prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
## Bot for adding Prop65 ID
from wikidataintegrator import wdi_core, wdi_login, wdi_helpers
from wikidataintegrator.ref_handlers import update_retrieved_if_new_multiple_refs
import pandas as pd
from pandas import read_csv
import requests
import time
from datetime import datetime
import copy
## Here are the object QI... | read_csv('data/prop65_chems.tsv',delimiter='\t',header=0, encoding='utf-8', index_col=0) | pandas.read_csv |
# -*- coding: utf-8 -*-
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
import pytest
import re
from numpy import nan as NA
import numpy as np
from numpy.random import randint
from pandas.compat import range, u
import pandas.compat as compat
from pandas import Index, Series, DataFrame, isn... | tm.assert_frame_equal(result, expected) | pandas.util.testing.assert_frame_equal |
import pandas as pd
import pytest
from feature_engine.encoding import OneHotEncoder
def test_encode_categories_in_k_binary_plus_select_vars_automatically(df_enc_big):
# test case 1: encode all categories into k binary variables, select variables
# automatically
encoder = OneHotEncoder(top_categories=None... | pd.DataFrame(transf) | pandas.DataFrame |
from datetime import (
datetime,
timedelta,
timezone,
)
import numpy as np
import pytest
import pytz
from pandas import (
Categorical,
DataFrame,
DatetimeIndex,
NaT,
Period,
Series,
Timedelta,
Timestamp,
date_range,
isna,
)
import pandas._testing as tm
class TestS... | tm.assert_series_equal(result, expected) | pandas._testing.assert_series_equal |
# -*- coding: utf-8 -*-
"""
@author: hkaneko
"""
import math
import sys
import numpy as np
import pandas as pd
import sample_functions
from sklearn import metrics, model_selection, svm
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split, GridSearchCV
... | pd.DataFrame(estimated_y_test_count, index=x_test.index, columns=class_types) | pandas.DataFrame |
import os
import random
import soundfile as sf
import torch
import yaml
import json
import argparse
import numpy as np
import pandas as pd
from tqdm import tqdm
from pprint import pprint
from asteroid import DCUNet
from asteroid.metrics import get_metrics
from asteroid.losses import PITLossWrapper, pairwise_neg_sisdr
... | pd.Series(utt_metrics) | pandas.Series |
import unittest
import qteasy as qt
import pandas as pd
from pandas import Timestamp
import numpy as np
import math
from numpy import int64
import itertools
import datetime
from qteasy.utilfuncs import list_to_str_format, regulate_date_format, time_str_format, str_to_list
from qteasy.utilfuncs import maybe_trade_day, ... | pd.to_datetime(prev_seems_trade_day) | pandas.to_datetime |
from functools import partial
from itertools import product
from string import ascii_letters
import numpy as np
from pandas import (
Categorical,
DataFrame,
MultiIndex,
Series,
Timestamp,
date_range,
period_range,
)
from .pandas_vb_common import tm
method_blocklist = {
"object": {
... | DataFrame(arr, columns=self.cols) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import pytest
import os
import numpy as np
import pandas as pd
from pandas.testing import assert_frame_equal, assert_series_equal
import numpy.testing as npt
from numpy.linalg import norm, lstsq
from numpy.random import randn
from flaky import flaky
from lifelines import CoxPHFitter, WeibullA... | assert_frame_equal(observedw, observed) | pandas.testing.assert_frame_equal |
# Copyright (c) 2018-2022, NVIDIA CORPORATION.
import numpy as np
import pandas as pd
import pytest
from pandas.api import types as ptypes
import cudf
from cudf.api import types as types
@pytest.mark.parametrize(
"obj, expect",
(
# Base Python objects.
(bool(), False),
(int(), False)... | pd.Series(dtype="object") | pandas.Series |
# -*- coding: utf-8 -*-
"""
Created on Sat Aug 29 19:14:15 2020
@author: hp 3006tx
"""
import pandas as pd
import dash
from dash.dependencies import Input , State, Output
import dash_core_components as dcc
import dash_html_components as html
import webbrowser
import plotly.graph_objects as go
import plotl... | pd.read_csv(globalterror) | pandas.read_csv |
####################
# Import Libraries
####################
import os
import sys
from PIL import Image
import cv2
import numpy as np
import pandas as pd
import pytorch_lightning as pl
from pytorch_lightning.metrics import Accuracy
from pytorch_lightning import loggers
from pytorch_lightning import seed_e... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
@author: hkaneko
"""
# サンプルプログラムで使われる関数群
import matplotlib.figure as figure
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy.spatial.distance import cdist
