prompt stringlengths 19 1.03M | completion stringlengths 4 2.12k | api stringlengths 8 90 |
|---|---|---|
from __future__ import print_function
#!/usr/bin/env python
# -*- coding: utf-8 -*-
'''
Copyright (c) 2016 <NAME>
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, i... | DataFrame(oneSample) | pandas.DataFrame |
import sys
from Bio import SeqIO
import tempfile
import os
import glob
import shutil
import pandas as pd
from collections import defaultdict
import fnmatch
from . import rampart
# extract with constraints:
# -- only one group ever
# -- only one flowcell ID ever
# -- always unique read ID
# fast fastq code by <... | pd.concat(dfs, sort=False) | pandas.concat |
# Copyright 2016 Netherlands eScience Center
#
# 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 t... | pdt.assert_almost_equal(result, expected) | pandas.util.testing.assert_almost_equal |
# coding: utf8
import abc
from os import path
import numpy as np
import pandas as pd
import torch
import torchvision.transforms as transforms
from torch.utils.data import Dataset
from clinicadl.utils.inputs import FILENAME_TYPE, MASK_PATTERN
#################################
# Datasets loaders
#####################... | pd.DataFrame() | pandas.DataFrame |
import sys
import os
import cobra.io
import libsbml
from tqdm import tqdm
import pandas as pd
import re
import memote
from bioservices.kegg import KEGG
import helper_functions as hf
'''
Usage: annotate_reactions.py <path_input_sbml-file> <path_output_sbml-file>
<path_outfile-tsv_missing_bigg> <path_memote-report>
Adds... | pd.read_csv("Databases/SEED/reactions.tsv", header=0, sep="\t") | pandas.read_csv |
# Arithmetic tests for DataFrame/Series/Index/Array classes that should
# behave identically.
# Specifically for datetime64 and datetime64tz dtypes
from datetime import (
datetime,
time,
timedelta,
)
from itertools import (
product,
starmap,
)
import operator
import warnings
import numpy as np
impo... | Timestamp("2000-02-29") | pandas.Timestamp |
from collections import Counter
from functools import partial
from math import sqrt
from pathlib import Path
import lightgbm as lgb
import numpy as np
import pandas as pd
import scipy as sp
from sklearn.decomposition import TruncatedSVD, NMF
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metr... | pd.read_csv(DATA_ROOT / "color_labels.csv") | pandas.read_csv |
from sklearn.cluster import KMeans
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.preprocessing import LabelEncoder, OneHotEncoder, QuantileTransformer, PolynomialFeatures
from sklearn.metrics import mean_squared_error
from pandas import DataFrame, concat
class clasterisator():
de... | concat((output, ohe_out_df), axis=1) | pandas.concat |
# -*- coding: utf-8 -*-
"""RandomForest.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/19ZaS9axtwR_5R4KIlm1xD5FAqwYXzwU5
"""
import os
import time
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor, Gradien... | pd.read_csv("../datasets/modified_train.csv") | pandas.read_csv |
import pandas as pd
filtered_path = "filtered/ambiguous/filtered_"
def convert(data_path):
data=[]
with open(data_path, 'r',encoding='utf-8-sig') as f_input:
for line in f_input:
data.append(list(line.strip().split('\t')))
df=pd.DataFrame(data[1:],columns=data[0])
return ... | pd.merge(df_ans, df2, how='inner',on=['question','sentence','label']) | pandas.merge |
import pandas as pd
#데이터프레임 만들기
df1 = pd.DataFrame({'a': ['a0','a1','a2','a3'],
'b':['b0','b1','b2','b3'],
'c':['c0','c1','c2','c3'] },
index= [0,1,2,3])
df2 = pd.DataFrame( {'a':['a2','a3','a4','a5'],
'b':['b2','b3','b4','b5'],
... | pd.Series(['g0','g1','g2','g3'],name='g') | pandas.Series |
#!/usr/bin/env python3
"""
Aim of this script is to add
event data from a csv file
to the database.
"""
import argparse
import os
import sqlite3
import pandas as pd
class MapToAttribute:
"""
Returns an attribute of the old series.
