prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import gpxpy
import pandas as pd
def path_to_gpx(path_to_tcx):
return path_to_tcx.split('.')[0] + '.gpx'
def get_workout_info(path_to_tcx):
"""Get name and type of a workout from its gpx file."""
path = path_to_gpx(path_to_tcx)
with open(path) as f:
gpx = gpxpy.parse(f)
# assert len(gpx.... | pd.DataFrame(dic) | pandas.DataFrame |
# Copyright (c) 2021. <NAME>. All rights Reserved.
import numpy
import numpy as np
import pandas as pd
from bm.datamanipulation.AdjustDataFrame import remove_null_values
class DocumentProcessor:
custom_dtypes = []
model_types = []
def __init__(self):
self.custom_dtypes = ['int64', 'float64', 'd... | pd.isna(df[col]) | pandas.isna |
"""Tests for misc module."""
import mock
import numpy as np
import pandas as pd
from pandas.testing import assert_frame_equal
from numpy.testing import assert_almost_equal
import pytest
import causalimpact
standardize = causalimpact.misc.standardize_all_variables
unstandardize = causalimpact.misc.unstandardize
df_... | pd.DataFrame(data) | pandas.DataFrame |
from __future__ import absolute_import, print_function
from builtins import object, str
import copy, numpy, pandas, pyarrow as pa, sys, uuid
from .pygraphistry import PyGraphistry
from .pygraphistry import util
from .pygraphistry import bolt_util
from .nodexlistry import NodeXLGraphistry
from .tigeristry import Tigeri... | pandas.DataFrame(lnodes, columns=[nodeid]) | pandas.DataFrame |
"""Prepare feature data from Universal Dependencies and UniMorph datasets.
We need to know the feature values of each word in BERT's vocab. For the
multilingual model, we want to know the feature values for all the languages it
models.
This module is intended to be run as a script:
$ python src/features.py
"""
i... | pd.DataFrame(result) | pandas.DataFrame |
#!/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["最新价"]) | pandas.to_numeric |
from pathlib import Path
import numpy as np
import pandas as pd
import pandas.testing as tm
import pytest
from tableauhyperapi import Connection, CreateMode, HyperProcess, TableName, Telemetry
import pantab
import pantab._compat as compat
def assert_roundtrip_equal(result, expected):
"""Compat helper for compar... | pd.concat([expected, expected]) | pandas.concat |
######
# Author: <NAME>
# this file loads and organizes
# Foundation data for further use
######
import numpy as np
import networkx as nx
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import numpy as np
from datetime import datetime,timedelta
from tqdm import tqdm
import matplot... | pd.read_csv("data/nft_metadata.csv") | pandas.read_csv |
# ----------------------------------------------------------------------------
# Name: Read/Write/Helper functions for HDF5 based CML data format cmlH5
# Purpose:
#
# Authors:
#
# Created:
# Copyright: (c) <NAME> 2016
# Licence: The MIT License
# -----------------------------------------------... | pd.DataFrame(index=t, data=data_dict) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
爬虫抓取工具
"""
import numpy as np
import time
import uuid
import sys
from mllib.utils import seleniumutil as util
import re
import lxml.html
import pandas as pd
from lxml import etree
from urllib.request import urlopen, Request
import requests
from pandas.compat import StringIO
from mllib.uti... | StringIO(text_1) | pandas.compat.StringIO |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import featuretools as ft
import pandas as pd
import pytest
from numpy import nan
from cardea.data_loader import EntitySetLoader
from cardea.problem_definition.predicting_diagnosis import DiagnosisPrediction
@pytest.fixture()
def diagnosis_prediction():
