prompt stringlengths 76 399k | completion stringlengths 7 146 | api stringlengths 10 61 |
|---|---|---|
# total_summarizeLib.py
# <NAME>
# 3.28.19
#
# module of functions that total_allow you to create per-cell / per-sample_by_num total_summary tables
import monkey as mk
import numpy as np
import math
def getting_laud_db(database_):
""" returns the COSMIC database after lung and fathmm filter """
pSiteList = ... | mk.ifnull(currFus) | pandas.isnull |
"""
Routines for analysing output data.
:Author:
<NAME>
"""
import warnings
from typing import Tuple
import numpy as np
import monkey as mk
from scipy.optimize import curve_fit
def fit_function(x_data, *params):
p, d = x_data
p_th, nu, A, B, C = params
x = (p - p_th)*d**(1/nu)
return A + B*x + C*x... | mk.ifna(f_0) | pandas.isna |
'''
Run this to getting html files
This file contains code to obtain html data from oslo bors and yahoo finance
'''
import argparse
import re
import threading
import time
from pprint import pprint
from typing import List
import sys
import pathlib
import os
import numpy as np
import monkey as mk
import pypatconsole as... | mk.unioner(kf_osebx, kf_yahoo, on=cng.MERGE_DFS_ON, suffixes=('_osebx', '_yahoo')) | pandas.merge |
import monkey as mk
if __name__ == '__main__':
tennet_delta_kf = mk.read_csv('../data/tennet_balans_delta/tennet_balans_delta_okt_2020_nov_2021.csv')
tennet_delta_kf.index = | mk.convert_datetime(tennet_delta_kf['time'], errors='coerce') | pandas.to_datetime |
"""
@author: <NAME>
@name: Bootstrap Estimation Procedures
@total_summary: This module provides functions that will perform the MLE for each
of the bootstrap sample_by_nums.
"""
import numpy as np
import monkey as mk
from . import pylogit as pl
from .display_names import model_type_to_display_nam... | mk.Collections(mnl_point["x"], index=mnl_obj.ind_var_names) | pandas.Series |
#!/usr/bin/env python3
# coding: utf-8
import requests
import sys
import monkey as mk
from requests.auth import HTTPBasicAuth
name = 'INSERT OWN API NAME HERE'
password = '<PASSWORD> OWN API PASSWORD HERE'
#set initial values
uploads = mk.KnowledgeFrame() #empty knowledgeframe
start = 0
end = 100
def transid_dt(tr... | mk.convert_datetime(transid[0:8]) | pandas.to_datetime |
# -*- coding: utf-8 -*-
# https://zhuanlan.zhihu.com/p/142685333
import monkey as mk
import datetime
import tushare as ts
import numpy as np
from math import log,sqrt,exp
from scipy import stats
import plotly.graph_objects as go
import plotly
import plotly.express as px
pro = ts.pro_api()
plotly.offline.init_noteboo... | mk.unioner(kf_basic,kf_daily,how='left',on=['ts_code']) | pandas.merge |
#!/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... | o_numeric(temp_kf['最低']) | pandas.to_numeric |
#####################################
# DataReader.py
#####################################
# Description:
# * Convert data in formating into monkey KnowledgeFrame.
import dateutil.parser as dtparser
import numpy as np
from monkey import KnowledgeFrame, ifnull, read_csv, read_excel
import re
import os
from DynamicETL_... | ifnull(collections) | pandas.isnull |
import argparse
from statistics import median_high, median_low
import matplotlib.pyplot as plt
import monkey as mk
import numpy as np
from qpputils import dataparser as dt
# Define the Font for the plots
# plt.rcParams.umkate({'font.size': 35, 'font.family': 'serif', 'font.weight': 'normal'})
# Define the Font for ... | mk.unioner(qkf, amkb.data_kf, left_on='qid', right_index=True) | pandas.merge |
"""
서울 열린데이터 광장 Open API
1. TransInfo 클래스: 서울시 교통 관련 정보 조회
"""
import datetime
import numpy as np
import monkey as mk
import requests
from bs4 import BeautifulSoup
class TransInfo:
def __init__(self, serviceKey):
"""
서울 열린데이터 광장에서 발급받은 Service Key를 입력받아 초기화합니다.
