prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import requests_html, openpyxl, ntpath, os, datetime
import PySimpleGUI as sg
import numpy as np
import pandas as pd
from pathlib import Path
from bs4 import BeautifulSoup as BSoup
from requests.exceptions import ConnectionError
from requests.exceptions import ReadTimeout
from openpyxl.utils.dataframe import da... | pd.DataFrame(nomatch_array) | pandas.DataFrame |
"""
Tests that work on both the Python and C engines but do not have a
specific classification into the other test modules.
"""
from datetime import datetime
from inspect import signature
from io import StringIO
import os
from pathlib import Path
import sys
import numpy as np
import pytest
from pandas.compat import P... | DataFrame({"A": [1, 10], "B": [2334, 13], "C": [5, 10.0]}) | pandas.DataFrame |
# -*- 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... | Series(['a']) | pandas.Series |
import cv2
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt
from scipy import ndimage
import scipy.misc
from tqdm import tqdm
import utils.data as data
from utils.filename import *
from utils.image import *
from utils.params import *
from utils.preprocess import *
impor... | pd.DataFrame(res) | pandas.DataFrame |
#!/usr/bin/env python
"""
Command-line tool to control the concavity constraining tools
Mudd et al., 2018
So far mostly testing purposes
B.G.
"""
from lsdtopytools import LSDDEM # I am telling python I will need this module to run.
from lsdtopytools import argparser_debug as AGPD # I am telling python I will need this ... | pd.DataFrame(df_perimeter) | pandas.DataFrame |
from bokeh.models import HoverTool
from bokeh.io import curdoc
from bokeh.layouts import column,row
import pandas as pd
from statement_parser import parse_ofx_statements
import holoviews as hv
from bokeh.models.formatters import DatetimeTickFormatter
pd.options.plotting.backend = 'holoviews'
merged_df = parse_ofx_stat... | pd.read_csv("temp.csv") | pandas.read_csv |
import pandas as pd
import numpy as np
from rdtools import energy_from_power
import pytest
# Tests for resampling at same frequency
def test_energy_from_power_calculation():
power_times = pd.date_range('2018-04-01 12:00', '2018-04-01 13:00', freq='15T')
result_times = power_times[1:]
power_series = pd.Ser... | pd.to_timedelta('15 minutes') | pandas.to_timedelta |
"""
"""
from __future__ import print_function
from future.utils import listvalues
import random
from KSIF.core import utils
from .utils import fmtp, fmtn, fmtpn, get_period_name
import numpy as np
import pandas as pd
from pandas.core.base import PandasObject
from tabulate import tabulate
from matplotlib im... | pd.DateOffset(years=10) | pandas.DateOffset |
# Copyright 2018 Twitter, Inc.
# Licensed under the Apache License, Version 2.0
# http://www.apache.org/licenses/LICENSE-2.0
""" This module contains classes and methods for extracting metrics from the
Heron Topology Master instance. """
import logging
import warnings
import datetime as dt
from typing import Dict, ... | pd.DataFrame() | pandas.DataFrame |
# Standard packages
from netCDF4 import Dataset, num2date
from datetime import datetime
import numpy as np
import pandas as pd
#____________Selecting a season (DJF,DJFM,NDJFM,JJA)
def sel_season(var,dates,season,timestep):
#----------------------------------------------------------------------------------------
... | pd.to_datetime(dates) | pandas.to_datetime |
import time
from definitions_toxicity import ROOT_DIR
import pandas as pd
from src.preprocessing import custom_transformers as ct
from sklearn.pipeline import Pipeline
import nltk
import pickle
from src.preprocessing.text_utils import tokenize_by_sentences, fit_tokenizer, tokenize_text_with_sentences
import numpy as... | pd.DataFrame(y_test, columns=labels) | pandas.DataFrame |
#%%
import ee
from ee.data import exportTable
import eemont
import re
from datetime import datetime
import pandas as pd
import numpy as np
from pandas.core import frame
import geopandas as gpd
import matplotlib.pyplot as plt
import dload
from py01_helper_functions import ee_collection_pull, process_gdf
# %%
ee.Au... | pd.concat([landsat_5_nbr, landsat_7_nbr]) | pandas.concat |
import datetime as dt
import unittest
import pandas as pd
import numpy as np
import numpy.testing as npt
import seaice.nasateam as nt
import seaice.tools.plotter.daily_extent as de
class Test_BoundingDateRange(unittest.TestCase):
def test_standard(self):
today = dt.date(2015, 9, 22)
month_bound... | pd.Index([6, 7, 8], name='day of year') | pandas.Index |
