prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
# -*- coding: utf-8 -*-
"""
Created on Fri Apr 22 15:07:09 2016
@author: advena
"""
#import re
from datetime import datetime
#import numpy as np
import pandas as pd
import os
import sys
import shutil
from dateutil import parser
########################################################################
#... | pd.merge(branch_df1, branch_df2, how='left', on=['Fr_Bus_Num','To_Bus_Num']) | pandas.merge |
# INSERT LICENSE
# This script contains statistical functions
import copy
import random
from collections import Counter
from typing import Optional, Union, Tuple
import navis
import navis.interfaces.neuprint as nvneu
import numpy as np
import pandas as pd
from scipy import stats
from scipy.stats import ks_2samp
from ... | pd.DataFrame() | pandas.DataFrame |
# Copyright 2019 QuantRocket - All Rights Reserved
#
# 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 ... | pd.Timedelta(bar_size) | pandas.Timedelta |
import pytest
import os
from mapping import util
from pandas.util.testing import assert_frame_equal, assert_series_equal
import pandas as pd
from pandas import Timestamp as TS
import numpy as np
@pytest.fixture
def price_files():
cdir = os.path.dirname(__file__)
path = os.path.join(cdir, 'data/')
files = ... | TS('2015-01-03') | pandas.Timestamp |
"""
Copyright 2019 <NAME>.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing,
software distribut... | pd.date_range('2019-01-01', periods=3, freq='D') | pandas.date_range |
from sklearn import datasets
from sklearn.datasets import load_breast_cancer
from tensorflow import keras
import pandas as pd
import numpy as np
from src.auxiliary_functions.auxiliary_functions import fd
def fetch_data_set(name: str, samples_per_class_synthetic: int = 100, noise_synthetic: float = 0.1):
"""
L... | pd.read_excel("data_sets/lsvt/LSVT_voice_rehabilitation.xlsx", sheet_name="Data") | pandas.read_excel |
"""
Script for plotting Figures 3, 5, 6
"""
import numpy as np
import pandas as pd
from datetime import datetime
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from matplotlib.lines import Line2D
import matplotlib.ticker as mtick
import seaborn as sns
from datetime import timedelta
i... | pd.read_csv('predictions/RF_infer_preds.csv') | pandas.read_csv |
import os
from root import *
import xgboost
from xgboost import XGBRegressor
import pickle
import pandas as pd
import datetime
from preprocessing.data_utils import *
from datetime import datetime, timedelta
pd.set_option('display.max_columns', 100)
train_all_x = pd.read_csv(root+"/data/interim/train_all_x.csv")
train... | pd.read_csv(root+"/data/interim/test_preds_plot.csv") | pandas.read_csv |
'''THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND
NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR ANYONE
DISTRIBUTING THE SOFTWARE BE LIABLE FOR ANY DAMAGES OR OT... | pd.Series(server_vers) | pandas.Series |
import sys
import nltk
import time
import pickle
import re
import pandas as pd
import numpy as np
from sqlalchemy import create_engine
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem.wordnet import WordNetLemmatizer
from sklearn.pipeline import Pipeline
from sklearn.metrics im... | pd.read_sql_table("messages", engine) | pandas.read_sql_table |
import pandas as pd
import numpy as np
import os
import requests
import time
directory = 'C:/Users/phil_/OneDrive/Documents/GitHub/rocket-league-stats/stat_files/'
playerli = []
# loop through player files and add to data frame
for filename in os.listdir(directory):
if filename.startswith("PLAYER_"):
#p... | pd.read_csv(directory+filename, sep=';', index_col=None, header=0) | pandas.read_csv |
'''
Para o r/brasil
Pesquisa, análise e gráficos por:
u/Drunpy
• Estrutura do código:
• Apresentação
• Imports
• Separação dos dados
• Gráficos
'''
#IMPORTS
import pandas as pd
impor... | pd.DataFrame.from_dict(estado_x_jatraiu, orient='index') | pandas.DataFrame.from_dict |
from itertools import groupby, zip_longest
from fractions import Fraction
from random import sample
import json
import pandas as pd
import numpy as np
import music21 as m21
from music21.meter import TimeSignatureException
m21.humdrum.spineParser.flavors['JRP'] = True
from collections import defaultdict
#song has no ... | pd.array([ix[4][1] for ix in pgram_span_ixs], dtype="Int16") | pandas.array |
"""
Download, transform and simulate various datasets.
