5 ways to drop rows in pandas DataFrame [Practical Examples]

Different methods to drop rows in pandas DataFrame

In this tutorial we will discuss how to drop rows  using the following methods:

  • Drop a single Row in DataFrame by Row Index Label
  • Drop a multiple Rows in DataFrame by Row Index Label
  • Drop a single Row in DataFrame by Row Index Position
  • Drop a multiple Rows in DataFrame by Row Index Position
  • Drop Rows in a DataFrame with conditions

 

Create pandas DataFrame with example data

DataFrame is a data structure used to store the data in two dimensional format. It is similar to table that stores the data in rows and columns. Rows represents the records/ tuples and columns refers to the attributes.

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We can create the DataFrame by using pandas.DataFrame() method.

Syntax:

pandas.DataFrame(input_data,columns,index)

Parameters:

It will take mainly three parameters

  1. input_data is represents a list of data
  2. columns represent the columns names for the data
  3. index represent the row numbers/values

We can also create a DataFrame using dictionary by skipping columns and indices.

Let’s see an example.

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Example:

Python Program to create a dataframe for market data from a dictionary of food items by specifying the column names.

#import the module
import pandas

#consider the food data
food_input={'id':['foo-23','foo-13','foo-02','foo-31'],
                  'name':['ground-nut oil','almonds','flour','cereals'],
                  'cost':[567.00,562.56,67.00,76.09],
                  'quantity':[1,2,3,2]}

#pass this food to the dataframe by specifying rows 
dataframe=pandas.DataFrame(food_input,index = ['item-1', 'item-2', 'item-3', 'item-4'])

#display the dataframe
print(dataframe)

Output:

            id            name    cost  quantity
item-1  foo-23  ground-nut oil  567.00         1
item-2  foo-13         almonds  562.56         2
item-3  foo-02           flour   67.00         3
item-4  foo-31         cereals   76.09         2

 

Method 1 - Drop a single Row in DataFrame by Row Index Label

Here we are going to delete/drop single row from the dataframe using index name/label.

Syntax:

dataframe.drop('index_label')

where,

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  1. dataframe is the input dataframe
  2. index_label represents the index name

 

Example 1: Drop last row in the pandas.DataFrame

In this example we are going to drop last row using row label

#import the module
import pandas

#consider the food data
food_input={'id':['foo-23','foo-13','foo-02','foo-31'],
                  'name':['ground-nut oil','almonds','flour','cereals'],
                  'cost':[567.00,562.56,67.00,76.09],
                  'quantity':[1,2,3,2]}

#pass this food to the dataframe by specifying rows 
dataframe=pandas.DataFrame(food_input,index = ['item-1', 'item-2', 'item-3', 'item-4'])

#drop item-4
print(dataframe.drop('item-4'))

Output:

            id            name    cost  quantity
item-1  foo-23  ground-nut oil  567.00         1
item-2  foo-13         almonds  562.56         2
item-3  foo-02           flour   67.00         3

 

Example 2: Drop nth row in the pandas.DataFrame

In this example we are going to drop second row using row label

#import the module
import pandas

#consider the food data
food_input={'id':['foo-23','foo-13','foo-02','foo-31'],
                  'name':['ground-nut oil','almonds','flour','cereals'],
                  'cost':[567.00,562.56,67.00,76.09],
                  'quantity':[1,2,3,2]}

#pass this food to the dataframe by specifying rows 
dataframe=pandas.DataFrame(food_input,index = ['item-1', 'item-2', 'item-3', 'item-4'])

#drop item-2
print(dataframe.drop('item-2'))

Output:

            id            name    cost  quantity
item-1  foo-23  ground-nut oil  567.00         1
item-3  foo-02           flour   67.00         3
item-4  foo-31         cereals   76.09         2

 

Method 2 - Drop multiple Rows in DataFrame by Row Index Label

Here we are going to delete/drop multiple rows from the dataframe using index name/label. We have to use comma operator to separate the index_labels though a list