return Diagn... | pd.DataFrame({"object_id": [0, 2, 1, 7]}) | pandas.DataFrame |
#
# Build a graph describing the layout of each station based on data
# from the MTA's elevator and escalator equipment file. We also
# incorporate an override file, since some of the MTA descriptions
# too difficult for this simple program to understand. Writes to
# stdout.
#
import argparse
import pandas as pd
import... | pd.read_csv(master_file) | pandas.read_csv |
import os
import joblib
import numpy as np
import pandas as pd
from joblib import Parallel
from joblib import delayed
from Fuzzy_clustering.version2.common_utils.logging import create_logger
from Fuzzy_clustering.version2.dataset_manager.common_utils import check_empty_nwp
from Fuzzy_clustering.version2.dataset_manag... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
# pylint: disable=E1101
# flake8: noqa
from datetime import datetime
import csv
import os
import sys
import re
import nose
import platform
from multiprocessing.pool import ThreadPool
from numpy import nan
import numpy as np
from pandas.io.common import DtypeWarning
from pandas import DataFr... | StringIO(csv) | pandas.compat.StringIO |
#! /usr/bin/env python3
# -*- coding: utf-8 -*-
##########################################################################
# Copyright (c) 2017-2018 <NAME>. All rights reserved. #
# Use of this source code is governed by a BSD-style license that can be #
# found in the LICENSE file. ... | pd.MultiIndex.from_tuples([(mvt, muscle) for muscle in summary.index], names=index_names) | pandas.MultiIndex.from_tuples |
from __future__ import absolute_import, division, print_function
import pytest
from datetime import datetime, timedelta
import numpy as np
import pandas as pd
import pandas.util.testing as tm
from pandas import DataFrame, Series
from string import ascii_lowercase
from blaze.compute.core import compute
from blaze ... | Series(('a', 'b', 'c')) | pandas.Series |
from __future__ import division
import pytest
import numpy as np
from datetime import timedelta
from pandas import (
Interval, IntervalIndex, Index, isna, notna, interval_range, Timestamp,
Timedelta, compat, date_range, timedelta_range, DateOffset)
from pandas.compat import lzip
from pandas.tseries.offsets imp... | IntervalIndex(index, copy=False) | pandas.IntervalIndex |
#####################################################################
####### Dash Plotly with Bootstrap Components #########
#####################################################################
import os
import pandas as pd
import numpy as np
from datetime import datetime
import dash_bootstrap_compone... | pd.Timestamp.today() | pandas.Timestamp.today |
# This file is part of the
# Garpar Project (https://github.com/quatrope/garpar).
# Copyright (c) 2021, 2022, <NAME>, <NAME> and QuatroPe
# License: MIT
# Full Text: https://github.com/quatrope/garpar/blob/master/LICENSE
# =============================================================================
# IMPORTS
# =... | pdt.assert_series_equal(result, expected) | pandas.testing.assert_series_equal |
import pandas as pd
from scipy.io.arff import loadarff
def data_albrecht():
raw_data = loadarff("../data_experiment/classic/albrecht.arff")
df_data = pd.DataFrame(raw_data[0])
new_alb = df_data.drop(columns=['FPAdj', 'RawFPcounts', 'AdjFP'])
return new_alb
def data_china():
raw_data = loadarff("... | pd.DataFrame(raw_data[0]) | pandas.DataFrame |
"""
Collection of functions to prepare the master curve for the identification
of the Prony series parameters. Methods are provided to shift the raw
measurement data into a master curve and remove measurement outliers through
smoothing of the master curve.
"""
import numpy as np
import pandas as pd
import matplotlib... | pd.DataFrame(Temp, columns=['T']) | pandas.DataFrame |
from sklearn.metrics.ranking import roc_auc_score, roc_curve
from sklearn.model_selection import train_test_split
from keras.layers import Dense, Dropout, Activation
from imblearn.keras import balanced_batch_generator
from imblearn.under_sampling import NearMiss
from keras.models import Sequential
from keras.optimizers... | pd.DataFrame(shuffled_X_test) | pandas.DataFrame |
"""
Test for utility class functionality
"""
from core.utility import Utility
import pandas as pd
import numpy as np
d1 = [4,3,2,1]
d2 = [1,2,3,4]
d3 = [2,np.nan,6,8]
df = | pd.DataFrame() | pandas.DataFrame |
import numpy as np
import pandas as pd
import itertools
import collections
def findDuplicates(N, L, MOI):
'''
This function takes as an input the number of cells (N), the library size (L)
and the average MOI for the virus and returns the number of duplicate cells.
