prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import pandas as pd
import os
import time
from minder_utils.configurations import config
from .format_util import iter_dir
from minder_utils.download.download import Downloader
from minder_utils.util.decorators import load_save
from minder_utils.formatting.format_tihm import format_tihm_data
import numpy as np
from min... | pd.to_datetime(data_out.time, utc=True) | pandas.to_datetime |
# previously we looked into numpy and its ndarrayobject in particular. Here
# we build on that knowledge by looking at the data structures provided by the Pandas Library.
# Pandas is a newer package built on top of NumPz and proveides an efficient
# implementation of a DataFrame.
# Here we will focus on the mechani... | pd.Series([0.25, 0.5, 0.75, 1.0]) | pandas.Series |
"""
Data: Temperature and Salinity time series from SIO Scripps Pier
Salinity: measured in PSU at the surface (~0.5m) and at depth (~5m)
Temp: measured in degrees C at the surface (~0.5m) and at depth (~5m)
- Timestamp included beginning in 1990
"""
# imports
import sys,os
import pandas as pd
import numpy as np
im... | pd.to_datetime(sal_data[['YEAR', 'MONTH', 'DAY']]) | pandas.to_datetime |
# Arithmetic tests for DataFrame/Series/Index/Array classes that should
# behave identically.
# Specifically for datetime64 and datetime64tz dtypes
from datetime import (
datetime,
time,
timedelta,
)
from itertools import (
product,
starmap,
)
import operator
import warnings
import numpy as np
impo... | Series([NaT, NaT], dtype="datetime64[ns]") | pandas.Series |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
Date: 2022/2/14 18:19
Desc: 新浪财经-股票期权
https://stock.finance.sina.com.cn/option/quotes.html
期权-中金所-沪深 300 指数
https://stock.finance.sina.com.cn/futures/view/optionsCffexDP.php
期权-上交所-50ETF
期权-上交所-300ETF
https://stock.finance.sina.com.cn/option/quotes.html
"""
import json
i... | pd.DataFrame(data_json) | pandas.DataFrame |
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import os
breakout_index = 0
prev_breakout_index = 0
num_samples = 90
month = "july"
num_samples_split = 10
path = str(num_samples_split) + "_normalized_refined_lfc/"
#filename_breakout = "normalized datasets 2/normalized_breakout_" + month + ".xl... | pd.DataFrame(data_required) | pandas.DataFrame |
from BoostInference_no_parallelization import Booster
import sys, pandas as pd, numpy as np
import glob, pickle
from sklearn.metrics import roc_auc_score, precision_recall_curve, auc
if len(sys.argv)<5:
print('python file.py df-val-PhyloPGM-input df-test-PhyloPGM-output info_tree fname_df_pgm_output')
exit(0)... | pd.read_csv(fname_dtrain, index_col=0) | pandas.read_csv |
# Import libraries
import pandas as pd
from bs4 import BeautifulSoup
import matplotlib.pyplot as plt
from urllib.request import urlopen, Request
from nltk.sentiment.vader import SentimentIntensityAnalyzer
# Parameters
n = 10 # the # of article headlines displayed per ticker
tickers = ['AAPL', 'TSLA', 'AMZN']
# Get D... | pd.DataFrame(scores) | pandas.DataFrame |
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import LabelEncoder
from twitter.twitter_data_model import User
import pandas as pd
def get_most_likely_author(usernames, tweet_to_classify, nlp):
vects = []
# Puts vectorized tweets in dataframe for each user
for username in u... | pd.DataFrame([tweet.vect for tweet in user_0.tweets]) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import numpy as np
import pytest
from numpy.random import RandomState
from numpy import nan
from datetime import datetime
from itertools import permutations
from pandas import (Series, Categorical, CategoricalIndex,
Timestamp, DatetimeIndex, Index, IntervalIndex)
import pan... | tm.assert_numpy_array_equal(result, expected) | pandas.util.testing.assert_numpy_array_equal |
# MIT License
#
# Copyright (c) 2021. <NAME> <<EMAIL>>
#
# 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.isna(v) | pandas.isna |
import re
import numpy as np
import pandas as pd
import pytest
import cudf
from cudf import melt as cudf_melt
from cudf.core import DataFrame
from cudf.tests.utils import (
ALL_TYPES,
DATETIME_TYPES,
NUMERIC_TYPES,
assert_eq,
)
@pytest.mark.parametrize("num_id_vars", [0, 1, 2, 10])
@pytest.mark.para... | pd.MultiIndex.from_tuples([("c", 1), ("c", 2)], names=[None, None]) | pandas.MultiIndex.from_tuples |
"""Test attributing simple impact."""
