prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
"""
WIP method to predict column embeddings using remainder of row as context
"""
import pandas as pd
import fasttext
from scipy.spatial import distance
from itertools import product
import sys
import numpy as np
from configurations import *
from tuple_embedding_models import AutoEncoderTupleEmbedding, AutoEncoderTuple... | pd.read_csv(table_files[0]) | pandas.read_csv |
# Copyright (c) 2018-2021, NVIDIA CORPORATION.
import array as arr
import datetime
import io
import operator
import random
import re
import string
import textwrap
from copy import copy
import cupy
import numpy as np
import pandas as pd
import pyarrow as pa
import pytest
from numba import cuda
import cudf
from cudf.c... | pd.DataFrame({"first_col": [], "second_col": [], "third_col": []}) | pandas.DataFrame |
import pandas
from google.cloud import bigquery
from google_pandas_load import LoadConfig
from tests.context.loaders import gpl1, gpl2, gpl3, gpl4, gpl5
from tests.context.resources import project_id, bq_client, \
dataset_ref, dataset_name
from tests.utils import BaseClassTest, populate_dataset, \
populate, pop... | pandas.DataFrame(data={'x': [3]}) | pandas.DataFrame |
# -*- coding: utf-8 -*-
import os
import sys
import pandas as pd
import numpy as np
import json
# webscraping
import requests
import wget
from bs4 import BeautifulSoup
from selenium import webdriver
from datetime import datetime, timedelta
import time
from tqdm import tqdm
# if __package__:
# from ..imports imp... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
from pathlib import Path
from itertools import islice
class My_dict(dict):
def __init__(self):
self = dict()
def add(self, key, value):
self[key] = value
class Df():
def __init__(self, raw_data_location):
... | pd.DataFrame() | pandas.DataFrame |
import numpy as np
import pandas as pd
import streamlit as st
import importlib
import os
import sys
import time
def file_selector(folder_path='.'):
filenames = os.listdir(folder_path)
filenames_ = [f for f in filenames if f[-3:] == "txt"]
selected_filename = st.selectbox('Select a file', filenames_)
... | pd.DataFrame({"TAGS":new_tag}, index=[0]) | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Dec 6 10:25:47 2019
@author: <NAME>
Input files: host and pathogen quantification tables (e.g. pathogen_quant_salmon.tsv, host_quantification_uniquely_mapped_htseq.tsv), raw read statistics, star statistics
Output file: tsv file
Description: Used to ... | pd.read_csv(args.cross_mapped,sep="\t", header=None, index_col=0, names=['cross_mapped_reads']) | pandas.read_csv |
#! /usr/bin/env python
import cyvcf2
import argparse
import sys
from collections import defaultdict, Counter
import pandas as pd
import signal
import numpy as np
from shutil import copyfile
import pyfaidx
from random import choice
from pyliftover import LiftOver
from Bio.Seq import reverse_complement
from mutyper imp... | pd.DataFrame(ksfs_data, index) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""experiment0_baseline_nn
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1fD-X5sxQmLpGu1VhKF6I73vLX7swU2kV
"""
"""### Imports"""
import numpy as np
import matplotlib.pyplot as plt
import random
from sklearn.model_selection im... | pd.DataFrame({'Labels':labels, "precision":precision, "recall":recall, "f1":f1 }) | pandas.DataFrame |
#! /usr/bin/env python
# -*- coding: utf-8 -*-
""" Aplicación para resolver el Challenge Python L1
Desarrollado por <NAME>
version 1.0
Bitacora:
fecha: 2021-02-27 observacion: Version 1 por: <NAME>
"""
import requests
import pandas as pd
import json
import hashlib
from datetime im... | pd.DataFrame(data=respuestaJson) | pandas.DataFrame |
import numpy as np
import pandas as pd
import random as random
import pickle
def formatRank_german(df):
tmp = pd.DataFrame()
tmp['y']=df.sort_values('y_pred',ascending=False).index
tmp['y_pred']=tmp.index
tmp['g']=df.sort_values('y_pred',ascending=False).reset_index()['g']
return tmp
def forma... | pd.read_pickle(inpath+'FairRanking03PercentProtected.pickle') | pandas.read_pickle |
import dash
import dash_core_components as dcc
import dash_html_components as html
import dash_bootstrap_components as dbc
import plotly.graph_objs as go
import pandas as pd
import sqlite3
from dash.dependencies import Input, Output, State
import time
# import datetime
from datetime import datetime
from pandas import S... | pd.DataFrame({'DATE':df_100_count.index, '100 Degree Days':df_100_count.values}) | pandas.DataFrame |
import os
import datetime
import pandas as pd
from dataactcore.config import CONFIG_BROKER
from dataactcore.scripts import load_duns_exec_comp
from dataactcore.models.domainModels import DUNS
from dataactcore.utils.duns import DUNS_COLUMNS, EXCLUDE_FROM_API
def mock_get_duns_props_from_sam(duns_list):
