prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
import seaborn as sns
import matplotlib.py as plt
import pandas as pd
s = pd.Series([0,2,4,18,32,50])
t= pd.Series([1,2,3,4,5,6])
motion_graph(specify="p-t",s=s,t=t,color="#1a2b3f");
v = pd.Series([0,2,4,6,8,8,8,6,4,2,0])
t= pd.Series([1,2,3,4,5,6,7,8,9,10,11])
motion_graph(specify="v-t",v=v,t=t);
a = pd.Series([... | pd.Series([1,2,3,4,5,6,7,8,9,10,11]) | pandas.Series |
import numpy as np
import pytest
import pandas as pd
from pandas import (
CategoricalDtype,
CategoricalIndex,
DataFrame,
Index,
IntervalIndex,
MultiIndex,
Series,
Timestamp,
)
import pandas._testing as tm
class TestDataFrameSortIndex:
def test_sort_index_and_reconstruction_doc_exa... | DataFrame(sorted_dict, index=output_index) | pandas.DataFrame |
# Authors: <NAME> (<EMAIL>), <NAME> (<EMAIL>), <NAME> (<EMAIL>)
import pandas as pd
import numpy as np
from scipy.integrate import solve_ivp
from scipy.optimize import minimize
from datetime import datetime, timedelta
from DELPHI_utils import (
DELPHIDataCreator, DELPHIAggregations, DELPHIDataSaver, get_initial_con... | pd.concat(list_df_global_predictions_since_today) | pandas.concat |
import os
import pandas as pd
import geopandas as gpd
files = ['prop_urban_2000_2010.csv',
'pop_women_2010.csv',
'pop_men_2010.csv',
'idhm_2000_2010.csv',
'estimativas_pop.csv',
'interest_real.csv',
'num_people_age_gender_AP_2010.csv',
'qualification_APs_... | pd.read_csv(path, sep=sep) | pandas.read_csv |
"""
Copyright (c) 2021 <NAME> as part of Airlab Amsterdam
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 applic... | pd.DataFrame({'y':y, 'yhat_lgb':yhat}) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Wed Jun 20 10:47:30 2018
@author: SilverDoe
"""
#============ Selecting a column ==============================================
import pandas as pd
d = {'one' : pd.Series([1, 2, 3], index=['a', 'b', 'c']),
'two' : pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])}
df =... | pd.Series([1, 2, 3], index=['a', 'b', 'c']) | pandas.Series |
import pandas as pd
import numpy as np
import os
def import_schedules(file_path, file_name):
"""
Given the file path and file name (of the schedule file that is inputted into the Maccor Cycler), this
function will import and clean the schedule file and return it as a df.
Parameters
-----------
... | pd.isnull(df['step'][ind]) | pandas.isnull |
# coding: utf-8
import numpy as np
import pandas as pd
import scipy
import scipy.sparse as sp
import os
import time
import multiprocessing
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression, Ridge
from sklearn.model_selection import cross_val_predict
from sklearn.metrics import mean_squar... | pd.read_csv(cur_embedding_path, sep=self.file_sep, index_col=0) | pandas.read_csv |
from collections import Counter, defaultdict
from pprint import pprint
import pandas as pd
from util import data_io
def to_datetime(df, key):
df[key] = | pd.to_datetime(df[key]) | pandas.to_datetime |
"""
.. module: security_monkey.views.GuardDutyEventMapPointsList
:platform: Unix
.. version:: $$VERSION$$
.. moduleauthor:: <NAME> <<EMAIL>> @nuagedm
"""
import datetime
from flask import jsonify, request
from security_monkey import db, rbac
from security_monkey.views import AuthenticatedService
from security_m... | pd.DataFrame(flatten_records) | pandas.DataFrame |
import os
import unittest
import random
import sys
import site # so that ai4water directory is in path
ai4_dir = os.path.dirname(os.path.dirname(os.path.abspath(sys.argv[0])))
site.addsitedir(ai4_dir)
import scipy
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from ai4wa... | pd.date_range('20110101', periods=40, freq='D') | pandas.date_range |
from __future__ import absolute_import
from __future__ import print_function
import h5py
import argparse
import logging
import re
import numpy as np
import pandas as pd
import os
import apache_beam as beam
from apache_beam.io import ReadFromText
from apache_beam.io import WriteToText
from apache_beam.... | pd.read_csv(known_args.input,header=None,nrows=1) | pandas.read_csv |
"""A module providing the `Model` class representing the global model and tying together
all the other classes defined in the `pygmol` package (concrete subclasses of
`Chemistry`, `PlasmaParameters` and `Equations`.)
