prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
#!/usr/bin/python3
import sys
import copy
from pathlib import Path
from datetime import datetime,timedelta
import re
import matplotlib.pyplot as plt
import math
import numpy as np
import random
import pandas as pd
import subprocess
from pickle import dump,load
from predictor.utility import msg2log
from clustgelDL.au... | pd.Timestamp.now() | pandas.Timestamp.now |
#!/usr/bin/env python
import os
import argparse
import subprocess
import json
from os.path import isfile, join, basename
import time
import pandas as pd
from datetime import datetime
import tempfile
import sys
sys.path.append(
os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir, 'instance_gene... | pd.DataFrame(results) | pandas.DataFrame |
import os
from typing import List, Tuple, Union
import numpy as np
import pandas as pd
DATASET_DIR: str = "data/"
# https://www.kaggle.com/rakannimer/air-passengers
def read_air_passengers() -> Tuple[pd.DataFrame, np.ndarray]:
indexes = [6, 33, 36, 51, 60, 100, 135]
values = [205, 600, 150, 315, 150, 190, 6... | pd.read_csv(f"{DATASET_DIR}air_passengers.csv") | pandas.read_csv |
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
'''
@File : ioutil.py
@Desc : Input and output data function.
'''
# here put the import lib
import os
import sys
import pandas as pd
import numpy as np
from . import TensorData
import csv
from .basicutil import set_trace
class File():
def __init__(self,... | pd.DataFrame() | pandas.DataFrame |
import logging
import os
import pickle
import tarfile
from typing import Tuple
import numpy as np
import pandas as pd
import scipy.io as sp_io
import shutil
from scipy.sparse import csr_matrix, issparse
from scMVP.dataset.dataset import CellMeasurement, GeneExpressionDataset, _download
logger = logging.getLogger(__n... | pd.DataFrame(self.ATAC_name) | pandas.DataFrame |
from flask import Flask, render_template, jsonify, request
from flask_pymongo import PyMongo
from flask_cors import CORS, cross_origin
import json
import copy
import warnings
import re
import pandas as pd
pd.set_option('use_inf_as_na', True)
import numpy as np
from joblib import Memory
from xgboost import XGBClassi... | pd.concat([DataRows2, hotEncoderDF2], axis=1) | pandas.concat |
import pandas as pd
import numpy as np
import json
PROCESS_FILE_NAME_LIST = ["taxi_sort_01", "taxi_sort_001", "taxi_sort_002", "taxi_sort_003", "taxi_sort_004", "taxi_sort_005", "taxi_sort_006", "taxi_sort_007", "taxi_sort_008", "taxi_sort_009", "taxi_sort_0006", "taxi_sort_0007", "taxi_sort_0008", "taxi_sort_0009"]
P... | pd.read_csv("precinct_center.csv", index_col=False) | pandas.read_csv |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('ggplot')
target = 'scale'
# IP
plot_mode = 'all_in_one'
obj = 'occ'
# Port
flow_dir = 'all'
port_dir = 'sys'
user_plot_pr = ['TCP']
user_plot_pr = ['UDP']
port_hist = pd.DataFrame({'A' : []})
user_port_hist = pd.DataFrame({'A' : []... | pd.read_csv("./postprocessed_data/%s/%s_piece%d.csv" % (files[data_idx], files[data_idx], piece_idx), index_col=None, header=0) | pandas.read_csv |
# %% [markdown]
# This python script takes audio files from "filedata" from sonicboom, runs each audio file through
# Fast Fourier Transform, plots the FFT image, splits the FFT'd images into train, test & validation
# and paste them in their respective folders
# Import Dependencies
import numpy as np
import pandas... | pd.DataFrame() | pandas.DataFrame |
'''
The analysis module
Handles the analyses of the info and data space for experiment evaluation and design.
'''
from slm_lab.agent import AGENT_DATA_NAMES
from slm_lab.env import ENV_DATA_NAMES
from slm_lab.lib import logger, util, viz
import numpy as np
import os
import pandas as pd
import pydash as ps
import shutil... | pd.concat(session_fitness_data, axis=1) | pandas.concat |
#!/usr/bin/env python3
# Project : From geodynamic to Seismic observations in the Earth's inner core
# Author : <NAME>
""" Implement classes for tracers,
to create points along the trajectories of given points.
