prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import unittest
from nose.tools import assert_equal, assert_list_equal, nottest, raises
from py_stringmatching.tokenizer.delimiter_tokenizer import DelimiterTokenizer
from py_stringmatching.tokenizer.qgram_tokenizer import QgramTokenizer
import numpy as np
import pandas as pd
from py_stringsimjoin.filter.overlap_filt... | pd.DataFrame([{'B.id':1, 'B.attr':'world', 'B.int_attr':6}]) | pandas.DataFrame |
# coding: utf-8
"""Mapping of production and consumption mixes in Europe and their effect on
the carbon footprint of electric vehicles
This code performs the following:
- Import data from ENTSO-E (production quantities, trades relationships)
- Calculates the production and consumption electricity mixes for Europ... | pd.ExcelWriter(results_filepath) | pandas.ExcelWriter |
#%%
from sklearn import preprocessing
from sklearn.preprocessing import MinMaxScaler
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from textblob import TextBlob
import twitterscraper as ts
import os
import re
import json
import datetime as dt
import yfinance as yf
import plotly
import plotly.ex... | pd.Timedelta(days=3) | pandas.Timedelta |
# Celligner
from re import sub
from celligner.params import *
from celligner import limma
from genepy.utils import helper as h
from genepy.utils import plot
from sklearn.decomposition import PCA, IncrementalPCA
from sklearn.linear_model import LinearRegression
from scipy.spatial import cKDTree
import umap.umap_ as um... | pd.concat([self.transform_input, self.fit_input]) | pandas.concat |
from __future__ import division
from builtins import str
from builtins import range
from builtins import object
__copyright__ = "Copyright 2015 Contributing Entities"
__license__ = """
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the Lice... | pd.isnull(pathset_links_df[Route.ROUTES_COLUMN_MODE_NUM]) | pandas.isnull |
# -*- coding: utf-8 -*-
from __future__ import absolute_import, division, print_function
import operator
import warnings
from functools import wraps, partial
from numbers import Number, Integral
from operator import getitem
from pprint import pformat
import numpy as np
import pandas as pd
from pandas.util import cach... | pd.DataFrame(array, index=index, columns=self.columns) | pandas.DataFrame |
import re
from copy import copy
from typing import Iterable, Optional, Union
import pandas as pd
import requests
from bs4 import BeautifulSoup
from pvoutput.consts import (
MAP_URL,
PV_OUTPUT_COUNTRY_CODES,
PV_OUTPUT_MAP_COLUMN_NAMES,
REGIONS_URL,
)
_MAX_NUM_PAGES = 1024
def get_pv_systems_for_coun... | pd.Series(outputs_col, name="timeseries_duration", index=index) | pandas.Series |
import json
import os
import copy
import numpy as np
import pandas as pd
import pytest
from ..utils import sanitize_dataframe, nested_update, prepare_spec
PANDAS_DATA = pd.DataFrame({'x': [1, 2, 3], 'y': [4, 5, 6]})
JSON_DATA = {
"values": [
{"x": 1, "y": 4},
{"x": 2, "y": 5},
{"x": 3, "... | pd.date_range('2012-01-01', periods=5, freq='H') | pandas.date_range |
def read_table(filename, datadir='./out', levels=None):
import pandas as pd
import os
file = os.path.join(datadir, filename)
if levels is None:
levels = 0
with open(file, 'r') as fd:
for i in fd.readline().split(','):
if i: break
else: levels +... | pd.concat(out) | pandas.concat |
import pandas as pd
import sqlite3
from sqlite3 import Error as SQLError
from datetime import datetime
import re
import csv
import os
import json
from fuzzywuzzy import fuzz
import sys
sys.path.insert(1, "../")
from settings import DB_FP, CORPUS_META
sql_get_members ="""
SELECT c.PimsId, m.name, c.constituency
FROM ... | pd.read_csv(fp, header=0) | pandas.read_csv |
import requests
from bs4 import BeautifulSoup
from time import sleep
import time
from datetime import datetime
import itertools
import inspect
import pandas as pd
import numpy as np
import re
from classes import LRTlinks
startTime = time.time()
# To do
# rename the Task class
# create a class for the scraping of link... | pd.read_csv(file_to_be_read) | pandas.read_csv |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', None)
import pandas as pd
from sklearn import preprocessing
from pandas.plotting import scatter_matrix
from matplotlib import pyplot
from sklearn.preprocessing import LabelEncoder