'''
n_tags_per_cell = np.random.po... | pd.DataFrame() | pandas.DataFrame |
from typing import List, Dict, Union
import pickle
from pathlib import Path
import pandas as pd
import numpy as np
import h5py
def extract_result(results: Dict, key: str) -> pd.Series:
df = pd.concat({(int(res['hp_ix']), int(bs_ix), k): | pd.DataFrame(v, index=[0]) | pandas.DataFrame |
### 第一批数据：1:敏感语料（短语） 2:微博评论原文（senti100k,未处理）,各6754条，测试集比例0.1
import pandas as pd
df_1 = pd.read_excel('/Users/leo/Data/项目数据/文德数慧-文本内容审核/分类实验/数据/网络信息语料 文德 20210122.xlsx', sheet_name='测试集')
df_0 = pd.read_csv('/Users/leo/Data/项目数据/文德数慧-文本内容审核/分类实验/数据/weibo_senti_100k.csv')
df_0 = df_0.sample(n=6754).reset_index(drop=... | pd.DataFrame({'label':[label],'text':[text]}) | pandas.DataFrame |
"""
Author: <EMAIL> / <EMAIL>
Purpose: ease OCT image access and analyses
"""
import pandas as pd
import numpy as np
import os
import glob
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import plotly.graph_objects as go
class TopconSegmentationData:
# extract thickness data from ... | pd.DataFrame(columns=columns_names) | pandas.DataFrame |
# -*- coding:utf-8 -*-
# /usr/bin/env python
"""
Date: 2021/8/28 21:26
Desc: 东方财富网-行情首页-上证 A 股-每日行情
获取最近一个交易日的交易信息
使用示例（直接运行main函数，获取最近一个交易日的交易信息）：
main()
"""
import time
import json
import pandas as pd
import os
from data_urls import a_detail_url as url
from comm_funcs import requests_get
from comm_funcs import get... | numeric(save_df["涨跌幅"], errors="coerce") | pandas.to_numeric |
import pandas as pd
import streamlit as st
@st.cache(suppress_st_warning=True)
def load_zero_data(fast_file) -> pd.DataFrame:
"""
Load a Zero Fasting data export CSV file and return a pandas DataFrame version of the file.
DataFrame is reindexed chronologically, oldest to newest, before returned.
Args:... | pd.concat([start_dt, end_dt], axis=1) | pandas.concat |
import os
""" First change the following directory link to where all input files do exist """
os.chdir("D:\\Book writing\\Codes\\Chapter 2")
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
import seaborn as sns
#from sklearn.model_selection impor... | pd.DataFrame(y_test) | pandas.DataFrame |
"""Read in hourly weather file."""
import os
import glob
import yaml
from datetime import datetime
from dateutil import tz
import numpy as np
import pandas as pd
import xarray as xr
from timezonefinder import TimezoneFinder
from ideotype import DATA_PATH
from ideotype.utils import CC_RH, CC_VPD
from ideotype.data_pr... | pd.read_csv(fpath_stations_info) | pandas.read_csv |
import logging
from tqdm import tqdm
import pandas as pd
import kex
logging.basicConfig(format='%(asctime)s %(levelname)-8s %(message)s', level=logging.INFO, datefmt='%Y-%m-%d %H:%M:%S')
types = {
"Inspec": "Abst",
"www": "Abst",
"kdd": "Abst",
"Krapivin2009": "Full",
"SemEval2010": "Full",
"... | pd.DataFrame(each_data) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# # Simple tool to analyze data from www.data.gouv.fr
#
# **Note:** This is a Jupyter notebook which is also available as its executable export as a Python 3 script (therefore with automatically generated comments).
# # Libraries
# In[ ]:
import sys,os
addPath= [os.path.absp... | PAN.set_option('display.max_colwidth', None) | pandas.set_option |
import io
import os
from datetime import datetime
import pandas as pd
import scrapy
from scrapy import Request
from scrapy import signals
from fooltrader.api.quote import get_security_list
from fooltrader.contract.data_contract import KDATA_COLUMN_STOCK, KDATA_COLUMN_163
from fooltrader.contract.files_contract import... | pd.to_datetime(df_current.index) | pandas.to_datetime |
import datetime as dt
import itertools
import json
import logging
import re
from functools import cached_property
from itertools import product
from typing import Callable, List, Mapping, Optional, Sequence, Union
import numpy as np
import pandas as pd
import tushare as ts
from ratelimiter import RateLimiter
from retr... | pd.concat([data, cache], axis=1) | pandas.concat |
import time
import numpy as np
import pandas as pd
from sklearn.decomposition import PCA
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.model_selection import KFold
from tqdm import tqdm
def timeit(method):
def timed(*args, **kw):
ts = time.time()
result = metho... | pd.concat([df_test, df_test_lda], axis=1) | pandas.concat |
import numpy as np
import pandas as pd
import spacy
from spacy.lang.de.stop_words import STOP_WORDS
from nltk.tokenize import sent_tokenize
from itertools import groupby
import copy
import re
import sys
import textstat
# Method to create a matrix with contains only zeroes and a index starting by 0
def cr... | pd.DataFrame(d_end_punct_list) | pandas.DataFrame |
# Neural network for pop assignment
# Load packages
import tensorflow.keras as tf
from kerastuner.tuners import RandomSearch
from kerastuner import HyperModel
import numpy as np
import pandas as pd
import allel
import zarr