from sklearn import metrics
## 目的変数の実測値と推定値との間で、散布図を描いたり、r2, RMSE, MAE を計算したりする関数
# de... | pd.concat([estimated_y_train, y_train_for_save, y_error_train], axis=1) | pandas.concat |
import pandas as pd
import json
import bids
import matplotlib.pyplot as plt
import plotje
# Download data from here: <NAME>. et al. Crowdsourced MRI quality metrics
# and expert quality annotations for training of humans and machines. Sci Data 6, 30 (2019).
# Then run make_distributions.py to summarize the data from t... | pd.read_csv(summary_path + qc + '_summary.csv', index_col=[0]) | pandas.read_csv |
import pandas
import math
import csv
import random
import numpy
from sklearn import linear_model
from sklearn.model_selection import cross_val_score
# 当每支队伍没有elo等级分时，赋予其基础elo等级分
base_elo = 1600
team_elos = {}
team_stats = {}
x = []
y = []
folder = 'data'
# 根据每支队伍的Micellaneous, Opponent, Team统计数据csv文件进行初始化
def initia... | pandas.merge(miscellaneous_stats, opponent_per_game_stats, how='left', on='Team') | pandas.merge |
from sklearn.linear_model import LinearRegression, Ridge, Lasso, ElasticNet
from sklearn.linear_model import LassoCV , ElasticNetCV , RidgeCV
from sklearn.pipeline import Pipeline, FeatureUnion
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metric... | pd.read_csv(f"{path}{filename}.csv") | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Sat May 5 00:27:52 2018
@author: sindu
About: Feature Selection on Genome Data"""
import pandas as pd
import numpy as np
import math
import operator
from sklearn import metrics
from sklearn import svm
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neighbors.nea... | pd.read_csv('GenomeTop100TrainData.txt', header=-1) | pandas.read_csv |
import os.path
import ast
import pickle
import pandas as pd
Run = False
if not os.path.isfile('sp.txt'):
Run = True
print('process file sp')
if __name__ == "__main__":
Run = True
print("process file sp")
# functions to extract certain information in the data
def get_genre(dataframe):
"""
Th... | pd.Series(x['countries']) | pandas.Series |
"""
Copyright 2019 <NAME>.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing,
software distribut... | pd.DataFrame(self) | pandas.DataFrame |
import pandas as pd
import repackage
import re
from camel_tools.utils.charsets import UNICODE_PUNCT_CHARSET
import logging
from funcy import log_durations
import argparse
from pathlib import Path
repackage.up()
from data.make_dataset import recompose, puncs
project_dir = Path(__file__).resolve().parents[2]
def loa... | pd.concat(parallel_tokens) | pandas.concat |
"""Water network transfers maps
"""
import os
import sys
from collections import OrderedDict
import numpy as np
import geopandas as gpd
import pandas as pd
import cartopy.crs as ccrs
import cartopy.io.shapereader as shpreader
import matplotlib.pyplot as plt
import numpy as np
from shapely.geometry import LineString
fr... | pd.merge(region_file,flow_file,how='left', on=['edge_id']) | pandas.merge |
from datetime import datetime
import numpy as np
import pytest
from pandas import (
DataFrame,
Series,
bdate_range,
notna,
)
@pytest.fixture
def series():
"""Make mocked series as fixture."""
arr = np.random.randn(100)
locs = np.arange(20, 40)
arr[locs] = np.NaN
series = Series(a... | DataFrame(s) | pandas.DataFrame |
"""the_pile dataset"""
import io
import os
import pandas as pd
from ekorpkit import eKonf
from ekorpkit.io.download.web import web_download
from tqdm.auto import tqdm
try:
import simdjson as json
except ImportError:
print("Installing simdjson library")
os.system("pip install -q pysimdjson")
import jso... | pd.DataFrame(documents) | pandas.DataFrame |
from twembeddings.build_features_matrix import format_text, find_date_created_at, build_matrix
from twembeddings.embeddings import TfIdf
from twembeddings import ClusteringAlgoSparse
from twembeddings import general_statistics, cluster_event_match
from twembeddings.eval import cluster_acc
import logging
import sklearn.... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Thu Feb 22 11:05:21 2018
@author: 028375
"""
from __future__ import unicode_literals, division
import pandas as pd
import os.path
import numpy as np
def Check2(lastmonth,thismonth,collateral):
ContractID=(thismonth['ContractID'].append(lastmonth['ContractID'])).append(coll... | meric(thismonth['Upfront结算货币'],errors='coerce') | pandas.to_numeric |
