prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
# Common Python library imports
import difflib
from concurrent.futures import ThreadPoolExecutor as TPE
from multiprocessing import cpu_count
# Pip package imports
import pandas as pd
from loguru import logger
# Internal package imports
from miner.core import IHandler, Converter
from miner.footballdata.scrapper impor... | pd.to_datetime(football_df['Date'], format='%d/%m/%y') | pandas.to_datetime |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.linear_model import Lasso
import pickle
import os
import warnings
currentpath = os.getcwd()
warnings.filterwarnings('ignore')
rating_path = 'analysisapp/data/ratings.csv'
my_rating_path = 'analysisapp/data/my_ra... | pd.read_csv(rating_path) | pandas.read_csv |
from src.typeDefs.iexRtmRecord import IIexRtmRecord, ISection_1_1
import datetime as dt
from src.repos.metricsData.metricsDataRepo import MetricsDataRepo
import pandas as pd
def fetchIexRtmTableContext(appDbConnStr: str, startDt: dt.datetime, endDt: dt.datetime) -> IIexRtmRecord:
mRepo = MetricsDataRepo(appDbConn... | pd.DataFrame(iexRtmMcvVals) | pandas.DataFrame |
""" test the scalar Timestamp """
import pytz
import pytest
import dateutil
import calendar
import locale
import numpy as np
from dateutil.tz import tzutc
from pytz import timezone, utc
from datetime import datetime, timedelta
import pandas.util.testing as tm
import pandas.util._test_decorators as td
from pandas.ts... | Timestamp('2013-05-01 07:15:45.123456789', tz='US/Eastern') | pandas.Timestamp |
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 31 19:28:58 2020
@author: hcb
"""
import pandas as pd
import numpy as np
import lightgbm as lgb
import os
from tqdm import tqdm
from sklearn.model_selection import KFold
from sklearn.metrics import f1_score
from config import config
import warnings
from sklearn.feature_ex... | pd.DataFrame(svd_tmp) | pandas.DataFrame |
from collections import defaultdict
import pandas as pd
from ..sql.functions import Column, AggColumn, min as F_min, max as F_max, col, _SpecialSpandaColumn
from spanda.core.typing import *
from .utils import wrap_col_args, wrap_dataframe
class DataFrameWrapper:
"""
DataFrameWrapper takes in a Pandas Datafr... | pd.merge(self._df, other._df, on=on, how=how) | pandas.merge |
import os
import pickle
import re
from pathlib import Path
from typing import Tuple, Dict
import pandas as pd
import requests
from bs4 import BeautifulSoup
from selenium import webdriver
from brFinance.scraper.cvm.financial_report import FinancialReport
from brFinance.scraper.cvm.search import SearchDFP, SearchITR
fr... | pd.DataFrame() | pandas.DataFrame |
import os
import re
import datetime
import copy
import codecs
from lxml import etree
import pandas as pd
from sqlalchemy import create_engine
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.dialects import postgresql as psql
from sqlalchemy import Column, Integer, String, DATE
from sq... | pd.to_datetime(s, errors='ignore', format=f) | pandas.to_datetime |
import os
import pandas as pd
from Utils import Truncate
from Cajero import Cajero
from Cliente import Cliente
from Evento import Inicializacion, FinSimulacion, LlegadaCliente, FinAtencion, FinEspera
class Controlador:
def __init__(self, cant_iteraciones, tiempo, mostrar_desde, media_llegada, media_fin):
... | pd.DataFrame() | pandas.DataFrame |
import os
from collections import defaultdict
from concurrent.futures import ProcessPoolExecutor
import celescope
import pysam
import numpy as np
import pandas as pd
import logging
from celescope.tools.utils import format_number, log, read_barcode_file
from celescope.tools.utils import format_stat
from celescope.tools.... | pd.DataFrame(columns=['barcode', 'gene', 'UMI', 'read_count']) | pandas.DataFrame |
# *****************************************************************************
# Copyright (c) 2019-2020, Intel Corporation All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# Redistributions o... | pd.Series([0, 1, 2, np.nan, 4]) | pandas.Series |
import io
import time
import json
from datetime import datetime
import pandas as pd
from pathlib import Path
import requests
drop_cols = [