import h5py
from sklearn.model_selection import RepeatedStratifiedKFold, train_test_split
from s... | pd.DataFrame(top_freqs["freq"]) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Wed Oct 7 20:41:59 2020
@author: prasa
"""
import pandas as pd
import re
import nltk
from nltk.stem import WordNetLemmatizer
from nltk.stem import PorterStemmer
from nltk.corpus import stopwords
#import the file and here label and message is separeted with ... | pd.read_csv('D:/Work space/smsspamcollection/SMSSpamCollection', sep='\t', names=["labels", "Text_Message"]) | pandas.read_csv |
import numpy as np
import scipy.io as sio
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
from matplotlib.collections import LineCollection
from matplotlib.colors import ListedColormap, BoundaryNorm
import matplotlib.cm as cm
import pandas as pd
import copy
#Filters
from sklearn.model_selecti... | pd.concat([df_f] * q_signals_file, ignore_index=False) | pandas.concat |
# *****************************************************************************
# Copyright (c) 2019, 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 of sou... | pandas.Series(result) | pandas.Series |
import cv2
from datetime import datetime
import pandas
from bokeh.plotting import figure
from bokeh.io import output_file, show
first_frame = None
status_list = [None, None]
time_stamp = []
video = cv2.VideoCapture(0)
while True:
check, frame= video.read()
status = 0
gray_frame = cv2.cv... | pandas.DataFrame(columns=['start', 'end']) | pandas.DataFrame |
# Copyright (c) 2018-2021, NVIDIA CORPORATION.
import re
from concurrent.futures import ThreadPoolExecutor
import numpy as np
import pandas as pd
import pytest
import cudf
from cudf.datasets import randomdata
from cudf.testing._utils import assert_eq, assert_exceptions_equal
params_dtypes = [np.int32, np.uint32, np... | pd.Series([1.0, 2.0, 3.0, np.nan, None]) | pandas.Series |
import os
import re
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import netdice.experiments.sri_plot_helper as sph
from netdice.experiments.compare_approaches import bf_states_for_target_precision, \
hoeffding_samples_for_target_precision
from netdice.my_logging import log
class Analyz... | pd.DataFrame(data_list, columns=["precision"]) | pandas.DataFrame |
# import app components
from app import app, data
from flask_cors import CORS
CORS(app) # enable CORS for all routes
# import libraries
from flask import request
import pandas as pd
import re
from datetime import datetime
from functools import reduce
# define functions
## process date args
def date_arg(arg):
try... | pd.read_csv(data.ccodwg[k]) | pandas.read_csv |
#! /usr/bin/env python3
import re
import math
import json
import inspect
import pkg_resources
import numpy as np
import pandas as pd
from time import time
from joblib import Parallel, delayed
from typing import Any, Dict, List, Optional, Union
from pathlib import Path
from pkg_resources import resource_filename
from p... | pd.read_stata(infile, columns=tokeep, chunksize=chunksize) | pandas.read_stata |
# spikein_utils.py
# Single Cell Sequencing Quality Assessment: scqua
#
# Copyright 2018 <NAME> <<EMAIL>>
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the Lice... | pd.read_csv(tpm_file, index_col=0) | pandas.read_csv |
# -*- coding: utf-8 -*-
import pandas as pd
class JPY(object):
"""docstring for JPY"""
def __init__(self, usd_filename, btc_filename, bch_filename):
usd = pd.read_csv(usd_filename, parse_dates=['snapped_at'])
btc = pd.read_csv(btc_filename, parse_dates=['snapped_at'])
bch = pd.read_csv(... | pd.merge(btc, bch, how='left') | pandas.merge |
from Task1 import *
from Task3 import *
from Task5 import *
from Task6 import *
import csv
import pandas as pd
from pandas import read_csv
from sympy import *
import sqlalchemy
from sqlalchemy.orm import sessionmaker
#Напишите скрипт, читающий во всех mp3-файлах указанной директории ID3v1-теги и выводящий... | pd.concat([titles, table[['capital', 'ccn3', 'area', 'currencies']], lat, lng], axis=1) | pandas.concat |
import numpy as np
import pandas as pd
import time # count clock time
import psutil # access the number of CPUs
import pyomo.environ as pyo
from pyomo.environ import Set, Var, Binary, NonNegativeReals, RealSet, Constraint, ConcreteModel, Objective, minimize, Suffix, DataPortal
from... | pd.read_csv(CaseName+'/oT_Data_UpwardOperatingReserve_' +CaseName+'.csv', index_col=[0,1,2]) | pandas.read_csv |
import pandas as pd
import string
import numpy as np
import pkg_resources
import seaborn as sns
from PIL import Image
from wordcloud import WordCloud
import matplotlib.pyplot as plt
from pdfminer.high_level import extract_text
from tqdm import tqdm
import os
class wording:
def __init__(self):
self.res... | pd.DataFrame() | pandas.DataFrame |
"""
Программа создает файлы-исходники в папке it\Иван\ИВАН\НовыйАвтомат(НК/ВБ/МАЙ)\Исходники(НК/ВБ/МАЙ)CRM , необходимые для автоматов Ивана.