""" Mock f... | pd.DataFrame(columns=columns) | pandas.DataFrame |
from __future__ import print_function
import os
import pandas as pd
import xgboost as xgb
import time
import shutil
from sklearn import preprocessing
from sklearn.cross_validation import train_test_split
import numpy as np
from sklearn.utils import shuffle
def archive_results(filename,results,algo,script):
"""
... | pd.DataFrame({"patient_id": id_test, 'predict_screener': predictions}) | pandas.DataFrame |
import numpy as np # deal with data
import pandas as pd # deal with data
import re # regular expression
from bs4 import BeautifulSoup # resolver review
from nltk.corpus import stopwords # Import the stop word list
from gensim.models import word2vec# use word2Vec(... | pd.DataFrame(columns=["T2V", "ZeroR", "Random Guessing"]) | pandas.DataFrame |
"""
Código para crear las variables de decisión del baseline
"""
import pandas as pd
import time
from baseline_ajustes import variables_decision_nacional, variables_decision_exp
from limpieza_masters import limpieza_data, ajustar_tarifario
from output import guardar_outputs
# Carga de datos. Retorna diccionario de DFs... | pd.concat([decision_nal, decision_exp]) | pandas.concat |
# -*- coding: utf-8 -*-
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
# Author: <EMAIL>
from mlhub.pkg import mlask, mlcat
MOVIELENS = '100k' # Select Movielens data size: 100k, 1m, 10m, or 20m.
TOPK = 10 # Top k items to recommend.
TITLEN = 45 # Trunca... | pd.to_numeric(topk['MovieId']) | pandas.to_numeric |
import unittest
from unittest.mock import patch, PropertyMock
import time
import mt5_correlation.correlation as correlation
import pandas as pd
from datetime import datetime, timedelta
from test_mt5 import Symbol
import random
import os
class TestCorrelation(unittest.TestCase):
# Mock symbols. 4 Symbols, 3 visibl... | pd.DataFrame(columns=columns, data=[[starttime, price_base * 0.1]]) | pandas.DataFrame |
import datetime
import numpy as np
import pandas as pd
import pandas.testing as pdt
from cape_privacy.pandas import dtypes
from cape_privacy.pandas.transformations import DateTruncation
from cape_privacy.pandas.transformations import NumericRounding
def _make_apply_numeric_rounding(input, expected_output, ctype, dt... | pd.DataFrame({"date": [input_date]}) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# Copyright 2017 The <NAME>. 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 require... | pd.read_csv('dataset/%s.csv' % f) | pandas.read_csv |
import base64
import io
import textwrap
import dash
import dash_core_components as dcc
import dash_html_components as html
import gunicorn
import plotly.graph_objs as go
from dash.dependencies import Input, Output, State
import flask
import pandas as pd
import urllib.parse
from sklearn.preprocessing import StandardSca... | pd.DataFrame(data=features_outlier, columns=['line_group']) | pandas.DataFrame |
import pandas as pd
import numpy as np
from time import perf_counter
from utils import View, Rule
class DataReader:
def __init__(self, raw_data_path='data/unigram_freq.csv', data_path='data/data.csv'):
# https://www.kaggle.com/rtatman/english-word-frequency
# This dataset was well sorted by freque... | pd.read_csv(self.data_path) | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
This module contains the ReadSets class that is in charge
of reading the sets files, reshaping them to be used in
the build class, creating and reading the parameter files and
checking the errors in the definition of the sets and parameters
"""
import itertools as it
from openpyxl import lo... | pd.Index(self.main_years, name="Years") | pandas.Index |
import numpy as np # We recommend to use numpy arrays
import gc
import pandas as pd
import time
from multiprocessing import Pool
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import train_test_split
#from gensim.models.word2vec import Word2Vec
from sklearn.model_s... | pd.concat([df, data_vec], axis=1) | pandas.concat |
# coding: utf-8
import logging
import multiprocessing
import os
import pickle
import sys
import time
from math import sqrt
import enchant
import numpy as np
import codecs
import simplejson
import pandas as pd
from glob import glob
from nltk import word_tokenize
from nltk.corpus import stopwords
from nltk.tokenize impo... | pd.read_pickle(self.lexicons_output) | pandas.read_pickle |
# -------------------------------------------------- ML 02/10/2019 ----------------------------------------------------#
#
# This is the class for poisson process
#
# -------------------------------------------------------------------------------------------------------------------- #
import numpy as np
import pandas ... | pd.DataFrame([p]) | pandas.DataFrame |
"""This module contains auxiliary functions for the creation of tables in the main notebook."""