'3-day average of daily number of positive tests (may count people more than once)',
'daily total tests completed (may count people more than once)',
'3-day average of ... | pd.to_datetime(date) | pandas.to_datetime |
#
# Copyright (C) 2019 Databricks, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to i... | pd.MultiIndex.from_arrays(arrays, names=("number", "color")) | pandas.MultiIndex.from_arrays |
# -*- coding: utf-8 -*-
"""
Created on Tue Jul 2 09:25:41 2019
@author: michaelek
"""
import os
import pandas as pd
from pdsql import mssql
from matplotlib.pyplot import show
pd.options.display.max_columns = 10
date_col = 'Date_Time_Readings'
output_path = r'C:\ecan\git\water-use-advice\2020-08-17'
csv1 = 'L37-08... | pd.to_datetime(df1['Date'] + ' ' + df1['Time'], dayfirst=True) | pandas.to_datetime |
import pandas as pd
import numpy as np
from openpyxl import load_workbook
from matplotlib import pyplot as plt
from matplotlib import rcParams
import matplotlib.ticker as ticker
from collections import namedtuple
import inspect
import os
from lcmod.core import make_shape, get_forecast
def spend_mult(sav_rate, k1=None... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Tue Aug 17 18:15:38 2021
@author: johan
"""
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import tifffile as tf
from scipy import ndimage
from skimage.measure import regionprops, label
import napari
# Use GPU for processing
import pyclespera... | pd.DataFrame(meas, index=[0]) | pandas.DataFrame |
import pandas as pd
import urllib3 as urllib
import urllib.request as urllib2
import json
import glob
import IPython.display
import re
pd.options.display.max_columns = None
http = urllib.PoolManager()
# Load Facility Name to CMS ID json file
fac2CMS_file = 'IL_FacilityName_to_CMS_ID.json'
with open(fac2CMS_file) as... | pd.DataFrame(ltc_data['FacilityValues']) | pandas.DataFrame |
"""
Helpers for metrics
"""
import altair as alt
import numpy as np
import pandas as pd
import streamlit as st
from sklearn import metrics
from xai_fairness.toolkit_perf import (
cumulative_gain_curve, binary_ks_curve)
def confusion_matrix_chart(source, title="Confusion matrix"):
"""Confusion matrix."""
... | pd.DataFrame({"x": percentages, "y": recall}) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
Date: 2021/8/20 18:02
Desc: 东方财富网-数据中心-特色数据-股权质押
东方财富网-数据中心-特色数据-股权质押-股权质押市场概况: http://data.eastmoney.com/gpzy/marketProfile.aspx
东方财富网-数据中心-特色数据-股权质押-上市公司质押比例: http://data.eastmoney.com/gpzy/pledgeRatio.aspx
东方财富网-数据中心-特色数据-股权质押-重要股东股权质押明细: http://data.eastmoney.com/gpz... | pd.DataFrame(data_json["font"]["FontMapping"]) | pandas.DataFrame |
# -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import logging
import requests
import json
from rasa_core_sdk import Action
import pandas as pd
from duckling import DucklingWrapper
# from rasa_c... | pd.to_datetime(dt[0][:10]) | pandas.to_datetime |
# https://imaddabbura.github.io/post/kmeans_clustering/
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.cluster import KMeans
import pandas as pd
import numpy as np
from PIL import Image
def rgb_to_hex(rgb):
return '#%02x%02x%02x' % (int(rgb[0]), int(rgb[1]), int(rgb[2]))
# Convert PIL image t... | pd.DataFrame(new_array, columns=["col1", "col2", "col3"]) | pandas.DataFrame |
import argparse
import itertools
import multiprocessing as mp
import os
from inspect import signature
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from Timer import Timer, timer
import qpputils as dp
try:
from crossval import InterTopicCrossValidation, IntraTopicCrossValidation
from... | pd.DataFrame.from_dict(result, orient='index') | pandas.DataFrame.from_dict |
"""
# install the package
pip install deepctr
# tutorial
https://deepctr-doc.readthedocs.io/en/latest/Quick-Start.html#getting-started-4-steps-to-deepctr
# github
https://github.com/shenweichen/DeepCTR
しかし、これは binary しか出来ないので適応不可能。
binary を無理矢理適応させるばあいは、非クリックデータを何らかの方法で生成する必要がある。
# ---- 次のアイデア ----
# github
https:/... | pd.DataFrame(feature_embeddings) | pandas.DataFrame |
import asyncio
from collections import defaultdict, namedtuple
from dataclasses import dataclass, fields as dataclass_fields
from datetime import date, datetime, timedelta, timezone
from enum import Enum