import numpy as np
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
import plotly.express as px
# train-test split by a percentage.
# input: dataframe, label column name, split ration, and random state
# returns: x_train, x_test, y_train, y_test
def split_df(user_df: pd.DataFrame, label_name: str, ... | pd.DataFrame(columns=y.columns) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# Arithmetc tests for DataFrame/Series/Index/Array classes that should
# behave identically.
from datetime import timedelta
import operator
import pytest
import numpy as np
import pandas as pd
import pandas.util.testing as tm
from pandas.core import ops
from pandas.errors import NullFrequency... | tm.box_expected(tdser, box) | pandas.util.testing.box_expected |
# Import libraries
import os
import sys
import anemoi as an
import pandas as pd
import numpy as np
import pyodbc
from datetime import datetime
import requests
import collections
import json
import urllib3
def return_between_date_query_string(start_date, end_date):
if start_date != None and end_date != None:
... | pd.to_datetime(turbine_data['TimeStampLocal'], format='%Y-%m-%d %H:%M:%S') | pandas.to_datetime |
from datetime import datetime, timedelta
import warnings
import operator
from textwrap import dedent
import numpy as np
from pandas._libs import (lib, index as libindex, tslib as libts,
algos as libalgos, join as libjoin,
Timedelta)
from pandas._libs.lib import is_da... | make_invalid_op('__neg__') | pandas.core.ops.make_invalid_op |
from pandas import DataFrame, read_excel, ExcelFile, read_csv, concat, Series, \
notnull
from pathlib import Path
from re import match
from typing import Optional, List, Union, Callable
from survey import Survey
from survey.attributes import PositiveMeasureAttribute
from survey.mixins.data_types.categorical_mixin ... | notnull(row['categories']) | pandas.notnull |
#!/home/brian/miniconda3/bin/python3.7
# encoding: utf-8
"""
Read the docs, obey PEP 8 and PEP 20 (Zen of Python, import this)
Build on: Spyder
Python ver: 3.7.3
Created on Thu Oct 17 21:14:04 2019
@author: brian
"""
# %% modules:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sea... | pd.set_option('display.max_columns', 500) | pandas.set_option |
from datetime import datetime
import re
import unittest
import nose
from nose.tools import assert_equal
import numpy as np
from pandas.tslib import iNaT
from pandas import Series, DataFrame, date_range, DatetimeIndex, Timestamp
from pandas import compat
from pandas.compat import range, long, lrange, lmap, u
from pand... | com.take_1d(data, indexer, fill_value=fill_value) | pandas.core.common.take_1d |
from abc import abstractmethod
from collections import OrderedDict
import os
import pickle
import re
from typing import Tuple, Union
import pandas as pd
import numpy as np
import gym
from gridworld.log import logger
from gridworld import ComponentEnv
from gridworld.utils import to_scaled, to_raw, maybe_rescale_box_s... | pd.Timestamp(start_time) | pandas.Timestamp |
import numpy as np
import pandas as pd
from scipy.optimize import least_squares
from scipy.optimize import OptimizeResult
from numba.typed import List
from mspt.diff.diffusion_analysis_functions import calc_msd, calc_jd_nth, lin_fit_msd_offset, lin_fit_msd_offset_iterative
from mspt.diff.diffusion_analysis_func... | pd.concat([df_jdd_msd, traj_df_temp], axis=1) | pandas.concat |
import numpy as np
import pytest
from pandas import (
Categorical,
CategoricalDtype,
NaT,
Timestamp,
array,
to_datetime,
)
import pandas._testing as tm
class TestAstype:
def test_astype_str_int_categories_to_nullable_int(self):
# GH#39616
dtype = CategoricalDtype([str(i) f... | Timestamp("2021-03-27 00:00:00") | pandas.Timestamp |
import math as math
import numpy as np
import re
import pandas as pd
import spimcube.functions as fct
def initialization(path, basename):
"""Return a dictionary with: NStepsX, NStepsY, Npixel, Matrix, tab_of_lambda, Xstep, Ystep, Xrange, Yrange."""