import json
import scipy
import numpy as np
from numpy import nan
import pandas as pd
import pandas.io.formats.style
import seaborn as sns
import statsmodels as sm
import statsmodels.formula.api as smf
import statsmodels.ap... | pd.DataFrame() | pandas.DataFrame |
"""Backtesting Controller Module"""
__docformat__ = "numpy"
import argparse
import os
from typing import List
import matplotlib as mpl
import matplotlib.pyplot as plt
import pandas as pd
from prompt_toolkit.completion import NestedCompleter
from gamestonk_terminal import feature_flags as gtff
from gamestonk_terminal... | pd.DataFrame() | pandas.DataFrame |
import os
import sys
import argparse
import pandas as pd
from utils import simplify_string_for_hdf5
parser = argparse.ArgumentParser(description='S-PrediXcan results processor.')
parser.add_argument('--spredixcan-hdf5-folder', required=True, type=str)
parser.add_argument('--spredixcan-hdf5-file-template', required=... | pd.read_csv(args.phenotypes_info_file, sep='\t', index_col='pheno_id') | pandas.read_csv |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import pickle
import os
import yaml
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import r2_score
# from .utils import Boba_Utils as u
# from ._03_Modeling ... | pd.merge(evaluation_df,temp_df,left_index=True,right_index=True) | pandas.merge |
import pandas as pd
from pandas import DataFrame
import sys
#--------
# Imports medi dataset with icd9 and rxcui descriptions to .csv file
# PARAMETERS:
# medi = medi spreadsheet
# icd9_desc = contains icd9 codes and their descriptions
# rxcui_desc = contains rxcui codes and their descriptions
def add_info_to_medi(med... | pd.merge(df_rxcui_icd9, drug_rxcui, how='left', on='RXCUI_IN') | pandas.merge |
# coding: utf-8
import json
import pandas as pd
import numpy as np
import glob
import ast
from modlamp.descriptors import *
import re
import cfg
import os
def not_in_range(seq):
if seq is None or len(seq) < 1 or len(seq) > 80:
return True
return False
def bad_terminus(peptide):
if peptide.nTermi... | pd.concat([combined_toxic_neg, commont]) | pandas.concat |
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
# Copyright 2011-2020, <NAME>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... | Series([record[field] for record in self], index=index, dtype=dtype) | pandas.Series |
# Copyright 2021 AI Singapore. 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 or... | pd.DataFrame([[0.1, 2.5, 3.6], [0.5, 2.2, 6.6]], columns=['x1', 'x2', 'x3']) | pandas.DataFrame |
################################################################################
# Module: schedule.py
# Description: Functions for handling conversion of EnergyPlus schedule objects
# License: MIT, see full license in LICENSE.txt
# Web: https://github.com/samuelduchesne/archetypal
#####################################... | pd.Series([False] * periods, index=index) | pandas.Series |
# author: DSCI-522 Group-21
# date: 2021-11-26
"""Score the model with the test set and generate a confusion matrix
Usage: scoring.py --input=<input> --output=<output>
Options:
--input=<input> The directory where the data and model is
--output=<output> Directory specifying where to store output figure(s)... | pd.DataFrame(scores, index=["test_scores"]) | pandas.DataFrame |
#!/usr/bin/env python3
import cv2
# from cv2 import aruco
from tqdm import trange
import numpy as np
import os, os.path
from glob import glob
from collections import defaultdict
import pandas as pd
## TODO: rewrite this whole file with aniposelib
from .common import \
get_calibration_board, get_board_type, \
... | pd.DataFrame() | pandas.DataFrame |
# Copyright (c) 2016 The UUV Simulator 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 b... | pandas.concat([df_input, df_output, df_wrench], ignore_index=True) | pandas.concat |
