prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import pathlib
from pathlib import Path
from typing import Union, Tuple
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import re
class QuestionnaireAnalysis:
"""
Reads and analyzes data generated by the questionnaire experiment.
Should be able to accept strings and pathlib.Path obj... | pd.Series(scores_df.score.values, dtype="UInt8") | pandas.Series |
# -*- coding: utf-8 -*-
"""
Created on Sat Feb 8 18:04:32 2020
@author: mofarrag
"""
import os
import pandas as pd
import numpy as np
import datetime as dt
from scipy.stats import gumbel_r
import Hapi.Raster as Raster
import matplotlib.pyplot as plt
import zipfile
import Hapi.Raster as Raster
class River():
# cl... | pd.date_range(self.start,self.end, freq='D') | pandas.date_range |
#!/usr/bin/env python
"""
The script converts the .dat files from afphot to .nc files for M2 pipeline.
Before running this script, afphot should be ran (usually in muscat-abc)
and its results copied to /ut2/muscat/reduction/muscat/DATE.
To convert .dat to .nc, this script does the following.
1. read the .dat files in... | pd.read_csv(dat_file, delim_whitespace=True, comment='#', names=column_names) | pandas.read_csv |
from __future__ import print_function, unicode_literals
def warn(*args, **kwargs):
pass
import warnings
warnings.warn = warn
import sys
import os
if not sys.warnoptions:
warnings.simplefilter("ignore")
import click
from tabulate import tabulate
import emoji
from pyfiglet import Figlet
import gensim
impor... | pd.DataFrame({"keywords": document_keywords}) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Sat Nov 3 12:24:27 2018
@author: <NAME>
"""
"""
python script to scrape the results from unitedstateszipcodes and save to a file
"""
from bs4 import BeautifulSoup
import os
import pandas as pd
from selenium import webdriver
from fake_useragent import UserAgent
... | pd.read_csv("E:/Cognitive Computing BIA662/Project/scraped_results.csv", na_values=0, dtype={'zipcode':str}) | pandas.read_csv |
# %%
import os
import pandas as pd
import numpy as np
import threading
import time
base_dir = os.getcwd()
# %%
# 初始化表头
header = ['user', 'n_op', 'n_trans', 'op_type_0', 'op_type_1', 'op_type_2', 'op_type_3', 'op_type_4', 'op_type_5',
'op_type_6', 'op_type_7', 'op_type_8', 'op_type_9', 'op_type_perc', 'op_ty... | pd.read_csv(base_dir + '/dataset/dataset2/encoders/enc_trans_platform.csv') | pandas.read_csv |
from datetime import datetime
import backtrader as bt
from backtrader import cerebro
from django.conf import settings
from django.contrib.auth.models import User
from django.http import HttpResponse
from rest_framework import permissions, viewsets
from rest_framework.response import Response
from rest_framework.views ... | pd.to_numeric(df.Close, downcast="float") | pandas.to_numeric |
import itertools
import numpy as np
import pandas as pd
import pytest
from estimagic.estimation.msm_weighting import assemble_block_diagonal_matrix
from estimagic.estimation.msm_weighting import get_weighting_matrix
from numpy.testing import assert_array_almost_equal as aaae
@pytest.fixture
def expected_values():
... | pd.DataFrame([[1, 2], [3, 4]]) | pandas.DataFrame |
import pandas as pd
import numpy as np
def btk_data_decoy_old():
df = pd.read_csv('btk_active_decoy/BTK_2810_old.csv')
df_decoy = pd.read_csv('btk_active_decoy/btk_finddecoy.csv')
df_decoy = pd.DataFrame(df_decoy['smile'])
df_decoy['label'] = 0
df_active = df[df['target2']<300]
df_active['tar... | pd.read_csv('btk_active_decoy/BTK_2810.csv') | pandas.read_csv |
# Python for Healthcare
## Hospital Spending
### Import Libraries
import pandas as pd
import statsmodels.api as sm
### Import Data
df_cms = | pd.read_csv('C:/Users/drewc/GitHub/python-for-healthcare/pynarratives/hospital_spending/_data/cms_mspb_stage.csv') | pandas.read_csv |
import numpy as np
import pandas as pd
import random
import pickle
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
import torch
from torch.nn.utils.rnn import pad_sequence
from utils import _get_parcel, _get_behavioral
from cc_utils import _get_clip_labels
K_RUNS = 4
K_SEED = 330
def _get_cl... | pd.read_csv('data/videoclip_tr_lookup.csv') | pandas.read_csv |
#!/usr/bin/env python