Логика основанана на сверке данных из JSON застройщика, где содержатся свободные квартиры, и "Эталонных выгрузок", в которых содержатся данные по всем квартирам вообще
При выв... | pd.read_csv(path+'_'+file+'.csv',sep=';', encoding='cp1251',engine='python', index_col=False) | pandas.read_csv |
#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not us... | pd.Series(arr, dtype=dtype, name=name) | pandas.Series |
# -*- coding: utf-8 -*-
import pytest
import pandas as pd
from numpy import nan, float64
from jqfactor_analyzer.prepare import get_clean_factor_and_forward_returns
from jqfactor_analyzer.performance import (
factor_information_coefficient,
factor_autocorrelation,
mean_information_coefficient,
quantil... | pd.testing.assert_frame_equal(avgrt, expected) | pandas.testing.assert_frame_equal |
"""Provides helper functions for reading input data and configuration files.
The default configuration values are provided in aneris.RC_DEFAULTS.
"""
from collections import abc
import os
import yaml
import pandas as pd
from aneris.utils import isstr, isnum, iamc_idx
RC_DEFAULTS = """
config:
default_luc_method... | pd.ExcelWriter(f, engine='xlsxwriter') | pandas.ExcelWriter |
import os
import unittest
import pandas as pd
from sklearn import datasets
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, Imputer, LabelEncoder, LabelBinarizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.tree import DecisionTreeRegressor
from sklearn... | pd.DataFrame(iris.data,columns=iris.feature_names) | pandas.DataFrame |
#import AYS_Environment as ays_env
import c_global.cG_LAGTPKS_Environment as c_global
import numpy as np
import pandas as pd
import sys,os
import matplotlib.pyplot as plt
from matplotlib.offsetbox import AnchoredText
pars=dict( Sigma = 1.5 * 1e8,
Cstar=5500,
a0=0.03,
aT=3.2*1e3,
... | pd.DataFrame(actions[start_time:end_time]) | pandas.DataFrame |
# -*- coding: utf-8 -*-
#
# License: This module is released under the terms of the LICENSE file
# contained within this applications INSTALL directory
"""
Utility functions for model generation
"""
# -- Coding Conventions
# http://www.python.org/dev/peps/pep-0008/ - Use the Python s... | pd.Timedelta(23, unit='h') | pandas.Timedelta |
""":func:`~pandas.eval` parsers
"""
import ast
import operator
import sys
import inspect
import tokenize
import datetime
import struct
from functools import partial
import pandas as pd
from pandas import compat
from pandas.compat import StringIO, zip, reduce, string_types
from pandas.core.base import StringMixin
fro... | com.pprint_thing(self.terms) | pandas.core.common.pprint_thing |
import os
import glob
import psycopg2
import pandas as pd
from sql_queries import *
def process_song_file(cur, filepath):
"""Reads songs log file row by row, selects needed fields and inserts them into song and artist tables.
Parameters:
cur (psycopg2.cursor()): Cursor of the sparkifydb databa... | pd.read_json(filepath, lines=True) | pandas.read_json |
#!/usr/bin/env python
import sys
import PySimpleGUI as sg
import pandas as pd
import numpy as np
from icon import icon
def file_picker():
"""shows a file picker for selecting a postQC.tsv file. Returns None on Cancel."""
chooser = sg.Window('Choose file', [
[sg.Text('Filename')],
[sg.Input(), ... | pd.unique(df['UID']) | pandas.unique |
from flask import *
from flask_cors import CORS,cross_origin
import warnings
import os
import dash
import plotly.express as px
from flask import Flask, render_template #this has changed
import plotly.graph_objs as go
import numpy as np
import dash_core_components as dcc
import uuid
from werkzeug.utils import secure_fil... | pd.DataFrame.from_dict(data_scraped['data']) | pandas.DataFrame.from_dict |
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import linear_model
from logistic_regression import LogisticRegression
def Cal_accuracy(predictions, y):
correct = [1 if ((a == 1 and b == 1) or (a == 0 and b == 0)) else 0 for (a, b) in zip(predictions, y)]... | pd.DataFrame(dict) | pandas.DataFrame |
import pandas as pd
import numpy as np
import re as re
from base import Feature, get_arguments, generate_features
Feature.dir = 'features'
# """sample usage
# """
# class Pclass(Feature):
# def create_features(self):
# self.train['Pclass'] = train['Pclass']
# self.test['Pclass'] = test['Pclass']... | pd.to_datetime(test["publishedAt"]) | pandas.to_datetime |
# -----------------------------------------------------------------------------
'''A Feature Module of classes and functions related to stress distributions.'''