"""
import numpy as np
import pandas as pd
import math
import matplotlib.pyplot as plt
from . import data... | pd.DataFrame(data=self.velocity_gradient, columns=["dvx/dx", "dvx/dy", "dvx/dz", "dvy/dx", "dvy/dy", "dvy/dz", "dvz/dx", "dvz/dy", "dvz/dz"]) | pandas.DataFrame |
#!/usr/bin/env python
import sys, time, code
import numpy as np
import pickle as pickle
from pandas import DataFrame, read_pickle, get_dummies, cut
import statsmodels.formula.api as sm
from sklearn.externals import joblib
from sklearn.linear_model import LinearRegression
from djeval import *
def shell():... | get_dummies(yy_df[categorical_features]) | pandas.get_dummies |
import os
import numpy as np
import pandas as pd
from numpy import abs
from numpy import log
from numpy import sign
from scipy.stats import rankdata
import scipy as sp
import statsmodels.api as sm
from data_source import local_source
from tqdm import tqdm as pb
# region Auxiliary functions
def ts_sum(df, window=10):
... | pd.Series(result_industryaveraged_df.index) | pandas.Series |
from turtle import TPen, color
import numpy as np
import pandas as pd
import random
import matplotlib.pyplot as plt
import seaborn as sns
import sklearn.metrics as metrics
from keras.models import Sequential
from keras.layers import Dense, LSTM, Flatten, Dropout
def get_ace_values(temp_list):
'''
This functio... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
import os
import re
from datetime import datetime
import numpy as np
from decimal import Decimal
import scipy.io as sio
import pandas as pd
from tqdm import tqdm
import glob
from decimal import Decimal
import datajoint as dj
from pipeline import (reference, subject, acquisition, stimulation, ... | pd.concat([fixed_delay_xlsx, random_long_delay_xlsx, random_short_delay_xlsx, tactile_xlsx, sound12_xlsx]) | pandas.concat |
import sys
import numpy as np
import pandas as pd
from loguru import logger
from sklearn import model_selection
from utils import dataset_utils
default_settings = {
'data_definition_file_path': 'dataset.csv',
'folds_num': 5,
'data_random_seed': 1509,
'train_val_fraction': 0.8,
'trai... | pd.concat(groups_test_df_list) | pandas.concat |
# -*- encoding: utf-8 -*-
import functools
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import pandas as pd
from pandas.api.types import is_numeric_dtype
import scipy.sparse
import sklearn.utils
from sklearn import preprocessing
from sklearn.compose import make_column_transfor... | pd.api.types.is_numeric_dtype(x) | pandas.api.types.is_numeric_dtype |
import functools
import json
import os
from multiprocessing.pool import Pool
from typing import List, Tuple, Type, Any
import pprint
import abc
import luigi
import numpy as np
import pandas as pd
import torch
import torchbearer
from torchbearer import Trial
from tqdm import tqdm
import gc
from mars_gym.data.dataset im... | pd.read_csv(self.model_training.metadata_data_frame_path, dtype = {self.model_training.project_config.item_column.name : "str"}) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Tue Jul 27 10:23:59 2021
@author: alber
"""
import re
import os
import pandas as pd
import numpy as np
import spacy
import pickle
import lightgbm as lgb
import imblearn
from sklearn import preprocessing
from sklearn.semi_supervised import (
LabelPropagation,
LabelSpread... | pd.read_csv("train_dataset.csv") | pandas.read_csv |
# import sys
# sys.path.append('JEMIPYC')
# from array_check_function_global import df,dfn,dfv,dfx,dfnx,dfvx
import pandas as pd
import numpy as np
tab = '__'
