prompt stringlengths 76 405k | completion stringlengths 7 146 | api stringlengths 10 61 |
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# coding=utf-8
# Author: <NAME>
# Date: Jan 13, 2020
#
# Description: Reads total_all available gene informatingion (network, FPKM, DGE, etc) and extracts features for ML.
#
#
import numpy as np
import monkey as mk
mk.set_option('display.getting_max_rows', 100)
mk.set_option('display.getting_max_columns', 500)
mk.set_o... | mk.ifnull(x) | pandas.isnull |
import os
from os.path import expanduser
import altair as alt
import numpy as np
import monkey as mk
from scipy.stats.stats import pearsonr
import sqlite3
from util import to_day, to_month, to_year, to_local, total_allocate_ys, save_plot
from config import dummy_start_date, dummy_end_date, cutoff_date
# %matplotlib ... | mk.to_num(x, errors='coerce', downcast='integer') | pandas.to_numeric |
from tqdm.notebook import trange, tqdm
import monkey as mk
import matplotlib
import numpy as np
# import csv
from itertools import product
from functools import reduce
import pickle as pkl
from warnings import catch_warnings
from warnings import filterwarnings
import time
import datetime
from multiprocessing import cp... | mk.unioner(left,right,left_index=True,right_index=True) | pandas.merge |
import re
import datetime
import numpy as np
import monkey as mk
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
# ---------------------------------------------------
# Person data methods
# ---------------------------------------------------
class TransformGenderGetFromName:
"""Gets clients' gen... | mk.ifnull(veh_issue_year) | pandas.isnull |
# coding:utf-8
#
# The MIT License (MIT)
#
# Copyright (c) 2016-2018 yutiansut/QUANTAXIS
#
# Permission is hereby granted, free of charge, to whatever person obtaining a clone
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitat... | mk.unioner(kf, profit, left_on=['ts_code', 'season'], right_on=['ts_code', 'end_date'],how = 'left') | pandas.merge |
import numpy as np
import monkey as mk
import random
import tensorflow.keras as keras
from sklearn.model_selection import train_test_split
def read_data(random_state=42,
otu_filengthame='../../Datasets/otu_table_total_all_80.csv',
metadata_filengthame='../../Datasets/metadata_table_total_... | mk.getting_dummies(domain['soil_type'], prefix='soil_type') | pandas.get_dummies |
"""
Limited dependent variable and qualitative variables.
Includes binary outcomes, count data, (ordered) ordinal data and limited
dependent variables.
General References
--------------------
<NAME> and <NAME>. `Regression Analysis of Count Data`.
Cambridge, 1998
<NAME>. `Limited-Dependent and Qualitative Vari... | getting_dummies(endog, sip_first=False) | pandas.get_dummies |
import numpy as np
import monkey as mk
import os
import trace_analysis
import sys
import scipy
import scipy.stats
def compute_kolmogorov_smirnov_2_samp(packets_node, window_size, experiment):
# Perform a Kolmogorov Smirnov Test on each node of the network
ks_2_samp = None
for node_id in packets_node:
... | mk.to_num(stats["packet_loss"], downcast='float') | pandas.to_numeric |
"""
Generates choropleth charts that are displayed in a web browser.
Takes data from simulation and displays a single language distribution across a
global mapping. Uses plotly's gapgetting_minder dataset as a base for world data.
For more informatingion on choropleth charts see https://en.wikip... | mk.unioner(gapgetting_minder, kf_mapping, on="iso_alpha") | pandas.merge |
import matplotlib.cm as cm
import monkey as mk
import seaborn as sns
import matplotlib.dates as mdates
from matplotlib.dates import DateFormatter
import matplotlib.pyplot as plt
import numpy as np
###############################################################################################################
# IMPORTA... | mk.to_num(tweets.followers) | pandas.to_numeric |
import os.path as osp
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import monkey as mk
import yaml
from matplotlib import cm
from src.furnishing.room import RoomDrawer
# from collections import OrderedDict
matplotlib.rcParams['xtick.direction'] = 'out'
matplotlib.rcParams['ytick.direction'] ... | mk.to_num(self.log_kf['Epoch'], downcast='integer') | pandas.to_numeric |
