prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
"""Technical analysis on a trading Pandas DataFrame"""
from numpy import floor
from re import compile
from numpy import maximum, mean, minimum, nan, ndarray, round
from numpy import sum as np_sum
from numpy import where
from pandas import DataFrame, Series
from statsmodels.tsa.statespace.sarimax import SARIMAX
clas... | DataFrame() | pandas.DataFrame |
import numpy as np
import pandas as pd
from scipy.stats import mode
from sklearn.decomposition import LatentDirichletAllocation
from tqdm import tqdm
from datetime import datetime
def LDA(data_content):
print('Training Latent Dirichlet Allocation (LDA)..', flush=True)
lda = LatentDirichletAllocation(n_compo... | pd.merge(df, data_content.bikers_df, on='biker_id', how='left') | pandas.merge |
#Author: <NAME>. Email: <EMAIL>
#Packaged by: <NAME>. Email: <EMAIL>
#This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
#To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/ or send a letter to Creative Commons,
#PO Box... | pd.DataFrame({'Probability':P_dict,'Number of occurences':num_instances,'Sequences':motifs}) | pandas.DataFrame |
import pandas as pd
import numpy as np
from newsapi import NewsApiClient
from requests import get
from requests.exceptions import RequestException
from contextlib import closing
from bs4 import BeautifulSoup
from os import path
from .. import app, db
from .news_formatting import NewsFormatting
"""
This file contain... | pd.DataFrame.from_dict(all_articles) | pandas.DataFrame.from_dict |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# This file is part of CbM (https://github.com/ec-jrc/cbm).
# Author : <NAME>
# Credits : GTCAP Team
# Copyright : 2021 European Commission, Joint Research Centre
# License : 3-Clause BSD
import json
import numpy as np
import pandas as pd
import matplotlib.pyplot ... | pd.read_csv(file_ts, index_col=0) | pandas.read_csv |
#!/usr/bin/python3
# -*- coding: utf-8 -*-
# *****************************************************************************/
# * Authors: <NAME>
# *****************************************************************************/
"""transformCSV.py
This module contains the basic functions for creating the content of... | pandas.StringDtype() | pandas.StringDtype |
import json
import math
import operator
import warnings
import numpy as np
import pandas as pd
warnings.simplefilter(action='ignore', category=FutureWarning)
warnings.simplefilter(action='ignore', category=np.RankWarning)
np.seterr(divide='ignore', invalid='ignore')
def ema(series, period):
values = np.zeros(le... | pd.Timedelta(period) | pandas.Timedelta |
#
# Copyright 2018 Quantopian, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wr... | pd.Timestamp(self.minutes_nanos[0], tz=UTC) | pandas.Timestamp |
import warnings
import geopandas as gpd
import numpy as np
import pandas as pd
from shapely.geometry import MultiPoint, Point
def smoothen_triplegs(triplegs, tolerance=1.0, preserve_topology=True):
"""
Reduce number of points while retaining structure of tripleg.
A wrapper function using shapely.simplif... | pd.concat((trips, sp_tpls_only_act, gaps, user_change), axis=0, ignore_index=True) | pandas.concat |
""" test the scalar Timestamp """
import pytz
import pytest
import dateutil
import calendar
import locale
import numpy as np
from dateutil.tz import tzutc
from pytz import timezone, utc
from datetime import datetime, timedelta
import pandas.util.testing as tm
import pandas.util._test_decorators as td
from pandas.ts... | tm.get_locales() | pandas.util.testing.get_locales |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from ddf_library.context import COMPSsContext
from ddf_library.utils import generate_info
from pycompss.api.task import task
import pandas as pd
import numpy as np
import time
from pandas.testing import assert_frame_equal
@task(returns=2)
def _generate_partition(size,... | pd.DataFrame([['b', 3], ['a', 1]], columns=cols) | pandas.DataFrame |
import pkg_resources
from unittest.mock import sentinel
import pandas as pd
import pytest
import osmo_jupyter.dataset.combine as module
@pytest.fixture
def test_picolog_file_path():
return pkg_resources.resource_filename(
"osmo_jupyter", "test_fixtures/test_picolog.csv"
)
@pytest.fixture
def test_... | pd.to_datetime("2022") | pandas.to_datetime |
#!/usr/bin/env python
# inst: university of bristol
# auth: <NAME>
# mail: <EMAIL> / <EMAIL>
import os
import shutil
from glob import glob
import zipfile
import numpy as np
import pandas as pd
import gdalutils
from osgeo import osr
def _secs_to_time(df, date1):
df = df.copy()
