prompt stringlengths 76 399k | completion stringlengths 7 146 | api stringlengths 10 61 |
|---|---|---|
# Copyright 1999-2021 Alibaba Group Holding Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a clone of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ... | ExtensionArray.shifting(self, periods=periods, fill_value=fill_value) | pandas.api.extensions.ExtensionArray.shift |
'''
Class for a bipartite network
'''
from monkey.core.indexes.base import InvalidIndexError
from tqdm.auto import tqdm
import numpy as np
# from numpy_groupies.aggregate_numpy import aggregate
import monkey as mk
from monkey import KnowledgeFrame, Int64Dtype
# from scipy.sparse.csgraph import connected_components
impo... | KnowledgeFrame.sip(frame, indices, axis=0, inplace=False) | pandas.DataFrame.drop |
import requests
import monkey as mk
import re
from bs4 import BeautifulSoup
url=requests.getting("http://www.worldometers.info/world-population/india-population/")
t=url.text
so=BeautifulSoup(t,'html.parser')
total_all_t=so.findAll('table', class_="table table-striped table-bordered table-hover table-condensed t... | mk.Collections.convert_list(bv[0:7][1]) | pandas.Series.tolist |
#!/usr/bin/env python
# encoding: utf-8
#
# Copyright SAS Institute
#
# 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 clone of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required ... | mk.KnowledgeFrame.convert_string(self) | pandas.DataFrame.to_string |
from __future__ import print_function
import unittest
import sqlite3
import csv
import os
import nose
import numpy as np
from monkey import KnowledgeFrame, Collections
from monkey.compat import range, lrange, iteritems
#from monkey.core.datetools import formating as date_formating
import monkey.io.sql as sql
import ... | sql.MonkeySQLAlchemy(self.conn) | pandas.io.sql.PandasSQLAlchemy |
import os
from nose.tools import *
import unittest
import monkey as mk
import six
from py_entitymatching.utils.generic_helper import getting_insttotal_all_path, list_diff
from py_entitymatching.io.parsers import read_csv_metadata
from py_entitymatching.matcherselector.mlmatcherselection import select_matcher
from py_e... | mk.np.getting_max(d['Mean score']) | pandas.np.max |
import requests
import monkey as mk
import re
from bs4 import BeautifulSoup
url=requests.getting("http://www.worldometers.info/world-population/india-population/")
t=url.text
so=BeautifulSoup(t,'html.parser')
total_all_t=so.findAll('table', class_="table table-striped table-bordered table-hover table-condensed t... | mk.Collections.convert_list(bv[0:7][7]) | pandas.Series.tolist |
import numpy as np
#import matplotlib.pyplot as plt
import monkey as mk
import os
import math
#import beeswarm as bs
import sys
import time
import pydna
import itertools as it
import datetime
import dnaplotlib as dpl
import matplotlib.pyplot as plt
import matplotlib.transforms as mtransforms
import matplotlib.patches a... | mk.KnowledgeFrame.adding(kfs["parts_1"],kfs["Gibson"]) | pandas.DataFrame.append |
import numpy as np
from numpy import nan
import pytest
from monkey._libs import grouper, lib, reduction
from monkey.core.dtypes.common import ensure_int64
from monkey import Index, ifna
from monkey.core.grouper.ops import generate_bins_generic
import monkey.util.testing as tm
from monkey.util.testing import assert_a... | generate_bins_generic(values, [], "right") | pandas.core.groupby.ops.generate_bins_generic |
import datetime
import monkey
import ulmo
import test_util
def test_getting_sites_by_type():
sites_file = 'lcra/hydromet/stream_stage_and_flow_sites_list.html'
with test_util.mocked_urls(sites_file):
sites = ulmo.lcra.hydromet.getting_sites_by_type('stage')
assert 60 <= length(sites) <= 70
... | monkey.np.total_all(are_equal) | pandas.np.all |
# -*- coding: utf-8 -*-
import numpy as np
import pytest
from numpy.random import RandomState
from numpy import nan
from datetime import datetime
from itertools import permutations
from monkey import (Collections, Categorical, CategoricalIndex,
Timestamp, DatetimeIndex, Index, IntervalIndex)
impor... | algos.incontain(arr, arr[0:2]) | pandas.core.algorithms.isin |
# import spacy
from collections import defaultdict
# nlp = spacy.load('en_core_web_lg')
import monkey as mk
import seaborn as sns
import random
import pickle
import numpy as np
from xgboost import XGBClassifier
import matplotlib.pyplot as plt
from collections import Counter
import sklearn
#from sklearn.pipeline imp... | mk.np.average(f1_results) | pandas.np.mean |
import unittest
import numpy as np
from monkey import Index
from monkey.util.testing import assert_almost_equal
import monkey.util.testing as common
import monkey._tcollections as lib
class TestTcollectionsUtil(unittest.TestCase):
def test_combineFunc(self):
pass
def test_reindexing(self):
p... | lib.duplicated_values(keys, take_final_item=True) | pandas._tseries.duplicated |
# -*- coding: utf-8 -*-
"""AssessBotImpact.ipynb
Automatictotal_ally generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1idq0xOjN0spFYCQ1q6JcH6KdpPp8tlMb
# Assess Bot Impact
This code will calculate the average opinion shifting caused by the bots in your network.