# coding: utf-8
# In[1]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import time
# In[2]:
df1=pd.read_csv("loan_data.csv")
df1.head()
# In[3]:
df1 = | pd.get_dummies(df1,['purpose'],drop_first=True) | pandas.get_dummies |
"""
Tests the usecols functionality during parsing
for all of the parsers defined in parsers.py
"""
from io import StringIO
import numpy as np
import pytest
from pandas._libs.tslib import Timestamp
from pandas import DataFrame, Index
import pandas._testing as tm
_msg_validate_usecols_arg = (
"'usecols' must eit... | Timestamp("2008-02-07 10:00") | pandas._libs.tslib.Timestamp |
"""
This module tests high level dataset API functions which require entire datasets, indices, etc
"""
from collections import OrderedDict
import pandas as pd
import pandas.testing as pdt
from kartothek.core.dataset import DatasetMetadata
from kartothek.core.index import ExplicitSecondaryIndex
def test_dataset_ge... | pd.Index(["part1", "part1", "part2", "part2"], name="partition") | pandas.Index |
# Licensed to Modin Development Team under one or more contributor license
# agreements. See the NOTICE file distributed with this work for additional
# information regarding copyright ownership. The Modin Development Team
# licenses this file to you under the Apache License, Version 2.0 (the
# "License"); you may not... | pandas.DataFrame(frame_data2) | pandas.DataFrame |
# encoding: utf-8
"""
.. codeauthor:: <NAME> <<EMAIL>>
"""
from __future__ import absolute_import, print_function, unicode_literals
import collections
import re
from textwrap import dedent
import pytablewriter as ptw
import pytest
import six # noqa: W0611
from pytablewriter.style import Align, FontSize, Style, Tho... | pd.DataFrame({"A": [1, 2], "B": [10, 11]}, index=["a", "b"]) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jun 14 14:54:39 2017
@author: dhingratul
"""
import pandas as pd
countries = [
'Afghanistan', 'Albania', 'Algeria', 'Angola', 'Argentina',
'Armenia', 'Australia', 'Austria', 'Azerbaijan', 'Bahamas',
'Bahrain', 'Bangladesh', 'Barbados', 'Be... | pd.Series(employment_values, index=countries) | pandas.Series |
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
from sklearn import datasets, linear_model
from difflib import SequenceMatcher
import seaborn as sns
from statistics import mean
from ast import literal_eval
from scipy import stats
from sklearn.linear_model import LinearRegression
from s... | pd.to_numeric(telo_data.iloc[:,0], errors='coerce') | pandas.to_numeric |
# SPDX-FileCopyrightText: : 2020 @JanFrederickUnnewehr, The PyPSA-Eur Authors
#
# SPDX-License-Identifier: GPL-3.0-or-later
"""
This rule downloads the load data from `Open Power System Data Time series <https://data.open-power-system-data.org/time_series/>`_. For all countries in the network, the per country load ti... | pd.read_csv(IGGINL_df_path, sep=';') | pandas.read_csv |
import pandas as pd
import numpy as np
import random
from human_ISH_config import *
import math
import os
import sklearn
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import f1_score
from sklearn.metrics import roc_auc_score
from sklearn... | pd.DataFrame(scores,columns=['level', 'AUC', 'f1']) | pandas.DataFrame |
import numpy as np
import os
import io
import glob
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import tensorflow as tf
import tensorflow_datasets as tfds
import itertools
import pickle
from collections import defaultdict
from sklearn.manifold import TSNE
from dtaid... | pd.DataFrame(labels) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
@created: 01/29/21
@modified: 01/29/21
@author: <NAME>
CentraleSupelec
MICS laboratory
9 rue <NAME>, Gif-Sur-Yvette, 91190 France
Defines internal classes user-level functions for building and plotting double heatmaps.
"""
import copy
from dataclasses import dataclass, fiel... | pd.qcut(vals, q=n-1) | pandas.qcut |
import pandas as pd
from SALib.analyze.radial_ee import analyze as ee_analyze
from SALib.analyze.sobol_jansen import analyze as jansen_analyze
from SALib.plotting.bar import plot as barplot
# results produced with
# python launch.py --specific_inputs oat_mc_10_samples.csv --num_cores 48
# python launch.py --specific_... | pd.DataFrame(data=res) | pandas.DataFrame |
#!/usr/bin/env python
import os
import glob
import sys
import shutil
import pdb
import re
from argparse import ArgumentParser
import pandas as pd
import numpy as np
import math
import matplotlib.pyplot as plt
import seaborn as sns
sys.path.insert(0,'..')