"""
from typing import Union, Mapping
import numpy as np
import pandas
import pandas as pd
from numpy ... | pd.DataFrame(final_values, columns=final_columns) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""Compute statistical description of datasets"""
import multiprocessing
import itertools
from functools import partial
import numpy as np
import pandas as pd
import matplotlib
from pkg_resources import resource_filename
import pandas_profiling.formatters as formatters
import pandas_profiling.b... | pd.Index([names]) | pandas.Index |
#!/usr/bin/env python
# coding: utf-8
import datetime
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pytz
import random
# Date and Time
# =============
print(datetime.datetime(2000, 1, 1))
print(datetime.datetime.strptime("2000/1/1", "%Y/%m/%d"))
print(datetime.datetime(2000, 1, 1, 0, ... | pd.Timedelta('-1 days 2 min 10s 3us') | pandas.Timedelta |
import numpy as np;
import pandas as pd;
import os
raw_data_path = os.path.join(os.path.pardir,'data','raw')
train_file_path = os.path.join(raw_data_path,'train.csv')
test_file_path = os.path.join(raw_data_path,'test.csv')
#read data as dataframe
train_df = pd.read_csv(train_file_path,index_col='PassengerId')
test_df ... | pd.concat((train_df,test_df),axis=0) | pandas.concat |
from typing import Tuple, Union
import datetime
import os
from xlrd import XLRDError
import pandas as pd
def load_df(url: str, sheet_name: Union[int, str] = 0) -> Tuple[pd.DataFrame, bool]:
from_html = os.path.splitext(url)[1] in ['.htm', '.html']
# Read from input file
if from_html:
try:
... | pd.DatetimeIndex(df['DateTime']) | pandas.DatetimeIndex |
import numpy as np
import pandas as pd
import datetime as dt
import matplotlib.pyplot as plt
import matplotlib.dates as date
import seaborn as sns
import urllib
sns.set_context('talk')
data_crime_raw = pd.read_csv('.\\NYPD_Complaint_Data_Historic.csv',
usecols=['CMPLNT_FR_DT', 'OFNS_DESC'... | pd.cut(data_311['Longitude'], lonrange) | pandas.cut |
# pylint: disable-msg=W0612,E1101,W0141
import nose
from numpy.random import randn
import numpy as np
from pandas.core.index import Index, MultiIndex
from pandas import Panel, DataFrame, Series, notnull, isnull
from pandas.util.testing import (assert_almost_equal,
assert_series_equal... | assert_frame_equal(result, expected) | pandas.util.testing.assert_frame_equal |
#!/usr/bin/python
# https://media.readthedocs.org/pdf/pynag/latest/pynag.pdf
# first try to export hosts, will be expanded over the time.
from pynag.Model import Parsers
import os
from tempfile import mkstemp
from shutil import move
from os import remove, close
import re
import time
import pandas as pd
from pandas.io.... | json_normalize(hosts) | pandas.io.json.json_normalize |
import pygame
import math
import numpy as np
import networkx as nx
import itertools as it
import pandas as pd
import colorsys
import generateTreeWithPrior as generateTree
import generatePartitionGivenTreeWithPrior as generatePartition
class SampleNodesFeatureMeans():
def __init__(self, allFeatureMeans):
s... | pd.concat([tree.node[leafNode]['featureMeans'] for leafNode in leafNodes]) | pandas.concat |
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import f1_score
from sklearn.preprocessing import LabelEnc... | pd.read_csv("/home/matilda/PycharmProjects/FailurePrediction/4_analysis/clog/data/NOVA/resources/"+TIME_INTERVAL+"/classification_data/classification_TFIDF_"+ TIME_INTERVAL +"_.csv") | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Tests that comments are properly handled during parsing
for all of the parsers defined in parsers.py
"""
import numpy as np
import pandas.util.testing as tm
from pandas import DataFrame
from pandas.compat import StringIO
class CommentTests(object):
def test_comment(self):
d... | StringIO(data) | pandas.compat.StringIO |
# -*- coding: utf-8 -*-
"""
Created on Wed Jun 3 10:54:45 2020
@author: Janusz
"""
import logging
import tkinter as tk
import tkinter.font as tkFont
from collections import namedtuple
import easyocr
import pandas as pd
from cv2 import cv2 as cv
import dss
from windowcapture import WindowCapture
# REMEMBER TO SET G... | pd.read_csv("champions_data_scaled.csv") | pandas.read_csv |
import pandas as pd
from sodapy import Socrata
import datetime
import definitions
# global variables for main data:
hhs_data, test_data, nyt_data_us, nyt_data_state, max_hosp_date = [],[],[],[],[]
"""
get_data()
Fetches data from API, filters, cleans, and combines with provisional.