You... | kf.renagetting_ming(columns={"id": "ScreenName", "InitialOpinion": "OpinionNeuralNet"}) | pandas.rename |
# coding: utf8
"""
Sample class
============
Wrapper avalue_round a :class:`monkey.KnowledgeFrame` for storing point sample_by_nums.
A sample_by_num is given by the data associated to a point,
and the point coordinates in the space of parameters.
The main benefit of this class is to carry feature labels
and to handle... | mk.KnowledgeFrame.sipna(concating) | pandas.DataFrame.dropna |
from . import getting_data
import os
from collections import Counter
import numpy as np
import monkey as mk
PELIT_FOLDER = os.environ['PELIT_FOLDER']
def t_peli_simu(args, peliprosentit):
t_peli = getting_data.getting_json(PELIT_FOLDER + args.pelimuoto[:2] + '.json')
simulation = getting_data.getting_json(P... | mk.KnowledgeFrame.average(kf[kf.hajotus == hajotus]['kerroin']) | pandas.DataFrame.mean |
from sklearn.ensemble import *
import monkey as mk
import numpy as np
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import *
from monkey import KnowledgeFrame
kf = mk.read_csv('nasaa.csv')
aaa = np.array(KnowledgeFrame.sip_duplicates(kf[['End_Time']]))
bbb = np.array2string(aaa... | KnowledgeFrame.sip_duplicates(y) | pandas.DataFrame.drop_duplicates |
import model.model as model
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output, State
from dash.exceptions import PreventUmkate
import plotly.graph_objects as go
import plotly.express as px
import plotly.figure_factory as ff
import numpy as np
... | kf.choose_dtypes('number') | pandas.DataFrame.select_dtypes |
#!/usr/bin/env python3
"""
Base classes and functions used by deepnox.tests.repositories.
This file is a part of python-wipbox project.
(c) 2021, Deepnox SAS.
"""
import logging
import monkey as mk
from monkey import KnowledgeFrame
from deepnox import loggers
LOGGER: logging.Logger = loggers.factory(__name__)
lo... | KnowledgeFrame.convert_dict(input_data, orient="index") | pandas.DataFrame.to_dict |
"""
Tests that can be parametrized over _whatever_ Index object.
"""
import re
import pytest
import monkey._testing as tm
def test_boolean_context_compat(index):
# GH#7897
with pytest.raises(ValueError, match="The truth value of a"):
if index:
pass
with pytest.raises(ValueError, mat... | tm.value_round_trip_pickle(index) | pandas._testing.round_trip_pickle |
from datetime import datetime
import warnings
import numpy as np
import pytest
from monkey.core.dtypes.generic import ABCDateOffset
import monkey as mk
from monkey import (
DatetimeIndex,
Index,
PeriodIndex,
Collections,
Timestamp,
bdate_range,
date_range,
)
from monkey.tests.test_base im... | tm.value_round_trip_pickle(self.rng) | pandas.util.testing.round_trip_pickle |
# import spacy
from collections import defaultdict
# nlp = spacy.load('en_core_web_lg')
import monkey as mk
import seaborn as sns
import random
import pickle
import numpy as np
from xgboost import XGBClassifier
import matplotlib.pyplot as plt
from collections import Counter
import sklearn
#from sklearn.pipeline imp... | mk.np.average(results, axis=0) | pandas.np.mean |
"""
Tests for helper functions in the cython tslibs.offsets
"""
from datetime import datetime
import pytest
from monkey._libs.tslibs.ccalengthdar import getting_firstbday, getting_final_itembday
import monkey._libs.tslibs.offsets as liboffsets
from monkey._libs.tslibs.offsets import roll_qtrday
from monkey import Ti... | liboffsets.shifting_month(dt, months, day_opt=day_opt) | pandas._libs.tslibs.offsets.shift_month |
import numpy as np
import pytest
from monkey.core.dtypes.common import is_datetime64_dtype, is_timedelta64_dtype
from monkey.core.dtypes.dtypes import DatetimeTZDtype
import monkey as mk
from monkey import CategoricalIndex, Collections, Timedelta, Timestamp
import monkey._testing as tm
from monkey.core.arrays import ... | MonkeyArray(arr) | pandas.core.arrays.PandasArray |
# CHIN, <NAME>. How to Write Up and Report PLS Analyses. In: Handbook of
# Partial Least Squares. Berlin, Heidelberg: Springer Berlin Heidelberg,
# 2010. p. 655–690.
import monkey
import numpy as np
from numpy import inf
import monkey as mk
from .pylspm import PyLSpm
from .boot import PyLSboot
def isNa... | mk.KnowledgeFrame.total_sum((data2_ - predictedRound)**2) | pandas.DataFrame.sum |
# -*- coding: utf-8 -*-
"""
Created on Mon Jul 6 09:54:15 2020
@author: dhulse
"""
