import csv
import os
import shutil
import codecs
import pandas as pd
import numpy as np

from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import LabelBinarizer
from sklearn.preprocessing import MultiLabelBinarizer

dir_name = ‘C:UsersThuang6DesktopMaxWellDataOneHotcsv_to_csv.csv’

path = os.chdir(‘C:UsersThuang6DesktopMaxWellDataOneHot’)
df = pd.read_csv(dir_name,names=[‘Time’,’Process’,’Component’,’Operation’,’Action’,’Control’,’Category’,’Context’],index_col = False)
df = df.fillna(value= ‘NULL’)
process = LabelBinarizer().fit_transform(df[‘Process’])
print(process)

component = LabelBinarizer().fit_transform(df[‘Component’])
print(component)

operation = LabelBinarizer().fit_transform(df[‘Operation’])
print(operation)

action = LabelBinarizer().fit_transform(df[‘Action’])
print(action)

control = LabelBinarizer().fit_transform(df[‘Control’])
print(control)

category = LabelBinarizer().fit_transform(df[‘Category’])
print(category)

final_output = np.hstack((process,component,operation,action,control,category))

print(final_output)

final_split = np.vsplit(final_output,21)

print(final_split)

print(np.shape(final_split))

print(“nihao”)
d = []
for i in range(21):
a = final_split[i]
#print(a)
b = np.ndarray.flatten(a)
c = b.tolist()
d.append(c)
#print(d)
#print(len(d))
#print(type(b))

b = np.ndarray.flatten(a)

print(np.ndarray.flatten(a))

print(d)