After running, global variables are... | pd.Timestamp.today() | pandas.Timestamp.today |
# --------------
import pandas as pd
from sklearn.model_selection import train_test_split
#path - Path of file
df= | pd.read_csv(path) | pandas.read_csv |
import sys
import os
from time import time, sleep
import shutil
import datetime
import csv
import json
import tempfile
from ast import literal_eval
import re
import unittest2 as unittest
from mock import Mock, patch
import os.path
import numpy as np
import pandas as pd
from tsfresh import extract_features, extract_rel... | pd.read_csv(tmp_csv, delimiter=',', header=None, names=['metric', 'timestamp', 'value']) | pandas.read_csv |
from sqlite3.dbapi2 import Timestamp
import sqlalchemy
import pandas as pd
from sqlalchemy.orm import sessionmaker
import requests
import json
from datetime import datetime
import datetime
import sqlite3
DATABASE_LOCATION = "sqlite:///my_played_tracks.sqlite"
USER_ID = "21cxorcxlyiwautslytprkgmq" # your Spotify usern... | pd.DataFrame(song_dict, columns = ["song_name", "artist_name", "played_at", "timestamp"]) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon May 31 16:59:28 2021
@author: liang
"""
import random
import pandas as pd
from make_random_date import make_random_time
from tqdm import tqdm
import pickle
import os
import numpy as np
from multiprocessing import Pool
import time
fea_config = pickl... | pd.concat(result_parts) | pandas.concat |
"""
Collection of function to pre-process the master curve and perform the Prony
series parameter identification.
"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy.optimize import minimize, nnls
from . import shift
"""
-------------------------------------------------------------... | pd.concat(df_list, axis=1) | pandas.concat |
# -*- coding: utf-8 -*-
from datetime import datetime, timedelta, date, time
import numpy as np
import pandas as pd
import pandas.lib as lib
import pandas.util.testing as tm
from pandas import Index
from pandas.compat import long, u, PY2
class TestInference(tm.TestCase):
def test_infer_dtype_bytes(self):
... | lib.convert_sql_column(arr) | pandas.lib.convert_sql_column |
import json
import pandas as pd
from web import *
class Stock(object):
@staticmethod
def get_realtime_stock(symbol):
output = {'symbol': symbol}
url = yahoo_api_v8_template.replace('{symbol}', symbol)
url = url.replace('{interval}', '1d')
url = url.replace('{range}', '1d')
... | pd.Series(quote['quote'][0]['volume']) | pandas.Series |
import numpy as np
from numpy.testing import assert_array_equal
from pandas.testing import assert_frame_equal
import pandas as pd
from pdpbox.pdp_calc_utils import _calc_ice_lines, _calc_ice_lines_inter, _prepare_pdp_count_data
import pytest
class TestCalcICELinesBinary(object):
def test_ice_binary(self, titanic... | assert_frame_equal(count_data, expected, check_like=True, check_dtype=False) | pandas.testing.assert_frame_equal |
from datetime import datetime
from pickle import dump, load
import numpy as np
import pandas as pd
from sklearn.kernel_ridge import KernelRidge
from sklearn.linear_model import Ridge, LinearRegression
from sklearn.preprocessing import OneHotEncoder
import seaborn as sns
import matplotlib.pyplot as plt
from tensorflow.... | pd.read_csv(url, sep=";") | pandas.read_csv |
import pandas as pd
import numpy as np
import lightgbm as lgb
from sklearn.model_selection import KFold
from catboost import CatBoostRegressor
from utils import *
import argparse
from sklearn import preprocessing
import wordbatch
from wordbatch.extractors import WordBag
from wordbatch.models import FM_FTRL
... | pd.concat([trn_series, target], axis=1) | pandas.concat |
#coding:utf-8
from typing import Set
from scipy.optimize.optimize import main
from basic_config import *
import seaborn as sns
import pandas as pd
def hist_attr(data, attr_names, logs, outpath, col=2, indexed=True):
indexes = 'abcdefghijklmn'
attr_num = len(attr_names)
if attr_num == 0:
logging... | pd.DataFrame.from_dict({'interval': poses}) | pandas.DataFrame.from_dict |
from io import BytesIO
import os
from typing import Optional, Tuple
import pandas as pd
from .loader_base import MovieLensBase
class MovieLens100kDataManager(MovieLensBase):
"""The Data manager for MovieLens 100k dataset."""