from sklearn.... | pd.DataFrame({"original":y_test,"predictions":preds}) | pandas.DataFrame |
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.to_datetime(migrations.startCommitTime, utc=True) | pandas.to_datetime |
import json
import pandas as pd
from pprint import pprint
def reader(reader_csv="reader_results.csv"):
model_rename_map = {
"deepset/roberta-base-squad2": "RoBERTa",
"deepset/minilm-uncased-squad2": "MiniLM",
"deepset/bert-base-cased-squad2": "BERT base",
"deepset/bert-large-uncase... | pd.read_csv(index_csv) | pandas.read_csv |
# -*- coding: utf-8 -*-
# This code is initially based on the Kaggle kernel from <NAME>, which can be found in the following link
# https://www.kaggle.com/neviadomski/how-to-get-to-top-25-with-simple-model-sklearn/notebook
# and the Kaggle kernel from <NAME>, which can be found in the link below
# https://www.kag... | pd.read_csv("../../test.csv") | pandas.read_csv |
import pandas as pd
import json
import io
from datetime import datetime, timedelta
| pd.set_option('display.max_rows', None) | pandas.set_option |
# authors: <NAME>, <NAME>, <NAME>, <NAME>
# date: 2020-11-25
"""Fits a SVR model on the preprocessed data from the IMDB review data set.
Saves the model with optimized hyper-parameters, as well as the search result.
Usage:
imdb_rating_predict_model.py <train> <out>
imdb_rating_predict_model.py (-h | --help)
Op... | pd.DataFrame(hyper_parameters_search.cv_results_) | pandas.DataFrame |
'''
This code will clean the OB datasets and combine all the cleaned data into one
Dataset name: O-27-Da Yan
semi-automate code, needs some hands work. LOL But God is so good to me.
1. 9 different buildings in this dataset, and each building has different rooms
3. each room has different window, door, ac, indoor, out... | pd.read_excel(door_name, usecols=[0, 1]) | pandas.read_excel |
"""
*** <NAME> ***
_________Shubbair__________
TODO Naive Bias
"""
from sklearn.naive_bayes import GaussianNB, MultinomialNB
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_wine
wine = load_wine()
print(dir(wine))
data_frame = | pd.DataFrame(wine.data, columns=wine.feature_names) | pandas.DataFrame |
import pandas as pd
import STRING
import numpy as np
import datetime
from sklearn.cluster import AgglomerativeClustering
from models.cluster_model import cluster_analysis
pd.options.display.max_columns = 500
# SOURCE FILE
offer_df = pd.read_csv(STRING.path_db + STRING.file_offer, sep=',', encoding='utf-8', ... | pd.read_csv(STRING.path_db_aux + STRING.file_bonus, sep=';', encoding='latin1') | pandas.read_csv |
# Globals #
import re
import numpy as np
import pandas as pd
import dateutil.parser as dp
from nltk import word_tokenize
from nltk.corpus import stopwords
from nltk.stem.porter import *
from itertools import islice
from scipy.stats import boxcox
from scipy.integrate import simps
from realtime_talib import Indicator
fr... | pd.Series(ema12[:min_length]) | pandas.Series |
# -*- coding: utf-8 -*-
# pylint: disable=E1101,E1103,W0232
import os
import sys
from datetime import datetime
from distutils.version import LooseVersion
import numpy as np
import pandas as pd
import pandas.compat as compat
import pandas.core.common as com
import pandas.util.testing as tm
from pandas import (Categor... | Series([1, 2, 3]) | pandas.Series |
# -*- coding: utf-8 -*-
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
import pytest
import re
from numpy import nan as NA
import numpy as np
from numpy.random import randint
from pandas.compat import range, u
import pandas.compat as compat
from pandas import Index, Series, DataFrame, isn... | u('f_g_h') | pandas.compat.u |
import datetime
from pathlib import Path
import numpy as np
import pandas as pd
import pytest
from pointcloudset import Dataset, PointCloud
@pytest.fixture()
def testdata_path() -> Path:
return Path(__file__).parent.absolute() / "testdata"
@pytest.fixture()
def testbag1():
return Path(__file__).parent.abs... | pd.read_pickle(filename) | pandas.read_pickle |
import pymanda
import pandas as pd
import numpy as np
import warnings
"""
ChoiceData
---------
A container for a DataFrame that maintains relevant columns for mergers and
acquisitions analyses
"""
class ChoiceData():
"""
Two-dimensional, size-mutable, potentially heterogeneous tabular data.
