Reindexing Dataframe after rows filter in Pandas and preserving previous index

I have an excel sheet with below records

Lets filter out all those rows where customer name contain “ika”

import pandas as pd
import numpy as np

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

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

#implementing filter  not like condition
df2 = df1[~df1.customer_name.str.contains('ika', na=False)]
print('Rows where customer Name not contain ika \n',df2)

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 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

Now here if you see the output of df2, you will there are two rows with index 1 and 4, which simply indicates that it require reindexing. Lets put the logic of reindexing

#reindexing DF2 dataframe
df3 = df2.reset_index(drop=True)
print('After reindexing of DF3 \n',df3)

Output:

After reindexing of DF3

    account_no  branch  city_code customer_name  amount

0        2119     NaN      215.0      Prayansh   12000

1        2356  7548.0      256.0           NaN   15000

Now here if you see in output, the index are correct and in sequence i.e. 0 and 1.

Now lets check the syntax

df2.reset_index(drop=True)

there is a parameter “drop=True”, this actually drops the existing index on the rows and create new one starting with 0.

But what if we want to preserve the actual index of the rows… just simple remove the optional parameter  “drop=True”

#what if we remove "drop=True" parameter og reset index

df4 = df2.reset_index()
print('After reindexing of DF2 \n',df4)

Output

After reindexing of DF2

    index  account_no  branch  city_code customer_name  amount

0      1        2119     NaN      215.0      Prayansh   12000

1      4        2356  7548.0      256.0           NaN   15000

So here you can see, it creates a new column called “index”, and preserve the existing index numbering.

Full code:

import pandas as pd
import numpy as np

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

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

#implementing filter  not like condition
df2 = df1[~df1.customer_name.str.contains('ika', na=False)]
print('Rows where customer Name not contain ika \n',df2)

#reindexing DF2 dataframe
df3 = df2.reset_index(drop=True)
print('After reindexing of DF2 \n',df3)

#what if we remove "drop=True" parameter og reset index

df4 = df2.reset_index()
print('After reindexing of DF2 \n',df4)

Data Science with…Python :

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