# create the complete file name with extension
... | pd.DataFrame(data={'B': B_values, 'wavelength': wavelength, 'energy': energy, 'intensity': intensity}) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
# dataset src: https://data.london.gov.uk/dataset/smartmeter-energy-use-data-in-london-households
# file: UKPN-LCL-smartmeter-sample (986.99 kB)
# In[2]:
# A Time series is a collection of data points indexed,
# listed or graphed in time order.
# Most commonly, a ... | pd.to_datetime(raw_date_kwh_df.loc[:, 'DateTime']) | pandas.to_datetime |
import re
from pathlib import Path
from typing import List
import pandas as pd
from scipy.io import arff
from common import write_arff_file
def create_index_partitions() -> List[int]:
partitions = []
for i in range(10, 200, 10):
partitions.append(i)
for i in range(75, 126):
partitions.a... | pd.DataFrame(data) | pandas.DataFrame |
import ast
import argparse
import warnings
import logging
import os
import json
import boto3
import pickle
# from prettytable import PrettyTable
import subprocess
import sys
from urllib.parse import urlparse
#os.system('pip install autogluon')
# from autogluon import TabularPrediction as task
import pandas as pd #... | pd.set_option('display.max_rows', 500) | pandas.set_option |
# -*- coding: utf-8 -*-
"""
Created on Sun May 21 13:13:26 2017
@author: ning
"""
import pandas as pd
import os
import numpy as np
import matplotlib.pyplot as plt
import pickle
try:
function_dir = 'D:\\NING - spindle\\Spindle_by_Graphical_Features'
os.chdir(function_dir)
except:
function_dir = 'C:\\Users\... | pd.read_csv(f) | pandas.read_csv |
import gzip
import pickle5 as pickle
# import pickle
from collections import defaultdict
import numpy as np
import pandas as pd
import os
from copy import deepcopy
import datetime
import neat
from tensorflow.python.framework.ops import default_session
from scipy.optimize import curve_fit
from ongoing.prescriptors.ba... | pd.DataFrame(df_dict) | pandas.DataFrame |
import requests
from bs4 import BeautifulSoup
import pandas as pd
from difflib import SequenceMatcher
desired_width = 320
pd.set_option('display.width', desired_width)
pd.set_option('display.max_columns', 10)
pd.set_option('display.max_rows', 1000)
pd.set_option('display.max_colwidth', None) #To display full URL in dat... | pd.read_excel('Dataframes/' + company_name + '.xlsx', index_col = [0], dtype = object) | pandas.read_excel |
import unittest
import numpy as np
import pandas as pd
from pyalink.alink import *
class TestDataFrame(unittest.TestCase):
def setUp(self):
data_null = np.array([
["007", 1, 1, 2.0, True],
[None, 2, 2, None, True],
["12", None, 4, 2.0, False],
["1312", 0,... | pd.Int32Dtype() | pandas.Int32Dtype |
import re
import pandas as pd
from gensim.models import KeyedVectors
from nltk.corpus import stopwords
import keras.backend as K
from keras.layers import Input, Embedding, LSTM, Lambda
from keras.models import Model
from keras.optimizers import Adadelta
from random import sample
from keras.preprocessing.sequence import... | pd.DataFrame({'is_dupl': val_dupl}) | pandas.DataFrame |
import os
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from GenNet_utils.hase.config import basedir, PYTHON_PATH
os.environ['HASEDIR'] = basedir
if PYTHON_PATH is not None:
for i in PYTHON_PATH: sys.path.insert(0, i)
from GenNet_utils.hase.hdgwas.tools import HaseAnalyse... | pd.DataFrame.from_dict(Analyser.results) | pandas.DataFrame.from_dict |
import numpy as np
import pandas as pd
from shapely.geometry import box
import flopy
from sfrmaker.routing import find_path, make_graph
from gisutils import shp2df
from mfexport.budget_output import read_sfr_output
from .fileio import read_tables
from .routing import get_next_id_in_subset
from sfrmaker.fileio import lo... | pd.concat(to_concat) | pandas.concat |
# -*- coding: utf-8 -*-
#%% NumPyの読み込み
import numpy as np
# SciPyのstatsモジュールの読み込み
import scipy.stats as st
# Pandasの読み込み
import pandas as pd
# PyMCの読み込み
import pymc3 as pm
# MatplotlibのPyplotモジュールの読み込み
import matplotlib.pyplot as plt
# tqdmからプログレスバーの関数を読み込む
from tqdm import trange
# 日本語フォントの設定
from matplotl... | pd.DataFrame(stats, index=param_string, columns=stats_string) | pandas.DataFrame |
# encoding: utf-8
from opendatatools.common import RestAgent