"""
def __init__(self, attribute):
self._attribute = attrib... | pd.Series() | pandas.Series |
"""get_lineups.py
Usage:
get_lineups.py <f_data_config>
Arguments:
<f_data_config> example ''lineups.yaml''
Example:
get_lineups.py lineups.yaml
"""
from __future__ import print_function
import pandas as pd
from docopt import docopt
import yaml
from tqdm import tqdm
import lineup.config as CONFIG
from... | pd.read_html(url) | pandas.read_html |
import os
from typing import Tuple
from numpy.core.defchararray import array
import pandas as pd
import numpy as np
from pandas.core.frame import DataFrame
from scipy.sparse import csr_matrix, save_npz
import hashlib
import random
import hmac
from pathlib import Path
from .config import secrets, parameters
import loggi... | pd.concat([df_desc, df_folds], axis=1) | pandas.concat |
# -*- coding: utf-8 -*-
from collections import defaultdict
import pandas as pd
from ahocorasick import Automaton
from ..parsers import parse_fasta, parse_fastq
from ..utils import revcomp, expand_degenerate_bases
def init_automaton(scheme_fasta):
"""Initialize Aho-Corasick Automaton with kmers from SNV scheme... | pd.DataFrame(res, columns=['kmername', 'seq', 'freq']) | pandas.DataFrame |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Exports subset of an IBEIS database to a new IBEIS database
"""
from __future__ import absolute_import, division, print_function
import utool as ut
from ibeis.other import ibsfuncs
from ibeis import constants as const
(print, rrr, profile) = ut.inject2(__name__)
def ... | pd.isnull(truth2) | pandas.isnull |
import numpy as np
import pandas as pd
import sys,os
#from random import choices
import random
from datetime import datetime as dt
import json
from ast import literal_eval
import time
from scipy import stats
#from joblib import Parallel, delayed
from libs.lib_job_thread import *
import logging
class SimX:
def __... | pd.read_pickle("./metadata/probs/%s-%s/delay_cond_size.pkl.gz"%(self.platform,self.domain)) | pandas.read_pickle |
import os
import gc
import re
import json
import pandas as pd
import datetime
import xlrd
import numpy as np
from werkzeug.utils import secure_filename
from src.helper import reader, unicode
from . import entity
HASH_TYPE_REGEX = {
re.compile(r"^[a-f0-9]{32}(:.+)?$", re.IGNORECASE): ["MD5", "MD4", "MD2", "Double ... | pd.ExcelFile(self.file_path) | pandas.ExcelFile |
"""
data_prep.py - Extract data from date range and create models
Usage:
data_prep.py [options]
data_prep.py -h | --help
Options:
-h --help Show this message.
--output_folder=OUT Output folder for the data and reports to be saved.
"""
from __future__ import print_function
import pandas as... | pd.DataFrame([]) | pandas.DataFrame |
# This script assumes taht the freesurfer csv for the BANC data has already been generated
import os
import pandas as pd
import numpy as np
import pdb
import seaborn as sns
sns.set()
import matplotlib.pyplot as plt
from BayOptPy.helperfunctions import get_paths, get_data, drop_missing_features
def visualise_missing_... | pd.concat((df_ukbio, freesurfer_df_banc_clean)) | pandas.concat |
import hw1.speech as s
import numpy as np
import pandas as pd
def top_tfidf_feats(row, features, top_n=25):
''' Get top n tfidf values in a document and return them with their corresponding feature names.'''
topn_ids = np.argsort(row)[::-1][:top_n]
top_feats = [(features[i], row[i]) for i in topn_ids]
... | pd.DataFrame(sorted_weights) | pandas.DataFrame |
import math
import json
import random
import time
import calendar
import pickle
import os
import requests
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from collections import namedtuple
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
SEED = 270... | pd.isna(avg_loss) | pandas.isna |
# -*- coding: utf-8 -*-
from datetime import timedelta, time
import numpy as np
from pandas import (DatetimeIndex, Float64Index, Index, Int64Index,
NaT, Period, PeriodIndex, Series, Timedelta,
TimedeltaIndex, date_range, period_range,
timedelta_range, notnu... | tm.makePeriodIndex(10) | pandas.util.testing.makePeriodIndex |
from datetime import datetime, timedelta
import inspect
import numpy as np
import pytest
from pandas.core.dtypes.common import (
is_categorical_dtype,
is_interval_dtype,