#!/usr/bin/env python
# -- coding: utf-8 --
# PAQUETES PARA CORRER OP.
import netCDF4
import pandas as pd
import numpy as np
import datetime as dt
import json
import wmf.wmf as wmf
import hydroeval
import glob
import MySQLdb
#modulo pa correr modelo
import hidrologia
from sklearn.linear_model import LinearRegression
... | pd.to_datetime(start) | pandas.to_datetime |
"""
Creates a new Database
"""
import re
from app import APPLOG
import pandas as pd
from core.settings import (
DB_MOD,
DB_DIR,
ARTLIST_DIR,
SQL_T_ARTLIST,
SQL_T_BOM,
SQL_CONN,
)
from sqlalchemy import create_engine
def createBomDB(bom_df: pd.DataFrame, items_df: pd.DataFrame) -> tuple:
""... | pd.to_numeric(bom_df["childqty"], errors="coerce") | pandas.to_numeric |
r"""Submodule frequentist_statistics.py includes the following functions: <br>
- **normal_check():** compare the distribution of numeric variables to a normal distribution using the
Kolmogrov-Smirnov test <br>
- **correlation_analysis():** Run correlations for numerical features and return output in different forma... | pd.DataFrame(df_normality_check) | pandas.DataFrame |
# coding=utf-8
# pylint: disable-msg=E1101,W0612
from datetime import timedelta
from numpy import nan
import numpy as np
import pandas as pd
from pandas import (Series, isnull, date_range,
MultiIndex, Index)
from pandas.tseries.index import Timestamp
from pandas.compat import range
from pandas.u... | Timestamp('20130103 9:01:01') | pandas.tseries.index.Timestamp |
import pandas as pd
import psycopg2
import pickle
import numpy as np
# counterS = 0
# global counterS
# global valGlob
# from sqlalchemy import create_engine
# -*- coding: utf-8 -*-
import os
import sys
import copy
# fileName = '/Users/alessandro/Documents/PhD/OntoHistory/WDTaxo_October2014.csv'
# connection paramet... | pd.read_sql(classesDataQuery, con=conn) | pandas.read_sql |
""" test get/set & misc """
from datetime import timedelta
import re
import numpy as np
import pytest
from pandas import (
DataFrame,
IndexSlice,
MultiIndex,
Series,
Timedelta,
Timestamp,
date_range,
period_range,
timedelta_range,
)
import pandas._testing as tm
def test_basic_ind... | Timedelta("1 days") | pandas.Timedelta |
# Copyright 1999-2020 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, raw) | pandas.testing.assert_frame_equal |
# -*- coding: utf-8 -*-
import sys
import os
import random
import pdb
import glob
import numpy as np
import pandas as pd
from skimage import io
from skimage.color import rgba2rgb
from . import util
from .util import kconfig
from .transformers import resize_image
#----------------------------------------------... | pd.DataFrame(data=test_info, columns=columns) | pandas.DataFrame |
#===============================================
# PROJETO <NAME>
# funcoes para preprocessamento e visualizacao
#
# @claudioalvesmonteiro
#===============================================
# funcao para gerar dummies com base em threshold
def categoryToDummyThreshold(dataframe, data, column, threshold):
import pand... | pd.get_dummies(dataframe[('SIG_'+column)], prefix=column) | pandas.get_dummies |
import sys
import click
import requests, requests_cache
import configparser
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from pathlib import Path
from datetime import datetime
from mpl_toolkits.axes_grid1 import make_axes_locatable
from pynance.auth import signed_params
from pynance.util... | pd.to_datetime(trades.time, unit="ms") | pandas.to_datetime |
import matplotlib.pyplot as plt
import seaborn as sns
import pdb
import requests
import re
import threading
import concurrent.futures
import numpy as np
import pandas as pd
from functools import reduce
from collections import Counter
from sklearn.preprocessing import normalize, StandardScaler, Normalizer, RobustSca... | pd.DataFrame(edge_weights, columns=["source_node", "target_node", "edge_weight"]) | pandas.DataFrame |
from binance.client import Client
import keys
from pandas import DataFrame as df
from datetime import datetime
import trading_key
client=Client(api_key=keys.Pkeys, api_secret=keys.Skeys)
#get candle data
def candle_data(symbols, intervals):
candles=client.get_klines(symbol=symbols, interval=interv... | df(final_date) | pandas.DataFrame |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import os
import math
import itertools
import pandas as pd
import datetime
from fclib.dataset.retail.benchmark_paths import DATA_DIR
import fclib.dataset.retail.benchmark_settings as bs
# Utility functions
def week_of_month(dt):
"""Get the ... | pd.DataFrame.from_records(item_list, columns=["store", "brand", "week"]) | pandas.DataFrame.from_records |
# -*- coding: utf-8 -*-
"""
Created on Mon Oct 12 16:44:24 2020
@author: Borja
"""
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
"""
- Ultra Trail Mont Blanc. Clasificación desde 2003 hasta 2017.
https://www.kaggle.com/ceruleansea/ultratrail-du-montblanc-20032017?select... | pd.read_csv('Data/csv/utmb_2008.csv', sep=',', decimal='.') | pandas.read_csv |
# -*- coding: utf-8 -*-
"""
Spyder Editor
This is a temporary script file.