"""
# Author: <NAME> <<EMAIL>>
# <NAME> <<EMAIL>>
# License: MIT
from os.path import join
from re import sub
from collections import Counter
from itertools import product
from urllib.parse import urljoin
from string import ascii_lowercase
from zipfile imp... | pd.read_csv(FETCH_URLS["heart"], header=None, delim_whitespace=True) | pandas.read_csv |
import unittest
import pandas as pd
import numpy as np
from pandas.testing import assert_frame_equal
from msticpy.analysis.anomalous_sequence import sessionize
class TestSessionize(unittest.TestCase):
def setUp(self):
self.df1 = pd.DataFrame({"UserId": [], "time": [], "operation": []})
self.df1_... | pd.to_datetime("2020-01-03 01:00:00") | pandas.to_datetime |
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import smtplib
from email.mime.image import MIMEImage
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
import time
def send_email(con="你好！"):
_user = "<EMAIL>"
_pwd = "<PASSWORD>"
_to = "... | pd.set_option('precision', 2) | pandas.set_option |
# The MIT License (MIT)
# Copyright (c) 2021 by the xcube development team and contributors
#
# 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... | pd.Timestamp.now() | pandas.Timestamp.now |
import numpy as np
import numpy.testing as npt
import pandas as pd
import pandas.testing as pdt
import pytest
from plateau.utils.pandas import (
aggregate_to_lists,
concat_dataframes,
drop_sorted_duplicates_keep_last,
is_dataframe_sorted,
mask_sorted_duplicates_keep_last,
merge_dataframes_robus... | pdt.assert_frame_equal(actual, df) | pandas.testing.assert_frame_equal |
# coding=utf-8
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
import operator
from itertools import product, starmap
from numpy import nan, inf
import numpy as np
import pandas as pd
from pandas import (Index, Series, DataFrame, isnull, bdate_range,
NaT, date_range, ti... | Timedelta('-2days') | pandas.tseries.tdi.Timedelta |
#!/usr/bin/env python
ONLINE_RETAIL_XLSX = 'OnlineRetail.xlsx'
ONLINE_RETAIL_CSV = 'OnlineRetail.csv'
ONLINE_RETAIL_JSON = 'OnlineRetail.json'
def download_spreadsheet():
print('Starting download_spreadsheet() ...')
# support python 2 and 3
try:
# python 3
import urllib.request as url... | pd.DatetimeIndex(df['InvoiceDate']) | pandas.DatetimeIndex |
# -*- coding: utf-8 -*-
"""
Created on Thu Jun 11 13:38:13 2020
@author: zhanghai
"""
'''
Input parameters： ticker，interval, test start date, test end date, model name
Output ： dataframe： initial deposit， gross profit，gross loss， total net profit，profit factor，
expected payoff， absolute drawdown， m... | pd.DataFrame({'position size':0,'total':self.total_value,'profit':profit},index=[cur_date]) | pandas.DataFrame |
import enum
import numpy as np
import pandas as pd
import pytest
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import Column, Integer, String, Float, Enum
from cascade.dismod.db.wrapper import DismodFile, _get_engine, _validate_data
from cascade.dismod.db import DismodFileError
@pytest.f... | pd.DataFrame({"enum_column": [1, 2, 3], "nonnullable_column": [1, 2, 3]}) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
import pandas as pd
import sys
from pyfaidx import Faidx
from clonesig.data_loader import PAT_LIST, DataWriter
import pathlib
from scipy.stats import beta
import numpy as np
tumor = sys.argv[1]
seq_depth = sys.argv[2]
"""
tumor = "T2"
seq_depth = "8X"
#sed 's/^\#\#/\&/g'... | pd.read_csv(vcf_filename, sep='\t', comment='&') | pandas.read_csv |
from linearmodels.compat.statsmodels import Summary
import warnings
import numpy as np
from numpy.linalg import pinv
from numpy.testing import assert_allclose, assert_equal
import pandas as pd
from pandas.testing import assert_series_equal
import pytest
import scipy.linalg
from statsmodels.tools.tools import add_cons... | assert_series_equal(res.params, res_cat.params) | pandas.testing.assert_series_equal |
#!/usr/bin/env python3
import glob
import os
import pprint
import traceback
import pandas as pd
from tensorboard.backend.event_processing.event_accumulator import EventAccumulator
# Extraction function
def tflog2pandas(path: str) -> pd.DataFrame:
"""convert single tensorflow log file to pandas DataFrame
Par... | pd.DataFrame(r) | pandas.DataFrame |
import pandas as pd
import numpy as np
from tkinter import *
from tkinter import filedialog
# Importing Chen Values
chen_67_to_69 = pd.read_csv('chcof1.id', index_col=0)
chen_70_to_74 = | pd.read_csv('chcof2.id', index_col=0) | pandas.read_csv |
from __future__ import division
# import libraries
from datetime import datetime, timedelta
import pandas as pd
# %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import plotly as py
import plotly.offline as pyoff
import plotly.graph_objs as go
#inititate Plotly
pyoff.init... | pd.to_datetime(tx_data['InvoiceDate']) | pandas.to_datetime |
# Bereken het percentuele verschil tov een x-aantal dagen ervoor
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import seaborn as sn
import platform
import datetime
import datetime as dt
import streamlit as st
from streamlit import caching
from helpers import * # cell_background, select_period... | pd.to_datetime(df[datumveld], format="%Y-%m-%d") | pandas.to_datetime |
# -*- coding: utf-8 -*-
# Copyright (c) 2015-2016, Exa Analytics Development Team
# Distributed under the terms of the Apache License 2.0
"""
NWChem Output
#######################
Parse NWChem output files and convert them into an exatomic Universe container.