@property
def DOWNLOAD_URL(self) -> str:
"http://files.grouplens.org/da... | pd.to_datetime(df_mov.release_date) | pandas.to_datetime |
from __future__ import print_function, division
# MIMIC IIIv14 on postgres 9.4
import os, psycopg2, re, sys, time, numpy as np, pandas as pd
from sklearn import metrics
from datetime import datetime
from datetime import timedelta
from os.path import isfile, isdir, splitext
import argparse
import pickle as cPickle
imp... | pd.to_datetime(data['outtime']) | pandas.to_datetime |
import pandas as pd
import numpy as np
from io import StringIO
from sklearn.preprocessing import Imputer
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
from sklearn.linear_mode... | pd.get_dummies(df[['price', 'color', 'size']]) | pandas.get_dummies |
import pandas as pd
import pytest
from powersimdata.input.tests.test_helpers import check_dataframe_matches
from powersimdata.tests.mock_scenario import MockScenario
from pytest import approx
from postreise.analyze.generation.capacity import (
calculate_net_load_peak,
calculate_NLDC,
get_capacity_by_resour... | pd.DataFrame({101: mock_pg[101] / 9000}) | pandas.DataFrame |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# custom
import data_processing as dp
dfs, sh_int, fin_sh = dp.load_stocks(stocks=None, TAs=False, finra_shorts=False, short_interest=False, earliest_date=None)
# full_df = pd.concat([dfs[s] for s in dfs.keys()])
stocks = ['LNG', 'CHK', 'AMD']
sm... | pd.DataFrame({**feat_dict, **targ_dict}) | pandas.DataFrame |
import importlib
import os
from pathlib import Path
import pandas as pd
from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from livelossplot.inputs.keras import PlotLossesCallback
from sklearn.model_selection import KFold
from tensorflow.keras.layers import Dropout
from tensorflow.keras.opti... | pd.concat(train_result_df) | pandas.concat |
import pandas as pd
import pytest
from evalml.exceptions import MethodPropertyNotFoundError
from evalml.pipelines.components import (
ComponentBase,
FeatureSelector,
RFClassifierSelectFromModel,
RFRegressorSelectFromModel
)
def make_rf_feature_selectors():
rf_classifier = RFClassifierSelectFromMo... | pd.DataFrame() | pandas.DataFrame |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import pandas
from pandas.compat import string_types
from pandas.core.dtypes.cast import find_common_type
from pandas.core.dtypes.common import (
is_list_like,
is_numeric_dtype,
... | pandas.DataFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Tests dtype specification during parsing
for all of the parsers defined in parsers.py
"""
import pytest
import numpy as np
import pandas as pd
import pandas.util.testing as tm
from pandas import DataFrame, Series, Index, MultiIndex, Categorical
from pandas.compat import StringIO
from pan... | tm.assert_frame_equal(result, expected) | pandas.util.testing.assert_frame_equal |
from openpyxl import load_workbook
import numpy as np
import datetime as dt
import matplotlib.pyplot as plt
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as dates
import plotly.graph_objs as go
import plotly.io as pio
from tabulate import tabulate
def border_msg(msg):
row = len(msg)
... | pd.read_excel('excels/lite/lite-Ages-Gender.xlsx') | pandas.read_excel |
import json
import os
import sqlite3
import pyAesCrypt
import pandas
from os import stat
from datetime import datetime
import time
import numpy
# Global variables for use by this file
bufferSize = 64*1024
password = os.environ.get('ENCRYPTIONPASSWORD')
# py -c 'import databaseAccess; databaseAccess.reset()'
def reset... | pandas.to_datetime(storedActivities['start_date_local'],errors='coerce') | pandas.to_datetime |
'''
Title: QuickView
Purpose: Provides a Glance at the dataset with one line of code!