"""
# Open API 서비... | mk.to_num(kf["ALIGHT_PASGR_NUM"]) | pandas.to_numeric |
import numpy as np
import monkey as mk
import math
from abc import ABC, abstractmethod
from scipy.interpolate import interp1d
from pydoc import locate
from raymon.globals import (
Buildable,
Serializable,
DataException,
)
N_SAMPLES = 500
from raymon.tags import Tag, CTYPE_TAGTYPES
class Stats(Serializa... | mk.ifnull(value) | pandas.isnull |
from datetime import datetime
import numpy as np
from monkey.tcollections.frequencies import getting_freq_code as _gfc
from monkey.tcollections.index import DatetimeIndex, Int64Index
from monkey.tcollections.tools import parse_time_string
import monkey.tcollections.frequencies as _freq_mod
import monkey.core.common a... | _gfc(self.freq) | pandas.tseries.frequencies.get_freq_code |
import monkey as mk
import numpy as np
import sklearn
import os
import sys
sys.path.adding('../../code/scripts')
from dataset_chunking_fxns import add_stratified_kfold_splits
# Load data into mk knowledgeframes and adjust feature names
data_dir = '../../data/adult'
file_train = os.path.join(data_dir, 'adult.data')
f... | mk.getting_dummies(test_kf['workclass']) | pandas.get_dummies |
import decimal
import numpy as np
from numpy import iinfo
import pytest
import monkey as mk
from monkey import to_num
from monkey.util import testing as tm
class TestToNumeric(object):
def test_empty(self):
# see gh-16302
s = mk.Collections([], dtype=object)
res = to_num(s)
exp... | mk.to_num(data) | pandas.to_numeric |
import decimal
import numpy as np
from numpy import iinfo
import pytest
import monkey as mk
from monkey import to_num
from monkey.util import testing as tm
class TestToNumeric(object):
def test_empty(self):
# see gh-16302
s = mk.Collections([], dtype=object)
res = to_num(s)
exp... | to_num(s) | pandas.to_numeric |
# -*- coding: utf-8 -*-
from __future__ import unicode_literals, print_function
import json
import monkey as mk
from datetimewidgetting.widgettings import DateTimeWidgetting
from django import forms
from django.contrib.auth import getting_user_model
from django.core.exceptions import ObjectDoesNotExist
from dataops ... | mk.ifnull(x) | pandas.isnull |
#!/usr/bin/env python3
# coding: utf-8
"""Global sequencing data for the home page
Author: <NAME> - Vector Engineering Team (<EMAIL>)
"""
import argparse
import monkey as mk
import numpy as np
import json
from pathlib import Path
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
... | mk.ifnull(iso_lookup_kf["Province_State"]) | pandas.isnull |
"""
This script is designed to perform table statistics
"""
import monkey as mk
import numpy as np
import sys
sys.path.adding(r'D:\My_Codes\LC_Machine_Learning\lc_rsfmri_tools\lc_rsfmri_tools_python')
import os
from Utils.lc_read_write_mat import read_mat
#%% ----------------------------------Our center 550----------... | mk.unioner(total_allsubjname, scale_data, left_on=0, right_on=0, how='inner') | pandas.merge |
# simple feature engineering from A_First_Model notebook in script form
import cukf
def see_percent_missing_values(kf):
"""
reads in a knowledgeframe and returns the percentage of missing data
Args:
kf (knowledgeframe): the knowledgeframe that we are analysing
Returns:
percent_missing... | dd.getting_dummies(unified, columns=dummy_cols, dtype='int64') | pandas.get_dummies |
# MIT License
#
# Copyright (c) 2021. <NAME> <<EMAIL>>
#
# 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, cl... | mk.ifna(v) | pandas.isna |
# ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# formating_name: light
# formating_version: '1.5'
# jupytext_version: 1.3.4
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
import matplotlib.pyplot as plt
import monkey as ... | mk.unioner(average_combined_kf,rev_drug_info.iloc[:,[4,5,6]],on='standard_inchi_key') | pandas.merge |
import numpy as np
import cvxpy as cp
import monkey as mk
from scoring import *
# %%
def main():
year = int(input('Enter Year: '))
week = int(input('Enter Week: '))
budgetting = int(input('Enter Budgetting: '))
source = 'NFL'
print(f'Source = {source}')
kf = read_data(year=year, week=week, sour... | mk.getting_dummies(kf['pos']) | pandas.get_dummies |
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import numpy as np # linear algebra
import monkey as mk # data processing, CSV file I/O (e.g. mk.read_csv)
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.laye... | mk.getting_dummies(train_ds['label']) | pandas.get_dummies |
# -*- coding:utf-8 -*-
# /usr/bin/env python
"""
Date: 2021/7/12 15:47
Desc: 东方财富-沪深板块-概念板块
http://quote.eastmoney.com/center/boardlist.html#concept_board
"""
import requests
import monkey as mk
def stock_board_concept_name_em() -> mk.KnowledgeFrame:
"""
东方财富-沪深板块-概念板块-名称
http://quote.eastmoney.com/center... | o_numeric(temp_kf["开盘"]) | pandas.to_numeric |
import monkey as mk
