You can use this solution implemented to pandas by Series.apply:

from Crypto.Cipher import XOR
import base64

def encrypt(key, plaintext):
  cipher = XOR.new(key)
  return base64.b64encode(cipher.encrypt(plaintext))

def decrypt(key, ciphertext):
  cipher = XOR.new(key)
  return cipher.decrypt(base64.b64decode(ciphertext))

load['Encoded_Column'] = load['F'].apply(lambda x: encrypt('password',x))
load['Decoded_Column'] = (load['Encoded_Column'].apply(lambda x: decrypt('password', x))
                                                .str.decode("utf-8"))
print (load)
   A  B  C  D  E  F Encoded_Column Decoded_Column
0  a  4  7  1  5  a        b'EQ=='              a
1  b  5  8  3  3  a        b'EQ=='              a
2  c  4  9  5  6  a        b'EQ=='              a
3  d  5  4  4  9  b        b'Eg=='              b
4  e  5  2  2  2  b        b'Eg=='              b
5  f  4  0  0  4  b        b'Eg=='              b

Another solution:

import base64
def encode(key, clear):
    enc = []
    for i in range(len(clear)):
        key_c = key[i % len(key)]
        enc_c = chr((ord(clear[i]) + ord(key_c)) % 256)
        enc.append(enc_c)
    return base64.urlsafe_b64encode("".join(enc).encode()).decode()

def decode(key, enc):
    dec = []
    enc = base64.urlsafe_b64decode(enc).decode()
    for i in range(len(enc)):
        key_c = key[i % len(key)]
        dec_c = chr((256 + ord(enc[i]) - ord(key_c)) % 256)
        dec.append(dec_c)
    return "".join(dec)

load['Encoded_Column'] = load['F'].apply(lambda x: encode('password',x))
load['Decoded_Column'] = load['Encoded_Column'].apply(lambda x: decode('password', x))

Or use list comprehension:

load['Encoded_Column'] = [encode('password',x) for x in load['F']]
load['Decoded_Column'] = [decode('password', x) for x in load['Encoded_Column']]

print (load)
   A  B  C  D  E  F Encoded_Column Decoded_Column
0  a  4  7  1  5  a           w5E=              a
1  b  5  8  3  3  a           w5E=              a
2  c  4  9  5  6  a           w5E=              a
3  d  5  4  4  9  b           w5I=              b
4  e  5  2  2  2  b           w5I=              b
5  f  4  0  0  4  b           w5I=              b

You can using category dtype in sklearn , it should be labelencoder

df.city=df.city.astype('category').cat.codes
df
Out[385]: 
   school  city  category  capacity
0       1     0        45        23
1       2     1        12       236
2       3     2         8        63
3       4     0         7       234