conversion = 86400 # 86400s =... | pd.concat([bdy, df[0]], axis=1) | pandas.concat |
# -*- coding: utf-8 -*-
"""
Created on Fri Oct 5 10:04:19 2018
@author: <NAME> and <NAME>
--Collector Probe W Analysis and Mapping--
"""
# set imports
import os
import pandas as pd
import numpy as np
#import matplotlib.pyplot as plt
# define file directory and file name
os.chdir(r'C:\Users\jduran2\Documents\Pyth... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding: utf_8 -*
#
# Script to reformat xlsx tables from Akamai State of the Internet into a single SQL table
#
# <NAME> 2014
# <EMAIL>
# requires pandas, xlrd
import pandas as pd
import sqlite3
def importdata(infile,table,connection):
# Get list of sheets or count of sheets
xfile... | pd.ExcelFile(infile) | pandas.ExcelFile |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# Copyright (c) 2021 snaketao. All Rights Reserved
#
# @Version : 1.0
# @Author : snaketao
# @Time : 2021-10-21 12:21
# @FileName: insert_mongo.py
# @Desc : insert data to mongodb
import appbk_mongo
import pandas as pd
#数据处理，构造一个movies对应多个tagid的字典，并插入 mongodb 的mo... | pd.merge(grouped, file3, how='inner', on='tagId',left_index=False, right_index=False, sort=False,suffixes=('_x', '_y'), copy=True) | pandas.merge |
# -*- coding: utf-8 -*-
""" Simple multi-area model for Nordic electricity market
Created on Wed Jan 16 11:31:07 2019
@author: elisn
Notes:
1 - For conversion between dates (YYYYMMDD:HH) and weeks (YYYY:WW) weeks are counted as starting during the first hour
in a year and lasting 7 days, except for the last week wh... | pd.DataFrame(0.0,index=self.timerange_p1,columns=self.solar_areas) | pandas.DataFrame |
# -*- coding: utf-8 -*-
from copy import deepcopy
import warnings
from itertools import chain, combinations
from collections import Counter
from typing import Dict, Iterable, Iterator, List, Optional, Tuple, Union
import numpy as np
import pandas as pd
from scipy.stats import (pearsonr as pearsonR,
... | pd.concat([preserved, active, inactive]) | pandas.concat |
# -*- coding: utf-8 -*-
"""Tracker REITs (Real Estate Investment Trust) investments."""
import datetime
import os
from concurrent.futures import ProcessPoolExecutor
import numpy as np
import pandas as pd
import requests
import requests_cache
from .portutils import NUM_PROCESS, Singleton, UnitsTransactions
# Cac... | pd.json_normalize(df_tmp.dividends) | pandas.json_normalize |
from abc import abstractmethod, ABC
from pandas import DataFrame
from database.db_reader import read_table_header
class DataSource(ABC):
@staticmethod
def convert_header(table_name, dataframe):
columns = read_table_header(table_name)
dataframe.columns = columns
return dataframe
@... | DataFrame() | pandas.DataFrame |
from PyDSS.pyContrReader import pySubscriptionReader as pySR
from PyDSS.pyLogger import getLoggerTag
from PyDSS.unitDefinations import type_info as Types
from PyDSS.unitDefinations import unit_info as Units
from PyDSS.pyContrReader import pyExportReader as pyER
from PyDSS import unitDefinations
from PyDSS.exceptions im... | pd.MultiIndex.from_tuples(tuples, names=['timestamp', 'frequency', 'Simulation mode']) | pandas.MultiIndex.from_tuples |
"""ops.syncretism.io model"""
__docformat__ = "numpy"
import configparser
import logging
from typing import Tuple
import pandas as pd
import requests
import yfinance as yf
from gamestonk_terminal.decorators import log_start_end
from gamestonk_terminal.rich_config import console
from gamestonk_terminal.stocks.options... | pd.to_datetime(entry["timestamp"], unit="s") | pandas.to_datetime |
import os
import sys
import numpy as np
import pandas as pd
from pycompss.api.api import compss_wait_on
from pycompss.api.task import task
from data_managers.fundamentals_extraction import FundamentalsCollector
from data_managers.price_extraction import PriceExtractor
from data_managers.sic import load_sic
from model... | pd.cut(r.y, bins) | pandas.cut |
__all__ = [
'PrettyPachydermClient'
]
import logging
import re
from typing import Dict, List, Iterable, Union, Optional
from datetime import datetime
from dateutil.relativedelta import relativedelta
import pandas.io.formats.style as style
import pandas as pd
import numpy as np
import yaml
from IPython.core.displa... | pd.isna(x) | pandas.isna |
import numpy as np
import pandas as pd
from typing import List
from astropy.time import Time
from .orbits import Orbits
from .utils import assignPatchesSquare
__all__ = [
"findAverageOrbits",
"findTestOrbitsPatch",
"selectTestOrbits",
]
def findAverageOrbits(
ephemeris: pd.DataFrame,
orbi... | pd.DataFrame() | pandas.DataFrame |
# -*- coding:utf-8 -*-
# /usr/bin/env python
"""
Date: 2021/7/8 22:08