GitHub: http://github.com/avannaldas/QuickView
Author: <NAME> (Twitter @avannaldas)
'''
import pandas as _pd
import matplotlib.pyplot as _plt
from matplotlib.pyplot import cm
'''Number of rows in the dataframe'''
row_count = -1
'''... | _pd.DataFrame() | pandas.DataFrame |
# coding: utf8
import collections
import argparse
import pprint
import json
from pathlib import Path
from .score import subtaskA, subtaskB, compute_metrics
from .utils import Collection
def evaluate_scenario(submit, gold, scenario):
submit_input = submit / ("output_scenario%i.txt" % scenario)
if not submit... | pd.DataFrame(items) | pandas.DataFrame |
from datetime import time
from decimal import Decimal
import numpy as np
import pandas as pd
import pytest
from multipledispatch.conflict import ambiguities
from pandas.api.types import CategoricalDtype, DatetimeTZDtype
import ibis
import ibis.expr.datatypes as dt
import ibis.expr.schema as sch
import ibis.expr.types... | pd.Period('2011-03') | pandas.Period |
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import glob
import pandas as pd
import matplotlib
import os
import math
import random
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy import optimize, signal
import numpy as np
import matplotlib.pyplot as plt
fro... | pd.DataFrame(y) | pandas.DataFrame |
# -*- 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... | tm.assert_series_equal(result, exp) | pandas.util.testing.assert_series_equal |
#--------------------------------------------------
import pandas as pd
import numpy as np
import Auxiliary.auxiliary_functions as aux_fun
#--------------------------------------------------
def read_and_rename():
'''
Function that reads the original data from the VH DB and renames the
columns to the names ... | pd.isnull(x) | pandas.isnull |
import numpy as np
import pytest
import pandas as pd
from pandas import (
DataFrame,
Index,
Series,
concat,
date_range,
)
import pandas._testing as tm
class TestEmptyConcat:
def test_handle_empty_objects(self, sort):
df = DataFrame(np.random.randn(10, 4), columns=list("abcd"))
... | Series(dtype="float64") | pandas.Series |
import pandas as pd
import re
import numpy as np
import matplotlib.pyplot as plt
"""
References:
Title: matplotlib
Author: matplotlib Team
Availability: https://github.com/matplotlib/matplotlib
Version: 3.4.2
Title: numpy
Author: numpy Team
Availability: https://github.com/numpy/numpy
Version: 1.19.5... | pd.read_csv('newfightinfo.csv') | pandas.read_csv |
# Import libraries
import os
import sys
import anemoi as an
import pandas as pd
import numpy as np
import pyodbc
from datetime import datetime
import requests
import collections
import json
import urllib3
def return_between_date_query_string(start_date, end_date):
if start_date != None and end_date != None:
... | pd.read_sql(sql_query_assets, self.conn) | pandas.read_sql |
# -*- coding: utf-8 -*-
"""
Created on Wed Aug 29 11:36:45 2018
@author: suvod
"""
from __future__ import division
from . import git_access
import json
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import math
import networkx as nx
class git_api_access(object):
def __init__(self,toke... | pd.DataFrame(comment_details, columns = ['issue_number','user_logon','author_association','body','created_at']) | pandas.DataFrame |
import warnings
from pandas import DataFrame, to_datetime, read_csv, notnull
from pyramm.helpers import _map_json
from pyramm.geometry import transform, loads
DEFAULT_DATE_COLUMNS = ["added_on", "chgd_on"]
class BaseTable:
table_name = None
index_name = None
get_geometry = False
date_columns = []
... | notnull(self.df) | pandas.notnull |
import numpy as np
import xml.etree.ElementTree as ET
import gzip
import pandas as pd
import os
import gavia
def loadlog(projectdir):
'''
Load the gps log file for the specified project, given by the projectdir
parameter
Parameters
----------
projectdir : string
path to ... | pd.DataFrame() | pandas.DataFrame |
import traceback
from typing import Union
import sys
import numpy as np
import pandas as pd
from importers.base import BaseImporter
from .state_decorator import ImporterStatus, Status
from .attr_range_decorator import update_attribute_ranges
sys.path.append("../..")