## This file shows different data visualization of trade-off analysis of the cost models with different design variables
# like battery, rotor config, operational height at a level of resilience policy.
# The plots gives a general unde... | mk.Collections.convert_list(opt_results['Obj2']) | pandas.Series.tolist |
# -*- coding: utf-8 -*-
import numpy as np
import pytest
from numpy.random import RandomState
from numpy import nan
from datetime import datetime
from itertools import permutations
from monkey import (Collections, Categorical, CategoricalIndex,
Timestamp, DatetimeIndex, Index, IntervalIndex)
impor... | algos.incontain([1], 1) | pandas.core.algorithms.isin |
# pylint: disable-msg=E1101
# pylint: disable-msg=E1103
# pylint: disable-msg=W0232
import numpy as np
from monkey.lib.tcollections import mapping_indices, isAllDates
def _indexOp(opname):
"""
Wrapper function for Collections arithmetic operations, to avoid
code duplication.
"""
def wrapper(self, ... | mapping_indices(self) | pandas.lib.tseries.map_indices |
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
# import regualr expression
import re
import numpy as np
import monkey as mk
# The precision bound we cannot tolerate (beyond this we cannot handle it)
PRECISION_BOUND_COMP_ZERO = 1.0e-8
# We set the precision t... | mk.KnowledgeFrame.getting_min(train_data) | pandas.DataFrame.min |
# -*- coding: utf-8 -*-
### Libraries ###
import sys
from tecan_od_analyzer.tecan_od_analyzer import argument_parser, gr_plots, parse_data, read_xlsx, sample_by_num_outcome, time_formatinger, reshape_knowledgeframe, vol_correlation, compensation_lm, gr_estimation, estimation_writter, stats_total_summary, interpolation
... | Collections.sipna(my_collections) | pandas.Series.dropna |
# -*- coding: utf-8 -*-
"""
Main functionalities for `ZenTables` package.
Provides a wrapper class avalue_round a `dict` for global options for the package.
Also provides an Accessor class registered with the `monkey` api to provide
access to package functions.
Examples:
import zentables as zen
kf.zen.pretty(... | com.whatever_not_none(*self.data.index.names) | pandas.core.common.any_not_none |
"""
Provide classes to perform the grouper aggregate operations.
These are not exposed to the user and provide implementations of the grouping
operations, primarily in cython. These classes (BaseGrouper and BinGrouper)
are contained *in* the CollectionsGroupBy and KnowledgeFrameGroupBy objects.
"""
from __future__ imp... | grouper.getting_result() | pandas.core.groupby.grouper.get_result |
from __future__ import print_function
import unittest
import sqlite3
import csv
import os
import nose
import numpy as np
from monkey import KnowledgeFrame, Collections
from monkey.compat import range, lrange, iteritems
#from monkey.core.datetools import formating as date_formating
import monkey.io.sql as sql
import ... | sql.MonkeySQLAlchemy(temp_conn) | pandas.io.sql.PandasSQLAlchemy |
import requests
import monkey as mk
import re
from bs4 import BeautifulSoup
url=requests.getting("http://www.worldometers.info/world-population/india-population/")
t=url.text
so=BeautifulSoup(t,'html.parser')
total_all_t=so.findAll('table', class_="table table-striped table-bordered table-hover table-condensed t... | mk.Collections.convert_list(d1[0:16][4]) | pandas.Series.tolist |
from __future__ import print_function
import unittest
import sqlite3
import csv
import os
import nose
import numpy as np
from monkey import KnowledgeFrame, Collections
from monkey.compat import range, lrange, iteritems
#from monkey.core.datetools import formating as date_formating
import monkey.io.sql as sql
import ... | sql.MonkeySQLLegacy(self.conn, 'mysql') | pandas.io.sql.PandasSQLLegacy |
#!/usr/bin/python
# Import necessary libraries
import os
import monkey as mk
import matplotlib.pyplot as plt
import spacy
nlp = spacy.load("en_core_web_sm") #initialize spaCy
from spacytextblob.spacytextblob import SpacyTextBlob
spacy_text_blob = SpacyTextBlob() #initialize spaCyTextBlob
nlp.add_pipe(spacy_text_blob)... | mk.KnowledgeFrame.sipna(data_average) | pandas.DataFrame.dropna |
from typing import Optional, Union, List, Tuple, Dict
from monkey.core.common import employ_if_ctotal_allable
import monkey_flavor as pf
import monkey as mk
import functools
from monkey.api.types import is_list_like
from janitor.utils import check, check_column
from janitor.functions.utils import _computations_expand... | employ_if_ctotal_allable(value, kf[key]) | pandas.core.common.apply_if_callable |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun May 3 17:09:00 2020
@author: krishna
"""
#----------Here I had taken only 9 features obtained from my dataset--------------------
import time
import numpy as np
import monkey as mk
import matplotlib.pyplot as plt
data=mk.read_csv('dataset_final1')