# MIT License
#
# Copyright (c) 2019 <NAME>
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy... | pd.DataFrame(data, columns=columns) | pandas.DataFrame |
import pandas as pd
from datascope.importance.shapley import ImportanceMethod
from enum import Enum
from pandas import DataFrame
from typing import Any, Optional, Dict
from .base import Scenario, attribute, result
from ..dataset import Dataset, DEFAULT_TRAINSIZE, DEFAULT_VALSIZE, DEFAULT_TESTSIZE
from ..pipelines imp... | pd.DataFrame() | pandas.DataFrame |
import numpy as np
import pandas as pd
import joblib
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
def impute_age(cols):
age=cols[0]
pClass = cols[1]
if pd.isnull(age):
if pClass == 1:
... | pd.get_dummies(train_df['Embarked'],drop_first=True) | pandas.get_dummies |
import glob
import pandas as pd
files = glob.glob('Corpus_mda/*')
files.sort()
df_agg1 = pd.DataFrame()
for i, file in enumerate(files[0:2000]):
# print(i)
df_agg1 = df_agg1.append(pd.read_pickle(file))
df_agg1.to_pickle('mda_agg/mda_agg1.pkl')
df_agg2 = | pd.DataFrame() | pandas.DataFrame |
# The EsmcolValidate class defined below is an adaptation of the
# stac-validator: https://github.com/sparkgeo/stac-validator
# For reference, here is a copy of the stac-validator copyright notice:
# Copyright 2019 Sparkgeo
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use ... | pd.read_csv(catalog_content, index_col=0) | pandas.read_csv |
# %%
import os
import urllib
from bs4 import BeautifulSoup
import pandas as pd
import yfinance as yf
import pandas_datareader as dtr
import datetime
import time
from tqdm import tqdm
from copy import deepcopy
from talib import WILLR
from talib import EMA
# %%
HEADERS = {
'Access-Control-Allow-Origin': '*',
... | pd.DataFrame(tickers) | pandas.DataFrame |
import numpy as np
import pandas as pd
from sklearn import linear_model
def get_round(df, num_teams):
matches = int(num_teams / 2)
matchday_list = sum([[i] * matches for i in range(1, 50)], [])
df["round"] = matchday_list[:df.shape[0]]
return df
def get_team_encoding(df):
"""
Creates unique... | pd.concat([df["HomeTeam"], df["AwayTeam"]]) | pandas.concat |
"""
Training script for scene graph detection. Integrated with my faster rcnn setup
"""
from dataloaders.visual_genome import VGDataLoader, VG
import numpy as np
from torch import optim
import torch
import pandas as pd
import time
import os
from tensorboardX import SummaryWriter
from config import ModelConfig, BOX_SC... | pd.concat(tr[-conf.print_interval:], axis=1) | pandas.concat |
import pandas as pd
import sys
import utils
import config
nrows = None
tr = utils.load_df(config.data+'train.csv',nrows=nrows)
te = utils.load_df(config.data+'test.csv',nrows=nrows)
actions = ['interaction item image','interaction item info','interaction item deals','interaction item rating','search for item']
df = ... | pd.concat([trs,tes]) | pandas.concat |
# coding: utf-8
import numpy as np
import pandas as pd
import os
import time
import multiprocessing
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score, accuracy_score
from sklearn.multiclass import OneVsRestClassifier
from sklearn import preprocessing
from utils import check_a... | pd.DataFrame({'node': val_nodes, 'label': val_labels}) | pandas.DataFrame |
import numpy as np
import pandas as pd
import os
def load_stats_dataframe(files, aggregated_results=None):
if os.path.exists(aggregated_results) and all([os.path.getmtime(f) < os.path.getmtime(aggregated_results) for f in files]):
return | pd.read_pickle(aggregated_results) | pandas.read_pickle |
import glob
import os
import sys
# these imports and usings need to be in the same order
sys.path.insert(0, "../")
sys.path.insert(0, "TP_model")
sys.path.insert(0, "TP_model/fit_and_forecast")
from Reff_functions import *
from Reff_constants import *
from sys import argv
from datetime import timedelta, datetime
from ... | pd.DataFrame(sim_R) | pandas.DataFrame |
# coding: utf-8
# In[1]:
from __future__ import division, print_function, absolute_import
from past.builtins import basestring
import os
import gzip
import pandas as pd
from twip.constant import DATA_PATH
from gensim.models import TfidfModel, LsiModel
from gensim.corpora import Dictionary
# In[2]:
import matp... | pd.DataFrame.from_csv(f, encoding='utf8') | pandas.DataFrame.from_csv |
