExtension\\'
path_files = project_path + os.sep
sys.pat... | pd.DataFrame(index=processed_temp.index) | pandas.DataFrame |
import argparse
from ast import literal_eval
from astropy.io import fits
import base64
from bson.json_util import loads
import confluent_kafka
from copy import deepcopy
import datetime
import fastavro
import gzip
import io
from matplotlib.colors import LogNorm
import matplotlib.pyplot as plt
import multiprocessing
impo... | pd.DataFrame(alert['prv_candidates']) | pandas.DataFrame |
from collections import Counter
from importlib.machinery import SourceFileLoader
import numpy as np
from os.path import join
import warnings
warnings.filterwarnings("ignore")
import nltk
nltk.download('punkt')
import seaborn as sns
import matplotlib
from nltk.tokenize import sent_tokenize, word_tokenize
from nltk.stem... | pd.get_dummies(df_train['Label']) | pandas.get_dummies |
# -*- coding: utf-8 -*-
"""
These the test the public routines exposed in types/common.py
related to inference and not otherwise tested in types/test_common.py
"""
from warnings import catch_warnings, simplefilter
import collections
import re
from datetime import datetime, date, timedelta, time
from decimal import De... | lib.infer_dtype(arr) | pandas._libs.lib.infer_dtype |
from datetime import datetime
import unittest
import numpy as np
import pandas.core.datetools as datetools
from pandas.core.daterange import DateRange, XDateRange
####
## XDateRange Tests
####
def eqXDateRange(kwargs, expected):
assert(np.array_equal(list(XDateRange(**kwargs)), expected))
def testXDateRange1(... | DateRange(START, periods=20, offset=datetools.bday) | pandas.core.daterange.DateRange |
# Copyright (c) <NAME>
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import chronos_utils
import pandas as pd
import numpy as np
import torch
from torch.optim import Rprop
from torch.distributions import constraints
import pyro
import py... | pd.date_range(start="01-01-2020", periods=366) | pandas.date_range |
import itertools
import re
import pandas as pd
import numpy as np
from catboost import Pool, FeaturesData
from constants import SCHOOLS_REVERSED, TARGET_LABELS
def _parse_str_nums(num_string):
"""
parse strings of numbers and take averages if there are multiple
:param num_string: a string of numbers ... | pd.DataFrame({school: labels[school]}) | pandas.DataFrame |
# coding: utf-8
import numpy as np
import netCDF4 as nc
import pandas as pd
from glob import glob
from datetime import datetime
from os import path
from j24 import home
arm_dir = path.join(home(), 'DATA', 'arm')
SOUNDING_DIR = path.join(arm_dir, 'sounding')
GROUND_DIR = path.join(arm_dir, 'ground')
MWR_DIR = path.joi... | pd.to_datetime(t0 + ncdata.variables['time_offset'][:], unit='s') | pandas.to_datetime |
import unittest
import pandas as pd
import numpy as np
from scipy.sparse.csr import csr_matrix
from string_grouper.string_grouper import DEFAULT_MIN_SIMILARITY, \
DEFAULT_REGEX, DEFAULT_NGRAM_SIZE, DEFAULT_N_PROCESSES, DEFAULT_IGNORE_CASE, \
StringGrouperConfig, StringGrouper, StringGrouperNotFitException, \
... | pd.DataFrame({'master_side': master, 'dupe_side': dupe_side, 'similarity': similarity}) | pandas.DataFrame |
#coding=utf-8
import pandas as pd
import numpy as np
import sys
import os
from sklearn import preprocessing
import datetime
import scipy as sc
from sklearn.preprocessing import MinMaxScaler,StandardScaler
from sklearn.externals import joblib
#import joblib
class FEbase(object):
"""description of class"""
def ... | pd.merge(df_data, df_adj_all, how='inner', on=['ts_code','trade_date']) | pandas.merge |
from typing import Union, List
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from sklearn.metrics import roc_auc_score, roc_curve
def plot_bars(df, path, title=None):
sns.set(style="whitegrid", font_scale=1.5)
pl = df.plot(figsize=(10, 10), kind='bar', cmap='Acc... | pd.concat(samples, axis=0, ignore_index=True) | pandas.concat |
from re import X
from dash import Dash, dcc, html, Input, Output
import dash_bootstrap_components as dbc
import pandas as pd
import altair as alt
import os
alt.data_transformers.disable_max_rows()
# import data
# absolute path to this file
FILE_DIR = os.path.dirname(os.path.abspath(__file__))
# absolute path to this ... | pd.read_json(filter_df) | pandas.read_json |
import pytest
import os
import sys
import json
from random import randint
from mercury_ml.common.artifact_storage.local import store_dict_json, store_pandas_json, store_pandas_pickle, \
store_h2o_frame
import shutil
input_dict = {"hello": [randint(0,100),randint(0,100),randint(0,100),randint(0,100)]}
dir = "./resu... | pd.DataFrame(input_dict) | pandas.DataFrame |
#! /usr/bin/env python3.5
from __future__ import print_function
import argparse
import csv
import pandas
import random
import numpy
from keras import backend as K
from keras.models import Sequential
from keras.layers import Dense, Dropout
from collections import Counter, OrderedDict
# Label regularization loss, accor... | pandas.read_csv(args.meta_file, sep=";", encoding='utf-8-sig') | pandas.read_csv |
# coding: utf-8
import numpy as np
from itertools import product
from collections import Counter
import pandas as pd
import os
import re
import json
import openslide
from matplotlib import pyplot as plt
import cv2
#cell#
from extract_rois_svs_xml import extract_rois_svs_xml
from slideutils import (plot_contour, get_... | pd.Series([roi["name"] for roi in roilist]) | pandas.Series |
"""Tests for system parameter identification functions."""
