prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
#Calculate the Linear Regression between Market Caps
import pandas as pd
import numpy as np
import datetime as date
today = date.datetime.now().strftime('%Y-%m-%d')
from plotly.subplots import make_subplots
import plotly.graph_objects as go
import plotly.io as pio
pio.renderers.default = "browser"
from checkonchain.g... | pd.merge_asof(BTC_data,XMR2,on='date') | pandas.merge_asof |
import os
import pytest
from shapely.geometry import LineString
from network_wrangler import haversine_distance
from network_wrangler import create_unique_shape_id
from network_wrangler import offset_location_reference
slug_test_list = [
{"text": "I am a roadway", "delim": "_", "answer": "i_am_a_roadway"},
{... | assert_series_equal(df["time"], df["time_results"], check_names=False) | pandas.testing.assert_series_equal |
import json
import os
import numpy as np
import pandas as pd
import sqlalchemy
import logging
# Constants / definitions
# Database constants
SENSOR_LOG_TABLE = 'firefighter_sensor_log'
ANALYTICS_TABLE = 'firefighter_status_analytics'
FIREFIGHTER_ID_COL = 'firefighter_id'
# mySQL needs to be told the firefighter_id c... | pd.concat([window_twa_df, window_gauge_df], axis='columns') | pandas.concat |
'''
Created with love by Sigmoid
@Author - <NAME> - <EMAIL>
'''
import numpy as np
import pandas as pd
import random
import sys
from random import randrange
from .SMOTE import SMOTE
from sklearn.mixture import GaussianMixture
from .erorrs import NotBinaryData, NoSuchColumn
def warn(*args, **kwargs):
... | pd.concat([cluster_df,self.new_df],axis=0) | pandas.concat |
"""
Copyright 2021 <NAME>
Licensed under the Apache License, Version 2.0 (the "License"); you may not use
this work 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 distributed... | pd.ExcelWriter(filename + ".xlsx") | pandas.ExcelWriter |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
Date: 2022/1/26 13:10
Desc: 申万指数-申万一级、二级和三级
http://www.swsindex.com/IdxMain.aspx
https://legulegu.com/stockdata/index-composition?industryCode=851921.SI
"""
import time
import json
import pandas as pd
from akshare.utils import demjson
import requests
from bs4 import Bea... | numeric(temp_df["最低价"]) | pandas.to_numeric |
"""Auxiliary functions to generate dataframes.
"""
##########
# Imports
##########
import pandas as pd
import random
##########
# Int df Size
##########
def create_int_df_size(cols: int, rows: int) -> "dataframe":
"""Returns test dataframe with passed number of columns and rows.
"""
df_dict = {
... | pd.DataFrame(data) | pandas.DataFrame |
import os
import pandas as pd
import sys
from collections import defaultdict
import matplotlib.pyplot as plt
import numpy as np
import random
import statistics
import itertools
JtokWh = 2.7778e-7
weight_factor = [1.50558832,0.35786005,1.0]
path_test = os.path.join(sys.path[0])
representative_days_path= ... | pd.DataFrame(statistics_table) | pandas.DataFrame |
from __future__ import division
import numpy as np
import pandas as pd
from sklearn.dummy import DummyClassifier
from sklearn.model_selection import StratifiedShuffleSplit, LeavePGroupsOut
from sklearn.utils import resample, check_X_y
from sklearn.utils.validation import check_is_fitted
from prep import SITES
from me... | pd.concat([csmf_actual, csmf_pred], axis=1) | pandas.concat |
# Author: <NAME>
# Homework 1
# CAP 5610: Machine Learning
# required libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import random
from scipy.stats import chi2_contingency
# import data
train_df = pd.read_csv('/Users/mdjibanulhaquejiban/PhD_CRCV/Semesters/Spring2021/ML/HW/HW1/Titanic... | pd.DataFrame(Avg) | pandas.DataFrame |
from datetime import datetime
from flask import render_template, flash, redirect, url_for, request, g, \
jsonify, current_app, make_response
from flask_login import current_user, login_required
from app import db
#from app.main.forms import EditProfileForm, PostForm, SearchForm, MessageForm
from app.models import U... | pd.DataFrame(data) | pandas.DataFrame |
"""A dataframe concatenation function for PySpark."""
from collections import abc
import functools
from typing import (
Iterable,
Mapping,
Optional,
Sequence,
Tuple,
Union,
)
import warnings
import pandas as pd
from pyspark.sql import (
DataFrame as SparkDF,
functions as F,
)
from ons_... | pd.Series(dtypes, index=col_names) | pandas.Series |
import importlib.util
spec = importlib.util.spec_from_file_location("BoundaryLayerToolbox", "/Users/claudiopierard/VC/BoundaryLayerToolbox.py")
blt = importlib.util.module_from_spec(spec)
spec.loader.exec_module(blt)
import matplotlib
import numpy as np
import h5py
import matplotlib.pyplot as plt
import scipy as spy
i... | pd.to_datetime('2015-04-08 16:00:00') | pandas.to_datetime |
'''
Urban-PLUMBER processing code
Associated with the manuscript: Harmonized, gap-filled dataset from 20 urban flux tower sites
Copyright (c) 2021 <NAME>
Licensed under the Apache License, Version 2.0 (the "License").
