prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
# This file aims to iterate portfolios listed in scanner.csv,
# perform the same operations of iteratEternity.py but using the
# input excel as instructions to do opetations at scale.
# By changing scanner.csv we maintain the DATABASE updated.
# Reset format of excel with function BacktoBasiscs, so it can be re iterat... | pd.ExcelWriter(f'{newName}',engine='xlsxwriter') | pandas.ExcelWriter |
# coding: utf-8
'''
feature list
- order_number_rev()
- dep_prob()
- aisle_prob()
- dow_prob()
- hour_prob()
- organic_prob()
- latest_order()
- model()
'''
import pymysql
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import csv
import xgboost as xgb
from nu... | pd.merge(prior_df, orders_df, how='inner', on=['order_id']) | pandas.merge |
import matplotlib
from matplotlib import collections as mc
from matplotlib import pyplot as plt
import seaborn as sns
sns.set(style='white', palette='Blues')
import numpy as np
import pandas as pd
from collections import namedtuple
from mpl_toolkits.mplot3d import Axes3D
from time import time as t
import os
notebo... | pd.Series(stats.episode_rewards) | pandas.Series |
"""
An exhaustive list of pandas methods exercising NDFrame.__finalize__.
"""
import operator
import re
import numpy as np
import pytest
import pandas as pd
# TODO:
# * Binary methods (mul, div, etc.)
# * Binary outputs (align, etc.)
# * top-level methods (concat, merge, get_dummies, etc.)
# * window
# * cumulative ... | pd.DataFrame({"A": [1]}) | pandas.DataFrame |
from PreProcessing.metaPipeline import PipelineMeta
import pandas as pd
import numpy as np
import librosa
class MelSpectrogram(PipelineMeta):
def __init__(self, metaFileName='Meta.csv'):
"""
Initialize class, try to load meta.csv containing filepath metadata
Upon failure inherit... | pd.read_csv(metaFileName) | pandas.read_csv |
from flask import Flask, render_template, request
import numpy as np
import pandas as pd
import scipy.stats as stats
import matplotlib.pyplot as plt
import sklearn
import seaborn as sns
sns.set_style("whitegrid")
from sklearn.model_selection import cross_val_score
from sklearn import preprocessing
from sklearn import d... | pd.read_csv('features.csv') | pandas.read_csv |
#!/usr/bin/env python
# coding: utf-8
"""
EMCEE deconvolution using the fast parameterised model of the 750l radon
detector based on W&Z's 1996 paper
"""
from __future__ import (absolute_import, division,
print_function)
import glob
import datetime
import os
import pandas as pd
import nump... | pd.DataFrame(data=d, index=df.index) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from constants import *
import numpy as np
import pandas as pd
import utils
import time
from collections import deque, defaultdict
from scipy.spatial.distance import cosine
from scipy import stats
import math
seed = SEED
cur_stage = CUR_STAGE
mode = cur_mode... | pd.DataFrame(left_result,index=feat_left.index,columns=['left_allitem_item_textsim_max','left_allitem_item_textsim_sum']) | pandas.DataFrame |
# TODO make it handle missing data
from __future__ import unicode_literals
__all__ = [
'clean_FIPS',
'fix_FIPS',
'get_custom_bins',
'make_choropleth',
'AreaPopDataset',
'CityInfo',
'CityLabel',
'ChoroplethStyle',
'Choropleth'
]
import geopandas as gpd
import numpy... | pd.read_csv(data_csv) | pandas.read_csv |
# Notebook to transform OSeMOSYS output to same format as EGEDA
# Import relevant packages
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
from openpyxl import Workbook
import xlsxwriter
import pandas.io.formats.excel
import glob
import re
# Path for OSeMOSYS output
path_output = './d... | pd.DataFrame() | pandas.DataFrame |
import datetime
import os
import pickle
import urllib.parse
import urllib.request as request
from collections import Counter
from contextlib import closing
from datetime import timedelta
from pathlib import Path
import numpy as np
import pandas as pd
import tika
import wget
from dateutil import parser
os.environ['TIK... | pd.concat([done, finish], axis=0, join='outer', ignore_index=False, copy=True) | pandas.concat |
import json
from os import listdir
import pandas as pd
import multiprocessing as mp
THRESHOLD=0.82
DIR='/mnt/ceph/storage/data-in-progress/data-research/web-search/SIGIR-21/sigir21-deduplicate-trec-run-files/'
def analyze_jsonl_line(line):
dedup_data = json.loads(line)
docs_to_remove = []
for... | pd.DataFrame(rows) | pandas.DataFrame |
import unittest
import pandas as pd
import numpy as np
from scipy.sparse.csr import csr_matrix
from string_grouper.string_grouper import DEFAULT_MIN_SIMILARITY, \
