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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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 :) 

Post Reference: Vikram Aristocratic Elfin Share

Removing Null Value rows from Dataframe in Pandas

I have an excel sheet with below records

Here if you see second and fourth rows having null value, so our objective to remove these rows from process, lets see how we can do with panda package of python

import pandas as pd
import numpy as np

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

print('Original DF with NULL value in rows \n',df1 , '\n')

for col in df1.columns:
    df1=df1[df1[col].notnull()]

print('After removing rows with NULL Value \n',df1)

Output:

Original DF with NULL value in 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

After removing rows with NULL Value

    account_no  branch  city_code customer_name  amount

0        2112  3212.0      321.0       Sidhika   19000

2        2115  4321.0      212.0       Rishika   15000

Data Science with…Python J

Post Reference: Vikram Aristocratic Elfin Share