You may obtain a copy of the License at: http://www.apache.org/licenses/LICENSE-2.0
'''
__title__ =... | pd.read_csv(f'{datapath}/{sitename}/Lodz_Lublinek_2006-2015_precipitation.txt',delim_whitespace=True) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Fri Dec 20 02:13:41 2019
@author: islam
"""
import numpy as np
import pandas as pd
from sklearn.metrics import accuracy_score, roc_auc_score,f1_score,recall_score
import heapq # for retrieval topK
from utilities import get_instances_with_random_neg_samples, get_test_i... | pd.read_csv("train-test/train_concentrationsID.csv",names=['like_id']) | pandas.read_csv |
import numpy as np
import pandas as pd
from popmon.hist.histogram import (
HistogramContainer,
project_on_x,
project_split2dhist_on_axis,
sum_entries,
sum_over_x,
)
from popmon.hist.patched_histogrammer import histogrammar as hg
def get_test_data():
df = pd.util.testing.makeMixedDataFrame()
... | pd.Timedelta(days=1) | pandas.Timedelta |
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import plotly.graph_objs as go
from dash.dependencies import Input, Output
import datetime as dt
import pandas_datareader as web
app = dash.Dash()
server = app.server
start = dt.datetime(2000,1,1)
end = dt.datetim... | pd.to_datetime(D_validationData.Date,format="%Y-%m-%d") | pandas.to_datetime |
"""Risk Premiums from Fama-Macbeth Cross-sectional Regression
- pandas datareader, Fama French data library
<NAME>
License: MIT
"""
import os
import numpy as np
import pandas as pd
from pandas import DataFrame, Series
import matplotlib.pyplot as plt
import statsmodels.formula.api as smf
import pandas_datareader as pd... | DataFrame(b, columns=x, index=y) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# Copyright (c) 2018-2021, earthobservations developers.
# Distributed under the MIT License. See LICENSE for more info.
from datetime import datetime
import numpy as np
import pandas as pd
import pytest
import pytz
from freezegun import freeze_time
from pandas import Timestamp
from pandas._tes... | pd.Categorical(["01048"] * 28) | pandas.Categorical |
# Utility functions
import re
import pandas as pd
from collections import Counter
from nltk.tokenize import wordpunct_tokenize
from nltk.corpus import stopwords
import requests
import simplejson
def my_replacements(text):
"""
Quick function to clean up some of my review text. It clears HTML and some extra ch... | pd.read_csv(filename, delim_whitespace=True, skiprows=45, header=None, names=['word', 'affect', 'flag']) | pandas.read_csv |
#!/usr/bin/env python
# coding: utf-8
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import itertools
from tqdm import tqdm
from lidopt.model import evaluate, calculate_metrics
from lidopt import PARAM_GRID, METRICS, EXP, SIM, MODE
from lidopt.parsers import parse_experiment
def run(event=None,... | pd.DataFrame.from_records(all_combinations, columns=col_names) | pandas.DataFrame.from_records |
import re
import datetime as dt
import numpy as np
import pandas as pd
from path import Path
from PIL import Image
import base64
from io import BytesIO
import plotly
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from skimage import io
import onion_trees as ot
im... | pd.merge(dists_df, sd_meta, on='fasta_hdr') | pandas.merge |
import os
from datetime import datetime
import pandas as pd
from pytest import fixture
from socceraction.data.opta import (
OptaCompetitionSchema,
OptaGameSchema,
OptaPlayerSchema,
OptaTeamSchema,
)
from socceraction.data.opta.parsers import MA1JSONParser
@fixture()