Syntax:

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dataframe.drop(['index_label',...............'index_label'])

where,

  1. dataframe is the input dataframe
  2. index_label represents the index name

 

Example 1: In this example, we are going to drop 2 nd and 4 th row

#import the module
import pandas

#consider the food data
food_input={'id':['foo-23','foo-13','foo-02','foo-31'],
                  'name':['ground-nut oil','almonds','flour','cereals'],
                  'cost':[567.00,562.56,67.00,76.09],
                  'quantity':[1,2,3,2]}

#pass this food to the dataframe by specifying rows 
dataframe=pandas.DataFrame(food_input,index = ['item-1', 'item-2', 'item-3', 'item-4'])

#drop item-2 and item-4
print(dataframe.drop(['item-2','item-4']))

Output:

            id            name   cost  quantity
item-1  foo-23  ground-nut oil  567.0         1
item-3  foo-02           flour   67.0         3

 

Example 2: In this example, we are going to drop 1 st , 2 nd and 4 th row

#import the module
import pandas

#consider the food data
food_input={'id':['foo-23','foo-13','foo-02','foo-31'],
                  'name':['ground-nut oil','almonds','flour','cereals'],
                  'cost':[567.00,562.56,67.00,76.09],
                  'quantity':[1,2,3,2]}

#pass this food to the dataframe by specifying rows 
dataframe=pandas.DataFrame(food_input,index = ['item-1', 'item-2', 'item-3', 'item-4'])

#drop item-1 , item-2 and item-4
print(dataframe.drop(['item-1','item-2','item-4']))

Output:

            id   name  cost  quantity
item-3  foo-02  flour  67.0         3

 

Method 3 - Drop a single Row in DataFrame by Row Index Position

Here we are going to delete/drop single row from the dataframe using index position. we have to pass index by using index() method. indexing starts with .

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Syntax:

dataframe.drop([dataframe.index[index_position]])

where,

  1. dataframe is the input dataframe
  2. index_position represents the index position

 

Example 1: In this example we are going to drop last row using row position

#import the module
import pandas

#consider the food data
food_input={'id':['foo-23','foo-13','foo-02','foo-31'],
                  'name':['ground-nut oil','almonds','flour','cereals'],
                  'cost':[567.00,562.56,67.00,76.09],
                  'quantity':[1,2,3,2]}

#pass this food to the dataframe by specifying rows 
dataframe=pandas.DataFrame(food_input,index = ['item-1', 'item-2', 'item-3', 'item-4'])

#drop item-4
print(dataframe.drop([dataframe.index[3]]))

Output:

            id            name    cost  quantity
item-1  foo-23  ground-nut oil  567.00         1
item-2  foo-13         almonds  562.56         2
item-3  foo-02           flour   67.00         3

 

Example 2- In this example we are going to drop second row using row position

#import the module
import pandas

#consider the food data
food_input={'id':['foo-23','foo-13','foo-02','foo-31'],
                  'name':['ground-nut oil','almonds','flour','cereals'],
                  'cost':[567.00,562.56,67.00,76.09],
                  'quantity':[1,2,3,2]}

#pass this food to the dataframe by specifying rows 
dataframe=pandas.DataFrame(food_input,index = ['item-1', 'item-2', 'item-3', 'item-4'])

#drop item-2
print(dataframe.drop([dataframe.index[1]]))

Output:

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            id            name    cost  quantity
item-1  foo-23  ground-nut oil  567.00         1
item-3  foo-02           flour   67.00         3
item-4  foo-31         cereals   76.09         2

 

Method 4 - Drop multiple Rows in DataFrame by Row Index Position

Here we are going to delete/drop multiple rows from the dataframe using index Position.

