prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
#!/usr/bin/env python
# Copyright 2017 Calico LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | pd.read_csv('%s/genes.tsv' % data_dir, sep='\t', index_col=0) | pandas.read_csv |
from bs4 import BeautifulSoup
import json
import os
import pandas as pd
import re
import requests
import subprocess
def text_from_pdf(pdf_path, temp_path):
if os.path.exists(temp_path):
os.remove(temp_path)
subprocess.call(["pdftotext", pdf_path, temp_path])
f = open(temp_path, encoding="utf8")
... | pd.DataFrame(papers, columns=["id", "year", "title", "event_type", "pdf_name", "abstract", "paper_text"]) | pandas.DataFrame |
#!/usr/bin/env python3
#Author: <NAME>
#Contact: <EMAIL>
from __future__ import print_function
from . import SigProfilerMatrixGenerator as matGen
import os
import SigProfilerMatrixGenerator as sig
import re
import sys
import pandas as pd
import datetime
from SigProfilerMatrixGenerator.scripts import convert_input_t... | pd.DataFrame(0, index=indel_types, columns=samples) | pandas.DataFrame |
'''
Auther: littleherozzzx
Date: 2022-01-13 16:48:51
LastEditTime: 2022-03-08 12:42:39
'''
import base64
import json
import logging
import os.path
import sys
import threading
import time
import pandas as pd
import requests
import rsa
import yaml
from getpass4 import getpass
from bs4 import BeautifulSoup
import config
... | pd.DataFrame() | pandas.DataFrame |
#================================================================
#
# File name : utils.py
# Author : PyLessons
# Created date: 2021-01-20
# Website : https://pylessons.com/
# GitHub : https://github.com/pythonlessons/RL-Bitcoin-trading-bot
# Description : additional functions
#
#===========... | pd.to_datetime(df.Date) | pandas.to_datetime |
import random
import pandas as pd
import pytest
from suda import suda, find_msu
@pytest.fixture
def data():
persons = [
{'gender': 'female', 'region': 'urban', 'education': 'secondary incomplete', 'labourstatus': 'employed'},
{'gender': 'female', 'region': 'urban', 'education': 'secondary incomple... | pd.DataFrame(persons) | pandas.DataFrame |
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import scipy.signal as S
import scipy.io.wavfile as wavfile
import os
from functools import reduce
import matrices as M
import waveforms as W
import tuning as T
def get_signal(path):
return wavfile.read(path)[1]
def signal_to_csv(path):
... | pd.DataFrame(sound) | pandas.DataFrame |
from __future__ import print_function
import os
import pandas as pd
import xgboost as xgb
import time
import shutil
from sklearn import preprocessing
from sklearn.cross_validation import train_test_split
import numpy as np
from sklearn.utils import shuffle
from sklearn import metrics
import sys
def archive_results(fi... | pd.DataFrame({"patient_id": id_test, 'predict_screener': predictions}) | pandas.DataFrame |
import abc
import logging
from typing import Union, Dict, Tuple, List, Set, Callable
import pandas as pd
import warnings
import numpy as np
import scipy.sparse
import xarray as xr
import patsy
try:
import anndata
except ImportError:
anndata = None
import batchglm.data as data_utils
from batchglm.xarray_sparse... | pd.DataFrame(retval, columns=self.full_estim.features) | pandas.DataFrame |
from pathlib import Path
import abc
import logging
import io
import importlib
import time
from _collections import OrderedDict
import traceback
import pandas as pd
import numpy as np
import shutil
from graphviz import Digraph
from ibllib.misc import version
import one.params
from one.alf.files import add_uuid_string
... | pd.DataFrame(columns=self.one._cache.datasets.columns) | pandas.DataFrame |
import os
import sys
import pytest
from shapely.geometry import Polygon, GeometryCollection
from pandas import DataFrame, Timestamp
from pandas.testing import assert_frame_equal
from tests.fixtures import *
from tests.test_core_components_route import self_looping_route, route
from tests.test_core_components_service im... | Timestamp('1970-01-01 13:07:00') | pandas.Timestamp |
import datetime
from collections import Counter
import warnings
warnings.filterwarnings('ignore')
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn import tree
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif'] = ['SimHei'] # 显示中文标签
plt.rcParams['axes.unico... | pd.read_csv('D:/data/A5GX_1.csv') | pandas.read_csv |
# This gets all the census data, can be filted by level and state.
# Should play with all the chunk sizes, to see how that affects speed. I'm leaving a message in censusreporter_api.py for now that will alert you if the size gets too big and it does a json_merge. json_merge is slow, we want to avoid those.