import ESM_utils as esm
from scipy.optimize import curve_fi... | pd.read_csv("../../data/DIAN/participant_metadata/GENETIC_D1801.csv") | pandas.read_csv |
#testing_framework.py
#This script is to evaluate an arbitrary number of classifier objects and output the results
#Build a class that takes in a model object and outputs a dataframe with the predictions
#Benefits of this approach are that we can initialize the class a single time, then feed different datasets in to ... | pd.DataFrame(X) | pandas.DataFrame |
from simulationClasses import DCChargingStations, Taxi, Bus, BatterySwappingStation
import numpy as np
import pandas as pd
from scipy import stats, integrate
import matplotlib.pyplot as plt
from matplotlib.ticker import FuncFormatter
from matplotlib.dates import DateFormatter, HourLocator, MinuteLocator, AutoDateLocato... | pd.DataFrame(taxiIncome,columns=["time","income","running","charging","waiting"]) | pandas.DataFrame |
import time
import os
import math
import argparse
from glob import glob
from collections import OrderedDict
import random
import warnings
from datetime import datetime
import yaml
import gc
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
import pandas as pd
import joblib
import cv2
from sklea... | pd.read_csv('processed/pose_train.csv') | pandas.read_csv |
""" Stockgrid View """
__docformat__ = "numpy"
import logging
from typing import List, Tuple
import pandas as pd
import requests
from gamestonk_terminal.decorators import log_start_end
logger = logging.getLogger(__name__)
@log_start_end(log=logger)
def get_dark_pool_short_positions(sort_field: str, ascending: boo... | pd.to_datetime(df["dates"]) | pandas.to_datetime |
# pylint: disable-msg=E1101,W0612
from datetime import datetime, time, timedelta, date
import sys
import os
import operator
from distutils.version import LooseVersion
import nose
import numpy as np
randn = np.random.randn
from pandas import (Index, Series, TimeSeries, DataFrame,
isnull, date_ran... | assert_series_equal(result, ts[:0]) | pandas.util.testing.assert_series_equal |
from flask import Flask, render_template, request
import numpy as np
import pandas as pd
import scipy.stats as stats
import matplotlib.pyplot as plt
import sklearn
import seaborn as sns
sns.set_style("whitegrid")
from sklearn.model_selection import cross_val_score
from sklearn import preprocessing
from sklearn import d... | pd.to_numeric(dftest['Month']) | pandas.to_numeric |
import pandas as pd
import numpy as np
import xgboost as xgb
train_data_df = pd.read_csv('train.csv')
test_data_df = | pd.read_csv('test.csv') | pandas.read_csv |
import inspect
import operator as op
from typing import *
import pandas as pd
import pypika as pk
from dateutil.relativedelta import relativedelta
from pandas.io.formats.style import Styler
from pypika import DatePart # noqa
from pypika import Order # noqa
from pypika import Case, Criterion # noqa
from pypika impor... | pd.to_datetime(x.index.start_time.date) | pandas.to_datetime |
from decouple import config
import pandas as pd
import pymssql
import utility
import os
# Pobranie zmiennych srodowiskowych
server = config('SERVER')
user = config('DB_USER')
password = config('DB_PASSWORD')
database = config('DATABASE')
try:
conn = pymssql.connect(server, user, password, database)
except pymssq... | pd.DataFrame(sql_query) | pandas.DataFrame |
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import statsmodels
from matplotlib import pyplot
from scipy import stats
import statsmodels.api as sm
import warnings
from itertools import product
import datetime as dt
from stat... | pd.concat([df_month2, future]) | pandas.concat |
import pandas as pd
import config
from data_process import get_data
from visualization import plot_indicator, plot_monthly_return_comp_etf, plot_yearly_return_comp_etf
from trade import get_trading_records
import yfinance as yf
def run() -> None:
for indicator in config.TECHNICAL_INDICATORS:
# visualize i... | pd.DataFrame() | pandas.DataFrame |
import timeit
from typing import Union
import numpy as np
import pandas as pd
import copy
from carla.evaluation.distances import get_distances
from carla.evaluation.nearest_neighbours import yNN, yNN_prob, yNN_dist
from carla.evaluation.manifold import yNN_manifold, sphere_manifold
from carla.evaluation.process_nans ... | pd.DataFrame([[ynn]], columns=columns) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Thu Feb 18 10:32:02 2021
@author: Avram
"""
import time
import math
import numpy as np
import pandas as pd
from pathlib import Path
import glob
from .utilities import t_to_d
class PtracMod:
"""A class to store ptrac information for moderator studies."""
... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Oct 14 18:12:10 2018
@author: Kazuki
"""
import numpy as np
import pandas as pd
import os, gc
from tqdm import tqdm
from multiprocessing import cpu_count, Pool
import utils
os.system(f'rm -rf ../data')
os.system(f'mkdir ../data')
os.system(f'rm -rf .... | pd.merge(train_log, train[['object_id', 'distmod']], on='object_id', how='left') | pandas.merge |
import matplotlib
# matplotlib.use('pgf')
# pgf_with_pdflatex = {
# "pgf.texsystem": "pdflatex",
# "pgf.preamble": [
# r"\usepackage[utf8x]{inputenc}",
# r"\usepackage[T1]{fontenc}",
# r"\usepackage{cmbright}",
# ]
# }
# matplotlib.rcParams.update(pgf_with_pdflatex)
import ... | pandas.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
# @Time : 2021/9/7 21:16
# @Author : <NAME>
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
from matplotlib.ticker import MultipleLocator, FormatStrFormatter
import re
import ast
file1 = 'output_search-cell-nas-bench-201_GDAS-cifa... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
'''
@test($$;type(pd))
@alt(全ての|すべての|全)
@alt(の名前|名)
@alt(丸める|四捨五入する)
@alt(丸めて|四捨五入して)
@prefix(df;データフレーム)
@prefix(ds;データ列)
@prefix(col;カラム)
@alt(日付データ|タイムスタンプ[型|]|Pandasの日付型|datetime64型)
@prefix(value;[文字列|日付|])
データ列を使う
データ列をインポートする
'''
dateList = [ | pd.to_datetime('12-12-12') | pandas.to_datetime |
# -*- coding: utf-8 -*-
"""
Produce a JSON file used to enhance structural metadata in the IIIF manifests.
"""
import json
import tqdm
import click
import pandas as pd
from get_annotations import get_annotations_df
from helpers import write_to_csv, get_tag, get_transcription, get_source
from helpers import CACHE
def... | pd.DataFrame(out) | pandas.DataFrame |
#!/usr/bin/python3
import math as m
from datetime import datetime
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from tkinter import *
from pandastable import Table, TableModel
import pandas as pd
import h5_spectrum as H5
STAT_NORMAL = np.dtype([(H5.MEAN_MEMBER, np.float64),
... | pd.DataFrame(df_data) | pandas.DataFrame |
from flask import Flask, render_template, request, session, redirect, url_for
from datetime import datetime, timedelta
import pandas as pd
import sqlite3, hashlib, os, random, os, dotenv
app = Flask(__name__)
app.secret_key = "super secret key"
dotenv.load_dotenv()
MAPBOX_TOKEN = os.getenv('MAPBOX_TOKEN')
conn = sqlit... | pd.read_sql("select * from w_branch", conn) | pandas.read_sql |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
Date: 2022/3/21 17:40
Desc: 天天基金网-基金档案-投资组合
http://fundf10.eastmoney.com/ccmx_000001.html
"""
import pandas as pd
import requests
from bs4 import BeautifulSoup
from akshare.utils import demjson
def fund_portfolio_hold_em(symbol: str = "162411", date: str = "2020") -> ... | numeric(big_df["持股数"], errors="coerce") | pandas.to_numeric |
#!/usr/bin/python3
# -*- coding: utf-8 -*-
# *****************************************************************************/
# * Authors: <NAME>
# *****************************************************************************/
"""transformCSV.py
This module contains the basic functions for creating the content of... | pandas.StringDtype() | pandas.StringDtype |
"""
Tests for CBMonthEnd CBMonthBegin, SemiMonthEnd, and SemiMonthBegin in offsets
"""
from datetime import (
date,
datetime,
)
import numpy as np
import pytest
from pandas._libs.tslibs import Timestamp
from pandas._libs.tslibs.offsets import (
CBMonthBegin,
CBMonthEnd,
CDay,
SemiMonthBegin,
... | SemiMonthBegin() | pandas._libs.tslibs.offsets.SemiMonthBegin |
# coding: utf-8
"""Extract AA mutations from NT mutations
Author: <NAME> - Vector Engineering Team (<EMAIL>)
"""
import pandas as pd
from scripts.fasta import read_fasta_file
from scripts.util import translate
def extract_aa_mutations(
dna_mutation_file, gene_or_protein_file, reference_file, mode="gene"
):
... | pd.read_json(gene_or_protein_file) | pandas.read_json |
"""Class definition for the DataSetParser ABC and FeaturizerMixin."""