"""
import six
from os import sep, path
import numpy as np
i... | pd.concat((alpha_orbital, beta_orbital), ignore_index=True) | pandas.concat |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Nov 23 11:40:16 2017
@author: tobias
"""
import os
import re
import glob
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
# Get data for axes
contig_input_file = '/Users/tobias/Desktop/target_contigs/matc... | pd.read_csv(contig_input_file,sep='\t',index_col=0) | pandas.read_csv |
from __future__ import division
import pandas as pd
import numpy as np
# In[2]:
import gc
import subprocess
from ImageDataGenerator import *
import os
import pickle
from keras.models import Model
from keras.layers import Dense, Dropout, Input
from keras.optimizers import Adam, Nadam
from sklearn.model_selection impo... | pd.concat([train, test], axis=0, sort=False) | pandas.concat |
#This auxiliary code prepares for each date and country the cases, a table of greylisted status, deaths and tests as publicly reported
#for a window starting before and ending after the date. This is used in evaluating the value of public data.
import numpy as np
import pandas as pd
history_start=20 #---how far i... | pd.read_csv('../OPE_Outputs/ope_dat_TRUE_Window_3_MinTest_30_SmoothPrior_TRUE_2001_0.9.csv') | pandas.read_csv |
import sys, os
sys.path.append('yolov3_detector')
from yolov3_custom_helper import yolo_detector
from darknet import Darknet
sys.path.append('pytorch-YOLOv4')
from tool.darknet2pytorch import Darknet as DarknetYolov4
import argparse
import cv2,time
import numpy as np
from tool.plateprocessing import find_coordinates, p... | pd.read_csv(fp2, sep='\t', header=0) | pandas.read_csv |
import copy
import warnings
import catboost as cgb
import hyperopt
import lightgbm as lgb
import pandas as pd
import xgboost as xgb
from wax_toolbox import Timer
from churnchall.constants import MODEL_DIR, RESULT_DIR
from churnchall.datahandler import DataHandleCookie, to_gradboost_dataset
from churnchall.tuning impo... | pd.DataFrame({label: pred}) | pandas.DataFrame |
# -*- coding: utf-8 -*-
from __future__ import division, print_function
import numpy as np, pandas as pd
from astropy.table import Table
from astropy.coordinates import SkyCoord
from astropy import units as u
from glob import glob
import os
def make_prioritycut_ctl(datadir='/Users/luke/local/TIC/CTL71/',
... | pd.read_csv(subcat, names=columns) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Mon Feb 15 17:07:38 2021
@author: perger
"""
# import packages
import pandas as pd
from datetime import timedelta, datetime
import pyam
import FRESH_clustering
from pathlib import Path
import glob
# Model name and version, scenario, region
model_name = 'FRESH:COM v2.0'
scenari... | pd.read_csv(filename_grid, sep=';') | pandas.read_csv |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Dec 31 13:31:13 2019
@author: mehrdad
"""
import pandas as pd
import numpy as np
import tslib.trip_detection
# Compute the difference between observed trips and computed trips ----------------------
# Any mode to any mode
def compute_observed_vs_compu... | pd.merge(alts, observed_[['month']], left_index=True, right_index=True, how='inner') | pandas.merge |
"""
Plaster-specific plots
These fall into two categories:
* Mature: plots that are ready to be used in notebook report templates
* Development: Plots that are still being worked on across various notebooks
Note:
* All plots are free-functions
* All plots should accept a run parameters and *optional* ... | pd.merge(df_ln, df, how="left", on=["peak_i", "channel_i"]) | pandas.merge |
#!/usr/bin/env python
# -*- coding: UTF-8 -*-
# Created by <NAME>
import unittest
import pandas as pd
import pandas.testing as pdtest
