prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
"""
Import necessary libraries
"""
from itertools import chain
import sqlalchemy as db
import pandas as pd
from bs4 import BeautifulSoup
from urllib.request import urlopen
import re
import json
from time import sleep
# Few component's idea adapted from/Reference from - # https://github.com/erilu/web-scraping-NBA-stati... | pd.Series(career_info, index=career_stats_df.columns, name=player_index) | pandas.Series |
# -------------------------------------------------- ML 02/10/2019 ----------------------------------------------------#
#
# This is the class for poisson process
#
# -------------------------------------------------------------------------------------------------------------------- #
import numpy as np
import pandas ... | pd.DataFrame() | pandas.DataFrame |
import random
import pandas as pd
from tqdm import tqdm
from shared.utils import make_dirs
from shared.utils import load_from_json
import sys
class Training_Data_Generator(object):
""" Class for generating ground-truth dataset used for feature learning
:param random_seed: parameter used for reproducibilit... | pd.DataFrame.from_records(query_answer_pairs) | pandas.DataFrame.from_records |
import pandas as pd
import numpy as np
import re
import os
from pandas import json_normalize
import json
from alive_progress import alive_bar
class PrepareNSMCLogs:
def __init__(self, config):
self.raw_logs_dir = config.raw_logs_dir
self.prepared_logs_dir = config.prepared_logs_dir
self.fi... | json_normalize(json_log) | pandas.json_normalize |
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import functools
import math
import warnings
from typing import Callable, Dict, List, Optional, Tuple
import numpy as np
import pandas as p... | pd.concat([frames, out_df]) | pandas.concat |
# Copyright (c) 2019-2021 - for information on the respective copyright owner
# see the NOTICE file and/or the repository
# https://github.com/boschresearch/pylife
#
# 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 co... | pd.Index(['foo', 'bar', 'baz']) | pandas.Index |
import pandas
from msdss_models_api.models import Model
def create_init_method(can_input=True, can_output=True, can_update=True):
"""
Create model init method for scikit-learn models to be compatible with :class:`msdss_models_api:msdss_models_api.models.Model`.
See :class:`msdss_models_api:msdss_models_a... | pandas.DataFrame(data) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""Functions from market data"""
__author__ = "<NAME>"
__version__ = "1"
import pandas as pd
import numpy as np
from pyquanttrade.engine.utils import (
max_drawdown_ratio,
max_drawdown_value,
safe_div,
safe_min,
safe_sum,
safe_mean,
)
class DailyStats:
def __init_... | pd.DataFrame(index=index_data) | pandas.DataFrame |
import osmnx as ox
import networkx as nx
import geopandas
import pandas as pd
from pylab import *
print('EXECUTING')
# Get graph
g = ox.graph_from_place(
'Brentwood - Darlington, Portland, Oregon, USA', network_type='all')
# tranfer to GDF
g_gdf_nodes, g_gdf_edges = ox.graph_to_gdfs(g)
# transfer to data fram
g_... | pd.DataFrame(g_gdf_nodes) | pandas.DataFrame |
"""
NetSQL is a network query tool which helps to collect and filter data about your network.
Requires access to network devices, but also can process raw command output.
"""
from __future__ import print_function, unicode_literals
import json
import re
import csv
import getpass
import ipaddress
import argparse
import ... | pd.read_csv(file1) | pandas.read_csv |
import numpy as np
import pandas as pd
from bach import Series, DataFrame
from bach.operations.cut import CutOperation, QCutOperation
from sql_models.util import quote_identifier
from tests.functional.bach.test_data_and_utils import assert_equals_data
PD_TESTING_SETTINGS = {
'check_dtype': False,
'check_exact... | pd.Interval(79.2, 89.1, closed='right') | pandas.Interval |
#!/usr/bin/env python3
import os
import functools
import subprocess
import numpy as np
import pandas as pd
from multiprocessing import Pool
from sklearn.model_selection import train_test_split
import deepmp.utils as ut
import deepmp.merge_h5s as mh5
names_all = ['chrom', 'pos', 'strand', 'pos_in_strand', 'readname... | pd.read_csv(positions, sep='\t') | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Created on Wed Aug 20 12:06:23 2019
Elexon Data API
@author: <NAME>
"""
######################### Libraries ###########################
from datetime import date, timedelta, datetime
import requests
import os
from io import StringIO
import pandas as pd
import fnmatch
#########... | pd.to_numeric(data['Quantity']) | pandas.to_numeric |
# License: Apache-2.0
import databricks.koalas as ks
import numpy as np
import pandas as pd
import pytest
from pandas.testing import assert_frame_equal, assert_series_equal
from gators.model_building.train_test_split import TrainTestSplit
@pytest.fixture()
def data_ordered():
X = pd.DataFrame(np.arange(40).resha... | pd.Series([0, 1, 2, 0, 1, 2, 0, 1], name=y_name) | pandas.Series |
#!/usr/bin/env python
"""Given rows from a parse job script, generates tables/figures for certain
performance metrics. Each output from the parse job script is expected to have
one header row and one metrics row. The first 5 columns are expected to be the
task profile information.