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Callable, Generator, List, Tuple, Type
import numpy as np
import pandas as pd
from sklearn.preprocessing import RobustScaler
class FeaturizerMixin:
"""Mixin to pr... | pd.api.types.is_numeric_dtype(series) | pandas.api.types.is_numeric_dtype |
from hddm.simulators import *
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
import seaborn as sns
import pymc as pm
import os
import warnings
import hddm
import pandas as pd
from kabuki.analyze import _post_pred_generate, _parents_to_random_posterior_sample
from statsmodels.... | pd.concat(samples) | pandas.concat |
#!/usr/bin/env python3
import os, re, sys, logging, csv, multiprocessing
import pandas as pd
from itertools import groupby
import itertools, functools
try:
from Bio.Alphabet import generic_dna, IUPAC
Bio_Alphabet = True
except ImportError:
Bio_Alphabet = None
# usages of generic_dna, IUPAC are not suppo... | pd.DataFrame(columns=nameslist) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = "<NAME>"
__contact__ = "gambrosio[at]uma.es"
__copyright__ = "Copyright 2021, <NAME>"
__date__ = "2021/07/27"
__license__ = "MIT"
import sys
import datetime as dt
import sqlite3
import os
import cv2
import numpy as np
import pandas as pd
import mxnet as mx
fr... | pd.read_sql_query(sql_str, self.__con) | pandas.read_sql_query |
import os
import logging
from notion.client import NotionClient
import numpy as np
import pandas as pd
import yfinance as yf
from datetime import datetime
import matplotlib.pyplot as plt
import seaborn as sns
from telegram.ext import Updater, CommandHandler, MessageHandler, Filters
from telegram import ParseMode, Repl... | pd.DataFrame(notion_data, columns=['id', 'balance_time', 'Credit', 'Cash', 'USD']) | pandas.DataFrame |
from functools import lru_cache
from pyiso import client_factory
from datetime import datetime, timedelta
from funcy import compose, identity, retry
from itertools import repeat
from urllib.error import HTTPError
import pandas as pd
import numpy as np
from app.model import RENEWABLES, NON_RENEWABLES
from app.util impo... | pd.to_datetime(x["hour"], unit="h", origin=x["date"]) | pandas.to_datetime |
# -*- coding: utf-8 -*-
"""
Created on Fri Apr 22 09:23:26 2022
@author: <NAME> willi
"""
#%% Packages
import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets, linear_model, metrics
import pandas as pd
from sklearn.model_selection import train_test_split
#%% Loading data (induvidual fund)
y_... | pd.DataFrame(r2_test) | pandas.DataFrame |
# import libraries
import os, os.path
import numpy as np
import pandas as pd
# import geopandas as gpd
import sys
from IPython.display import Image
# from shapely.geometry import Point, Polygon
from math import factorial
import scipy
from statsmodels.sandbox.regression.predstd import wls_prediction_std
from sklearn.lin... | pd.concat([randomly_chosen_fields_DT, curr_F]) | pandas.concat |
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
from selenium.webdriver.common.keys import Keys
import requests
import time
from datetime import datetime
import pandas as pd
from urllib import parse
from config import ENV_VARIABLE
from os.path import getsize
fold_path = ... | pd.DataFrame() | pandas.DataFrame |
import os
import numpy as np
import pandas as pd
import torch
from torch.utils.data import Dataset, DataLoader
# from sklearn.preprocessing import StandardScaler
from utils.tools import StandardScaler
from utils.timefeatures import time_features
import warnings
warnings.filterwarnings('ignore')
class Dataset_ETT_ho... | pd.to_datetime(df_stamp.date) | pandas.to_datetime |
import numpy as np
import pandas as pd
import logging
logging.getLogger(__name__).addHandler(logging.NullHandler())
logger = logging.getLogger(__name__)
try:
from sklearn.base import TransformerMixin, BaseEstimator
except ImportError:
msg = "scikit-learn not installed"
logger.warning(msg)
try:
from ... | pd.isnull(X[mask, :]) | pandas.isnull |
from datetime import datetime, timedelta
import unittest
from pandas.core.datetools import (
bday, BDay, BQuarterEnd, BMonthEnd, BYearEnd, MonthEnd,
DateOffset, Week, YearBegin, YearEnd, Hour, Minute, Second,
format, ole2datetime, to_datetime, normalize_date,
getOffset, getOffsetName, inferTimeR... | BQuarterEnd(1, startingMonth=2) | pandas.core.datetools.BQuarterEnd |
