Using ‘drop’ method we can drop the rows from a DataFrame either using row index or row label.
I am using below dataset to demonstrate the examples.
Name Age City Gender Rating 0 Krishna 34 Bangalore Male 81 1 Sailu 35 Hyderabad Female 76 2 Joel 29 Hyderabad Male 67 3 Chamu 35 Chennai Female 100 4 Jitendra 52 Bangalore Male 87 5 Raj 34 Chennai Male 89
Drop a row at specific index
Syntax
df.drop(row_index)
Example
df_without_row_2 = df.drop(2)
df_without_row_2 contain below dataset.
Name Age City Gender Rating 0 Krishna 34 Bangalore Male 81 1 Sailu 35 Hyderabad Female 76 3 Chamu 35 Chennai Female 100 4 Jitendra 52 Bangalore Male 87 5 Raj 34 Chennai Male 89
Drop rows at indexes 3 and 5
Syntax
df.drop([row_index_1, row_index_2])
Example
df_drop_rows_3_and_5 = df.drop([3, 5])
df_drop_rows_3_and_5 contain below dataset.
Name Age City Gender Rating 0 Krishna 34 Bangalore Male 81 1 Sailu 35 Hyderabad Female 76 2 Joel 29 Hyderabad Male 67 4 Jitendra 52 Bangalore Male 87
Unless you set the argument ‘inplace’ to True, drop method do not have any impact on original DataFrame. To reflect the changes in original data frame set inplace to True.
df.drop([3, 5], inplace=True)
Find the below working application.
drop_rows_using_index.py
import pandas as pd
# Create a sample DataFrame
data = {'Name': ['Krishna', 'Sailu', 'Joel', 'Chamu', 'Jitendra', "Raj"],
'Age': [34, 35, 29, 35, 52, 34],
'City': ['Bangalore', 'Hyderabad', 'Hyderabad', 'Chennai', 'Bangalore', 'Chennai'],
'Gender': ['Male', 'Female', 'Male', 'Female', 'Male', 'Male'],
'Rating': [81, 76, 67, 100, 87, 89]}
df = pd.DataFrame(data)
df_without_row_2 = df.drop(2)
df_drop_rows_3_and_5 = df.drop([3, 5])
print('Original DataFrame')
print(df)
print('\ndf_without_row_2')
print(df_without_row_2)
print('\ndf_drop_rows_3_and_5')
print(df_drop_rows_3_and_5)
df.drop([3, 5], inplace=True)
print('\nOriginal DataFrame after dropping rows 3 and 5')
print(df)
Output
Original DataFrame
Name Age City Gender Rating
0 Krishna 34 Bangalore Male 81
1 Sailu 35 Hyderabad Female 76
2 Joel 29 Hyderabad Male 67
3 Chamu 35 Chennai Female 100
4 Jitendra 52 Bangalore Male 87
5 Raj 34 Chennai Male 89
df_without_row_2
Name Age City Gender Rating
0 Krishna 34 Bangalore Male 81
1 Sailu 35 Hyderabad Female 76
3 Chamu 35 Chennai Female 100
4 Jitendra 52 Bangalore Male 87
5 Raj 34 Chennai Male 89
df_drop_rows_3_and_5
Name Age City Gender Rating
0 Krishna 34 Bangalore Male 81
1 Sailu 35 Hyderabad Female 76
2 Joel 29 Hyderabad Male 67
4 Jitendra 52 Bangalore Male 87
Original DataFrame after dropping rows 3 and 5
Name Age City Gender Rating
0 Krishna 34 Bangalore Male 81
1 Sailu 35 Hyderabad Female 76
2 Joel 29 Hyderabad Male 67
4 Jitendra 52 Bangalore Male 87
Drop rows using row labels
I am using below data set to demonstrate the examples.Age City Gender Rating Name Krishna 34 Bangalore Male 81 Sailu 35 Hyderabad Female 76 Joel 29 Hyderabad Male 67 Chamu 35 Chennai Female 100 Jitendra 52 Bangalore Male 87 Raj 34 Chennai Male 89
Drop a row with row label
Syntax
df.drop[row_label]
Example
df_drop_krishna = df.drop('Krishna')
df_drop_krishna contain below dataset.
Age City Gender Rating Name Sailu 35 Hyderabad Female 76 Joel 29 Hyderabad Male 67 Chamu 35 Chennai Female 100 Jitendra 52 Bangalore Male 87 Raj 34 Chennai Male 89
Drop multiple rows using row labels
Syntax
df.drop[row_label_1, row_label_2]
Example
df_drop_krishna_and_chamu = df.drop(['Krishna', 'Chamu'])
df_drop_krishna_and_chamu contain below dataset.
Age City Gender Rating Name Sailu 35 Hyderabad Female 76 Joel 29 Hyderabad Male 67 Jitendra 52 Bangalore Male 87 Raj 34 Chennai Male 89
Unless you set the argument ‘inplace’ to True, drop method do not have any impact on original DataFrame. To reflect the changes in original data frame set inplace to True.
df.drop(['Krishna', 'Chamu'], inplace=True)
Find the below working application.
drop_rows_using_labels.py
import pandas as pd
# Create a sample DataFrame
data = {'Name': ['Krishna', 'Sailu', 'Joel', 'Chamu', 'Jitendra', "Raj"],
'Age': [34, 35, 29, 35, 52, 34],
'City': ['Bangalore', 'Hyderabad', 'Hyderabad', 'Chennai', 'Bangalore', 'Chennai'],
'Gender': ['Male', 'Female', 'Male', 'Female', 'Male', 'Male'],
'Rating': [81, 76, 67, 100, 87, 89]}
df = pd.DataFrame(data)
df.set_index('Name', inplace=True)
df_drop_krishna = df.drop('Krishna')
df_drop_krishna_and_chamu = df.drop(['Krishna', 'Chamu'])
print('Original DataFrame')
print(df)
print('\nRow with label "Krishna" is deleted')
print(df_drop_krishna)
print('\nRow with label "Krishna" and "Chamu" are deleted')
print(df_drop_krishna_and_chamu)
df.drop(['Krishna', 'Chamu'], inplace=True)
print('\nOriginal DataFrame after dropping rows with labels "Krishna" and "Chamu"')
print(df)
Output
Original DataFrame
Age City Gender Rating
Name
Krishna 34 Bangalore Male 81
Sailu 35 Hyderabad Female 76
Joel 29 Hyderabad Male 67
Chamu 35 Chennai Female 100
Jitendra 52 Bangalore Male 87
Raj 34 Chennai Male 89
Row with label "Krishna" is deleted
Age City Gender Rating
Name
Sailu 35 Hyderabad Female 76
Joel 29 Hyderabad Male 67
Chamu 35 Chennai Female 100
Jitendra 52 Bangalore Male 87
Raj 34 Chennai Male 89
Row with label "Krishna" and "Chamu" are deleted
Age City Gender Rating
Name
Sailu 35 Hyderabad Female 76
Joel 29 Hyderabad Male 67
Jitendra 52 Bangalore Male 87
Raj 34 Chennai Male 89
Original DataFrame after dropping rows with labels "Krishna" and "Chamu"
Age City Gender Rating
Name
Sailu 35 Hyderabad Female 76
Joel 29 Hyderabad Male 67
Jitendra 52 Bangalore Male 87
Raj 34 Chennai Male 89
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