from opendatatools.common import date_convert, remove_non_numerical
from bs4 import BeautifulSoup
import datetime
import json
import pandas as pd
import io
from opendatatools.futures.futures_agent import _concat_df
import zipfile
class SHExAgent(RestAgent):... | pd.DataFrame(data) | pandas.DataFrame |
"""
This module merges temperature, humidity, and influenza data together
"""
import pandas as pd
import ast
__author__ = '<NAME>'
__license__ = 'MIT'
__status__ = 'release'
__url__ = 'https://github.com/caominhduy/TH-Flu-Modulation'
__version__ = '1.0.0'
def merge_flu(path='data/epidemiology/processed_CDC_2008_2021... | pd.DataFrame(frames) | pandas.DataFrame |
import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from sklearn import preprocessing
matplotlib.use("Agg")
import datetime
import torch
from finrl.config import config
from finrl.marketdata.yahoodownloader import YahooDownloader
from finrl.preprocessing.preprocessors import Featu... | pd.DataFrame() | pandas.DataFrame |
from movie import app
from flask import render_template,flash
from movie.forms import MovieForm
@app.route('/',methods=['GET','POST'])
def movierec():
form=MovieForm()
if form.validate_on_submit():
import pandas as pd
import numpy as np
ratings=pd.read_csv('ratings.csv')
movies... | pd.DataFrame(movies_like_movie,columns=['Correlation']) | pandas.DataFrame |
# -*- coding: utf-8 -*-
from unittest import TestCase
import pandas as pd
from alphaware.base import (Factor,
FactorContainer)
from alphaware.enums import (FactorType,
OutputDataFormat,
FreqType,
FactorNo... | assert_frame_equal(calculate, expected) | pandas.util.testing.assert_frame_equal |
import pandas as pd
import matplotlib as mpl
import numpy as np
from sklearn import metrics
import itertools
import warnings
from dateutil.relativedelta import relativedelta
from statsmodels.tsa.stattools import adfuller
from statsmodels.tsa.seasonal import seasonal_decompose
from statsmodels.tsa.statespace.sarimax im... | pd.to_datetime('2021-02-28') | pandas.to_datetime |
from seedsKmeans import SEEDS
import pandas as pd
import BaseDados as BD
import numpy as np
tamanhos= [0.05, 0.06, 0.07, 0.08, 0.09, 0.1]
resultado = []
for tam in tamanhos:
X,Y = BD.base_qualquer('D:/basedados/vinhos.csv')
Y -= 1
dados = pd.DataFrame(X, columns=np.arange(np.size(X, axis=1))... | pd.DataFrame(resultado, columns=colunas) | pandas.DataFrame |
import pandas as pd
import dash
from dash.dependencies import Input, Output, State, MATCH, ALL
import dash_core_components as dcc
import dash_html_components as html
from dash.exceptions import PreventUpdate
import dash_bootstrap_components as dbc
import dash_table
import plotly.graph_objs as go
from threading impor... | pd.DataFrame(header_data) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Mon 9/2/14
Using python pandas to post process csv output from Pacejka Tire model
@author: <NAME>, 2014
"""
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
import pylab as py
class PacTire_panda:
'''
@class: loads, manages and plots various output... | pd.read_table(compare_adams_file, sep='\t', header=0) | pandas.read_table |
import os
import pandas as pd
import numpy as np
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import cross_val_score
##### UTILITIES #######
def generate_keywords(keywords = "../data/keywords/keywords_italy.txt"):
"""
Generate a list of keywords (Wikipedia's pages) which are us... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
### Read data from strip theory reference dataset
### Folder must contain List*.txt and Data*.*.bin files
from array import array
import pandas as pd
import matplotlib.pyplot as mpl
import os.path as path # to check either .csv file exists or not on disk
Ncfd = 2 # No. ... | pd.concat([df1,df2]) | pandas.concat |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# License: BSD-3 (https://tldrlegal.com/license/bsd-3-clause-license-(revised))
# Copyright (c) 2016-2021, <NAME>; <NAME>
# Copyright (c) 2022, QuatroPe
# All rights reserved.
# =============================================================================
# DOCS
# ========... | pd.get_option("display.max_colwidth") | pandas.get_option |
"""Tools for generating and forecasting with ensembles of models."""