D... | pd.DataFrame(index=all_choices) | pandas.DataFrame |
"""
Created by adam on 11/8/16
"""
__author__ = 'adam'
import pandas as pd
import environment as env
import Models.TweetORM as TweetORM
pd.options.display.max_rows = 999 # let pandas dataframe listings go long
def isRetweet(text):
"""
Classifies whether a tweet is a retweet based on how it starts
"""
... | pd.concat(frames) | pandas.concat |
# -*- coding: utf-8 -*-
from datetime import timedelta
import operator
from string import ascii_lowercase
import warnings
import numpy as np
import pytest
from pandas.compat import lrange
import pandas.util._test_decorators as td
import pandas as pd
from pandas import (
Categorical, DataFrame, MultiIndex, Serie... | tm.assert_index_equal(the_sum.index, the_mean.index) | pandas.util.testing.assert_index_equal |
"""Get data into JVM for prediction and out again as Spark Dataframe"""
import logging
logger = logging.getLogger('nlu')
import pyspark
from pyspark.sql.functions import monotonically_increasing_id
import numpy as np
import pandas as pd
from pyspark.sql.types import StringType, StructType, StructField
class DataConv... | pd.DataFrame({raw_text_column:data}) | pandas.DataFrame |
import datetime
import os
import sys
import geopandas as gpd
import numpy as np
import pandas as pd
from bokeh.io import output_file, save
from bokeh.layouts import column
from bokeh.models.widgets import Panel, Tabs
from .plotting import PLOT_HEIGHT, PLOT_WIDTH, plot_map, plot_time_series
from .utils import Data, ge... | pd.to_datetime(d.Date) | pandas.to_datetime |
"""
Monte Carlo-type tests for the BM model
Note that that the actual tests that run are just regression tests against
previously estimated values with small sample sizes that can be run quickly
for continuous integration. However, this file can be used to re-run (slow)
large-sample Monte Carlo tests.
"""
import numpy... | pd.period_range(endog.index[0] - 1, endog.index[-1], freq='M') | pandas.period_range |
import pytest
import numpy as np
import pandas as pd
from pandas import Categorical, Series, CategoricalIndex
from pandas.core.dtypes.concat import union_categoricals
from pandas.util import testing as tm
class TestUnionCategoricals(object):
def test_union_categorical(self):
# GH 13361
data = [
... | Categorical(['a', 'b'], categories=['a', 'b', 'c']) | pandas.Categorical |
import pandas as pd
import numpy as np
import json
import io
import random
def prepareSalesData(csvfile):
#Read store 20 sales
store20_sales = pd.read_csv(csvfile, index_col=None)
# Create Year column for grouping data
store20_sales['Date'] = pd.to_datetime(store20_sales['Date'])
store20_sales['Yea... | pd.DataFrame(dept_list, index=cat_values, columns=['Dept']) | pandas.DataFrame |
"""
"""
import os
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from src.utils.constants import REGIONS, LANDCOVER_PERIODS, DICTIONARY
if __name__ == "__main__":
# Project's root
os.chdir("../..")
fig, axs = plt.subplots(2, 2, figsize=(11.69, 4.14))
correlations = | pd.read_csv("results/csv/burned_area_landcover_change_corr.csv") | pandas.read_csv |
#%%
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
import networkx as nx
import psycopg2
import datatable as dt
import pickle
import plotly.express as px
# from plotly.subplots import make_subplots
from collections import namedtuple, defaultdict
from datetime import datetime
import torch
from torch.uti... | pd.to_datetime(df.loc[:,'elapsed_time']) | pandas.to_datetime |
#!/usr/bin/env python3
import os
from datetime import date
from pathlib import Path
import pandas as pd
import sys
def load(path: Path, d: date, sex: str) -> pd.DataFrame:
print(f"Loading input file {path}")
df = pd.read_excel(
path,
header=2
)
# rename the columns to NUTS? code
... | pd.concat([df_2019_12_31_B, df_2019_12_31_M, df_2019_12_31_F]) | pandas.concat |
from datetime import datetime
import os
import re
import numpy as np
import pandas as pd
from fetcher.extras.common import MaRawData, zipContextManager
from fetcher.utils import Fields, extract_arcgis_attributes
NULL_DATE = datetime(2020, 1, 1)
DATE = Fields.DATE.name
TS = Fields.TIMESTAMP.name
DATE_USED = Fields.DA... | pd.DataFrame(collected) | pandas.DataFrame |
import numpy as np
import copy
import logging
from IPython.display import display, clear_output
from collections import defaultdict
import pailab.analysis.plot as paiplot
import pailab.analysis.plot_helper as plt_helper
import ipywidgets as widgets
from pailab import MLObjectType, RepoInfoKey, FIRST_VERSION, LAST_VERS... | pd.DataFrame(model_rows) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# In[2]:
import sys
sys.path.append('..')