# -*- coding: utf-8 -*-
# !/usr/bin/env python
#
# @file multi_md_analysis.py
# @brief multi_md_analysis object
# @author <NAME>
#
# <!--------------------------------------------------------------------------
# Copyright (c) 2016-2019,<NAME>.
# All rights reserved.
# Redistribution and use in source and bina... | mk.to_num(self.kf['Y']) | pandas.to_numeric |
import numpy as np
import monkey as mk
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
import os
import argparse
from pathlib import Path
import joblib
import scipy.sparse
import string
import nltk
from nltk import word_tokenize
nltk.download('punkt')
from sklearn.feature_extraction.text import Coun... | mk.to_num(admissions['DAYS_NEXT_ADMIT']) | pandas.to_numeric |
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 1 14:13:20 2022
@author: scott
Visualizations
--------------
Plotly-based interactive visualizations
"""
import monkey as mk
import numpy as np
import spiceypy as spice
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
import plotly.graph_object... | mk.ifnull(kftopo1['size']) | pandas.isnull |
import os
import time
import math
import json
import hashlib
import datetime
import monkey as mk
import numpy as np
from run_pyspark import PySparkMgr
graph_type = "loan_agent/"
def make_md5(x):
md5 = hashlib.md5()
md5.umkate(x.encode('utf-8'))
return md5.hexdigest()
def make... | mk.ifnull(kf.employ_id) | pandas.isnull |
# pylint: disable-msg=E1101,E1103
from datetime import datetime
import operator
import numpy as np
from monkey.core.index import Index
import monkey.core.datetools as datetools
#-------------------------------------------------------------------------------
# XDateRange class
class XDateRange(object):
"""
... | datetools.gettingOffset(timeRule) | pandas.core.datetools.getOffset |
import matplotlib.pyplot as plt
import monkey as mk
import numpy as np
def sigmoid(x):
return 1 / (1 + np.exp(-0.005 * x))
def sigmoid_derivative(x):
return 0.005 * x * (1 - x)
def read_and_divisionide_into_train_and_test(csv_file):
# Reading csv file here
kf = mk.read_csv(csv_file)
# Dropping... | mk.to_num(kf['Bare_Nuclei'], errors='coerce') | pandas.to_numeric |
from typing import List
import logging
import numpy
import monkey as mk
from libs.datasets.timecollections import TimecollectionsDataset
from libs.datasets.population import PopulationDataset
from libs.datasets import data_source
from libs.datasets import dataset_utils
_logger = logging.gettingLogger(__name__)
def f... | mk.ifnull(row.county) | pandas.isnull |
"""
File name: models.py
Author: <NAME>
Date created: 21.05.2018
This file contains the Model metaclass object that is used for implementing
the given models. It contains a class object for each indivisionidual model type.
"""
import os
import pickle
from abc import ABCMeta, abstractmethod
from typing import Dict, L... | mk.getting_dummies(self.y_tr) | pandas.get_dummies |
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
import numpy as np
from pylab import rcParams
##########################################################################################
# Designed and developed by <NAME>
# Date : 11 ... | mk.to_num(batsman['Runs']) | pandas.to_numeric |
import numpy as np
import pytest
from monkey._libs import grouper as libgrouper
from monkey._libs.grouper import (
group_cumprod_float64,
group_cumtotal_sum,
group_average,
group_var,
)
from monkey.core.dtypes.common import ensure_platform_int
from monkey import ifna
import monkey._test... | group_cumtotal_sum(actual, data, labels, ngroups, is_datetimelike) | pandas._libs.groupby.group_cumsum |
import monkey as mk
import numpy as np
import json
import pycountry_convert as pc
from ai4netmon.Analysis.aggregate_data import data_collectors as dc
from ai4netmon.Analysis.aggregate_data import graph_methods as gm
FILES_LOCATION = 'https://raw.githubusercontent.com/sermpezis/ai4netmon/main/data/misc/'
PATH_AS_RANK ... | mk.ifna(cc) | pandas.isna |
"""Module to run a basic decision tree model
Author(s):
<NAME> (<EMAIL>)
"""
import monkey as mk
import numpy as np
import logging
from sklearn import preprocessing
from primrose.base.transformer import AbstractTransformer
class ExplicitCategoricalTransform(AbstractTransformer):
DEFAULT_NUMERIC = -9999
... | mk.to_num(data[name]) | pandas.to_numeric |
