How does Label Encoder assigns the same number

Question

I have the column in my data frame

city 

London
Paris
New York 
.

I am label encoding the column and it assigns the 0 to London , 1 to Paris and 2 to New York . But when I pass single value for predictions from model I gives city name New York and it assigns the 0 to it . How it shall remains same , I want that if New York values assigns 2 by label encoder in training phase, it should assign 2 again at the predictions .

Code
from sklearn.preprocessing import LabelEncoder
labelencoder=LabelEncoder()
df['city']=labelencoder.fit_transform(df['city'])

Answer 1

I am creating a dummy data set by using list and using the zip function.

city = ['London','Paris','New York ']
continent = ['Europe', 'Europe' ,'North America']

data = list(zip(city, continent))
data

Output

[('London', 'Europe'), ('Paris', 'Europe'), ('New York ', 'North America')]

Converting the data set into data frame

import pandas as pd
from sklearn.preprocessing import LabelEncoder
labelencoder=LabelEncoder()

df= pd.DataFrame(data, columns=['city', 'continent'])
df

df['label'] = labelencoder.fit_transform(df['city'])
df

City        Continent
London      Europe
Paris       Europe
New York   North America

You need to use fit_transform to fit the encoder and then transform the data. This will encode the labels as you want and will not re-fit the encoder.
Output

City Continent label London Europe 0 Paris Europe 2 New York North America 1