# In[3]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from datetime import timedelta, datetime, date
import os
from utils import data_paths, load_config
from pathlib import Path
from nltk.metrics import edit... | pd.read_csv(deaths_url, error_bad_lines=False) | pandas.read_csv |
import time
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm as cm
import seaborn as sns
sns.set_style("whitegrid")
import sys
import os
from pathlib import Path
from sklearn import metrics
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection i... | pd.Series(train_scores_std, name='training_score_std') | pandas.Series |
#!/usr/bin/env python
# coding: utf-8
# # Exploratory Data Analysis
# The purpose of this section of the notebook is to provide some key highlights of the baseline data being used. This showcases the various attributes, any specific transformations, and key relationships.
# In[50]:
import pandas as pd
import matpl... | pd.Series(data=clf.feature_importances_,index=X.columns) | pandas.Series |
import os
import time
import re
import requests
import pandas as pd
from datetime import datetime, timedelta
from dateutil import parser
from concha.environment import FileHandler
class NOAA:
"""Handles NOAA weather operations for finding stations, getting historical weather, and forecasts.
The only setting... | pd.NamedAgg(column="snow", aggfunc="any") | pandas.NamedAgg |
"""
Testing interaction between the different managers (BlockManager, ArrayManager)
"""
from pandas.core.dtypes.missing import array_equivalent
import pandas as pd
import pandas._testing as tm
from pandas.core.internals import (
ArrayManager,
BlockManager,
SingleArrayManager,
SingleBlockMana... | pd.option_context("mode.data_manager", "array") | pandas.option_context |
# coding=utf-8
# pylint: disable-msg=E1101,W0612
""" test get/set & misc """
import pytest
from datetime import timedelta
import numpy as np
import pandas as pd
from pandas.core.dtypes.common import is_scalar
from pandas import (Series, DataFrame, MultiIndex,
Timestamp, Timedelta, Categorical)
... | pd.set_option('chained_assignment', 'raise') | pandas.set_option |
import nose
import unittest
from numpy import nan
import numpy as np
from pandas import Series, DataFrame
from pandas.util.compat import product
from pandas.util.testing import (assert_frame_equal,
assert_series_equal,
assert_almost_equal)
class Tes... | assert_series_equal(res2, expected) | pandas.util.testing.assert_series_equal |
# -*- 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... | numeric(temp_df["总市值"]) | pandas.to_numeric |
#!/usr/bin/env python
# coding: utf-8
from install import *
from solvers import *
from params import *
import pandas as pd
import matplotlib.pyplot as plt
from scipy.stats import rayleigh, norm, kstest
def plot_maxwell(vel, label=None, draw=True):
speed = (vel*vel).sum(1)**0.5
loc, scale = rayleigh.fit(speed, fl... | pd.isnull(checks.fit_speed) | pandas.isnull |
# Copyright 2021 Huawei Technologies Co., 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 agreed to... | pd.DataFrame(eval_name_list) | pandas.DataFrame |
"""
omg: Omics Mock Generator
Generates a mock dataset of omics data (importable in EDD):
transcriptomics, proteomics, and metabolomics
Requirements: Python 3.7.2, cobra, numpy, pandas.