import p... | pd.concat(context_df_list) | pandas.concat |
# Import relevant libraries
import pandas as pd # to deal with the dataset
import plotly.express as px #to plot with beauty
from download_file import download_file
import json
## Get around pandas freezing when opening the file
url_name = 'https://base-covid19.pt/export3.json'
output_file = 'export3.json'
downl... | pd.read_json(output_file) | pandas.read_json |
# pylint: disable=W0231
import numpy as np
from pandas.core.common import save, load
from pandas.core.index import MultiIndex
import pandas.core.datetools as datetools
#-------------------------------------------------------------------------------
# Picklable mixin
class Picklable(object):
def save(self, path... | datetools.to_datetime(after) | pandas.core.datetools.to_datetime |
import pandas as pd
import os
from typing import List, Tuple, Dict
from collections import defaultdict
from datetime import datetime
import json
def get_game_data_path() -> str:
current_dir = os.path.dirname(os.path.realpath(__file__))
data_dir = os.path.join(current_dir, os.pardir, os.pardir, "data... | pd.concat(df_list, sort=False, ignore_index=True) | pandas.concat |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Nov 16 10:37:14 2020
Ferrain and horst Together
@author: nikorose
"""
from DJSFunctions import plot_ankle_DJS, ankle_DJS, Plotting
import os
import pandas as pd
import numpy as np
import matplotlib.colors as mcolors
from utilities_QS import ttest, hyperp... | pd.MultiIndex.from_product([['C vs Y', 'Y vs A'],['Ankle angle', 'Ankle moment']]) | pandas.MultiIndex.from_product |
import pytest
import numpy as np
import pandas as pd
from pandas._testing import assert_frame_equal
from wetterdienst.dwd.util import (
coerce_field_types,
build_parameter_set_identifier,
)
from wetterdienst.util.enumeration import parse_enumeration_from_template
from wetterdienst.dwd.observations import (
... | pd.Int64Dtype() | pandas.Int64Dtype |
import json
import pandas as pd
from pandas import DataFrame
import matplotlib.pyplot as plt
import os
import collections
import nltk.classify
import nltk.metrics
import numpy as np
"""
read all business id
"""
business=[]
users=[]
scores=[]
rates=[]
t=0
review= | pd.read_csv('dataset_review_emo_bayes.tsv', sep="\t") | pandas.read_csv |
import logging
import os
import numpy as np
import pandas as pd
from opencell.database import utils, constants
logger = logging.getLogger(__name__)
def parseFloat(val):
try:
val = float(val)
except ValueError:
val = float(str(val).replace(',', ''))
return val
def load_library_snapshot(... | pd.read_csv(filepath) | pandas.read_csv |
from elasticsearch import Elasticsearch
from elasticsearch.helpers import scan
import seaborn as sns
import matplotlib.pylab as plt
import numpy as np
import pandas as pd
load_from_disk = True
start_date = '2018-04-01 00:00:00'
end_date = '2018-05-01 23:59:59'
site = 'MWT2'
es = Elasticsearch(['atlas-kibana.mwt2.or... | pd.DataFrame(requests) | pandas.DataFrame |
import math
from abc import ABC
from typing import Optional, Iterable
import pandas as pd
from django.db import connection
from pandas import DataFrame
from recipe_db.analytics import METRIC_PRECISION, POPULARITY_START_MONTH, POPULARITY_CUT_OFF_DATE
from recipe_db.analytics.scope import RecipeScope, StyleProjection, ... | pd.Categorical(smoothened['kind_id'], trending_ids) | pandas.Categorical |
'''Reads data files in input folder(home by default, -Gi is flag for passing new one) then calls GDDcalculator.py,
passes lists of maximum and minimum temperatures also base and upper, takes list of GDD from that and concatenates it
with associated Data Frame'''
from GDDcalculate import *
import argparse
import ... | pd.Series.dropna(tempmin) | pandas.Series.dropna |
# Code derived from tensorflow/tensorflow/models/image/imagenet/classify_image.py
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import os
import sys
import tarfile
import warnings
from collections import defaultdict
import numpy as np
impo... | pd.DataFrame(scores) | pandas.DataFrame |
"""
* Copyright (c) 2021, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law... | pd.DataFrame(name_to_series) | pandas.DataFrame |