"""
import argparse
from common impo... | pd.Categorical(table['Program']) | pandas.Categorical |
import pandas as pd
from pattern.en import conjugate
import global_variables as v
from generic_operations import print_to_file
def detect_activities(transformed_text_list, dictionary_list):
tagged_records = []
try: conjugate('hello', 'inf') # dirty fix to python 3.7 / pattern error
except: pass
for... | pd.DataFrame(dict_data, columns=v.dictionary_headings) | pandas.DataFrame |
# importar panda
import numpy as np
# importar metodos de tabela
from pandas import Series, DataFrame
# gerar uma array(uma lista que é uma tupla)
dados = np.arange(6)
linha = ['linha1', 'linha2', 'linha3', 'linha4', 'linha5', 'linha6']
coluna = ['coluna1', 'coluna2', 'coluna3']
# indexar(numerar) o array
serie = | Series(dados, index=linha) | pandas.Series |
"""
conjoin_tables.py.
Bring together two tables:
- Reading times by subject by token
- Surprisal by token
This should be the final step before R analysis.
Ideally, this process would be included in the R analysis to lower the number
of steps needed to get data visualizations, but this Python script will fill
that... | pd.read_csv(rts_file, sep='\t', header=0) | pandas.read_csv |
import pandas as pd
import plotly.graph_objects as go
import plotly.express as px
import plotly.io as pio
import plotly as pl
import re
import requests
from .DataFrameUtil import DataFrameUtil as dfUtil
class CreateDataFrame():
"""Classe de serviços para a criação de dataframes utilizados para a construção dos gr... | pd.merge(dfTimeSeriesRecoverSomado, dfRegioesNew, on="Name") | pandas.merge |
from datetime import datetime, timedelta
import warnings
import operator
from textwrap import dedent
import numpy as np
from pandas._libs import (lib, index as libindex, tslib as libts,
algos as libalgos, join as libjoin,
Timedelta)
from pandas._libs.lib import is_da... | is_object_dtype(self.categories) | pandas.core.dtypes.common.is_object_dtype |
__author__ = "<NAME>"
__copyright__ = "Sprace.org.br"
__version__ = "1.0.0"
import os
import numpy as np
import pandas as pd
#from torch.utils.data import Dataset, DataLoader
from sklearn.preprocessing import MinMaxScaler, StandardScaler
from enum import Enum
from pickle import dump, load
class FeatureType(Enum):
... | pd.DataFrame(y_data) | pandas.DataFrame |
# Import packages
import os
import pandas as pd
import scipy
from scipy.optimize import curve_fit
import hplib as hpl
# Functions
def import_heating_data():
# read in keymark data from *.txt files in /input/txt/
# save a dataframe to database_heating.csv in folder /output/
Modul = []
Manufacturer = []... | pd.DataFrame() | pandas.DataFrame |
"""
Initial population
======
This module generates initial population for the genetic algorithm.