"""
__author__ = 'LBL-QMM'
__copyright__ = 'Copyright (C) 2019 Berkeley Lab'
__license__ = ''
__status__ = 'Alpha'
__date__ = 'Dec ... | pd.DataFrame(edd) | pandas.DataFrame |
# %%
from bs4 import BeautifulSoup
import requests
import math
import pandas as pd
import numpy as np
import sys, os, fnmatch
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from datetime import datetime as dt
# %%
def get_version(s, version):
for v in version:
# split up '(F)S... | pd.to_numeric(stats["times"][s], errors="coerce") | pandas.to_numeric |
# 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_frame_equal(expected, result) | pandas.testing.assert_frame_equal |
import pandas as pd
from datetime import datetime
from multiprocessing import Pool
import seaborn as sns
from matplotlib import pyplot as plt
from pathlib import Path
# ================================
# MARKING SCHEME NOTES
# ===============================
# 1. In the accompanying assignment Python file, students ar... | pd.read_csv(csv_file) | pandas.read_csv |
# allocation.py (flowsa)
# !/usr/bin/env python3
# coding=utf-8
"""
Methods of allocating datasets
"""
import pandas as pd
from flowsa.common import fbs_activity_fields, sector_level_key, \
load_crosswalk, check_activities_sector_like
from flowsa.settings import log, vLogDetailed
from flowsa.dataclean import repla... | pd.DataFrame() | pandas.DataFrame |
# Data container for ESI data
from pathlib import Path
import geopandas as gpd
import numpy as np
import pandas as pd
from scipy.spatial import cKDTree
from .grs import GRS
class ESI:
"""
ESI data container.
Attributes:
-----------
path: Path
Path to ESI data
gdf: geopandas.GeoDataF... | pd.Series(esi_rows, dtype='i4') | pandas.Series |
import numpy as np
import pandas as pd
from imblearn.over_sampling import SMOTE
from sklearn import preprocessing
from sklearn.decomposition import PCA
from sklearn.model_selection import train_test_split
from sklearn.neighbors import LocalOutlierFactor
from sklearn.preprocessing import StandardScaler
train_data_path ... | pd.read_csv(train_data_path, index_col=False, header=None) | pandas.read_csv |
from contextlib import contextmanager, ExitStack
from copy import deepcopy
from functools import partial, reduce
import itertools
import re
import tempfile
from typing import Callable, Iterable, Optional, Union
import warnings
import humanize
import IPython.display
from IPython.core.getipython import get_ipython
impor... | pd.DataFrame({k: df}) | pandas.DataFrame |
# -*- 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... | inference.is_list_like(result) | pandas.core.dtypes.inference.is_list_like |
import re
import pandas
import spacy
from spacytextblob.spacytextblob import SpacyTextBlob
from nltk.sentiment.vader import SentimentIntensityAnalyzer
sid = SentimentIntensityAnalyzer()
# nlp = spacy.load('en_core_web_lg')
nlp = spacy.load('en_core_web_md')
nlp.add_pipe('spacytextblob')
def subcatego(cat_mess: str)... | pandas.to_datetime(df[col], origin='unix', unit='s') | pandas.to_datetime |
""" MCH API ver 0.1
Author: <NAME>
License: CC-BY-SA 4.0
2020 Mexico
"""
import os
from flask import Flask, jsonify, json, Response
from flask_restful import Api, Resource, reqparse, abort
from flask_mysqldb import MySQL
import pandas as pd
import numpy as np
import json
from os.path import abspath, dirname, join
app... | pd.DataFrame(jdata) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Fri Jun 19 15:36:56 2020
@author: suyu
"""
from surprise import SVD
from surprise import Dataset
from surprise import Reader
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error,roc_auc_score,mean_absolute_error,log_loss
import nump... | pd.DataFrame(tr_ratings_dict) | pandas.DataFrame |
"""
Analysis dashboards module.