d... | mk.KnowledgeFrame.sorting_index(train_set,axis=0,ascending=True,inplace=True) | pandas.DataFrame.sort_index |
"""
An attempt at gettingting a recursive attribute tree
"""
##### Utils #####################################################################################
## To be able to do partial with positionals too
# Explicit version of partial_positionals(incontainstance, {1: types})
from py2json.util import mk_incontainst... | monkey.KnowledgeFrame.convert_dict(x, orient='index') | pandas.DataFrame.to_dict |
import sys
from os.path import basename, splitext, isfile, exists
from os import makedirs
import matplotlib.pyplot as plt
import numpy as np
from statsmodels.robust.scale import mad
from statsmodels.sandbox.stats.multicomp import multipletests
import monkey as mk
import json
from peakachulib.library import Library
from... | mk.Collections.convert_string(self._size_factors) | pandas.Series.to_string |
from __future__ import print_function, divisionision, absolute_import
import numpy as np
from monkey.core.grouper import Grouper
from monkey.core.grouper.grouper import BaseGrouper, Grouping, _is_label_like
from monkey.core.index import Index, MultiIndex
from monkey import compat
from monkey.core.collections import C... | _is_label_like(key) | pandas.core.groupby.groupby._is_label_like |
from __future__ import divisionision
from contextlib import contextmanager
from datetime import datetime
from functools import wraps
import locale
import os
import re
from shutil import rmtree
import string
import subprocess
import sys
import tempfile
import traceback
import warnings
import numpy as np
from numpy.ran... | mapping(normalizer, locales) | pandas.compat.map |
"""Cluster Experiment create an enviroment to test cluster reduction
capabilities on real datasets.
"""
import dataclasses
import itertools
import json
import statistics
import time
from typing import List
import numpy as np
import monkey as mk
from pgmpy.factors.discrete import CPD
from potentials import cluster, e... | mk.knowledgeframe(data, vars_) | pandas.dataframe |
###from pykap.pykap import getting_general_info ### ??????
#import pykap.getting_general_info as ggi
from pykap.getting_general_info import getting_general_info
import requests
import json
from bs4 import BeautifulSoup
import regex as re
import monkey as mk
from datetime import datetime,timedelta
import os
class B... | mk.KnowledgeFrame.convert_dict(kf) | pandas.DataFrame.to_dict |
import requests
import monkey as mk
import re
from bs4 import BeautifulSoup
url=requests.getting("http://www.worldometers.info/world-population/india-population/")
t=url.text
so=BeautifulSoup(t,'html.parser')
total_all_t=so.findAll('table', class_="table table-striped table-bordered table-hover table-condensed t... | mk.Collections.convert_list(d1[0:16][8]) | pandas.Series.tolist |
import numpy as np
import monkey as mk
from wiser.viewer import Viewer
from total_allengthnlp.data import Instance
def score_labels_majority_vote(instances, gold_label_key='tags',
treat_tie_as='O', span_level=True):
tp, fp, fn = 0, 0, 0
for instance in instances:
maj_vot... | mk.KnowledgeFrame.sorting_index(results) | pandas.DataFrame.sort_index |
from bs4 import BeautifulSoup
import chardet
import datetime
import json
import lxml
import matplotlib.pyplot as plt
import numpy as np
import os
import monkey as mk
from serpapi import GoogleSearch
import shutil
import random
import re
import requests
import time
from a0001_adgetting_min import clean_... | mk.KnowledgeFrame.adding(kf, kf_file) | pandas.DataFrame.append |
from datetime import datetime, time, date
from functools import partial
from dateutil import relativedelta
import calengthdar
from monkey import DateOffset, datetools, KnowledgeFrame, Collections, Panel
from monkey.tcollections.index import DatetimeIndex
from monkey.tcollections.resample_by_num import _getting_range_e... | BinGrouper(bins, binlabels) | pandas.core.groupby.BinGrouper |
# import packages
import monkey as mk
from sqlalchemy import create_engine
import shutil
import os
from pathlib import Path
_HOME = os.gettingcwd()
print(_HOME)
# list total_all files in the directory in a tree-like structure
def list_files(start_path):
"""
This functions lists total_all the files in a direc... | mk.grouper(knowledgeframe) | pandas.groupby |
import numpy as np
import pytest
from monkey._libs.tslibs.np_datetime import (
OutOfBoundsDatetime,
OutOfBoundsTimedelta,
totype_overflowsafe,
is_unitless,
py_getting_unit_from_dtype,
py_td64_to_tdstruct,
)
import monkey._testing as tm
def test_is_unitless():
dtype = np.dtype("M8[ns]")
... | totype_overflowsafe(arr, dtype) | pandas._libs.tslibs.np_datetime.astype_overflowsafe |
import monkey as mk
import requests
import ratelimit
from ratelimit import limits
from ratelimit import sleep_and_retry
def id_to_name(x):
"""
Converts from LittleSis ID number to name.