# -*- coding: utf-8 -*-
# pylint: disable-msg=E1101,W0612
import nose
import numpy as np
from numpy import nan
import pandas as pd
from distutils.version import LooseVersion
from pandas import (Index, Series, DataFrame, Panel, isnull,
date_range, period_range)
from pandas.core.index import MultiIn... | tm.assertRaises(ValueError) | pandas.util.testing.assertRaises |
import numpy as np
import pandas as pd
from numba import njit, typeof
from numba.typed import List
from datetime import datetime, timedelta
import pytest
from copy import deepcopy
import vectorbt as vbt
from vectorbt.portfolio.enums import *
from vectorbt.generic.enums import drawdown_dt
from vectorbt.utils.random_ im... | pd.Series([5., 4., 3., 2., 1.], index=price.index) | pandas.Series |
from logging import getLogger
logger = getLogger("__name__")
from sklearn.decomposition import PCA
import pandas as pd
import numpy as np
import matplotlib.pylab as plt
import warnings
from .plot import annotate_points, _def_label_alignment
import seaborn as sns
from matplotlib.patches import Ellipse
import matplot... | pd.Series(radius, self.components.columns) | pandas.Series |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Apr 5, 2020
@authors: enzoampil & jpdeleon
"""
# Import standard library
import os
from inspect import signature
from datetime import datetime
import warnings
from pathlib import Path
from string import digits
import requests
import json
import re
# Im... | pd.to_datetime(combined.time, unit="s") | pandas.to_datetime |
"""
Copyright 2021 Novartis Institutes for BioMedical Research Inc.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law... | pd.DataFrame(data=samples_decoded) | pandas.DataFrame |
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
#import stationary_block_bootstrap as sbb
import pandas as pd
import numpy as np
import scipy.stats
import numpy
import time
import random
#import state_variables
import os
import scipy.stats
import sklearn.feature_selection
import matplotlib.gridspec as... | pd.read_csv(main_folders[title] + '/_overview/'+ filename) | pandas.read_csv |
"""
Script to generate new train test splits
"""
import pandas as pd
import numpy as np
import random
import argparse
total_frames = 1950
def get_df_all(data_path):
names = ['path', 'x1', 'y1', 'x2', 'y2', 'class_name']
df_annotations_train = pd.read_csv('{}/annotation_train.csv'.format(data_path), names=n... | pd.concat([df_annotations_train, df_annotations_val, df_annotations_test]) | pandas.concat |
import os
from datetime import datetime, timedelta, timezone
import logging
from typing import List
from itertools import repeat
import tarfile
import pandas as pd
import pyarrow.parquet as pq
import pyarrow.dataset as ds # put this later due to some numpy dependency
from suzieq.shared.utils import humanize_timest... | pd.DataFrame({'file': fname_list, 'timestamp': fts_list}) | pandas.DataFrame |
import os
import torch
import numpy as np
import pandas as pd
import torchvision
from torchvision import datasets, models as torch_models, transforms
import datetime
import time
import sys
import copy
import warnings
from metric_test_eval import MetricEmbeddingEvaluator, LogitEvaluator
import logging
log... | pd.read_csv(args.all_imgs_csv) | pandas.read_csv |
# 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... | Series([np.nan]) | pandas.Series |
import pandas as pd
import numpy as np
import pdfplumber
import json
import os
import re
import datetime
from utils import bdday_to_date, district_correction, \
district_th_to_en, find_similar_word
from get_pdf import ensure_pdf
THAIMONTH_TO_MONTH = {
"ม.ค.": "01",
"ก.พ.": "02",
"มี.ค.": "03",
"เม.... | pd.notnull(df["province_en"]) | pandas.notnull |
from datetime import datetime, timezone
import numpy as np
import pandas as pd
from suncalc import get_position, get_times
date = datetime(2013, 3, 5, tzinfo=timezone.utc)
lat = 50.5
lng = 30.5
height = 2000
testTimes = {
'solar_noon': '2013-03-05T10:10:57Z',
'nadir': '2013-03-04T22:10:57Z',
'sunrise': ... | pd.Timestamp(date) | pandas.Timestamp |
# ----------------------------------------------------------------------------
# Copyright (c) 2016-2021, QIIME 2 development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE, distributed with this software.