Desc: 金十数据中心-经济指标-美国
https://datacenter.jin10.com/economic
"""
import json
import time
import pandas as pd
import demjson
import requests
from akshare.economic.cons import (
JS_USA_NON_FARM_URL,
JS_USA_UNEMPLOYMENT_RATE_URL,
JS_USA_EIA_... | pd.to_datetime(temp_se.iloc[:, 0]) | pandas.to_datetime |
import nltk
import numpy as np
import pandas as pd
import bokeh as bk
from math import pi
from collections import Counter
from bokeh.transform import cumsum
from bokeh.palettes import Category20c
from bokeh.models.glyphs import VBar
from bokeh.models import ColumnDataSource, DataRange1d, Plot, LinearAxis, Grid
from bok... | pd.concat([analysis_df, temp], sort=True) | pandas.concat |
import pandas as pd
# import copy
from pathlib import Path
import pickle
pd.set_option('display.max_colwidth', -1)
pd.options.display.max_rows = 999
pd.options.mode.chained_assignment = None
import numpy as np
import math
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from s... | pd.concat(ldf, keys=keys_ldf) | pandas.concat |
from __future__ import division
'''
NeuroLearn Statistics Tools
===========================
Tools to help with statistical analyses.
'''
__all__ = ['pearson',
'zscore',
'fdr',
'holm_bonf',
'threshold',
'multi_threshold',
'winsorize',
'trim... | pd.Series(index=cutoff['std'], data=std) | pandas.Series |
import common, cost, fetchresults
import logging, sys, datetime
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
matplotlib.style.use('ggplot')
import pickle
logging.basicConfig(level=logging.INFO)
#logging.basicConfig(level=logging.DEBUG)
if 1:
results = fetchresults.get_a... | pd.DataFrame(compute_hours_all, index=date) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import pandas.util.testing as tm
from pandas.tseries import offsets
from pandas._libs.tslibs.frequencies import (get_rule_month,
_period_str_to_code,
INVALID_FREQ_ERR_MSG,
... | offsets.Minute() | pandas.tseries.offsets.Minute |
import numpy as np
import pandas as pd
import requests
from bs4 import BeautifulSoup
from Helpers import find_between
class CJH_Archives:
def __init__(self, repo, url=False):
self.repo = repo
self.url = url
def get_meta_data(self, object_type, page_to_start_at, maximum_pages_to_scrape):
... | pd.DataFrame.from_records(tupleList, columns=['Names', 'Link', 'Location']) | pandas.DataFrame.from_records |
import pandas as pd
import numpy as np
import math
import os
from scipy.interpolate import interp1d
import time
from sklearn.ensemble import RandomForestRegressor
import xgboost as xgb
from lightgbm import LGBMRegressor
from catboost import CatBoostRegressor
from information_measures import *
from joblib import Para... | pd.DataFrame(all_time_ids_byStock,columns=['time_id']) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Wed Oct 27 01:31:54 2021
@author: yoonseok
"""
import os
import pandas as pd
from tqdm import tqdm
from scipy.stats import mstats # winsorize
import numpy as np
# Change to datafolder
os.chdir(r"C:\data\car\\")
# 기본 테이블 입력
df = pd.read_csv("dataframe_h1.txt")
del df["Unnamed:... | pd.merge(result, asset[["key", "asset"]], how="inner", on=["key"]) | pandas.merge |
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.DataFrame([[1.0, 0], [0, 1.0]], index=idx, columns=cols) | pandas.DataFrame |
import logging
l = logging.getLogger("abg")
import flask
from flask import Blueprint, flash, redirect, render_template, request, url_for
from flask_login import login_required, login_user, logout_user
from flask import Markup
from flask import send_file
from flask import abort
l.error("flask")
from abg_stats.extension... | pd.concat([player_winner, player_loser]) | pandas.concat |
# Copyright 2020 Google 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 agreed to in writing, ... | testing.assert_frame_equal(output, expected) | pandas.testing.assert_frame_equal |
import pandas as pd
import numpy as np
file1 = '../data/STRIDE_PATIENT.xlsx'
x1 = pd.ExcelFile(file1)
stride_patient = x1.parse('Sheet1')
file2 = '../data//SURGERY.xlsx'
x2 = | pd.ExcelFile(file2) | pandas.ExcelFile |
# general
import math
import logging
import json
import os,sys
from pIMZ.regions import SpectraRegion
import random
from collections import defaultdict, Counter
import glob
import shutil, io, base64
# general package
from natsort import natsorted
import pandas as pd
import numpy as np
from numpy.ctypeslib import ndpo... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
# ---
# jupyter:
# jupytext:
# formats: ipynb,py:light
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.4.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
#... | pd.read_sql("SELECT * FROM processed", DB.conn) | pandas.read_sql |
