Table of Contents
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.
We can create the DataFrame by using pandas.DataFrame() method.
Syntax:
pandas.DataFrame(input_data,columns,index)Parameters:
It will take mainly three parameters
- input_data is represents a list of data
columnsrepresent the columns names for the dataindexrepresent the row numbers/values
We can also create a DataFrame using dictionary by skipping columns and indices.
Let’s see an example.
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,
- dataframe is the input dataframe
- 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:
dataframe.drop(['index_label',...............'index_label'])where,
- dataframe is the input dataframe
index_labelrepresents 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 0.
Syntax:
dataframe.drop([dataframe.index[index_position]])where,
- dataframe is the input dataframe
index_positionrepresents 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:
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,
- dataframe is the input dataframe
index_positionrepresents 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
#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,
- column refers the column name to be checked with
condition operatorcan be a relational operatorindexis 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