DEFAULT_REGEX, DEFAULT_NGRAM_SIZE, DEFAULT_N_PROCESSES, DEFAULT_IGNORE_CASE, \
StringGrouperConfig, StringGrouper, StringGrouperNotFitException, \
... | pd.testing.assert_series_equal(expected_result, result) | pandas.testing.assert_series_equal |
from typing import *
import numpy as np
import argparse
from toolz.itertoolz import get
import zarr
import re
import sys
import logging
import pickle
import pandas as pd
from sympy import Point, Line
from skimage import feature, measure, morphology, img_as_float
from skimage.filters import rank_order
from scipy import ... | pd.concat([fov_subdataset_df]*counts_df.shape[0],axis=0) | pandas.concat |
import pandas as pd
df=pd.read_csv("C:/Users/Administrator/Desktop/at1.csv")
import cv2
cam = cv2.VideoCapture(0)
detector = cv2.CascadeClassifier('C:/Users/Administrator/Desktop/haarcascade_frontalface_default.xml')
Id = input('Enter your id:')
Name=input('Enter your name:')
df2 = | pd.DataFrame({"Id":[Id],"Name":[Name]}) | pandas.DataFrame |
import subprocess
from pandas.io.json import json_normalize
import pandas as pd
import os
import PIL
import glob
import argparse
import numpy as np
import pandas as pd
from PIL import Image
import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as tr... | pd.read_csv(input_dir+'/dev.csv') | pandas.read_csv |
#%%
import docx
from datetime import date
import pandas as pd
import numpy as np
from pandas.core.frame import DataFrame
from pandas.core.reshape.merge import merge_ordered
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler
scale = StandardScaler()
from sklearn.cluster impor... | pd.DataFrame(label) | pandas.DataFrame |
import sys, os
import unittest
import pandas as pd
import numpy
import sys
from sklearn import datasets
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, Imputer, LabelEncoder, LabelBinarizer, MinMaxScaler, MaxAbsScaler, RobustScaler,\
Binarizer, PolynomialFeatures, OneHotEn... | pd.DataFrame(iris.data, columns=iris.feature_names) | pandas.DataFrame |
from collections import abc, deque
from decimal import Decimal
from io import StringIO
from warnings import catch_warnings
import numpy as np
from numpy.random import randn
import pytest
from pandas.core.dtypes.dtypes import CategoricalDtype
import pandas as pd
from pandas import (
Categorical,
DataFrame,
... | DataFrame([[1, 2], [3, 4]], columns=["a", "b"]) | pandas.DataFrame |
import hashlib
import neptune.new as neptune
import pandas as pd
import xgboost as xgb
from neptune.new.integrations.xgboost import NeptuneCallback
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
# (neptune) create run
run = neptune.init(
project="<WORKSPACE/PR... | pd.DataFrame(enc_data) | pandas.DataFrame |
# 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... | tm.assert_equal(NaT != left, expected) | pandas._testing.assert_equal |
# last edited: 04/10/2021
#
# The functions pca_initial, pca_initial_, pca_final, and pca_final_ are adapted
# from a post by <NAME> here:
# https://nirpyresearch.com/classification-nir-spectra-principal-component-analysis-python/
#
# Retrieved in December 2020 and is licensed under Creative Commons Attribution 4.0
# I... | pd.DataFrame(Xt1) | pandas.DataFrame |
import glob
import pandas as pd
import datetime
import re
from constants import CAT_TO_SUBCAT, DATA_PATH_PATTERN
def get_expenses_df():
expenses = read_newest_csv()
expenses = clean_df(expenses)
expenses = aggregate_categories(expenses)
expenses = expenses.sort_values('Date', ascending=False)
prin... | pd.to_numeric(cleaned.Cost, errors='coerce') | pandas.to_numeric |
import logging
import pandas as pd
import requests
import io
import re
import datetime
def create_elo_dict(db):
elo_dict = pd.read_csv('data/raw/elo_dictionary.csv', sep=';')[['fd.name', 'elo.name']]
elo_dict = elo_dict.rename(columns={'fd.name':'fd_name', 'elo.name':'elo_name'})
elo_dict['updated_untill'... | pd.to_datetime(eloRank.From) | pandas.to_datetime |
#!/usr/bin/env python
import pandas as pd
import click
from bokeh.io import vform
from bokeh.plotting import figure, show, output_file
from bokeh.models import CustomJS, ColumnDataSource
from bokeh.models.widgets import Select
from bokeh.palettes import (Blues9, BrBG9, BuGn9, BuPu9, GnBu9, Greens9,
... | pd.Series(colors, index=index) | pandas.Series |
import torch
import torch.nn as nn
from torchvision import transforms as TF
from models.NIMA_model.nima import NIMA
import argparse
import os
from PIL import Image
import pandas as pd
import numpy as np
from tqdm import tqdm
import matplotlib.pyplot as plt
transforms = TF.Compose([
TF.Resize((224,224)),
TF.To... | pd.DataFrame(data) | pandas.DataFrame |
"""Unit tests for track_reanalysis.py."""