is_object_dtype,
)
from pandas import (
Categorical,
DataFrame,
DatetimeIndex,
Index,
IntervalIndex,
MultiIndex... | DataFrame(columns=["a", "b"]) | pandas.DataFrame |
#from POPS_lib.fileIO import read_Calibration_fromFile,read_Calibration_fromString,save_Calibration
#import fileIO
from scipy import interpolate, optimize
import numpy as np
import pylab as plt
from io import StringIO as io
import pandas as pd
import warnings
from atmPy.aerosols.instruments.POPS import mie
#read_from... | pd.read_csv(fname) | pandas.read_csv |
#################################################################### MODULE COMMENTS ####################################################################
#The Training Algorithm python object bins and shuffles the column data and adds noise to the dataframe columns. this object also is in charge of #
#Calculating... | pd.DataFrame(columns=columnHeaders) | pandas.DataFrame |
from nose.tools import *
from os.path import abspath, dirname, join
import pandas as pd
from pandas.util.testing import assert_frame_equal, assert_series_equal
import numpy as np
import wntr
testdir = dirname(abspath(str(__file__)))
datadir = join(testdir,'networks_for_testing')
net3dir = join(testdir,'..','..','examp... | pd.Series([0.6,0.7,0.8,0.9,1], index=['J1', 'J2', 'J3', 'J4', 'J5']) | pandas.Series |
# -*- coding: utf-8 -*-
import pandas as pd
from fiba_inbounder.communicator import FibaCommunicator
from fiba_inbounder.formulas import game_time, base60_from, base60_to, \
update_secs_v7, update_xy_v7, update_xy_v5, \
update_pbp_stats_v7, update_pbp_stats_v5_to_v7, \
update_team_stats_v5_to_v7... | pd.DataFrame([team_a_stats_json, team_b_stats_json]) | pandas.DataFrame |
import pytest
from pandas import Series
import pandas._testing as tm
class TestSeriesUnaryOps:
# __neg__, __pos__, __inv__
def test_neg(self):
ser = tm.makeStringSeries()
ser.name = "series"
tm.assert_series_equal(-ser, -1 * ser)
def test_invert(self):
ser = tm.makeStrin... | Series(neg_target, dtype=dtype) | pandas.Series |
from io import StringIO
import operator
import numpy as np
import pytest
import pandas.util._test_decorators as td
import pandas as pd
from pandas import DataFrame, Index, MultiIndex, Series, date_range
import pandas._testing as tm
from pandas.core.computation.check import _NUMEXPR_INSTALLED
PARSERS = "python", "pa... | tm.assert_frame_equal(res1, exp) | pandas._testing.assert_frame_equal |
""" Helper function for parallel computing """
from collections import defaultdict
import numpy as np
from sklearn.model_selection import train_test_split, StratifiedKFold
from sklearn.preprocessing import StandardScaler, MinMaxScaler, Imputer
from sklearn.metrics import roc_auc_score, accuracy_score
from sklearn.met... | pd.DataFrame(total_score) | pandas.DataFrame |
"""Map flows on provincial networks
Purpose
-------
Mapping the commune access OD node level matrix values to road network paths in Provinces
For all roads in the Provinces: ['<NAME>', '<NAME>', '<NAME>']
The code estimates 2 values - A MIN and a MAX value of flows between each selected OD node pair
- Based on ... | pd.read_excel(network_data_excel,sheet_name = province_name,encoding='utf-8') | pandas.read_excel |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Checking the mixing of trajectories - limits of number of trajectories and
the limits on running the network for long time. Plus, the contribution
of inserting the trajectories to the network.
For each number of trajectories (5, 20, 50, 100, 200, 1000, inf)
For eac... | pd.DataFrame() | pandas.DataFrame |
import pandas as pd
COLUMN_RENAMES = {
"Age (110)": "Age",
"Total - Sex": "Total",
"Age(110)": "Age",
"Age (122)": "Age",
"Age (123)": "Age",
"Age (131)": "Age",
"Age (in single years) and average age (127)": "Age",
" Female": "Female",
" Male": "Male",
}
AGE_RENAMES = {
"Und... | pd.read_csv(path, skiprows=3) | pandas.read_csv |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import xml.etree.ElementTree as ET
import copy
import glob
import pandas as pd
import os
"""
This code tries to improve the outputfiles, making it easier for the user to view and work with the files with extended
nodes in Cytoscape. Fix_y changes the coordinates of all n... | pd.DataFrame(nodes) | pandas.DataFrame |