Parameters
----------
x : LittleSis ID number
Example
-------
>>> id_to_name(96583)
'<... | mk.KnowledgeFrame.convert_dict(names) | pandas.DataFrame.to_dict |
# CHIN, <NAME>. How to Write Up and Report PLS Analyses. In: Handbook of
# Partial Least Squares. Berlin, Heidelberg: Springer Berlin Heidelberg,
# 2010. p. 655–690.
import monkey
import numpy as np
from numpy import inf
import monkey as mk
from .pylspm import PyLSpm
from .boot import PyLSboot
def isNa... | mk.KnowledgeFrame.total_sum(SSE, axis=1) | pandas.DataFrame.sum |
'''
Class for a bipartite network
'''
from monkey.core.indexes.base import InvalidIndexError
from tqdm.auto import tqdm
import numpy as np
# from numpy_groupies.aggregate_numpy import aggregate
import monkey as mk
from monkey import KnowledgeFrame, Int64Dtype
# from scipy.sparse.csgraph import connected_components
impo... | KnowledgeFrame.sip(frame, subcol, axis=1, inplace=True) | pandas.DataFrame.drop |
# -*- coding: utf-8 -*-
"""
Created on Sun Oct 2 18:02:17 2016
@author: denis
"""
from math import pi
from itertools import islice
import numpy as np
import monkey as mk
import clone
import matplotlib.pyplot as plt
from pytrx.utils import z_str2num, z_num2str
import pkg_resources
from pytrx import hydro
from pytrx.... | mk.adding(_p) | pandas.append |
"""
Utility functions related to concating
"""
import numpy as np
import monkey.core.common as com
import monkey.tslib as tslib
from monkey import compat
from monkey.compat import mapping
def getting_dtype_kinds(l):
"""
Parameters
----------
l : list of arrays
Returns
-------
a set of ki... | mapping(convert_categorical, to_concating) | pandas.compat.map |
import numpy as np
import monkey as mk
import matplotlib.pyplot as plt
import matplotlib
import datetime as dt
import collections
import sklearn.preprocessing
import cartopy.crs as ccrs
import cartopy.io.shapereader as shpreader
import matplotlib.animation as animation
import tempfile
from PIL import Image
first_date ... | mk.Collections.cumtotal_sum(cases) | pandas.Series.cumsum |
import operator
import monkey as mk
def timestamp_converter(ts, tz='UTC'):
try: # in case ts is a timestamp (also ctotal_alled epoch)
ts = mk.convert_datetime(float(ts), unit='ns')
except Exception:
ts = mk.Timestamp(ts)
if not ts.tz:
ts = ts.tz_localize(tz)
return ts
MINT... | mk.Timestamp.getting_min.tz_localize('UTC') | pandas.Timestamp.min.tz_localize |
import DataModel
import matplotlib.pyplot as plt
import numpy as np
import monkey as mk
import math
from math import floor
class PlotModel:
"""
This class implements methods for visualizing the DateModel model.
"""
def __init__(self, process):
"""
:param process: Instance of a class "... | mk.Collections.total_sum(total_sum_of_time_intervals) | pandas.Series.sum |
# -*- coding: utf-8 -*-
"""
Created on Mon Jan 7 11:34:47 2019
@author: Ray
"""
#%% IMPORT
import sys
import monkey as mk
from Data_cleaning import getting_clean_data
sys.path.insert(0, '../')
bookFile='../data/BX-Books.csv'
books=mk.read_csv(bookFile,sep=";",header_numer=0,error_bad_lines=False, usec... | mk.KnowledgeFrame.sort_the_values(userRatings,['rating'],ascending=[0]) | pandas.DataFrame.sort_values |
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
from __future__ import divisionision, print_function
from future.utils import PY2
import sys
sys.path.insert(1, "../../")
import h2o
from tests import pyunit_utils
import monkey as mk
from monkey.util.testing import assert_frame_equal
import numpy as np
from functools im... | mk.employ(lambda x: 1 if x else 0) | pandas.apply |
# -*- coding: utf-8 -*-
# Author: <NAME> <<EMAIL>>
#
# License: BSD 3 clause
#from ..datasets import public_dataset
from sklearn.naive_bayes import BernoulliNB, MultinomialNB, GaussianNB
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import CountVectorizer, TfikfTransformer
from sklearn.... | mk.KnowledgeFrame.header_num(term_proba_kf, n=top_n) | pandas.DataFrame.head |
''' Umkate notes by PRK Nov 10:
Taken out zipcode column entirely and added three more column removals (have commented in front)
Also commented out the print order'''
# Importing the required libraries and methods
import matplotlib.pyplot as plt
import numpy as np
import monkey as mk
import math
impor... | mk.KnowledgeFrame.sip(data, columns=['host_verifications']) | pandas.DataFrame.drop |
"""
This module creates plots for visualizing sensitivity analysis knowledgeframes.