"""
from BOFdat.util.update import _import_csv_file,_import_base_biomass,_import_model,_import_essentiality
from BOFdat.util.update import _get_biomass_objective_function, determine_coefficients
import warnings
import ra... | pd.DataFrame({'Metab': metab, 'Number of metab': number_of_rxn}) | pandas.DataFrame |
import streamlit as st
import streamlit.components.v1 as components
import pandas as pd
import numpy as np
import yfinance as yf
from datetime import datetime, date
import matplotlib.pyplot as plt
import talib
#import ta
import numpy as np
import matplotlib.ticker as mticker
import pandas as pd
import requests
from bs... | pd.concat(colOne, ignore_index=True) | pandas.concat |
"""
oil price data source: https://www.ppac.gov.in/WriteReadData/userfiles/file/PP_9_a_DailyPriceMSHSD_Metro.pdf
"""
import pandas as pd
import numpy as np
import tabula
import requests
import plotly.express as px
import plotly.graph_objects as go
import time
from pandas.tseries.offsets import MonthEnd
import re
impor... | pd.concat([petrol_monthly_average,diesel_monthly_average]) | pandas.concat |
# -*- coding: utf-8 -*-
import copy
import os
import shutil
from builtins import range
from datetime import datetime
import numpy as np
import pandas as pd
import pytest
from ..testing_utils import make_ecommerce_entityset
import featuretools as ft
from featuretools import variable_types
from featuretools.entityset... | pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6], 3: ['a', 'b', 'c']}) | pandas.DataFrame |
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... | BQuarterEnd(startingMonth=1) | pandas.core.datetools.BQuarterEnd |
import pandas as pd
from telethon import TelegramClient, sync, events
import json
import re
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', None)
api_id = 'your api_id'
api_hash = 'yout api hash'
... | pd.DataFrame(data) | pandas.DataFrame |
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 |
import pytest
import os
from mapping import util
from pandas.util.testing import assert_frame_equal, assert_series_equal
import pandas as pd
from pandas import Timestamp as TS
import numpy as np
@pytest.fixture
def price_files():
cdir = os.path.dirname(__file__)
path = os.path.join(cdir, 'data/')
files = ... | TS('2015-01-05') | pandas.Timestamp |
from bs4 import BeautifulSoup as Bt4
import requests
import json
import datetime
import pandas as pd
import urllib
import time
import re
import random
import platform
from datetime import datetime
import platform
import shutil
from lxml import etree
# ---------- 爬取 主幹航線準班率 ----------
# 顯卡網址
website = f"https://www.ss... | pd.DataFrame({0:logistic}) | pandas.DataFrame |
# Copyright 2019 <NAME>.
# 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,
# software d... | Series() | pandas.Series |
# import modules ----------------------
import nba_py
import nba_py.game
import nba_py.player
import nba_py.team
import pandas as pd
import numpy as np
import datetime
import pytz
old_settings = np.seterr(all='print')
np.geterr()
print('modules imported')
# define functions ----------------------
def get_games(... | pd.merge(players, team_team, on='TEAM_ID') | pandas.merge |
"""
Functions for data cleaning.
:author: <NAME>
"""
# Imports
import itertools
import numpy as np
import pandas as pd
import re
from sklearn.base import BaseEstimator, TransformerMixin
from typing import List, Optional, Union
from klib.describe import corr_mat
from klib.utils import (
_diff_report,
_drop_du... | pd.DataFrame(excluded_cols) | pandas.DataFrame |
"""
Folium operations.
save_map,
create_base_map,
heatmap,
heatmap_with_time,
cluster,
faster_cluster,
plot_markers,
plot_trajectories_with_folium,
plot_trajectory_by_id_folium,
plot_trajectory_by_period,
plot_trajectory_by_day_week,
plot_trajectory_by_date,
plot_trajectory_by_hour,
plot_stops,
plot_bbox,
plot_points_... | pd.to_datetime(e_datetime + delta_event) | pandas.to_datetime |
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
from sklearn.metrics import accuracy_score
from sklearn.model_selection import TimeSeriesSplit
from keras.layers import Dropout
from keras.layers import Dense, LSTM
from keras.models import Sequential
import numpy as np
from sklearn.preprocessing impo... | pd.to_datetime(Nikkei_df['Date']) | pandas.to_datetime |
#!/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(tsnc["time"][-1].values) | pandas.to_datetime |
# -*- coding: utf-8 -*-
import csv
import os
import platform
import codecs
import re
import sys
from datetime import datetime
import pytest
import numpy as np
from pandas._libs.lib import Timestamp
import pandas as pd
import pandas.util.testing as tm
from pandas import DataFrame, Series, Index, MultiIndex
from pand... | tm.assert_raises_regex(ValueError, msg) | pandas.util.testing.assert_raises_regex |
import numpy as np
import pandas as pd
import scanpy as sc
from termcolor import colored
import time
import matplotlib
import matplotlib.pyplot as plt
from sklearn.metrics.pairwise import euclidean_distances
import umap
import phate
import seaborn as sns
from pyVIA.core import *
def cellrank_Human(ncomps=80, knn=30, v... | pd.DataFrame(data_sub, columns=data.columns) | pandas.DataFrame |
# authors: <NAME>, <NAME>, <NAME>
# date: 2020-01-25
'''The script loads previously trained model and performs validation on test data. It then
stores sample excerpt in data folder
Usage: test_model.py [--TEST_FILE_PATH=<TEST_FILE_PATH>] [--MODEL_DUMP_PATH=<MODEL_DUMP_PATH>] [--TEST_SIZE=<TEST_SIZE>]
Options:
--TEST... | pd.read_csv(test_file_path) | pandas.read_csv |
"""
Tests for statistical pipeline terms.