import pandas as pd
import tushare as ts
from StockAnalysisSystem.core.config import TS_TOKEN
from StockAnalysisSystem.core.Utility.common import *
from StockAnalysisSystem.core.Utility.time_utility import *
from StockAnalysisSystem.core.Utility.CollectorUtility import *
# -------------------------------------------... | pd.concat([result_product, result_area]) | pandas.concat |
import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression,RidgeClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn import preprocessing
from sklearn.model_selection import GridSearchCV
from tensorflow.keras.wrapper... | pd.DataFrame(X_scaled, columns=X.columns) | pandas.DataFrame |
#!/usr/bin/python
# encoding: utf-8
"""
@author: Ian
@file: test.py
@time: 2019-05-15 15:09
"""
import pandas as pd
if __name__ == '__main__':
mode = 1
if mode == 1:
df = | pd.read_excel('zy_all.xlsx', converters={'出险人客户号': str}) | pandas.read_excel |
# coding:utf-8
# 用 ARMA 进行时间序列预测
import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
from statsmodels.tsa.arima_model import ARMA
from statsmodels.graphics.api import qqplot
# 创建数据
data = [5922, 5308, 5546, 5975, 2704, 1767, 4111, 5542, 4726, 5866, 6183, 3199, 1471, 1325, 6618, 6644, 5337, ... | pd.Index(data_index) | pandas.Index |
from datetime import timedelta
from functools import partial
import itertools
from parameterized import parameterized
import numpy as np
from numpy.testing import assert_array_equal, assert_almost_equal
import pandas as pd
from toolz import merge
from zipline.pipeline import SimplePipelineEngine, Pipeline, CustomFacto... | pd.Timestamp("2015-01-20") | pandas.Timestamp |
"""
分析模块
"""
import warnings
from typing import Tuple, Union
import re
import numpy as np
import pandas as pd
from scipy import stats
from statsmodels.api import OLS, add_constant
from QUANTAXIS.QAFactor import utils
from QUANTAXIS.QAFactor.parameters import DAYS_PER_MONTH, DAYS_PER_QUARTER, DAYS_PER_YEA... | pd.isnull(pret) | pandas.isnull |
import pandas as pd
import numpy as np
import pytest
from features_creator.features_creator import *
@pytest.fixture
def data_df():
data = {
"week_payment1": [1.0, 2, 3],
"week_payment2": [4, 5.0, 6],
"week_payment3": [7, 8, 9.0],
"othercolumn": [1, 1, 1]}
df = pd.DataFrame(dat... | pd.DataFrame([]) | pandas.DataFrame |
import pull_mdsplus as pull
import pandas as pd
import numpy as np
import meas_locations as geo
import MDSplus as mds
import itertools
from scipy import interpolate
def load_gfile_mds(shot, time, tree="EFIT01", exact=False, connection=None, tunnel=True):
"""
This is scavenged from th... | pd.DataFrame() | pandas.DataFrame |
import math
import load_data
import pickle
import pandas as pd
import numpy as np
import datetime
from collections import deque
import scipy.stats as st
import ast
import astpretty
import re
def main():
# Used first in Organization.ipynb
print('\nCell Output')
get_cell_output()
print('\nCell Stats')
... | pd.concat(output_dfs) | pandas.concat |
import datetime as dt
import os.path
import re
import numpy as np
import pandas as pd
import pandas.testing as pdt
import pint.errors
import pytest
import scmdata.processing
from scmdata import ScmRun
from scmdata.errors import MissingRequiredColumnError, NonUniqueMetadataError
from scmdata.testing import _check_pand... | pd.Series(exp_vals, index=exp_idx) | pandas.Series |
#!/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["市盈率ttm"], errors="coerce") | pandas.to_numeric |
import sys, os
sys.path.append(os.path.abspath(__file__).split('test')[0])
import pandas as pd
import numpy as np
from pyml.supervised.linear_regression.LinearRegression import LinearRegression
"""
----------------------------------------------------------------------------------------------------------------------... | pd.read_csv("../../../data/Salary_Data.csv", sep=",") | pandas.read_csv |
import os
import numpy as np
import matplotlib.pyplot as pp
import pandas as pd
#########################
## INTIALISE VARIABLES ##
#########################
newDesk=[]
selectedList=[]
yPlotlabel=""
flow=["red", "orange","brown","tan", "lime", "purple", "teal", "black", "blue", "grey", "pink", "violet", "... | pd.DataFrame() | pandas.DataFrame |