import numpy as np
from flask_socketio import SocketIO, emit
import time
import warnings
warnings.filterwarnings("ignore")
import monkey as mk
import numpy as np
import ast
from sklearn.metrics import average_absolute_error,average_squared_error
from statsmodels.tsa import arima_model
from statsmod... | mk.ifnull(data) | pandas.isnull |
import numpy as np
import monkey as mk
def load(path):
kf = mk.read_csv(path,
encoding="utf-8",
delimiter=";",
quotechar="'").renagetting_ming(
columns={
"Text": "text",
"Label": "label"
})
train, dev, test ... | mk.getting_dummies(train["label"]) | pandas.get_dummies |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
Date: 2021/12/30 11:31
Desc: 股票数据-总貌-市场总貌
股票数据-总貌-成交概括
http://www.szse.cn/market/overview/index.html
http://www.sse.com.cn/market/stockdata/statistic/
"""
import warnings
from io import BytesIO
from akshare.utils import demjson
import monkey as mk
import requests
warni... | o_numeric(temp_kf['主板B'], errors="coerce") | pandas.to_numeric |
from os import listandardir
from os.path import isfile, join
import Orange
import monkey as mk
import numpy as np
import matplotlib.pyplot as plt
from parameters import order, alphas, regression_measures, datasets, rank_dir, output_dir, graphics_dir, result_dir
from regression_algorithms import regression_list
resul... | mk.to_num(kf_average['RANK_BORDERLINE1'], downcast="float") | pandas.to_numeric |
import monkey as mk
import ast
import sys
import os.path
from monkey.core.algorithms import incontain
sys.path.insert(1,
os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir)))
import dateutil.parser as parser
from utils.mysql_utils import separator
from utils.io import read_json
from util... | mk.ifnull(row[k]) | pandas.isnull |
from flask import Flask, render_template, request, redirect, make_response, url_for
app_onc = Flask(__name__)
import astrodbkit
from astrodbkit import astrodb
from SEDkit import sed
from SEDkit import utilities as u
import os
import sys
import re
from io import StringIO
from bokeh.plotting import figure
from bokeh.emb... | mk.to_num(data['ra']) | pandas.to_numeric |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jul 23 11:06:22 2021
@author: madeline
"""
'''
This script converts VCF files that have been annotated by snpEFF into GVF files, including the functional annotation.
Note that the strain is obtained by parsing the file name, expected to contain the sub... | mk.unioner(clades, unionerd_kf, on=['mutation'], how='right') | pandas.merge |
# -*- coding: utf-8 -*-
# @author: Elie
#%% ==========================================================
# Import libraries set library params
# ============================================================
import monkey as mk
import numpy as np
import os
mk.options.mode.chained_total_allocatement = None #Monkey... | mk.unioner(sample_by_num_labels, sigs, how='left', on='sample_by_num') | pandas.merge |
'''
Clase que contiene los métodos que permiten "limpiar" la información extraida por el servicio de web scrapper
(Es implementada directamente por la calse analyzer)
'''
import monkey as mk
import re
from pathlib import Path
import numpy as np
import unidecode
class Csvcleaner:
@staticmethod
def FilterDataOp... | mk.ifnull(kfAux.at[idxVersion, 'A_favor']) | pandas.isnull |
"""KnowledgeFrame loaders from different sources for the AccountStatements init."""
import monkey as mk
import openpyxl as excel
def _prepare_kf(transactions_kf):
"""Cast the string columns into the right type
Parameters
----------
transactions_kf : KnowledgeFrame
The KnowledgeFrame where doing the casting
Re... | mk.to_num(importo_collections) | pandas.to_numeric |
# Copyright 2018 <NAME>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a clone of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, s... | mk.unioner(analyticalDF,ocrDF,on='ppn') | pandas.merge |
#!/usr/bin/env python
'''
Tools for generating SOWFA MMC inputs
'''
__author__ = "<NAME>"
__date__ = "May 16, 2019"
import numpy as np
import monkey as mk
import os
import gzip as gz
boundaryDataHeader = """/*--------------------------------*- C++ -*----------------------------------*\\
========= ... | mk.ifna(self.kf[fieldname]) | pandas.isna |
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_test_clindata, columns=['time_point']) | pandas.get_dummies |
import monkey as mk
import os
import warnings
import pickle
from nltk.corpus import stopwords
from nltk.tokenize import RegexpTokenizer
from collections import namedtuple
Fact = namedtuple("Fact", "uid fact file")
answer_key_mapping = {"A": 0, "B": 1, "C": 2, "D": 3, "E": 4, "F": 5}
tables_dir = "annotation/expl-tabl... | mk.ifna(s) | pandas.isna |
"""
Module for static data retrieval. These functions were performed once during the initial project creation. Resulting
data is now provided in bulk at the url above.