import copy
import unittest
import numpy
import pandas
from gewittergefahr.gg_utils import track_reanalysis
from gewittergefahr.gg_utils import temporal_tracking
from gewittergefahr.gg_utils import storm_tracking_utils as tracking_utils
TOLERANCE = 1e-6
ORIG_X_COORDS_METRES ... | pandas.DataFrame.from_dict(THIS_DICT) | pandas.DataFrame.from_dict |
# 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... | pd.Timedelta('5 days') | pandas.Timedelta |
"""
**EMR Data Censoring Function**
Contains source code for :ref:`censorData` tool.
"""
import pandas as pd
import numpy as np
def censor_diagnosis(genotype_file, phenotype_file, final_pfile, final_gfile, efield, delta_field=None, start_time=np.nan, end_time=np.nan):
"""
Specify a range of ages for censoring even... | pd.read_csv(phenotype_file) | pandas.read_csv |
# Copyright (c) 2019-2021 - for information on the respective copyright owner
# see the NOTICE file and/or the repository
# https://github.com/boschresearch/pylife
#
# 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 co... | pd.Interval(expected-1./96., expected+1./96.) | pandas.Interval |
from scipy.sparse import issparse, isspmatrix
import numpy as np
import pandas as pd
from multiprocessing.dummy import Pool as ThreadPool
import itertools
from tqdm import tqdm
from anndata import AnnData
from typing import Union
from .utils import normalize_data, TF_link_gene_chip
from ..tools.utils import flatten, e... | pd.isna(t1_df) | pandas.isna |
import logging
import numpy as np
import pandas as pd
from collections import Counter as counter
from tardis.plasma.properties.base import (
ProcessingPlasmaProperty,
HiddenPlasmaProperty,
BaseAtomicDataProperty,
)
from tardis.plasma.exceptions import IncompleteAtomicData
logger = logging.getLogger(__nam... | pd.DataFrame(updated_index) | pandas.DataFrame |
import calendar as cal
import pandas as pd
from hidrocomp.series.exceptions import StationError
from hidrocomp.statistic.pearson3 import Pearson3
from hidrocomp.series.series_build import SeriesBuild
from hidrocomp.series.partial import Partial
from hidrocomp.series.maximum import MaximumFlow
from hidrocomp.series.min... | pd.DataFrame(self.data[self.station]) | pandas.DataFrame |
import pandas as pd
import numpy as np
import sklearn as sk
import matplotlib.pyplot as plt
from sklearn import metrics
from json import *
import requests
pd.set_option('display.max_rows', 21000)
pd.set_option('display.max_columns', 500)
| pd.set_option('display.width', 150) | pandas.set_option |
import numpy as np
import pandas as pd
from matplotlib import *
# .........................Series.......................#
x1 = np.array([1, 2, 3, 4])
s = | pd.Series(x1, index=[1, 2, 3, 4]) | pandas.Series |
"""
This class contains all parameters for all models for different countries.
It contains methods to obtain observed data.
It also contains the common methods to use the model itself.
