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Drop Rows from Pandas DataFrame Based on Column Value

In this tutorial, we will learn how to drop rows from Pandas DataFrame based on column value with the help of example? By Pranit Sharma Last updated : April 12, 2023

Drop Rows from DataFrame

To drop rows from DataFrame based on column value, use DataFrame.drop() method by passing the condition as a parameter. Since rows and columns are based on index and axis values respectively, by passing the index or axis value inside DataFrame.drop() method we can delete that particular row or column.

Syntax

DataFrame.drop(
    labels=None, 
    axis=0, 
    index=None, 
    columns=None, 
    level=None, 
    inplace=False, 
    errors='raise'
    )

# short forms
df.drop(axis=None)  # deletes a specified column
df.drop(index=None) # deletes a specified row

Let us understand with the help of an example.

Example to Drop Rows from Pandas DataFrame Based on Column Value

# Importing pandas package
import pandas as pd

# Creating a dictionary
d = {
    "Name":['Hari','Mohan','Neeti','Shaily','Ram','Umesh'],
    "Age":[25,36,26,21,30,33],
    "Gender":['Male','Male','Female','Female','Male','Male'],
    "Profession":['Doctor','Teacher','Singer','Student','Engineer','CA'],
    "Title":['Mr','Mr','Ms','Ms','Mr','Mr']
}

# Now, Create DataFrame
df = pd.DataFrame(d)

# Printing the original DataFrame
print("Original DataFrame:\n")
print(df,"\n\n")

# Now, Deletes a row where column Title is 'Ms'
# For this we will find the index value i.e. 
# the row number where the column value is 'Ms'
index_to_be_deleted = df[ df['Title'] == 'Ms' ].index

# Now, Pass the index_to_be_deleted as a parameter inside 
# df.drop() method to delete those rows having 
# column value = 'Ms'
df.drop(index_to_be_deleted,inplace=True)

# Now, Printing the modified DataFrame
print("Updated DataFrame:\n")
print(df)

Output

Original DataFrame:

     Name  Age  Gender Profession Title
0    Hari   25    Male     Doctor    Mr
1   Mohan   36    Male    Teacher    Mr
2   Neeti   26  Female     Singer    Ms
3  Shaily   21  Female    Student    Ms
4     Ram   30    Male   Engineer    Mr
5   Umesh   33    Male         CA    Mr 


Updated DataFrame:

    Name  Age Gender Profession Title
0   Hari   25   Male     Doctor    Mr
1  Mohan   36   Male    Teacher    Mr
4    Ram   30   Male   Engineer    Mr
5  Umesh   33   Male         CA    Mr

Output (Screenshot)

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