"""
from numpy import (
arange,
full,
full_like,
nan,
where,
)
from pandas import (
DataFrame,
date_range,
Int64Index,
Timestamp,
)
from pandas.util.testing import assert_frame_equal
from scipy.stats import linregress, pearsonr, spearmanr
fr... | assert_frame_equal(pearson_results, expected_pearson_results) | pandas.util.testing.assert_frame_equal |
import operator
import re
import warnings
import numpy as np
import pytest
from pandas._libs.sparse import IntIndex
import pandas.util._test_decorators as td
import pandas as pd
from pandas import isna
from pandas.core.sparse.api import SparseArray, SparseDtype, SparseSeries
import pandas.util.testing as tm
from pan... | tm.assert_sp_array_equal(result, expected) | pandas.util.testing.assert_sp_array_equal |
import pandas as pd
import numpy as np
import pdb
import sys
import os
from sklearn.ensemble import GradientBoostingRegressor
from joblib import dump, load
import re
##################################################################3
# (Sept 2020 - Jared) - PG-MTL training script on 145 source lake
# Features and hyp... | pd.read_feather("../../metadata/diffs/target_nhdhr_"+lake_id+".feather") | pandas.read_feather |
import pandas as pd
import re
import ijson
import json
import numpy as np
import csv
class jsonData:
percent_critical = 0
percent_high = 0
percent_medium = 0
percent_low = 0
total = 0
def __init__(self):
pass
def print_full(self, x): # function that prints full dataframe for dis... | pd.DataFrame() | pandas.DataFrame |
import os
from os.path import join
import numpy as np
import pandas as pd
from collections import OrderedDict
from itertools import chain
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import ShuffleSplit
from sklearn.model_selectio... | pd.DataFrame(X_test, index=y_test.index) | pandas.DataFrame |
import pandas as pd
import numpy as np
from web.pickle_helper import *
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
from django.http import HttpResponse
import io
from io import BytesIO
import random
import base64
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
f... | pd.Series(df[column_name]) | pandas.Series |
""" Format data """
from __future__ import division, print_function
import pandas as pd
import numpy as np
import re
from os.path import dirname, join
from copy import deepcopy
import lawstructural.lawstructural.constants as lc
import lawstructural.lawstructural.utils as lu
#TODO: Take out entrant stuff from lawData
... | pd.isnull(worst_schools) | pandas.isnull |
# -*- 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.Index([1., 1., 2., 3., 4.]) | pandas.Index |
import numpy as np
import py2neo
import pandas as pd
import networkx as nx
from scipy import sparse
DATA_DIR = "data/mag"
def get_db():
username = "neo4j"
password = "<PASSWORD>"
uri = "http://localhost:7474"
graph = py2neo.Graph(uri=uri, user=username, password=password)
return graph
def const... | pd.concat(cartel_table_list, ignore_index=True) | pandas.concat |
import numpy as np
import pandas as pd
import pytest
from src.policies.single_policy_functions import (
_identify_who_attends_because_of_a_b_schooling,
)
from src.policies.single_policy_functions import mixed_educ_policy
@pytest.fixture
def fake_states():
states = pd.DataFrame(index=np.arange(10))
states... | pd.testing.assert_series_equal(res, expected) | pandas.testing.assert_series_equal |
import luigi
import os
import pandas as pd
from db import extract
from db import sql
from forecast import util
import shutil
import luigi.contrib.hadoop
from sqlalchemy import create_engine
from pysandag.database import get_connection_string
from pysandag import database
from db import log
class EmpPopulation(luigi.T... | pd.read_hdf('temp/data.h5', 'econ_sim_rates') | pandas.read_hdf |
"""
Module contains tools for processing files into DataFrames or other objects
"""
from collections import abc, defaultdict
import csv
import datetime
from io import StringIO
import itertools
import re
import sys
from textwrap import fill
from typing import (
Any,
Dict,
Iterable,
Iterator,
List,
... | lib.map_infer_mask(values, conv_f, mask) | pandas._libs.lib.map_infer_mask |
"""
This module handles data and provides convenient and efficient access to it.