# for adding data(bills,elevator,etc.) as input please type append(root_out) in python console which root_out is the path including the name of the csv file that your inputs will be saved in there
# for dividing expenses please type execute_all_division_funcs(root_in, root_out, root_info) in python console
# for acqu... | pd.read_excel(root_info) | pandas.read_excel |
import os
import time
import torch
import argparse
import scipy.io
import warnings
from torch.autograd import Variable
from torchvision import datasets, transforms
import dataset
from darknet import Darknet
from utils import *
from MeshPly import MeshPly
import argparse
import pandas as pd
# Create new directory
def... | pd.read_csv(csv_output_name) | pandas.read_csv |
import inspect
import os
import datetime
from collections import OrderedDict
import numpy as np
from numpy import nan, array
import pandas as pd
import pytest
from pandas.util.testing import assert_series_equal, assert_frame_equal
from numpy.testing import assert_allclose
from pvlib import tmy
from pvlib import pvsy... | pd.Series([np.nan, 50, 100]) | pandas.Series |
'''
Author: <NAME>
File: composite_frame
Trello: Goal 1
'''
from typing import List
import numpy as np
import pandas as pd
class Composite_Frame(object):
'''
The Composite_Frame class takes a pandas data frame containing network flow
information and splits into a list of frames, each representing the t... | pd.concat([df1, df2]) | pandas.concat |
#!/usr/bin/env python
import networkx as nx, pandas as pd, sys, csv
from argparse import ArgumentParser
def rowsplit(s): return s.rstrip(";").split(";")
def tab_to_dataframe(infile,families):
df = pd.DataFrame(columns=['Node1','Node2'])
j = 0
with open(infile, 'r') as fh:
for i,row in enumerate(c... | pd.concat([df,tmp]) | pandas.concat |
"""
Testing that functions from rpy work as expected
"""
import pandas as pd
import numpy as np
import unittest
import nose
import pandas.util.testing as tm
try:
import pandas.rpy.common as com
from rpy2.robjects import r
import rpy2.robjects as robj
except ImportError:
raise nose.SkipTest('R not inst... | tm.getSeriesData() | pandas.util.testing.getSeriesData |
import os, codecs
import pandas as pd
import numpy as np
PATH = '../input/'
# 共享单车轨迹数据
bike_track = pd.concat([
pd.read_csv(PATH + 'gxdc_gj20201221.csv'),
pd.read_csv(PATH + 'gxdc_gj20201222.csv'),
pd.read_csv(PATH + 'gxdc_gj20201223.csv'),
pd.read_csv(PATH + 'gxdc_gj20201224.csv'),
pd.r... | pd.to_datetime(bike_order['UPDATE_TIME']) | pandas.to_datetime |
import pandas as pd
import sys
import os
import numpy as np
import signatureanalyzer as sa
from typing import Union
import nimfa
from tqdm import tqdm
import sklearn
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
from qtl.norm import deseq2_size_factors
import warnings
warnings.filterwarnings("igno... | pd.DataFrame(bmf_fit.fit.W, index=df.index) | pandas.DataFrame |
from sklearn.metrics import confusion_matrix, classification_report
from matplotlib.colors import LinearSegmentedColormap
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib as mpl
from matplotlib.pyplot import figure
import os
import warnings
warnings.filterwarnings("i... | pd.read_csv(homepath+"/train_data/mul_Normal.txt.bz2",header=None, delimiter = "\t") | pandas.read_csv |
import seaborn as sns
import pandas as pd
import geopandas as gpd
import numpy as np
import matplotlib.pyplot as plt
from pandas.io.json import json_normalize
from pysal.lib import weights
from sklearn import cluster
from shapely.geometry import Point
# # # # # PET DATA # # # # #
# filename = "pets.json"
# with ope... | pd.read_json(urlD) | pandas.read_json |
import os
import cv2
import json
import dlib
import shutil
import joblib
import exifread
import warnings
import numpy as np
import pandas as pd
import face_recognition
from pathlib import Path
from joblib import Parallel, delayed
def get_model(cfg):
from tensorflow.keras import applications
from tensorflow.ke... | pd.read_hdf(network_dir+"FaceDatabase.h5") | pandas.read_hdf |
# 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... | assert_frame_equal(result, expected) | pandas.util.testing.assert_frame_equal |
import etherscan as es
from pycoingecko import CoinGeckoAPI
import pandas as pd
import numpy as np
import datetime as dt
import time
from functools import reduce