def ma1json_parser() -> MA1JSONPa... | pd.DataFrame.from_dict(games, orient="index") | pandas.DataFrame.from_dict |
import numpy as np
import pytest
import pandas as pd
from pandas import DataFrame, Index, Series, date_range, offsets
import pandas._testing as tm
class TestDataFrameShift:
def test_shift(self, datetime_frame, int_frame):
# naive shift
shiftedFrame = datetime_frame.shift(5)
tm.assert_inde... | pd.Timestamp("2020-01-01") | pandas.Timestamp |
#!/usr/bin/env python
import os, sys
import pandas as pd
import subprocess as sp
from pdb import set_trace
sOutput_dir = sys.argv[1]
def Parsing_summary():
if not os.path.isdir("{outdir}/Summary_result".format(outdir=sOutput_dir)):
os.mkdir("{outdir}/Summary_result".format(outdir=sOutput_dir))
sp.ca... | pd.concat([dfCount_INDEL, dfSummary.loc[:,['Total_indel', 'Total', 'IND/TOT']]],axis=1) | pandas.concat |
# -*- 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... | pd.Index([20, 30, 40]) | pandas.Index |
# -*- coding: utf-8 -*-
"""
Created on Fri Aug 2 16:39:25 2019
@author: Shane
"""
import numpy as np
import pandas as pd
from pandas import Series, DataFrame
import scipy
import scipy.stats as stats
import glob
import statsmodels.stats.api as sms
#import matplotlib for plotting
import matplotlib.pyplo... | pd.cut(df.cell_volume, v_bins) | pandas.cut |
import itertools
import json
import logging
import os
import traceback
import uuid
from copy import deepcopy
from typing import Union, List, Dict
import genet.auxiliary_files as auxiliary_files
import genet.exceptions as exceptions
import genet.modify.change_log as change_log
import genet.modify.graph as modify_graph
... | pd.DataFrame(edges_attributes) | pandas.DataFrame |
import pandas as pd
import cv2
import pygame
import numpy as np
from movement_detector.detectors import AbstractMovementDetector
class Interface:
"""
This class displays the video, overlays metadata, and enables user-control.
"""
def __init__(self, detector: AbstractMovementDetector):
self.d... | pd.isna(meta_data['flagged'].iloc[0]) | pandas.isna |
import pytest
import numpy as np
import pandas as pd
from rapidfuzz import fuzz
from polyfuzz.models import BaseMatcher
from tests.utils import get_test_strings
from_list, to_list = get_test_strings()
class MyIncorrectModel(BaseMatcher):
pass
class MyCorrectModel(BaseMatcher):
def match(self, from_list, to... | pd.DataFrame({'From': from_list, 'To': mappings, 'Similarity': scores}) | pandas.DataFrame |
"""Tests for the sdv.constraints.tabular module."""
import uuid
from datetime import datetime
from unittest.mock import Mock
import numpy as np
import pandas as pd
import pytest
from sdv.constraints.errors import MissingConstraintColumnError
from sdv.constraints.tabular import (
Between, ColumnFormula, CustomCon... | pd.to_datetime('2020-10-03') | pandas.to_datetime |
#!/usr/bin/env python
### Up to date as of 10/2019 ###
'''Section 0: Import python libraries
This code has a number of dependencies, listed below.
They can be installed using the virtual environment "slab23"
that is setup using script 'library/setup3env.sh'.
Additional functions are housed in file ... | pd.concat([elistAA, AAadd],sort=True) | pandas.concat |
import numpy as np
import warnings
warnings.filterwarnings("ignore")
import talib as ta
import yfinance as yf
import matplotlib.pyplot as plt
from datetime import datetime
import matplotlib.dates as mdates
from yahooquery import Ticker
import pandas as pd
import streamlit as st
from src.tools import functions as f0
... | pd.DataFrame(data) | pandas.DataFrame |
"""
This module aims to standardize the training and evaluation procedure.