import numpy as np
import pandas as pd
import pytest
from nbaspa.data.endpoints.pbp import EventTypes
from nbaspa.player_rating.tasks import SimplePlayerImpact
@pytest.mark.parametrize(
"evt",
[
EventTypes.REBOUND,
EventTypes.FREE_THROW,
EventTyp... | pd.Series([0.0, 0.0]) | pandas.Series |
# coding=utf-8
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
import operator
from itertools import product, starmap
from numpy import nan, inf
import numpy as np
import pandas as pd
from pandas import (Index, Series, DataFrame, isnull, bdate_range,
NaT, date_range, ti... | tm.assert_frame_equal(res, exp) | pandas.util.testing.assert_frame_equal |
import os
import pytz
import logging
import pymongo
import multiprocessing
import pandas as pd
from datetime import datetime
from collections import Counter, defaultdict
from typing import List, Set, Tuple
# For non-docker use, change to your url (e.g., localhost:27017)
MONGO_URL = "mongodb://localhost:27... | pd.read_excel("data/rules.xlsx", engine="openpyxl") | pandas.read_excel |
import os
from multiprocessing import Value, context
import pandas as pd
import socket
import threading
import multiprocessing
import re
from contextlib import contextmanager
from pathlib import Path
import json
from portalocker import RLock, AlreadyLocked
import shutil
import pytest
from aljpy import humanhash
from fn... | pd.to_datetime(df['_created']) | pandas.to_datetime |
import json
import numpy as np
import pandas as pd
import requests
def get_state_fips_codes():
"""
Returns dataframe of state FIPS codes and state names
from the BLS JT series reference
"""
url = "https://download.bls.gov/pub/time.series/jt/jt.state"
data = requests.get(url)
data_fmt = da... | pd.concat(dfs) | pandas.concat |
"""
Msgpack serializer support for reading and writing pandas data structures
to disk
portions of msgpack_numpy package, by <NAME> were incorporated
into this module (and tests_packers.py)
License
=======
Copyright (c) 2013, <NAME>.
All rights reserved.
Redistribution and use in source and binary forms, with or wit... | Timestamp(obj['value'], tz=obj['tz'], freq=freq) | pandas.Timestamp |
import datetime
import logging
import os
import shutil
import geopandas as gpd
import numpy as np
import pandas as pd
import pytz
from berlin_hp import electricity
from demandlib import bdew as bdew
from demandlib import particular_profiles as profiles
from matplotlib import cm
from matplotlib import dates as mdates
f... | pd.to_datetime(re_en["utc_timestamp"], utc=True) | pandas.to_datetime |
"""
The MIT License (MIT)
Copyright (c) 2016 <NAME>
Copyright (c) 2019 <NAME>
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, ... | pd.concat([alldf, df], axis=0) | pandas.concat |
import os
import pandas as pd
import numpy as np
import copy
from pprint import pprint
def work(pres):
count = [0, 0]
for i in pres:
count[i] += 1
out = count.index(max(count))
return out
def simple_vote(model_name, date, dataset, pseudo=False):
if pseudo:
DATA_DIR = '../predict_... | pd.DataFrame(humor_final_data, columns=['ID', 'Speaker', 'Sentence', 'Label']) | pandas.DataFrame |
import re
import logging
from functools import reduce, partial
from concurrent import futures
from concurrent.futures import ThreadPoolExecutor, as_completed
import pandas as pd
import numpy as np
from pandas.api.types import is_numeric_dtype
from influxdb.resultset import ResultSet
from requests.exceptions import Re... | pd.to_datetime(right.time, unit='ms') | pandas.to_datetime |
import os
import pandas as pd
import mysql.connector as mysql
from mysql.connector import Error
def DBConnect(dbName=None):
"""
Parameters
----------
dbName :
Default value = None)
Returns
-------
"""
conn = mysql.connect(host='localhost', user='root', password='<PASSWORD>',
... | pd.read_csv('data/station_summary.csv') | pandas.read_csv |
import itertools
import warnings
import networkx as nx
import numpy as np
import pandas as pd
from tqdm import tqdm
from AppGenerator import AppGenerator
from ServerlessAppWorkflow import ServerlessAppWorkflow
warnings.filterwarnings("ignore")
class PerfOpt:
def __init__(self, Appworkflow, generate_perf_profile=... | pd.Series(aRow) | pandas.Series |
# Copyright 2021 Rikai Authors
#
# 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 wri... | pd.DataFrame(data) | pandas.DataFrame |
"""