from allfreqs import AlleleFreqs
from allfreqs.classes import Reference, MultiAlignment
from allfreqs.tests.constants import (
REAL_ALG_X_FASTA, REAL_ALG_X_NOREF_FASTA, REAL_RSRS_F... | pdtest.assert_frame_equal(result, expected) | pandas.testing.assert_frame_equal |
from bs4 import BeautifulSoup
from urllib.request import urlopen
import re
import requests
import urllib.request
import json
import pandas as pd
import csv
# 利用colab云上编译
# 保存文件
# from pydrive.auth import GoogleAuth
# from pydrive.drive import GoogleDrive
# from google.colab import auth
# from oauth2client.client impo... | pd.DataFrame(x) | pandas.DataFrame |
import inspect
import json
import os
import re
from urllib.parse import quote
from urllib.request import urlopen
import pandas as pd
import param
from .configuration import DEFAULTS
class TutorialData(param.Parameterized):
label = param.String(allow_None=True)
raw = param.Boolean()
verbose = param.Bool... | pd.read_csv(self._data_url, **kwds) | pandas.read_csv |
import sys
import os
import pandas as pd
import numpy as np
import scipy as sp
import camoco as co
from itertools import chain
from camoco.Tools import log
# Initialize a new log object
log = log()
def snp2gene(args):
"""
Perform SNP (locus) to candidate gene mapping
"""
if args.out != sys.st... | pd.concat([data, genes]) | pandas.concat |
# -*- coding: utf-8 -*-
# ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.7.1
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# # These are queries to valida... | pd.read_gbq(query, dialect='standard') | pandas.read_gbq |
from functools import reduce
from os.path import join, exists
from src.log import create_experiment
from joblib import Parallel, delayed, dump, load
import numpy as np
import pandas as pd
from itertools import product
from time import time
from sklearn import svm
from sklearn.metrics import f1_score
from sklearn.pipel... | pd.concat(y_tests, axis=1, keys=ticks) | pandas.concat |
'''
Program: LBplot v3.1
Author: <NAME>
Released: 06/10/2020
Available in <https://github.com/HectorKroes/LBplot>
'''
##REFERENCES##
References = ('''
-<NAME>., & <NAME>. (1934). The Determination of Enzyme Dissociation Constants. Journal of the American Chemical Society, 56(3), 658–666. doi:10.1021/ja01318... | pd.DataFrame(data, columns = ['','V0','1/V0','[S]','1/[S]']) | pandas.DataFrame |
from functools import partial
import os
import unittest
from nose.tools import assert_equal, assert_list_equal, nottest, raises
from py_stringmatching.tokenizer.delimiter_tokenizer import DelimiterTokenizer
from py_stringmatching.tokenizer.qgram_tokenizer import QgramTokenizer
from six import iteritems
import pandas a... | pd.DataFrame([{'A.id':1, 'A.attr':'hello', 'A.int_attr':5}]) | pandas.DataFrame |
import pandas as pd
import os
from scipy import signal
import matplotlib.pyplot as plt
data1n = []
data2n = []
root = 'Filtered'
emosi = ['kaget','marah','santai','senang']
def lowpass_filter(sinyal,fcl):
sampleRate = 200
wnl = fcl/(sampleRate)
b,a = signal.butter(3,wnl,'lowpass')
fil = signal.filt... | pd.read_csv('Data_raw/nice1.csv') | pandas.read_csv |
#!/usr/bin/env python3
# (c) 2017-2020 L.Spiegelberg
# validates output of flights query
import pandas as pd
import os
import glob
import numpy as np
import json
import re
from tqdm import tqdm
root_path = '.'
def compare_dfs(dfA, dfB):
if len(dfA) != len(dfB):
print('not equal, lengths do not coincide... | pd.DataFrame() | pandas.DataFrame |
"""Loading example datasets."""
from os.path import dirname, join
import datetime
import io
import requests
import numpy as np
import pandas as pd
import time
def load_daily(long: bool = True):
"""2020 Covid, Air Pollution, and Economic Data.