# Case() : A collection of LaminateModel objects
# Cases() : A collection of Cases
# flake8 distributions.py --ignore E265,E501,N802,N806
import os
import imp... | pd.Series(self.load_params) | pandas.Series |
import time
import datetime
start_time = time.time()
date = str(datetime.datetime.now().strftime(format='%m%d'))
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
# from sklearn import pipeline, model_selection
from sklearn import pipeline, grid_search
# from sklearn.fea... | pd.merge(all_details, hd_pro_desc, how='left', on='product_uid') | pandas.merge |
from typing import List
from bs4 import BeautifulSoup
from pandas.core.frame import DataFrame
import requests
import pandas as pd
import numpy as np
import json
# Scraping Target api to create a database
stores = np.arange(0000, 4000, 1).tolist()
stores = [str(store).zfill(4) for store in stores]
# stores =... | pd.DataFrame(dot_data) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
@author: efourrier
Purpose : Automated test suites with unittest
run "python -m unittest -v test" in the module directory to run the tests
The clock decorator in utils will measure the run time of the test
"""
#########################################################
# Import Packages a... | pd.Series(['A']*300 + ['B']*200 + ['C']*200 +['A']*300) | pandas.Series |
# -*- coding: utf-8 -*-
#-----------------------------------------------------------------------------------
#Framework:
#1. In this framework, only set parameters then train model for you.
#2. Automatically recommend best models for you. Give you insights that what model
# is fitting your problem best.
#3. Give y... | pd.concat([X_norm, Y], axis=1) | pandas.concat |
import pandas as pd
import numpy as np
import re
import marcformat
class MarcExtractor(object):
tag_marc_file = 'MARC_FILE'
tag_filter_columns = 'FILTER_COLUMNS'
tag_marc_output_file = 'MARC_OUTPUT_FILE'
marcFile = ''
marcOutFile = ''
filteredColumns = []
df = | pd.DataFrame() | pandas.DataFrame |
"""
GLM fitting utilities based on NeuroGLM by <NAME>, <NAME>:
https://github.com/pillowlab/neuroGLM
<NAME>
International Brain Lab, 2020
"""
from warnings import warn, catch_warnings
import numpy as np
from numpy.linalg.linalg import LinAlgError
import pandas as pd
from brainbox.processing import bincount2D
from sk... | pd.Series(stimvecs, index=self.trialsdf.index) | pandas.Series |
# ----------------------------------------------------------------------------
# Copyright (c) 2017-2019, QIIME 2 development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE, distributed with this software.
# ------------------------------------------------... | pd.Index(['sample_a', 'sample_b'], name='id') | pandas.Index |
"""
"""
import io
import os
import pandas as pd
import numpy as np
from datetime import datetime
import yaml
import tethys_utils as tu
import logging
from time import sleep
from pyproj import Proj, CRS, Transformer
pd.options.display.max_columns = 10
#############################################
### Parameters
bas... | pd.read_csv(permit_csv) | pandas.read_csv |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Date : 2018-12-12 15:00:35
# @Author : <NAME> (<EMAIL>)
# @Link : github.com/taseikyo
# @Version : python3.5
"""
obtain video information that exceeds the play threshold
"""
import os
import sys
import csv
import requests
import pandas as pd
PLAY_THRESHOLD = 50... | pd.concat([df1, df2], axis=0, ignore_index=True, sort=False) | pandas.concat |
# coding=utf-8
"""
数据源解析模块以及示例内置数据源的解析类实现
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import logging
import numpy as np
import pandas as pd
from .ABuSymbol import EMarketTargetType
from ..CoreBu.ABuFixes import six
from ..UtilBu import ABuDate... | pd.DataFrame(klines, index=dates_pd) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
import panel as pn
from patchwork._sample import PROTECTED_COLUMN_NAMES, find_partially_labeled
class SingleImageTagger():
def __init__(self, f, classname="class", size=200):
self.classname = classname
# determine PNG or JPG and ... | pd.isna(self.df[c]) | pandas.isna |
from pathlib import Path
from typing import List, Tuple
import matplotlib.pyplot as plt
import pandas as pd
from pylossmap import BLMData
from pylossmap.lossmap import LossMap
from tqdm.auto import tqdm
def ufo_stable_proton(ufo_meta: pd.DataFrame) -> pd.DataFrame:
ufo_meta = ufo_meta[ufo_meta["beam_mode"] == "S... | pd.Timedelta("1s") | pandas.Timedelta |
import requests
import pandas as pd
from datetime import timedelta
import numpy as np
def ba_timezone(ba, format):
"""
Retrieves the UTC Offset (for standard time) for each balancing area.
