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Drop rows from the dataframe based on certain condition applied on a column

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  • Difficulty Level : Easy
  • Last Updated : 20 Sep, 2022
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In this post, we are going to discuss several approaches on how to drop rows from the Dataframe based on certain conditions applied to a column. Retain all those rows for which the applied condition on the given column evaluates to True. 

We have already discussed earlier how to drop rows or columns based on their labels. However, in this post we are going to discuss several approaches on how to drop rows from the dataframe based on certain condition applied on a column. Retain all those rows for which the applied condition on the given column evaluates to True.

To download the CSV (“nba.csv” dataset) used in the code, click here

Creating Dataframe to drop rows

 In this Dataframe, currently, we are having 458 rows and 9 columns.

Python3




# importing pandas as pd
import pandas as pd
 
# Read the csv file and construct the
# dataframe
df = pd.read_csv('nba.csv')
 
# Visualize the dataframe
print(df.head(15)
 
# Print the shape of the dataframe
print(df.shape)


Output:

 

 

Delete rows based on the condition of a column

We will use vectorization to filter out such rows from the dataset which satisfy the applied condition. Let’s use the vectorization operation to filter out all those rows which satisfy the given condition.

Python3




# Filter all rows for which the player's
# age is greater than or equal to 25
df_filtered = df[df['Age'] >= 25]
 
# Print the new dataframe
print(df_filtered.head(15)
 
# Print the shape of the dataframe
print(df_filtered.shape)


Output:

As we can see in the output, the returned Dataframe only contains those players whose age is greater than or equal to 25 years. 

 

 

Delete rows based on multiple conditions on a column

As we can see in the output, the returned Dataframe only contains those players whose age is not between 20 to 25 age using df.drop()

Python3




# delete all rows with column 'Age' has value 30 to 40
indexAge = df[ (df['Age'] >= 20) & (df['Age'] <= 25) ].index
df.drop(indexAge , inplace=True)
df.head(15)


Output:

 

Delete rows based on multiple conditions on different columns

Here, we drop all the rows whose names and Positions are associated with ‘John Holland‘ or ‘SG’ using df.drop().

Python3




# delete all rows with column 'Age' has value 30 to 40
indexAge = df[ (df['Name'] == 'John Holland') | (df['Position'] == 'SG') ].index
df.drop(indexAge , inplace=True)
df.head(15)


Output:

 

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