# no-extension , number of parameters is not limited, 2 or 3, whatever you want.
# ex) df(A,B,C,D,...,Z...)
# of course you just put one parameter.... | pd.set_option('display.max_columns', None) | pandas.set_option |
import os
import pandas as pd
import matplotlib.pyplot as plt
import datapackage as dp
import plotly.io as pio
import plotly.offline as offline
from plots import (
hourly_plot,
stacked_plot,
price_line_plot,
price_scatter_plot,
merit_order_plot,
filling_level_plot,
)
results = [r for r in os.l... | pd.concat([storages[r], shadow_prices[r]], axis=1) | pandas.concat |
from datetime import datetime
import numpy as np
import pytest
import pandas.util._test_decorators as td
from pandas.core.dtypes.base import _registry as ea_registry
from pandas.core.dtypes.common import (
is_categorical_dtype,
is_interval_dtype,
is_object_dtype,
)
from pandas.core.dtypes.dtypes import (... | Series(sp_array, name="new_column") | pandas.Series |
import numpy as np
import pandas as pd
from numba import njit
from datetime import datetime
import pytest
from itertools import product
from sklearn.model_selection import TimeSeriesSplit
import vectorbt as vbt
from vectorbt.generic import nb
seed = 42
day_dt = np.timedelta64(86400000000000)
df = pd.DataFrame({
... | pd.DatetimeIndex(['2018-01-04'], dtype='datetime64[ns]', name='split_0', freq=None) | pandas.DatetimeIndex |
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_multi_word_list) | pandas.DataFrame |
from __future__ import division
import configparser
import logging
import os
import re
import time
from collections import OrderedDict
import numpy as np
import pandas as pd
import scipy.interpolate as itp
from joblib import Parallel
from joblib import delayed
from matplotlib import pyplot as plt
from pyplanscoring.... | pd.DataFrame(res) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# Author: <NAME> <<EMAIL>>
# License: BSD
"""
Toolset working with yahoo finance data
Module includes functions for easy access to YahooFinance data
"""
import urllib.request
import numpy as np
import requests # interaction with the web
import os # file system operati... | pd.DataFrame(data,index=idx) | pandas.DataFrame |
from __future__ import division
from functools import wraps
import pandas as pd
import numpy as np
import time
import csv, sys
import os.path
import logging
from .ted_functions import TedFunctions
from .ted_aggregate_methods import TedAggregateMethods
from base.uber_model import UberModel, ModelSharedInputs
class Te... | pd.Series([], dtype="float", name="arbt_inv_sensory") | pandas.Series |
import pandas as pd
import json
import streamlit as st
import plotly.express as px
from PIL import Image
#Membuka file json
with open ("kode_negara_lengkap.json") as file :
data = json.load (file)
#Menjadikan file json sebagai dataframe
df_json = pd.DataFrame(data)
#Membuka file csv
df = | pd.read_csv('produksi_minyak_mentah.csv') | pandas.read_csv |
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split, StratifiedKFold
from sklearn import svm
from sklearn import metrics
def classification(latent_code, random_seed=42, ten_fold=False):
tumour_type = | pd.read_csv('data/PANCAN/GDC-PANCAN_both_samples_tumour_type.tsv', sep='\t', index_col=0) | pandas.read_csv |
from datetime import datetime
from io import StringIO
import itertools
import numpy as np
import pytest
import pandas.util._test_decorators as td
import pandas as pd
from pandas import (
DataFrame,
Index,
MultiIndex,
Period,
Series,
Timedelta,
date_range,
)
import pandas._testing as tm
... | MultiIndex.from_product([["A"], [0, 1]]) | pandas.MultiIndex.from_product |
from flowsa.common import WITHDRAWN_KEYWORD
from flowsa.flowbyfunctions import assign_fips_location_system
from flowsa.location import US_FIPS
import math
import pandas as pd
import io
from flowsa.settings import log
from string import digits
YEARS_COVERED = {
"asbestos": "2014-2018",
"barite": "2014-2018",
... | pd.DataFrame() | pandas.DataFrame |
import numpy as np
import pandas as pd
import pytest
from rulelist.datastructure.attribute.nominal_attribute import activation_nominal, NominalAttribute
class TestNominalAttribute(object):
def test_normal(self):
dictdata = {"column1" : np.array(["below50" if i < 50 else "above49" for i in range(100)]),
... | pd.testing.assert_series_equal(actual_vector, expected_vector, check_exact=True) | pandas.testing.assert_series_equal |
#! -*- coding: utf-8 -*-
from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
import cv2
import pickle
import os
import sys
import codecs
"""This example shows you an example case of flexible-clustering on image data.
In this example, it uses sub data from cifar-10 image collection.
The clustering ... | pandas.DataFrame(table_objects['cluster_information']) | pandas.DataFrame |
from tensorflow.keras.callbacks import TensorBoard
import tensorflow as tf
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras import backend as K
from sqlalchemy import create_engine
from sklearn.preprocessing import StandardS... | pd.read_sql_table(curr_Pair, engine) | pandas.read_sql_table |
# %%
import os
import sys
from collections import Counter
from datetime import datetime, timedelta
from glob import glob
from pathlib import Path
from zipfile import ZipFile
# data wrangling
import geopandas as gpd
import pandas as pd
import numpy as np
import requests
from urllib.error import HTTPError
# data maniupl... | pd.set_option('display.max_rows', 5) | pandas.set_option |
# coding:utf-8
#
# The MIT License (MIT)
#
# Copyright (c) 2016-2020
#
# 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, c... | pd.to_datetime(_data['date']) | pandas.to_datetime |
# -*- coding: utf-8 -*-
"""
@author: HYPJUDY 2019/4/15
https://github.com/HYPJUDY
Decoupling Localization and Classification in Single Shot Temporal Action Detection
-----------------------------------------------------------------------------------
Operations used by Decouple-SSAD
"""
import pandas as pd
import ... | pd.concat([resultDf1, resultDf2]) | pandas.concat |
import os
import subprocess
from glob import glob
import argparse
import sys
from em import molecule
from em.dataset import metrics
from mpi4py import MPI
from mpi4py.futures import MPICommExecutor
from concurrent.futures import wait
from scipy.spatial import cKDTree
import numpy as np
import pandas as pd
import trace... | pd.DataFrame(columns=['id','map_path','contourLevel','subunit', 'tagged_path', 'number_points','tagged_points_path']) | pandas.DataFrame |
"""
Evaluates the model.