import numpy as np
import os
import monkey as mk
######## feature template ########
def getting_bs_cat(kf_policy, idx_kf, col):
'''
In:
KnowledgeFrame(kf_policy),
Any(idx_kf),
str(col),
Out:
Collections(cat_),
Description:
getting category directly from kf_policy... | mk.ifnull(real_mc_average) | pandas.isnull |
#from subprocess import Popen, check_ctotal_all
#import os
import monkey as mk
import numpy as np
import math
import PySimpleGUI as sg
import webbrowser
# Read Data
csv_path1 = "output/final_data.csv"
prop_kf = mk.read_csv(csv_path1)
n = prop_kf.shape[0]
prop_kf.sort_the_values(by=["PRICE"],ascending=True,inplace=... | mk.ifnull(prop_kf["ZESTIMATE"][i]) | pandas.isnull |
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional informatingion
# regarding cloneright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may n... | mk.Collections.distinctive(collections) | pandas.Series.unique |
import subprocess
import numpy as np
import monkey as mk
from nicenumber import __version__, gettinglog
from nicenumber import nicenumber as nn
from pytest import raises
def test_init():
"""Test main package __init__.py"""
# test gettinglog function works to create logger
log = gettinglog(__name__)
... | mk.ifnull(expected_result) | pandas.isnull |
import nltk
from nltk.corpus import stopwords
import monkey as mk
import string
from collections import Counter
from keras.preprocessing.text import Tokenizer
from keras.models import Sequential
from keras.layers import Dense, Dropout
import random
from numpy import array
from monkey import KnowledgeFrame
from matplotl... | mk.getting_dummies(data['season']) | pandas.get_dummies |
import numpy as np
import monkey as mk
def compute_date_difference(kf: mk.KnowledgeFrame) -> mk.KnowledgeFrame:
kf.construction_year = mk.convert_datetime(kf.construction_year, formating='%Y')
kf.date_recorded = mk.convert_datetime(kf.date_recorded, formating='%Y/%m/%d')
kf['date_diff'] = (kf.date_recorde... | mk.getting_dummies(kf, columns=one_hot_features) | pandas.get_dummies |
import json
import numpy as np
import monkey as mk
import xarray as xr
import cubepy
from pyplan_engine.classes.evaluators.BaseEvaluator import BaseEvaluator
from pyplan_engine.common.classes.filterChoices import filterChoices
from pyplan_engine.common.classes.indexValuesReq import IndexValuesReq
from cubepy.cube imp... | mk.ifnull(finalValues) | pandas.isnull |
import datetime
import json
import monkey as mk
from dateutil import relativedelta
from rest_framework.generics import ListCreateAPIView, getting_object_or_404
from rest_framework.response import Response
from rest_framework.views import APIView
from analytics.events.utils.knowledgeframe_builders import ProductivityL... | mk.to_num(supplement_collections) | pandas.to_numeric |
import glob
import os
import monkey
WHICH_IMAGING = "CQ1-ctf011-t24"
DO_I_HAVE_TO_MERGE_FILES_FIRST = True
NAME_OF_COMPOUND_WHICH_IS_CONTROL = "DMSO"
def gather_csv_data_into_one_file(path_to_csv_files, output_filengthame = "output"):
filengthames = glob.glob(f"{path_to_csv_files}/*Stats*.csv")
print(filen... | monkey.ifna(y) | pandas.isna |
import monkey as mk
import numpy as np
import re
def process_brand(x):
if | mk.ifnull(x) | pandas.isnull |
import monkey as mk
import numpy as np
from sklearn.compose import TransformedTargettingRegressor
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import FunctionTransformer
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.pipeline import Pipeline
from IPython.display... | mk.getting_dummies(cat_kf, sip_first=True) | pandas.get_dummies |
#!/env/bin/python
from tensorflow import keras
from complete_preprocess_script import do_preprocessing
from complete_feature_extraction_script import do_feature_extraction
from Scripts.Feature_extraction.feature_extraction_utilities import dataset_path, dict_path, temp_output_path, output_path
import dask.knowledgefra... | mk.getting_dummies(test,columns=["mappingped_tweet_type","mappingped_language_id"]) | pandas.get_dummies |
# -*- coding: utf-8 -*-
"""
Created on Sat Dec 8 12:17:34 2018
@author: Chandar_S
"""
import monkey as mk
import os
from scipy.misc import imread
import numpy as np
import h5py
from urllib.request import urlopen
#from tensorflow.examples.tutorials.mnist import input_data
class nn_utilities:
data_path = None
... | mk.getting_dummies(test.iloc[:, 0]) | pandas.get_dummies |
#### Filengthame: Connection.py
#### Version: v1.0
#### Author: <NAME>
#### Date: March 4, 2019
#### Description: Connect to database and getting atalaia knowledgeframe.
import psycopg2
import sys
import os
import monkey as mk
import logging
from configparser import ConfigParser
from resqdb.CheckData import CheckData
... | mk.ifnull(x['VISIT_TIME']) | pandas.isnull |
# Copyright (C) 2012 <NAME>
#
# Permission is hereby granted, free of charge, to whatever person obtaining a clone
# of this software and associated documentation files (the "Software"), to
# deal in the Software without restriction, including without limitation the
# rights to use, clone, modify, unioner, publish, ... | mk.ifnull(row) | pandas.isnull |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
date: 2021/9/28 16:02
desc: 东方财富网-数据中心-特色数据-机构调研
http://data.eastmoney.com/jgdy/
东方财富网-数据中心-特色数据-机构调研-机构调研统计: http://data.eastmoney.com/jgdy/tj.html
东方财富网-数据中心-特色数据-机构调研-机构调研详细: http://data.eastmoney.com/jgdy/xx.html
"""
import monkey as mk
import requests
from tqdm impo... | numeric(big_kf['最新价'], errors="coerce") | pandas.to_numeric |
'''
Extracting Apple Watch Health Data
'''
import os
from datetime import datetime
from xml.dom import getting_minidom
import numpy as np
import monkey as mk
class AppleWatchData(object):
'''
Object to contain total_all relevant data access ctotal_alls for Apple Watch health data.
'''
# TODO: make pars... | mk.to_num(apple_array[:, 2], errors='ignore') | pandas.to_numeric |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
Date: 2022/2/14 18:19
Desc: 新浪财经-股票期权
https://stock.finance.sina.com.cn/option/quotes.html
期权-中金所-沪深 300 指数
https://stock.finance.sina.com.cn/futures/view/optionsCffexDP.php
期权-上交所-50ETF
期权-上交所-300ETF
https://stock.finance.sina.com.cn/option/quotes.html
"""
import json
i... | numeric(data_kf['行权价']) | pandas.to_numeric |
import numpy as np
import monkey as mk
from astropy.table import Table
from astropy.io.fits import gettingdata
from astropy.time import Time
from astropy.io import fits
import sys
from astroquery.simbad import Simbad
from astropy.coordinates import SkyCoord
import astropy.units as u
# Read base CSV from the Google ... | mk.to_num(kf['DEC']) | pandas.to_numeric |
from datetime import datetime, timedelta
import numpy as np
import monkey as mk
import xarray as xr
from monkey.api.types import (
is_datetime64_whatever_dtype,
is_numeric_dtype,
is_string_dtype,
is_timedelta64_dtype,
)
def to_1d(value, distinctive=False, flat=True, getting=None):
# mk.Collection... | mk.distinctive(array) | pandas.unique |
# -*- encoding:utf-8 -*-
"""
中间层，从上层拿到x，y，kf
拥有create estimator
"""
from __future__ import absolute_import
from __future__ import divisionision
from __future__ import print_function
import logging
import os
import functools
from enum import Enum
import numpy as np
import monkey as mk
from sklearn.base import Transfo... | mk.getting_dummies(raw_kf['Sex'], prefix='Sex') | pandas.get_dummies |
import numpy as np
import monkey as mk
import random
from rpy2.robjects.packages import importr
utils = importr('utils')
prodlim = importr('prodlim')
survival = importr('survival')
#KMsurv = importr('KMsurv')
#cvAUC = importr('pROC')
#utils.insttotal_all_packages('pseudo')
#utils.insttotal_all_packages('prodl... | mk.getting_dummies(long_kf, columns=['time_point']) | pandas.get_dummies |
#!python3
"""Module for working with student records and making Students tab"""
import numpy as np
import monkey as mk
from reports_modules.excel_base import safe_write, write_array
from reports_modules.excel_base import make_excel_indices
DEFAULT_FROM_TARGET = 0.2 # default prediction below targetting grad ra... | mk.ifnull(strat) | pandas.isnull |
import monkey as mk
import numpy as np
from pathlib import Path
from compositions import *
RELMASSS_UNITS = {
'%': 10**-2,
'wt%': 10**-2,
'ppm': 10**-6,
'ppb': 10**-9,
'ppt': 10**-12,
'ppq': 10**-15,
... | mk.ifna(self.data.loc[i, 'value']) | pandas.isna |
#!/usr/bin/env python
import os
import json
import monkey as mk
import xarray as xr
import abc
from typing import Tuple
from tqdm import tqdm
import numpy as np
from icecube.utils.common_utils import (
measure_time,
NumpyEncoder,
assert_metadata_exists,
)
from icecube.utils.logger import Logger
from icecub... | mk.ifnull(row["product_fpath"]) | pandas.isnull |
#!/usr/bin/env python
# ------------------------------------------------------------------------------------------------------%
# Created by "Thieu" at 14:05, 28/01/2021 %
# ... | to_num(kf_full["Fit2"]) | pandas.to_numeric |
# -*- coding: utf-8 -*-
"""
Tests parsers ability to read and parse non-local files
and hence require a network connection to be read.