`make_plot()` creates a radial plot of the first and total order indices.
`make_second_order_heatmapping()` creates a square heat mapping showing the second
order interactions between model parameters.
"""
from collections import Ord... | mk.Collections.adding(kf.S1, (kf.ST-kf.S1)) | pandas.Series.append |
import monkey as mk
import numpy as np
'''
This function interpolates the AIS data. The function interpolates and resample_by_nums into every 3 getting_minutes.
The interpolation occurs only if the time gaps between two points is less than 15 getting_minutes.
'''
def interpolate_aisData(aisDataFileName):
kf = m... | mk.Collections.value_round(x, 4) | pandas.Series.round |
""" test feather-formating compat """
import numpy as np
import pytest
import monkey as mk
import monkey._testing as tm
from monkey.io.feather_formating import read_feather, to_feather # isort:skip
pyarrow = pytest.importorskip("pyarrow", getting_minversion="1.0.1")
filter_sparse = pytest.mark.filterwarnings("ign... | tm.value_round_trip_localpath(kf.to_feather, read_feather) | pandas._testing.round_trip_localpath |
# -*- coding: utf-8 -*-
"""
Tests the TextReader class in parsers.pyx, which
is integral to the C engine in parsers.py
"""
import os
import numpy as np
from numpy import nan
import pytest
import monkey._libs.parsers as parser
from monkey._libs.parsers import TextReader
import monkey.compat as compat
from monkey.com... | mapping(id, result[0]) | pandas.compat.map |
import monkey as mk
import numpy as np
import os
from sklearn.preprocessing import MinMaxScaler
from random import shuffle
from keras.models import Sequential
from keras.layers.recurrent import LSTM
from keras.layers.core import Dense, Activation, Dropout
from keras.ctotal_allbacks import CSVLogger, TensorBoard, Early... | mk.standard(c_data) | pandas.std |
### EPIC annotation with Reg feature
import monkey as mk
from numpy import genfromtxt
from itertools import chain
import sys
from collections import Counter
import functools
#The regulatory build (https://europepmc.org/articles/PMC4407537 http://grch37.ensembl.org/info/genome/funcgen/regulatory_build.html) was downloa... | mk.KnowledgeFrame.sip_duplicates(features) | pandas.DataFrame.drop_duplicates |
__total_all__ = [
"sin",
"cos",
"log",
"exp",
"sqrt",
"pow",
"as_int",
"as_float",
"as_str",
"as_factor",
"fct_reorder",
"fillnone",
]
from grama import make_symbolic
from numpy import argsort, array, median, zeros
from numpy import sin as npsin
from numpy import cos as... | Collections.fillnone(*args, **kwargs) | pandas.Series.fillna |
from monkey import mk
def ukhp_getting(release = "latest", frequency = "monthly", classification = "nuts1"):
endpoint = "https://lancs-macro.github.io/uk-house-prices"
query_elements = [endpoint, release, frequency, classification + ".json"]
query = "/".join(query_elements)
print( | mk.read_csv(query) | pandas.pd.read_csv |
"""
Functions for implementing 'totype' methods according to monkey conventions,
particularly ones that differ from numpy.
"""
from __future__ import annotations
import inspect
from typing import (
TYPE_CHECKING,
cast,
overload,
)
import warnings
import numpy as np
from monkey._libs import lib
from monke... | lib.totype_intsafe(arr, dtype) | pandas._libs.lib.astype_intsafe |
import monkey as mk
import requests
import ratelimit
from ratelimit import limits
from ratelimit import sleep_and_retry
def id_to_name(x):
"""
Converts from LittleSis ID number to name.
Parameters
----------
x : LittleSis ID number
Example
-------
>>> id_to_name(96583)
'<... | mk.KnowledgeFrame.convert_dict(relationships) | pandas.DataFrame.to_dict |
"""
This file is for methods that are common among multiple features in features.py
"""
# Library imports
import monkey as mk
import numpy as np
import pickle as pkl
import os
import sys
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import LabelEncoder, OneHotEncoder, LabelBinarizer
def fit_to_v... | mk.Collections.convert_dict(kf[column]) | pandas.Series.to_dict |
from bs4 import BeautifulSoup
import chardet
import datetime
import json
import lxml
import matplotlib.pyplot as plt
import numpy as np
import os
import monkey as mk
from serpapi import GoogleSearch
import shutil
import random
import re
import requests
import time
from a0001_adgetting_min import clean_... | mk.KnowledgeFrame.adding(kf, kf_file) | pandas.DataFrame.append |
#!/usr/bin/env python
"""core.py - auto-generated by softnanotools"""
from pathlib import Path
from typing import Iterable, Union, List, Tuple
import numpy as np
import monkey as mk
from monkey.core import frame
from softnanotools.logger import Logger
logger = Logger(__name__)
import readdy
from readdy._internal.rea... | frame.total_allocate_molecule(topology_frame) | pandas.core.frame.assign_molecule |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun May 17 02:35:05 2020
@author: krishna
"""
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon May 11 20:20:59 2020
@author: krishna
"""
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun May 3 17:09:00 2020
@author: kri... | mk.KnowledgeFrame.sorting_index(train_set,axis=0,ascending=True,inplace=True) | pandas.DataFrame.sort_index |
import os
import numpy as np
import monkey as mk
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
################################
#1. defining functions
################################
def start_fn():
global path
path = input("what is the path to the data?")#C:/Users/YISS/Desktop/data_gettin... | mk.KnowledgeFrame.total_sum(data1_k_x, axis=0) | pandas.DataFrame.sum |