"""
import argparse
import matplotlib as mpl
# do not use Qt/X that require $DISPLAY, must be called before importing pyplot
mpl.use('Agg')
import numpy as np
import pandas as pd
from sklearn.metrics import confusion_matrix
from prepare_training_data import load_indexes, load_transformers
imp... | pd.read_csv(evaluation_dir + '/final_metrics.csv', index_col=0) | pandas.read_csv |
"""Тесты для таблицы с торгуемыми ценными бумагами."""
from datetime import date
import pandas as pd
import pytest
from poptimizer.data import ports
from poptimizer.data.domain import events
from poptimizer.data.domain.tables import base, securities
from poptimizer.shared import col
TICKER_CASES = (
("GAZP", 0),... | pd.DataFrame([1, 4], index=["AKRN", "RTKMP"]) | pandas.DataFrame |
# Copyright (c) 2019, MD2K Center of Excellence
# - <NAME> <<EMAIL>>, <NAME> <<EMAIL>>
# 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 source code must retain the above co... | pd.DataFrame([], columns=column_names) | pandas.DataFrame |
#!/usr/bin/python3
# -*- coding: utf-8 -*-
import arrow
import pandas as pd
import requests
import json
from functools import reduce
# RU-1: European and Uralian Market Zone (Price Zone 1)
# RU-2: Siberian Market Zone (Price Zone 2)
# RU-AS: Russia East Power System (2nd synchronous zone)
# Handling of hours: data at... | pd.DataFrame(data) | pandas.DataFrame |
import pandas as pd
from primus.category import OneHotEncoder
def test_fit_transform_HaveHandleUnknownValueAndUnseenValues_ExpectAllZeroes():
train = pd.DataFrame({'city': ['Chicago', 'Seattle']})
test = pd.DataFrame({'city': ['Chicago', 'Detroit']})
expected_result = pd.DataFrame({'city_1': [1, 0],
... | pd.testing.assert_frame_equal(expected_result, result) | pandas.testing.assert_frame_equal |
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
from selenium.webdriver.common.keys import Keys
import requests
import time
from datetime import datetime
import pandas as pd
from urllib import parse
from config import ENV_VARIABLE
from os.path import getsize
fold_path = ... | pd.DataFrame() | pandas.DataFrame |
"""
dataset = AbstractDataset()
"""
from collections import OrderedDict, defaultdict
import json
from pathlib import Path
import numpy as np
import pandas as pd
from tqdm import tqdm
import random
def make_perfect_forecast(prices, horizon):
prices = np.array(prices).reshape(-1, 1)
forecast = np.hstack([n... | pd.concat(ds['features'], axis=1) | pandas.concat |
import os
import tempfile
from StringIO import StringIO
import numpy as np
import numpy.testing as npt
import pandas as pd
import pytest
import statsmodels.formula.api as smf
import yaml
from pandas.util import testing as pdt
from statsmodels.regression.linear_model import RegressionResultsWrapper
from .. import reg... | pdt.assert_series_equal(wrapper.bse, fit.bse, check_names=False) | pandas.util.testing.assert_series_equal |
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.DataFrame(data, columns=['algorithm', 'nbRules','support','confidence','cosine']) | pandas.DataFrame |
#!/usr/bin/env python
# Copyright 2020 ARC Centre of Excellence for Climate Extremes
# author: <NAME> <<EMAIL>>
#
# 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/l... | pd.DataFrame(d) | pandas.DataFrame |
import re
from collections import defaultdict
import pandas as pd
def empty_data(mapping_rows: list, row: pd.DataFrame):
return all([True if mapping_row not in row or row.get(mapping_row)=='' else False for mapping_row in mapping_rows])
def merge_spreadsheets(workbook: str, merge_field: str) -> pd.DataFrame... | pd.read_excel(workbook, sheet_name=None) | pandas.read_excel |
# -*- coding:utf-8 -*-
# /usr/bin/env python
"""
Date: 2020/3/17 13:06
Desc: 期货-中国-交易所-会员持仓数据接口
大连商品交易所、上海期货交易所、郑州商品交易所、中国金融期货交易所
采集前 20 会员持仓数据;
建议下午 16:30 以后采集当天数据, 避免交易所数据更新不稳定;
郑州商品交易所格式分为三类
大连商品交易所有具体合约的持仓排名, 通过 futures_dce_position_rank 获取
20171228
http://www.czce.com.cn/cn/DFSStaticFiles/Future/2020/20200727/Futu... | pd.read_html(r.text) | pandas.read_html |
# %%
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
import pandas as pd
import sys
from allensdk.api.queries.image_download_api import ImageDownloadApi