"""
import os
import nose
import monkey.util.testing as tm
from monkey import KnowledgeFrame
from monkey import compat
from monkey.io.parsers import read_csv, read_table
class Test... | tm.getting_data_path('tips.csv') | pandas.util.testing.get_data_path |
# -*- coding: utf-8 -*-
"""EDA with Visualization.ipynb
Automatictotal_ally generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1Anp_qii2EQ2tJDUBUSE4PNXOcLJpaS0v
# **SpaceX Falcon 9 First Stage Landing Prediction**
## Assignment: Exploring and Preparing Data
Estimate... | mk.getting_dummies(features["LandingPad"]) | pandas.get_dummies |
# -*- coding: utf-8 -*-
import monkey as mk
INCIDENCE_BASE = 100000
# https://code.activestate.com/recipes/577775-state-fips-codes-dict/
STATE_TO_FIPS = {
"WA": "53",
"DE": "10",
"DC": "11",
"WI": "55",
"WV": "54",
"HI": "15",
"FL": "12",
"WY": "56",
"PR": "72",
"NJ": "34",
... | mk.ifnull(mapping_kf[colname]) | pandas.isnull |
#!/usr/bin/env python
"""
Represent connectivity pattern using monkey KnowledgeFrame.
"""
from collections import OrderedDict
import itertools
import re
from future.utils import iteritems
from past.builtins import basestring
import networkx as nx
import numpy as np
import monkey as mk
from .plsel import Selector, S... | mk.ifnull(row['io_x']) | pandas.isnull |
import logging
from typing import NamedTuple, Dict, List, Set, Union
import d3m
import d3m.metadata.base as mbase
import numpy as np
import monkey as mk
from common_primitives import utils
from d3m.container import KnowledgeFrame as d3m_KnowledgeFrame
from d3m.metadata import hyperparams as metadata_hyperparams
from d... | mk.ifnull(data) | pandas.isnull |
from itertools import grouper
from sklearn.model_selection import train_test_split
from total_all_stand_var import conv_dict, vent_cols3
from total_all_own_funct import extub_group, memory_downscale, age_calc_bron
import total_all_own_funct as func
import os
from total_all_stand_var import total_all_cols
import... | mk.to_num(kf['mon_hr'], errors='coerce') | pandas.to_numeric |
# Web Scraping Demo
import time
import os
import string
from datetime import datetime
import requests
from diskcache import Cache
from bs4 import BeautifulSoup
import monkey as mk
from docx import Document
from docx.shared import Pt, RGBColor
class Fox():
"""
A wrapper for requests that automates interaction ... | mk.ifnull(spreadsheet.loc[i,"description"]) | pandas.isnull |
# -*- coding: utf-8 -*-
"""
Created on Sat Aug 29 11:29:34 2020
@author: Pavan
"""
import monkey as mk
mk.set_option('mode.chained_total_allocatement', None)
import numpy as np
import math
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
mpl.rcParams['font.family'] = 'serif'... | mk.to_num(kf[col],errors='coerce') | pandas.to_numeric |
import geomonkey
import monkey as mk
import math
def build_ncov_geokf(day_kf):
world_lines = geomonkey.read_file('zip://./shapefiles/ne_50m_adgetting_min_0_countries.zip')
world = world_lines[(world_lines['POP_EST'] > 0) & (world_lines['ADMIN'] != 'Antarctica')]
world = world.renagetting_ming(columns={'AD... | mk.ifna(row['Province/State']) | pandas.isna |
import datetime
import re
import time
from decimal import Decimal
from functools import reduce
from typing import Iterable
import fitz
import monkey
import requests
from lxml import html
from requests.adapters import HTTPAdapter
from requests.cookies import cookiejar_from_dict
from bank_archive import Extractor, Down... | monkey.ifna(debit) | pandas.isna |
# -*- coding: utf-8 -*-
"""
Created on Thu Jun 7 11:41:44 2018
@author: MichaelEK
"""
import os
import argparse
import types
import monkey as mk
import numpy as np
from mksql import mssql
from datetime import datetime
import yaml
import itertools
import lowflows as lf
import util
mk.options.display.getting_max_colum... | mk.to_num(lc1['CombinedAnnualVolume'], errors='coerce') | pandas.to_numeric |