Syntax:

dataframe.drop([dataframe.index[index_position],...............,dataframe.index[index_position]])

where,

  1. dataframe is the input dataframe
  2. index_position represents the index position

 

Example 1: In this example, we are going to drop 2 nd and 4 th row

#import the module
import pandas

#consider the food data
food_input={'id':['foo-23','foo-13','foo-02','foo-31'],
                  'name':['ground-nut oil','almonds','flour','cereals'],
                  'cost':[567.00,562.56,67.00,76.09],
                  'quantity':[1,2,3,2]}

#pass this food to the dataframe by specifying rows 
dataframe=pandas.DataFrame(food_input,index = ['item-1', 'item-2', 'item-3', 'item-4'])

#drop item-2 and item-4
print(dataframe.drop([dataframe.index[1],dataframe.index[3]]))

Output:

            id            name   cost  quantity
item-1  foo-23  ground-nut oil  567.0         1
item-3  foo-02           flour   67.0         3

 

Example 2: In this example, we are going to drop 1 st , 2 nd and 4 th row

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#import the module
import pandas

#consider the food data
food_input={'id':['foo-23','foo-13','foo-02','foo-31'],
                  'name':['ground-nut oil','almonds','flour','cereals'],
                  'cost':[567.00,562.56,67.00,76.09],
                  'quantity':[1,2,3,2]}

#pass this food to the dataframe by specifying rows 
dataframe=pandas.DataFrame(food_input,index = ['item-1', 'item-2', 'item-3', 'item-4'])

#drop item-1, item-2 and item-4
print(dataframe.drop([dataframe.index[0],dataframe.index[1],dataframe.index[3]]))

Output:

            id   name  cost  quantity
item-3  foo-02  flour  67.0         3

 

Method 5 - Drop Rows in a DataFrame with conditions

Now , we have to drop rows based on the conditions.

Just specify the column name with a condition.

Syntax:

dataframe.drop(dataframe[dataframe['column'] operator value].index)

where,

  1. column refers the column name to be checked with condition
  2. operator can be a relational operator
  3. index is the function to drop row

 

Example 1: In this example, we are going to drop the rows based on cost column

#import the module
import pandas

#consider the food data
food_input={'id':['foo-23','foo-13','foo-02','foo-31'],
                  'name':['ground-nut oil','almonds','flour','cereals'],
                  'cost':[567.00,562.56,67.00,76.09],
                  'quantity':[1,2,3,2]}

#pass this food to the dataframe by specifying rows 
dataframe=pandas.DataFrame(food_input,index = ['item-1', 'item-2', 'item-3', 'item-4'])

#drop rows based on cost column with value greater than 300
print(dataframe.drop(dataframe[dataframe['cost'] > 300].index))

Output:

            id     name   cost  quantity
item-3  foo-02    flour  67.00         3
item-4  foo-31  cereals  76.09         2

 

Example 2:  In this example, we are going to drop the rows based on quantity column

#import the module
import pandas

#consider the food data
food_input={'id':['foo-23','foo-13','foo-02','foo-31'],
                  'name':['ground-nut oil','almonds','flour','cereals'],
                  'cost':[567.00,562.56,67.00,76.09],
                  'quantity':[1,2,3,2]}

#pass this food to the dataframe by specifying rows 
dataframe=pandas.DataFrame(food_input,index = ['item-1', 'item-2', 'item-3', 'item-4'])

#drop rows nased on quantity column with value less than 3
print(dataframe.drop(dataframe[dataframe['quantity'] < 3].index))

#drop rows based on quantity column with value less than 1
print(dataframe.drop(dataframe[dataframe['quantity'] < 1].index))

Output:

            id   name  cost  quantity
item-3  foo-02  flour  67.0         3
            id            name    cost  quantity
item-1  foo-23  ground-nut oil  567.00         1
item-2  foo-13         almonds  562.56         2
item-3  foo-02           flour   67.00         3
item-4  foo-31         cereals   76.09         2

 

Summary

We discussed how to drop the row in the Pandas dataframe using four methods with index label and index position. We seen that drop function is the common in all methods and we can also drop/delete the rows conditionally from the dataframe using column.

 

Further Reading

Pandas - drop()

 

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