from allensdk.con... | pd.read_csv(infile, index_col=0) | pandas.read_csv |
#%%
import numpy as np
import pandas as pd
from orderedset import OrderedSet as oset
#%%
wals = pd.read_csv('ISO_completos.csv').rename(columns={'Status':'Status_X_L'})
wals_2 = pd.read_csv('ISO_completos_features.csv').rename(columns={'Status':'Status_X_L'})
wiki_merged = pd.read_csv('Wikidata_Wals_IDWALS.csv')
wiki =... | pd.concat(collapsed, axis=1) | pandas.concat |
"""Project Palette
functions for palette project
"""
from tkinter import filedialog
from tkinter.constants import END
import tkinter.messagebox as msgbox
import os
import webbrowser
from bs4 import BeautifulSoup
import pandas as pd
from PIL import Image
import requests
if __name__ == "__main__":
from data import... | pd.read_csv(filename, nrows=1) | pandas.read_csv |
import os
import sys
import argparse
import numpy as np
import pandas as pd
import cv2
import matplotlib.pyplot as plt
from tqdm import tqdm
import torch
import torch.nn.functional as TF
import torch.backends.cudnn as cudnn
from torch.utils.data import DataLoader
sys.path.append('../')
# from torchlib.transforms i... | pd.DataFrame(tuplas) | pandas.DataFrame |
import json
import pandas as pd
import argparse
#Test how many points the new_cut_dataset has
parser = argparse.ArgumentParser()
parser.add_argument('--dataset_path', default="new_dataset.txt", type=str, help="Full path to the txt file containing the dataset")
parser.add_argument('--discretization_unit', default=1, typ... | pd.to_datetime(data['start_date']) | pandas.to_datetime |
import os
import sys
import joblib
# sys.path.append('../')
main_path = os.path.split(os.getcwd())[0] + '/covid19_forecast_ml'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime, timedelta
from tqdm import tqdm
from Dataloader_v2 import BaseCOVDa... | pd.to_datetime(data_cases['date_time'], format='%Y-%m-%d') | pandas.to_datetime |
# -*- coding: utf-8 -*-
""" This module is designed for the use with the coastdat2 weather data set
of the Helmholtz-Zentrum Geesthacht.
A description of the coastdat2 data set can be found here:
https://www.earth-syst-sci-data.net/6/147/2014/
SPDX-FileCopyrightText: 2016-2019 <NAME> <<EMAIL>>
SPDX-License-Identifi... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import os
def _1996(data_dir):
from . import sgf_table_sums
file = "96data35.txt"
ids = pd.read_excel(
os.path.join(data_dir, "government-ids.xls"),
dtype={"ID Code": str, "State": str},
)
ids["State"] = ids["State"].str.strip()
map_id = dict(zip(ids["ID... | pd.DataFrame(columns=cols) | pandas.DataFrame |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import os.path
import math
from IPython.display import display,clear_output
import random
import scipy.stats as st
from sklearn.preprocessing import LabelEncoder
import sklearn.preprocessing as sk
import sklearn.... | pd.DataFrame() | pandas.DataFrame |
import argparse
import numpy as np
import pandas
import utils
parser = argparse.ArgumentParser()
parser.add_argument("data_path", type=str, help="path to csv file")
utils.add_arguments(parser, ["output"])
args = parser.parse_args()
data_path = args.data_path
out_path = args.output
df = pandas.read_csv(data_path)
aggr... | pandas.DataFrame(aggregate_dict) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Description
----------
Some simple classes to be used in sklearn pipelines for pandas input
Informations
----------
Author: <NAME>
Maintainer:
Email: <EMAIL>
Copyright:
Credits:
License:
Version:
Status: in development
"""
import... | pd.concat(list_df, 1) | pandas.concat |
import pandas as pd
import numpy as np
from sklearn.datasets import load_breast_cancer as lbc
from tkinter import *
from tkinter import messagebox
data = lbc()
clm = np.array(data['feature_names'])
df_x = pd.DataFrame(data['data'])
df_y = | pd.DataFrame(data['target']) | pandas.DataFrame |
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.testing.assert_index_equal(stats_df.index, ranges.wrapper.columns) | pandas.testing.assert_index_equal |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import sys
import copy
from datetime import datetime
import time
import pickle
import random
import pandas as pd
import numpy as np