import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
from sklearn.impute import SimpleImputer
rand_dataset = | pd.read_csv("sample_submission.csv") | pandas.read_csv |
import os
import numpy as np
import pandas as pd
import tsplib95
import networkx as nx
from tqdm import tqdm
import sys
import re
def prepare_testset_FINDER(data_dir, scale_factor=0.000001):
graph_list = []
atoi = lambda text : int(text) if text.isdigit() else text
natural_keys = lambda text : [atoi(c) fo... | pd.read_csv(f'{result_dir}/{f}', index_col=0) | pandas.read_csv |
import re
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import statsmodels.api as sa
import statsmodels.formula.api as sfa
import scikit_posthocs as sp
import networkx as nx
from loguru import logger
from GEN_Utils import FileHandling
from ... | pd.merge(cluster_summary, inter_vs_intra, on='cluster_filter_type') | pandas.merge |
import re
import json
from itertools import filterfalse
from luigi import Task
from luigi.task import logger as luigi_logger
import pandas as pd
from lxml import objectify
from datapackage import Package
from ..utils import TargetOutput
from ..utils import SaltedOutput
from ..utils import SuffixPreservingLocalTarget... | pd.DataFrame(data=as_lists, columns=columns) | pandas.DataFrame |
# coding: utf-8
# In[ ]:
import re
import sys
import os
import argparse
import pandas as pd
from pprint import pprint
from collections import OrderedDict
from operator import itemgetter
import json
def main():
# Parse args
args = parse_args()
# read manifest + EFO mappings
FINNGEN_EFO=pd.read_csv... | pd.read_json(args.in_manifest, lines=True) | pandas.read_json |
"""
"""
import pandas as pd
import os, sys
import numpy as np
# tie-breaker for RQ1
TIE = 'avg' #'MAX'
def read_and_add_flag(filename):
"""
"""
df = pd.read_csv(filename)
indices = df.index.values
df['flag'] = df.true == df.pred
return df
def compute_acc(init_pred_df, aft_pred_df, classes):
"""
"""
from sk... | pd.DataFrame(data = {
'true':init_pred_df.true.values,
'pred':init_pred_df.pred.values,
'new_pred':aft_pred_df.pred.values,
'init_flag':init_pred_df.flag}) | pandas.DataFrame |
from flask import Flask, render_template, request, redirect, url_for, session
import pandas as pd
import pymysql
import os
import io
#from werkzeug.utils import secure_filename
from pulp import *
import numpy as np
import pymysql
import pymysql.cursors
from pandas.io import sql
#from sqlalchemy import create... | pd.DataFrame(result) | pandas.DataFrame |
import pandas as pd
import itertools
class Aprioripy:
def __init__(self,
table,
convert=True,
items=[],
excluded_items=[],
positive_label=1):
def converter(table):
item_list = list(set(sum([x.split(", ")... | pd.DataFrame(new_table) | pandas.DataFrame |
from cmath import nan
from tokenize import endpats
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d.art3d import Poly3DCollection, Line3DCollection
import chart_studio.plotly as py
import plotly.graph_objects as go
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
import plotly... | pd.DataFrame(geodesic_points, columns = ['x1', 'x2','x3','x4']) | pandas.DataFrame |
import h5py
from pathlib import Path
from typing import Union, Tuple
import pickle
import json
import os
import gc
from tqdm import tqdm
import numpy as np
import pandas as pd
# TODO output check, verbose
def load_all_libsdata(path_to_folder: Union[str, Path]) -> Tuple[pd.DataFrame, list, pd.Series]:
"""
Functio... | pd.Series(samples) | pandas.Series |
#%%
# CARGO LOS DATASETS
import pandas as pd
import numpy as np
from shapely.geometry import Point
import shapely as shp
import geopandas as gpd
from geopandas.array import points_from_xy
path = "merged1_listas.pkl"
df_merge1 = pd.read_pickle(path)
df_merge1.reset_index(inplace=True)
#%%
#region PART 1... | pd.read_pickle('paso1.pkl') | pandas.read_pickle |
#!/usr/bin.env/python
# -*- coding: utf-8 -*-
"""
Gates are traditionally used to subset single cell data in one
or two dimensional space by hand-drawn polygons in a manual and laborious
process. cytopy attempts to emulate this using autonomous gates, driven
by unsupervised learning algorithms. The gate module contains... | pd.concat(data) | pandas.concat |
import time
import datetime
import numpy as np
import pandas as pd
import lightgbm as lgb
from dateutil.parser import parse
from sklearn.cross_validation import KFold
from sklearn.metrics import mean_squared_error
data_path = './'
train = pd.read_csv(data_path + 'f_train_20180204.csv', encoding='gb2312')
test = | pd.read_csv(data_path + 'f_test_a_20180204.csv', encoding='gb2312') | pandas.read_csv |