"""
import numpy as np
import pandas as pd
import xarray as xr
from os.path import join, exists
from os import listdir
from ninolearn.utils import print_header, small_print_header
from ninolearn.pathes import modeldir, processeddir
# evaluati... | pd.to_datetime('1963-01-01') | pandas.to_datetime |
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import itertools
from datetime import datetime
import numpy as np
import sklearn.mixture as mix
from matplotlib.dates import YearLocator, MonthLocator
import warnings
from pylab import rcParams
from matplotlib.pyplot import cm
rcParams['figure.fi... | pd.to_datetime(time_series.index) | pandas.to_datetime |
import json
import time
import uuid
import numpy as np
import pandas as pd
from great_expectations.dataset import PandasDataset
from feast import (
Client,
Entity,
Feature,
FeatureTable,
FileSource,
KafkaSource,
ValueType,
)
from feast.contrib.validation.ge import apply_validation, create_... | pd.DataFrame(columns=["key", "num", "set", "event_timestamp"]) | pandas.DataFrame |
#!/usr/bin/env python3
import sys
import argparse
import seaborn
from evalys import *
from evalys.jobset import *
from evalys.mstates import *
from evalys.pstates import *
from evalys.visu.legacy import *
import pandas as pd
import matplotlib.pyplot as plt
def main():
# Argument parsing
parser = argparse.Ar... | pd.concat([df, diff], axis=1) | pandas.concat |
import json
import os
import csv
import socket
import pandas as pd
import numpy as np
import glob
import logging
from datetime import datetime, timedelta
from flask import flash, current_app
from flask_login import current_user
from pathlib import Path
from specter_importer import Specter
from pricing_engine.engine im... | pd.merge(main_df, df_tmp, on='trade_asset_ticker') | pandas.merge |
# ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.7.1
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# # QA queries on new CDR_deid Row Suppression-ICD10IC... | pd.read_gbq(query, dialect='standard') | pandas.read_gbq |
from .baseManager import BaseManager
from ..busSim import BusSim
from ...result.searchResult import SearchResult
import os
import pandas as pd
import geopandas as gpd
from shapely.geometry import Point
from zipfile import ZipFile
import time
from tqdm import tqdm
class LocalManager(BaseManager):
def __init__(self,... | pd.DataFrame(columns=["geometry", "start_time", "map_identifier"]) | pandas.DataFrame |
import networkx as nx
import numpy as np
import pandas as pd
from quetzal.analysis import analysis
from quetzal.engine import engine, nested_logit, optimal_strategy
from quetzal.engine.pathfinder import PublicPathFinder
from quetzal.engine.road_pathfinder import RoadPathFinder
from quetzal.model import preparationmodel... | pd.merge(self.pt_los, right, on=['origin', 'destination']) | pandas.merge |
import sys
import unittest
import numpy as np
import pandas as pd
sys.path.append("../../")
from thex_data.data_consts import TARGET_LABEL, UNDEF_CLASS
from mainmodel.helper_compute import *
from mainmodel.helper_plotting import *
from models.binary_model.binary_model import BinaryModel
from models.ind_model.ind_mode... | pd.DataFrame(preds) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Hosmer-Lemeshow test
@author: Alex (stackoverflow)
"""
import pandas as pd
import numpy as np
from scipy.stats import chi2
def hosmer_lemeshow_test(pihat,real_label):
# pihat=model.predict()
pihatcat=pd.cut(pihat, np.percentile(pihat,[0,25,50,75,100]),labels=False,include_lowest=T... | pd.DataFrame(data2) | pandas.DataFrame |
__author__ = 'thor'
# import ut
import ut.util.ulist
import ut.daf.ch
import ut.daf.get
import pandas as pd
def group_and_count(df, count_col=None, frequency=False):
if isinstance(df, pd.Series):
t = | pd.DataFrame() | pandas.DataFrame |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from __future__ import division
from __future__ import print_function
import os
import sys
import stat
import time
import pickle
import traceback
import redis_lock
import contextlib
import abc
from pathlib import Path
import numpy as np
import ... | pd.HDFStore(self.index_path, mode="r") | pandas.HDFStore |
import blpapi
import logging
import datetime
import pandas as pd
import contextlib
from collections import defaultdict
from pandas import DataFrame
@contextlib.contextmanager
def bopen(debug=False):
con = BCon(debug=debug)
con.start()
try:
yield con
finally:
con.stop()
class BCon(obj... | DataFrame(data) | pandas.DataFrame |
# PyLS-PM Library
# Author: <NAME>
# Creation: November 2016
# Description: Library based on <NAME>'s simplePLS,
# <NAME>'s plspm and <NAME>'s matrixpls made in R
import pandas as pd
import numpy as np
import scipy as sp
import scipy.stats
from .qpLRlib4 import otimiza, plotaIC
import scipy.linalg
from col... | pd.DataFrame.dot(implied_, self.outer_loadings.T) | pandas.DataFrame.dot |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Main entry point for data_inspection. This script reads in
tabular patient data and analyzes it for outliers. First, it inspects specified
columns for data integrity (missing values) and produces histograms if appropriate.