"""
import copy
from datetime import timedelta
import numpy as np
import pandas as pd
import logging
from flask_login import login_required
from flask import render_template, request
from sqlalchemy import and_
from app.dashboards import blueprint
from utilities.utils import parse_... | pd.to_datetime(energy_hour["timestamp"].dt.date) | pandas.to_datetime |
from datetime import datetime, timedelta
import numpy as np
import pandas as pd
from pandas.api.types import is_numeric_dtype
import matplotlib as plt
from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.model_selection import train_test_split as sk_train_test_split
from multiprocessing impor... | pd.merge(df, new_df, how='left', on=['time', self.assetId]) | pandas.merge |
#encoding=utf-8
from nltk.corpus import stopwords
from sklearn.preprocessing import LabelEncoder
from sklearn.pipeline import FeatureUnion
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.linear_model import Ridge
from scipy.sparse import hstack, csr_matrix
import pandas as pd
i... | pd.read_csv("../input/region_income.csv", sep=";", names=["region", "income"]) | pandas.read_csv |
from tkinter import ttk,filedialog
from tkinter import *
import pandas as pd
# import argparse
from openpyxl import Workbook,worksheet
from openpyxl.styles import Border, Side, Font, Alignment
from openpyxl.utils.dataframe import dataframe_to_rows
from openpyxl.utils import get_column_letter
root = Tk()
root.title('ea... | pd.DataFrame() | pandas.DataFrame |
from .database import CodingSystem, CodingProperty, GlobalProperty, GlobalRating, GlobalValue, Interview, \
PropertyValue, Utterance, UtteranceCode
from .utils import sanitize_for_spss
from pandas import DataFrame, Index, MultiIndex, notna
from pandas.api.types import is_string_dtype, is_object_dtype
from peewee im... | ptypes.is_float_dtype(data_frame[col].dtype) | pandas.api.types.is_float_dtype |
import ccxt
import pandas as pd
import datetime
import os
import time
import numpy as np
class binance_data():
now = datetime.datetime.now()
timestamp_now = int(time.time()*1000)
addtime = {'1m':60000, '15m':900000, '30m':1800000,'1h':3600000, '12h':43200000,'1d':86400000}
def __init__(self,... | pd.read_csv(self.file) | pandas.read_csv |
# Imports
import streamlit as st
import streamlit.components.v1 as components
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import time
import os.path
# ML dependency imports
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.decomposition import PCA
from sklearn.manif... | pd.get_dummies(masterMerge) | pandas.get_dummies |
#@title 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, software
# distributed under... | pd.DataFrame(test_results, index=['Mean absolute error [MPG]']) | pandas.DataFrame |
#
# 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.Series(False, index=self.sessions) | pandas.Series |
from __future__ import print_function
from datetime import datetime, timedelta
import numpy as np
import pandas as pd
from pandas import (Series, Index, Int64Index, Timestamp, Period,
DatetimeIndex, PeriodIndex, TimedeltaIndex,
Timedelta, timedelta_range, date_range, Float64Index... | pd.period_range('2009', '2019', freq='A') | pandas.period_range |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import division
from __future__ import print_function
import timeit
__author__ = ['<NAME>']
__email__ = ['<EMAIL>']
__package__ = 'Gemm testing'
NUM_REPEATS = 10
NUMBER = 500
def gemm_nn (N, M, K):
SETUP_CODE = '''
import numpy as... | pd.DataFrame(data=times_nt, columns=['Times']) | pandas.DataFrame |
import logging
import os
import gc
import pandas as pd
from src.data_models.tdidf_model import FrequencyModel
from src.evaluations.statisticalOverview import StatisticalOverview
from src.globalVariable import GlobalVariable
from src.kemures.tecnics.content_based import ContentBased
from src.preprocessing.preferences_... | pd.concat([scenario_results_df, application_results_df]) | pandas.concat |
#!/usr/bin/env python3
import sys
import numpy as np
import pandas as pd
from functools import partial
from multiprocessing import Pool
from sklearn.ensemble import RandomForestClassifier
def input_validator(filename, indel_class):
"""Validate and shuffle data
Args:
filename (str): path to input trai... | pd.read_csv(filename, sep="\t") | pandas.read_csv |
import numpy as np
import pandas as pd
import pandas.core.computation.expressions as expressions