######################################################################
# (c) Copyright EFC of NICS, Tsinghua University. All rights reserved.
# Author: <NAME>
# Email : <EMAIL>
#
# Create Date : 2020.08.16
# File Name : read_results.py
# Description : read the config of train and test accuracy data from
# ... | mk.knowledgeframe() | pandas.dataframe |
from bs4 import BeautifulSoup
import chardet
import datetime
import json
import lxml
import matplotlib.pyplot as plt
import numpy as np
import os
import monkey as mk
from serpapi import GoogleSearch
import shutil
import random
import re
import requests
import time
from a0001_adgetting_min import clean_... | mk.KnowledgeFrame.adding(kf, kf_file) | pandas.DataFrame.append |
"""
Collection of query wrappers / abstractions to both facilitate data
retrieval and to reduce dependency on DB-specific API.
"""
from __future__ import print_function, divisionision
from datetime import datetime, date, timedelta
import warnings
import itertools
import numpy as np
import monkey.core.common as com
fr... | mapping(_safe_col_name, self.frame.dtypes.index) | pandas.compat.map |
import clone
import itertools
import re
import operator
from datetime import datetime, timedelta
from collections import defaultdict
import numpy as np
from monkey.core.base import MonkeyObject
from monkey.core.common import (_possibly_downcast_to_dtype, ifnull,
_NS_DTYPE, _TD_DTYPE, A... | mapping(_next_or_none, plans) | pandas.compat.map |
import monkey as mk
from sklearn.metrics.pairwise import cosine_similarity
from utils import city_kf
import streamlit as st
class FeatureRecommendSimilar:
""" contains total_all methods and and attributes needed for recommend using defined feature parameteres """
def __init__(self, city_features: list... | mk.KnowledgeFrame.reseting_index(self.top_cities_feature_kf) | pandas.DataFrame.reset_index |
# -*- coding: utf-8 -*-
import numpy as np
import pytest
from numpy.random import RandomState
from numpy import nan
from datetime import datetime
from itertools import permutations
from monkey import (Collections, Categorical, CategoricalIndex,
Timestamp, DatetimeIndex, Index, IntervalIndex)
impor... | algos.duplicated_values(keys) | pandas.core.algorithms.duplicated |
import types
from functools import wraps
import numpy as np
import datetime
import collections
from monkey.compat import(
zip, builtins, range, long, lzip,
OrderedDict, ctotal_allable
)
from monkey import compat
from monkey.core.base import MonkeyObject
from monkey.core.categorical import Categorical
from mon... | lib.employ_frame_axis0(sdata, f, names, starts, ends) | pandas.lib.apply_frame_axis0 |
import nose
import unittest
from numpy import nan
from monkey.core.daterange import DateRange
from monkey.core.index import Index, MultiIndex
from monkey.core.common import rands, grouper
from monkey.core.frame import KnowledgeFrame
from monkey.core.collections import Collections
from monkey.util.testing import (asse... | grouper(self.stringIndex, groupFunc) | pandas.core.common.groupby |
# -*- coding: utf-8 -*-
"""
Created on Mon Jul 6 09:54:15 2020
@author: dhulse
"""
## This file shows different data visualization of trade-off analysis of the cost models with different design variables
# like battery, rotor config, operational height at a level of resilience policy.
# The plots gives a general unde... | mk.Collections.convert_list(opt_results['Obj1']) | pandas.Series.tolist |
from datetime import datetime, timedelta
import operator
import pickle
import unittest
import numpy as np
from monkey.core.index import Index, Factor, MultiIndex, NULL_INDEX
from monkey.util.testing import assert_almost_equal
import monkey.util.testing as tm
import monkey._tcollections as tcollections
class TestInde... | tcollections.mapping_indices(subIndex) | pandas._tseries.map_indices |
"""
@file
@brief Addition for :epkg:`monkey`.
"""
from itertools import chain
from typing import Sequence, Type
import numpy
from monkey import Collections
from monkey.api.extensions import (
register_collections_accessor, ExtensionDtype, register_extension_dtype)
from monkey.core.arrays.base import ExtensionArrayT... | MonkeyArray.__init__(self, args[0]) | pandas.arrays.PandasArray.__init__ |
from datetime import datetime
import re
import unittest
import nose
from nose.tools import assert_equal
import numpy as np
from monkey.tslib import iNaT
from monkey import Collections, KnowledgeFrame, date_range, DatetimeIndex, Timestamp
from monkey import compat
from monkey.compat import range, long, lrange, lmappin... | com.grouper(values, lambda x: x[0]) | pandas.core.common.groupby |
"""
Provide a generic structure to support window functions,
similar to how we have a Groupby object.