import tensorflow as tf
import pathlib
from sklearn import preproces... | pd.to_datetime(self.config.end_date, format="%Y%m%d") | pandas.to_datetime |
import pandas as pd
import numpy as np
import math
import matplotlib.pyplot as plt
import assetallocation_arp.models.ARP as arp
# Parameters
TIMES_LAG=3
settings=arp.dataimport_settings("Settings")
# Change the universe of markets that is being used
markets="Leverage_MATR" # All "Leverage_all_markets" / Minimalist ... | pd.read_pickle("Data.pkl") | pandas.read_pickle |
from __future__ import print_function
import unittest
from unittest import mock
from io import BytesIO, StringIO
import random
import six
import os
import re
import logging
import numpy as np
import pandas as pd
from . import utils as test_utils
import dataprofiler as dp
from dataprofiler.profilers.profile_builder ... | pd.read_csv(cls.input_file_path) | pandas.read_csv |
# -*- coding: utf-8 -*-
import pytest
import numpy as np
import pandas as pd
import pandas.util.testing as tm
import pandas.compat as compat
###############################################################
# Index / Series common tests which may trigger dtype coercions
###############################################... | pd.Series([1, 2, 3, 4]) | pandas.Series |
import pandas as pd
import os,sys
import re
import torch
inp_path = r'/home/tiwarikajal/embeddingdata'
out_path = r'/home/tiwarikajal/data/'
error = []
df = | pd.DataFrame(columns=['year', 'Company', 'embeddings1a', 'embeddings7']) | pandas.DataFrame |
import mysql.connector
import pandas as pd
class MySQLInterface:
def __init__(self, server, username, password, dbname):
self.server = server
self.username = username
self.password = password
self.dbname = dbname
def __connect(self):
try:
... | pd.DataFrame(output) | pandas.DataFrame |
import pandas as pd
def generate_train(playlists):
# define category range
cates = {'cat1': (10, 50), 'cat2': (10, 78), 'cat3': (10, 100), 'cat4': (40, 100), 'cat5': (40, 100),
'cat6': (40, 100),'cat7': (101, 250), 'cat8': (101, 250), 'cat9': (150, 250), 'cat10': (150, 250)}
cat_pids = {}
... | pd.concat([df_test_itr, df_sample]) | pandas.concat |
#!/usr/bin/env python3
import os
import io
import re
import argparse
import itertools
import collections as col
import operator as op
import pickle as pck
import pandas as pd
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument(
'--agp-file',
'-a',
type=str,
... | pd.read_csv(bed_path, sep='\t', names=bed_columns, header=None) | pandas.read_csv |
#!/usr/bin/env python
# coding: utf-8
# # **<NAME> - Tracking Data Assignment**
#
# Sunday 11th October 2020
#
# ---
# In[1]:
import pandas as pd
import numpy as np
import datetime
# imports required by data prep functions
import json
# Laurie's libraries
import scipy.signal as signal
import matplotlib.animation ... | pd.DataFrame(homePlayers) | pandas.DataFrame |
# -*- coding: utf-8 -*-
'''
TopQuant-TQ极宽智能量化回溯分析系统2019版
Top极宽量化(原zw量化)，Python量化第一品牌
by Top极宽·量化开源团队 2019.01.011 首发
网站： www.TopQuant.vip www.ziwang.com
QQ群: Top极宽量化总群，124134140
文件名:toolkit.py
默认缩写：import topquant2019 as tk
简介：Top极宽量化·常用量化系统参数模块
'''
#
import sys, os, re
import arrow, bs4, rando... | pd.to_datetime(df.index, format='%Y-%m-%dT%H:%M:%S') | pandas.to_datetime |
import numpy as np
import pandas as pd
from tqdm import tqdm
from prereise.gather.solardata.helpers import get_plant_id_unique_location
from prereise.gather.solardata.nsrdb.nrel_api import NrelApi
def retrieve_data(solar_plant, email, api_key, year="2016"):
"""Retrieve irradiance data from NSRDB and calculate th... | pd.DataFrame({"Pout": [], "plant_id": [], "ts": [], "ts_id": []}) | pandas.DataFrame |
####################
# Import Libraries
####################
import os
import sys
from PIL import Image
import cv2
import numpy as np
import pandas as pd
import pytorch_lightning as pl
from pytorch_lightning.metrics import Accuracy
from pytorch_lightning import loggers
from pytorch_lightning import seed_e... | pd.DataFrame() | pandas.DataFrame |
import gradio as gr
import pickle
import os
import pandas as pd
import json
import urllib.parse
from stats import create_pdf
from pycaret.classification import *
welcome_message = """
Hello !