from itertools import chain, repeat
import logging
import pickle
from typing import Collection, Dict, Generator, Ite... | pd.Index([]) | pandas.Index |
# coding: utf-8
# ### SHL project
#
# * training module: shl_tm (under construction)
#
# * prediction module: shl_pm (completed)
#
# * simulation module: shl_sm (completed, pending OCR)
#
# * misc module: shl_mm (under construction)
#
#
# ### data feeds:
#
# * historical bidding price, per second, time series ... | pd.to_datetime(shl_data_time_field, format='%H:%M:%S') | pandas.to_datetime |
import os
import json
import pandas as pd
import numpy as np
import logging
import shutil
from linker.core.base import (link_config,
COLUMN_TYPES,
LINKING_RELATIONSHIPS)
from linker.core.files import LinkFiles
from linker.core.memory_link_base impor... | pd.notnull(x) | pandas.notnull |
import os
import tempfile
import unittest
import numpy as np
import pandas as pd
from sqlalchemy import create_engine
from tests.settings import POSTGRESQL_ENGINE, SQLITE_ENGINE
from tests.utils import get_repository_path, DBTest
from ukbrest.common.pheno2sql import Pheno2SQL
class Pheno2SQLTest(DBTest):
@unitt... | pd.isnull(query_result.loc[1000060, 'c103_0_0']) | pandas.isnull |
"""
"""
import numpy as np
import pandas as pd
def parse_data_elecciones_esp(votation_file):
#Headers as rows for now
df = pd.read_excel(votation_file, 0)
## circunscripcion
circunscripcion = df.loc[:, :14]
circunscripcion = pd.DataFrame(circunscripcion.loc[1:, :].as_matrix(), columns = circun... | pd.DataFrame(cs, columns=circunscripcion.columns) | pandas.DataFrame |
import pandas as pd
from texthero import nlp
from . import PandasTestCase
import unittest
import string
class TestNLP(PandasTestCase):
"""
Named entity.
"""
def test_named_entities(self):
s = | pd.Series("New York is a big city") | pandas.Series |
# To add a new cell, type '# %%'
# To add a new markdown cell, type '# %% [markdown]'
# %%
import pandas as pd
import glob
import os
# %%
home=os.path.dirname(__file__)+"/../"
# %%
df = pd.read_csv(home+'/COVID-19/dati-province/dpc-covid19-ita-province.csv')
provdata = pd.read_csv(home+'/other_info/provinceData.csv')... | pd.to_timedelta(1,unit='D') | pandas.to_timedelta |
"""Test the surface_io module."""
from collections import OrderedDict
import logging
import shutil
import pandas as pd
import yaml
import fmu.dataio
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
CFG = OrderedDict()
CFG["template"] = {"name": "Test", "revision": "AUTO"}
CFG["masterdata"] = {
... | pd.DataFrame({"STOIIP": [123, 345, 654], "PORO": [0.2, 0.4, 0.3]}) | pandas.DataFrame |
from numpy import mean,cov,double,cumsum,dot,linalg,array,rank
from pylab import plot,subplot,axis,stem,show,figure
import numpy
import pandas
import math
import matplotlib.pyplot as plt
import numpy as np
from sklearn.preprocessing import scale
from sklearn.decomposition import PCA
from sklearn import cross_validation... | pandas.DataFrame(temp) | pandas.DataFrame |
from pathlib import Path
import pandas as pd
import openpyxl
class CompareFiles(object):
def __init__(self, file_one_path: str, file_two_path: str):
self.file_one_path: str = file_one_path
self.file_two_path: str = file_two_path
self.__validate__()
def __validate__(self):
"""
... | pd.read_csv(self.file_two_path) | pandas.read_csv |
from pandas import DataFrame
import numpy as np
import nltk
from collections import Counter
from collections import OrderedDict
from sklearn.feature_extraction.text import TfidfVectorizer
def extract_sim_words(model, brand, result_path, freq_dist, min_count, save=True, topn=20):
df = DataFrame(columns=[['word', 's... | DataFrame(columns=[col_name]) | pandas.DataFrame |
import numpy as np
import pandas as pd
import talib
from talib import stream
def test_streaming():
a = np.array([1,1,2,3,5,8,13], dtype=float)
r = stream.MOM(a, timeperiod=1)
assert r == 5
r = stream.MOM(a, timeperiod=2)
assert r == 8
r = stream.MOM(a, timeperiod=3)
assert r == 10
r = ... | pd.Series([40.29, 40.29, 40.29, 40.29, 40.29, 40.29, 40.29, 40.29, 40.29, 40.29, 40.29, 40.29, 40.29, 40.29, 40.46, 37.08, 33.37, 30.03]) | pandas.Series |
import os
import pandas as pd
from numpy.random import default_rng