# Part of localization phase
# suspected bug detection:
# 1. Tensorflow,Theano,CNTK
# 2. Tensorflow,Theano,MXNET
#
# voting process
# -> a. inconsistency -> error backend,error layer.
# b. check error backend in new container(whether inconsistency disappears).
# """
#
import numpy as np
import os
import sys
imp... | pd.DataFrame(columns=unsolved_columns) | pandas.DataFrame |
import numpy as np
from scipy.special import softmax
import torch
import torch.nn as nn
import torch.optim as optim
import torch.utils.data as tdata
import pandas as pd
import time
from tqdm import tqdm
from utils import validate, get_logits_targets, sort_sum
import pdb
# Conformalize a model with a calibration set.
#... | pd.DataFrame({'size': size, 'correct': correct}) | pandas.DataFrame |
import cv2
from collections import OrderedDict
import numpy as np
import itertools
from datetime import datetime
from configparser import ConfigParser, MissingSectionHeaderError, NoOptionError, NoSectionError
import pandas as pd
import os
import warnings
warnings.filterwarnings("ignore")
def shap_summary_... | pd.read_csv(feat_cat_csv_path, header=[0, 1]) | pandas.read_csv |
from qfengine.data.price.price_source import MySQLPriceDataSource as SQLTable
from qfengine.asset import assetClasses
import pandas as pd
from typing import Union, List, Dict
import os
import numpy as np
import logging
import functools
from qfengine import settings
import concurrent.futures
logger = logging.getLogge... | pd.Timedelta(hours=9, minutes=30) | pandas.Timedelta |
# cheat sheet https://share.streamlit.io/daniellewisdl/streamlit-cheat-sheet/app.py
# https://docs.streamlit.io/en/stable/index.html
import streamlit as st
import pandas as pd
import numpy as np
import time
# title
st.title('Uber pickups in NYC')
st.write(f" Streamlit version:{st.__version__}")
# get data
DATE_COLU... | pd.to_datetime(data[DATE_COLUMN]) | pandas.to_datetime |
# this program breaks down into a csv where phosphosite orthologs could be lost in the PhosphositeOrthology program
# PhosphositePlus is being used to verify that my orthologs are correct but PSP does not have everything which is the
# reason for using dbPAF
# we want to make sure that the UniprotIDs contained in BO... | pd.read_table('oma-uniprot_clean.txt', dtype=object) | pandas.read_table |
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import os
import argparse
from pathlib import Path
parser = argparse.ArgumentParser(description="Analyse the logs produced by torchbeast")
parser.add_argument("--dir", type=str, default="~/logs/torchbeast", help="Directory fo... | pd.concat(dfs) | pandas.concat |
import torch
import pandas as pd
import numpy as np
import os
#os.environ['CUDA_VISIBLE_DEVICES'] = '2'
import transformers as ppb
from transformers import BertForSequenceClassification, AdamW, BertConfig
from torch.utils.data import TensorDataset,DataLoader, RandomSampler, SequentialSampler
from keras.utils import to... | pd.DataFrame(data=training_stats) | pandas.DataFrame |
import pandas as pd
import pytest
from pandas.testing import assert_frame_equal
from sklearn.pipeline import Pipeline
from hcrystalball.feature_extraction import HolidayTransformer
@pytest.mark.parametrize(
"X_y_with_freq, country_code, country_code_column, country_code_column_value, extected_error",
[
... | pd.DataFrame(expected, index=X.index) | pandas.DataFrame |
"""Helper Functions to Support Pairs Trading
This file can be imported as a module and contains the following functions:
* create_and_save_historicals - returns a df with all coin information
* binance_data_to_df - historical information for a single coin
* two_coin_pricing - historical log pricing of two ... | pd.DataFrame(klines, columns = ['timestamp', 'open', 'high', 'low', 'close', 'volume', 'close_time', 'quote_av', 'trades', 'tb_base_av', 'tb_quote_av', 'ignore' ]) | pandas.DataFrame |
import numpy as np
import numpy.testing as npt
import pandas as pd
from stumpy import aampi, core, config
import pytest
import naive
substitution_locations = [(slice(0, 0), 0, -1, slice(1, 3), [0, 3])]
substitution_values = [np.nan, np.inf]
def test_aampi_int_input():
with pytest.raises(TypeError):
aampi... | pd.Series(T) | pandas.Series |
#!/usr/bin/env python
# coding: utf-8
# # Starbucks Capstone Challenge
#
# ### Introduction
#
# This data set contains simulated data that mimics customer behavior on the Starbucks rewards mobile app. Once every few days, Starbucks sends out an offer to users of the mobile app. An offer can be merely an advertisemen... | pd.read_json('data/profile.json', orient='records', lines=True) | pandas.read_json |
import inspect
import os
from unittest.mock import MagicMock, patch
import numpy as np
import pandas as pd
import pytest
import woodwork as ww
from evalml.model_understanding.graphs import visualize_decision_tree
from evalml.pipelines.components import ComponentBase
from evalml.utils.gen_utils import (
SEED_BOUND... | pd.Series([1, 2, 3, 4], dtype="Int64") | pandas.Series |
"""
Functions to scrape by season, games, and date range
"""