from settings import GetConfig
@GetConfig("TfL_B... | pd.to_datetime(x) | pandas.to_datetime |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import pandas as pd
import cv2
#データ読み込みクラス
class Class_File_Data_Reader():
'''
各種データ形式をpandasに読み込むクラス
日本語データに対応 (Shift-JIS前提 xlsx⇒csvした日本語では使える)
'''
def __init__(self, ext="ALL"):
'''
コンストラクタ
(読み込み対象と... | pd.concat([result,input_pd_list[dnameindx+1]]) | pandas.concat |
#
# Copyright (C) 2019 Databricks, 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 i... | pd.Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], name='x') | pandas.Index |
# -*- coding: utf-8 -*-
# """@author: Elie"""
# run locally on python 3.8.5('dec1st_py38_xgboostetal':conda)
# =============================================================================
# %% Libraries
# =============================================================================
import pandas as pd
import nu... | pd.read_csv(cohort_data, sep='\t', low_memory=False) | pandas.read_csv |
# coding=utf-8
import os
import os.path
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from loganalysis.const import *
class Log(object):
''' 调度模块Log分析接口类
主要提供如下3类功能：
a) 信息呈现
b）问题发现
c）问题定位
要求所有文件命名符合EI命名格式：子系统_时间.csv
'''
def __init__(self, ... | pd.read_csv(filename, na_values='-', usecols=col) | pandas.read_csv |
"""
This script is interesting but has abug that needs fixing.
"""
import seaborn as sns
from tqdm import tqdm
import matplotlib.pyplot as plt
import ast
from sklearn.model_selection import StratifiedKFold
import os
import warnings
from datetime import datetime
from collections import Counter
import gc
from pathlib im... | pd.read_csv("data/train.csv") | pandas.read_csv |
from __future__ import annotations
import numpy as np
import pandas as pd
from sklearn import datasets
from IMLearn.metrics import mean_square_error
from IMLearn.utils import split_train_test
from IMLearn.model_selection import cross_validate
from IMLearn.learners.regressors import PolynomialFitting, LinearRegression, ... | pd.Series(y) | pandas.Series |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
url = "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/" + \
"csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_{}_global.csv"
deaths = pd.read_csv(url.format('deaths'), index_col=1)
cases = pd.read_csv(url.form... | pd.to_datetime(c.index, errors="coerce", format="%m/%d/%y") | pandas.to_datetime |
import streamlit as st
import pandas as pd
from utils import *
from modules import *
import os
import numpy as np
import altair as alt
import plotly.graph_objects as go
absolute_path = os.path.abspath(__file__)
path = os.path.dirname(absolute_path)
ipl_ball = pd.read_csv(path+'/2008_2021_updated_ball.csv')
ipl_ma... | pd.DataFrame() | pandas.DataFrame |
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(outliers_df) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import warnings
from datetime import datetime, timedelta
import pytest
import numpy as np
import pandas as pd
import pandas.util.testing as tm
from pandas.errors import PerformanceWarning
from pandas import (Timestamp, Timedelta, Series,
DatetimeIndex, TimedeltaIndex,
... | tm.assert_index_equal(res2, expected) | pandas.util.testing.assert_index_equal |
import streamlit as st
import numpy as np
import pandas as pd
import plotly.graph_objects as go
from datetime import datetime
import requests
class DataFetcher:
def __init__(self):
self.url_brazil_general = 'https://covid19-brazil-api.now.sh/api/report/v1/brazil/'
self.url_brazil_states = 'https://... | pd.DataFrame({'state': list_states, 'lat': lat_coords, 'long': long_coords}) | pandas.DataFrame |
# 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(result, expected) | pandas.testing.assert_frame_equal |
import sys
import uncertainty_rfr
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestRegressor
import pandas.api.types as ptypes
sys.path.append("../")
df_test = pd.read_csv('./xiaofeng_lasso/unittest_dummy.csv', nrows=5)
X_test, y_test = uncertainty_rfr.descriptors_outputs(df_test, d_st... | pd.DataFrame(data={'err_int': [1, 2, 3], 'std_dev': [4, 5, 6]}) | pandas.DataFrame |
import pandas as pd
from sklearn import linear_model
import statsmodels.api as sm
import numpy as np
from scipy import stats
df_all = pd.read_csv("/mnt/nadavrap-students/STS/data/imputed_data2.csv")
# df_all = pd.read_csv("/tmp/pycharm_project_723/new data sum info surg and Hosp numeric values.csv")
# # print(df_... | pd.merge(d10, df_PredComp_all, left_on=['HospID','surgyear'], right_on=['HospID','surgyear'], how='outer') | pandas.merge |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jul 17 09:11:58 2020
@author: ets
"""
import datetime as dt
import logging
import re
import warnings
from pathlib import Path
from typing import List, Tuple
# import climpred
import numpy as np
import pandas as pd
import xarray as xr
from climpred imp... | pd.to_datetime(ds.time[0].values) | pandas.to_datetime |
# getFamaFrenchFactors.py
# Author: Vash
# Version 0.0.4
# Last updated: 18 May 2019
"""
This programme gets cleaned versions of factors including:
* Fama French 3 factor (MRP, SMB, HML)
* Momentum (MOM)
* Carhart 4 factors (MRP, SMB, HML, MOM)
* Fama French 5 factors (MRP, SMB, HML, RMW, CMA)
Updates... | pd.to_numeric(ff3_factors[col]) | pandas.to_numeric |
import pandas as pd
import numpy as np
import scipy
import seaborn as sns
import matplotlib.pyplot as plt
import os
from functools import reduce
from statsmodels.tsa.stattools import coint
############### 一、pearson_corr begin
sns.set(style='white')