# Written and maintained by <NAME>
# <EMAIL>
#
# A project of XamPak Open Source Software
# Follow my github for more !
# https://github.com/XamNalpak
#
# Last updated 2/22/21
#
#
# Interested in more ideas? Let me know !!##
#
#
#
#
# importing Python Packages
import praw
import pandas as pd
from datetime import dat... | pd.read_csv('nbatop.csv') | pandas.read_csv |
####################
# 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 matplotlib.pyplot as plt
import matplotlib.font_manager
import plotly.graph_objects as go
import funct... | pd.read_html('https://en.wikipedia.org/wiki/List_of_S%26P_500_companies') | pandas.read_html |
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib as mpl
import netCDF4 as nc
import datetime as dt
from salishsea_tools import evaltools as et, places, viz_tools, visualisations, geo_tools
import xarray as xr
import pandas as pd
import pickle
import os
import gsw
#... | pd.notna(df['phytopeaks'].iloc[i]) | pandas.notna |
#!/usr/bin/env python3
import numpy as np
import pandas as pd
from functools import partial
from .model_selection import features
from .model_selection import make_k_folds
from .model_selection import perform_k_fold_cv
from .model_selection import make_score_dict
from .model_selection import report_result
def select... | pd.DataFrame(scores) | pandas.DataFrame |
import os
import requests
from time import sleep, time
import pandas as pd
from polygon import RESTClient
from dotenv import load_dotenv, find_dotenv
from FileOps import FileReader, FileWriter
from TimeMachine import TimeTraveller
from Constants import PathFinder
import Constants as C
class MarketData:
... | pd.json_normalize(data) | pandas.json_normalize |
import pandas as pd
a = {"Bir": 1, "İki": 2, "Üç": 3, "Dört": 4, "Beş": 5}
b = {"Bir": 10, "İki": 20, "Üç": 30, "Dört": 40, "Altı": 60}
x = pd.Series(a)
y = | pd.Series(b) | pandas.Series |
from sequana.viz import ANOVA
from pylab import normal
def test_anova():
import pandas as pd
A = normal(0.5,size=10000)
B = normal(0.25, size=10000)
C = normal(0, 0.5,size=10000)
df = | pd.DataFrame({"A":A, "B":B, "C":C}) | pandas.DataFrame |
from copy import deepcopy
import inspect
import pydoc
import numpy as np
import pytest
import pandas.util._test_decorators as td
from pandas.util._test_decorators import (
async_mark,
skip_if_no,
)
import pandas as pd
from pandas import (
DataFrame,
Series,
date_range,
timedelta_range,
)
impo... | DataFrame({"A": [1, 2]}) | pandas.DataFrame |
from datetime import datetime
from decimal import Decimal
from io import StringIO
import numpy as np
import pytest
from pandas.errors import PerformanceWarning
import pandas as pd
from pandas import DataFrame, Index, MultiIndex, Series, Timestamp, date_range, read_csv
import pandas._testing as tm
from pa... | pd.MultiIndex.from_tuples(values, names=["date", None]) | pandas.MultiIndex.from_tuples |
import time
from Bio import Entrez
import xml.etree.ElementTree as ET
import pandas as pd
import numpy as np
from io import StringIO
import unidecode
# Create output file
output_file = 'BIOI4870-Tumor-Sample-Database-DML.sql'
with open(output_file, 'w+') as f:
pass
# Email for connecting to Entrez, en... | pd.DataFrame.from_dict([biosample_add]) | pandas.DataFrame.from_dict |
from __future__ import division
from datetime import timedelta
from functools import partial
import itertools
from nose.tools import assert_true
from parameterized import parameterized
import numpy as np
from numpy.testing import assert_array_equal, assert_almost_equal
import pandas as pd
from toolz import merge
fro... | pd.Timestamp('2015-01-12') | pandas.Timestamp |
"""
Packages to use :
tsfresh
tsfel https://tsfel.readthedocs.io/en/latest/
sktime
feature tools : https://docs.featuretools.com/en/stable/automated_feature_engineering/handling_time.html
Cesium http://cesium-ml.org/docs/feature_table.html
Feature Tools for advacned fewatures `https://github.com/Featuretools/pr... | pd.merge(out_df, month_state_lag, left_on="state_id", right_index=True, how="left") | pandas.merge |
# -*- coding:utf-8 -*-
# !/usr/bin/env python
"""
Date: 2021/10/14 12:19
Desc: 巨潮资讯-数据中心-专题统计-债券报表-债券发行
http://webapi.cninfo.com.cn/#/thematicStatistics
"""
import time
import pandas as pd
import requests
from py_mini_racer import py_mini_racer
js_str = """
function mcode(input) {
var keyStr = "... | meric(temp_df["发行面值"]) | pandas.to_numeric |
import matplotlib.pyplot as plt
import os
import seaborn as sns
import numpy as np
from matplotlib.colors import ListedColormap
import pandas as pd
from sklearn.manifold import TSNE
from src.Utils.Fitness import Fitness
class Graphs:
def __init__(self,objectiveNames,data,save=True,display=False,path='./Figures/'... | pd.read_csv(p) | pandas.read_csv |