import pytest
import pandas as pd
import numpy as np
import pvlib
from pvlib import location, pvsystem, tracking, modelchain, irradiance
from pvanalytics import system
from .conftest import requires_pvlib
@pytest.fixture(scope='module')
def summer_times():
... | pd.Series(False, index=power_half_tracking.index) | pandas.Series |
# -*- coding: utf-8 -*-
import csv
import os
import platform
import codecs
import re
import sys
from datetime import datetime
import pytest
import numpy as np
from pandas._libs.lib import Timestamp
import pandas as pd
import pandas.util.testing as tm
from pandas import DataFrame, Series, Index, MultiIndex
from pand... | pd.concat(result) | pandas.concat |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
data_path = 'Bike-Sharing-Dataset/hour.csv'
rides = pd.read_csv(data_path)
dummy_fields = ['season', 'weathersit', 'mnth', 'hr', 'weekday']
for each in dummy_fields:
dummies = pd.get_dummies(rides[each], prefix=each, drop_first=False)
ride... | pd.concat([rides, dummies], axis=1) | pandas.concat |
#!/usr/bin/env python
"""
DataExplore pluin differential expression using R
Created June 2017
Copyright (C) <NAME>
This program is free software; you can redistribute it and/or
modify it under the terms of the GNU General Public License
as published by the Free Software Foundation; either versi... | pd.Categorical(idx) | pandas.Categorical |
import numpy as np
import scipy as sp
import pandas as pd
import scipy.stats
import scanpy
import csv
import glob
import random
from sklearn import preprocessing
min_max_scaler = preprocessing.MinMaxScaler()
# <- todo
predefined_col_order = "./data/col_index"
string_gene_list_pwd = "./data/genesort_string_hit.txt"
# ... | pd.concat([out_te,test_set], axis=1) | pandas.concat |
# fetch_california_housing
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# 加载数据集
california = fetch_california_housing()
# 数据格式化的一些操作... | pd.Series(california.target) | pandas.Series |
import string
import numpy as np
import pandas as pd
from pandas import DataFrame
import pandas._testing as tm
from pandas.api.types import (
is_extension_array_dtype,
pandas_dtype,
)
from .pandas_vb_common import (
datetime_dtypes,
extension_dtypes,
numeric_dtypes,
string_dtypes,
)
_numpy_d... | DataFrame(data, index=self.index, columns=self.columns) | pandas.DataFrame |
import itertools
import jax.numpy as jnp
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from skillmodels.params_index import get_params_index
from skillmodels.parse_params import create_parsing_info
from skillmodels.parse_params import parse_params
from skillmodels.proces... | pd.concat(to_concat) | pandas.concat |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import logging
from pathlib import Path
import shutil
from tempfile import NamedTemporaryFile
from typi... | pd.DataFrame.from_dict(manifest) | pandas.DataFrame.from_dict |
import unittest
import pandas as pd
import numpy as np
from scipy.sparse.csr import csr_matrix
from string_grouper.string_grouper import DEFAULT_MIN_SIMILARITY, \
DEFAULT_REGEX, DEFAULT_NGRAM_SIZE, DEFAULT_N_PROCESSES, DEFAULT_IGNORE_CASE, \
StringGrouperConfig, StringGrouper, StringGrouperNotFitException, \
... | pd.testing.assert_frame_equal(expected_df, sg._matches_list) | pandas.testing.assert_frame_equal |
name = 'nfl_data_py'
import pandas
import numpy
import datetime
def import_pbp_data(years, columns=None, downcast=True):
"""Imports play-by-play data
Args:
years (List[int]): years to get PBP data for
columns (List[str]): only return these columns
downcast (bool): convert float64... | pandas.read_csv(r'https://github.com/nflverse/nflfastR-data/raw/master/teams_colors_logos.csv') | pandas.read_csv |
import logging
from typing import Tuple
import pandas as pd
from pandas import DataFrame
from dbnd import task
from dbnd.testing.helpers_pytest import assert_run_task
from dbnd_test_scenarios.test_common.targets.target_test_base import TargetTestBase
logger = logging.getLogger(__name__)
@task(result=("features"... | pd.DataFrame(data=[[p, 1]], columns=["c1", "c2"]) | pandas.DataFrame |
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