# Retrieve intraday price data and combine them into a DataFrame.
#... | pd.merge(x, y, on='date') | pandas.merge |
import os
import pandas as pd
import datetime
import matplotlib.pyplot as plt
df = pd.read_csv(os.path.join('data', 'lake_mendota.csv'))
df['year'] = df['close_year']
df['month'] = df['close_month']
df['day'] = df['close_day']
df['close_date'] = pd.to_datetime(df[['year', 'month', 'day']])
df['year'] = df['open_yea... | pd.to_datetime(df[['year', 'month', 'day']]) | pandas.to_datetime |
"""This module provides access to the Vicon and biplane fluoroscopy filesystem-based database."""
from pathlib import Path
import itertools
import functools
import numpy as np
import pandas as pd
import quaternion
from lazy import lazy
from typing import Union, Callable, Type, Tuple
from biokinepy.cs import ht_r, chan... | pd.read_csv(self.torso_vicon_file_v3d, header=0, dtype=TORSO_FILE_HEADERS) | pandas.read_csv |
# -*- coding: utf-8 -*-
from __future__ import print_function
from datetime import datetime, timedelta
import functools
import itertools
import numpy as np
import numpy.ma as ma
import numpy.ma.mrecords as mrecords
from numpy.random import randn
import pytest
from pandas.compat import (
PY3, PY36, OrderedDict, ... | tm.assert_series_equal(result['foo'], expected) | pandas.util.testing.assert_series_equal |
# Copyright 2015-2016 ARM Limited
#
# 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 w... | pd.DataFrame(run_id_dict) | pandas.DataFrame |
#!/usr/bin/env python
from __future__ import division
from __future__ import print_function
from builtins import zip
from builtins import str
from builtins import range
import pandas as pd
import os
import sys
from collections import Counter
import operator
from itertools import takewhile
import multiprocessing
from ... | pd.merge(allmtseq2, summed_counter, how='left', on='sequence') | pandas.merge |
import pandas as pd
import os
#
from .... import global_var
from . import transcode, paths
def load(map_code = None):
"""
Loads the load data provided by ENTSO-E.
:param map_code: The delivery zone
:type map_code: string
:return: The load data
:rtype: pd.DataFrame
""... | pd.to_datetime(df[global_var.load_dt_UTC]) | pandas.to_datetime |
import time
import pandas as pd
import numpy as np
CITY_DATA = { 'chicago': 'chicago.csv',
'new york city': 'new_york_city.csv',
'washington': 'washington.csv' }
MONTHS = ["january","february", "march", "april",\
"may","june","all"]
DAYS = ["monday","tuesday","wednesday","thursd... | pd.read_csv(CITY_DATA[city]) | pandas.read_csv |
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer, TfidfTransformer
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import StratifiedKFold
from sklearn import metrics
from utils impor... | pd.read_excel('datasets/raw_data.xlsx', engine='openpyxl') | pandas.read_excel |
#!/usr/bin/env python
import os
import pandas as pd
def get_supertwists(qmc_out):
""" read supercell twists from QMCPACK output
Args:
qmc_out (str): QMCPACK output, must contain "Super twist #"
Return:
np.array: an array of twist vectors (ntwist, ndim)
"""
from qharv.reel import ascii_out
mm = asc... | pd.read_json(ofile) | pandas.read_json |
import numpy as np
import csv
import sys
import os
import h5py
import pandas as pd
import simplejson as json
import sqlite3
import copy
# structure followed in this file is based on : https://github.com/nhammerla/deepHAR/tree/master/data
# and https://github.com/IRC-SPHERE/sphere-challenge
class data_reader:
def ... | pd.DataFrame(index=acceleration.index) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import sys, os
import pandas as pd
from importlib import reload
from bs4 import BeautifulSoup
import requests
from tqdm import tqdm
import numpy as np
import itertools
import shutil
import grimsel_h.auxiliary.timemap as timemap
import grimsel_h.auxiliary.aux_sql_func as ... | pd.read_csv(fn, delimiter=';') | pandas.read_csv |