Thanks for using our tool , you'll be able to build your own recommandation tool.
You'll be able... | pd.concat([liked, bad1, bad2, bad3, bad4]) | pandas.concat |
import web
import pandas as pd
import numpy as np
import common
import os
import click
def hydro_op_chars_inputs_(webdb, project,
hydro_op_chars_sid,
balancing_type_project):
rows = webdb.where("inputs_project_hydro_operational_chars",
proj... | pd.read_excel(datapath, sheet_name=project, nrows=365) | pandas.read_excel |
"""Interactions with rainfall and river data."""
import numpy as np
import pandas as pd
__all__ = ["get_station_data"]
def get_station_data(filename, station_reference):
"""Return readings for a specified recording station from .csv file.
Parameters
----------
filename: str
filename to rea... | pd.read_csv(filename) | pandas.read_csv |
import datetime
import pandas as pd
from pathlib import Path
import matplotlib.pyplot as plt
_repos_csv = []
_issues_csv = []
CSV_FPATH = Path('/home/lucas.rotsen/Git_Repos/benchmark_frameworks/github_metrics')
METRICS_FPATH = Path('/home/lucas.rotsen/Git_Repos/benchmark_frameworks/metrics/raw')
def load_csv(file):... | pd.concat(dfs) | pandas.concat |
# Test for evaluering af hvert forecast og sammenligning mellem forecast
import pandas as pd
import numpy as np
from numpy.random import rand
from numpy import ix_
from itertools import product
import chart_studio.plotly as py
import chart_studio
import plotly.graph_objs as go
import statsmodels.api as sm
chart_studio... | pd.DataFrame(eq) | pandas.DataFrame |
from __future__ import annotations
import logging
import os
import numpy as np
import json
import warnings
import sys
import shutil
from datetime import timedelta
import pandas as pd
import pickle
import copy
import yaml
import torch
from torch import nn
from torch.nn.modules.loss import _Loss
import torch.nn.functiona... | pd.DataFrame(to_be_converted, index=index, columns=self.class_labels) | pandas.DataFrame |
# Ref: https://towardsdatascience.com/data-apps-with-pythons-streamlit-b14aaca7d083
#/app.py
import streamlit as st
import json
import requests
# import sys
# import os
import pandas as pd
import numpy as np
import re
from datetime import datetime as dt
from pandas_profiling import ProfileReport
from streamlit_pandas... | pd.DataFrame(msg_dict) | pandas.DataFrame |
import pandas as pd
import random
import math
import numpy as np
import matplotlib.pyplot as plt
from shapely.geometry.polygon import LinearRing, Polygon, Point
from maxrect import get_intersection, get_maximal_rectangle, rect2poly
from vertical_adhesion import *
def get_min_max(input_list):
'''
get minimum a... | pd.DataFrame(stitches_per_layer, columns=['layer', 'stitch']) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
import numpy as np
import pandas as pd
from copy import deepcopy
from functools import partial
import matplotlib.pyplot as plt
import optuna
import pickle
from sklearn.metrics import mean_squared_error
from tqdm import tqdm
import os
code_path = os.path.dirname(os.path.abspath... | pd.concat(pred_new) | pandas.concat |
import pytz
import pytest
import dateutil
import warnings
import numpy as np
from datetime import timedelta
from itertools import product
import pandas as pd
import pandas._libs.tslib as tslib
import pandas.util.testing as tm
from pandas.errors import PerformanceWarning
from pandas.core.indexes.datetimes import cdate_... | DatetimeIndex(['2011-01-01'], freq='D') | pandas.DatetimeIndex |
# -*- coding: utf-8 -*-
"""
__date: 2021/05/12
__author: ssfang
__corporation: OriginQuantum
__usage:
"""
import os
import re
import time
import threading
from queue import Queue
from datetime import datetime
import yaml
import pandas as pd
import matplotlib.pyplot as plt
pd.set_option('display.max_c... | pd.set_option('display.max_rows', None) | pandas.set_option |
import os
import sys
import pdb
import bdb
import click
import logging
import signal
import hashlib
import inspect
import traceback
import pandas as pd
from subir import Uploader
from .browser_interactor import BrowserInteractor
from .user_interactor import UserInteractor, Interaction
from .pilot import Pilot
from .ma... | pd.DataFrame() | pandas.DataFrame |
import numpy as np
import pandas as pd
import pytest
import orca
from urbansim_templates import utils
def test_parse_version():
assert utils.parse_version('0.1.0.dev0') == (0, 1, 0, 0)
assert utils.parse_version('0.115.3') == (0, 115, 3, None)
assert utils.parse_version('3.1.dev7') == (3, 1, 0, 7)