Then it analyzes specified 2d relationships, ... | pd.DataFrame(x_scaled) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Copyright 2014-2019 OpenEEmeter contributors
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/LIC... | pd.isnull(df.iloc[-1].n_days_kept) | pandas.isnull |
import urllib.request as url
from bs4 import BeautifulSoup
import pandas as pd
import os
import re
import csv
metadata = []
datasets_to_download = []
page_no = 1
seed_url = 'https://catalog.data.gov'
files_written = 0
while len(metadata) <= 1000:
try:
page = url.urlopen(seed_url + '/dataset?page=' + s... | pd.read_csv(save_file) | pandas.read_csv |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Monday 3 December 2018
@author: <NAME>
"""
import os
import pandas as pd
import numpy as np
import feather
import time
from datetime import date
import sys
from sklearn.cluster import MiniBatchKMeans, KMeans
from sklearn.metrics import silhouette_score
fr... | pd.DataFrame(A) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
class Sink:
def write(self, key, obj):
raise Exception('Virtual function is not overriden')
def flush(self):
raise Exception('Virtual function is not overriden')
class CompositeSink(Sin... | pd.HDFStore(self._file_path, complib='blosc', complevel=9) | pandas.HDFStore |
import numpy as np
import pandas as pd
from pandas import DataFrame
import matplotlib.pyplot as plt
import seaborn as sns
from os.path import exists
from pathlib import Path
def load_consistency_result(filename):
data = pd.read_csv(filename, header=None)
data = data.iloc[:, 0:2].copy()
#print(data)
d... | pd.DataFrame(new_mal_data) | pandas.DataFrame |
#!/usr/bin/env python3
import random, os, sys, logging, re
import pandas as pd
from Bio import SeqIO
try:
from Bio.Alphabet import generic_dna, IUPAC
Bio_Alphabet = True
except ImportError:
Bio_Alphabet = None
# usages of generic_dna, IUPAC are not supported in Biopython 1.78 (September 2020).
print... | pd.DataFrame(columns=colnames) | pandas.DataFrame |
# encoding: utf-8
import logging
import re
from io import BytesIO
from zipfile import ZipFile
from collections import OrderedDict
import pandas as pd
from urllib.request import urlopen
import os.path
from .helpers import pitch_count, progress, game_state
from .version import __version__
from .event import event
cl... | pd.DataFrame(pitchings, columns = ['game_id','order','stat','player_id']) | pandas.DataFrame |
import numpy as np
import pandas as pd
import pandas.util.testing as tm
import pandas.tseries.period as period
from pandas import period_range, PeriodIndex, Index, date_range
def _permute(obj):
return obj.take(np.random.permutation(len(obj)))
class TestPeriodIndex(tm.TestCase):
def setUp(self):
pa... | period_range('1/1/2000', '1/20/2000', freq='2D') | pandas.period_range |
# -*- coding: utf-8 -*-
import pandas as pd
d = {'one' : pd.Series([1., 2., 3.], index=['a', 'b', 'c']),
'two' : pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])}
df = | pd.DataFrame(d) | pandas.DataFrame |
import requests
import pandas as pd
from typing import Dict, List, Union, Tuple
PATH_LEGISLATIVAS_2019 = "https://raw.githubusercontent.com/Politica-Para-Todos/ppt-archive/master/legislativas/legislativas-2019/data.json"
# mapping between party and manifesto inside PPT repo
PARTY_TO_MANIFESTO_LEGISLATIVAS_2019 = {... | pd.DataFrame(candidates) | pandas.DataFrame |
"""Tests for gate.py"""
import numpy as np
import pandas as pd
import xarray as xr
from timeflux.helpers.testing import DummyData, DummyXArray
from timeflux.nodes.gate import Gate
xarray_data = DummyXArray()
pandas_data = DummyData()
node = Gate(event_opens='foo_begins', event_closes='foo_ends', truncate=True)
de... | pd.Timestamp('2018-01-01 00:00:00.300986584') | pandas.Timestamp |
# coding=utf-8
import os
import os.path
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from loganalysis.const import *
class Log(object):
''' 调度模块Log分析接口类
主要提供如下3类功能：
a) 信息呈现
b）问题发现
c）问题定位
要求所有文件命名符合EI命名格式：子系统_时间.csv
'''
def __init__(self, ... | pd.read_csv(filename, na_values='-', usecols=totcols) | pandas.read_csv |
import numpy as np
import pytest
import pandas as pd
from pandas import DataFrame, Series, concat
from pandas.core.base import DataError
from pandas.util import testing as tm
def test_rank_apply():
lev1 = tm.rands_array(10, 100)
lev2 = tm.rands_array(10, 130)
lab1 = np.random.randint(0, 100, size=500)
... | pd.Timestamp("2018-01-06") | pandas.Timestamp |
# ref: alt-ed-covid-2...analysis_1_vars_and_regression.py
# ref: alt-ed-matching-effects-2...analysis_1_vars_and_regression.py
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
import statsmodels.api as sm
from statsmodels.iolib.summary2 import summary_col
def fsImproveProviderN... | pd.get_dummies(df, columns=['education']) | pandas.get_dummies |
#!/usr/bin/env python
'''
This script generates training dataset for DeepAnchor.
