MSWon

import numpy as np
import pandas as pd


train = pd.read_json('./KorQuAD_v1.0_train.json')
valid = pd.read_json('./KorQuAD_v1.0_dev.json')

valid.head(5)

valid['data'][0]['paragraphs'][0]

MSWon

import json
import numpy as np
import pandas as pd
from sklearn.utils import shuffle

filename = './yelp_academic_dataset_review.json'

def make_dataset(filename):
    
    data =[]

MSWon

from hgtk.text import decompose as decom

a = decom("감스트")
b = a.split("ᴥ")
del(b[-1])

MSWon

import pandas as pd

months = ['Jan','Apr','Mar','June']
days = [31,30,31,30]

d = {'Month':months,'Day':days}
df = pd.DataFrame(d)

'''
 Day Month

MSWon

from glob import glob

glob("./*")  ## Get list of file names in  current dir

MSWon

dir /b > list.txt

MSWon

import pandas as pd
import numpy as np
from matplotlib import pyplot as plt

data = pd.read_csv("./loss_accuracy.csv")

plt.figure(figsize = (10,7))
plt.plot(range(1,11),data['train_loss'], label = 'train', marker = "D",linewidth = 2.5, markersize=8,
         color = "C1")
plt.plot(range(1,11),data['test_loss'], label = 'test', marker = "D",linewidth = 2.5, markersize=8,

MSWon

import pandas as pd

## df is pd.DataFrame
h = [g for _, g in df.groupby('v1')]

MSWon

import re

test = "What do you need?"
re.sub("do", "", test)
## "What  you need?" ##

MSWon

from keras.preprocessing.sequence import pad_sequences

train_seq = [[1,2,3],[4,7,9,1]]
pad_sequences(train_seq, maxlen = 5, padding = "post")