import hockey_scraper.json_schedule as json_schedule
import hockey_scraper.game_scraper as game_scraper
import hockey_scraper.shared as shared
import pandas as pd
import time
import random
# This hold the scraping errors in a string format.
# This may seem... | pd.concat(master_pbps) | pandas.concat |
# %%
import airtablecache.airtablecache as AC
import pandas as pd
import os
import sys
testDataLoc = os.path.join(os.path.dirname(sys.modules['airtablecache'].__file__), 'data/testData.csv')
sampleDF = pd.DataFrame([
['Alpha', 10, 'India'],
['Beta', 15, 'Australia']
], columns=['Name', 'Age', 'Country'])
new... | pd.testing.assert_frame_equal(df, sampleDF) | pandas.testing.assert_frame_equal |
import random
import timeit
from decimal import Decimal
import h5py
import hdf5plugin
import numpy as np
import pandas as pd
import gym
from gym import logger
from gym import spaces
import matplotlib.pyplot as plt
import os
from decimal import getcontext
os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'
| pd.set_option('display.float_format', lambda x: '%.10f' % x) | pandas.set_option |
from datetime import datetime
from pandas.compat import range, long, zip
from pandas import compat
import re
import numpy as np
from pandas.core.algorithms import unique
from pandas.tseries.offsets import DateOffset
from pandas.util.decorators import cache_readonly
import pandas.tseries.offsets as offsets
import pand... | compat.iteritems(_reso_str_map) | pandas.compat.iteritems |
# Title: Weather Data Aggregator
# Description: Aggregates data from the weather station on Cockcroft from the OnCall API.
# Author: <NAME>
# Date: 17/12/2020
# Version: 1.0
# Import libraries
import pandas as pd
from pandas import json_normalize
import json
import requests
from datetime import datetime, timedelta
fr... | pd.DataFrame() | pandas.DataFrame |
import typer
import spotipy
import pandas as pd
import os
from loguru import logger
from spotify_smart_playlists.helpers import spotify_auth
from toolz import thread_last, mapcat, partition_all
from typing import List
def main(library_file: str, artists_file: str):
logger.info("Initializing Spotify client.")
... | pd.read_csv(artists_file) | pandas.read_csv |
from datetime import datetime, timedelta
import unittest
from pandas.core.datetools import (
bday, BDay, BQuarterEnd, BMonthEnd, BYearEnd, MonthEnd,
DateOffset, Week, YearBegin, YearEnd, Hour, Minute, Second,
format, ole2datetime, to_datetime, normalize_date,
getOffset, getOffsetName, inferTimeR... | Week(weekday=2) | pandas.core.datetools.Week |
#!/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 pandas as pd
import matplotlib.dates as mdates
from matplotlib impor... | pd.read_csv(index_csv_file) | pandas.read_csv |
# -*- coding: utf-8 -*-
import os
import numpy as np
import pandas as pd
from dramkit.gentools import isnull
from dramkit.iotools import load_csv
from dramkit.datetimetools import today_date
from dramkit.datetimetools import get_date_format
from dramkit.datetimetools import date_reformat
from dramkit.datetimetools imp... | pd.DataFrame(df.iloc[0, :]) | pandas.DataFrame |
# from IPython.core.display import display, HTML
# display(HTML("<style>.container { width:100% !important; }</style>"))
_ = None
import argparse
import json as J
import os
import shutil
import tempfile
import joblib
import mlflow
import functools as F
from importlib import reload as rl
import copy
import pandas as ... | pd.Series(mp) | pandas.Series |
# coding=utf-8
# pylint: disable-msg=E1101,W0612
from datetime import timedelta
from numpy import nan
import numpy as np
import pandas as pd
from pandas import (Series, isnull, date_range,
MultiIndex, Index)
from pandas.tseries.index import Timestamp
from pandas.compat import range
from pandas.u... | assert_series_equal(result, expected) | pandas.util.testing.assert_series_equal |
import os
from os import listdir
from os.path import isfile, join
import re
from path import Path
import numpy as np
import pandas as pd
from poor_trader import utils
from poor_trader.utils import quotes_range
from poor_trader.config import INDICATORS_OUTPUT_PATH