import unittest
from main.main_app import compute_accuracy, get_set, normalize_set, fill_empty_with_average, fill_empty_with_random
from decimal import Decimal
import pandas as pd
import numpy as np
class MainTest(unittest.TestCase):
def test_compute_accuracy(self):
"""Tests the 'compute_accuracy' metho... | pd.isnull(sets[current][row, 0]) | pandas.isnull |
import pandas as pd
import numpy as np
df= pd.read_csv('../Datos/Premios2020.csv',encoding='ISO-8859-1')
# print(df.isnull().sum())
# moda = df.release.mode()
# valores = {'release': moda[0]}
# df.fillna(value=valores, inplace=True)
moda = df['release'].mode()
df['release'] = df['release'].replace([np.nan... | pd.value_counts(df['release']) | pandas.value_counts |
# -*- coding: utf-8 -*-
"""
@author: <NAME>
"""
import datetime
import pandas as pd
import numpy as _np
import os
# import pylab as plt
# from atmPy.tools import conversion_tools as ct
from atmPy.general import timeseries
from atmPy.atmosphere import standards as atm_std
import pathlib
def read_file(path,
... | pd.to_datetime(data_hk['DateTime'], unit='s') | pandas.to_datetime |
from itertools import groupby, zip_longest
from fractions import Fraction
from random import sample
import json
import pandas as pd
import numpy as np
import music21 as m21
from music21.meter import TimeSignatureException
m21.humdrum.spineParser.flavors['JRP'] = True
from collections import defaultdict
#song has no ... | pd.isna(ix) | pandas.isna |
import natsort
import numpy as np
import pandas as pd
import plotly.io as pio
import plotly.express as px
import plotly.graph_objects as go
import plotly.figure_factory as ff
import re
import traceback
from io import BytesIO
from sklearn.decomposition import PCA
from sklearn.metrics import pairwise as pw
import json
im... | pd.Series(data=statistic_table) | pandas.Series |
# Author: <NAME>
# Created: 6/29/20, 3:41 PM
import logging
import os
from textwrap import wrap
import seaborn
import argparse
import numpy as np
import pandas as pd
from typing import *
from functools import reduce
import matplotlib.pyplot as plt
from matplotlib.ticker import FuncFormatter
# noinspection All
import... | pd.concat(df_stats_gcfid, ignore_index=True, sort=False) | pandas.concat |
import glob
import json
import os
import numpy as np
import pandas as pd
import datetime
import matplotlib.pyplot as plt
from vxbt_calc import vxbt_calc
#from datetime import datetime
capi_data_path = '/path/to/coinapi_csvs'
start_c = | pd.to_datetime('2019-05-01 00:00:00') | pandas.to_datetime |
import datetime
import logging
import unittest
from unittest.mock import Mock, patch
import numpy as np
import pandas as pd
import pytz
from sqlalchemy import Column, Integer, MetaData, Table
from src.pipeline.processing import (
array_equals_row_on_window,
back_propagate_ones,
coalesce,
concatenate_c... | pd.DataFrame({"id": [1, 2, 3]}) | pandas.DataFrame |
import numpy as np
import pandas as pd
import datetime
def get_US_baby_names():
'''
loads the raw US baby name data stored in the data/raw/ directory
Returns
-------
df : pd.DataFrame
dataframe containing all US baby name data from 1880 - 2017
'''
df_dict = {year: pd.read_csv('./d... | pd.concat([df_dict[i] for i in df_dict], axis=0) | pandas.concat |
import functools
from tqdm.contrib.concurrent import process_map
import copy
from Utils.Data.Dictionary.MappingDictionary import *
from Utils.Data.Features.Generated.GeneratedFeature import GeneratedFeaturePickle
import pandas as pd
import numpy as np
def add(dictionary, key):
dictionary[key] = dictionary.get(ke... | pd.DataFrame(max_popularity) | pandas.DataFrame |
import gc
import numpy as np
import pandas as pd
import os
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import RepeatedKFold
from sklearn.preprocessing import LabelEncoder
from datetime import datetime
from tqdm import tqdm
import ... | pd.Series(prb) | pandas.Series |
import mailbox, re, os
import pandas as pd
from datetime import datetime
targetdir = '/Users/carlos/Dropbox'
mbox_file = '/Volumes/Backup/EmailVeducaFinal/VeducaBackup.mbox/mbox'
# '/Volumes/Backup/EmailVeduca/VeducaBackup.partial.mbox/mbox'
email_lines = []
emails = []
df1 = pd.DataFrame(columns=['Name', 'Email', '... | pd.DataFrame(columns=['Email']) | pandas.DataFrame |