import yaml
import pandas as pd
import numpy as np
from os.path import join
from os import makedirs
import glob
import sys
import re
def parse_samplesheet(fp_samplesheet):
#print(fp_samplesheet.split('/')[-1])
# in a first iteration, open the file, read line by line and determine start
# of sample informa... | pd.isnull(row['spike_entity_id']) | pandas.isnull |
import calendar
import datetime as dt
from datetime import timedelta
import holidays
import math
import os
from dateutil.relativedelta import relativedelta
import json
import numpy as np
import pandas as pd
import pickle
from pyiso import client_factory
from pyiso.eia_esod import EIAClient
import requests
from sklearn... | pd.get_dummies(load_df[feature], prefix=feature, drop_first=True) | pandas.get_dummies |
# coding: utf-8
# In[1]:
# Implementation from https://github.com/dougalsutherland/opt-mmd
import sys, os
import numpy as np
from math import sqrt
CHANNEL_MEANS = (129.38732832670212/255, 124.35894414782524/255, 113.09937313199043/255)
CHANNEL_STDS = (67.87980079650879/255, 65.10988622903824/255, 70.0480176508426... | pd.Series({'energy': lsun_crop_energy}) | pandas.Series |
"""
Auxiliar standarization functions
"""
import pandas as pd
import numpy as np
import os
from ..IO.aux_functions import parse_xlsx_sheet
from ..Preprocess.geo_filters import check_correct_spain_coord
extra_folder = 'extra'
servicios_columns = ['nom', 'nif', 'cp', 'cnae', 'localidad', 'x', 'y', 'es-x',
... | pd.isnull(df['cp']) | pandas.isnull |
# Copyright 2015 Novo Nordisk Foundation Center for Biosustainability, DTU.
#
# 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 requi... | pandas.DataFrame(data=0, index=index, columns=['stoichiometry'], dtype=dtype) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# viewer.py - View aggregated i2p network statistics.
# Author: <NAME> <<EMAIL>>
# License: This is free and unencumbered software released into the public domain.
#
# NOTE: This file should never write to the database, only read.
import argparse
import datetime
import m... | pd.to_datetime(df['sh'], unit='s') | pandas.to_datetime |
import requests
import re
from bs4 import BeautifulSoup
import pandas as pd
import gzip
import csv
import os
import boto3
import json
from decimal import Decimal
cveTable = os.environ['CVE_TABLE']
awsRegion = os.environ['AWS_REGION']
dynamodb = boto3.resource('dynamodb', region_name = awsRegion)
def collect_exploit(... | pd.read_json('./Exploit_CVE.json') | pandas.read_json |
# pylint: disable=E1101
from pandas.util.py3compat import StringIO, BytesIO, PY3
from datetime import datetime
from os.path import split as psplit
import csv
import os
import sys
import re
import unittest
import nose
from numpy import nan
import numpy as np
from pandas import DataFrame, Series, Index, MultiIndex, D... | mkdf(nrows, ncols, r_idx_nlevels=i, c_idx_nlevels=j) | pandas.util.testing.makeCustomDataframe |
import csv
import pprint
import datetime
import time
import pandas as pd
## Filenames
chicago = 'chicago.csv'
new_york_city = 'new_york_city.csv'
washington = 'washington.csv'
def get_city():
'''Asks the user for a city and returns the filename for that city's bike share data.
Args:
none.
Ret... | pd.read_csv(city) | pandas.read_csv |
import argparse
import pandas as pd
from util.util_funcs import load_json, load_jsonl
def main():
parser = argparse.ArgumentParser(
description="Merges table and sentence data for input to the veracity prediction model"
)
parser.add_argument(
"--tapas_csv_file",
default=None,
... | pd.DataFrame(claim_id_label_map) | pandas.DataFrame |
import sys
import psutil
import pandas as pd
from tornado import gen
from functools import wraps
from distributed import Client, LocalCluster
from concurrent.futures import CancelledError
from ..static import DatasetStatus
from ..util import listify, logger
_cluster = None
tasks = {}
futures = {}
class StartCluste... | pd.DataFrame.from_dict(task_list, orient='index') | pandas.DataFrame.from_dict |
import pandas as pd
import sys
# from urllib import urlopen # python2
from urllib.request import urlopen
#try:
# from rpy2.robjects.packages import importr
# try:
# biomaRt = importr("biomaRt")
# except:
# print "rpy2 could be loaded but 'biomaRt' could not be found.\nIf you want to use 'biomaRt'... | pd.merge(kegg_paths_genes,df,on=["pathID"],how="outer") | pandas.merge |
# ---
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# text_representation:
# extension: .py
# format_name: light
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# # VIP This ... | pd.Series(sample_colors[:male_code.shape[0]]) | pandas.Series |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import sys
sys.path.append('..')
