Fetch rows on the basis of condition in Pandas Dataframe

I have an excel sheet with below records

Here we are trying to implement various filter criteria

·          Implementing value search

·          Implementing like condition

·          Implementing not like condition

And while doing these we will try to ignore the NULL condition with the help of “na=Falase” parameter. Lets code it  

import pandas as pd
import numpy as np

df1 = pd.read_csv("NullFilterExample.csv")

print('Original DF rows \n',df1 , '\n')

#implementing value search
df2=df1[df1.customer_name == 'Rishika']
print('Rows where customer Name like Rishika \n',df2)

#implementing like condition
df3 = df1[df1.customer_name.str.contains('ika', na=False)]
print('Rows where customer Name contain ika \n',df3)

#implementing not like condition
df4 = df1[~df1.customer_name.str.contains('ika', na=False)]
print('Rows where customer Name not contain ika \n',df4)

Output:

Original DF rows

    account_no  branch  city_code customer_name  amount

0        2112  3212.0      321.0       Sidhika   19000

1        2119     NaN      215.0      Prayansh   12000

2        2115  4321.0      212.0       Rishika   15000

3        2435  2312.0        NaN      Sagarika   13000

4        2356  7548.0      256.0           NaN   15000

Rows where customer Name like Rishika

    account_no  branch  city_code customer_name  amount

2        2115  4321.0      212.0       Rishika   15000

Rows where customer Name contain ika

    account_no  branch  city_code customer_name  amount

0        2112  3212.0      321.0       Sidhika   19000

2        2115  4321.0      212.0       Rishika   15000

3        2435  2312.0        NaN      Sagarika   13000

Rows where customer Name not contain ika

    account_no  branch  city_code customer_name  amount

1        2119     NaN      215.0      Prayansh   12000

4        2356  7548.0      256.0           NaN   15000

Data Science with…Python :) 

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