Sources: Covid Tracking Project, EPA, and FRED
Args:
... | pd.to_numeric(df_wide.index, errors='coerce', downcast='integer') | pandas.to_numeric |
import argparse
import warnings
from io import StringIO
import joblib
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import GridSearchCV
from xgboost import XGBRegressor
import volcengine_ml_platform
from volcengine_ml_platform import constant
fro... | pd.notna(total.LotFrontage) | pandas.notna |
from shapely import geometry
import numpy as np
import xarray as xr
import pandas as pd
import geopandas as gpd
from .base_class_for_query_of_nearest_points import Query_Nearest_Points
def _get_nearest_pixels(ground_pixel_tree,
xy,
radius=100 # in meters
... | pd.to_datetime(xy[gdf_time_coord_name]) | pandas.to_datetime |
import pandas as pd
from pandas._testing import assert_frame_equal
import pytest
import numpy as np
from scripts.my_normalize_data import (
normalize_expedition_section_cols,
remove_bracket_text,
remove_whitespace,
normalize_columns
)
class XTestNormalizeColumns:
def test_replace_column_name_with... | pd.DataFrame(data) | pandas.DataFrame |
# -*- coding: utf-8 -*-
from __future__ import print_function
from datetime import datetime, time
from numpy import nan
from numpy.random import randn
import numpy as np
from pandas import (DataFrame, Series, Index,
Timestamp, DatetimeIndex,
to_datetime, date_range)
import pa... | pd.Timestamp('2012-05-01') | pandas.Timestamp |
# -*- coding:utf-8 -*-
# !/usr/bin/env python
"""
Date: 2022/5/2 15:58
Desc: 东方财富-股票-财务分析
"""
import pandas as pd
import requests
from tqdm import tqdm
def stock_balance_sheet_by_report_em(symbol: str = "SH600519") -> pd.DataFrame:
"""
东方财富-股票-财务分析-资产负债表-按报告期
https://emweb.securities.eastmoney.com/PC_HSF1... | pd.DataFrame(data_json["data"]) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
@author:XuMing(<EMAIL>)
@description:
"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from .discretize import discretize
from .feature import ContinuousFeature, CategoricalFeature, MultiCategoryFeature
from ..utils.logger import logger
class Feat... | pd.concat([feat_value_continuous, feat_value_category], axis=1) | pandas.concat |
# encoding=utf-8
from nltk.corpus import stopwords
from sklearn.preprocessing import LabelEncoder
from sklearn.pipeline import FeatureUnion
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.cross_validation import KFold
from sk... | pd.read_csv("../input/periods_test.csv", nrows=1000, parse_dates=["date_from", "date_to"]) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Thu Feb 13 10:31:11 2020
@author: <NAME>
"""
# Imports
import os
import pandas as pd
import itertools
import numpy as np
# import multiprocessing as mp
from operator import itemgetter
from support_modules.readers import log_reader as lr
from support_modules import role_discove... | pd.DataFrame.from_dict(ranges, orient='index') | pandas.DataFrame.from_dict |
from tkinter import *
from datetime import timedelta, datetime
from urllib.request import urlopen, Request, urlretrieve
import urllib
from urllib import request
from pathlib import Path
import urllib.error
from urllib.request import Request, urlopen
import os
import sys
import pandas as pd
import numpy as np... | pd.DataFrame(data=[nifty_close]) | pandas.DataFrame |
from typing import List
import numpy as np
import pandas as pd
from trader.core.model import Position
from trader.core.const.candle_index import OPEN_TIME_INDEX
from trader.core.util.common import Storable
import plotly.graph_objects as go
class TradeReport(Storable):
def __init__(
self,
... | pd.to_datetime(self.start_timestamp, unit='s') | pandas.to_datetime |
# -*- coding: utf-8 -*-
"""
Created on Tue Aug 13 16:30:16 2019
@author: <NAME>
"""
### Program for controlling BK8542B DC Electronic Load for IV curve measurement of solar panel ###
import serial, time, csv, os
import pandas as pd
import itertools as it
from time import strftime
from array import array... | pd.to_datetime(df_env['TIMESTAMP'],format='%Y-%m-%d %H:%M:%S') | pandas.to_datetime |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
import datetime as dt
import collections
import sklearn.preprocessing
import cartopy.crs as ccrs
import cartopy.io.shapereader as shpreader
import matplotlib.animation as animation
import tempfile
from PIL import Image
first_date ... | pd.Series(['Austria', 'Belgium', 'Bulgaria', 'Croatia', 'Cyprus', 'Czechia', 'Denmark', 'Estonia', 'France', 'Germany', 'Greece', 'Hungary', 'Ireland', 'Italy', 'Latvia', 'Lithuania', 'Luxembourg', 'Malta', 'Netherlands', 'Poland', 'Portugal', 'Romania', 'Slovakia', 'Slovenia', 'Spain', 'Sweden']) | pandas.Series |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import sys
cardData = pd.read_csv('CardData.csv', header=0, encoding='utf-8-sig')
coinData = pd.read_csv('CoinData.csv', header=0, encoding='utf-8-sig')
baseCost = pd.read_csv('BaseCost.csv', header=0, encoding='utf-8-sig')
numCards = len(c... | pd.DataFrame(costEquip, columns=['Initial Investment']) | pandas.DataFrame |
# coding=utf-8
"""
Porównanie skuteczności metod uczenia maszynowego.