# In[2]:
import os
import gc
import yaml
import numpy as np
import pandas as pd
import warnings
warnings.filterwarnings('ignore')
from joblib import Parallel, delayed
from utils import optimize_dtypes, get_files, int16_repr, downc... | pd.DataFrame() | pandas.DataFrame |
import io
import requests
import pandas as pd
from bokeh.models import ColumnDataSource, HoverTool, ResizeTool, SaveTool
from bokeh.models.widgets import TextInput, Button
from bokeh.plotting import figure, curdoc
from bokeh.layouts import row, widgetbox
TICKER = ""
base = "https://api.iextrading.com/1.0/"
data = Col... | pd.to_datetime(prices_df["time"], unit="ms") | pandas.to_datetime |
import json
import boto3
import logging
import pandas as pd
import glob
logger = logging.getLogger()
logger.setLevel(logging.INFO)
#https://stackoverflow.com/questions/43355074/read-a-csv-file-from-aws-s3-using-boto-and-pandas
def flight_data_df_from_response(response):
initial_df = | pd.read_csv(response['Body']) | pandas.read_csv |
import pandas as pd
import numpy as np
import openml
from pandas.api.types import is_numeric_dtype
from sklearn.model_selection import cross_validate, train_test_split, GridSearchCV, RandomizedSearchCV
from sklearn.metrics import f1_score, mean_squared_error
from sklearn.pipeline import Pipeline
from statistics import ... | pd.DataFrame() | pandas.DataFrame |
"""
This script is for analysing the outputs from the implementation of DeepAR in GluonTS
"""
import os, time
from pathlib import Path
import streamlit as st
import pandas as pd
import numpy as np
from gluonts.model.predictor import Predictor
from gluonts.dataset.common import ListDataset
from gluonts.transform import ... | pd.read_csv(path) | pandas.read_csv |
import torch
import os
import sys
import pandas as pd
class WeeBitDataset(torch.utils.data.Dataset):
def __init__(self, datapath):
self.dirs = os.listdir(datapath)
self.data_dict = {'WRLevel2': [], 'WRLevel4': [], 'WRLevel3': [], 'BitGCSE': [], 'BitKS3': []}
for dir in self.dirs:
... | pd.DataFrame(columns=['text', 'label']) | pandas.DataFrame |
# %%
import os
from google.cloud.bigquery.client import Client
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import typing
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
from sklearn.manifold import TSNE
from sklearn.metrics import silhouette_score
from c... | pd.DataFrame(tsne_scale_results, columns=['t-SNE 1', 't-SNE 2', 't-SNE 3']) | pandas.DataFrame |
from collections import defaultdict
from typing import Any, Callable, Dict, Iterable, Sequence
import numpy as np
import pandas as pd
import scipy.optimize
from invoice_net.parsers import (
parses_as_full_date,
parses_as_amount,
parses_as_invoice_number,
)
from invoice_net.data_handler import DataHandler
... | pd.DataFrame(predictions) | pandas.DataFrame |
"""
Common routines to work with raw MS data from metabolomics experiments.
Functions
---------
detect_features(path_list) : Perform feature detection on several samples.
feature_correspondence(feature_data) : Match features across different samples
using a combination of clustering algorithms.