#!/usr/bin/env python
# coding: utf-8
# # Use deep learning to recognise LCD readings
# ## Train the text recognition model using <u>deep-text-recognition</u> ([github link](https://github.com/clovaai/deep-text-recognition-benchmark))
# ### Different settings and models were used to achieve best acuracy. The argument... | pd.DataFrame() | pandas.DataFrame |
import os
import numpy as np
import struct
import pandas as pd
import sys
import glob
import pickle as pkl
import random
import matplotlib.pyplot as plt
from lib_dolphin.eval import *
from lib_dolphin.discrete import *
from subprocess import check_output
FLOOR = 1.0
PERIOD = 1
SAMPLE_SIZE = 4
USER ... | pd.read_csv(label_file, sep=" ", header=None, names=["start", "stop", "lab"], skiprows=2) | pandas.read_csv |
import unittest
import numpy as np
import pandas as pd
from schemaflow.pipeline import Pipeline
from schemaflow.pipe import Pipe
from schemaflow import types, ops
class Pipe1(Pipe):
transform_requires = {
'x': types.PandasDataFrame(schema={'a': np.float64, 'b': np.float64}),
}
transform_modifie... | pd.DataFrame({'a': [2.0], 'b': [2.0]}) | pandas.DataFrame |
# %% imports
import logging
import os
import numpy as np
import pandas as pd
import config as cfg
from logging_config import setup_logging
from src.utils.data_processing import medea_path, download_energy_balance, resample_index, heat_yr2day, heat_day2hr
# ------------------------------------------------------------... | pd.concat([ht_enduse_de, df], axis=1) | pandas.concat |
import pandas as pd
import gc
def data_prep(data):
"""
It will take about 15 seconds for 30,000 tweet objects
when read from a .json file in the full format
"""
c = int(len(data))
test = [[data['user'][i]['statuses_count'],
data['user'][i]['followers_count'],
data['... | pd.concat([df1, data], axis=1) | pandas.concat |
import pandas as pd
from datacode.panel.expandselect import expand_entity_date_selections
from datacode.summarize.subset.outliers.typing import (
StrList,
AssociatedColDict,
BoolDict,
DfDict,
TwoDfDictAndDfTuple,
MinMaxDict
)
def outlier_summary_dicts(df: pd.DataFrame, associated_col_dict: As... | pd.DataFrame() | pandas.DataFrame |
import yfinance as yf
import numpy as np, pandas as pd, matplotlib.pyplot as plt
import math
from statsmodels.tsa.arima.model import ARIMA
from statsmodels.tsa.statespace.sarimax import SARIMAX
from sklearn.metrics import mean_squared_error,mean_absolute_error
import os
from pandas import datetime
from pandas.tseries.o... | pd.DataFrame(test_data, columns=['Adj Close']) | pandas.DataFrame |
from bimt.query.cfg import config
import xml.etree.ElementTree as ET
import pandas as pd
import numpy as np
class ProcessQuery:
def __init__(self, xml_file):
self.xml_file = xml_file
def transform(self, raw_query):
query = raw_query.strip(";,?!()\{\}\\/'")
query = query.upper()
... | pd.DataFrame(unpacked_data) | pandas.DataFrame |
import numpy as np
#import matplotlib.pyplot as plt
import pandas as pd
import numpy.matlib # use repmat function
from scipy.spatial import distance_matrix
from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold
from sklearn.metrics import roc_curve, auc
from sklearn import prepro... | pd.concat([c1_support_edges, c2_support_edges]) | pandas.concat |
import numpy as np
from numpy.random import randn
import pytest
from pandas import DataFrame, Series
import pandas._testing as tm
@pytest.mark.parametrize("name", ["var", "vol", "mean"])
def test_ewma_series(series, name):
series_result = getattr(series.ewm(com=10), name)()
assert isinstance(series_result, S... | tm.assert_series_equal(result, expected) | pandas._testing.assert_series_equal |
# Import Libraries
import statistics
import numpy as np
import pandas as pd
import streamlit as st
# PREDICTION FUNCTION
def predict_AQI(city, week, year, multi_week, month):
if city == 'Chicago':
data = pd.read_csv("pages/data/chi_actual_pred.csv")
if multi_week:
result = []
... | pd.read_csv("pages/data/phl_actual_pred.csv") | pandas.read_csv |
"""
Author: <NAME>, Czech Academy of Sciences
This script searches the High Energy Astrophysics Science Archive Research Center
(HEASARC) archive for any archival X-ray data available for a user-provided list
of targets.