"""
import numpy as np
import pandas as pd
import math
from Communication import Database
np.set_printoptions(suppress=True)
# Insp... | pd.DataFrame(data=data_dict) | pandas.DataFrame |
from __future__ import annotations
import copy
import itertools
from typing import (
TYPE_CHECKING,
Sequence,
cast,
)
import numpy as np
from pandas._libs import (
NaT,
internals as libinternals,
)
from pandas._libs.missing import NA
from pandas._typing import (
ArrayLike,
DtypeObj,
M... | DatetimeArray(i8values, dtype=empty_dtype) | pandas.core.arrays.DatetimeArray |
from unittest import TestCase
import pandas as pd
import numpy as np
from skbio import OrdinationResults
from q2_convexhull.convexhull import convex_hull
from q2_convexhull.convexhull import validate
from pandas.testing import assert_frame_equal
from qiime2 import Metadata
class TestConvexHull(TestCase):
def set... | assert_frame_equal(hulls, expected) | pandas.testing.assert_frame_equal |
# -*- coding: utf-8 -*-
try:
import json
except ImportError:
import simplejson as json
import math
import pytz
import locale
import pytest
import time
import datetime
import calendar
import re
import decimal
import dateutil
from functools import partial
from pandas.compat import range, StringIO, u
from pandas.... | ujson.encode(i, orient="records") | pandas._libs.json.encode |
from hyperopt import hp
import pandas as pd
import numpy as np
from pyFTS.models import hofts
from pyFTS.models.multivariate import granular
from pyFTS.partitioners import Grid, Entropy
from pyFTS.models.multivariate import variable
from pyFTS.common import Membership
from spatiotemporal.models.clusteredmvfts.fts impor... | pd.DataFrame(data) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# In[2]:
import os
import sys
import pandas as pd
import numpy as np
# In[3]:
# In[4]:
#file 불러오기
#filepath = sys.argv[1]
#filename = sys.argv[2]
filepath = "/home/data/projects/rda/workspace/rda/files/"
filename = "input3.csv"
data = pd.read_csv(filepath + "/" + filena... | pd.Series(nmi) | pandas.Series |
# Essentials
import pandas as pd
import numpy as np
# Plots
import matplotlib.pyplot as plt
from tqdm import tqdm
# Models
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier, VotingClassifier
import xgboost as xgb
# Misc
from rdkit import Chem
from sklearn.model_selection import GridSear... | pd.DataFrame(results_average_precision, columns=sizes) | pandas.DataFrame |
import locale
import numpy as np
import pytest
from pandas.compat import (
is_platform_windows,
np_version_under1p19,
)
import pandas as pd
import pandas._testing as tm
from pandas.core.arrays import FloatingArray
from pandas.core.arrays.floating import (
Float32Dtype,
Float64Dtype,
)
def test_uses... | FloatingArray(arr, mask) | pandas.core.arrays.FloatingArray |
#!/usr/bin/env python
import argparse
import csv
import json
import sys
import time
from confluent_kafka import Producer
import socket
from newsapi import NewsApiClient
import http.client
import urllib.parse
import pandas as pd
import numpy as np
offset = 0
def acked(err, msg):
if err is not None:
prin... | pd.DataFrame(articles) | pandas.DataFrame |
from IPython import embed
from requests import get
from requests.exceptions import RequestException
from contextlib import closing
import pandas as pd
def simple_get(url):
"""
Attempts to get the content at `url` by making an HTTP GET request.
If the content-type of response is some kind of HTML/XML, retur... | pd.DataFrame(data, columns=['Name', 'Type', 'Description']) | pandas.DataFrame |
"""Utility functions shared across the Aquarius project."""
import ftplib
import os
import logging
import gzip
import numpy as np
import pandas as pd
import yaml
import json
from datetime import timedelta, date, datetime
from dfply import (
X,
group_by,
summarize,
mask,
n,
transmute,
select,... | pd.concat([station_name, country_name, continent_name], axis=0) | pandas.concat |
from IPython.core.error import UsageError
from mock import MagicMock
import numpy as np
from nose.tools import assert_equals, assert_is
import pandas as pd
from pandas.testing import assert_frame_equal
from sparkmagic.livyclientlib.exceptions import BadUserDataException
from sparkmagic.utils.utils import parse_argstri... | assert_frame_equal(expected, df) | pandas.testing.assert_frame_equal |
import re
from unittest.mock import Mock, call, patch
import numpy as np
import pandas as pd
import pytest
from rdt.transformers.categorical import (
CategoricalFuzzyTransformer, CategoricalTransformer, LabelEncodingTransformer,
OneHotEncodingTransformer)
RE_SSN = re.compile(r'\d\d\d-\d\d-\d\d\d\d')
class ... | pd.Series([1, 3, 3, 2, 1]) | pandas.Series |
# -*- coding: utf-8 -*-
# Copyright StateOfTheArt.quant.
#
# * Commercial Usage: please contact <EMAIL>
# * Non-Commercial Usage:
# 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.DataFrame(tensor_np) | pandas.DataFrame |
import sqlite3
import pandas as pd
import numpy as np
def save_results(env, agent, history, reward, scenario=None, agent_name=None, notes=None):
conn = sqlite3.connect('gym_battery_database.db')
result = conn.execute('SELECT MAX(scenario_id) FROM grid_flow_output;')
scenario_id = int(result.fetchone()[0])... | pd.DataFrame(history, columns=['episode_cnt', 'reward', 'new_demand', 'orig_reward', 'orig_demand']) | 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... | Series([False, True, False, True]) | pandas.Series |
# county level symptoms map for Sweden
import csv
import json
import os
import pandas as pd
import plotly.express as px
import requests
base_path = os.getenv("PYTHONPATH", ".")