Please include following data within a work_dir and arrange them like that:
work_dir
----raw
----loop.bedpe # ChIA-PET or other types of loop files in bedpe format
----CTCF_peak.bed.gz # The ChIP-seq peak ... | pd.read_csv(file_bedpe, sep='\t', names=bedpe_columns) | pandas.read_csv |
# -*- 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[1], panel4d[1]) | pandas.util.testing.assert_panel_equal |
import os
from glob import glob
import time
import json
from PIL import Image
import pandas as pd
import numpy as np
import torchvision as tv
from rsp.data import bilinear_upsample, BANDS
from tifffile import imread as tiffread
from d3m.container import DataFrame as d3m_DataFrame
from d3m.metadata import base as metad... | pd.DataFrame({'annotations': annotations}) | pandas.DataFrame |
import numpy as np
import pytest
import pandas.util._test_decorators as td
import pandas as pd
from pandas import (
Index,
Interval,
IntervalIndex,
Timedelta,
Timestamp,
date_range,
timedelta_range,
)
import pandas._testing as tm
from pandas.core.arrays import IntervalArray
@pytest.fixtu... | IntervalArray.from_arrays(expected_left, expected_right) | pandas.core.arrays.IntervalArray.from_arrays |
from bs4 import BeautifulSoup
from bs4.element import Comment
import pandas as pd
import requests
# Processa o sitemap principal, para encontrar os sitemaps internos (organizados po dia)
sitemap_url = 'https://towardsdatascience.com/sitemap/sitemap.xml'
xml = requests.get(sitemap_url).content
soup = Beautifu... | pd.DataFrame(dict) | pandas.DataFrame |
import pandas as pd
from genomics_data_index.storage.io.mutation.NucleotideSampleData import NucleotideSampleData
def test_combine_vcf_mask():
num_annotations = 9
data_vcf = [
['SampleA', 'ref', 10, 'A', 'T', 'SNP', 'file', 'ref:10:A:T'] + [pd.NA] * num_annotations,
]
data_mask = [
[... | pd.DataFrame(data_mask, columns=['SAMPLE', 'CHROM', 'POS', 'REF', 'ALT', 'TYPE', 'FILE', 'VARIANT_ID']) | pandas.DataFrame |
# USAGE
# python test_network.py --model santa_not_santa.model --image images/examples/santa_01.png
# import the necessary packages
from keras.preprocessing.image import img_to_array
from keras.models import load_model
import numpy as np
import argparse
import imutils
import cv2
from PIL import Image
import glob
impor... | ExcelWriter('abc.xlsx') | pandas.ExcelWriter |
from context import dero
import pandas as pd
from pandas.util.testing import assert_frame_equal
from pandas import Timestamp
from numpy import nan
import numpy
class DataFrameTest:
df = pd.DataFrame([
(10516, 'a', '1/1/2000', 1.01),
(10516, 'a'... | Timestamp('2000-01-19 00:00:00') | pandas.Timestamp |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
Date: 2022/2/24 15:02
Desc: 东方财富网-数据中心-新股数据-打新收益率
东方财富网-数据中心-新股数据-打新收益率
http://data.eastmoney.com/xg/xg/dxsyl.html
东方财富网-数据中心-新股数据-新股申购与中签查询
http://data.eastmoney.com/xg/xg/default_2.html
"""
import pandas as pd
import requests
from tqdm import tqdm
from akshare.utils i... | ric(big_df['行业市盈率']) | pandas.to_numeric |
"""MovieLens dataset"""
import numpy as np
import os
import re
import pandas as pd
import scipy.sparse as sp
import torch as th
import dgl
from dgl.data.utils import download, extract_archive, get_download_dir
_urls = {
'ml-100k' : 'http://files.grouplens.org/datasets/movielens/ml-100k.zip',
'ml-1m' : 'http:/... | pd.concat([self.all_train_rating_info, self.test_rating_info]) | pandas.concat |
from collections import OrderedDict
from datetime import datetime, timedelta
import numpy as np
import numpy.ma as ma
import pytest
from pandas._libs import iNaT, lib
from pandas.core.dtypes.common import is_categorical_dtype, is_datetime64tz_dtype
from pandas.core.dtypes.dtypes import (
CategoricalDtype,
Da... | Series(data, name=n) | pandas.Series |
'''
Copyright <NAME> and <NAME>
2015, 2016, 2017, 2018
'''
from __future__ import print_function # Python 2.7 and 3 compatibility
import os
import sys
import time
import shutil
#import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# Standard imports
from numpy import pi
fr... | pd.Series({}) | pandas.Series |
import sys
# Do not show error traceback
sys.tracebacklimit=0
# Check if all packages installed
try:
from pandas.core.frame import DataFrame
import pandas as pd
except ImportError as e:
print("Package <pandas> needed to be installed before getting data ! ")
raise e
try:
import requests
except Impo... | pd.DataFrame() | pandas.DataFrame |
import boto3
import json,io
from fbprophet.serialize import model_to_json, model_from_json
import pandas as pd
from fbprophet import Prophet
import datetime
import matplotlib.pyplot as plt
from fastapi import FastAPI
app = FastAPI()
def gen_datetime(datetime_str):
"""
input: datetime string : format examp... | pd.DataFrame(dates, columns=["ds"]) | pandas.DataFrame |
# -*- coding: utf-8 -*-
from datetime import timedelta
import pytest
import numpy as np
import pandas as pd