########################################################################
#
# Functions for loading financial data.
#
# Data from several different files are combined into a single
# Pandas DataFrame for each stock or stock-index.
#
# The price-data is read from CSV-files from Yahoo Finance.
# Other financial data (Sale... | pd.read_csv(path, sep=",", parse_dates=[3], index_col=3) | pandas.read_csv |
from itertools import groupby, zip_longest
from fractions import Fraction
from random import sample
import json
import pandas as pd
import numpy as np
import music21 as m21
from music21.meter import TimeSignatureException
m21.humdrum.spineParser.flavors['JRP'] = True
from collections import defaultdict
#song has no ... | pd.isna(ix) | pandas.isna |
import numpy as np
import pandas as pd
import tensorflow as tf
from shfl.data_base.data_base import DataBase
from shfl.data_distribution.data_distribution_iid import IidDataDistribution
class TestDataBase(DataBase):
def __init__(self):
super(TestDataBase, self).__init__()
def load_data(self):
... | pd.testing.assert_frame_equal(all_data, train_data.iloc[all_data.index.values]) | pandas.testing.assert_frame_equal |
from __future__ import print_function, absolute_import
import unittest, math
import pandas as pd
import numpy as np
from . import *
class T(base_pandas_extensions_tester.BasePandasExtensionsTester):
def test_concat(self):
df = pd.DataFrame({'c_1':['a', 'b', 'c'], 'c_2': ['d', 'e', 'f']})
df.en... | pd.DataFrame({'n_1': [1, 2, 3], 'n_2': [4, 5, 6]}) | pandas.DataFrame |
"Test suite of AirBnbModel.source.processing module"
import numpy as np
import pandas as pd
import pytest
from pandas._testing import assert_index_equal
from AirBnbModel.source.processing import intersect_index
class TestIntersectIndex(object):
"Test suite for intersect_index method"
def test_first_input_n... | pd.Series(data=[1, 2, 3, 4], index=["foo", "bar", "bar", np.nan]) | pandas.Series |
"""Run unit tests.
Use this to run tests and understand how tasks.py works.