import matplotlib.pyplot as plt
# Global variables
cg_api = CoinGeckoAPI()
coin_dict = pd.DataFrame(cg_api.get_coins_list())
class MarketData:
def _... | pd.MultiIndex.from_tuples(cols) | pandas.MultiIndex.from_tuples |
import statsmodels.formula.api as smf
import numpy as np
import torch
from torch import nn
import pandas as pd
import scipy as sp
from tqdm.auto import tqdm
from boardlaw import sql, elos
import aljpy
from pavlov import stats, runs
import pandas as pd
from boardlaw import arena
# All Elos internally go as e^d; Elos in... | pd.concat(yhats, 1) | pandas.concat |
""" configuration run result """
import pandas
from datetime import datetime
from decimal import Decimal
from .connection import get_connection
def _get_connection():
_cnxn = get_connection()
return _cnxn
def insert(result):
_cnxn = _get_connection()
cursor = _cnxn.cursor()
with cursor.execute(... | pandas.DataFrame(rows) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from datetime import datetime
import pandas as pd
from kavalkilu import LogWithInflux
from servertools import (
SlackWeatherNotification,
OpenWeather,
OWMLocation,
Plants,
Plant
)
# Initiate Log, including a suffix to the log name to denote which insta... | pd.Timedelta(hours=24) | pandas.Timedelta |
# $Id$
# $HeadURL$
################################################################
# The contents of this file are subject to the BSD 3Clause (New)
# you may not use this file except in
# compliance with the License. You may obtain a copy of the License at
# http://directory.fsf.org/wiki/License:BSD_3Clause
# Softw... | pd.Series() | pandas.Series |
# -*- coding: utf-8 -*-
"""
Created on Tue Apr 30 09:33:17 2019
@author: WENDY
"""
import os
import csv
import pandas as pd
from jieba import analyse
from utils.config import EXPERIMENT_DIR, RAW_DATA
from src.graphviz.func import init
def textrank_extract(text, keyword_num=200):
"""
使用 text rank 提取关键词
:... | pd.DataFrame({'feature_name': kn}) | pandas.DataFrame |
import random
import pandas as pd
import copy
import math
from util import weight, check_maze, maze, order
def insert(block, map1, weight_map, count, area, df, flag, i, branch):
# print("반입 함수")
x_axis, y_axis = order.order(df)
minsize = 50
maxsize = 255
ran_num = random.randint(minsize, maxsize) ... | pd.Series(map1.data[obstruct_block_index]) | pandas.Series |
import plotly.express as px
import plotly.graph_objects as go
import pandas as pd
#naming convention
'''
render_figurename
e.g. :
render_annual_timeseries
#standardize color : #7febf5
'''
def load_data() :
data = pd.read_csv('Air_Traffic_Passenger_Statistics.csv')
data = data.replace('United Airline... | pd.to_datetime(passanger_count_group_period['Period'], format='%Y%m') | pandas.to_datetime |
"""
Evaluate Classifier Predictions
Modified from PDX PPTC Machine Learning Analysis
https://github.com/marislab/pdx-classification
Rokita et al. Cell Reports. 2019.
https://doi.org/10.1016/j.celrep.2019.09.071
<NAME>, 2018
Modified by <NAME> for OpenPBTA, 2020
This script evaluates the predictions made by the NF1 an... | pd.read_table(status_file, low_memory=False) | pandas.read_table |
from strategy.rebalance import get_relative_to_expiry_rebalance_dates, \
get_fixed_frequency_rebalance_dates, \
get_relative_to_expiry_instrument_weights
from strategy.calendar import get_mtm_dates
import pandas as pd
import pytest
from pandas.util.testing import assert_index_equal, assert_frame_equal
def ass... | pd.Timestamp("2015-03-18") | pandas.Timestamp |
""" test fancy indexing & misc """
from datetime import datetime
import re
import weakref
import numpy as np
import pytest
import pandas.util._test_decorators as td
from pandas.core.dtypes.common import (
is_float_dtype,
is_integer_dtype,
)
import pandas as pd
from pandas import (
DataFrame,
Index,... | tm.assert_index_equal(s2.index, s.index) | pandas._testing.assert_index_equal |
from elasticsearch_dsl.query import Query
from fastapi import APIRouter
from elasticsearch import Elasticsearch
from elasticsearch_dsl import Search, Q, Index
from typing import List, Dict, Any
from app.types import Node, Keyphrase
import json
import os
import pandas as pd
import spacy
import pytextrank
... | pd.DataFrame(elastic_list) | pandas.DataFrame |
"""
Calculate MQA scores only for the resolved region from local score.