import pandas.util.testing as tm
from pandas import (Timedelta,
period_range, Period, PeriodIndex,
_np_version_under1p10)
import pandas.core.indexes.period as period
cla... | pd.Index([12, np.nan, 10, 9], name='idx') | pandas.Index |
#!/usr/bin/python
# Imports
import pandas as pd
import numpy as np
from collections import Counter
import tqdm
import math, os
from sklearn.metrics import mean_squared_error
from scipy.sparse.csgraph import minimum_spanning_tree as mst_nsim
from scipy.sparse.linalg import svds
from scipy.sparse import csr_matrix
from... | pd.concat([concat_preds, flat_preds], axis=0) | pandas.concat |
# -*- coding: utf-8 -*-
"""
Created on Tue Nov 7 15:14:59 2017
@author: 028375
"""
from __future__ import unicode_literals, division
import pandas as pd
import os.path
import numpy as np
def get_Cost(Outputs,DataFrame0,DateType0,Date0,CostType0,flag0):
if flag0==1:
Cost0=DataFrame0[DataFrame0[DateType0]... | pd.to_datetime('2017-11-30') | pandas.to_datetime |
# notebook: 00-oh-preprocess_data.ipynb
# %% [markdown]
# # Data Cleanup and Pre-processing
#
# Before we can analyze the data we need to clean the raw data and bring it to a format suited for the analyses.
# %%
# Basic imports and setup.
import sys
import logging
from pathlib import Path
import pandas as pd
from... | pd.Int8Dtype() | pandas.Int8Dtype |
"""
DOCSTRING
"""
import matplotlib.pyplot
import numpy
import os
import pandas
import PIL
import seaborn
import skimage
import time
class EDA:
"""
DOCSTRING
"""
def __init__(self):
dict_labels = {
0: "No DR",
1: "Mild",
2: "Moderate",
3: "Severe"... | pandas.read_csv("../labels/trainLabels_master_256_v2.csv") | pandas.read_csv |
# Copyright 2021 <NAME>. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agree... | pd.read_csv(ngram_gbc_path) | pandas.read_csv |
import streamlit as st
import pandas as pd
import numpy as np
import altair as alt
import pydeck as pdk
import matplotlib.pyplot as plt
from nltk.tokenize import TweetTokenizer
from nltk.corpus import stopwords
import string
from wordcloud import WordCloud
import json
import business_weekday_plot
# [TODO] put a divid... | pd.to_datetime(review_df["date"]) | pandas.to_datetime |
import datetime
import numpy as np
import streamlit as st
import pandas as pd
from sqlalchemy import create_engine
import visualization
import joblib
import random
import SessionState
VALUES = [
0,
1,
5,
10,
25,
50,
75,
100,
200,
300,
400,
500,
750,
1_000,
5... | pd.DataFrame(round_data, index=[0]) | pandas.DataFrame |
import sys
import os
import json
import pandas as pd
from sklearn.utils import class_weight
import numpy as np
from keras import optimizers, callbacks
import tensorflow as tf
from sklearn.metrics import accuracy_score
from utils.ml_utils import data_to_pkl
from arg_parser import UserArgs, ArgParser
import matplotlib
f... | pd.DataFrame(columns=['reg_acc', 'per_class_acc', 'wgt_acc']) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Mon Feb 19 16:49:28 2018
@author: <NAME>
__________________________________________________
### MacPyver.vector ###
### The Swissknife like Python-Package for ###
### work in general and with ... | pd.DataFrame(dic) | pandas.DataFrame |
import od_lib.definitions.path_definitions as path_definitions
import pandas as pd
import datetime
import os
# output directory
ELECTORAL_TERMS = path_definitions.ELECTORAL_TERMS
save_path = os.path.join(ELECTORAL_TERMS, "electoral_terms.csv")
if not os.path.exists(ELECTORAL_TERMS):
os.makedirs(ELECTORAL_TERMS)
... | pd.DataFrame(electoral_terms) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import pandas as pd
# In[2]:
train = pd.read_csv("D:/ML/Dataset/MedicalInsurance/Train-1542865627584.csv")
beneficiary = pd.read_csv("D:/ML/Dataset/MedicalInsurance/Train_Beneficiarydata-1542865627584.csv")
inpatient = pd.read_csv("D:/ML/Dataset/Medica... | pd.concat([fraud_provider_op_df["ClmProcedureCode_1"], fraud_provider_op_df["ClmProcedureCode_2"], fraud_provider_op_df["ClmProcedureCode_3"], fraud_provider_op_df["ClmProcedureCode_4"], fraud_provider_op_df["ClmProcedureCode_5"], fraud_provider_op_df["ClmProcedureCode_6"]], axis=0, sort=True).dropna() | pandas.concat |
# -*- coding: utf-8 -*-
"""
Created on Fri Apr 26 17:17:19 2019
@author: sdenaro
"""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
prices= | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python3
from itertools import product
import numpy as np
import pandas as pd
from scipy import stats
import torch
from tensorqtl import core
from src import logger
def QTL_pairwise(genotypes_df, phenotypes_df, residualizer=None, report_maf=False, return_r_matrix=False):
"""
Wrapper for `tensorqtl... | pd.concat(results) | pandas.concat |
from itertools import product
from typing import Iterator, Optional
import numpy as np
import pandas as pd
from glycan import Glycan, PTMComposition
class Glycoprotein:
"""
A protein with glycans.
