Drop a list of rows from a Pandas DataFrame

Let us see how to drop a list of rows in a Pandas DataFrame. We can do this using the Pandas drop() function. We will also pass inplace = True and axis=0 to denote row, as it makes the changes we make in the instance stored in that instance without doing any assignment.

Creating Dataframe to drop a list of rows

# import the module
import pandas as pd

# creating a DataFrame
dictionary = {'Names': ['Simon', 'Josh', 'Amen', 'Habby',
                        'Jonathan', 'Nick'],
              'Countries': ['AUSTRIA', 'BELGIUM', 'BRAZIL',
                            'FRANCE', 'INDIA', 'GERMANY']}
table = pd.DataFrame(dictionary, columns=['Names', 'Countries'],
                     index=['a', 'b', 'c', 'd', 'e', 'f'])

display(table)

Output: 

Drop a list of rows from a Pandas DataFrame using df.drop

In this example, we are simply dropping the row with the index 3.

# drop 3rd row
display("Dropped 3rd row")
display(table.drop('c'))

Delete rows from pandas without mentioning the index labels

Here, we are simply dropping rows 1 and 3 from the Dataframe table. At first, we dropped using the index value and after that, we use the row name to drop the row. 

# gives the table with the dropped rows
display("Table with the dropped rows")
display(table.drop(table.index[[1, 3]]))

# You can also use index no. instead rows name
# display(table.drop(['a', 'd']))

Output: 

Drop a list of rows from a Pandas DataFrame using inplace

In this example, we are dropping the rows with and without inplace. Here, we use inplace=True which performs the drop operation in the same Dataframe, rather than creating a new Dataframe object during the drop operation.

table = pd.DataFrame(dictionary, columns=['Names', 'Countries'],
                     index=['a', 'b', 'c', 'd', 'e', 'f'])

# it gives none but it makes changes in the table
display(table.drop(['a', 'd'], inplace=True))

# final table
print("Final Table")
display(table)

Output:

Drop Rows by Index Range in Pandas DataFrame

The range's lower and upper limits are inclusive and exclusive, respectively. Accordingly, rows 0 and 1 will be removed, but row 2 won't be.

# table after removing range of rows from 0 to 2(not included)
table.drop(table.index[0:2], inplace=True)

display(table)

Output:

Drop Rows with Conditions in Pandas

The Josh name from the Dataframe is dropped based on the condition that if df['Names'] == 'Josh'], then drop that row. You can drop multiple rows with more conditions by following the same syntax.

df = table
index_names = df[ df['Names'] == 'Josh'].index
  
# drop these row indexes
# from dataFrame
df.drop(index_names, inplace = True)

display(table)

Output:

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