import datetime
import numpy as np
import pandas as pd
import json
from autots.models.base import PredictionObject
from autots.models.model_list import no_shared
from autots.tools.impute import fill_median
horizontal_aliases = ['horizontal', 'probab... | pd.concat([upload, missing_rows]) | pandas.concat |
import numpy as np
import pandas as pd
from sklearn.model_selection import GridSearchCV, KFold
from sklearn.metrics import f1_score, roc_curve, auc, precision_recall_curve, \
precision_recall_fscore_support, average_precision_score
import os
import matplotlib.pyplot as plt
plt.rcParams.updat... | pd.DataFrame(columns=columns) | pandas.DataFrame |
import itertools
import numpy
import os
import random
import re
import scipy.spatial.distance as ssd
import scipy.stats
from scipy.cluster.hierarchy import dendrogram, linkage
import pandas
from matplotlib import colors
from matplotlib import pyplot as plt
import vectors
from libs import tsne
rubensteinGoodenoughDat... | pandas.DataFrame.from_dict(metrics) | pandas.DataFrame.from_dict |
from datetime import datetime, timedelta
import pandas as pd
import argparse
# My home instition(s)
_home_insts = ['Argonne National Laboratory', 'University of Chicago']
# Read in the command-line options
parser = argparse.ArgumentParser()
parser.add_argument('--date', help='Date of proposal submission in MM-DD-YYYY... | pd.read_excel('collaborators.xlsx', sheet_name='Coauthors') | pandas.read_excel |
# -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
import matplotlib.dates as dates
import numpy as np
import pandas as pd
from datetime import datetime, timedelta, time
import calendar
import seaborn as sns
from hypnospy import Wearable
from hypnospy import Experiment
import warnings
class Viewer(object):
"... | pd.DataFrame(group[1]) | pandas.DataFrame |
'''
Module : Stats
Description : Statistical calculations for Hatch
Copyright : (c) <NAME>, 16 Oct 2019-2021
License : MIT
Maintainer : <EMAIL>
Portability : POSIX
'''
import argparse
import logging
import pandas as pd
import numpy as np
from itertools import combinations
import math
import scipy
from ... | pd.melt(corr_df_wide, id_vars='index') | pandas.melt |
"""
/*---------------------------------------------------------------------------------------------
* Copyright (c) Microsoft Corporation. All rights reserved.
* Licensed under the MIT License. See License.txt in the project root for license information.
*----------------------------------------------------------... | pd.concat([df_clips, df_gold, df_trap, df_general], axis=1, sort=False) | pandas.concat |
#!/usr/bin/env python
# coding: utf-8
# > Note: KNN is a memory-based model, that means it will memorize the patterns and not generalize. It is simple yet powerful technique and compete with SOTA models like BERT4Rec.
# In[1]:
import os
project_name = "reco-tut-itr"; branch = "main"; account = "sparsh-ai"
project_p... | pd.DataFrame(index=rating_matrix.columns, columns=['Rating']) | pandas.DataFrame |
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 |
import csv
import itertools
import numpy
import numpy as np
import pandas as pd
import sklearn
from matplotlib import pyplot as plt
from pandas import DataFrame
import tsv
import experiments
import utils
from granularity import *
from sklearn.metrics import f1_score, accuracy_score
input_df = | pd.read_csv("data/answer_weather_ordinal.csv", sep=",") | pandas.read_csv |
# Arithmetic tests for DataFrame/Series/Index/Array classes that should
# behave identically.
# Specifically for datetime64 and datetime64tz dtypes
from datetime import (
datetime,
time,
timedelta,
)
from itertools import (
product,
starmap,
)
import operator
import warnings
import numpy as np
impo... | get_upcast_box(left, NaT, True) | pandas.tests.arithmetic.common.get_upcast_box |
"""
A collection of plotting functions to use with pandas, numpy, and pyplot.
Created: 2016-36-28 11:10
"""
import sys
from operator import itemgetter
from itertools import groupby, cycle
import numpy as np
import scipy as sp
import scipy.stats as sts
import pandas as pd
import statsmodels.api as sm
from stat... | pd.DataFrame([x, y, lx, ly]) | pandas.DataFrame |
from geopandas import GeoDataFrame
import pandas as pd
import numpy as np
import geopandas as gp
OLR= gp.read_file('Roadways_gridV6.shp')
OLR1=pd.DataFrame(OLR)
def label_race (row):
if row['cat'] == 'trunk' :
tcc=(row.length/263.778046)*0.38
return tcc
elif row['cat'] == 'primary' :