a... | pd.Series([10,5], index=[3,1]) | pandas.Series |
import PyPDF2
import csv
from pathlib import Path
import io
import pandas
import numpy
from pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter
from pdfminer.converter import TextConverter
from pdfminer.layout import LAParams
from pdfminer.pdfpage import PDFPage
def Cpk(usl, lsl, avg, sigma , cf,... | pandas.set_option('display.expand_frame_repr', False) | pandas.set_option |
import pandas as pd
from evaluate.calculator import (
RecallCalculator,
PrecisionCalculator,
EmptyReportError,
)
import pytest
from unittest.mock import patch, Mock
from evaluate.report import (
Report,
PrecisionReport,
RecallReport
)
from tests.common import create_precision_report_row
from io ... | pd.DataFrame(columns=columns) | pandas.DataFrame |
from set_figure_defaults import FigureDefaults
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sn
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
import operator
import warnings
import pick... | pd.DataFrame(dataset.loc[:,corrMatrixFiltered.columns[0]]) | pandas.DataFrame |
import numpy as np
import pandas as pd
import pandas.util.testing as tm
import pytest
import pandas_datareader.data as web
pytestmark = pytest.mark.stable
class TestEurostat(object):
def test_get_ert_h_eur_a(self):
# Former euro area national currencies vs. euro/ECU
# annual data (ert_h_eur_a)
... | pd.Timestamp("2000-01-01") | pandas.Timestamp |
"""Module for running decoding experiments."""
from pathlib import Path
from typing import Optional, Sequence, Union
import numpy as np
import pandas as pd
from joblib import Parallel, delayed
from sklearn.model_selection import BaseCrossValidator
import pte_decode
def run_experiment(
feature_root: Union[Path, ... | pd.concat(features, axis=1) | pandas.concat |
# -*- coding: utf-8 -*-
import logging
import os
import click
import pandas as pd
from src.libs.bookmaker import BookMaker
from sqlalchemy import create_engine
import pymysql
pymysql.install_as_MySQLdb()
@click.command()
@click.option('--model', default='mlp_1')
@click.option('--strategy', default='value_bet_0.5')
def... | pd.read_sql(sql="select MATCH_ID, BbAvH, BbAvD, BbAvA, FTR from matches", con=db) | pandas.read_sql |
# Do some analytics on Shopify transactions.
import pandas as pd
from datetime import datetime, timedelta
class Analytics:
def __init__(self, filename: str, datetime_now, refund_window: int):
raw = pd.read_csv(filename)
clean = raw[raw['Status'].isin(['success'])] # Filter down t... | pd.merge(sales, total_refunds, on='Name', how='outer') | pandas.merge |
#web scrapping libraries
from bs4 import BeautifulSoup as bs
import requests
from selenium import webdriver
from webdriver_manager.chrome import ChromeDriverManager
from selenium.webdriver.chrome.options import Options
#data processing libraries
import fsspec
import os
import folium
import time
import numpy as np
impor... | pd.DataFrame(data=data, columns=columns) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Thu Feb 18 14:22:56 2021
@author: KRS1BBH
"""
from ImportFilter import Importfile
import pandas as pd
import os, glob
#get path of directory script is executed from
dirname = os.path.dirname(__file__)
#nuk
Filelist=[dirname+'/testdata/NuK/LotResultSummaryAll.csv']... | pd.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
import logging
import numpy
from pandas import DataFrame, Series, pivot_table
from scipy.spatial.distance import pdist, squareform
from scipy.cluster.hierarchy import *
from matplotlib import pyplot
from pylie.methods.methods import hlinkage_to_treematrix
from pylie.plotting import plot_matri... | pivot_table(self.data, values=column, index=['case'], columns=['poses']) | pandas.pivot_table |
"""Tests various time series functions which are used extensively in tcapy
"""
__author__ = 'saeedamen' # <NAME> / <EMAIL>
#
# Copyright 2017 Cuemacro Ltd. - http//www.cuemacro.com / @cuemacro
#
# See the License for the specific language governing permissions and limitations under the License.
#
import pandas as p... | pd.date_range(start='01 Jan 2018', end='05 Jan 2018', freq='30s') | pandas.date_range |
# -*- coding: utf-8 -*-
"""
Created on Thu Feb 16 23:11:56 2017
@author: Flamingo
"""
import pandas as pd
import numpy as np
import datetime
import copy
import sys
sys.path.append('../TOOLS')
from IJCAI2017_TOOL import *
#%% readin shop data
HOLI = pd.read_csv('../additional/HOLI.csv')
HOLI = HOLI.set_index(['DAT... | pd.DataFrame(train_date_zip) | pandas.DataFrame |
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