# ------------------------------------------------... | pd.Series(['ACGT', 'ACCT'], index=['f1', 'f2']) | pandas.Series |
from collections import OrderedDict
import contextlib
from datetime import datetime, time
from functools import partial
import os
from urllib.error import URLError
import warnings
import numpy as np
import pytest
import pandas.util._test_decorators as td
import pandas as pd
from pandas import DataFrame, Index, Multi... | tm.assert_frame_equal(actual, expected) | pandas.util.testing.assert_frame_equal |
import numpy as np
import pandas as pd
from . import util as DataUtil
from . import cols as DataCol
"""
The main data loader.
TODO: population & common special dates
"""
class DataCenter:
def __init__(self):
self.__kabko = None
self.__dates_global = pd.DataFrame([], columns=DataCol.DATES_GLOBAL)
... | pd.to_datetime(df[date_col]) | pandas.to_datetime |
import contextlib
import logging
import os
import psutil
import re
import shutil
from packaging import version
import pandas as pd
import h2o
from h2o.automl import H2OAutoML
from frameworks.shared.callee import FrameworkError, call_run, output_subdir, result
from frameworks.shared.utils import Monitoring, Namespac... | pd.DataFrame.from_records(arr, columns=columns) | pandas.DataFrame.from_records |
# -*- coding: utf-8 -*-
"""
Created on Sat Nov 27 17:50:08 2021
@author: Dropex
"""
import TAClass
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
def smas_graph(symbol,interval,exchange):
asset = TAClass.TradeAsset(symbol,interval,exchange)
asset.getklines()
asset.dataframe()
... | pd.to_datetime(asset.df['OpenTime'], unit='ms') | pandas.to_datetime |
import pandas as pd
import numpy as np
from scipy.stats.mstats import theilslopes
# Custom exception
class NoValidIntervalError(Exception):
'''raised when no valid rows appear in the result grame'''
pass
class pm_frame(pd.DataFrame):
'''Class consisting of dataframe for analysis constructed from system ... | pd.DataFrame(result_list) | pandas.DataFrame |
import requests
import re
import ipaddress
import pandas as pd
import openpyxl
from tkinter import *
from tkinter import filedialog
import tkinter.messagebox
import os
import configparser
from openpyxl.styles import Border, Side
from openpyxl.formatting.rule import ColorScaleRule, FormulaRule
config = co... | pd.DataFrame({'ipAddress': IpaddList}) | pandas.DataFrame |
import webbrowser
import pandas as pd
import sqlite3
import matplotlib.pyplot as plt
import folium
def top_ties(data, num, sort_by='summ'):
"""
A function that handles top ties problem.
:param sort_by: the name of columns which dataframe was sorted by
:param data: pandas dataframe type that contains s... | pd.read_sql_query(command, conn) | pandas.read_sql_query |
import numpy as np
import pandas as pd
from app import db
from app.fetcher.fetcher import Fetcher
from app.models import OckovaniLide
class VaccinatedFetcher(Fetcher):
"""
Class for updating vaccinated people table.
"""
VACCINATED_CSV = 'https://onemocneni-aktualne.mzcr.cz/api/v2/covid-19/ockovani-p... | pd.merge(df, orp, how='left') | pandas.merge |
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 7 09:40:49 2018
@author: yuwei
"""
import pandas as pd
import numpy as np
import math
import random
import time
import scipy as sp
import xgboost as xgb
def loadData():
"下载数据"
trainSet = pd.read_table('round1_ijcai_18_train_20180301.txt',sep=' ')
testSet ... | pd.merge(result,feat,on=['user_id'],how='left') | pandas.merge |
from __future__ import print_function
import os
import csv
import gidcommon as gc
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
from time import sleep
import random
import numpy as np
import pandas as pd
os.chdir(gc.datadir)
email = '<EMAIL>'
password = '<PASSWORD>'
driver = webdrive... | pd.DataFrame(diseases_by_country) | pandas.DataFrame |
import pandas as pd
import numpy as np
data_path = "/home/clairegayral/Documents/openclassroom/data/P4/"
res_path = "/home/clairegayral/Documents/openclassroom/res/P4/"
from sklearn import preprocessing
from sklearn.impute import KNNImputer
###################
#### open data ####
###################
product_catego... | pd.read_csv(data_path + "olist_customers_dataset.csv") | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Copyright (c) German Cancer Research Center,
Division of Medical Image Computing.