"""
from __future__ import annotations
import os
import pickle
import sys
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
import pandas as pd
from bs4 import BeautifulSoup
from scipy import sparse
import util.t... | pd.DataFrame.sparse.from_spmatrix(matrix, index=mss_ids, columns=text_names) | pandas.DataFrame.sparse.from_spmatrix |
""" pandaspyomo: read data from coopr.pyomo models to pandas DataFrames
Pyomo is a GAMS-like model description language for mathematical
optimization problems. This module provides functions to read data from
Pyomo model instances and result objects. Use list_entities to get a list
of all entities (sets, params, vari... | pd.DataFrame() | pandas.DataFrame |
import re
import pandas as pd
import numpy as np
from datasets.constants import signal_types
from datasets.sources.source_base import SourceBase
import logging
logger = logging.getLogger(__name__)
class EverionSource(SourceBase):
FILES = {
'signals': r'^CsvData_signals_EV-[A-Z0-9-]{14}\.csv$',
'... | pd.DataFrame() | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
##################################################
Extract Active Entries from ChEMBL SQLite Database
##################################################
*Created on Tue Feb 02, 2022 by <NAME>*
Extract active molecule entries from the SQLite version of the ChEMBL data... | pd.merge(df, df_mc, how="inner", on="chembl_id") | pandas.merge |
import pandas as pd
import path_utils
from Evolve import Evolve, replot_evo_dict_from_dir
import traceback as tb
import os, json, shutil
import numpy as np
import matplotlib.pyplot as plt
import itertools
from copy import deepcopy
import pprint as pp
from tabulate import tabulate
import seaborn as sns
import shutil
imp... | pd.read_csv(all_scores_fname) | pandas.read_csv |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.tree import DecisionTreeClassifier
from brightics.common.report import ReportBuilder, strip_margin, pandasDF2MD, plt2MD, dict2MD
from brightics.function.utils import _model_dict
from sklearn.tree.export import export_graphviz
from brigh... | pd.DataFrame(data=feature_importance, index=feature_cols) | pandas.DataFrame |
def hover(x):
index=x.find(".")
if index==-1: return x
else: return x[:index]
def morph(x):
index=x.find(".")
if index==-1: return ""
else: return x[index+1:]
def stransform(inputw):
if len(inputw)>0 and inputw[0]=="[":
return " ʔăḏōnāy"
elif len(inputw)>1 and inputw[0]==input... | pd.DataFrame() | pandas.DataFrame |
# Copyright 2017 Regents of the University of Colorado. All Rights Reserved.
# Released under the MIT license.
# This software was developed at the University of Colorado's Laboratory for Atmospheric and Space Physics.
# Verify current version before use at: https://github.com/MAVENSDC/Pydivide
import calendar
import ... | pd.concat(temp_data, axis=0, sort=True) | pandas.concat |
import os
from urllib.request import urlretrieve
import pandas as pd
Fremont_URL = 'https://data.seattle.gov/api/views/65db-xm6k/rows.csv?accessType=DOWNLOAD'
def get_fremont_data(filename="Fremont.csv",url=Fremont_URL
,force_download=False):
"""
Download and cache the fremont data
... | pd.to_datetime(data.index) | pandas.to_datetime |
import os, sys, json, warnings, logging as log
import pandas as pd, tqdm, dpath
import annotate, collect
from pprint import pprint
def make_items(iter_labeled_meta, iter_all_meta, n_unlabeled, read_rows):
'''Generate metadata from gold-standard and unlabled'''
labeled_items = [(meta, read_rows(meta['url'... | pd.Series(y_match) | pandas.Series |
""" merge predictions and generate submission.