"""
offset_dict = {'AEC': 6,
'AECI': 6,
'AVA': 8,
'AVRN': ... | pd.to_datetime(f'{start_date}T00:00:00{utc_offset}') | pandas.to_datetime |
# %matplotlib notebook
import pandas as pd
import numpy as np
import seaborn as sns
sns.set(color_codes=True)
from sklearn import preprocessing
# from imblearn.over_sampling import SMOTE
from imblearn.over_sampling import SMOTE,RandomOverSampler
from sklearn.model_selection import train_test_split
from sklearn.ensembl... | pd.read_pickle(fileNameToSave, compression='infer') | pandas.read_pickle |
"""
To fix the yield from the UPTSO preprocessed files
both Schwaller & Lowe versions
Keeps all data, no filtration done.
Version: 1.31: 2021-04-16; A.M.
@author: <NAME> (DocMinus)
license: MIT License
Copyright (c) 2021 DocMinus
"""
import pandas as pd
import numpy as np
def deconvolute_yield(row):
text_yiel... | pd.to_numeric(data["TxtYield"], errors="coerce") | pandas.to_numeric |
import numpy as np
import random as rand
import copy
import time
import matplotlib.pyplot as plt
import sys
import os
import pandas as pd
from .toric_model import *
from .util import Action
from .mcmc import *
from .toric_model import Toric_code
from matplotlib import rc
#rc('font',**{'family':'sans-serif'})#,'sans-... | pd.DataFrame(columns=['SEQ', 'eps', 'kld', 'tvd', 'steps']) | pandas.DataFrame |
__author__ = "saeedamen" # <NAME>
#
# Copyright 2016 Cuemacro
#
# 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 la... | pandas.DataFrame(self._returns_df[d]) | pandas.DataFrame |
import os
from os.path import join as pjoin
import numpy as np
import pandas as pd
import scipy.stats
import dask
from cesium import featurize
from cesium.tests.fixtures import (sample_values, sample_ts_files,
sample_featureset)
import numpy.testing as npt
import pytest
DATA_PATH ... | pd.Series({'meta1': 0.5}) | pandas.Series |
import numpy as np
import pytest
from pandas import (
DataFrame,
Index,
MultiIndex,
Series,
Timestamp,
date_range,
to_datetime,
)
import pandas._testing as tm
import pandas.tseries.offsets as offsets
class TestRollingTS:
# rolling time-series friendly
# xref GH13327
def set... | Timestamp("20130101 09:00:00") | pandas.Timestamp |
"""Test functions in owid.datautils.dataframes module.
"""
import numpy as np
import pandas as pd
from pytest import warns
from typing import Any, Dict
from owid.datautils import dataframes
class TestCompareDataFrames:
def test_with_large_absolute_tolerance_all_equal(self):
assert dataframes.compare(
... | pd.DataFrame({"col_01": [1, 2]}) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Wed Jan 16 11:27:05 2019
@author: <NAME>
"""
""" Quick Start
In order to use this program, you will need to do these things:
* Specify a value for the variable 'server' to indicate whether local files
will be input for, perhaps, debugging mode or file path... | pd.read_csv(path1+files1[i]) | pandas.read_csv |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
'''
functions that disambiguate names using an external file.