# map
with open(f"{base_path}/sweden-counties.geojson", "r") as sw:
jdata = json.load(sw)
# dictionary to match data and map
counties_i... | pd.to_numeric(df1["Uppskattning"], errors="coerce") | pandas.to_numeric |
# Copyright 2018 BBVA
#
# 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, softwar... | pd.concat([week_df, aux_df]) | pandas.concat |
#!/usr/bin/env python
# coding: utf-8
# # EDA + CenterNet Baseline
#
# References:
# * Took 3D visualization code from https://www.kaggle.com/zstusnoopy/visualize-the-location-and-3d-bounding-box-of-car
# * CenterNet paper https://arxiv.org/pdf/1904.07850.pdf
# * CenterNet repository https://github.com/xingyizhou/Cen... | pd.read_csv(PATH + 'sample_submission.csv') | pandas.read_csv |
import pandas as pd
import datetime
from pandas import DataFrame
from pandasql import sqldf
loc = locals()
def calculate_average_ticker_price(prices: {}, total_quantity: float) -> float:
"""
:param prices: a list of price * quantity needed to calculate the average price of each stock
:param total_quantity... | pd.set_option('display.max_columns', 500) | pandas.set_option |
#!/usr/bin/python
print('Loading modules...')
import os, sys, getopt, datetime
import pickle as pkl
import pandas as pd
import numpy as np
from xgboost import XGBRegressor, XGBClassifier
from dairyml import XGBCombined
from skll.metrics import spearman, pearson
from sklearn.utils import shuffle
from sklearn.model_s... | pd.DataFrame(index=X.index) | pandas.DataFrame |
"""
timedelta support tools
"""
import re
from datetime import timedelta
import numpy as np
import pandas.tslib as tslib
from pandas import compat, _np_version_under1p7
from pandas.core.common import (ABCSeries, is_integer, is_integer_dtype, is_timedelta64_dtype,
_values_from_object, i... | is_timedelta64_dtype(arg) | pandas.core.common.is_timedelta64_dtype |
# -*- 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.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
# Original Code by <NAME> for VOST Portugal
# 18 MAR 2022
# -----------------------------------------------
# LIBRARIES
# -----------------------------------------------
# Import Dash and Dash Bootstrap Components
import dash
import dash_bootstrap_components as dbc
from dash... | pd.to_datetime(df_tl['timestamp']) | pandas.to_datetime |
import numpy as np
import pandas
import random
import re
import sys
from scipy.stats import pearsonr, spearmanr
# ausiliary functions
def buildSeriesByCategory(df, categories):
res = []
for cat in categories:
occ = df.loc[df["category"] == cat].shape[0]
res.append(occ)
res_series = pandas... | pandas.notna(df["reaction"]) | pandas.notna |
# -*- coding: utf-8 -*-
# -*- python 3 -*-
# -*- <NAME> -*-
# Import packages
import re
import numpy as np
import pandas as pd
import os ##for directory
import sys
import pprint
'''general function for easy use of python'''
def splitAndCombine(gene, rxn, sep0, moveDuplicate=False):
## one rxn has several gen... | pd.Series(list0) | pandas.Series |
# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
#
# 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 Licen... | pd.date_range('2015-10-01', periods=100) | pandas.date_range |
# Created by <NAME>
# email : <EMAIL>
import json
import os
import time
from concurrent import futures
from copy import deepcopy
from pathlib import Path
from typing import IO, Union, List
from collections import defaultdict
import re
from itertools import tee
import logging
# Non standard libraries
import pandas as p... | pd.to_datetime(df['created'], format='%Y-%m-%dT%H:%M:%SZ') | pandas.to_datetime |
# <NAME>
# <EMAIL>
import numpy as np
import pandas as pd
from flam2millijansky.flam2millijansky import flam2millijansky
from hstphot.container import Container
def prepare_KN_nebular_spc(wavelength_angstrom,luminosity_per_angstrom,luminosity_distance_mpc,container):
"""
prepare_KN_nebular_spc function prepar... | pd.DataFrame(out) | pandas.DataFrame |
# License: Apache-2.0
from gators.encoders.woe_encoder import WOEEncoder
from pandas.testing import assert_frame_equal