"""
import os
import sys
import glob
from pathlib import Path
import argparse
import cv2
import numpy as np
import pandas as pd
from tqdm import tqdm
import torch
from torch.utils.data import DataLoader
from torch.nn import functional as F
from albumentations import Co... | pd.read_csv('../input/sample_submission.csv') | pandas.read_csv |
import random
import math
import numpy as np
import pygeos
import pandas as pd
# Smallest enclosing circle - Library (Python)
# Copyright (c) 2017 Project Nayuki
# https://www.nayuki.io/page/smallest-enclosing-circle
# This program is free software: you can redistribute it and/or modify
# it under the terms of the G... | pd.Series(node_ids, index=df.index) | pandas.Series |
# This file is part of GridCal.
#
# GridCal is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# GridCal is distributed in the hope that... | pd.set_option('display.max_columns', 500) | pandas.set_option |
from sklearn.metrics import confusion_matrix, classification_report
from matplotlib.colors import LinearSegmentedColormap
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib as mpl
from matplotlib.pyplot import figure
import os
import warnings
warnings.filterwarnings("i... | pd.Series(y_true) | pandas.Series |
#!/usr/bin/env python3
import argparse
import collections
import copy
import datetime
import functools
import glob
import json
import logging
import math
import operator
import os
import os.path
import re
import sys
import typing
import warnings
import matplotlib
import matplotlib.cm
import matplotlib.dates
import ma... | pandas.read_csv(data_file_path) | pandas.read_csv |
import unittest
import os
from collections import defaultdict
from unittest import mock
import warnings
import pandas as pd
import numpy as np
from dataprofiler.profilers import FloatColumn
from dataprofiler.profilers.profiler_options import FloatOptions
test_root_path = os.path.dirname(os.path.dirname(os.path.real... | pd.Series([1, 1, 1, 1, 1, 1, 1]) | pandas.Series |
# Licensed to Elasticsearch B.V. under one or more contributor
# license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright
# ownership. Elasticsearch B.V. licenses this file to you under
# the Apache License, Version 2.0 (the "License"); you may
# not use ... | is_datetime_or_timedelta_dtype(self.pd_dtype) | pandas.core.dtypes.common.is_datetime_or_timedelta_dtype |
from __future__ import division
from sklearn.feature_extraction.text import ENGLISH_STOP_WORDS
from sklearn.pipeline import Pipeline
from sklearn.decomposition import TruncatedSVD
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.ensemble import RandomForestClassifier
from sklearn import pr... | pd.DataFrame(data=mv,index=['Accuracy','Precision','Recall','F-Measure']) | pandas.DataFrame |
#-*-coding=utf-8-*-
from emotion import emo
from collections import OrderedDict
from eval import getCNNDaata
import pandas as pd
from DBHandler import getStockList
from TuHandler import TuHandler
from datetime import date
class dataFetcher:
codeList = []
def __init__(self, listName):
self.codeList = ge... | pd.read_csv('SMB.csv') | pandas.read_csv |
# -*- 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... | DataFrame(data, index=['foo', 'bar', 'baz'], dtype='O') | pandas.DataFrame |
import pandas as pd
#import openpyxl
from openpyxl import workbook
from openpyxl import load_workbook
import numpy as np
from scipy.stats import spearmanr
from .general_functions import *
class Abundances():
def __init__(self):
self.abundance_df = pd.DataFrame(index=[], columns=[])
self.corr_matri... | pd.read_csv(filename, header=0, sep='\t') | pandas.read_csv |
from argparse import ArgumentParser
import pandas as pd
from fyne import heston
from utils import years_to_expiry
def get_heston_greeks(date, bbo, underlying, discount, vols, params):
_, kappa, theta, nu, rho = params
mid = bbo.mean(axis=1).unstack(['Class', 'Expiry', 'Strike'])
mid.name = 'Mid'
pr... | pd.read_parquet(args.discount_filename) | pandas.read_parquet |
import lightgbm as lgb
import pandas as pd
import pytest
import shap
from pyspark.ml.classification import RandomForestClassifier
from pyspark.sql import SparkSession
from sklearn.datasets import make_classification
from shapicant import PandasSelector, SparkSelector, SparkUdfSelector
@pytest.fixture
def data():
... | pd.DataFrame(data[0]) | pandas.DataFrame |
import os
# disable tensorflow debugging information
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import warnings
# suppress warnings:
warnings.filterwarnings("ignore")
from deep_utils import tf_set_seed
from utils.utils import save_params
from datetime import datetime
import tensorflow as tf
import numpy as np
from data... | pd.DataFrame(conf_matrix, index=["healthy", "schizophrenia"], columns=["healthy", "schizophrenia"]) | pandas.DataFrame |
import warnings
import pandas as pd
from sklearn.base import BaseEstimator, RegressorMixin
from sklearn.utils import check_array, check_X_y
from sklearn.utils.validation import check_is_fitted
class TimeSynchronousDownscaler(BaseEstimator):
def _check_X_y(self, X, y, **kwargs):
if isinstance(X, pd.DataFr... | pd.DataFrame(y, index=index) | pandas.DataFrame |
#!/usr/bin/env python
''' ---------------- About the script ----------------
Assignment 3: Sentiment Analysis
This script calculates sentiment scores of over a million headlines taken from the Australian news source ABC (Start Date: 2003-02-19 ; End Date: 2020-12-31) using the spaCyTextBlob approach, creates and s... | pd.read_csv(in_file) | pandas.read_csv |
'''Report for the entire Project.