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain... | pd.DataFrame(data.validation_x) | pandas.DataFrame |
# *****************************************************************************
# Copyright (c) 2019, Intel Corporation All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# Redistributions of sou... | pandas.Series(filled_data, self._index, self._name) | pandas.Series |
import numpy as np
import pytest
import pandas as pd
from pandas import DataFrame, Series, date_range
import pandas._testing as tm
class TestDataFrameUpdate:
def test_update_nan(self):
# #15593 #15617
# test 1
df1 = DataFrame({"A": [1.0, 2, 3], "B": date_range("2000", periods=3)})
... | Series([5, 6, 7, 8]) | pandas.Series |
from typing import List
import os
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pickle
def cka_wide(X, Y):
"""
Calculate CKA for two matrices. This algorithm uses a Gram matrix
implementation, which is fast when the data is wider than it is
tall... | pd.DataFrame(C) | pandas.DataFrame |
import os
from typing import Tuple
import numpy as np
import pandas as pd
from mydeep_api._deprecated.file_dataset import FileDataset
from sign_mnist.prepare_sign_mnist import name_provider
from stream_lib.stream import stream
from surili_core.surili_io.image_io import OpencvIO
from surili_core.worker import Worker
f... | pd.read_csv(csv_path) | pandas.read_csv |
# basics
from typing import Callable
import pandas as pd
import os
from pandas.core.frame import DataFrame
# segnlp
from segnlp import utils
from segnlp import metrics
from segnlp.utils.baselines import MajorityBaseline
from segnlp.utils.baselines import RandomBaseline
from segnlp.utils.baselines import Sentenc... | pd.DataFrame(all_metrics) | pandas.DataFrame |
'''
Tests for Naive benchmark classes
Tests currently cover:
1. Forecast horizons
2. Allowable input types: np.ndarray, pd.DataFrame, pd.Series
3. Failure paths for abnormal input such as np.nan, non numeric,
empty arrays and np.Inf
4. Predictions
- naive1 - carries forward last value
- snaive - carries fo... | pd.Series(data) | pandas.Series |
import argparse
from pathlib import Path
from typing import List
import numpy as np
import pandas as pd
def categorize_by_label_distribution(group: pd.DataFrame,
label: str,
dif_threshold: float = 0.1,
top_... | pd.merge(target_df, category_df, on='target') | pandas.merge |
import pathlib
import requests
import pandas as pd
from bs4 import BeautifulSoup
class Brasileiro:
def __init__(self, year: int, series: str) -> None:
if year < 2012:
raise ValueError('year must be greater than 2012')
elif series.lower() not in ['a', 'b']:
raise ValueError(... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import numpy as np
import re
import random
import ast
import warnings
import itertools
import time
from rdkit import Chem, rdBase
from rdkit.Chem import AllChem
from rdkit import RDLogger
from rdkit.Chem import Descriptors
from ast import literal_eval as leval
from copy import deepcopy
from tqdm ... | pd.DataFrame() | pandas.DataFrame |
import numpy as np
import pandas as pd
import pytest
from pandas.testing import assert_frame_equal
from pandas.testing import assert_series_equal
from sid.config import INDEX_NAMES
from sid.update_states import _kill_people_over_icu_limit
from sid.update_states import _update_immunity_level
from sid.update_states impor... | assert_series_equal(calculated, expected) | pandas.testing.assert_series_equal |
# python 2
try:
from urllib.request import Request, urlopen
# Python 3
except ImportError:
from urllib2 import Request, urlopen
import pandas as pd
import time
import datetime
import numpy as np
import re
import json
from bs4 import BeautifulSoup
from pytrends.request import TrendReq
cla... | pd.DataFrame(output) | pandas.DataFrame |
# Copyright (c) 2019 Microsoft Corporation
# Distributed under the MIT software license
import pytest
import numpy as np
import numpy.ma as ma
import pandas as pd
import scipy as sp
import math
from itertools import repeat, chain
from ..bin import *
from ..bin import _process_column_initial, _encode_categorical_exis... | pd.Series([1, 2, 3]) | pandas.Series |
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) | pandas.DataFrame |
#
# Like hypergraph(); adds engine = 'pandas' | 'cudf' | 'dask' | 'dask-cudf'
#
from typing import TYPE_CHECKING, Any, Dict, List, Optional