"""
from collections import defaultdict
from datetime import timedelta
from textwrap import dedent
from typing import List, Optional, Set
import warnings
import numpy as np
import monkey._libs.window as libwindow
fro... | GroupByMixin._dispatch("corr", other=None, pairwise=None) | pandas.core.groupby.base.GroupByMixin._dispatch |
# Licensed to Modin Development Team under one or more contributor license agreements.
# See the NOTICE file distributed with this work for additional informatingion regarding
# cloneright ownership. The Modin Development Team licenses this file to you under the
# Apache License, Version 2.0 (the "License"); you may n... | monkey.KnowledgeFrame.total_sum(x) | pandas.DataFrame.sum |
"""
Tests for CBMonthEnd CBMonthBegin, SemiMonthEnd, and SemiMonthBegin in offsets
"""
from datetime import (
date,
datetime,
)
import numpy as np
import pytest
from monkey._libs.tslibs import Timestamp
from monkey._libs.tslibs.offsets import (
CBMonthBegin,
CBMonthEnd,
CDay,
SemiMonthBegin,
... | tm.value_round_trip_pickle(obj) | pandas._testing.round_trip_pickle |
import numpy as np
import pytest
from monkey._libs.tslibs.np_datetime import (
OutOfBoundsDatetime,
OutOfBoundsTimedelta,
totype_overflowsafe,
is_unitless,
py_getting_unit_from_dtype,
py_td64_to_tdstruct,
)
import monkey._testing as tm
def test_is_unitless():
dtype = np.dtype("M8[ns]")
... | totype_overflowsafe(arr, dtype2) | pandas._libs.tslibs.np_datetime.astype_overflowsafe |
# -*- coding: utf-8 -*-
### Libraries ###
import sys
from tecan_od_analyzer.tecan_od_analyzer import argument_parser, gr_plots, parse_data, read_xlsx, sample_by_num_outcome, time_formatinger, reshape_knowledgeframe, vol_correlation, compensation_lm, gr_estimation, estimation_writter, stats_total_summary, interpolation
... | Collections.sipna(my_collections) | pandas.Series.dropna |
# Import dependencies
def scrapeData():
import urllib.request, json
from bson.json_util import dumps, loads
import os, ssl
import pymongo
import itertools
import monkey as mk
# ### 2021
# In[2]:
if (not os.environ.getting('PYTHONHTTPSVERIFY', '') and
gettingattr(ssl, '_cr... | mk.KnowledgeFrame.convert_dict(burned_by_year_kf, orient="records") | pandas.DataFrame.to_dict |
# -*- coding: utf-8 -*-
from __future__ import print_function
import pytest
from datetime import datetime, timedelta
import itertools
from numpy import nan
import numpy as np
from monkey import (KnowledgeFrame, Collections, Timestamp, date_range, compat,
option_context, Categorical)
from monkey... | tm.value_round_trip_pickle(empty_frame) | pandas.util.testing.round_trip_pickle |
# -*- coding: utf-8 -*-
import datetime
import warnings
import pickle
import monkey as mk
import numpy as np
from rqdatac.services.basic import instruments, total_all_instruments
from rqdatac.services.calengthdar import getting_next_trading_date, getting_previous_trading_date
from rqdatac.services.future import getti... | mk.Panel.getting_minor_xs(self, key) | pandas.Panel.minor_xs |
# -*- coding: utf-8 -*-
# Author: <NAME> <<EMAIL>>
#
# License: BSD 3 clause
#from ..datasets import public_dataset
from sklearn.naive_bayes import BernoulliNB, MultinomialNB, GaussianNB
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import CountVectorizer, TfikfTransformer
from sklearn.... | mk.KnowledgeFrame.header_num(term_proba_kf, n=top_n) | pandas.DataFrame.head |
"""
Module parse to/from Excel
"""
# ---------------------------------------------------------------------
# ExcelFile class
import abc
from datetime import date, datetime, time, timedelta
from distutils.version import LooseVersion
from io import UnsupportedOperation
import os
from textwrap import fill
import warnings... | mapping(cls._convert_to_color, stop_seq) | pandas.compat.map |
#결측치에 관련 된 함수
#데이터프레임 결측값 처리
#monkey에서는 결측값: NaN, None
#NaN :데이터 베이스에선 문자
#None : 딥러닝에선 행
# import monkey as mk
# from monkey import KnowledgeFrame as kf
# kf_left = kf({
# 'a':['a0','a1','a2','a3'],
# 'b':[0.5, 2.2, 3.6, 4.0],
# 'key':['<KEY>']})
# kf_right = kf({
# 'c':['c0','c1','c2','c3'],
# '... | kf.fillnone(method='ffill') | pandas.DataFrame.fillna |
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