Example:
Create directories::
mkdir -p test-data/input
mkdir -p test-data/output
Run tests::
pytest test_combine.py -s
Notes:
* this will create sample csv, xls and xlsx files
* test_combine_(... | pd.concat([df1, df2, df3], join='inner') | pandas.concat |
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... | pd.period_range('1/1/2000', freq='D', periods=5) | pandas.period_range |
import logging
import pandas as pd
import time
import wikifier
import typing
import hashlib
import json
import os
from .parser_base import ParserBase, PreParsedResult
from pandas.util import hash_pandas_object
from datamart_isi.materializers.general_materializer import GeneralMaterializer
from datamart_isi.materializ... | pd.read_csv(loaded_data, dtype=str) | pandas.read_csv |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jun 27 09:20:01 2018
@authors: <NAME>
Last modified: 2020-02-19
------------------------------------------
** Semantic Search Analysis: Start-up **
------------------------------------------
This script: Import search queries from Google Analytics, ... | pd.Series(foreignNo) | pandas.Series |
'''
Pulls data from xml and creates an array for each user consisting of PMID,
type, and annotation. Uses NLTK scoring metrics tools to determine
precision, recall, and f-score. By including PMID in the hash, this version
allows for examining user to user comparisons across multiple documents in the
... | pd.DataFrame(all_users_arr, columns=('user_id', 'pairings', 'total_f')) | pandas.DataFrame |
import pandas as pd
import numpy as np
import torch
import os.path
from glob import glob
from datetime import datetime
from base.torchvision_dataset import TorchvisionDataset
from torch.utils.data import TensorDataset
class HR_Dataset(TorchvisionDataset):
def __init__(self, root:str, normal_class):
supe... | pd.read_csv("/workspace/dim3_train.txt") | pandas.read_csv |
import pandas as pd
import numpy as np
import seaborn as sns
from scipy import stats
import matplotlib.pyplot as plt
import os
import re
from sklearn.model_selection import train_test_split
import random
import scorecardpy as sc
# split train into train data and test data
# os.chdir(r'D:\GWU\Aihan\DATS 6103 Data Mini... | pd.concat([X_train, y_train], axis=1) | pandas.concat |
import time
import numpy as np
import pandas as pd
import json
import matplotlib.pyplot as plt
class CFE():
def __init__(self, cfg_file=None):
super(CFE, self).__init__()
self.cfg_file = cfg_file
############################################################
# _______... | pd.Timestamp(year=1970, month=1, day=1, hour=0) | pandas.Timestamp |
import copy
import os
from functools import partial
import joblib
import numpy as np
import optuna
import pandas as pd
import lightgbm as lgbm
from .enums import ProblemType
from .logger import logger
from .metrics import Metrics
from .params import get_params
optuna.logging.set_verbosity(optuna.logging.INFO)
def... | pd.DataFrame.from_dict(final_valid_predictions, orient="index") | pandas.DataFrame.from_dict |
# Copyright 1999-2021 Alibaba Group Holding Ltd.
#
# 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 a... | pd.testing.assert_series_equal(result, expected) | pandas.testing.assert_series_equal |
#coding:utf-8
import pandas as pd
import numpy as np
# 读取个人信息
train_agg = pd.read_csv('../data/train_agg.csv',sep='\t')
test_agg = pd.read_csv('../data/test_agg.csv',sep='\t')
agg = | pd.concat([train_agg,test_agg],copy=False) | pandas.concat |
# -*- coding: utf-8 -*-
from __future__ import print_function
from datetime import datetime, time
from numpy import nan
from numpy.random import randn
import numpy as np
from pandas import (DataFrame, Series, Index,
Timestamp, DatetimeIndex,
to_datetime, date_range)
import pa... | pd.Timedelta('00:01:00') | pandas.Timedelta |
# 从Binance币安在线api下载1分钟k线，进行回测
import requests
import backtrader as bt
import backtrader.analyzers as btanalyzers
import json
import pandas as pd
import datetime as dt
import matplotlib.pyplot as plt
def get_binance_bars(symbol, interval, startTime, endTime):
url = "https://api.binance.com/api/v3/klines"
... | pd.concat(df_list) | pandas.concat |
'''
@Author: mendeslbruno
Date: 2021-01-26
Descr: Performs some simple analyzes for several actions of the index SP500.
'''
import pandas as pd
import yfinance as yf
import streamlit as st
import datetime as dt
import plotly.graph_objects as go
from plotly.subplots import make_subplots
snp500 = pd.read_csv("datasets/... | pd.DataFrame(data=marketInfo, index=[0]) | pandas.DataFrame |
# ----------------------------------------------------------------------------
# Copyright (c) 2013--, scikit-bio development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file COPYING.txt, distributed with this software.
# --------------------------------------------... | pd.DataFrame({'foo': [0], 'bar': [100]}) | pandas.DataFrame |
import pandas as pd
import numpy as np
from scipy import signal
import os
def get_timedeltas(login_timestamps, return_floats=True):
"""
Helper function that returns the time differences (delta t's) between consecutive logins for a user.
We just input the datetime stamps as an index, hence this method... | pd.Series(timedelta_sample) | pandas.Series |
import pandas as pd
import numpy as np
import torch
from scipy.io import arff
from abc import ABC, abstractmethod
from torch.utils.data import DataLoader, TensorDataset
class BaseADDataset(ABC):
"""Anomaly detection dataset base class."""