from .Engine import Engine, DataframeLike, DataframeLocalLike
import logging, numpy as np, pandas as pd, pyarrow as pa, sys
logger = logging.getLogger(__name__)
logger.setLevel(lo... | pd.concat([entities, event_entities], ignore_index=True, sort=False) | pandas.concat |
import os
import pandas as pd
import pytest
from pandas.testing import assert_frame_equal
from .. import read_sql
@pytest.fixture(scope="module") # type: ignore
def mysql_url() -> str:
conn = os.environ["MYSQL_URL"]
return conn
def test_mysql_without_partition(mysql_url: str) -> None:
query = "select... | pd.Series(["00:00:00", "23:59:59", "12:30:30"], dtype="object") | pandas.Series |
# coding=utf-8
# Copyright 2020 Google LLC
#
# 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 t... | pd.DataFrame.from_dict(data) | pandas.DataFrame.from_dict |
# -*- coding: utf-8 -*-
"""
Created on Sat Aug 7 16:36:59 2021
@author: LaoHu
"""
from docx import Document
import pandas as pd
document = Document("test.docx")
tables = []
for table in document.tables:
df = [["" for i in range(len(table.columns))] for j in range(len(table.rows))]
for i, row in enumerate(t... | pd.DataFrame(df) | pandas.DataFrame |
from datetime import datetime, time, timedelta
from pandas.compat import range
import sys
import os
import nose
import numpy as np
from pandas import Index, DatetimeIndex, Timestamp, Series, date_range, period_range
import pandas.tseries.frequencies as frequencies
from pandas.tseries.tools import to_datetime
impor... | frequencies.get_freq('W-MON') | pandas.tseries.frequencies.get_freq |
#!/usr/bin/env python3
# coding: utf-8
import argparse
import csv
import io
import logging
import numpy as np
import os
import pandas as pd
import pkg_resources
import sys
import yaml
from .version import __version__
logger = logging.getLogger('root')
provided_converters = [
'mq2pin',
'mq2pcq',
'mq2psea',
'... | pd.read_csv(f, sep=input_sep, low_memory=False) | pandas.read_csv |
import numpy as np
import pandas as pd
from statsmodels.formula.api import ols
from statsmodels.stats.anova import anova_lm
def one_way_anova(data, target, between, summary=None):
formula = "Q('%s') ~ " % target
formula += "C(Q('%s'))" % between
model = ols(formula, data=data).fit()
result = anov... | pd.DataFrame(columns=["Count", "Mean", "Median", "Std.", "Variance"]) | pandas.DataFrame |
"""Globwat diagnostic."""
import logging
from pathlib import Path
import numpy as np
import xarray as xr
import pandas as pd
import dask.array as da
import iris
from esmvalcore.preprocessor import regrid
from esmvaltool.diag_scripts.hydrology.derive_evspsblpot import debruin_pet
from esmvaltool.diag_scripts.hydrology... | pd.DataFrame(array.values, dtype=array.dtype) | pandas.DataFrame |
import calendar
import pickle as pkl
import pandas as pd
import numpy as np
import random
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import MinMaxScaler
from sklearn.pipeline import Pipeline
from sklearn.experimental import enable_hist_gradient_boosting
from sklearn.ensemble import HistG... | pd.DataFrame(X, columns=columns) | pandas.DataFrame |
import numpy as np
import pandas as pd
from bach import Series, DataFrame
from bach.operations.cut import CutOperation, QCutOperation
from sql_models.util import quote_identifier
from tests.functional.bach.test_data_and_utils import assert_equals_data
PD_TESTING_SETTINGS = {
'check_dtype': False,
'check_exact... | pd.qcut(p_series, q=[0.25, 0.5, 0.75], duplicates='drop') | pandas.qcut |
# Part 1 - Data Preprocessing
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import datetime
import warnings
warnings.filterwarnings('ignore')
# Importing the training set
df_raw = pd.read_csv('Google_Stock_Price_Train.csv')
df = df_raw
df.columns
df = df_raw.drop(['O... | pd.to_datetime(df['Date'], infer_datetime_format=True) | pandas.to_datetime |
import re
from datetime import datetime, timedelta
import numpy as np
import pandas.compat as compat
import pandas as pd
from pandas.compat import u, StringIO
from pandas.core.base import FrozenList, FrozenNDArray, DatetimeIndexOpsMixin
from pandas.util.testing import assertRaisesRegexp, assert_isinstance
from pandas i... | pd.Index(expected_list, dtype=object, name='idx') | pandas.Index |
import pandas as pd
def convert_to_datetime_idx_df(data):
df = | pd.DataFrame(data) | pandas.DataFrame |
"""The Model class is the main object for creating model in Pastas.