:ivar dict glycosylation_sites: glycosylation sites
:ivar int sites: number of glycosylation sites
... | pd.isnull(row.iloc[1]) | pandas.isnull |
# ' % kmergrammar
# ' % <NAME> mm2842
# ' % 15th May 2017
# ' # Introduction
# ' Some of the code below is still under active development
# ' ## Required libraries
# + name = 'import_libraries', echo=False
import os
import sys
import numpy as np
import pandas as pd
import sqlalchemy
import logging
import time
from m... | pd.DataFrame(LR_weights) | pandas.DataFrame |
import pandas as pd
from frozendict import frozendict
from copy import copy
import uuid
from pm4pymdl.objects.mdl.exporter import exporter as mdl_exporter
class Shared:
TSTCT = {}
EKBE_belnr_ebeln = {}
EKPO_matnr_ebeln = {}
EKPO_ebeln_banfn = {}
EKPO_ebeln_ebelp = {}
EKPO_objects = list()
... | pd.DataFrame(Shared.MARA_objects) | pandas.DataFrame |
import pytest
import numpy as np
import pandas
import pandas.util.testing as tm
from pandas.tests.frame.common import TestData
import matplotlib
import modin.pandas as pd
from modin.pandas.utils import to_pandas
from numpy.testing import assert_array_equal
from .utils import (
random_state,
RAND_LOW,
RAND_... | pandas.DataFrame(data) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Read the txt files containing the raw design tables from Chen, Sun and Wu (1993), format them and store them
in a new excel file, with one sheet per run size.
Created on Wed Jan 19 15:57:58 2022
@author: <NAME> - alexandre dot bohyn [at] kuleuven dot be
"""
import os
# % Packages
import re... | pd.DataFrame() | 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.concat(activities, copy=False) | pandas.concat |
import os
import subprocess
import re
import json
import time
import pandas as pd
from keyboard import press
from shutil import copy
from distutils.dir_util import copy_tree
class Script(object):
"""Master object for holding and modifying .cmd script settings,
creating .cmd files, and running them through Ven... | pd.concat(dflist, axis=1) | pandas.concat |
import pytest
from mapping import mappings
from pandas.util.testing import assert_frame_equal, assert_series_equal
import pandas as pd
from pandas import Timestamp as TS
import numpy as np
from pandas.tseries.offsets import BDay
@pytest.fixture
def dates():
return pd.Series(
[TS('2016-10-20'), TS('2016-11... | pd.MultiIndex.from_product([[0, 1], ['front', 'back']]) | pandas.MultiIndex.from_product |
import pytest
import numpy as np
import pandas as pd
from systrade.trading.brokers import PaperBroker
T_START = pd.to_datetime('2019/07/10-09:30:00:000000', format='%Y/%m/%d-%H:%M:%S:%f')
T_END = pd.to_datetime('2019/07/10-10:00:00:000000', format='%Y/%m/%d-%H:%M:%S:%f')
TIMEINDEX = pd.date_range(start=T_START,en... | pd.DateOffset(seconds=30) | pandas.DateOffset |
import os
from contextlib import contextmanager
import numpy as np
import pandas as pd
import pyarrow
import pyarrow.parquet as pq
import tarfile
from .tables import Table
from .cohort import ProcedureCohort
NEW_COLUMNS = {
'Table1_Encounter_Info.csv': None,
'Table2_Flowsheet_status.csv' : {
'flowsheet... | pd.to_numeric(proc_df.days_from_dob_procstart, errors='coerce') | pandas.to_numeric |
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