"""
import pandas as ... | pd.Series(data="-1", index=noise_index) | pandas.Series |
#Tools for mesh-based operations
#Smoothing,
import nibabel as nb
import numpy as np
import subprocess
import os
import potpourri3d as pp3d
import meld_classifier.paths as paths
# import paths as paths
def find_nearest_multi(array, value):
new_array = np.array([abs(x - value) for x in array])
min_array = new_... | pd.DataFrame(vertices) | pandas.DataFrame |
# --------------
import pandas as pd
import scipy.stats as stats
import math
import numpy as np
import warnings
warnings.filterwarnings('ignore')
#Sample_Size
sample_size=2000
#Z_Critical Score
z_critical = stats.norm.ppf(q = 0.95)
# path [File location variable]
#Code starts here
data = | pd.read_csv(path) | pandas.read_csv |
from datetime import datetime
import numpy as np
import pandas as pd
import pytest
from numba import njit
import vectorbt as vbt
from tests.utils import record_arrays_close
from vectorbt.generic.enums import range_dt, drawdown_dt
from vectorbt.portfolio.enums import order_dt, trade_dt, log_dt
day_dt = np.timedelta64... | pd.Index(['a', 'b'], dtype='object') | pandas.Index |
import pandas as pd
from typing import Tuple, Optional, List, Union
from ..tools import _to_list_if_str
DfListTuple = Tuple[pd.DataFrame, Optional[list]]
def fixed_effects_reg_df_and_cols_dict(df, fe_vars):
fe_vars = _to_list_if_str(fe_vars)
fe_cols_dict = {}
for fe_var in fe_vars:
df, cols = _... | pd.concat([dummy_calc_df[index_cols], dummies], axis=1) | pandas.concat |
# coding=utf-8
import pandas as pd
import re
import os
import json
from datetime import datetime
class dataset_object:
"""
This class allow you to store the data and the category of these data
"""
def __init__(self, dataset,name):
self.dataset, self.name = dataset, name
class file_ob... | pd.concat([dict_ds[f.category].dataset,ds]) | pandas.concat |
import argparse
import pandas as pd
import numpy as np
from tqdm import tqdm
import os
import pickle
from sklearn.decomposition import IncrementalPCA, MiniBatchDictionaryLearning
import gc
def load_subject(subject_filename):
with open(subject_filename, 'rb') as f:
subject_data = pickle.load(f)
return ... | pd.concat((data_pca, part), axis=1) | pandas.concat |
"""
Created on Jan 09 2021
<NAME> and <NAME>
database analysis from
https://data.gov.il/dataset/covid-19
Israel sities coordinates data
https://data-israeldata.opendata.arcgis.com/
"""
import json
import requests
import sys
import extract_israel_data
from Utils import *
import time
import pandas as pd
import os
impor... | pd.DataFrame(Other, columns=[fields[2]]) | pandas.DataFrame |
import logging
import os
import warnings
from pathlib import Path
from typing import Dict, Iterable, Union
import nibabel as nib
import numpy as np
import pandas as pd
import tqdm
from nilearn.image import resample_to_img
from nipype.interfaces.ants import ApplyTransforms
from nipype.interfaces.freesurfer import (
... | pd.Series(index=parcels.index) | pandas.Series |
""" Module for sleep periods from accelerometer """
import datetime
import numpy as np
import pandas as pd
import LAMP
from ..feature_types import primary_feature, log
from ..raw.accelerometer import accelerometer
@primary_feature(
name="cortex.feature.sleep_periods",
dependencies=[accelerometer]
)
def sleep... | pd.concat([df.loc[t0 <= df.time, :], df.loc[df.time <= t1, :]]) | pandas.concat |
import os
from functools import partial
import logging
import pandas as pd
import numpy as np
from scipy import stats
logger = logging.getLogger('pylearn')
def rank_varset(row, rank_coefficient=200):
khat = float(row['KHAT'])
nvar = int(row['NVAR'])
return khat - (1 - khat) * nvar / (rank_coefficient - n... | pd.concat(ranks) | pandas.concat |
from typing import Any, Dict, Optional, List
import argparse
import json
import os
import re
import pandas as pd
from matplotlib import pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
from azureml.automl.core.shared import constants
from azureml.automl.core.shared.types import GrainType
from azur... | pd.concat(dfs, sort=False, ignore_index=True) | pandas.concat |
import time
from definitions_toxicity import ROOT_DIR
import pandas as pd
from src.preprocessing import custom_transformers as ct
from sklearn.pipeline import Pipeline
import nltk
import pickle
from src.preprocessing.text_utils import tokenize_by_sentences, fit_tokenizer, tokenize_text_with_sentences
import numpy as... | pd.DataFrame(x_test, columns=columns) | pandas.DataFrame |
import warnings
import pytest
import pandas as pd
import pandas._testing as tm
from pandas.tests.extension.base.base import BaseExtensionTests
class BaseReduceTests(BaseExtensionTests):
"""
Reduction specific tests. Generally these only
make sense for numeric/boolean operations.