Klasyfikacja - czy klient banku spłaci pożyczkę.
Źródło danych: http://archive.ics.uci.edu/ml/machine-learning-databases/00350/
"""
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics im... | pd.concat([X, z, y], axis=1) | pandas.concat |
#!/usr/bin/env python3
#
# Copyright 2019 <NAME> <<EMAIL>>
#
# This file is part of Salus
# (see https://github.com/SymbioticLab/Salus).
#
# 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
#
#... | pd.concat(tables) | pandas.concat |
import argparse
from contextlib import redirect_stdout
import os
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
import configs
import plot_utils
# Set matplotlib font size
SMALL_SIZE = 12
MEDIUM_SIZE = 14
BIGGER_SIZE = 18
plt.rc('font', size=SMALL_SIZE) # controls default text s... | pd.concat([fg_results_df, results_df], axis=0, ignore_index=True) | pandas.concat |
import json
import os
import warnings
import casadi as ca
import numpy as np
import pandas as pd
import pandas.testing as pdt
import pytest
from scipy.signal import chirp
from skmid.integrator import RungeKutta4
from skmid.models import DynamicModel
from skmid.models import generate_model_attributes
@pytest.fixture... | pdt.assert_frame_equal(df_X, df_Y) | pandas.testing.assert_frame_equal |
"""
kbible.py - base bible object and commands
"""
import pandas as pd
import yaml
import os
import subprocess
__author__ = "<NAME> <<EMAIL>>"
__docformat__ = "restructuredtext en"
class KBible(object):
""" Bible text object """
def __init__(self, version="개역한글판성경", debug=False, **kwargs):
""" read ... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
import fasttext
import pandas as pd
import math
import re
import os
import sys
reload(sys)
sys.setdefaultencoding('utf-8')
# Folder Location
DIR = os.path.dirname(os.path.realpath(__file__))
# Read Raw Corpus data
dat = pd.read_csv(DIR + '/kosacCorpus.csv')
# column labels of data frame
# Ren... | pd.isnull(dat.intensity) | pandas.isnull |
# -*- coding: utf-8 -*-
# pylint: disable-msg=W0612,E1101
import itertools
import warnings
from warnings import catch_warnings
from datetime import datetime
from pandas.types.common import (is_integer_dtype,
is_float_dtype,
is_scalar)
from pandas.compat... | DataFrame({'a': [1, 2, 3], 'b': [3, 4, 5]}, index=[1., 2., 3.]) | pandas.core.api.DataFrame |
"""
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, modify, merge, publish, dis... | pd.concat(dicttimes, copy=False) | pandas.concat |
#TODO: DISTINCT
from abc import abstractmethod
from numpy.lib.arraysetops import isin
from models.instructions.Expression.expression import *
from pandas.core.frame import DataFrame
from models.instructions.DML.special_functions import *
from models.nodo import Node
import pandas as pd
class Instruction:
'''Clase... | pd.merge(left, right[index+1], on=['key']) | pandas.merge |
#!/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.
# moved from habitat_baselines due to dependency issues
import os
from typing import Any, Dict, List, Optional
import m... | pd.DataFrame(data) | pandas.DataFrame |
#!/usr/bin/python3.9
# -*- coding: utf-8 -*-
#
# Copyright (C) 2021 LinYulong. All Rights Reserved
#
# @Time : 2021/10/9 23:10
# @Author : LinYulong
import numpy
import pandas
from src.alg import cross_verify, math_helper
from src.excel import excel_helper
from src.train import train_cfg
from train import train
... | pandas.DataFrame(ret, dtype=int) | pandas.DataFrame |
import datetime
import os
import numpy as np
import pandas as pd
from tqdm import tqdm
from multiprocessing import Pool
from itertools import zip_longest
from os.path import isfile, join
import cProfile
import io
import pstats
import fitdecode
from utils import log, list_all_files
def profile(func):
def wrapper(*... | pd.DataFrame(columns=['time', 'distance', 'minutes_per_kilometer']) | pandas.DataFrame |
from preppy524 import datatype
import pandas as pd
import pytest
test_dict = {'cat1': ['apple', None, 'pear', 'banana', 'blueberry', 'lemon'],
'num1': [0, 1, 2, 3, 4, 5],
'cat2': [True, False, False, True, False, None],
'num2': [0, 16, 7, None, 10, 14],
'num3': [0.5... | pd.DataFrame(test_dict) | pandas.DataFrame |
import pandas as pd
import numpy as np
from ionsrcopt import source_stability as stab
class TestSourceStability:
def test_stability_mean_variance_classification(self):
def timedelta_to_seconds(timedelta):
if not pd.isnull(timedelta):
return timedelta.total_seconds()
... | pd.DatetimeIndex(df["Timestamp"]) | pandas.DatetimeIndex |
"""This file is the pipeline for data etl"""