import pytest
import numpy as np
import pandas as pd
import databricks.koalas as ks
ks.set_option('compute.default_index_type', 'distributed-sequence')
@pytest.fixture
def data():
X = pd.DataFram... | assert_frame_equal(X_new, X_expected) | pandas.testing.assert_frame_equal |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created in September 2020
@author: karliskanders
Functions and classes for generating and analysing career transition recommendations
"""
import pandas as pd
import numpy as np
import pickle
from time import time
import yaml
import os
from ast import literal_eval
fr... | pd.DataFrame(data=columns) | pandas.DataFrame |
# Needed libraries
import pandas as pd
from pandas import json_normalize
from coinsta.exceptions import BadSnapshotURL, WrongCoinCode, ApiKeyError
from coinsta.utils import _readable_date, _ticker_checker, _snapshot_readable_date, _parse_cmc_url
from datetime import date, datetime
from requests.exceptions import Connec... | pd.to_datetime(df['Date']) | pandas.to_datetime |
#! /usr/bin/python3
# Developer: <NAME>
# -*- coding: utf-8 -*-
import os
import subprocess
import pandas as pd
import openpyxl as xl
from pathlib import Path
import pyodbc
# Available functions
# Correct column's name by position
def correct(col_, head_):
for flag_, value_ in head_.items():
# match colum... | pd.read_csv(out_csv_, dtype=object) | pandas.read_csv |
#!/usr/bin/env python
# coding: utf-8
# In[47]:
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import plotly.express as px
from dash.dependencies import Input, Output
import numpy as np
import plotly.graph_objects as go
import dash_bootstrap_components as dbc
f... | pd.to_datetime(clinicalgov_dff['StartDate']) | pandas.to_datetime |
# -*- coding: utf-8 -*-
import pytest
import numpy as np
import pandas as pd
import pandas.util.testing as tm
import pandas.compat as compat
###############################################################
# Index / Series common tests which may trigger dtype coercions
###############################################... | pd.Index([0, 1, 2, 3, 1.1]) | pandas.Index |
import pandas as pd
import textacy
import textblob
import en_core_web_sm
nlp = en_core_web_sm.load()
# Multiprocessing Imports
from dask import dataframe as dd
from dask.multiprocessing import get
from multiprocessing import cpu_count
# Sentiment Imports
from vaderSentiment.vaderSentiment import SentimentIntensityAn... | pd.DataFrame(entity_sentiment_info) | pandas.DataFrame |
import numpy as np
import pandas as pd
from torch.utils.data import Dataset
from torch import tensor, float32
import json
from collections import defaultdict
# представление очищенного датасета в pytorch
class DatasetModel(Dataset):
def __init__(self, df, vectorizer):
self.df = df
self._vectorize... | pd.DataFrame(self.final_list) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Oct 25 09:35:31 2018
@author: <EMAIL>
Last modified: 2019-11-04
------------------------------------------------------
** Semantic Search Analysis: Build MeSH term list **
------------------------------------------------------
This script: Creates t... | pd.merge(TuisByCui, SemanticNetwork, left_on='TUI', right_on='TUI', how='left') | pandas.merge |
from unittest import TestCase
from unittest.mock import (
ANY,
Mock,
patch,
)
import numpy as np
import pandas as pd
from pandas.testing import assert_frame_equal
from pypika import Order
from fireant.queries.pagination import paginate
from fireant.tests.dataset.mocks import (
dimx2_date_bool_df,
... | assert_frame_equal(expected, paginated) | pandas.testing.assert_frame_equal |
def to_ascii(rows, n=None):
from terminaltables import AsciiTable
if n is None:
n = rows.max_display_rows
table_data = [rows.headers]
for each in rows.rows[:n]:
table_data.append(each)
if len(table_data) < len(rows):
table_data.append(['...']*len(table_dat... | pd.DataFrame(rows.rows, columns=rows.headers) | pandas.DataFrame |
"""
This script transforms the Semeval Task 5: Hyperpartisan News Detection data
provided in XML format, to CSV format for easier use.