from proto.common.v1 import common_pb2
from proto.aiengine.v1 import aiengine_pb2
from types import SimpleNamespace
import math
import threading
from exception import RewardInvalidException
from metrics import metrics
from ... | pd.isnull(newer_values) | pandas.isnull |
#
# extract_hourly_intervention.py
#
# Authors:
# <NAME>
# <NAME>
#
# This file extracts the hourly intervation for patients
import pandas as pd
import os
import numpy as np
import os
from scipy.stats import skew
import directories
import csv
import argparse
parser = argparse.ArgumentParser(description='Parser to pas... | pd.to_datetime(prescriptions.STARTDATE) | pandas.to_datetime |
import pandas as pd
import numpy as np
attr = | pd.read_csv('GTEx_Analysis_v8_Annotations_SampleAttributesDS.txt', sep='\t') | pandas.read_csv |
import copy
import unittest
import numpy as np
import pandas as pd
from sklearn.exceptions import NotFittedError
from pymatgen.core import Structure, Lattice
from matminer.featurizers.structure.bonding import (
MinimumRelativeDistances,
BondFractions,
BagofBonds,
StructuralHeterogeneity,
GlobalIns... | pd.DataFrame.from_dict({"s": s_list}) | pandas.DataFrame.from_dict |
from oauth2client import file, client, tools
from apiclient import discovery
from httplib2 import Http
from typing import Optional, Union, List
import os
from pandas.core.frame import DataFrame
from pandas import Timestamp, Timedelta
from functools import lru_cache
CLIENT_SECRET_PATH = '~/.gsheets2pandas/client_secret... | DataFrame(data_list) | pandas.core.frame.DataFrame |
from typing import Any, List, Tuple, Union, Mapping, Optional, Sequence
from types import MappingProxyType
from pathlib import Path
from anndata import AnnData
from cellrank import logging as logg
from cellrank._key import Key
from cellrank.tl._enum import _DEFAULT_BACKEND, Backend_t
from cellrank.ul._docs import d
f... | pd.DataFrame(all_models) | pandas.DataFrame |
import numpy as np
import pandas as pd
from sklearn import *
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from matplotlib import pyplot
import time
import os
showPlot=True
#prepare data
data_file_name = "../FinalCost.csv"
data_csv = pd.read_csv(data_file_na... | pd.concat([testData,preddData], axis=1) | pandas.concat |
"""Higher-level functions of automated time series modeling."""
import numpy as np
import pandas as pd
import random
import copy
import json
import sys
import time
from autots.tools.shaping import (
long_to_wide,
df_cleanup,
subset_series,
simple_train_test_split,
NumericTransformer,
clean_weig... | pd.json_normalize(model_df) | pandas.json_normalize |
import os
import sys
import time
import sqlite3
import warnings
import pythoncom
import numpy as np
import pandas as pd
from PyQt5 import QtWidgets
from PyQt5.QAxContainer import QAxWidget
sys.path.append(os.path.dirname(os.path.abspath(os.path.dirname(__file__))))
from utility.static import *
from utility.setting impo... | pd.DataFrame(data=df2, columns=items) | pandas.DataFrame |
#!/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 |
#!/usr/bin/env python
# encoding: utf-8
'''
editing.filter_known_snps
removes known SNPs (BED3) from a candidate list of editing sites (VCF).
@author: brian
@copyright: 2017 yeolab. All rights reserved.
@license: license
@contact: <EMAIL>
@deffield updated: 4-21-2017
'''
import sys
import os
import ... | pd.merge(eff_df, snp_df, how='left', on=['CHROM', 'POS']) | pandas.merge |
#!/usr/bin/env python
# coding: utf-8
# TODO:
#
#
# R1
# - get the Nyquist plot axis dimensions issue when $k=1$ fixed
# - figure out the failing of .pz with active elements
#
#
# R2
# - make the frequency analysis stuff happen
#
# In[1]:
from skidl.pyspice import *
#can you say cheeky
import PySpice as pspic... | pd.DataFrame(columns=['Type', 'Values']) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jul 3 10:06:21 2018
@author: rucsa
"""
import pandas as pd
import datetime
import numpy as np
import tables
import check_data_and_prices_helpers as help
def add_returns():
#fundamentals_2016 = pd.read_hdf("../sources/fundamentals_2016_msci_regio... | pd.read_hdf("../sources/fundamentals_2017_msci_regions.hdf5", "dataset1/x") | pandas.read_hdf |
import pytest
from siuba.tests.helpers import data_frame
import pandas as pd
from siuba.experimental.pd_groups.translate import method_agg_op, method_el_op, method_el_op2
from siuba.experimental.pd_groups.groupby import broadcast_agg
#TODO:
# - what if they have mandatory, non-data args?
# - support accessor method... | assert_series_equal(res.obj, dst, check_names=False) | pandas.testing.assert_series_equal |
import pandas as pd
import numpy as np
from functions.load_wtdata import load_wtdata
from pathlib import Path
import gc
import tempfile
import os
#Configs
db_config = {'table_cast_park_dic':'1_cast_park_table_dic','host':"127.0.0.1",'user':"itestit",'password':"<PASSWORD>",'db':"SCHistorical_DB"}
exclude_columns = ['al... | pd.DataFrame(y_pred) | pandas.DataFrame |
#
# Prepare the hvorg_movies
#
import os
import datetime
import pickle
import json
import numpy as np
import pandas as pd
from sunpy.time import parse_time
# The sources ids
get_sources_ids = 'getDataSources.json'
# Save the data
save_directory = os.path.expanduser('~/Data/hvanalysis/derived')
# Read in the data
di... | pd.read_csv(path) | pandas.read_csv |
import numpy as np
import pandas as pd
import pytest
from pandas.testing import assert_frame_equal
# from http://imachordata.com/2016/02/05/you-complete-me/
@pytest.fixture
def df1():
return pd.DataFrame(
{
"Year": [1999, 2000, 2004, 1999, 2004],
"Taxon": [
"Sacchar... | assert_frame_equal(result, output2) | pandas.testing.assert_frame_equal |
# -*- coding:utf-8 _*-
"""
@author:<NAME>
@time: 2019/12/02
"""
from urllib.parse import unquote
import pandas as pd
from redis import ConnectionPool, Redis
from scrapy.utils.project import get_project_settings
from dingxiangyuan import settings
from sqlalchemy import create_engine
from DBUtils.PooledDB import PooledD... | pd.read_sql(sql='''select board_name, to_char(posts_replies.post_time, 'YYYY') as year, author_identify, count(distinct dingxiangke.user_url) user_count from dingxiangke
inner join posts_replies on posts_replies.author_url=dingxiangke.user_url_unquote
where posts_... | pandas.read_sql |
# -*- 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.Period('2011-03', freq='M') | pandas.Period |
from utils import load_yaml
import pandas as pd
import click
from datetime import datetime, timedelta
import numpy as np
import os
cli = click.Group()
@cli.command()
@click.option('--lan', default='en')
@click.option('--config', default="configs/configuration.yaml")
def dump(lan, config, country_code):
# load th... | pd.DataFrame() | pandas.DataFrame |
# ------------------------------------------
# Copyright (c) Rygor. 2021.
# ------------------------------------------
""" Configuration file management """
import os
import pathlib
import sys
import datetime
import errno
import click
from appdirs import user_data_dir
import pandas as pd
from typing import Option... | pd.to_datetime(data["Cur_DateStart"]) | pandas.to_datetime |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score, mean_squared_error
import seaborn as sns
from scipy import stats
import math
def clean_data(df):
"""
... | pd.read_csv('data/reviews_boston.csv') | pandas.read_csv |
"""
Get data for past matches
"""
import requests
import pandas as pd
import json
import os
from mappings import regions_map, game_mode_map, match_cols, player_cols
# get the starting gameID for the API calls
try:
final_gameID_df = pd.read_csv(os.path.join('output', 'matchData.csv'), usecols=['match_id'])
if ... | pd.concat([match_df, match_missing_df], 1) | pandas.concat |
import os
from nose.tools import *
import unittest
import pandas as pd
import numpy as np
import py_entitymatching as em
from py_entitymatching.utils.generic_helper import get_install_path
import py_entitymatching.catalog.catalog_manager as cm
import py_entitymatching.utils.catalog_helper as ch
from py_entitymatching.... | pd.DataFrame([]) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Wed Sep 9 08:04:31 2020
@author: <NAME>
Functions to run the station characterization notebook on exploredata.
"""
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import math
import numpy as np
from netCDF4 import Dataset
import textwrap
import datetime... | pd.cut(df['degrees'], bins=dir_bins, labels=dir_labels, right=False) | pandas.cut |
# -*- 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... | assert_panel_equal(dropped, panel) | pandas.util.testing.assert_panel_equal |
#coding:utf-8
import json
import pandas as pd
import numpy as np
from sklearn.naive_bayes import GaussianNB
from sklearn import svm
def road_json(path = 'json/analyzeTarget.json'):
'''
pathからJSON形式のデータを読み取って返します。
'''
f = open(path, 'r')
jsonData = json.load(f)
f.close()
return jsonData
def ... | pd.read_csv(learn_path, encoding="SHIFT-JIS") | pandas.read_csv |
import pandas as pd
import sys
job_df = pd.read_csv(sys.argv[1])
my_index = | pd.MultiIndex(levels = [[],[]], codes=[[],[]], names=[u'labels', u'path_idx']) | pandas.MultiIndex |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.