"""
... | tm.assert_almost_equal(result, expected) | pandas._testing.assert_almost_equal |
import unittest
import numpy as np
import pandas as pd
from numpy.testing import assert_almost_equal
from pandas.testing import assert_frame_equal, assert_series_equal
import cvxpy as cvx
from zipline.optimize import MaximizeAlpha, TargetWeights
from zipline.optimize.constraints import (
Basket, CannotHold, Dolla... | pd.Series([-0.2, 0.1, 0.4], index=['000001', '000003','000004']) | pandas.Series |
import IMLearn.learners.regressors.linear_regression
from IMLearn.learners.regressors import PolynomialFitting
from IMLearn.utils import split_train_test
import numpy as np
import pandas as pd
import plotly.express as px
import plotly.io as pio
pio.templates.default = "simple_white"
def load_data(filename: str):
... | pd.to_datetime(full_data['Date'], format='%Y-%m-%d') | pandas.to_datetime |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue May 5 19:53:58 2020
@author: chaowu
"""
import pandas as pd
import numpy as np
import matplotlib.pylab as plt
import matplotlib.cbook as cbook
import matplotlib.dates as mdates
from scipy.signal import savgol_filter
def smooth_list(l, window=3, pol... | pd.read_csv("data/us-counties.csv") | pandas.read_csv |
import os
import pandas as pd
def _gen_photo_df_(photo_dir):
"""
"""
photo_dict = {"file_name": [], "dir_path": []}
# may have to pass photo_dir as a list to iterate through
# i.e. in case there are multiple locations
for root, _, files in os.walk(photo_dir):
for f in files:
... | pd.DataFrame(photo_dict) | pandas.DataFrame |
import pandas as pd
import csv
import math
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
#################################################################
# #
# #
# ... | pd.Series(newList) | pandas.Series |
# First Import Libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import warnings
# Read CSV File For RAW Heating And Electrical Consumption Pattern From Scraped Data
df = pd.read_csv("C:\\Users\\PowerMan\\Desktop\\KASR\Final\\Code and data\\Data\\Whole_scraped_data\\Total-Load-Who... | pd.read_csv("C:\\Users\\PowerMan\\Desktop\\KASR\\Final\\Code and data\\Codes\\datamunging\\sourcecity.csv") | pandas.read_csv |
import sqlite3
import glob
import pandas as pd
import sys
import time
import datetime
import os
import numpy as np
def main():
# save_irradiance_to_pickle_agg_by_station()
# save_irradiance_to_pickle_agg_by_day()
# plot_station_data()
load_irradiance_agg_by_station()
load_irradiance_agg_by_d... | pd.read_csv(filepath, header=1, parse_dates=[[0,1]]) | pandas.read_csv |
import json
import mmap
import os
import random
import re
from collections import Counter
from collections import defaultdict
import numpy as np
import pandas as pd
from sklearn.utils import shuffle
from tqdm import tqdm
class SUBEVENTKG_Processor(object):
"""
对EVENTKG数据集取适用于知识表示任务的子数据集
原数据集地址链接在https://... | pd.read_csv("all_triplets_data.txt", low_memory=False) | pandas.read_csv |
#coding=utf-8
import os
import re
import json
import time
import redis
import socket
import random
import requests
import threading
import pandas as pd
from threading import Thread
from multiprocessing import Process, Queue, Lock
agents = [ "Mozilla/5.0 (Windows; U; Windows NT 5.1; en-US) AppleWebKit/532.0 (KHTML, ... | pd.DataFrame.from_dict(data_dicts, orient='index') | pandas.DataFrame.from_dict |
"""
Mixture Class
=============
Mixture of expert using clustering machine learning to form local surrogate
models.
:Example:
::
>> from batman.surrogate import Mixture
>> import numpy as np
>> samples = np.array([[1., 5.], [2., 5.], [8., 5.], [9., 5.]])
>> data = np.array([[50., 51., 52.], [49., 48... | parallel_coordinates(df, "cluster") | pandas.plotting.parallel_coordinates |
# Copyright 2019 QuantRocket LLC - 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... | pd.MultiIndex.from_product([fields, dt_idx], names=["Field", "Date"]) | pandas.MultiIndex.from_product |
import asyncio
import sys
import random as rand
import os
from .integration_test_utils import setup_teardown_test, _generate_table_name, V3ioHeaders, V3ioError
from storey import build_flow, CSVSource, CSVTarget, SyncEmitSource, Reduce, Map, FlatMap, AsyncEmitSource, ParquetTarget, ParquetSource, \
DataframeSource... | pd.read_parquet(out_dir, columns=columns) | pandas.read_parquet |
"""
This script is for analysing the outputs from the implementation of DeepAR in GluonTS
"""
import os, time
from pathlib import Path
import streamlit as st
import pandas as pd
import numpy as np
from gluonts.model.predictor import Predictor
from gluonts.dataset.common import ListDataset
from gluonts.transform import ... | pd.concat([player_train_data, player_test_df]) | pandas.concat |
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