def create_sample(
input_file="../../classes_input/test_input.csv",
output_file=None,
percentage_sample=25,
exclude_samples=None,
):
if not output_file:
exclude = ""
if exclude_samples:
excluded_names = ... | pd.unique(input_df["class_id"]) | pandas.unique |
from pathlib import Path
import os
import pandas as pd
import numpy as np
def get_country_geolocation():
dir_path = os.path.dirname(os.path.realpath(__file__))
country_mapping = pd.read_csv(
dir_path + '/data_files/country_centroids_az8.csv', dtype=str)
country_mapping = country_mapping.iloc[:, [... | pd.read_excel(excel_file) | pandas.read_excel |
import numpy as np
import pandas as pd
import time, copy
import pickle as pickle
import sklearn
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import log_loss
from scipy.special import expit
import matplotlib.pyplot as plt
from sklearn.ensemble import AdaBoostClassifier
import statsmodels... | pd.DataFrame([1]) | pandas.DataFrame |
"""
Utilities to use with market_calendars
"""
import itertools
import warnings
import pandas as pd
def merge_schedules(schedules, how='outer'):
"""
Given a list of schedules will return a merged schedule. The merge method (how) will either return the superset
of any datetime when any schedule is open (ou... | pd.Timedelta("1D") | pandas.Timedelta |
import pandas as pd
def get_param_for_symbol(param, ary):
for dict in ary:
keys = dict.keys()
if param in keys:
return dict[param]
def build_param_ary_for_param(symbolary, paramset, start):
dict_df = {}
for param in paramset:
paramary = []
for symbol in symbol... | pd.Series(paramary, index=symbolary) | pandas.Series |
import functools
from tqdm.contrib.concurrent import process_map
import copy
from Utils.Data.Dictionary.MappingDictionary import *
from Utils.Data.Features.Generated.GeneratedFeature import GeneratedFeaturePickle
import pandas as pd
import numpy as np
def add(dictionary, key):
dictionary[key] = dictionary.get(ke... | pd.Series(out) | pandas.Series |
# -*- coding: utf-8 -*-
"""
@author: hkaneko
"""
import math
import sys
import numpy as np
import pandas as pd
import sample_functions
from sklearn import metrics, svm
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_predict, GridSearchCV
from sklearn.nei... | pd.DataFrame(model.feature_importances_, index=x.columns, columns=['importance']) | pandas.DataFrame |
#
# Copyright 2020 EPAM Systems
#
# 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 ag... | pd.DataFrame(input_matrix, columns=provided_columns_names) | pandas.DataFrame |
import os
import sys
import numpy as np
import pandas as pd
from pycompss.api.api import compss_wait_on
from pycompss.api.task import task
from data_managers.fundamentals_extraction import FundamentalsCollector
from data_managers.price_extraction import PriceExtractor
from data_managers.sic import load_sic
from model... | pd.concat(merged_dfs) | pandas.concat |
#!/usr/bin/env python
import argparse
import json
import os
import urllib
from collections import Counter
from datetime import date
import requests
import pandas
class _REST(object):
BASE_URL = 'https://qiita.com'
def __init__(self, headers: dict, **kwargs):
self.queries = {}
self.headers =... | pandas.read_json(output_path) | pandas.read_json |
# -*- 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... | Series(['ABCxx', ' BNSD', 'LDFJH xx']) | pandas.Series |
"""
test_exploreDA
--------------
The module which groups the main functions to explore a new data.
"""
import pandas as pd
import numpy as np
import datetime
from Plotting.contdistrib_plot import cont_distrib_plot
from Plotting.catdistrib_plot import barplot_plot
from Plotting.net_plotting import plot_net_distribu... | pd.DataFrame([longs, lats]) | pandas.DataFrame |
import bisect
import ifc.stockData as stockData
import pandas as pd
import numpy as np
from datetime import datetime
def get_series(ticker_sym, start, end):
df = stockData.get_data_from_google(ticker_sym, start, end)
return Series(stockData.get_data_from_google(ticker_sym, start, end))
class Series(object):
... | pd.Series(PosMF / TotMF) | pandas.Series |
#!/usr/bin/env python3
"""
Python tool to correct attenuation bias stemming from measurement error in polygenic scores (PGI).
