text_plain_output_1.png image_output_2.png image_output_1.png
import pandas as pd

train_set = pd.read_csv('/kaggle/input/aviakompaniya/train_dataset.csv')
test_set = pd.read_csv('/kaggle/input/aviakompaniya/test_dataset.csv')
sample = pd.read_csv('/kaggle/input/aviakompaniya/sample_submission.csv')

df = train_set.dropna()

df_100 = df[df['Flight Distance'] > 100]

df_100.select_dtypes('object').columns

Gender = list(df_100.Gender) + list(df_100.Gender)
Customer_Type = list(df_100['Customer Type']) + list(df_100['Customer Type'])
Type_of_Travel = list(df_100['Type of Travel']) + list(df_100['Type of Travel'])
Classes = list(df_100['Class']) + list(df_100['Class'])
print('\nGender ustunidagi takrorlanmas qiymatlar soni : \n ', len(set(Gender)))
print('\nGender ustunidagi takrorlanmas qiymatlari : \n ', set(Gender))
print('\nCustomer_Type ustunidagi takrorlanmas qiymatlar soni : \n ', len(set(Customer_Type)))
print('\nCustomer_Type ustunidagi takrorlanmas qiymatlari: \n ', set(Customer_Type))
print('\nType_of_Travel ustunidagi takrorlanmas qiymatlar soni : \n ', len(set(Type_of_Travel)))
print('\nType_of_Travel ustunidagi takrorlanmas qiymatlari : \n ', set(Type_of_Travel))
print('\nClass ustunidagi takrorlanmas qiymatlar soni : \n ', len(set(Classes)))
print('\nClass ustunidagi takrorlanmas qiymatlari : \n ', set(Classes))