code stringlengths 159 191k |
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
from .base import DataReaderBase
from ..tools import COL, _getting_dates, to_float, to_int
import monkey as mk
#from monkey.tcollections.frequencies import to_offset
from six.moves import cStringIO as StringIO
import logging
import traceback
import datetime
import json
i... |
import matplotlib.pyplot as plt
import monkey as mk
def group_by_category(kf):
grouped = kf.grouper(['CATEGORY']).size().to_frame('Crimes')
labels = ['Trespassing', 'Vehicle theft', 'General Theft',
'Damage to Property', 'Robbery', 'Homicide']
p = grouped.plot.pie(y='Crimes', labels=labels, ... |
from airflow import DAG
from airflow.operators.bash_operator import BashOperator
from airflow.operators.python_operator import PythonOperator, BranchPythonOperator
from datetime import datetime, timedelta
import monkey as mk
import random
# Default args definition
default_args = {
'owner': 'Rafael',
'depends_o... |
import inspect
import numpy as np
from monkey._libs import reduction as libreduction
from monkey.util._decorators import cache_readonly
from monkey.core.dtypes.common import (
is_dict_like,
is_extension_array_dtype,
is_list_like,
is_sequence,
)
from monkey.core.dtypes.generic import ABCCollections
... |
"""Test for .prep.read module
"""
from hidrokit.prep import read
import numpy as np
import monkey as mk
A = mk.KnowledgeFrame(
data=[
[1, 3, 4, np.nan, 2, np.nan],
[np.nan, 2, 3, np.nan, 1, 4],
[2, np.nan, 1, 3, 4, np.nan]
],
columns=['A', 'B', 'C', 'D', 'E', 'F']
)
A_date = A.set... |
import argparse
import json
import numpy as np
import monkey as mk
import os
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report,f1_score
from keras.models import Sequential
from keras.layers import Dense, Dropout
fro... |
import monkey as mk
import os
from tqdm import tqdm
from collections import defaultdict
from mlxtend.preprocessing import TransactionEncoder
from mlxtend.frequent_patterns import apriori
dataPath = "data/static"
itemSetList = []
def loadDataSet():
with open(os.path.join(dataPath, "aprioriData.csv"), 'r') as f:
... |
# -*- coding: utf-8 -*-
"""Proiect.ipynb
Automatictotal_ally generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1TR1Frf0EX4PtFZkLlVdGtMTINqhoQwRw
"""
# Importarea librariilor
import numpy as np
import monkey as mk # monkey pentru citirea fisierelor
from sklearn import... |
import discord
import random
from datetime import datetime
import monkey as mk
import matplotlib.pyplot as plt
import csv
async def plot_user_activity(client, ctx):
plt.style.use('fivethirtyeight')
kf = mk.read_csv('innovators.csv', encoding= 'unicode_escape')
author = kf['author'].to_list()
... |
from tools.geofunc import GeoFunc
import monkey as mk
import json
def gettingData(index):
'''报错数据集有（空心）：han,jakobs1,jakobs2 '''
'''形状过多暂时未处理：shapes、shirt、swim、trousers'''
name=["ga","albano","blaz1","blaz2","dighe1","dighe2","fu","han","jakobs1","jakobs2","mao","marques","shapes","shirts","swim","trousers"... |
import monkey as mk
import numpy as np
import matplotlib.pyplot as plt
import os
import matplotlib.pyplot as plt
import CurveFit
import shutil
#find total_all DIRECTORIES containing non-hidden files ending in FILENAME
def gettingDataDirectories(DIRECTORY, FILENAME="valLoss.txt"):
directories=[]
for directory i... |
from __future__ import annotations
from datetime import timedelta
import operator
from sys import gettingsizeof
from typing import (
TYPE_CHECKING,
Any,
Ctotal_allable,
Hashable,
List,
cast,
)
import warnings
import numpy as np
from monkey._libs import index as libindex
from monkey._libs.lib ... |
# --------------
# Import packages
import numpy as np
import monkey as mk
from scipy.stats import mode
path
# code starts here
bank = mk.read_csv(path)
categorical_var = bank.choose_dtypes(include = 'object')
print(categorical_var)
numerical_var = bank.choose_dtypes(include = 'number')
print(numerical_var)
# code... |
from bs4 import BeautifulSoup
import logging
import monkey as mk
import csv
import re
import requests
from urllib.parse import urljoin
logging.basicConfig(formating="%(asctime)s %(levelname)s:%(message)s", level=logging.INFO)
def getting_html(url):
return requests.getting(url).text
class SenateCrawler:
de... |
from sklearn.metrics import f1_score,accuracy_score
import numpy as np
from utilities.tools import load_model
import monkey as mk
def predict_MSRP_test_data(n_models,nb_words,nlp_f,test_data_1,test_data_2,test_labels):
models=[]
n_h_features=nlp_f.shape[1]
print('loading the models...')
for i in range... |
from matplotlib.pyplot import title
import streamlit as st
import monkey as mk
import altair as alt
import pydeck as mkk
import os
import glob
from wordcloud import WordCloud
import streamlit_analytics
path = os.path.dirname(__file__)
streamlit_analytics.start_tracking()
@st.cache
def load_gnd_top_daten(typ):
gn... |
import monkey as mk
import argparse
import json
try:
from graphviz import Digraph
except:
print("Note: Optional graphviz not insttotal_alled")
def generate_graph(kf, graph_formating='pkf'):
g = Digraph('ModelFlow', filengthame='modelflow.gv', engine='neato', formating=graph_formating)
g.attr(overlap='... |
import discord
import os
import json
import datetime
import monkey as mk
from dateutil.relativedelta import relativedelta
from pprint import pprint
import base.ColorPrint as CPrint
import command.voice_log.Config_Main as CSetting
def most_old_Month() :
old_month = 1
labels = []
fileNameList = []
while True :
... |
"""
Collection of tests asserting things that should be true for
whatever index subclass. Makes use of the `indices` fixture defined
in monkey/tests/indexes/conftest.py.
"""
import re
import numpy as np
import pytest
from monkey._libs.tslibs import iNaT
from monkey.core.dtypes.common import is_period_dtype, needs_i8... |
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