pcc=(r... | pd.DataFrame(aa) | pandas.DataFrame |
###############################################################################
# Omid55
# Start date: 16 Jan 2020
# Modified date: 14 Apr 2020
# Author: <NAME>
# Email: <EMAIL>
# Module to load group dynamics logs for every team.
###############################################################################... | pd.DataFrame(data, columns=columns) | pandas.DataFrame |
import gdax
import csv
import datetime as dt
import pandas_datareader.data as web
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.finance import candlestick_ohlc
import matplotlib.dates as mdates
#gets price data
public_client = gdax.PublicClient()
def coin_df_operations(df, coin_name):
#conve... | pd.to_datetime(df.index,unit='s') | pandas.to_datetime |
# In[]
import sys, os
sys.path.append('../')
sys.path.append('../src/')
import numpy as np
import pandas as pd
from scipy import sparse
import networkx as nx
import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.optim as optim
import matplotlib.pyplot as plt
from torch.utils.data import Dat... | pd.read_csv("../data/simulated/" + data_name + "/region2gene.txt", sep = "\t", header = None) | pandas.read_csv |
# Scientific Library
import numpy as np
import pandas as pd
from scipy import stats
from scipy.stats import norm as sp_norm
from scipy.stats.distributions import chi2 as sp_chi2
# Standard Library
from dataclasses import asdict, dataclass, field
from importlib.metadata import version
import importlib.resources as impo... | pd.to_numeric(df2[col], downcast="float") | pandas.to_numeric |
# -*- coding: utf-8 -*-
from datetime import datetime
from pandas.compat import range, lrange
import operator
import pytest
from warnings import catch_warnings
import numpy as np
from pandas import Series, Index, isna, notna
from pandas.core.dtypes.common import is_float_dtype
from pandas.core.dtypes.missing import re... | tm.assert_panel_equal(panel4dc[0], panel4d[0]) | pandas.util.testing.assert_panel_equal |
"""
Goals
------
Program should generate a report (Excel File) that shows
how data quality metrics for each HPO site change over time.
Data quality metrics include:
1. the number of duplicates per table
2. number of 'start dates' that precede 'end dates'
3. number of records that are >30 days after a patien... | pd.DataFrame({'table_type': valid_cols_tot}) | pandas.DataFrame |
import argparse
import json
import logging
import sys
import fiona
import geopandas as gpd
import numpy as np
import pandas as pd
import torch
from eolearn.core.utils.fs import get_aws_credentials, join_path
from sentinelhub import SHConfig
from hiector.utils.aws_utils import LocalFile
from hiector.utils.training_da... | pd.concat(dfs) | pandas.concat |
import numpy as np
from sklearn.ensemble import ExtraTreesRegressor, RandomForestRegressor
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn import preprocessing, svm
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn ... | pd.read_csv('Original_with_dummies.csv') | pandas.read_csv |
import os
import pandas as pd
import seaborn as sns
import matplotlib.dates as d
import matplotlib.pyplot as plt
from ..utils import everion_keys
from ..utils.plotter_helper import PlotterHelper
from ..utils.data_aggregator import DataAggregator
from ..patient.patient_data_loader import PatientDataLoader
sns.set()
... | pd.concat([df_right], keys=["right"], axis=1) | pandas.concat |
# -*- 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.pivot_table(dataFeat,index=['shop_review_num_level'],values='shop_review_num_level_count',aggfunc='count') | pandas.pivot_table |
import collections
import dask
from dask import delayed
from dask.diagnostics import ProgressBar
import logging
import multiprocessing
import pandas as pd
import numpy as np
import re
import six
import string
import py_stringsimjoin as ssj
from py_stringsimjoin.filter.overlap_filter import OverlapFilter
from py_string... | pd.isnull(val) | pandas.isnull |
# -*- coding: utf-8 -*-
"""
Created on Sat Dec 1 09:21:40 2018
@author: @gary.allison
This code is used to take ODNR files for Brine disposal fee and
eventually create a file to be used to show overall injection volumes.