def _true_range(df_quotes, indices):
cur = df_qu... | pd.DataFrame(columns=['top', 'mid', 'bottom'], index=df_quotes.index) | pandas.DataFrame |
# coding: utf-8
# In[ ]:
import numpy as np
import numpy.matlib as npml
import pandas as pd
import statistics as st
from copy import deepcopy
import networkx as nx
import simpy
import matplotlib.pyplot as plt
from simplekml import Kml, Style # for graph_kml
import math
import shapely.geometry
import pyproj
im... | pd.DataFrame() | pandas.DataFrame |
# ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.3.4
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
import matplotlib.pyplot as plt
import pandas as pd
imp... | pd.merge(average_combined_df,rev_drug_info.iloc[:,[4,5,6]],on='standard_inchi_key') | pandas.merge |
""" This file process the IO for the Text similarity index processor """
import math
import os
import pandas as pd
from similarity_processor.similarity_core import get_cosine
from similarity_processor.similarity_core import text_to_vector
import similarity_processor.similarity_logging as cl
LOG = cl.get_logger()
def ... | pd.ExcelWriter('%s.xlsx' % file_path, engine='xlsxwriter') | pandas.ExcelWriter |
#!/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 |
"""
"""
__version__='192.168.3.11.dev1'
import sys
import os
import logging
import pandas as pd
import re
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
logger = logging.getLogger('PT3S')
try:
from PT3S import Rm
except ImportError:... | pd.Timedelta('0 seconds') | pandas.Timedelta |
# -*- coding: utf-8 -*-
# pylint: disable=W0612,E1101
from datetime import datetime
import operator
import nose
from functools import wraps
import numpy as np
import pandas as pd
from pandas import Series, DataFrame, Index, isnull, notnull, pivot, MultiIndex
from pandas.core.datetools import bday
from pandas.core.n... | tm.assert_produces_warning(False) | pandas.util.testing.assert_produces_warning |
import numpy as np
import pandas as pd
from yews.cpic import pick
def detects2table(results_dict, wl, g, include_all=False, starttime=None):
'''
Converts dictionary of results from detect() function to pandas DataFrame
object. Columns include starttime, endtime, p probability and s probability
for each... | pd.DataFrame(data) | pandas.DataFrame |
import os, random
import pandas as pd
import re
import sys
import codecs
from shutil import copyfile
from datetime import datetime
def clean_text(text):
text = text.replace("<p>", "").replace("</p>", "\n")
return re.sub('\.+', ".", text)
def filecount(dir):
return len([f for f in os.listdir(dir)])
def... | pd.DataFrame(columns=['article_id', 'year']) | pandas.DataFrame |
import pandas as pd
import numpy as np
import warnings as w
import math
from scipy.signal import savgol_filter
from copy import deepcopy, copy
from fO2calculate import core
from fO2calculate import fO2bufferplotter
from fO2calculate import tavern as tv
def calc_dIW_from_fO2(P, T, fO2):
"""Translates from absolute... | pd.concat([_masses_data, fO2_data], axis=1) | pandas.concat |
import pandas as pd
import glob
import os
import numpy as np
import time
import fastparquet
import argparse
from multiprocessing import Pool
import multiprocessing as mp
from os.path import isfile
parser = argparse.ArgumentParser(description='Program to run google compounder for a particular file and setting')
parse... | pd.melt(cur_df,id_vars=['head','year','count'],value_vars=['modifier','w1','w2','w3'],value_name='context') | pandas.melt |
from django.shortcuts import render
from django.views.generic import FormView, UpdateView
from django.http import HttpResponse, JsonResponse, HttpResponseRedirect
from common.mixins import JSONResponseMixin, AdminUserRequiredMixin
from common.utils import get_object_or_none
from common.tools.data_fit import iter_regres... | pd.DataFrame(showjson) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Python Script related to:
Deep Neural Network model to predict the electrostatic parameters in the polarizable classical Drude oscillator force field
<NAME>, <NAME>, <NAME> and <NAME>.