def __init__(self, root: str):
super().__init__()
self... | pd.DataFrame(loaded[0]) | pandas.DataFrame |
# Copyright (c) 2021 ING Wholesale Banking Advanced Analytics
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy, mo... | pd.Series(data=obj, index=df.index) | pandas.Series |
"""
Unit test of Inverse Transform
"""
import unittest
import pandas as pd
import numpy as np
import category_encoders as ce
import catboost as cb
import sklearn
import lightgbm
import xgboost
from shapash.utils.transform import inverse_transform, apply_preprocessing, get_col_mapping_ce
class TestInverseTransformCate... | pd.testing.assert_frame_equal(train, original) | pandas.testing.assert_frame_equal |
#%%
import os
from pyteomics import mzid, mzml
import pandas as pd
import numpy as np
import glob
"""
Files are downloaded and manually randomly divided into different folders
the following code is repeated but has the same effect, it is applied to various folders to
generate pandas data frames and to store all the d... | pd.DataFrame({'file':mzml_location,'id':ids,'mz':mz,'intensities':intensities}) | pandas.DataFrame |
import os
from io import StringIO
from typing import List
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import roadrunner
import seaborn as sns
import tellurium as te
from tellurium.roadrunner.extended_roadrunner import ExtendedRoadRunner
from sres import SRES
mpl.us... | pd.DataFrame(dataValues, columns=["time", "L", "E", "P", "R"]) | pandas.DataFrame |
# flake8: noqa
import os
from carla import log
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
import warnings
import pandas as pd
warnings.simplefilter(action="ignore", category=FutureWarning)
import argparse
from typing import Dict, Optional
import numpy as np
import yaml
from tensorflow import Graph, Session
from c... | pd.concat([results, df_benchmark], axis=0) | pandas.concat |
# -*- coding: utf-8 -*-
import os
import numpy as np
import pandas as pd
from sqlalchemy import create_engine
from tablizer.inputs import Inputs, Base
from tablizer.defaults import Units, Methods, Fields
from tablizer.tools import create_sqlite_database, check_inputs_table, insert, \
make_session, check_existing_r... | pd.to_datetime(date) | pandas.to_datetime |
import os
import copy
import pytest
import numpy as np
import pandas as pd
import pyarrow as pa
from pyarrow import feather as pf
from pyarrow import parquet as pq
from time_series_transform.io.base import io_base
from time_series_transform.io.numpy import (
from_numpy,
to_numpy
)
from time_series_transfor... | pd.DataFrame(expect_collection_expandTime['remove']) | pandas.DataFrame |
import threading
import time
import datetime
import pandas as pd
from functools import reduce, wraps
from datetime import datetime, timedelta
import numpy as np
from scipy.stats import zscore
import model.queries as qrs
from model.NodesMetaData import NodesMetaData
import utils.helpers as hp
from utils.helpers import... | pd.merge(result, grouped, on=['site', 'lat', 'lon'], how='outer') | pandas.merge |
# flu prediction
import os
import pandas as pd
import feather
from utils.fastai.structured import *
from utils.fastai.column_data import *
from sklearn import preprocessing
from sklearn.metrics import classification_report, confusion_matrix
import keras
from keras.layers import Input, Embedding, Dense, Dropout
from ke... | pd.read_feather(data_path + 'joined_df.feather') | pandas.read_feather |
# -*- coding: utf-8 -*-
import logging
import numpy as np
import pandas as pd
import pymc3 as pm
from scipy.stats import wilcoxon
from sklearn.metrics import log_loss
from sklearn.model_selection import StratifiedKFold
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.base import BaseEstimator, Class... | pd.DataFrame(data=baseline_prediction) | pandas.DataFrame |
import os
import math
import copy
import random
import calendar
import csv
import pandas as pd
import numpy as np
import networkx as nx
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib.ticker as ticker
import sqlite3
import seaborn as sns
#from atnresilience import ... | pd.DataFrame() | pandas.DataFrame |
#code will get the proper values like emyield, marketcap, cacl, etc, and supply a string and value to put back into the dataframe.
import pandas as pd
import numpy as np
import logging
import inspect
from scipy import stats
from dateutil.relativedelta import relativedelta
from datetime import datetime
from scipy import... | pd.Series(ltcacls) | pandas.Series |
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
import pytest
from toolz.dicttoolz import valmap
from kartothek.core.factory import DatasetFactory
from kartothek.io.eager import store_dataframes_as_dataset
def assert_index_dct_equal(dict1, dict2):
dict1 = valmap(sorted, dict1)
dict2 = valmap(s... | pd.DataFrame({"p": [1, 2]}) | pandas.DataFrame |
# Created by fw at 8/14/20
import torch
import numpy as np
import pandas as pd
import joblib
from torch.utils.data import Dataset as _Dataset
# from typing import Union,List
import lmdb
import io
import os
def get_dataset(cfg, city, dataset_type):
cfg = cfg.DATASET
assert city.upper() in ["BERLIN", "ISTANBU... | pd.to_datetime("2019-01-02") | pandas.to_datetime |
import pandas as pd
from collections import OrderedDict
import csv
print("Started.")