Run this report with: `streamlit run 09-1_project-report.py`
This should provide an interactive mechanism to query the recommender system.
'''
import streamlit as st
import pandas as pd
import numpy as np
import sys
sys.path.insert(1, '..')
import recommender as rcmd
from recommende... | pd.DataFrame(embs) | pandas.DataFrame |
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.metrics import confusion_matrix
from mpl_toolkits.mplot3d import Axes3D
plt.rc('font', family='serif')
class Plots():
def boxcar(data):
f, (ax) = plt.subplots(1, 1, figsize=(12, 4))
... | pd.DataFrame(confusion_1,columns=['Shale','Brine Sands','Gas Sands'], index=['Shale',' Brine Sands','Gas Sands'] ) | pandas.DataFrame |
"""
Tests for character matrix formation.
"""
import unittest
import numpy as np
import pandas as pd
import cassiopeia as cas
class TestCharacterMatrixFormation(unittest.TestCase):
def setUp(self):
at_dict = {
"cellBC": ["cellA", "cellA", "cellA", "cellB", "cellC"],
"intBC": ["A... | pd.testing.assert_frame_equal(character_matrix, expected_df) | pandas.testing.assert_frame_equal |
#!/usr/bin/env python3
#
# - import a csv table of score files (and possibly edf files)
# - strip out spaces in column names
# - consolidate into trial datablocks (with consensus)
# TODO: use relative paths in csv?
#======================================
import pdb
import os
import argparse
import pandas as pd
... | pd.concat([df_index, df_data], axis=1) | pandas.concat |
import numpy as np
import pandas as pd
from pathlib import Path
import bw2data as bd
import bw_processing as bwp
from fs.zipfs import ZipFS
from consumption_model_ch.utils import get_habe_filepath
# Local files
from .sensitivity_analysis import get_mask
DATA_DIR = Path(__file__).parent.resolve() / "data"
KONSUMGUET... | pd.read_csv(path_ausgaben, sep='\t') | pandas.read_csv |
def getMetroStatus():
import http.client, urllib.request, urllib.parse, urllib.error, base64, time
headers = {
# Request headers
'api_key': '6b700f7ea9db408e9745c207da7ca827',}
params = urllib.parse.urlencode({})
try:
conn = http.client.HTTPSConnection('api.wmata.com')
conn.request("GET", "/StationPredi... | pd.concat([colorSeries, NEdirection, headerInfo.iloc[1:],secSince5B4,tripB4Table,tripTimeTable.iloc[1:]],axis=1) | pandas.concat |
# -*- coding: utf-8 -*-
"""Core logic for computing subtrees."""
# standard library imports
import contextlib
import os
import sys
from collections import Counter
from collections import OrderedDict
from itertools import chain
from itertools import combinations
from pathlib import Path
# third-party imports
import ne... | pd.DataFrame(stat_list) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
import random as rm
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.font_manager as fm
import os
import matplotlib.colors as colors
import matplotlib
#-----------------------------------------------------------------------------
# R... | pd.Grouper(freq="H") | pandas.Grouper |
import numpy as np
import scipy as sp
from scipy import stats as spstats
import pandas as pd
from six.moves import range
from numpy.testing import assert_array_equal, assert_array_almost_equal
import numpy.testing as npt
import nose.tools
import nose.tools as nt
from nose.tools import assert_equal, assert_almost_equal... | pdt.assert_frame_equal(out, want) | pandas.util.testing.assert_frame_equal |
import numpy as np
import pandas as pd
import rasterio
import statsmodels.formula.api as smf
from scipy.sparse import coo_matrix
import scipy.spatial
import patsy
from statsmodels.api import add_constant, OLS
from .utils import transform_coord
def test_linearity(x, y, n_knots=5, verbose=True):
"""Test linearity ... | pd.read_stata(SVY_IN_DIR) | pandas.read_stata |
import pandas as pd
from pandas.io.json import json_normalize
def venues_explore(client,lat,lng, limit=100, verbose=0, sort='popular', radius=2000, offset=1, day='any',query=''):
'''funtion to get n-places using explore in foursquare, where n is the limit when calling the function.