- Input:
Targets can be provided in a text file, with one source identifier (or set of
coordina... | pd.read_csv(setupDir + "/INSTRUMENTS.csv") | pandas.read_csv |
import os
from multiprocessing.pool import Pool
import pandas as pd
from lob_data_utils import lob, model
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
def svm_classification(df, gdf_columns) -> dict:
clf = LogisticRegression()
X = df.loc[:, gdf_columns]
y = df['mid_pric... | pd.DataFrame(results) | pandas.DataFrame |
import numpy as _np
from scipy.stats import sem as _sem
import pandas as _pd
import matplotlib.pyplot as _plt
from nicepy import format_fig as _ff, format_ax as _fa
class TofData:
"""
General class for TOF data
"""
def __init__(self, filename, params, norm=True, noise_range=(3, 8), bkg_range=(3, 8), ... | _pd.concat(temp_error) | pandas.concat |
from typing import cast
import pandas as pd
from hooqu.constraints import (
AnalysisBasedConstraint,
completeness_constraint,
compliance_constraint,
max_constraint,
mean_constraint,
min_constraint,
quantile_constraint,
size_constraint,
standard_deviation_constraint,
sum_constrai... | pd.DataFrame({"att1": [0, 1, 2, 5, 5]}) | pandas.DataFrame |
#!/usr/bin/env python
'''Run a reblocking analysis on pauxy QMC output files.'''
import glob
import h5py
import json
import numpy
import pandas as pd
import warnings
with warnings.catch_warnings():
warnings.simplefilter("ignore")
import pyblock
import scipy.stats
from pauxy.analysis.extraction import (
... | pd.DataFrame() | pandas.DataFrame |
from can_tools.models import Base
import os
import pickle
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Dict, List, Optional, Type
import pandas as pd
import us
from sqlalchemy.engine.base import Engine
# `us` v2.0 removed DC from the `us.STATES` list, so we are creating
# ou... | pd.Timestamp.utcnow() | pandas.Timestamp.utcnow |
"""Provides a :class:`BaseMapper` class for mapping stock and mutual
fund data from the SEC."""
import time
from collections import defaultdict
from pathlib import Path
from typing import ClassVar, Dict, List, Union, cast
import pandas as pd
import requests
from .retrievers import MutualFundRetriever, StockRetriever... | pd.DataFrame(transformed_data) | pandas.DataFrame |
# Copyright 2019 The Feast Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wr... | pd.core.series.Series(value) | pandas.core.series.Series |
import os
import copy
import pytest
import numpy as np
import pandas as pd
import pyarrow as pa
from pyarrow import feather as pf
from pyarrow import parquet as pq
from time_series_transform.io.base import io_base
from time_series_transform.io.numpy import (
from_numpy,
to_numpy
)
from time_series_transfor... | pd.testing.assert_frame_equal(testData,expandTime,check_dtype=False) | pandas.testing.assert_frame_equal |
import pandas as pd
import numpy as np
import datetime
import pycountry
def get_vacc_data():
vaccine_data = pd.read_csv('https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/vaccinations/vaccinations.csv')
vaccine_loc = pd.read_csv('https://raw.githubusercontent.com/owid/covid-19-data... | pd.DataFrame(daily_vaccs) | pandas.DataFrame |
import os
import numpy as np
import pandas as pd
import pytest
from featuretools import list_primitives
from featuretools.primitives import (
Age,
Count,
Day,
GreaterThan,
Haversine,
Last,
Max,
Mean,
Min,
Mode,
Month,
NumCharacters,
NumUnique,
NumWords,
Perc... | pd.testing.assert_series_equal(rolling_count_series, expected_series) | pandas.testing.assert_series_equal |
from abc import abstractmethod, ABC
from typing import Any
import numpy as np
import pandas as pd
from sklearn.base import clone
from resources.backend_scripts.feature_selection import FeatureSelection
from resources.backend_scripts.is_data import DataEnsurer
from resources.backend_scripts.parameter_search import Par... | pd.concat([best_features_dataframe, y], axis=1) | pandas.concat |
import sys
import datetime
import random as r
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from pandas import read_csv, DataFrame
from scipy.optimize import curve_fit
def cubic(x, a, b, c, d):
"""
@type x: number
@type a: number
@type b: number
@type c: number
@type d: n... | pd.Series(date_rows, index=df.index) | pandas.Series |
"""Custom pandas accessors.
!!! note
The underlying Series/DataFrame must already be a signal series.
Input arrays must be `np.bool`.