Examples
--------
>>> oseries = pd.Series([1,2,1], index=pd.to_datetime(range(3), unit="D"))
>>> ml = Model(oseries)
"""
from collections import OrderedDict
from copy import copy
from inspect import isclass
from logging import getLogger
from os im... | pd.Series(response, index=t, name=name) | pandas.Series |
import pandas as pd
from scipy.stats import linregress
df = pd.read_csv('Data/selected_100_normalized_merged.csv')
personality_features = ['reputation', 'Openness', 'Conscientiousness', 'Extraversion', 'Agreeableness', 'Emotional range']
numeric_features = ['question_count', 'answer_count']
# print(linregress(df['g... | pd.DataFrame(arr, columns=['x', 'R-value_answer', 'P-value_answer']) | pandas.DataFrame |
import glob
from shutil import copy2
import numpy as np
import matplotlib.pyplot as plt
import os
import sys
import pprint
import pandas as pd
import plotly.express as px
from plotly.subplots import make_subplots
prompt = lambda q : input("{} (y/n): ".format(q)).lower().strip()[:1] == "y"
def parse_log(filename, para... | pd.DataFrame.from_dict(metric) | pandas.DataFrame.from_dict |
# This is a sample Python program that trains a BYOC TensorFlow model, and then performs inference.
# This implementation will work on your local computer.
#
# Prerequisites:
# 1. Install required Python packages:
# pip install boto3 sagemaker pandas scikit-learn
# pip install 'sagemaker[local]'
# 2. Do... | pd.DataFrame(x_test) | pandas.DataFrame |
"""Functions for preprocessing cord-19 dataset."""
# -*- coding: utf-8 -*-
import json
import re
import tarfile
from datetime import datetime
from typing import List
from zipfile import ZipFile
import pandas as pd
# from pandas.io.json import json_normalize
def construct_regex_match_pattern(search_terms_file_path:... | pd.json_normalize(json_dict) | pandas.json_normalize |
"""
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... | com.convert_robj(r_dataframe.rownames) | pandas.rpy.common.convert_robj |
"""
(C) Copyright 2019 IBM Corp.
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
... | pd.Series(1, index=continuous_features) | pandas.Series |
import glob
import os
import pandas as pd
from fds.datax.utils.ipyexit import IpyExit
class FdsDataStoreLedger:
def __init__(self, dir_path):
self.dir_path = dir_path
def __load_cache_details__(self):
"""
This function will check for available FDS Caches within the
existing w... | pd.DataFrame(columns=cols) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# @Time : 2022/3/8 9:00 上午
# @Author : heisenberg
# @File : fuzzymatching.py
# @Project : sufe-cs-conf-ddl
# @Target : matching CCF info with Tenure Track info from short titles, likes v-lookup opreartion.
# 提前要安装的package
# pip install fuzzywuzzy
# pip install python-Levenshtein
import pandas... | pd.read_excel(sime_tenure_path) | pandas.read_excel |
#%%
import datetime
import time
import matplotlib.pyplot as plt
import networkx as nx
import numpy as np
import pandas as pd
import seaborn as sns
from giskard.plot import merge_axes, soft_axis_off
from pkg.data import load_network_palette, load_unmatched
from pkg.io import FIG_PATH
from pkg.io import glue as default... | pd.Series(data=weights, name="weights") | pandas.Series |
# -*- coding: utf-8 -*-
from __future__ import print_function
from distutils.version import LooseVersion
from numpy import nan, random
import numpy as np
from pandas.compat import lrange
from pandas import (DataFrame, Series, Timestamp,
date_range)
import pandas as pd
from pandas.util.testing im... | assert_frame_equal(result, expected) | pandas.util.testing.assert_frame_equal |
from unittest.mock import MagicMock, patch
import numpy as np
import pandas as pd
import pytest
from sklearn.model_selection import StratifiedKFold
from evalml import AutoMLSearch
from evalml.automl.callbacks import raise_error_callback
from evalml.automl.pipeline_search_plots import SearchIterationPlot
from evalml.e... | pd.Series(plot_data["x"]) | pandas.Series |
import pandas as pd
import torch
from sklearn.metrics import mean_squared_error
import os
import json
import random
from sklearn.model_selection import train_test_split
from pathlib import Path
import networkx as nx
import dgl
import numpy as np
from sklearn import preprocessing
import pdb
device = torch... | pd.DataFrame(A, columns=X.columns) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
"""
This script joins the following datasets`claim_vehicle_employee_line.csv`,
`Preventable and Non Preventable_tabDelimited.txt` and `employee_experience_V2.csv`
to create a CSV file that contains the required information for the interactive plot.