"""
from __future__ import division
from datetime import datetime
from sklearn import linear_model
import pandas as pd
import numpy as np
import scipy.stats as st
import statsmodels.distributions.empirical_distribution as edis
import seaborn a... | pd.read_csv('Synthetic_weather/synthetic_weather_data.csv',header=0) | pandas.read_csv |
#!/usr/bin/env python
# coding: utf-8
# # This will create plots for institutions of universities in THE WUR univs only and for the period of 2007-2017. The input dataset contains info of THE WUR univs only but for any period of time.
# #### The unpaywall dump used was from (April or June) 2018; hence analysis until ... | pd.concat([univ_papers_df_set1, univ_papers_df_set2]) | pandas.concat |
"""
Created on Mon Mar 23 17:06:41 2020
@author: diego
"""
from mip import *
import os
import pandas as pd
import numpy as np
import subprocess
# from .Configurations import Gurobi_license_path
# os.environ['GRB_LICENSE_FILE'] = Gurobi_license_path
#list_of_inputs
#
# P : number of working shifts
# T : time horizon... | pd.DataFrame.from_records([y_opt_1t,y_opt_2t]) | pandas.DataFrame.from_records |
#!/usr/bin/env python
"""
analyse Elasticsearch query
"""
import json
from elasticsearch import Elasticsearch
from elasticsearch import logger as es_logger
from collections import defaultdict, Counter
import re
import os
from datetime import datetime
# Preprocess terms for TF-IDF
import numpy as np
import pandas as pd... | pd.Grouper(key="date", freq="W") | pandas.Grouper |
#----------------------------------------------------------------------------------------------
####################
# IMPORT LIBRARIES #
####################
import streamlit as st
import pandas as pd
import numpy as np
import plotly as dd
import plotly.express as px
import seaborn as sns
import matplotl... | pd.DataFrame() | pandas.DataFrame |
import numpy as np
import pandas as pd
import pytest
from pandas.testing import assert_frame_equal
@pytest.fixture
def df_checks():
"""fixture dataframe"""
return pd.DataFrame(
{
"famid": [1, 1, 1, 2, 2, 2, 3, 3, 3],
"birth": [1, 2, 3, 1, 2, 3, 1, 2, 3],
"ht1": [2.... | assert_frame_equal(result, actual) | pandas.testing.assert_frame_equal |
import re
from unittest.mock import Mock, call, patch
import numpy as np
import pandas as pd
import pytest
from rdt.transformers.categorical import (
CategoricalFuzzyTransformer, CategoricalTransformer, LabelEncodingTransformer,
OneHotEncodingTransformer)
RE_SSN = re.compile(r'\d\d\d-\d\d-\d\d\d\d')
class ... | pd.Series([0.125, 0.375, 0.625, 0.875], index=[4, 3, 2, 1]) | pandas.Series |
import pandas as pd
df_ab = pd.DataFrame({'a': ['a_1', 'a_2', 'a_3'], 'b': ['b_1', 'b_2', 'b_3']})
df_ac = pd.DataFrame({'a': ['a_1', 'a_2', 'a_4'], 'c': ['c_1', 'c_2', 'c_4']})
print(df_ab)
# a b
# 0 a_1 b_1
# 1 a_2 b_2
# 2 a_3 b_3
print(df_ac)
# a c
# 0 a_1 c_1
# 1 a_2 c_2
# 2 a_4 c_4
... | pd.merge(df_ab, df_ac, on='a', how='left') | pandas.merge |
import pandas as pd
import numpy as np
from data import Data
import pickle
class Stats():
def __init__(self, data):
'''Enter dataclass of pandas dataframe'''
if isinstance(data, Data):
self.df = data.df
elif isinstance(data, pd.DataFrame):
self.df = data
... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
import pandas.rpy.common as rcom
import rpy2.robjects as robjects
from rpy2.robjects.vectors import SexpVector, ListVector, StrSexpVector
import rpy2.robjects.numpy2ri as numpy2ri
import trtools.rpy.conversion as rconv
import trtools.rpy.tools as rtools
from trtools.rpy.rmodule import get_func, R... | pd.DataFrame(o, index=o.index) | pandas.DataFrame |
# Arithmetic tests for DataFrame/Series/Index/Array classes that should
# behave identically.
from datetime import datetime, timedelta
import numpy as np
import pytest
from pandas.errors import (
NullFrequencyError, OutOfBoundsDatetime, PerformanceWarning)
import pandas as pd
from pandas import (
DataFrame, ... | tm.box_expected(expected, xbox) | pandas.util.testing.box_expected |
from bapiw.api import API
from datetime import datetime, date
import pandas as pd
import numpy as np
bapiw = API()
class DataParser:
# intervals used when calling kline data
# https://github.com/binance-exchange/binance-official-api-docs/blob/master/rest-api.md#enum-definitions
INTERVAL_1MIN = '1m'
... | pd.DataFrame.from_dict(kdata) | pandas.DataFrame.from_dict |
#%%
"""Combine article data from json file into a single dataframe.