# import relation package.
import pickle
import pandas as pd
from sklearn.preprocessing import OneHotEncoder, LabelEncoder
# import project package.
from config.config_setting import ConfigSetting
class DataEtlService:
def __init__(self):
config_setting = Co... | pd.read_csv(self.config['extract']['test_file']) | pandas.read_csv |
# 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.
# This file contains dummy data for the model unit tests
import numpy as np
import pandas as pd
AIR_FCST_LINEAR_95 = pd.DataFrame(
{
... | pd.Timestamp("2012-08-25 00:00:00") | pandas.Timestamp |
import pandas as pd
from sktime.transformers.series_as_features.base import \
BaseSeriesAsFeaturesTransformer
from sktime.utils.data_container import tabularize
from sktime.utils.validation.series_as_features import check_X
__author__ = "<NAME>"
class PAA(BaseSeriesAsFeaturesTransformer):
""" (PAA) Piecewise... | pd.Series(frames) | pandas.Series |
import re
import sys
import matplotlib.pyplot as plt
import pandas as pd
from bld.project_paths import project_paths_join as ppj
def data_prep(data):
"""Function calculating yearly and monthly change
Args:
data (pd.Dataframe): dataset with predicted values
Returns:
| monthly_change (pd... | pd.to_datetime(mean_df.index, format="%Y_%m_%d") | pandas.to_datetime |
import os
import time
import random
import argparse
import numpy as np
import pandas as pd
import cv2
import PIL.Image
from tqdm import tqdm
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import StratifiedKFold
import torch
from torch.utils.data import DataLoader, Dataset
import torch.nn as nn
i... | pd.concat(dfs) | pandas.concat |
# ActivitySim
# See full license in LICENSE.txt.
import logging
import numpy as np
import pandas as pd
from activitysim.core import simulate
from activitysim.core import tracing
from activitysim.core import pipeline
from activitysim.core import config
from activitysim.core import inject
from activitysim.core import e... | pd.Series(segment_spec.columns[choices.values], index=choices.index) | pandas.Series |
import numpy as np
import pytest
import pandas as pd
from pandas import (
DataFrame,
Index,
Series,
concat,
date_range,
)
import pandas._testing as tm
class TestEmptyConcat:
def test_handle_empty_objects(self, sort):
df = DataFrame(np.random.randn(10, 4), columns=list("abcd"))
... | Series(dtype=dtype) | pandas.Series |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Sep 24 16:43:33 2019
@author: jeremy_lehner
"""
import pandas as pd
import datetime
from selenium import webdriver
import time
from bs4 import BeautifulSoup
from os import path
def get_scrape_date():
"""
Gets the date on which data was scrape... | pd.DataFrame({'banrate': banrates, 'date': date}) | pandas.DataFrame |
#!/usr/bin/env python
import bz2
import gzip
import logging
import os
import subprocess
from collections import OrderedDict
from pathlib import Path
from pprint import pformat
import pandas as pd
import yaml
from Bio import SeqIO
def fetch_executable(cmd, ignore_errors=False):
executables = [
cp for cp ... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
from pomegranate import HiddenMarkovModel, DiscreteDistribution
import numpy as np
from pomegranate import *
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.metrics import f1_score, precision_score, recall_score, accuracy_score
from sklearn.metrics import confusion_matrix
from sk... | pd.read_csv("/home/matilda/PycharmProjects/RCA_logs/2_copy_original_data/Fault-Injection-Dataset-master/nova.tsv", sep="\t") | pandas.read_csv |
'''
Replica of Jupyter notebook - useful for debugging SA code.
'''
from sklearn.cluster import KMeans
from sklearn.datasets import make_blobs
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import cProfile
import pstats
from sa import SACluster, ExponentialCoolingSchedul... | pd.DataFrame(sa.search_history) | pandas.DataFrame |
from typing import Union
import pandas as pd
from sklearn.model_selection import train_test_split
from ..datastore import DataItem
def get_sample(
src: Union[DataItem, pd.core.frame.DataFrame], sample: int, label: str, reader=None
):
"""generate data sample to be split (candidate for mlrun)
Returns fea... | pd.DataFrame(data=data["ycal"].values, columns=[label]) | pandas.DataFrame |
import warnings
from collections import OrderedDict
from datetime import time
import tables as tb
import pandas as pd
import pandas.lib as lib
import numpy as np
import pandas.io.pytables as pdtables
from trtools.compat import izip, pickle
from trtools.io.common import _filename
from trtools.io.table_indexing import ... | pd.Timestamp(other) | pandas.Timestamp |
"""Test functions in owid.datautils.dataframes module.