"""
import argparse
import copy
import itertools
import logging
import multiprocessing
import os
import re
import stat
import sys
import tarfile
import tempfile
from typing import Any, Dict, L... | pd.DataFrame(columns=OUTPUT_COLUMNNAMES) | pandas.DataFrame |
import os
from functools import partial
from typing import Any, Dict
import numpy as np
import pandas as pd
import tensorflow as tf
import torch
from lenet_analysis import LenetAnalysis, get_class_per_layer_traced_edges
from PIL import Image
from torch.autograd import Variable
from torch.nn import Module
from torchvis... | pd.DataFrame(traces) | pandas.DataFrame |
import numpy
import yaml
import pathlib
import pandas
import geopandas as gpd
from decimal import *
def decimal_divide(numerator, denominator, precision):
"""Returns a floating point representation of the
mathematically correct answer to division of
a numerator with a denominator, up to precisio... | pandas.read_csv(monte_carlo_csv) | pandas.read_csv |
'''
/*******************************************************************************
* Copyright 2016-2019 Exactpro (Exactpro Systems Limited)
*
* 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
... | pandas.to_datetime(frame['Created_tr']) | pandas.to_datetime |
"""Summarise per-hazard total intersections (for the whole system)
Purpose
-------
Collect network-hazard intersection attributes
- Combine with boundary Polygons to collect network-boundary intersection attributes
- Write final results to an Excel sheet
Input data requirements
-----------------------
1. Co... | pd.DataFrame(return_periods,columns=['return period']) | pandas.DataFrame |
import numpy as np
import rasterio as rio
import geopandas as gpd
import pandas as pd
import random
#from osgeo import gdal, ogr, osr
from rasterio.mask import mask
from shapely.geometry import mapping, Polygon
from skimage.util import img_as_float
import os as os
os.chdir('E:/SLICUAV_manuscript_code/3_Landscape_mapp... | pd.DataFrame(feat_struct.featList) | pandas.DataFrame |
from __future__ import division #brings in Python 3.0 mixed type calculation rules
import datetime
import inspect
import numpy as np
import numpy.testing as npt
import os.path
import pandas as pd
import sys
from tabulate import tabulate
import unittest
print("Python version: " + sys.version)
print("Numpy version: " +... | pd.Series([], dtype='float') | pandas.Series |
#----------------------------------------------------------------------------------------------
####################
# IMPORT LIBRARIES #
####################
import streamlit as st
import pandas as pd
import numpy as np
import plotly as dd
import plotly.express as px
import seaborn as sns
import matplotl... | pd.DataFrame(model_full_results["GAM fitted"]) | pandas.DataFrame |
# libraries
import numpy as np
import pandas as pd
from pyliftover import LiftOver
import io
import os
import pyBigWig
import pickle
import time
"""Function to read and format data for process
Args:
input_path (str): The path of the input data.
Chr_col_name (str): The name of the column in the... | pd.DataFrame(temp, columns=["Chr", "BP", new_chr_name, new_pos_name]) | pandas.DataFrame |
###########################################################################################################
## IMPORTS
###########################################################################################################
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import math
import numpy as np
import pand... | pd.read_csv(__history_performance_file) | 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... | tm.box_expected([False, True, True], xbox) | pandas._testing.box_expected |
"""
One table verb implementations for a :class:`pandas.DataFrame`
"""
import warnings
import numpy as np
import pandas as pd
from ..types import GroupedDataFrame
from ..options import get_option
from ..operators import register_implementations
from ..utils import Q, get_empty_env, regular_index
from .common import E... | pd.DataFrame(verb.data) | pandas.DataFrame |
"""
This module illustrates how to retrieve the top-10 items with highest rating
prediction. We first train an SVD algorithm on the MovieLens dataset, and then
predict all the ratings for the pairs (user, item) that are not in the training
set. We then retrieve the top-10 prediction for each user.
"""
from __future__ ... | pd.DataFrame(columns=['userId', 'movieId', 'rating']) | pandas.DataFrame |
import warnings
import numpy as np
import pandas as pd
def create_initial_infections(
empirical_infections,
synthetic_data,
start,
end,
seed,
virus_shares,
reporting_delay,
population_size,
):
"""Create a DataFrame with initial infections.