Copyright (c) 2022, University of Maryland Baltimore
"""
import numpy as np
impor... | pd.read_pickle('dgenff_dataset.2021/test_alphathole_target.pkl') | pandas.read_pickle |
# coding=utf-8
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
import operator
from itertools import product, starmap
from numpy import nan, inf
import numpy as np
import pandas as pd
from pandas import (Index, Series, DataFrame, isnull, bdate_range,
NaT, date_range, ti... | Series([False, False, False]) | pandas.Series |
# -*- coding: utf-8 -*-
"""
Created on Fri Oct 29 09:20:13 2021
@author: bw98j
"""
import prose as pgx
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import seaborn as sns
import numpy as np
import itertools
import glob
import os
import random
from tqdm import ... | pd.read_csv('interim_files/klijn_panel_spearmanCorr.tsv', sep='\t',index_col=0) | pandas.read_csv |
from distutils.util import execute
import sqlite3
from datetime import datetime, timedelta, tzinfo
from importlib import resources
from pathlib import Path
from sqlite3.dbapi2 import OperationalError, Row
from typing import Any, Dict, List
import numpy as np
import pandas as pd
from DailyData import __version__
from D... | pd.DataFrame(fetch, columns=columns) | pandas.DataFrame |
import numpy as np
import cvxpy as cp
import pandas as pd
from scoring import *
# %%
def main():
year = int(input('Enter Year: '))
week = int(input('Enter Week: '))
budget = int(input('Enter Budget: '))
source = 'NFL'
print(f'Source = {source}')
df = read_data(year=year, week=week, source=sourc... | pd.get_dummies(df['pos']) | pandas.get_dummies |
# -*- coding: utf-8 -*-
"""
Tests the usecols functionality during parsing
for all of the parsers defined in parsers.py
"""
import nose
import numpy as np
import pandas.util.testing as tm
from pandas import DataFrame, Index
from pandas.lib import Timestamp
from pandas.compat import StringIO
class UsecolsTests(obj... | StringIO(s) | pandas.compat.StringIO |
# -*- coding: utf-8 -*-
"""
@file:maketrain.py
@time:2019/5/6 16:42
@author:Tangj
@software:Pycharm
@Desc
"""
import pandas as pd
import numpy as np
import gc
import time
name = ['log_0_1999', 'log_2000_3999', 'log_4000_5999','log_6000_7999', 'log_8000_9999', 'log_10000_19999',
'log_20000_29999', 'log_30000_39... | pd.concat([Train, train2]) | pandas.concat |
import pandas as pd
import tkinter as tk
from tkinter import filedialog
from tkinter import messagebox
import os
import joblib
import json, codecs
import numpy as np
from sklearn.cross_decomposition import PLSRegression
from datetime import date
import Classes.Configurations as cfg
from Classes import Configurations
... | pd.DataFrame(df_y_resume) | pandas.DataFrame |
from bs4 import BeautifulSoup
import os
import pandas as pd
HTML_FOLDER = 'yeet/'
file_names = os.listdir(HTML_FOLDER)
final_list = [] # TODO: find some other solution other than a global variable
def extract_data(soup):
'''
Function that takes in a single HTML file and returns a DataFrame with the required... | pd.DataFrame(final_list, columns=headings) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Tue Sep 1 15:26:36 2020
@author: <NAME>
"""
import pandas as pd
import numpy as np
data_ME = pd.read_csv("Portfolios_Formed_on_ME_monthly_EW.csv", index_col = 0)
def returns_anual(data_col):
returns = data_col / 100
n_months = returns.shape[0]
returns_annualized ... | pd.to_datetime(ind.index, format="%Y%m") | pandas.to_datetime |
# coding=utf-8
# pylint: disable-msg=E1101,W0612
import nose
import numpy as np
import pandas as pd
from pandas import (Index, Series, _np_version_under1p9)
from pandas.tseries.index import Timestamp
from pandas.types.common import is_integer
import pandas.util.testing as tm
from .common import TestData
class Test... | Series([np.nan], index=[0.5]) | pandas.Series |
from preprocessing.utils_communes import (
build_and_clean_df,
label_encoders_generator,
encode_df,
decode_df,
)
import pandas as pd
from preprocessing.preprocessing import (
standardize_education_level,
standardize_date,
standardize_tailmen,
standardize_bool_hors_nk,
standardize_mou... | pd.read_csv(aggregated_file, low_memory=False) | pandas.read_csv |
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn import metrics
from log import logger
def split_train_test(x, y, test_size, seed):
idx_norm = y == 0
idx_out = y == 1
n_f = x.shape[1]
... | pd.DataFrame(matrix, columns=columns) | pandas.DataFrame |
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.laye... | pd.get_dummies(train_ds['label']) | pandas.get_dummies |