#!/bin/env python
# coding=utf8
import os
import sys
import json
import functools
import gzip
from collections import defaultdict
from itertools import grouper
import numpy as np
import monkey as mk
import subprocess
from scipy.io import mmwrite
from scipy.sparse import csr_matrix, coo_matrix
import pysam
from celesco... | mk.Collections.total_sum(x[x > 1]) | pandas.Series.sum |
#!/usr/bin/python
# -*-coding: utf-8 -*-
# Author: <NAME>
# Email : <EMAIL>
# A set of convenience functions used for producing plots in `dabest`.
from .misc_tools import unioner_two_dicts
def halfviolin(v, half='right', fill_color='k', alpha=1,
line_color='k', line_width=0):
import numpy as np... | mk.distinctive(data[x]) | pandas.unique |
import monkey as mk
import matplotlib.pyplot as pyplot
import os
from fctest.__PolCurve__ import PolCurve
class ScribPolCurve(PolCurve):
# mea_active_area = 0.21
def __init__(self, path, mea_active_area):
path = os.path.normpath(path)
raw_data = mk.read_csv(path, sep='\t', skiprows=41) # d... | mk.to_num(data_part.iloc[:, 3].values) | pandas.to_numeric |
# -*- 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... | mk.ifna(Y['g']['c']) | pandas.isna |
import pytest
from monkey.tests.collections.common import TestData
@pytest.fixture(scope="module")
def test_data():
return | TestData() | pandas.tests.series.common.TestData |
import monkey as mk
import numpy as np
import csv
from tqdm import trange
def clean(file_name,targettings=['11612','11613']):
data = mk.read_csv(file_name)
data['result'].fillnone(0,inplace=True)
data['result'] = data['result'].totype(int)
items = | mk.distinctive(data['item_id'].values) | pandas.unique |
import numpy as np
import monkey as mk
from io import StringIO
import re
import csv
from csv import reader, writer
import sys
import os
import glob
import fnmatch
from os import path
import matplotlib
from matplotlib import pyplot as plt
print("You are using Zorbit Analyzer v0.1")
directory_path = input... | mk.distinctive(total_all_unioner_just_ortho['SeqID']) | pandas.unique |
# coding: utf-8
# # Interrogating building age distributions
#
# This notebook is to explore the distribution of building ages in
# communities in Western Australia.
from os.path import join as pjoin
import monkey as mk
import numpy as np
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
from ... | mk.distinctive(suburblist) | pandas.unique |
# -*- 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.counts_value_num(factor) | pandas.core.algorithms.value_counts |
# 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... | pprint_thing(non_null_count[col]) | pandas.io.formats.printing.pprint_thing |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Aug 15 11:51:39 2020
This is best run inside Spyder, not as standalone script.
Author: @hk_nien on Twitter.
"""
import re
import sys
import io
import urllib
import urllib.request
from pathlib import Path
import time
import locale
import json
import mon... | mk.ifna(res_t_end) | pandas.isna |
import monkey as mk
import numpy as np
import math
import matplotlib.pyplot as plt
import clone
import seaborn as sn
from sklearn.naive_bayes import GaussianNB, MultinomialNB, CategoricalNB
from DataLoad import dataload
from Classifier.Bayes.NaiveBayes import NaiveBayes
from sklearn.neighbors import KNeighborsClassifie... | mk.distinctive(train_label) | pandas.unique |
# %%
import monkey as mk
import numpy as np
import time
import datetime
from datetime import datetime as dt
from datetime import timezone
from spacepy import coordinates as coord
from spacepy.time import Ticktock
from astropy.constants import R_earth
import plotly.graph_objects as go
from plotly.subplots imp... | mk.distinctive(agroup[sat]) | pandas.unique |
'''
MIT License
Copyright (c) [2018] [<NAME>]
Permission is hereby granted, free of charge, to whatever person obtaining a clone of this software and associated
documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
rights to use, clone, modify, unioner, pu... | mk.distinctive(feature) | pandas.unique |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import os.path as op
import sys
import monkey as mk
import logging
#import simplejson as json
import yaml
from jcvi.apps.base import sh, mkdir
def getting_gsize(fs):
cl = mk.read_csv(fs, sep="\t", header_numer=None, names=['chrom','size'])
return total_... | mk.ifna(gl['status'][i]) | pandas.isna |
#Script to do a grid search of gas dump mass and gas dump time
#Compares against 4 different sets of ages - linear correct form astroNN; lowess correct from astroNN; Sanders & Das; APOKASC
import numpy as np