"""
import json
import pandas as pd
combined_df = pd.DataFrame() #columns=["Index_str", "Article_text"])
df = pd.read_csv("~/repo/StatMachLearn/NewsArticleClassification/data_to_group_copy.csv", header=0)
domain_list = list(set(df['domain'].values)... | pd.DataFrame.from_dict(article_dict, orient="index") | pandas.DataFrame.from_dict |
import nose
import warnings
import os
import datetime
import numpy as np
import sys
from distutils.version import LooseVersion
from pandas import compat
from pandas.compat import u, PY3
from pandas import (Series, DataFrame, Panel, MultiIndex, bdate_range,
date_range, period_range, Index, Categori... | bdate_range('2013-01-02', periods=10) | pandas.bdate_range |
import copy
import pandas as pd
from bokeh.plotting import figure, show, output_notebook
from bokeh.models import Legend, Span
# from bokeh.models import HoverTool
from ..utils import in_ipynb
from .plotobj import BasePlot
from .plotutils import get_color
_INITED = False
class BokehPlot(BasePlot):
def __init__(s... | pd.concat([df_bottom, df_top], ignore_index=True) | pandas.concat |
import itertools
import pathlib
import numpy as np
import pandas as pd
import os
from statistics import median_low
import click
import re
# Unimod parsing
import xml.etree.cElementTree as ET
from xml.etree.cElementTree import iterparse
# mzXML parsing
import pyopenms as po
class pepxml:
def __init__(self, pepxml_f... | pd.concat(peaks_list) | pandas.concat |
import inspect
import os
import sys
import time
import unittest
import warnings
from concurrent.futures.process import ProcessPoolExecutor
from contextlib import contextmanager
from glob import glob
from runpy import run_path
from tempfile import NamedTemporaryFile, gettempdir
from unittest import TestCase
from unittes... | pd.Timestamp('2013-03-01 00:00:00') | pandas.Timestamp |
import glob
import os
import random
import soundfile as sf
import torch
import yaml
import json
import argparse
import pandas as pd
from tqdm import tqdm
from pprint import pprint
from asteroid.metrics import get_metrics
from model import load_best_model
from local.preprocess_dns import make_wav_id_dict
parser = arg... | pd.Series(utt_metrics) | pandas.Series |
import pathlib
import yaml
import pandas as pd
from clumper import Clumper
from parse import compile as parse_compile
def nlu_path_to_dataframe(path):
"""
Converts a single nlu file with intents into a dataframe.
Usage:
```python
from taipo.common import nlu_path_to_dataframe
df = nlu_path_to... | pd.DataFrame(res) | pandas.DataFrame |
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
from scipy import stats
sns.set_context('talk')
data_crime_raw = pd.read_csv('.\\NYPD_Complaint_Data_Historic.csv',
usecols=['CMPLNT_FR_DT', ... | pd.to_datetime(data_crime_raw['CMPLNT_FR_DT'], format='%m/%d/%Y', errors='coerce') | pandas.to_datetime |
from time import time
import pandas as pd
from numpy import arange
results_df = pd.read_csv('../data/botbrnlys-rand.csv')
def extract_best_vals_index(results_df, df, classifier, hp):
final_df = pd.DataFrame()
temp_df = results_df[results_df.model == classifier]
temp_df_f = temp_df[temp_df.hp.round(3) == ... | pd.read_csv('../data/' + classifier + '0_results-nb.csv') | pandas.read_csv |
""" Script that contains the functions to perform sensitivity analysis. """
import itertools
import numpy as np
import pandas as pd
from sklearn.metrics import mean_squared_error
##### GLOBAL VARIABLES #####
legs = ['LF', 'LM', 'LH', 'RF', 'RM', 'RH']
joints = [
'Coxa',
'Coxa_yaw',
'Coxa_roll',
'Femur... | pd.DataFrame(leg_mse, columns=['Leg', 'Kp', 'Kv', 'MSE']) | pandas.DataFrame |
#
# Copyright 2015 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.concat([SPLITS, MERGERS, DIVIDENDS_EXPECTED], ignore_index=True) | pandas.concat |
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 18})
from textwrap import wrap
def plot_metrics_bar(result_dirs, plot_dir, metrics="Acc", prefix="pancreas",