# -*- coding:utf-8 -*-
# !/usr/bin/env python
"""
Date: 2021/11/2 21:08
Desc: 同花顺-数据中心-技术选股
http://data.10jqka.com.cn/rank/cxg/
"""
import pandas as pd
import requests
from bs4 import BeautifulSoup
from py_mini_racer import py_mini_racer
from tqdm import tqdm
from akshare.datasets import get_ths_js
def _get_file_co... | pd.DataFrame() | pandas.DataFrame |
# Authors: <NAME> <<EMAIL>>
# License: BSD 3 clause
import pandas as pd
import pytest
from categorical_encoder import CategoricalEncoder, RareLabelEncoder
def test_CategoricalEncoder_count():
df = {'category': ['A'] * 10 + ['B'] * 6 + ['C'] * 4,
'target' : [1,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0, 1,1,0,0,]}
... | pd.DataFrame(transf_df) | pandas.DataFrame |
# -*- coding:utf-8 -*-
# /usr/bin/env python
"""
Date: 2021/7/12 15:47
Desc: 东方财富-沪深板块-概念板块
http://quote.eastmoney.com/center/boardlist.html#concept_board
"""
import requests
import pandas as pd
def stock_board_concept_name_em() -> pd.DataFrame:
"""
东方财富-沪深板块-概念板块-名称
http://quote.eastmoney.com/center/boar... | o_numeric(temp_df["开盘"]) | pandas.to_numeric |
import os
import pickle
import sys
from pathlib import Path
from typing import Union
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from Bio import pairwise2
from scipy import interp
from scipy.stats import linregress
from sklearn.metrics import roc_curve, auc, precision_recall_curve
import th... | pd.Series(PR_AUC_dict) | pandas.Series |
import pandas as pd
from glob import glob
from collections import defaultdict
import csv
import time
import subprocess
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('start_well_index', help='key to identify which wells to do')
args = parser.parse_args()
start_well_index = int(args.start_well_i... | pd.concat(dats) | pandas.concat |
import os
import pandas as pd
import pytest
from pandas.testing import assert_frame_equal
from .. import read_sql
@pytest.fixture(scope="module") # type: ignore
def mssql_url() -> str:
conn = os.environ["MSSQL_URL"]
return conn
@pytest.mark.xfail
def test_on_non_select(mssql_url: str) -> None:
query ... | pd.Series([None, -2.23e-308, 1.79e308], dtype="float") | pandas.Series |
import pandas as pd
class BarBase(object):
pass
class Current_bar(BarBase):
def __init__(self):
self._cur_bar_list = []
def add_new_bar(self, new_bar):
"添加新行情，会缓存第n条当前行情，和第n+1条行情，共两条"
self._cur_bar_list.pop(0) if len(self._cur_bar_list) == 2 else None
self._cur_bar_list.... | pd.DataFrame(self._bar_dict[self.instrument]) | pandas.DataFrame |
import pandas as pd
import numpy as np
from flask_socketio import SocketIO, emit
import time
import warnings
warnings.filterwarnings("ignore")
import pandas as pd
import numpy as np
import ast
from sklearn.metrics import mean_absolute_error,mean_squared_error
from statsmodels.tsa import arima_model
from statsmodels.ts... | pd.isnull(data) | pandas.isnull |
import streamlit as st
import pandas as pd
import requests
import numpy as np
import wordcloud
import itertools
# Try this?
# https://towardsdatascience.com/topic-modelling-in-python-with-nltk-and-gensim-4ef03213cd21 ... pyLDAvis
num_words = 150
document_limit = 5000
st_time_to_live = 4 * 3600
# Display options
st... | pd.read_json(js["topics"], orient="split") | pandas.read_json |
import pandas as pd
import numpy as np
DATA_PATH = 'rawdata/' #where the raw data files are
ALT_OUTPUT_PATH = 'alt_output/' #there will be many files produced -- the files that are not "the main ones"
# are placed in this directory
feasDF = pd.read_csv(DATA_PATH+"mipdev_feasibility.csv") #read ... | pd.read_csv(DATA_PATH+"regions_time_data.csv") | pandas.read_csv |
import numpy as np
import pandas as pd
import remixt.bamreader
import os
empty_data = {
'fragments': remixt.bamreader.create_fragment_table(0),
'alleles': remixt.bamreader.create_allele_table(0),
}
def _get_key(record_type, chromosome):
return '/{}/chromosome_{}'.format(record_type, chromosome)
def ... | pd.HDFStore(in_filename, 'r') | pandas.HDFStore |
# -*- coding: utf-8 -*-
"""
Created on Tue Apr 9 18:48:55 2019
@author: shday
"""
import math
from collections import namedtuple
import pandas as pd
import numpy as np
from dateutil import parser
import dash_html_components as html
import plotly.express as px
import pytz
import plotly.graph_objects as... | pd.to_datetime(tweet_data['created_at'], errors='coerce') | pandas.to_datetime |
import numpy as np
import pandas as pd
def load(path):
df = pd.read_csv(path,
encoding="utf-8",
delimiter=";",
quotechar="'").rename(