MQA methods:
- DeepAccNet
- P3CMQA
- ProQ3D
- VoroCNN
"""
import argparse
import os
import subprocess
import tarfile
from pathlib import Path
from typing import Any, List, Union
import numpy as np
import pandas as pd
from prody i... | pd.DataFrame(results) | pandas.DataFrame |
"""This module is meant to contain the OpenSea class"""
from messari.dataloader import DataLoader
from messari.utils import validate_input
from string import Template
from typing import Union, List
import pandas as pd
# Reference: https://docs.opensea.io/reference/api-overview
# TODO, api key as header
ASSET_URL ... | pd.Series(response) | pandas.Series |
from flask import Flask, render_template, jsonify, request
from flask_pymongo import PyMongo
from flask_cors import CORS, cross_origin
import json
import collections
import numpy as np
import re
from numpy import array
from statistics import mode
import pandas as pd
import warnings
import copy
from joblib import Mem... | pd.DataFrame.from_dict(dicRF) | pandas.DataFrame.from_dict |
import networkx as nx
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import numpy as np
import pandas as pd
from ADvis.ADnum import ADnum
from mpl_toolkits.mplot3d import Axes3D
def gen_graph(y):
""" Function to create a directed graph from an ADnum.... | pd.DataFrame.from_dict(data) | pandas.DataFrame.from_dict |
""" test the scalar Timedelta """
import numpy as np
from datetime import timedelta
import pandas as pd
import pandas.util.testing as tm
from pandas.tseries.timedeltas import _coerce_scalar_to_timedelta_type as ct
from pandas import (Timedelta, TimedeltaIndex, timedelta_range, Series,
to_timedelta,... | Timedelta(milliseconds=1) | pandas.Timedelta |
# Import Module
import PyPDF2
from PyPDF2.utils import PdfReadError
import pdfx
from urlextract import URLExtract
import requests
import fitz
import click
import argparse
import os
from urllib.parse import urlparse, ParseResult
from fpdf import FPDF
import gspread
import pandas as pd
from gspread_datafram... | pd.isnull(df.at[index[0], 'Results URLs without check']) | pandas.isnull |
'''
Created on Jun 8, 2017
@author: husensofteng
'''
import matplotlib
matplotlib.use('Agg')
from matplotlib.backends.backend_pdf import PdfPages
import pybedtools
from pybedtools.bedtool import BedTool
from matplotlib.pyplot import tight_layout
import matplotlib.pyplot as plt
from pylab import gca
import pandas as pd... | pd.DataFrame(heatmap_dict) | pandas.DataFrame |
import os
import logging.config
import pandas as pd
from omegaconf import DictConfig
import hydra
from src.entities.predict_pipeline_params import PredictingPipelineParams, \
PredictingPipelineParamsSchema
from src.models import make_prediction
from src.utils import read_data, load_pkl_file
logger = logging.getL... | pd.DataFrame(predicts) | pandas.DataFrame |
# -----------------------------------------------------------------------------
# Copyright (c) 2014--, The Qiita Development Team.
#
# Distributed under the terms of the BSD 3-clause License.
#
# The full license is in the file LICENSE, distributed with this software.
# ------------------------------------------------... | pd.DataFrame.from_dict(metadata_dict, orient='index') | pandas.DataFrame.from_dict |
import pandas as pd
import numpy as np
def set_to_nan(array, n_nans):
array = array[:]
n = array.shape[0]
nan_indices = np.random.choice(np.arange(n), size=n_nans, replace=False)
array[nan_indices] = np.nan
return array
n_timesteps = 10000
x = np.linspace(0, 20, n_timesteps)
observable_1 = 10 *... | pd.date_range(start="1/1/2008", end="1/1/2015", periods=n_timesteps) | pandas.date_range |
#!/usr/bin/env python3
import pandas as pd
from os import path
cutoffs = pd.read_csv("data/ores_rcfilters_cutoffs.csv")
wikis = set(cutoffs.wiki_db)
sets = []
for wiki_db in wikis:
scores_file = "data/quarry_ores_scores/{0}_scores.csv".format(wiki_db)
if path.exists(scores_file):
scores = | pd.read_csv(scores_file) | pandas.read_csv |
import os
import sys
import inspect
import argparse
import importlib.util
from os import listdir
from os.path import isfile, join
# Lambda functions cannot raise exceptions so using higher order functions.
def _raise(e):
def raise_helper():
raise e
return raise_helper
from harvest.utils import debu... | pd.to_datetime(df["timestamp"]) | pandas.to_datetime |
import pandas as pd
import numpy as np
import sys
from openpyxl.utils.dataframe import dataframe_to_rows
from openpyxl import Workbook
import argparse
import re
from sklearn import linear_model
from sklearn.metrics import mean_squared_error, r2_score
import warnings
import os
import math
import datetime
from future.uti... | pd.DataFrame() | pandas.DataFrame |
import os
import time
import pandas as pd
import numpy as np
import functools
from functools import reduce
def time_pass(func):
@functools.wraps(func)
def wrapper(*args, **kw):
time_begin = time.time()
result = func(*args, **kw)
time_stop = time.time()
time_passed = time_stop... | pd.merge(x, y, on='id', how='outer') | pandas.merge |
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