groupby="features"):
'''Plot Acc, ARI, macroF1 for different methods
x-axis is... | pd.read_csv(result_dir+os.sep+method+suffix) | pandas.read_csv |
from operator import eq, ge
from functools import partial
import pandas as pd
from microsetta_public_api.resources import resources
ops = {
'equal': eq,
'greater_or_equal': ge,
}
conditions = {
"AND": partial(pd.DataFrame.all, axis=1),
"OR": partial(pd.DataFrame.any, axis=1)
}
def _is_rule(node):
... | pd.Series(True, index=self._metadata.index) | pandas.Series |
import numpy as np
import pandas as pd
from dateutil.parser import parse
import tldextract
def replace_basic_columns(dataframe):
pd.set_option('mode.chained_assignment', None)
dataframe["title"] = np.nan
dataframe["description"] = np.nan
dataframe["image"] = np.nan
for index in dataframe.index:... | pd.isnull(dataframe['meta_image'][index]) | pandas.isnull |
import functools
import warnings
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import pandas as pd
import os
from scipy.stats import poisson
import tensorflow.compat.v2 as tf
import tensorflow_probability as tfp
from tensorflow_probability import distributions as tfd
from tensorflow_proba... | pd.read_csv(gene_fname, index_col=0) | pandas.read_csv |
import os
import joblib
import numpy as np
import pandas as pd
from joblib import Parallel
from joblib import delayed
from Fuzzy_clustering.version2.common_utils.logging import create_logger
from Fuzzy_clustering.version2.dataset_manager.common_utils import check_empty_nwp
from Fuzzy_clustering.version2.dataset_manag... | pd.DateOffset(hours=1) | pandas.DateOffset |
import os
import numpy as np
import pandas as pd
import lightgbm as lgb
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression, RidgeClassifierCV, ElasticNetCV, LassoCV, LassoLarsCV
from sklearn.svm import SVC
from sklearn.model_selection import cross_val_score
... | pd.read_table('../data/raw/train.tsv', sep='\t') | pandas.read_table |
# -*- coding: utf-8 -*-
from __future__ import print_function
from distutils.version import LooseVersion
from numpy import nan, random
import numpy as np
from pandas.compat import lrange
from pandas import (DataFrame, Series, Timestamp,
date_range)
import pandas as pd
from pandas.util.testing im... | tm.assert_series_equal(smaller_frame['foo'], exp) | pandas.util.testing.assert_series_equal |
import datetime
import logging
import pandas
import sqlobject
def get_last_values(currency, frecuency, count=None):
"""Get last values."""
logging.debug('nb_values: %d', count)
result = Bollinger.select(
Bollinger.q.currency == currency
).orderBy(Bollinger.q.date_time)
if count:
... | pandas.Series(values[-frequency:]) | pandas.Series |
import warnings
import numpy as np
def to_dataframe(result):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
import pandas as pd
def collection_to_dataframe(n, x):
n = str(n).replace('[', '(').replace(']', ')')
df = | pd.DataFrame() | pandas.DataFrame |
import unittest
import pickle
import pathlib
import cobra
import pandas as pd
from BFAIR.mfa.sampling import (
model_rxn_overlap,
rxn_coverage,
split_lumped_rxns,
split_lumped_reverse_rxns,
find_reverse_rxns,
combine_split_rxns,
cobra_add_split_rxns,
add_constraints,
add_feasible_con... | pd.DataFrame({"rxn_id": "Biomass"}, index=[0]) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# # SW 1700
# In[2]:
import numpy as np
import os
#import ipdb
def connect_dataset(file_list, icond_file_list, outputdir,
topodx=15, roi=2500, offset=5000,gclass_num=5,test_data_num=500):
"""
複数のデータセットを連結する
"""
#ipdb.set_trace()
#Re... | pd.read_csv('../Journal_2/Thai_gs5.csv') | pandas.read_csv |
import sys
import pandas as pd
import numpy as np
import json
from sqlalchemy import create_engine
def load_data(messages_filepath, categories_filepath):
'''
This function load the datasets messages and categories and merge based on id column.