This returns a pandas datafr... | json_normalize(df1) | pandas.io.json.json_normalize |
import datetime as dt
import unittest
from unittest.mock import patch
import numpy as np
import numpy.testing as npt
import pandas as pd
from pandas.util.testing import assert_frame_equal, assert_series_equal, assert_index_equal
import seaice.timeseries.warp as warp
from seaice.timeseries.common import SeaIceTimeseri... | assert_frame_equal(expected, actual) | pandas.util.testing.assert_frame_equal |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Investing.com API - Market and historical data downloader
# https://github.com/crapher/pyinvesting.git
#
# Copyright 2020 <NAME>
#
# Licensed under the Apache License, Version 2.0 (the 'License');
# you may not use this file except in compliance with the License.
# You ... | pd.to_datetime(result['timestamp'], unit='s') | pandas.to_datetime |
from datetime import datetime
import operator
import numpy as np
import pytest
from pandas import DataFrame, Index, Series, bdate_range
import pandas._testing as tm
from pandas.core import ops
class TestSeriesLogicalOps:
@pytest.mark.parametrize("bool_op", [operator.and_, operator.or_, operator.xor])
def te... | Series([True, False, True], index=index) | pandas.Series |
# Copyright 2020 The SQLFlow Authors. All rights reserved.
# 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.read_csv('household_power_consumption.csv') | pandas.read_csv |
# Authors: <NAME> (<EMAIL>), <NAME> (<EMAIL>)
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
from typing import Union
from copy import deepcopy
from itertools import compress
import json
time_dict = {0: "Now", 7: "One Week", 14: "Two Weeks", 28: "Four Weeks", 42: "Six Weeks"}
class D... | pd.to_datetime(dsd) | pandas.to_datetime |
##############################################################################
#######################bibliotecas
##############################################################################
import pandas as pd
import numpy as np
# from eod_historical_data import (get_api_key,
# ... | pd.concat(prices) | pandas.concat |
# ----------------------------------------------------------------------------
# File name: NumericalEng.py
#
# Created on: Aug. 11 2020
#
# by <NAME>
#
# Description:
#
# 1) This module to engineer numerical features
#
#
#
# -----------------------------------------------------------------------------
#first l... | pd.to_datetime(X['timestamp']) | pandas.to_datetime |
''' Import modules for reidentification attack'''
import pandas as pd
import numpy as np
import random
import requests
import string
import uuid
import time
from faker import Faker
from datetime import datetime
import scipy.stats as ss
import matplotlib.pyplot as plt
import zipcodes as zc
from tqdm import tqdm
impor... | pd.DataFrame([custodian_id, gender, age, zipcode, diagnosis, treatment, severity]) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
@author: <NAME> - https://www.linkedin.com/in/adamrvfisher/
"""
#BTC strategy model with brute force optimization, need BTC data set to run
#BTC/USD time series can be found for free on Investing.com
#Import modules
import numpy as np
import random as rand
import pandas as p... | pd.Series(Empty) | pandas.Series |
import pandas as pd
import numpy as np
import pytest
import unittest
import datetime
import sys
import context
from fastbt.utils import *
def equation(a,b,c,x,y):
return a*x**2 + b*y + c
def test_multiargs_simple():
seq = pd.Series([equation(1,2,3,4,y) for y in range(20, 30)]).sort_index()
seq.index = ra... | pd.DataFrame() | pandas.DataFrame |
from argparse import ArgumentParser
from pathlib import Path
import pandas as pd
from tqdm.auto import tqdm
def run() -> None:
parser = ArgumentParser()
parser.add_argument('input_file', type=Path)
parser.add_argument('output_file', type=Path)
parser.add_argument('--no-finding-class', type=int, defau... | pd.DataFrame(rows, columns =['image_id', 'class_name', 'class_id', 'x_min', 'y_min', 'x_max', 'y_max']) | pandas.DataFrame |
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