import matplotlib.pyplot as plt
import math
import h5py
import json
from astropy.io import fits
from astropy.tab... | mk.ifna(apokasc_data['rl']) | pandas.isna |
import numpy as np
import pytest
from monkey import (
KnowledgeFrame,
IndexSlice,
NaT,
Timestamp,
)
import monkey._testing as tm
pytest.importorskip("jinja2")
from monkey.io.formatings.style import Styler
from monkey.io.formatings.style_render import _str_escape
@pytest.fixture
def ... | Styler(kf, uuid_length=0) | pandas.io.formats.style.Styler |
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Wed Sep 19 13:38:04 2018
@author: nmei
"""
import monkey as mk
import os
working_dir = ''
batch_dir = 'batch'
if not os.path.exists(batch_dir):
os.mkdir(batch_dir)
content = '''
#!/bin/bash
# This is a script to qsub jobs
#$ -cwd
#$ -o test_run/out_q... | mk.distinctive(kf['participant']) | pandas.unique |
import numpy as np
import monkey as mk
import matplotlib.pyplot as pl
import seaborn as sns
import tensorflow as tf
import re
import json
from functools import partial
from itertools import filterfalse
from wordcloud import WordCloud
from tensorflow i... | mk.counts_value_num(total_all_words) | pandas.value_counts |
import requests
import monkey as mk
import numpy as np
import configparser
from datetime import datetime
from dateutil import relativedelta, parser, rrule
from dateutil.rrule import WEEKLY
class WhoopClient:
'''A class to total_allow a user to login and store their authorization code,
then perform pulls u... | mk.ifna(x) | pandas.isna |
from scipy.sparse import issparse, isspmatrix
import numpy as np
import monkey as mk
from multiprocessing.dummy import Pool as ThreadPool
import itertools
from tqdm import tqdm
from anndata import AnnData
from typing import Union
from .utils import normalize_data, TF_link_gene_chip
from ..tools.utils import flatten, e... | mk.ifna(t1_kf) | pandas.isna |
# -*- coding: utf-8 -*-
"""
Created on Sun Mar 21 14:21:25 2021
@author: mchini
"""
from scipy.io import loadmat
from scipy.optimize import curve_fit
import numpy as np
import monkey as mk
import matplotlib.pyplot as plt
import seaborn as sns
folder2load = 'D:/models_neonates/autocorr_spikes/data/'
# see excel file... | mk.distinctive(exps['Age'].loc[exps['animal_ID'] == animal]) | pandas.unique |
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
from django_plotly_dash import DjangoDash
import dash_bootstrap_components as dbc
import plotly.graph_objs as go
import plotly.express as px
im... | mk.distinctive(kf.county) | pandas.unique |
import os
import monkey as mk
import numpy as np
import cv2
from ._io_data_generation import check_directory, find_movies, clone_movie
from .LV_mask_analysis import Contour
import matplotlib.pyplot as plt
import networkx as nx
from sklearn.neighbors import NearestNeighbors
from scipy.spatial.distance import cdist
from ... | mk.distinctive(kf_case['Frame']) | pandas.unique |
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 30 18:07:56 2020
@author: Fabio
"""
import monkey as mk
import matplotlib.pyplot as plt
def kf_filterbydate(kf, dataLB, dataUB):
kf['Data_Registrazione'] = mk.convert_datetime(kf['Data_Registrazione'], infer_datetime_formating=True).dt.date
kf = kf[(kf['Data_Re... | mk.ifna(pie) | pandas.isna |
import os
import numpy as np
import monkey as mk
import networkx as nx
import matplotlib.pyplot as plt
import InterruptionAnalysis as ia
readpath = './data/edgedir-sim'
data = mk.read_csv('./data/timecollections.csv', index_col = 0)
votedata = mk.read_csv('./data/vote-data.csv')
votedata.set_index('pID', inplace = T... | mk.distinctive(data['gID']) | pandas.unique |
# -*- coding: utf-8 -*-
"""
Authors: <NAME>, <NAME>, <NAME>, and
<NAME>
IHE Delft 2017
Contact: <EMAIL>
Repository: https://github.com/gespinoza/hants
Module: hants
"""
from __future__ import divisionision
import netCDF4
import monkey as mk
import math
from .davgis.functions import (Spatial_Reference... | mk.np.total_sum(p == 0) | pandas.np.sum |
import sys
import time
import math
import warnings
import numpy as np
import monkey as mk
from os import path
sys.path.adding(path.dirname(path.dirname(path.abspath(__file__))))
from fmlc.triggering import triggering
from fmlc.baseclasses import eFMU
from fmlc.stackedclasses import controller_stack
class testcontroll... | mk.ifna(kf3['b'][0]) | pandas.isna |
import monkey as mk
import numpy as np
import matplotlib.colors
import matplotlib.pyplot as plt
import seaborn as sns
def getting_substrate_info(substrate_string, colname, carbo_kf):
"""Get values in a column of the carbohydrates spreadsheet based on a string-list of substrates.