columns={
"Text": "text",
"Label": "label"
})
train, dev, test = split_df... | pd.get_dummies(train["label"]) | pandas.get_dummies |
# functions to run velocyto and scvelo
import numpy as np
import pandas as pd
# import velocyto as vcy
# import scvelo as scv
from scipy.sparse import csr_matrix
import matplotlib.pyplot as plt
from .moments import *
from anndata import AnnData
def vlm_to_adata(vlm, n_comps=30, basis="umap", trans_mats=None, cells_i... | pd.DataFrame.from_dict(scvelo_coef, orient="index") | pandas.DataFrame.from_dict |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
Date: 2021/12/30 11:31
Desc: 股票数据-总貌-市场总貌
股票数据-总貌-成交概括
http://www.szse.cn/market/overview/index.html
http://www.sse.com.cn/market/stockdata/statistic/
"""
import warnings
from io import BytesIO
from akshare.utils import demjson
import pandas as pd
import requests
warni... | o_numeric(temp_df['主板B'], errors="coerce") | pandas.to_numeric |
import numpy as np
import pytest
from pandas import DataFrame, Series, concat, isna, notna
import pandas._testing as tm
import pandas.tseries.offsets as offsets
@pytest.mark.parametrize(
"compare_func, roll_func, kwargs",
[
[np.mean, "mean", {}],
[np.nansum, "sum", {}],
[lambda x: np... | tm.assert_almost_equal(result0, result1) | pandas._testing.assert_almost_equal |
#!/usr/bin/env python3
import sys
sys.stderr = open(snakemake.log[0], "w")
import numpy as np
import pandas as pd
import allel
chroms = snakemake.params['chroms']
for chrom in chroms:
vcf = allel.read_vcf(f"results/variants/vcfs/annot.missense.{chrom}.vcf")
pos = vcf['variants/POS']
pos1 = pos+... | pd.DataFrame(data) | pandas.DataFrame |
import unittest
import pandas as pd
from data_profiler.profilers import OrderColumn
from . import test_utils
from unittest.mock import patch, MagicMock
from collections import defaultdict
# This is taken from: https://github.com/rlworkgroup/dowel/pull/36/files
# undo when cpython#4800 is merged.
unittest.case._Assert... | pd.Series(data3) | pandas.Series |
# -*- coding: utf-8 -*-
"""
These the test the public routines exposed in types/common.py
related to inference and not otherwise tested in types/test_common.py
"""
from warnings import catch_warnings, simplefilter
import collections
import re
from datetime import datetime, date, timedelta, time
from decimal import De... | lib.is_datetime64_array(arr) | pandas._libs.lib.is_datetime64_array |
import numpy as np
import pandas as pd
import csv
import os
from datetime import datetime
class Console_export(object):
def __init__(self, path):
self.path = path + "_sim_summary.txt"
def printLog(self, *args, **kwargs):
print(*args, **kwargs)
with open(self.path,'a') as file... | pd.concat([export_df, temp_df], axis=1) | pandas.concat |
# Copyright 2021 The TensorFlow Probability Authors.
#
# 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 o... | pd.to_datetime(['2012-12-25', '2013-01-01']) | pandas.to_datetime |
import datetime as dt
import pandas as pd
import numpy as np
import re
# Begin User Input Data
report_date = dt.datetime(2020, 8, 31)
wscf_market_value = 194719540.46
aqr_market_value = 182239774.63
delaware_market_value = 151551731.17
wellington_market_value = 149215529.22
qic_cash_market_value = 677011299.30
input... | pd.ExcelWriter(output_directory + 'CIO/#Data/output/holdings/top_holdings.xlsx', engine='xlsxwriter') | pandas.ExcelWriter |
import os
import csv
import pandas as pd
def main(dataset_path, dataset_mode):
data_label_pairs = []
data_path = 'leftImg8bit/'
label_path = 'gtFine/'
for subdirectory in os.listdir(dataset_path + '/' + data_path + dataset_mode):
for image_path in os.listdir(dataset_path + '/' + data_pat... | pd.DataFrame(data_label_pairs) | pandas.DataFrame |
import pandas as pd
from clr import AddReference
AddReference("System")
AddReference("QuantConnect.Algorithm")
AddReference("QuantConnect.Common")
AddReference("QuantConnect.Indicators")
from System import *
from QuantConnect import *
from QuantConnect.Algorithm import *
from QuantConnect.Algorithm.Framework import *... | pd.DataFrame(columns=self.colsU) | pandas.DataFrame |
import pandas as pd
import os
from upload_dados import *
import plotly.express as px
import numpy as np
os.system('cls')
#4. Qual a antecedência média das reservas?
# filtrando todos os dados dos anuncios alugados
data_df = data_df.loc[data_df['booked_on'] != 'blank']
data_df = pd.DataFrame(data_df)
#convertendo os... | pd.to_datetime(data_df['date']) | pandas.to_datetime |
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