It also cleans the resulting CS... | pd.merge(claims_with_employee, collision, on=['claim_id'], how='left') | pandas.merge |
import numpy as np
import pytest
from pandas import (
DataFrame,
Index,
MultiIndex,
Series,
Timestamp,
date_range,
to_datetime,
)
import pandas._testing as tm
import pandas.tseries.offsets as offsets
class TestRollingTS:
# rolling time-series friendly
# xref GH13327
def set... | tm.assert_frame_equal(result, expected) | pandas._testing.assert_frame_equal |
# coding: utf8
import torch
import pandas as pd
import numpy as np
from os import path
from torch.utils.data import Dataset
import torchvision.transforms as transforms
import abc
from clinicadl.tools.inputs.filename_types import FILENAME_TYPE
import os
import nibabel as nib
import torch.nn.functional as F
from scipy i... | pd.DataFrame() | pandas.DataFrame |
# Arithmetic tests for DataFrame/Series/Index/Array classes that should
# behave identically.
from datetime import datetime, timedelta
import numpy as np
import pytest
from pandas.errors import (
NullFrequencyError, OutOfBoundsDatetime, PerformanceWarning)
import pandas as pd
from pandas import (
DataFrame, ... | tm.box_expected(expected, box_with_array) | pandas.util.testing.box_expected |
from logging import log
import numpy as np
import pandas as pd
from scipy import interpolate
import matplotlib.pyplot as plt
from matplotlib.backends.backend_tkagg import (FigureCanvasTkAgg, NavigationToolbar2Tk)
from matplotlib.backend_bases import key_press_handler
from matplotlib.figure import Figure
from matplotlib... | pd.read_csv(filepath_or_buffer=filename, encoding="utf_8", sep=",") | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Thu Jan 30 15:16:34 2020
@author: thodoris
"""
import pandas
import numpy
import os
import sys
import re
import multiprocessing
sys.path.append('../')
from core_functions import uniprot_query_with_limit
from constant_variables import define_main_dir
def worker(_id, ref_proteo... | pandas.read_csv(f, sep='\t') | pandas.read_csv |
#!/usr/bin/python3
import os
import argparse
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
from scipy.interpolate import griddata
import torch
import torch.nn as nn
from core.model import MNISTNet
from core.dataset import dataset_fn
from utils.config import load_confi... | pd.read_csv(results_gridsearch_csv) | pandas.read_csv |
# Notebook - Tab 1
import tkinter as tk
from tkinter import filedialog
import pandas as pd
from mmm.functions import *
from mmm.moneyManager import *
class DataImportTab(tk.Frame):
def __init__(self, master):
tk.Frame.__init__(self, master)
self._frame = None
self.impDF = pd.DataFrame()
... | pd.DataFrame() | pandas.DataFrame |
import numpy as np
import pandas as pd
from collections import OrderedDict, Counter
import itertools
def select_columns_by_metebolic_parm(df, param_name, exclude=False):
if exclude == True:
mask = ~df.columns.str.contains(pat=param_name)
return df.loc[:, mask]
mask = df.columns.str.contains(pa... | pd.read_csv(path, date_parser=datetime_column_name) | pandas.read_csv |
"""
Functions for preparing various inputs passed to the DataFrame or Series
constructors before passing them to a BlockManager.
"""
from collections import abc
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
import numpy.ma as ma
from pandas._libs import lib
fro... | algorithms.take_1d(values, indexer) | pandas.core.algorithms.take_1d |
import pandas as pd
import numpy as np
from cleaner import ReviewCleaner
from datetime import datetime
import numpy as np
import os
data_today = datetime.now().strftime("_%d_%m_%Y__%H_%M")
current_directory = os.getcwd()
class Preprocessor:
@staticmethod
def load(namefile='dump.csv', lista_colonne=['FRASE',... | pd.read_csv('etichette\\'+namefile, header=0) | pandas.read_csv |
import string
import numpy as np
from numpy.testing import assert_array_equal
from pandas import DataFrame, MultiIndex, Series
from shapely.geometry import LinearRing, LineString, MultiPoint, Point, Polygon
from shapely.geometry.collection import GeometryCollection
from shapely.ops import unary_union
from geopandas ... | assert_frame_equal(test_df, expected_df) | pandas.testing.assert_frame_equal |
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