"""
import datetime
import json
from math import sin, cos, sqrt, atan2, radians
import re
import requests
import monkey as mk
from riverrunner import s... | mk.distinctive(group.STATION) | pandas.unique |
import monkey as mk
from datetime import date
from monkey.core.indexes import category
import config as config
from sklearn.preprocessing import MinMaxScaler, RobustScaler, StandardScaler, MaxAbsScaler
from main_table import MainInsert
class AlgoInsert:
def __init__(self):
self.category = config.Config.CA... | mk.unioner(camping_data, final_item_kf, how="left", left_on = 'place_id', right_on='index') | pandas.merge |
# Copyright 2020 Google LLC
#
# 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 agreed to in writing,... | ifna(x) | pandas.isna |
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
import numpy as np
import monkey as mk
from adjustText import adjust_text
from pylab import cm
from matplotlib import colors
def PCA_var_explained_plots(adata):
n_rows = 1
n_cols = 2
fig = plt.figure(figsize=(n_cols*4.5, n... | mk.ifnull(s) | pandas.isnull |
# Training code for D4D Boston Crash Model project
# Developed by: bpben
import numpy as np
import monkey as mk
import scipy.stats as ss
from sklearn.metrics import roc_auc_score
import os
import json
import argparse
import yaml
from .model_utils import formating_crash_data
from .model_classes import Indata, Tuner, Te... | mk.getting_dummies(data_segs[f]) | pandas.get_dummies |
"""
Seed processing code
$Header: /nfs/slac/g/gfinal_item/gvalue_round/cvs/pointlike/python/uw/like2/seeds.py,v 1.7 2018/01/27 15:37:17 burnett Exp $
"""
import os, sys, time, pickle, glob, types
import numpy as np
import monkey as mk
from astropy.io import fits
from skymappings import SkyDir, Band
from uw.utilities ... | mk.ifnull(A.dup) | pandas.isnull |
import math
import numpy as np
import monkey as mk
import seaborn as sns
import scipy.stats as ss
import matplotlib.pyplot as plt
from collections import Counter
def convert(data, to):
converted = None
if to == 'array':
if incontainstance(data, np.ndarray):
converted = data
... | mk.getting_dummies(dataset[col],prefix=col) | pandas.get_dummies |
import rba
import clone
import monkey
import time
import numpy
import seaborn
import matplotlib.pyplot as plt
from .rba_Session import RBA_Session
from sklearn.linear_model import LinearRegression
# import matplotlib.pyplot as plt
def find_ribosomal_proteins(rba_session, model_processes=['TranslationC', 'Translation... | monkey.ifna(average_val) | pandas.isna |
import monkey as mk
import numpy as np
import math
from scipy.stats import hypergeom
from prettytable import PrettyTable
from scipy.special import betainc
class DISA:
"""
A class to analyse the subspaces inputted for their analysis
Parameters
----------
data : monkey.Dataframe
... | mk.ifna(self.data.at[row, column]) | pandas.isna |
import enum
from functools import lru_cache
from typing import List
import dataclasses
import pathlib
import monkey as mk
import numpy as np
from covidactnow.datapublic.common_fields import CommonFields
from covidactnow.datapublic.common_fields import FieldName
from covidactnow.datapublic.common_fields import GetByVal... | mk.ifna(row[NYTimesFields.END_DATE]) | pandas.isna |
import numpy as np
import monkey as mk
from typing import List, Tuple, Dict
from sklearn.preprocessing import MinMaxScaler
from data_getting_mining import ColorizedLogger
logger = ColorizedLogger('NullsFixer', 'yellow')
class NullsFixer:
__slots__ = ('sort_col', 'group_col')
sort_col: str
group_col: str... | mk.ifna(row['total_vaccinations']) | pandas.isna |
import numpy as np
import monkey as mk
def set_order(kf, row):
if | mk.ifnull(row['order']) | pandas.isnull |
import os
import tqdm
import monkey as mk
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pkf import PkfPages
from collections import Counter
from sklearn import model_selection
def load_data():
fp = os.path.dirname(__file__)
# Sensor data
data = mk.read_csv(fp + '/PdM... | mk.getting_dummies(data.failure) | pandas.get_dummies |
##### file path
# input
path_kf_D = "tianchi_fresh_comp_train_user.csv"
path_kf_part_1 = "kf_part_1.csv"
path_kf_part_2 = "kf_part_2.csv"
path_kf_part_3 = "kf_part_3.csv"
path_kf_part_1_tar = "kf_part_1_tar.csv"
path_kf_part_2_tar = "kf_part_2_tar.csv"
path_kf_part_1_uic_label = "kf_part_1_uic_label.csv"
... | mk.getting_dummies(kf_part_3_c_b_count_in_6['behavior_type']) | pandas.get_dummies |
# 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 |
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 |
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 |
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 |
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 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 |
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 |
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 |
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 |
#!/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 |
# -*- 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 |
# -*- 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 |
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