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Tue Oct 9 16:34:12 2018
@author: nmei
"""
import pandas as pd
import numpy as np
from utils import (posthoc_multiple_comparison,
post_processing,
posthoc_multiple_comparison_interaction,
resample_tt... | pd.unique(c['Window']) | pandas.unique |
import pandas as pd
import pytest
from sklearn import datasets
from sklearn.metrics import f1_score
from sklearn.model_selection import train_test_split
from skqulacs.circuit.pre_defined import create_qcl_ansatz
from skqulacs.qnn import QNNClassifier
from skqulacs.qnn.solver import Adam, Bfgs, Solver
@pyt... | pd.DataFrame(iris.data, columns=iris.feature_names) | pandas.DataFrame |
from django.shortcuts import render
from django.http import HttpResponse
from django.views import View
import pytz
import numpy as np
from datetime import datetime, time
import pandas as pd
import os, subprocess, psutil
from django.conf.urls.static import static
from . forms import SubmitTickerSymbolForm
... | pd.read_csv(csvPathLive) | pandas.read_csv |
import os
from scipy.io import loadmat
import numpy as np
import pandas as pd
import torch
from sklearn.model_selection import train_test_split
from datasets.SequenceDatasets import dataset
from datasets.sequence_aug import *
from tqdm import tqdm
from itertools import islice
#Digital data was collected... | pd.DataFrame({"data": list_data[0], "label": list_data[1]}) | pandas.DataFrame |
# Copyright 2015 Cloudera 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 writing, so... | tm.assert_series_equal(result, expected) | pandas.util.testing.assert_series_equal |
# Arithmetic tests for DataFrame/Series/Index/Array classes that should
# behave identically.
# Specifically for Period dtype
import operator
import numpy as np
import pytest
from pandas._libs.tslibs.period import IncompatibleFrequency
from pandas.errors import PerformanceWarning
import pandas as pd
from pandas impo... | tm.assert_numpy_array_equal(pd.NaT == right, expected) | pandas.util.testing.assert_numpy_array_equal |
import sys
import pandas as pd
import numpy as np
from designer_summary import DesignerSummary
pd.set_option('display.expand_frame_repr', False)
pd.set_option('display.max_columns', 10)
class Grailed(object):
def __init__(self, feed_csv_path):
self.df = self.load_df_from_csv(feed_csv_path)
self.gr... | pd.isnull(b) | pandas.isnull |
import ipywidgets
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pymc3 as pm
import seaborn as sns
import sympy
# sympy symbol definition for confusion matrix (CM) entries
symbol_order = 'TP FN TN FP'.split()
tp, fn, tn, fp = cm_elements = sympy.symbols(symbol_order)
n = sum(cm_elements)... | pd.DataFrame({'k': predicted_k, 'j': predicted_j}) | pandas.DataFrame |
import pandas as pd
import numpy as np
import unittest
from dstools.preprocessing.Bucketizer import Bucketizer
class TestBucketizer(unittest.TestCase):
def compare_DataFrame(self, df_transformed, df_transformed_correct):
"""
helper function to compare the values of the transformed DataFrame with ... | pd.DataFrame({'x':[1,2,3]}) | pandas.DataFrame |
# -*- 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... | Series([1, 1, 1]) | pandas.Series |
# -*- coding: utf-8 -*-
__author__ = '<NAME>'
from pandas import (
concat,
read_csv,
Series
)
from sklearn.tree import DecisionTreeClassifier
class Titanic(object):
titanic_data = None
def __init__(self, titanic_csv):
self.titanic_data = read_csv(titanic_csv, index_col='PassengerId')
... | Series(['Pclass', 'Fare', 'Age', 'Sex']) | pandas.Series |
import csv
import sys
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import pandas as pd
import json
from os import listdir
from os.path import isfile, join
import re
monnomdistances={'C':0,'I':0,'D':1,'J':1,'K':2,'L':1,'M':2,'S':1,'T':2}
markersize=8
linewidth... | pd.DataFrame(extended) | pandas.DataFrame |
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