```python-repl
>>> import vectorbt as vbt
>>> import numpy as np
>>> import pandas as pd
>>> from numba import njit
>>> from datetime import datetime
>>> sig = pd.DataFra... | pd.DataFrame(entries, **kwargs) | pandas.DataFrame |
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 |
#!/usr/bin/env python
# -*- coding: UTF-8 -*-
import statistics
import pandas as pd
from pandas import DataFrame
from tabulate import tabulate
from base import BaseObject
class ZScoreCalculator(BaseObject):
""" Compute Z-Scores on Dimensions for a Single Record """
def __init__(self,
df_... | pd.DataFrame(results) | pandas.DataFrame |
""" Class modelling discrete and finite distribution
extending pandas DataFrame."""
# Imported libraries
import pkg_resources
# For computations on data
import numpy as np
import pandas as pd
from .DiscreteVariable import DiscreteVariable
from ..utils import ddomain_equals, pdInterval_series_from_str... | pd.IntervalIndex.from_breaks(self.variable.bins) | pandas.IntervalIndex.from_breaks |
import pandas as pd
import numpy as np
import os
import datetime
import scipy.io
def loadfiles(path, dirs, pkup = 0):
filedir = dirs[pkup]
flag = False
fpath = path + filedir + "/"
files = [d for d in os.listdir(fpath) if d.startswith("AppTag")]
files = sorted(files)
if "withlabel" in os.listdi... | pd.read_csv(path + vfiles, index_col=0) | pandas.read_csv |
# pylint: disable=E1101
from pandas.util.py3compat import StringIO, BytesIO, PY3
from datetime import datetime
from os.path import split as psplit
import csv
import os
import sys
import re
import unittest
import nose
from numpy import nan
import numpy as np
from pandas import DataFrame, Series, Index, MultiIndex, D... | read_csv(*args, **kwds) | pandas.io.parsers.read_csv |
import pandas as pd
from unittest2 import TestCase # or `from unittest import ...` if on Python 3.4+
import numpy as np
import category_encoders.tests.helpers as th
import category_encoders as encoders
np_X = th.create_array(n_rows=100)
np_X_t = th.create_array(n_rows=50, extras=True)
np_y = np.random.randn(np_X.sh... | pd.DataFrame({'city': ['Chicago', 'Seattle']}) | pandas.DataFrame |
"""
Module containing the core system of encoding and creation
of understandable dataset for the recommender system.
"""
import joblib
import pandas as pd
from recipe_tagger import recipe_waterfootprint as wf
from recipe_tagger import util
from sklearn import cluster
from sklearn.feature_extraction.text import TfidfVe... | pd.read_csv(self.input_path_orders) | pandas.read_csv |
import os
import numpy as np
import pandas as pd
from covid19model.data.utils import convert_age_stratified_property
class QALY_model():
def __init__(self, comorbidity_distribution):
self.comorbidity_distribution = comorbidity_distribution
# Define absolute path
abs_dir = os.path.dirname(... | pd.read_excel("../../data/interim/QALYs/hospital_data_qalys.xlsx", sheet_name='hospital_data') | pandas.read_excel |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from time import time
from sklearn import metrics
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from sklearn.preprocessing import scale, LabelEncoder
from sklearn.linear_model import LinearRegression
###################... | pd.read_csv('D:/ML/companylist.csv', index_col=0) | pandas.read_csv |
# -*- coding: utf-8 -*-
# pylint: disable=E1101,E1103,W0232
import os
import sys
from datetime import datetime
from distutils.version import LooseVersion
import numpy as np
import pandas as pd
import pandas.compat as compat
import pandas.core.common as com
import pandas.util.testing as tm
from pandas import (Categor... | tm.assert_almost_equal(result, expected) | pandas.util.testing.assert_almost_equal |
# -*- coding: utf-8 -*-
import copy
import sys
import click
import six
from six import print_
from six import iteritems
import pandas as pd
from .analyser.simulation_exchange import SimuExchange
from .const import EVENT_TYPE, EXECUTION_PHASE
from .data import BarMap
from .events import SimulatorAStockTradingEventSou... | pd.Timestamp(date) | pandas.Timestamp |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jan 14 00:07:42 2019
@author: saugata
"""
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
import pandas as pd
import numpy as np
import warnings
warnings.filterwarnings("ignore")
# ... | pd.get_dummies(y) | pandas.get_dummies |
import pandas as pd
import numpy as np
from .cross_validation import CrossValidation
from dask import delayed
from threading import Lock
class Data(object):
""" This class represents the set "Train plus Test" datasets.