.. warning::
In case a perso... | pd.Timestamp(end) | pandas.Timestamp |
import re
import unicodedata
from collections import Counter
from itertools import product
import numpy as np
import pandas as pd
from sklearn.decomposition import TruncatedSVD
from sklearn.model_selection import StratifiedKFold
from sklearn.preprocessing import LabelEncoder
from src import sentence_splitter, data_fr... | pd.read_csv("./data/input/train_data.csv") | pandas.read_csv |
import pandas as pd
import numpy as np
def inverse_sample_weights(df, target_col, weight_col,
new_col_name=None, min_class_weight = .01,
return_df = True):
""" Given a target class an column to use to derive training weights,
create a column of weights where the ne... | pd.Series(combined_weights, name=new_col_name) | pandas.Series |
import pandas as pd
import numpy as np
import logging
import os
import geojson
import math
import itertools
import geopandas as gpd
from geopy.geocoders import Nominatim
from shapely.geometry import Point, Polygon, MultiPolygon, shape
import shapely.ops
from pyproj import Proj
from bs4 import BeautifulSoup
import ... | pd.DataFrame(prob_array) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Sun Mar 31 15:54:06 2019
@author: Nathan
"""
import requests
import time
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.support.ui import WebDriverWait
from bs4 import BeautifulSoup, SoupStrainer
import pandas as pd
import... | Series(keywords_orig) | pandas.Series |
import sys
import pandas as pd
import pickle
from sqlalchemy import create_engine
from sklearn.pipeline import Pipeline, FeatureUnion
from sklearn.metrics import confusion_matrix
from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier
from sklearn.model_selection import train_test_split
from sklea... | pd.Series(X) | pandas.Series |
import ast
from datetime import datetime
import pandas as pd
import pytest
from pylighter import AdditionalOutputElement, Annotation
@pytest.mark.parametrize(
"labels, expected",
[
([["O", "O", "O", "O"]], [["O", "O", "O", "O"]]),
([["O", "B-1", "I-1", "I-1"]], [["O", "B-1", "I-1", "I-1"]]),... | pd.read_csv(save_path, sep=";") | pandas.read_csv |
from pykrx.website.krx.krxio import KrxWebIo
import pandas as pd
from pandas import DataFrame
# ------------------------------------------------------------------------------------------
# Ticker
class 상장종목검색(KrxWebIo):
@property
def bld(self):
return "dbms/comm/finder/finder_stkisu"
def fetch(se... | DataFrame(result['output']) | pandas.DataFrame |
#!/usr/bin/env python
import os
import sys
from time import time
import argparse
import numpy as np
import pandas as pd
from davis2017.evaluation import DAVISEvaluation
default_davis_path = 'data/ref-davis/DAVIS'
time_start = time()
parser = argparse.ArgumentParser()
parser.add_argument('--davis_path', type=str, hel... | pd.read_csv(csv_name_per_sequence_path) | pandas.read_csv |
# coding:utf-8
#
# The MIT License (MIT)
#
# Copyright (c) 2016-2018 yutiansut/QUANTAXIS
#
# 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 th... | pd.Series(var, index=A.index) | pandas.Series |
"""
python prepare_input.py
"""
import argparse
import pickle
import pandas as pd
import numpy as np
from tqdm import tqdm
from joblib import Parallel, delayed
from config import parallel, data_path, ID_col, t_col, var_col, val_col
# Ashutosh added extra imports
import gc
#import dask.dataframe as dd
from itertools ... | pd.to_numeric(df_v['variable_value'], errors='ignore') | pandas.to_numeric |
import pandas as pd
import numpy as np
from tensorboard.backend.event_processing.event_accumulator import EventAccumulator
from os import listdir
def load_tensorboard(path):
'''Function to load tensorboard file from a folder.
Assumes one file per folder!'''
event_file = next(filter(lambda filename: filen... | pd.Series(index=steps[idx], data=data[idx]) | pandas.Series |
from __future__ import annotations
from collections import namedtuple
from typing import TYPE_CHECKING
import warnings
from matplotlib.artist import setp
import numpy as np
from pandas.core.dtypes.common import is_dict_like
from pandas.core.dtypes.missing import remove_na_arraylike
import pandas as pd
import pandas... | pprint_thing(key) | pandas.io.formats.printing.pprint_thing |
"""Rank genes according to differential expression.
