How To Drop Rows From Pandas Dataframe Based on Column Values – Definitive Guide

Pandas Dataframe allows you to store data in rows and columns format.

You can drop rows from Pandas dataframe based on column values using df.drop(df.index[df[‘Column name’] == ‘value’], inplace=True) statement.

Basic Example

df.drop(df.index[df['Column_name'] == 'Column_value'], inplace=True)

df

It deletes the rows in which the column Column_name has the value of Column_value.

This tutorial teaches you the different methods to drop rows from pandas dataframe based on column values.

Sample Dataframe

You’ll create a pandas dataframe using the following code.

It contains rows with different types of values. You can use it to demonstrate the dropping of the columns.

import pandas as pd

data = {"product_name":["Keyboard","Mouse", "Monitor", "CPU","CPU", "Speakers",pd.NaT],
        "Unit_Price":[500,200, 5000.235, 10000.550, 10000.550, 250.50,None],
        "No_Of_Units":[5,6, 10, 20, 20, 8,0],
        "Available_Quantity":[5,6,10,10,"Not Available", pd.NaT,pd.NaT],
        "Available_Since_Date":['11/5/2021', '4/23/2021', '08/21/2021','09/18/2021','09/18/2021','01/05/2021',pd.NaT]
       }

df = pd.DataFrame(data)

df

Dataframe Will Look Like

	product_name	Unit_Price	No_Of_Units	Available_Quantity	Available_Since_Date
0	Keyboard	500.000	5	5	11/5/2021
1	Mouse	200.000	6	6	4/23/2021
2	Monitor	5000.235	10	10	08/21/2021
3	CPU	10000.550	20	10	09/18/2021
4	CPU	10000.550	20	Not Available	09/18/2021
5	Speakers	250.500	8	NaT	01/05/2021
6	NaT	NaN	0	NaT	NaT

Drop Rows Based On a Column Value

The drop() method is used to delete rows from the dataframe.

To drop rows based on a column value,

• Fetch the index of the rows with column values using the index attribute
• Pass the inplace=True to perform a drop in the same dataframe object.

Code

The following code demonstrates how to select the indexes of the rows containing Available_quantity = Not Available.

df.drop(df.index[df['Available_Quantity'] == 'Not Available'], inplace=True)

df

Dataframe Will Look Like

All the rows with Not Available values are dropped from the dataframe.

	product_name	Unit_Price	No_Of_Units	Available_Quantity	Available_Since_Date
0	Keyboard	500.000	5	5	11/5/2021
1	Mouse	200.000	6	6	4/23/2021
2	Monitor	5000.235	10	10	08/21/2021
3	CPU	10000.550	20	10	09/18/2021
5	Speakers	250.500	8	NaT	01/05/2021
6	NaT	NaN	0	NaT	NaT

Drop Rows Where Column Value is in List

To drop rows where the column value is in a list, use the drop() method and index attribute.

• Use the isin() method to fetch the rows with values available in the list
• Pass the rows from the isin() method to the index attribute, and it’ll return the indexes of the rows
• Pass those indexes to the drop() method, and those rows will be dropped from the dataframe.

Code

df.drop(df.index[df['No_Of_Units'].isin([8,10])], inplace = True)

df

Dataframe Will Look Like

The rows with column values 8 and 10 for the column Number of units are deleted.

	product_name	Unit_Price	No_Of_Units	Available_Quantity	Available_Since_Date
0	Keyboard	500.00	5	5	11/5/2021
1	Mouse	200.00	6	6	4/23/2021
3	CPU	10000.55	20	10	09/18/2021
4	CPU	10000.55	20	Not Available	09/18/2021
6	NaT	NaN	0	NaT	NaT

Drop Rows Where Column Value Not In List

This section teaches you how to drop rows where the column values are NOT available in the list.

• Use the tilde~ operator and isin() method to fetch the rows with values that are NOT available in the list
• Pass the rows from the ~ and isin() method to the index attribute, and it’ll return the indexes of the rows
• Pass those indexes to the drop() method, and those rows will be dropped from the dataframe.

Code

df.drop(df.index[~df['No_Of_Units'].isin([8,10])], inplace = True) df

Dataframe Will Look Like

The rows with column values other than 8 and 10 for the column Number of units are deleted.

	product_name	Unit_Price	No_Of_Units	Available_Quantity	Available_Since_Date
2	Monitor	5000.235	10	10	08/21/2021
5	Speakers	250.500	8	NaT	01/05/2021

Drop Rows Where Column Value is NaN

NaN denotes the missing values.

To drop the rows with NaN values,

• Use the notnull() method to select the rows with a column that doesn’t have any missing value.
• Pass the notnull() result to the dataframe again to select the subset of the rows that don’t contain missing values.

Code

The following code demonstrates how to delete the rows from the dataframe with NaN values in the column Available_Quantity.

df = df[df.Available_Quantity.notnull()]

df

Dataframe Will Look Like

	product_name	Unit_Price	No_Of_Units	Available_Quantity	Available_Since_Date
0	Keyboard	500.000	5	5	11/5/2021
1	Mouse	200.000	6	6	4/23/2021
2	Monitor	5000.235	10	10	08/21/2021
3	CPU	10000.550	20	10	09/18/2021
4	CPU	10000.550	20	Not Available	09/18/2021

Drop Rows Where Column Value is Zero

To delete rows where the column value is zero,

• Select the rows where the no_of_units is Zero
• Using the index attribute, select the index of those rows
• Using the drop() method, drop those indexes

Code

df.drop(df.index[df['No_Of_Units']==0], inplace=True)

df

Dataframe Will Look Like

The rows with Column values Zero are deleted.

	product_name	Unit_Price	No_Of_Units	Available_Quantity	Available_Since_Date
0	Keyboard	500.000	5	5	11/5/2021
1	Mouse	200.000	6	6	4/23/2021
2	Monitor	5000.235	10	10	08/21/2021
3	CPU	10000.550	20	10	09/18/2021
4	CPU	10000.550	20	Not Available	09/18/2021
5	Speakers	250.500	8	NaT	01/05/2021

Drop Rows Based On Multiple Column Values

This section teaches you to drop rows based on multiple column values.

• Combine the multiple conditions using the & operator
• Select the index of the rows matching the specific conditions.
• Drop using the drop() method

Code

The following code demonstrates how to drop rows based on multiple conditions and column values.

df.drop(df[(df['Unit_Price'] >400) & (df['Unit_Price'] < 600)].index, inplace=True)

df

Dataframe Will Look Like

	product_name	Unit_Price	No_Of_Units	Available_Quantity	Available_Since_Date
1	Mouse	200.000	6	6	4/23/2021
2	Monitor	5000.235	10	10	08/21/2021
3	CPU	10000.550	20	10	09/18/2021
4	CPU	10000.550	20	Not Available	09/18/2021
5	Speakers	250.500	8	NaT	01/05/2021

Additional Resources

• How To Select Rows From Pandas Dataframe Based On Condition
• How To Select Rows From Pandas Dataframe Based On Column Values