"""
import numpy as np
import pandas as pd
from pytest import warns
from typing import Any, Dict
from owid.datautils import dataframes
class TestCompareDataFrames:
def test_with_large_absolute_tolerance_all_equal(self):
assert dataframes.compare(
... | pd.DataFrame({"col_01": [2]}) | pandas.DataFrame |
import os
from datetime import datetime
import time
from sklearn.preprocessing import StandardScaler
import plotly.express as px
import plotly.graph_objs as go
import matplotlib.pyplot as plt
import seaborn as sns
import math
import statsmodels.api as sm
from statsmodels.tsa.ar_model import AutoReg
from statsmodels.... | pd.DataFrame(results) | pandas.DataFrame |
from datetime import datetime
import operator
import numpy as np
import pytest
from pandas import DataFrame, Index, Series, bdate_range
import pandas._testing as tm
from pandas.core import ops
class TestSeriesLogicalOps:
@pytest.mark.parametrize("bool_op", [operator.and_, operator.or_, operator.xor])
def te... | tm.assert_series_equal(result, expected) | pandas._testing.assert_series_equal |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Copyright [2020] [Indian Institute of Science, Bangalore]
SPDX-License-Identifier: Apache-2.0
"""
__name__ = "Instantiate a city and dump instantiations as json"
import os, sys
import json
import pandas as pd
import warnings
warnings.filterwarnings('ignore')
import ti... | pd.read_csv("data/base/"+city+"/households.csv") | pandas.read_csv |
# http://github.com/timestocome
# Attempt to predict nasdaq indexes and find outliers
# http://web.ipac.caltech.edu/staff/fmasci/home/astro_refs/TestForRandomness_RunsTest.pdf
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
#################################################################... | pd.read_csv('data/nasdaq.csv', parse_dates=True, index_col=0) | pandas.read_csv |
# Author: <NAME>, PhD
#
# Email: <EMAIL>
#
#
# Ref: https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html
# Ref: https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.jaccard.html#scipy.spatial.distance.jaccard
# Ref: https://docs.scipy.org/doc/scipy/reference/generated/scip... | pd.DataFrame ({'scaffold_id': ids, 'Dim_1': X_embedded[:,0], 'Dim_2': X_embedded[:,1]}) | pandas.DataFrame |
import pandas as pd
'''
Data pipeline for ingestion of 311-data datasets
General sections:
1. ACQUIRE: Download data from source
2. CLEAN: Perform data cleaning and organization before entering into SQL
3. INGEST: Add data set to SQL database
These workflows can be abstracted/encapsulated in order to better gener... | pd.to_datetime(dfb['CreatedDate']) | pandas.to_datetime |
# ---
# jupyter:
# jupytext:
# formats: ipynb,py:percent
# text_representation:
# extension: .py
# format_name: percent
# format_version: '1.3'
# jupytext_version: 1.13.7
# kernelspec:
# display_name: Python [conda env:bandit_38]
# language: python
# name: conda-env-bandi... | pd.read_csv(f'{hw_data_dir}/hw_hrus.csv') | pandas.read_csv |
"""Code used for notebooks and data exploration on
https://github.com/oscovida/oscovida"""
import datetime
import math
import os
import pytz
import time
import joblib
import numpy as np
import pandas as pd
import IPython.display
from typing import Tuple, Union
# choose font - can be deactivated
from matplotlib import... | pd.to_datetime(cases.columns[2:]) | pandas.to_datetime |
from os.path import join as opj
import numpy as np
from pandas import read_sql_query, concat
import matplotlib.pylab as plt
import seaborn as sns
from configs.nucleus_style_defaults import Interrater as ir, NucleusCategories as ncg
from interrater.interrater_utils import _maybe_mkdir, \
_connect_to_anchor_db, get_... | concat(overalldf, axis=0, ignore_index=True) | pandas.concat |
# Some utilites functions for loading the data, adding features
import numpy as np
import pandas as pd
from functools import reduce
from sklearn.preprocessing import MinMaxScaler
def load_csv(path):
"""Load dataframe from a csv file
Args:
path (STR): File path
"""
# Load the file
df ... | pd.to_numeric(df['hour_id']) | pandas.to_numeric |
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