"""
import pandas as pd
import xml.etree.cElementTree as et
import numpy as np
gfiles = ["./ground-truth-training-byarticle-20181122.xml",
"./ground-truth-training-bypublishe... | pd.read_csv(files[i][:-4] + ".csv", sep=',') | pandas.read_csv |
import ffn
import pandas as pd
import numpy as np
from numpy.testing import assert_almost_equal as aae
try:
df = pd.read_csv('tests/data/test_data.csv', index_col=0, parse_dates=True)
except FileNotFoundError as e:
try:
df = pd.read_csv('data/test_data.csv', index_col=0, parse_dates=True)
except Fi... | pd.Series([1, 2, 3, 4, 5]) | pandas.Series |
import numpy as np
import pytest
from pandas import Series
import pandas._testing as tm
def no_nans(x):
return x.notna().all().all()
def all_na(x):
return x.isnull().all().all()
@pytest.fixture(params=[(1, 0), (5, 1)])
def rolling_consistency_cases(request):
"""window, min_periods"""
return reque... | tm.assert_equal(rolling_f_result, rolling_apply_f_result) | pandas._testing.assert_equal |
import operator
import re
import warnings
import numpy as np
import pytest
from pandas._libs.sparse import IntIndex
import pandas.util._test_decorators as td
import pandas as pd
from pandas import isna
from pandas.core.sparse.api import SparseArray, SparseDtype, SparseSeries
import pandas.util.testing as tm
from pan... | SparseArray([1, 2, 3]) | pandas.core.sparse.api.SparseArray |
from . import wrapper_double, wrapper_float
import numpy as np, pandas as pd
from scipy.sparse import coo_matrix, csr_matrix, csc_matrix, issparse, isspmatrix_coo, isspmatrix_csr, isspmatrix_csc
import multiprocessing
import ctypes
import warnings
__all__ = ["CMF", "CMF_implicit",
"OMF_explicit", "OMF_impli... | pd.Categorical(X_col, self.item_mapping_) | pandas.Categorical |
import sys
import pytz
import hashlib
import numpy as np
import pandas as pd
from datetime import datetime
def edit_form_link(link_text='Submit edits'):
"""Return HTML for link to form for edits"""
return f'<a href="https://docs.google.com/forms/d/e/1FAIpQLScw8EUGIOtUj994IYEM1W7PfBGV0anXjEmz_YKiKJc4fm-tTg/... | pd.read_csv('data/candidate_statuses.csv') | pandas.read_csv |
###############################################
# #
# Interfacing with Excel Module to build DSM #
# #
# Contrib: uChouinard #
# V0 03/03/2019 #
# ... | pds.read_excel(self.input_filename, 'Input_Level') | pandas.read_excel |
from itertools import product
import pytest
import numpy as np
import pandas as pd
import iguanas.rule_scoring.rule_scoring_methods as rsm
import iguanas.rule_scoring.rule_score_scalers as rss
from iguanas.rule_scoring import RuleScorer
from iguanas.metrics.classification import Precision
@pytest.fixture
def create_d... | pd.Series({'A': 69, 'B': 100, 'C': 76}) | pandas.Series |
# This file contains functions that complete dataset with missing entries
import pandas as pd
import numpy as np
from utils.data import *
# Method 1
def complete_by_value(data, value=0, print_time=False):
"""
Replace NaN with `value` passed as argument
"""
if print_time:
tt = time.process_time... | pd.concat([data_unprotected, data_protected], axis=1) | pandas.concat |
# -*- coding: utf-8 -*-
"""
Library for demonstrating simple collaborative filtering
@author: <NAME>
"""
import os
import math
import numpy as np
import pandas as pd
import time
from statistics import mean
from math import sqrt
# convert the transaction data (long data) into a ratings matrix (wide data)
# assume the ... | pd.DataFrame(ratsA,index=[itemnames[i] for i in unseenitemids],columns=['predrating']) | pandas.DataFrame |
import numpy as np
import pandas as pd
from numba import njit
from datetime import datetime
import pytest
from itertools import product
from sklearn.model_selection import TimeSeriesSplit
import vectorbt as vbt
from vectorbt.generic import nb
seed = 42
day_dt = np.timedelta64(86400000000000)
df = pd.DataFrame({
... | pd.DatetimeIndex(['2018-01-03', '2018-01-04'], dtype='datetime64[ns]', name='split_1', freq=None) | pandas.DatetimeIndex |
import os
import numpy as np
import pandas as pd
from collections import defaultdict
from .io import save_data, load_data, exists_data, save_results
from . import RAW_DATA_DIR
DATASETS = ['password', 'keypad', 'fixed_text', 'free_text', 'mobile']
MOBILE_SENSORS = ['pressure', 'tool_major', 'x', 'x_acceleration', 'x_... | pd.concat([press, release], axis=1) | pandas.concat |
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from pandas import DataFrame, Series
# 这两行代码解决 plt 中文显示的问题
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
# read file
datafile = '../data/Sensitivity Analyse.xlsx'
data = pd.read_excel(... | DataFrame(data) | pandas.DataFrame |
#!/usr/bin/python
# encoding: utf-8
"""
@author: Ian
@file: plate_perspective.py
@time: 2019-09-04 13:26
"""
import pandas as pd
from datetime import datetime, timedelta
import os
import sys
sys.path.append('/Users/luoyonggui/PycharmProjects/mayiutils_n1/mayiutils/db')
from pymongo_wrapper import PyMongoWrapper
sys.pa... | pd.merge(dft, dfr, on='ts_code') | pandas.merge |
# -*- coding: utf-8 -*-
try:
import json
except ImportError:
import simplejson as json
import math
import pytz
import locale
import pytest
import time
import datetime
import calendar
import re
import decimal
import dateutil
from functools import partial
from pandas.compat import range, StringIO, u
from pandas.... | ujson.encode(list_input) | pandas._libs.json.encode |
import sys
import pandas as pd
from sklearn import preprocessing
from sklearn.decomposition import PCA
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.metrics import f1_score
from sklearn.model_selection import RepeatedStratifiedKFold, GridSearchCV
from sklearn.pipeline import Pipeline
# Use cros... | pd.read_csv(sys.argv[1]) | pandas.read_csv |
"""Functions to generate metafeatures using heuristics."""