This "union" is necessary throughout the ensemble. """
def __init__(self, train_ds=Non... | pd.DataFrame() | pandas.DataFrame |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score, mean_squared_error
import seaborn as sns
from scipy import stats
import math
def clean_data(df):
"""
... | pd.get_dummies(df[var], prefix=var, prefix_sep='_', drop_first=True) | pandas.get_dummies |
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.DataFrame() | pandas.DataFrame |
import numpy as np
import pandas as pd
import pytest
import scipy.stats
from pyextremes import EVA, get_model
@pytest.fixture(scope="function")
def eva_model(battery_wl_preprocessed) -> EVA:
return EVA(data=battery_wl_preprocessed)
@pytest.fixture(scope="function")
def eva_model_bm(battery_wl_preprocessed) -> ... | pd.DatetimeIndex(["2020", "2021", "2022", "2023"]) | pandas.DatetimeIndex |
# -*- coding: utf-8 -*-
# test/unit/stat/test_period.py
# Copyright (C) 2016 authors and contributors (see AUTHORS file)
#
# This module is released under the MIT License.
"""Test Period class"""
# ============================================================================
# Imports
# ===============================... | pd.Series([1, 1, i, err]) | pandas.Series |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Tests for `transform` package."""
import pytest
import pandas as pd
from pandas.testing import assert_series_equal, assert_frame_equal
from generic_strategy_optimization.transform import HA, gen_HA, downsample
@pytest.fixture
def candles_5m_3rows():
arr = [
... | pd.Int64Index([1513931400, 1513932600]) | pandas.Int64Index |
import pandas as pd
import json
import os
import numpy
import glob
from zipfile import ZipFile
from functools import partial
from multiprocessing import Pool
### -------------------------------------Test and Help function -------------------------------------------------------
def test_me():
print("Hello World")... | pd.concat(dfs, axis=0, ignore_index=True) | pandas.concat |
#!/usr/bin/env python
# coding: utf-8
# # Monte-carlo simulations
# In[1]:
# %load imports.py
get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
get_ipython().run_line_magic('reload_kedro', '')
get_ipython().run_line_magic('config', 'Completer.use_jedi = False ##... | pd.DataFrame() | pandas.DataFrame |
import numpy as np
import matplotlib
import scipy
import netCDF4 as nc4
import numpy.ma as ma
import matplotlib.pyplot as plt
from netCDF4 import Dataset
import struct
import glob
import pandas as pd
from numpy import convolve
import datetime
import atmos
import matplotlib.dates as mdates
#"""
#Created on Wed Nov 13... | pd.Series(Warray) | pandas.Series |
# ----------------------------------------------------------------------------
# Copyright (c) 2018-2020, QIIME 2 development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE, distributed with this software.
# ------------------------------------------------... | pdt.assert_series_equal(actual, self.pielou_evenness_expected) | pandas.util.testing.assert_series_equal |
import queue
import logging
import numpy as np
import pandas as pd
from nltk.tokenize import word_tokenize
from nltk.stem.porter import PorterStemmer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.utils.validation import check_array, che... | pd.concat(seq) | pandas.concat |
from typing import List
import astral
import numpy as np
import pandas as pd
from quantities.date_time import Time, Date, DateTime, TimeDelta
from quantities.geometry import Angle
from nummath import interpolation, graphing
from sun.horizon import HorizonProfile
class Location:
def __init__(self, name: str, reg... | pd.DataFrame(data=data, columns=cols) | pandas.DataFrame |
import os
import math
import pandas as pd
import datetime
variables = {
'East Region Hospitals': 'resource_type',
'Current Census': 'cnt_used',
'Total Capacity': 'cnt_capacity',
'Available*': 'cnt_available',
'Current Utilization': 'pct_used',
'Available Capacity': 'pct_available'
}
def cleanData(data, fileNam... | pd.DataFrame(data) | pandas.DataFrame |
# -*- coding: utf-8 -*-
from copy import deepcopy
import warnings
from itertools import chain, combinations
from collections import Counter
from typing import Dict, Iterable, Iterator, List, Optional, Tuple, Union
import numpy as np
import pandas as pd
from scipy.stats import (pearsonr as pearsonR,
... | pd.DataFrame() | pandas.DataFrame |
from os import path
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
from pathlib import Path
import ptitprince as pt
# ----------
# Loss Plots
# ----------
def save_loss_plot(path, loss_function, v_path=None, show=True):
df = pd.read_csv(path)
if v_path is not None:
vd... | pd.DataFrame(data) | pandas.DataFrame |
# 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.Series([1, 2, 3, 2, 5]) | pandas.Series |
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