Params:
messages_filepath (str): String ... | pd.read_csv(categories_filepath) | pandas.read_csv |
import sys
import csv
import pandas as pd
import ctdcal.sbe_reader as sbe_rd
import ctdcal.sbe_equations_dict as sbe_eq
import gsw
DEBUG = False
#lookup table for sensor data
###DOUBLE CHECK TYPE IS CORRECT###
short_lookup = {
'55':{'short_name': 'CTDTMP', 'long_name':'SBE 3+ Temperature', 'units': 'ITS-90', 'typ... | pd.read_csv(file_name, index_col=0, skiprows=[1], parse_dates=False) | pandas.read_csv |
import os
import joblib
import numpy as np
import pandas as pd
from joblib import Parallel
from joblib import delayed
from Fuzzy_clustering.version2.common_utils.logging import create_logger
from Fuzzy_clustering.version2.dataset_manager.common_utils import check_empty_nwp
from Fuzzy_clustering.version2.dataset_manag... | pd.DateOffset(hours=24) | pandas.DateOffset |
import ibeis
import six
import vtool
import utool
import numpy as np
import numpy.linalg as npl # NOQA
import pandas as pd
from vtool import clustering2 as clustertool
from vtool import nearest_neighbors as nntool
from plottool import draw_func2 as df2
np.set_printoptions(precision=2)
pd.set_option('display.max_rows',... | pd.Series(invindex.ax2_aid[invindex.idx2_ax], name='daid') | pandas.Series |
# ---
# jupyter:
# jupytext:
# formats: ipynb,py
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.6.0
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# ## Telecom Churn Case Study
#... | pd.get_dummies(telecom['MultipleLines'], prefix='MultipleLines') | pandas.get_dummies |
"""Class to process raw TomTom MultiNet data into a network dataset.
Copyright 2022 Esri
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/LICEN... | pd.DataFrame(cur, columns=fields) | pandas.DataFrame |
'''
Create a csv with inspection report, permit_id, date, time, inspec type, critical violation count, non-crit violation count, crit violation corrected on site, non-crit violation corrected on site, crit violation to be resolved, non-crit violation to be resolved, critical violation repeat violation, and non-crit vio... | pd.read_csv('cleaned_report_results.csv') | pandas.read_csv |
from typing import List, Any
from itertools import chain
from app_utils import AppFileHandler
import pandas as pd
import sqlite3
import logging
import os
import re
pd.set_option('display.max_rows', None)
class SansNotesApp(AppFileHandler):
APP_FILES = os.path.join(os.getcwd(),'SansNotesAppFiles')
APP_DATABAS... | pd.DataFrame(search_table_data,columns=[tuple[0] for tuple in self.__cur.description]) | pandas.DataFrame |
import inspect
import re
from functools import wraps
from typing import Union, Any, List
from uuid import uuid4
import pandas
import sys
from .six import string_types, integer_types
from .fields import (FIELD_DATAFRAME, FIELD_TEXT, FIELD_NUMERIC, FIELD_NO_INPUT,
FIELD_SELECT, FIELD_SELECT_MULTIPL... | pd.Series(result) | pandas.Series |
import pytest
from vetiver.vetiver_model import VetiverModel
from vetiver.mock import get_mock_data, get_mock_model
import pandas as pd
from numpy import int64
# Load data, model
X_df, y = get_mock_data()
X_array = | pd.DataFrame(X_df) | pandas.DataFrame |
#!/usr/bin/env python3
# Author: <NAME>
import numpy as np
import pandas as pd
import scipy.stats as stats
import warnings
def normalize_quantiles(df):
"""
Quantile normalization to the average empirical distribution
Note: replicates behavior of R function normalize.quantiles from library("preprocessCore")... | pd.DataFrame(M, index=df.index, columns=df.columns) | pandas.DataFrame |
import pandas as pd
from mfy_data_core.adapters.es_index_store import ESIndexStore
from mfy_data_core_fakes.adapters.fake_storage import FakeIndexStore, FakeEsPandasClient, FakeObjectStore
def test_fake_index_store():
index_store = FakeIndexStore()
df = | pd.DataFrame([{"col1": "A", "col2": "B"}]) | pandas.DataFrame |
# -*- coding: utf-8 -*-
"""
Created on Wed May 16 12:39:55 2018
@author: malopez
"""
import math
import numba
import numpy as np
import pandas as pd
from collisionTimes import CollisionDetector
from measure import MeasureClass
n_particles = 50
class EventList():
def __init__(self, n_particles, particle_radiu... | pd.concat((part_i, wallLeft, dt, eventType), axis=1) | pandas.concat |
"""
A warehouse for constant values required to initilize the PUDL Database.
This constants module stores and organizes a bunch of constant values which are
used throughout PUDL to populate static lists within the data packages or for
data cleaning purposes.
"""
import pandas as pd
import sqlalchemy as sa
##########... | pd.BooleanDtype() | pandas.BooleanDtype |
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