# define paths to your existing zooniverse classifications and metadata locations
# These are the files you get as output from the Jupyter Notebook convert_zooniverse.ipynb
classifs=pd.read_csv("../zooniverse_classifications/zoonive... | pd.read_csv("../metadata/filename_links.csv") | pandas.read_csv |
#!/usr/bin/env python
import numpy as np
import pandas as pa
import pb
from glccIndex import *
from collections import OrderedDict
def calc_area_from_LCCmatrix(veg5ini,list_LCCmatrix,area):
"""
Caculate the time series of area from initial vegetation
array (veg5ini) and a list of LCCmatrix.
P... | pa.DataFrame(glccReal_from_glccpftmtc,columns=['f','g','p','c'],index=['f','g','p','c']) | pandas.DataFrame |
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
import plotly.express as px
import plotly.graph_objects as go
import pandas as pd
import geopandas as gpd
import numpy as np
# for debugging purposes
import json
external_stylesheets = ['style... | pd.merge(gdf, df, on="neighborhood code") | pandas.merge |
'''
@Author: your name
@Date: 2020-05-25 10:31:42
@LastEditTime: 2020-05-25 11:03:34
@LastEditors: Please set LastEditors
@Description: In User Settings Edit
@FilePath: /dataprocess/transform/colconcat-neu.py
'''
import pandas as pd
import argparse
import os
import shutil
import random
parser = argparse.ArgumentParse... | pd.read_csv(left_dataset) | pandas.read_csv |
#!/usr/bin/python3
import json
from datetime import date, timedelta
from operator import itemgetter
from pprint import pprint
from typing import Dict, Tuple
import jinja2
import pandas as pd
from matplotlib import pyplot as plt
from rpm import labelCompare
# start statistics at this date (used as starting point for ... | pd.DataFrame(avg_bl_durs, index=dates) | pandas.DataFrame |
import re
import numpy as np
import pandas.compat as compat
import pandas as pd
from pandas.compat import u
from pandas.core.base import FrozenList, FrozenNDArray
from pandas.util.testing import assertRaisesRegexp, assert_isinstance
from pandas import Series, Index, DatetimeIndex, PeriodIndex
from pandas import _np_ver... | FrozenList(self.lst + [1, 2, 3]) | pandas.core.base.FrozenList |
"""
lcopt.model
-----------
Module containing the LcoptModel class.
"""
from lcopt.io import *
#from lcopt.ipython_interactive import IFS
from lcopt.interact import FlaskSandbox
from lcopt.bw2_export import Bw2Exporter
from lcopt.analysis import Bw2Analysis
# This is a copy straight from bw2data.query, extracted so ... | pd.DataFrame(to_df) | pandas.DataFrame |
#! /usr/bin/env python
### Script containing varous plotting functions for splitting Measurements
import pandas as pd
import sys
import os
import shlex
from subprocess import call
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
import matplotlib
import numpy as np
from stack import Stacker
from... | pd.read_csv(pdiff_l2,delim_whitespace=True,converters=date_time_convert) | pandas.read_csv |
import numpy as np
import pandas as pd
import os
DATA_FOLDER = 'data'
class Vehicle(object):
def __init__(self):
self.x = 0
self.y = 0
self.rides = []
def dist_to_ride(self, ride):
return np.abs(self.x - ride.start_x) + np.abs(self.y - ride.start_y)
def distance(coords1, coo... | pd.DataFrame(data=rides, columns=["start_x", "start_y", "end_x", "end_y", "min_start", "max_finish"]) | pandas.DataFrame |
from tkinter import *
import tkinter as tk
import os
import datetime as dt
from dateutil.relativedelta import relativedelta
import pytz
from openpyxl import load_workbook
import win32com.client
import win32api
import xlrd
from simple_salesforce import Salesforce
import pandas as pd
from openpyxl.styles import Font, Col... | pd.DataFrame(records3) | pandas.DataFrame |
import pdb
import unittest
import torch
import pandas as pd
import numpy as np
from agents.SACAgent import SACAgent
from cobs.model import Model
from test.test_config import state_name, sac_network_map, eplus_naming_dict, eplus_var_types, \
SatAction, BlindActionSingleZone, ThermActionSin... | pd.read_csv('test/agent_tests/saved_results/sac_blinds_multi_zone_single_stpt_obs.csv') | pandas.read_csv |
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