The ODNR data have several limitations that we must find and account for:
- data type con... | pd.merge(meta,dIn,how='left',on='API10',validate='1:1') | pandas.merge |
#!/usr/bin/python
# -*- coding: UTF-8 -*-
from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras.models import Model
from tensorflow.keras.preprocessing import image
from tensorflow.keras.preprocessing import sequence
from deprecated import deprecated
import os
import numpy as np
f... | pd.read_csv(image_feature_dir, header=None, iterator=True) | pandas.read_csv |
import logging
import pandas as pd
from nltk import tokenize
from nltk.sentiment.vader import SentimentIntensityAnalyzer
from lib.settings import DATA_DIR, LOG_LEVEL
from lib.characters import findAllMentionedCharacters
comments = | pd.read_csv(DATA_DIR / 'comments.csv') | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Tue Apr 6 09:44:04 2021
@author: <NAME>
"""
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from wu_rainfall import WuRainfall
import datetime
VER = "_01b" # Version, any string
f = 'sw_tph_g_data_LVR.csv' # Data file, csv format
df1 = ... | pd.to_datetime(df1.Date) | pandas.to_datetime |
import collections
import pandas as pd
import warnings
import PIL
from typing import Union, Optional, List, Dict, Tuple
from ..constants import (
NULL, CATEGORICAL, NUMERICAL, TEXT,
IMAGE_PATH, MULTICLASS, BINARY, REGRESSION,
)
def is_categorical_column(
data: pd.Series,
valid_data: pd.Series,... | pd.to_numeric(data) | pandas.to_numeric |
from convokit.model import Corpus, Conversation, User, Utterance
from typing import List, Callable, Union
from convokit import Transformer, CorpusObject
import pandas as pd
class Ranker(Transformer):
def __init__(self, obj_type: str,
score_func: Callable[[CorpusObject], Union[int, float]],
... | pd.DataFrame(obj_scores_ranks, columns=["id", self.score_feat_name, self.rank_feat_name]) | pandas.DataFrame |
#!/usr/bin/env python3
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import Ridge, Lasso
import pickle
import os
import yaml
import numpy as np
import scipy.signal as signal
import pandas as pd
from scipy.stats import pearsonr
from datetime import datetime
from urllib import request
from p... | pd.concat([df, df_vi_nf], axis=1, sort=False) | pandas.concat |
import numpy as np
import pandas as pd
import pytest
from tabmat.categorical_matrix import CategoricalMatrix
@pytest.fixture
def cat_vec():
m = 10
seed = 0
rng = np.random.default_rng(seed)
return rng.choice([0, 1, 2, np.inf, -np.inf], size=m)
@pytest.mark.parametrize("vec_dtype", [np.float64, np.f... | pd.get_dummies(cat_vec, drop_first=drop_first) | pandas.get_dummies |
import matplotlib.pyplot as plt
import seaborn as sns
import pdb
import requests
import re
import threading
import concurrent.futures
import numpy as np
import pandas as pd
from functools import reduce
from collections import Counter
from sklearn.preprocessing import normalize, StandardScaler, Normalizer, RobustSca... | pd.DataFrame(scaled_features, columns=columns) | pandas.DataFrame |
import numpy as np
import pandas as pd
from collections import OrderedDict
from pandas.api.types import is_numeric_dtype, is_object_dtype, is_categorical_dtype
from typing import List, Optional, Tuple, Callable
def inspect_df(df: pd.DataFrame) -> pd.DataFrame:
""" Show column types and null values in DataFrame d... | is_numeric_dtype(column) | pandas.api.types.is_numeric_dtype |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import pandas as pd
import numpy as np
import re
import xlrd
import pickle
import os
import requests
# from bs4 import BeautifulSoup
# or import bs4 as bs
# import json
# In[ ]:
# In[2]:
# setting directories for file loads and saves
logs_dir = "./data/logs/"
r... | pd.read_excel(file, sheet_name="School Profile") | pandas.read_excel |
# -*- coding: utf-8 -*-
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "... | tm.assert_frame_equal(result, expected) | pandas.util.testing.assert_frame_equal |
import argparse
import config
import todoist
import mystrings as s
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import os
def initialize_todoist_api():
# Used an access token obtained from https://developer.todoist.com/appconsole.html
# Stored this access token "access_toke... | pd.to_datetime(df[s.DATE_COMPLETED], utc=True) | pandas.to_datetime |
import os
import random
import math
import numpy as np
import pandas as pd
import itertools
from functools import lru_cache
##########################
## Compliance functions ##
##########################
def delayed_ramp_fun(Nc_old, Nc_new, t, tau_days, l, t_start):
"""
t : timestamp
current date
... | pd.Timestamp('2020-09-01') | pandas.Timestamp |
import argparse
from data_utils import get_n_examples_per_class
import os
import pandas as pd
from shutil import copyfile
def main():
parser = argparse.ArgumentParser()
parser.add_argument("DATA_DIRECTORY", type=str)
parser.add_argument("--N_train", type=int, nargs='+')
parser.add_argume... | pd.concat([df_reduced_train, df_valid]) | pandas.concat |
import pytest
import numpy as np
import numpy.testing as npt
import pandas as pd
import statsmodels.api as sm
import statsmodels.formula.api as smf
from scipy.stats import logistic
from scipy.optimize import root
from delicatessen import MEstimator
from delicatessen.utilities import inverse_logit
np.random.seed(23646... | pd.DataFrame() | pandas.DataFrame |
from datetime import datetime
import logging
import reader.cache
import hashlib
import dateutil.parser
from pandas import DataFrame, NaT
from clubhouse import ClubhouseClient
class Clubhouse:
def __init__(self, clubhouse_config: dict, workflow: dict) -> None:
super().__init__()
self.clubhouse_conf... | DataFrame(stories_data) | pandas.DataFrame |
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