"""
import numpy as np
import pandas as pd
from math import sqrt, floor
from scipy.sparse import issparse
from .. import utils
from .. import settings
from .. import logging as logg
from ..preprocessing._simple import _get_mean_var
def rank_genes_groups(
a... | pd.DataFrame(data=X[mask, left:right]) | pandas.DataFrame |
"""
Utility functions used by report.py
"""
import pypandoc
import pandas as pd
import numpy as np
import altair as alt
from jinja2 import Template
EPSILON = 1e-9
def run_calc(calc, year, var_list):
"""
Parameters
----------
calc: tax calculator object
year: year to run calculator for
var_li... | pd.melt(pltdata, id_vars="index") | pandas.melt |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jun 14 12:06:14 2018
@author: <NAME>
Spatial Wastewater Treatment and Allocation Tool
SWaTAT
"""
import glob
import pandas as pd
import numpy as np
import os
import logging
from functools import reduce
from math import pi, exp, sqrt
from sklearn.cluste... | pd.DataFrame({'X': energy_start[0], 'Y': energy_start[1].values, 'Z': energy_start[1].index}) | pandas.DataFrame |
import numpy as np
import pandas as pd
from itertools import combinations
class LabelEncoder:
"""
This class encodes the categorical values to either numerical values or the labels specified by the user.
Encoding categorical values is a must as ML models work only with numbers
"""
de... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import glob
import os
from matplotlib import rcParams
rcParams['font.family'] = 'sans-serif'
rcParams['font.sans-serif'] = ['Hiragino Maru Gothic Pro', 'Yu Gothic', 'Meirio', 'Takao', 'IPAexGothic',... | pd.to_datetime(df_xmean.index) | pandas.to_datetime |
import pickle
from io import BytesIO
from unittest.mock import Mock, patch
import numpy as np
import pandas as pd
from rdt.transformers import (
CategoricalTransformer, LabelEncodingTransformer, OneHotEncodingTransformer)
def test_categorical_numerical_nans():
"""Ensure CategoricalTransformer works on numer... | pd.testing.assert_frame_equal(reverse, data) | pandas.testing.assert_frame_equal |
import sox
import random
import yaml
import os
import numpy as np
from inspect import getmembers, signature, isclass, isfunction, ismethod
import librosa
import librosa.display
import yaml
import tempfile
import glob
import logging
import pandas as pd
import itertools
import sys
from collections import OrderedDict
imp... | pd.Series(chordnames) | pandas.Series |
from __future__ import print_function
from pprint import pprint
from time import time
import logging
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn import feature_extraction, model_selection, naive_bayes, metrics, svm
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline impor... | pd.read_json(read_file, encoding='utf-8') | pandas.read_json |
"""Module to read, check and write a HDSR meetpuntconfiguratie."""
__title__ = "histTags2mpt"
__description__ = "to evaluate a HDSR FEWS-config with a csv with CAW histTags"
__version__ = "0.1.0"
__author__ = "<NAME>"
__author_email__ = "<EMAIL>"
__license__ = "MIT License"
from meetpuntconfig.fews_utilities import Fe... | pd.DataFrame(idmap_wrong_section) | pandas.DataFrame |
#!/usr/bin/env python
"""
aperturephot.py - <NAME> (<EMAIL>) - Dec 2014
Contains aperture photometry routines for HATPI. Needs reduced frames.
The usual sequence is:
1. run parallel_extract_sources on all frames with threshold ~ 10000 to get
bright stars for astrometry.