Inputs are:
- a tab delimited csv with columns "Unique Names" (= found name, as in the
text) and "NameCopy" (the right name)
- output from 01_parse_xml.py
The "disambiguate_names" function will use the ... | pd.read_csv(original_path, delimiter=cf.CSV_SEP) | pandas.read_csv |
import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
@pytest.mark.parametrize("align_axis", [0, 1, "index", "columns"])
def test_compare_axis(align_axis):
# GH#30429
s1 = pd.Series(["a", "b", "c"])
s2 = pd.Series(["x", "b", "z"])
result = s1.compare(s2, align_axis=align_... | pd.Index(["self", "other"]) | pandas.Index |
# -*- coding: utf-8 -*-
from __future__ import print_function
from distutils.version import LooseVersion
from numpy import nan, random
import numpy as np
from pandas.compat import lrange
from pandas import (DataFrame, Series, Timestamp,
date_range)
import pandas as pd
from pandas.util.testing im... | pd.Timestamp('2013-01-02') | pandas.Timestamp |
#!/usr/bin/env python
# coding: utf-8
# # <font color='yellow'>How can we predict not just the hourly PM2.5 concentration at the site of one EPA sensor, but predict the hourly PM2.5 concentration anywhere?</font>
#
# Here, you build a new model for any given hour on any given day. This will leverage readings across a... | pd.set_option('display.max_columns', 500) | pandas.set_option |
import unittest
import keras
import numpy as np
import pandas as pd
import sklearn
from sklearn import preprocessing
import xrdos
class test_xrdos(unittest.TestCase):
def test_split(self):
data = {'column1': [2, 2, 3], 'column2': [1, 3, 5]}
df = pd.DataFrame(data)
one, two = xrdos.split... | pd.DataFrame(data) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import datetime as dt, logging, numpy, pandas as pd, pyarrow as pa, unittest
import graphistry, graphistry.plotter
from common import NoAuthTestCase
logger = logging.getLogger(__name__)
nid = graphistry.plotter.Plotter._defaultNodeId
triangleNodesDict = {
'id': ['a', 'b', 'c'],
'a1... | pd.DataFrame({'aa': [0, 1, 2], 'bb': ['a', 'b', 'c'], 'cc': ['b', 0, 1]}) | pandas.DataFrame |
"""
This code is copied from Philippjfr's notebook:
https://anaconda.org/philippjfr/sankey/notebook
"""
from functools import cmp_to_key
import holoviews as hv
import numpy as np
import pandas as pd
import param
from bokeh.models import Patches
from holoviews import Operation
from holoviews.core.util import basestrin... | pd.DataFrame(links) | pandas.DataFrame |
import numpy
import pandas
import sklearn
import seaborn
import matplotlib.pyplot as plot
from sklearn import datasets
from sklearn import model_selection
from sklearn import pipeline
from sklearn import preprocessing
from sklearn import linear_model
from sklearn import ensemble
from sklearn import metrics
from sklear... | pandas.Series(iris.target) | pandas.Series |
# -*- coding: utf-8 -*-
"""
Notes and tries on Chaper 03 (Think Stats 2, <NAME>)
Self-study on statistics using pyhton
@author: Github: @rafaelmm82
"""
import thinkstats2
import thinkplot
import nsfg
import math
import first
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
pmf=... | pd.DataFrame(array) | pandas.DataFrame |
import pandas as pd
from argparse import ArgumentParser
def read_metadata(f):
print(f"reading metadata file {f}...")
df = pd.read_csv(f,sep='\t',low_memory=False)
df['fulldate'] = df['date'].apply(lambda x: "XX" not in str(x))
df = df.query("fulldate == True").copy()
df['date'] = | pd.to_datetime(df['date']) | 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 gc
import math
import copy
import json
import numpy as np
import pandas as pd
import torch as th
import torch... | pd.read_csv('test.csv') | pandas.read_csv |
from copy import deepcopy
import elasticsearch
import pandas as pd
from suricate.base import ConnectorMixin
from suricate.dftransformers.cartesian import cartesian_join
import numpy as np
import time
ixname = 'ix'
ixnamesource = 'ix_source'
ixnametarget = 'ix_target'
ixname_pairs = [ixnamesource, ixnametarget]
class... | pd.DataFrame.from_dict(score, orient='columns') | pandas.DataFrame.from_dict |
## 1. Recap ##
import pandas as pd
loans = pd.read_csv("cleaned_loans_2007.csv")
print(loans.info())
## 3. Picking an error metric ##
import pandas as pd
# False positives.
fp_filter = (predictions == 1) & (loans["loan_status"] == 0)
fp = len(predictions[fp_filter])
# True positives.
tp_filter = (predictions == 1) ... | pd.Series(predictions) | pandas.Series |
#!/usr/bin/env python3
"""
dataframe_utils.py
Utilities for pd.DataFrame manipulation for ipy notebooks.
"""
import errno
import os
from astropy import units as u
from astropy.coordinates import SkyCoord
from astropy.io import fits
from astropy.table import Table
import matplotlib.pyplot as plt
import numpy as np
im... | pd.read_pickle(pickle_path) | pandas.read_pickle |
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