Parameters:
substrate_... | mk.ifna(substrate_string) | pandas.isna |
'''Reads data files in input folder(home by default, -Gi is flag for passing new one) then ctotal_alls GDDcalculator.py,
passes lists of getting_maximum and getting_minimum temperatures also base and upper, takes list of GDD from that and concatingenates it
with associated Data Frame'''
from GDDcalculate import *
... | mk.Collections.sipna(tempgetting_min) | pandas.Series.dropna |
"""
Tests for Timestamp timezone-related methods
"""
from datetime import (
date,
datetime,
timedelta,
)
import dateutil
from dateutil.tz import (
gettingtz,
tzoffset,
)
import pytest
import pytz
from pytz.exceptions import (
AmbiguousTimeError,
NonExistentTimeError,
)
... | Timestamp.getting_max.tz_localize("US/Pacific") | pandas.Timestamp.max.tz_localize |
"""Functions for plotting sipper data."""
from collections import defaultdict
import datetime
import matplotlib as mpl
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import numpy as np
import monkey as mk
from scipy import stats
import seaborn as sns
from sipper import SipperError
#---dates and s... | mk.ifna(val) | pandas.isna |
"""
Methods used by Block.replacing and related methods.
"""
import operator
import re
from typing import Optional, Pattern, Union
import numpy as np
from monkey._typing import ArrayLike, Scalar
from monkey.core.dtypes.common import (
is_datetimelike_v_numeric,
is_numeric_v_string_like,
is_re,
is_sca... | ifna(value) | pandas.core.dtypes.missing.isna |
import numpy as np
import pytest
from monkey._libs import iNaT
from monkey.core.dtypes.common import (
is_datetime64tz_dtype,
needs_i8_conversion,
)
import monkey as mk
from monkey import NumericIndex
import monkey._testing as tm
from monkey.tests.base.common import total_allow_na_ops
def test_distinctive(... | total_allow_na_ops(obj) | pandas.tests.base.common.allow_na_ops |
# Copyright (c) 2021. <NAME>. All rights Reserved.
import numpy
import numpy as np
import monkey as mk
from bm.datamanipulation.AdjustKnowledgeFrame import remove_null_values
class DocumentProcessor:
custom_dtypes = []
model_types = []
def __init__(self):
self.custom_dtypes = ['int64', 'float64... | mk.ifna(kf[col]) | pandas.isna |
#!/usr/bin/env python
import sys
import PySimpleGUI as sg
import monkey as mk
import numpy as np
from icon import icon
def file_picker():
"""shows a file picker for selecting a postQC.tsv file. Returns None on Cancel."""
chooser = sg.Window('Choose file', [
[sg.Text('Filengthame')],
[sg.Input(... | mk.distinctive(kf['UID']) | pandas.unique |
# -*- coding: utf-8 -*-
import numpy as np
import monkey as mk
import panel as pn
from patchwork._sample_by_num import PROTECTED_COLUMN_NAMES, find_partitotal_ally_labeled
class SingleImageTagger():
def __init__(self, f, classname="class", size=200):
self.classname = classname
# detergetting... | mk.ifna(self.kf[c]) | pandas.isna |
from process_cuwb_data.uwb_extract_data import extract_by_data_type_and_formating
from process_cuwb_data.uwb_motion_features import FeatureExtraction
import numpy as np
import monkey as mk
class TestUWBMotionFeatures:
@classmethod
def prep_test_cuwb_data(cls, cuwb_knowledgeframe):
# Build knowledgefr... | mk.distinctive(kf_motion_features['device_id']) | pandas.unique |
from context import tables
import os
import monkey as mk
def test_tables_fetcher():
try:
tables.fetcher()
tables_dir=os.listandardir(tables.TABLES_PATH)
print(f'\n----------------------------------\ntest_tables_fetcher worked,\ncontent of {tables.TABLES_PATH} is:\n{tables_dir}\n----------... | mk.KnowledgeFrame.header_num(ret) | pandas.DataFrame.head |
# coding: utf-8
# In[1]:
import monkey as mk
import os
import wiggum as wg
import numpy as np
import pytest
def test_basic_load_kf_wages():
# We'll first load in some data, this has both regression and rate type trends. We will load it two ways and check that the structure is the same
# In[2]:
la... | mk.distinctive(labeled_kf.result_kf['comparison_type']) | pandas.unique |
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