2. run parallel_anet to get WCS headers f... | pd.isnull(catmag) | pandas.isnull |
import argparse
import os.path as osp
from glob import glob
import cv2
import pandas as pd
from tqdm import tqdm
from gwd.converters import kaggle2coco
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--image-pattern", default="/data/SPIKE_images/*jpg")
parser.add_argument("--an... | pd.read_csv(ann_path, sep="\t", names=["x_min", "y_min", "x_max", "y_max"]) | pandas.read_csv |
"""
Optimal power flow in power distribution grids using
second-order cone optimization
by:
<NAME>
30/08/2021
Version 01
"""
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import cvxpy as cvx
"----- Read the database -----"
feeder = pd.read_csv(... | pd.DataFrame() | pandas.DataFrame |
from trueskill import TrueSkill
import pandas as pd
def get_elo(d, env, player):
if player not in d:
d[player] = env.create_rating()
return d[player]
def calc_elo(df):
env = TrueSkill(draw_probability=0)
ratings = {}
for idx, row in df.iterrows():
teams = [[get_elo(ratings, env, pl... | pd.DataFrame(results, columns=['date', 'game', 'teams', 'ranks']) | pandas.DataFrame |
# -*- coding: utf-8 -*-
'''
Rickshaw
-------
Python Pandas + Rickshaw.js
'''
from __future__ import division
import time
import json
from pkg_resources import resource_string
import numpy as np
import pandas as pd
from jinja2 import Environment, PackageLoader
class Chart(object):
'''Visualize Pandas Timeseries... | pd.isnull(objs['x']) | pandas.isnull |
import matplotlib.pyplot as plt
#from baseline_10day_avg import *
import pandas as pd
def run(res):
df = | pd.DataFrame.from_records(res) | pandas.DataFrame.from_records |
# ---
# jupyter:
# jupytext:
# formats: ipynb,py:percent
# text_representation:
# extension: .py
# format_name: percent
# format_version: '1.3'
# jupytext_version: 1.12.0
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# %%
BASE_SEED = 0
D... | pd.DataFrame(X, index=index) | pandas.DataFrame |
import math
import pandas as pd
import numpy as np
import sys
class MultiLayerPerceptron:
def __init__(self, batch_size, learning_rate, num_epochs):
self.batch_size = batch_size
self.learning_rate = learning_rate
self.num_epochs = num_epochs
# Layer 1: weight, bias
# w: 51... | pd.read_csv('train_label.csv', header=None) | pandas.read_csv |
# Collection of pandas scripts that may be useful
import pandas as pd
import os
from PIL import Image
import imagehash
# Image hash functions:
# https://content-blockchain.org/research/testing-different-image-hash-functions/
def phash(img_path):
# Identifies dups even when caption is different
phash = imag... | pd.concat([train, dev_seen, dev_unseen]) | pandas.concat |
"""
Tests for zipline/utils/pandas_utils.py
"""
from unittest import skipIf
import pandas as pd
from zipline.testing import parameter_space, ZiplineTestCase
from zipline.testing.predicates import assert_equal
from zipline.utils.pandas_utils import (
categorical_df_concat,
nearest_unequal_elements,
new_pan... | pd.Series([101, 102, 104], dtype='int64') | pandas.Series |
'''
<NAME>
Stanford University dept of Geophysics
<EMAIL>
Codes to process geospatial data in earth engine and python
'''
import os
import ee
import time
import tqdm
import fiona
import datetime
import numpy as np
import pandas as pd
import xarray as xr
import rasterio as rio
import geopandas as gp
from osgeo im... | MonthEnd(0) | pandas.tseries.offsets.MonthEnd |
import logging
import os
import shutil
import tempfile
from pathlib import Path
import h5py
import numpy as np
import pandas as pd
import pytest
from PyDSS.common import LimitsFilter
from PyDSS.dataset_buffer import DatasetBuffer
from PyDSS.export_list_reader import ExportListProperty
from PyDSS.metrics import Multi... | pd.DataFrame(values) | pandas.DataFrame |
#work on approaches to create a spark dataframe
#create the spark session object
from pyspark.sql import SparkSession
from pyspark.sql.types import *
from pyspark.sql.functions import *
spark = SparkSession.Builder().appName("create-df").master("local[3]").getOrCreate()
sc=spark.sparkContext
print('created spark ses... | pd.DataFrame(data=d) | pandas.DataFrame |
import time
import importlib
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output, State
import dash_table
import pandas as pd
import numpy as np
from sklearn.cluster import KMeans
from sklearn import preprocessing
from sklearn.model_selection... | pd.DataFrame([], columns=['ID', 'sqdist', 'cluster']) | pandas.DataFrame |
# Web-Scraper for Reddit Data
# Data used for paper and results were last scraped in September 2020.
# Adapted from (https://github.com/hesamuel/goodbye_world/blob/master/code/01_Data_Collection.ipynb
# data analysis imports
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pypl... | pd.read_csv('suicide_watch.csv') | pandas.read_csv |
import h5py
import numpy as np
from collections import Counter
import os
import pandas as pd
from multiprocessing import Pool
import time
def read_loom(loom_path):
assert os.path.exists(loom_path)
with h5py.File(loom_path, "r", libver='latest', swmr=True) as f:
gene_name = f["row_attrs/Gene"][...].ast... | pd.DataFrame(bc_gene_mat, columns=gene_name, index=cell_id) | pandas.DataFrame |
import pandas as pd
from pandas.io.json import json_normalize
class EsGroupBy:
def __init__(self,
es_connection,
index_pattern,
time_range_start,
time_range_end,
filters,
single_page_size=10000,
... | json_normalize(df_res['key']) | pandas.io.json.json_normalize |
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