import re
import numpy as np
import pandas as pd
from pandas.api import types
def _raise_if_not_pd_series(obj):
if not isinstance(obj, pd.Series):
raise TypeError(
f"Expecting `pd.Series type as input, instead of {type(obj)} type."
... | types.is_float_dtype(df[col]) | pandas.api.types.is_float_dtype |
#!/usr/bin/env python
# coding: utf-8
# In[ ]:
# to save report:
# clone the following repo: https://github.com/ihuston/jupyter-hide-code-html
# run in terminal: jupyter nbconvert --to html --template jupyter-hide-code-html/clean_output.tpl path/to/CGR_16S_Microbiome_QC_Report.ipynb
# name the above file... | pd.DataFrame(ids_list, columns=['externalid','replicate_1','replicate_2']) | pandas.DataFrame |
def three_way_ANOVA(df_list):
f3_len = len(df_list)
f1_len, f2_len = len(df_list[0].columns), len(df_list[0].index)
# それぞれの因子の効果を求める
f1_mean = sum([df.mean(axis=1) for df in df_list]) / f3_len
f2_mean = sum([df.mean() for df in df_list]) / f3_len
f3_mean = pd.Series([df.mean().mean() for df... | pd.DataFrame(f3_effect) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# @Time: 2020/6/30,030 13:36
# @Last Update: 2020/6/30,030 13:36
# @Author: 徐缘
# @FileName: lightGBM.py
# @Software: PyCharm
import os
import datetime
import requests
import time
import json
import pandas as pd
import numpy as np
from sklearn.model_selection import StratifiedKFold, GridSearc... | me(all_test_data['告警开始时间'], format='%Y-%m-%d %H:%M:%S') | pandas.to_datetime |
import os
# Reduce CPU load. Need to perform BEFORE import numpy and some other libraries.
os.environ['MKL_NUM_THREADS'] = '2'
os.environ['OMP_NUM_THREADS'] = '2'
os.environ['NUMEXPR_NUM_THREADS'] = '2'
import json
import numpy as np
import pandas as pd
from typing import Optional, List, Tuple, Union
from collections ... | pd.Series(result) | pandas.Series |
# -*- coding:utf-8 -*-
# /usr/bin/env python
"""
Date: 2021/7/8 22:08
Desc: 金十数据中心-经济指标-美国
https://datacenter.jin10.com/economic
"""
import json
import time
import pandas as pd
import demjson
import requests
from akshare.economic.cons import (
JS_USA_NON_FARM_URL,
JS_USA_UNEMPLOYMENT_RATE_URL,
JS_USA_EIA_... | pd.to_datetime(temp_se.iloc[:, 0]) | pandas.to_datetime |
import numpy as np
import pandas as pd
import pandas.util.testing as pdt
import pytest
from spandex.targets import scaling as scl
@pytest.fixture(scope='module')
def col():
return pd.Series([1, 2, 3, 4, 5])
@pytest.fixture(scope='module')
def target_col():
return 'target_col'
@pytest.fixture(scope='modul... | pdt.assert_index_equal(result.columns, df.columns) | pandas.util.testing.assert_index_equal |
#%%
# ANCHOR IMPORTS
import sys
import pandas as pd, numpy as np
import pickle
import re
from sklearn import feature_extraction , feature_selection
from scipy.sparse import csr_matrix, hstack
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction import DictVectorizer
from skl... | pd.concat(all_feats_df_list, axis=1, join='inner') | pandas.concat |
import os
import random
import warnings
warnings.filterwarnings('ignore')
import numpy as np
import pandas as pd
from sklearn.decomposition import LatentDirichletAllocation
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer, TfidfTransformer
from sumeval.metrics.rouge import RougeCalculator
... | pd.DataFrame(sentence_scores, columns=['Sentence', 'Importance-Score']) | pandas.DataFrame |
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