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Pandas is a popular library in Python used for data analysis and manipulation. One of its key functionalities is the ability to drop rows from a dataset based on their index. This can be achieved by using the “drop” function in Pandas, which takes in a list of index values as input and removes those rows from the dataset. This process is useful for removing unwanted or irrelevant data from a dataset, allowing for a more streamlined and accurate analysis. By utilizing the “drop” function in Pandas, users can easily drop rows by index and customize their dataset according to their specific needs.
Drop Rows by Index in Pandas (With Examples)
You can use the following syntax to drop one row from a pandas DataFrame by index number:
#drop first row from DataFrame df = df.drop(index=0)
And you can use the following syntax to drop multiple rows from a pandas DataFrame by index numbers:
#drop first, second, and fourth row from DataFrame df = df.drop(index=[0, 1, 3])
If your DataFrame has strings as index values, you can simply pass the names as strings to drop:
df = df.drop(index=['first', 'second', 'third'])
The following examples show how to drop rows by index in practice.
Example 1: Drop One Row by Index
The following code shows how to drop the second row in a pandas DataFrame:
import pandas as pd #create DataFrame df = pd.DataFrame({'team': ['Mavs', 'Lakers', 'Spurs', 'Cavs'], 'first': ['Dirk', 'Kobe', 'Tim', 'Lebron'], 'last': ['Nowitzki', 'Bryant', 'Duncan', 'James'], 'points': [26, 31, 22, 29]}) #view DataFrame df team first last points 0 Mavs Dirk Nowitzki26 1 Lakers Kobe Bryant 31 2 Spurs Tim Duncan 22 3 Cavs Lebron James 29 #drop second row from DataFrame df = df.drop(index=1) #view resulting dataFrame df team first last points 0 Mavs Dirk Nowitzki 26 2 Spurs Tim Duncan 22 3 Cavs Lebron James 29
Example 2: Drop Multiple Rows by Index
The following code shows how to drop multiple rows in a pandas DataFrame by index:
import pandas as pd #create DataFrame df = pd.DataFrame({'team': ['Mavs', 'Lakers', 'Spurs', 'Cavs'], 'first': ['Dirk', 'Kobe', 'Tim', 'Lebron'], 'last': ['Nowitzki', 'Bryant', 'Duncan', 'James'], 'points': [26, 31, 22, 29]}) #view DataFrame df team first last points 0 Mavs Dirk Nowitzki26 1 Lakers Kobe Bryant 31 2 Spurs Tim Duncan 22 3 Cavs Lebron James 29 #drop first, second, and fourth row from DataFrame df = df.drop(index=[0, 1, 3]) #view resulting dataFrame df team first last points 2 Spurs Tim Duncan 22
Example 3: Drop Rows When Index is a String
The following code shows how to drop rows from a pandas DataFrame by index when the index is a string instead of a number:
import pandas as pd
#create DataFrame
df = pd.DataFrame({'team': ['Mavs', 'Lakers', 'Spurs', 'Cavs'],
'last': ['Nowitzki', 'Bryant', 'Duncan', 'James'],
'last': ['Nowitzki', 'Bryant', 'Duncan', 'James'],
'points': [26, 31, 22, 29]},
index=['A', 'B', 'C', 'D'])
#view DataFrame
df
team first last points
A Mavs Dirk Nowitzki26
B Lakers Kobe Bryant 31
C Spurs Tim Duncan 22
D Cavs Lebron James 29
#remove rows with index values 'A' and 'C'
df = df.drop(index=['A', 'C'])
#view resulting DataFrame
df
team first last points
B Lakers Kobe Bryant 31
D Cavs Lebron James 29Cite this article
stats writer (2024). How can I drop rows by index in Pandas?. PSYCHOLOGICAL SCALES. Retrieved from https://scales.arabpsychology.com/stats/how-can-i-drop-rows-by-index-in-pandas/
stats writer. "How can I drop rows by index in Pandas?." PSYCHOLOGICAL SCALES, 30 Apr. 2024, https://scales.arabpsychology.com/stats/how-can-i-drop-rows-by-index-in-pandas/.
stats writer. "How can I drop rows by index in Pandas?." PSYCHOLOGICAL SCALES, 2024. https://scales.arabpsychology.com/stats/how-can-i-drop-rows-by-index-in-pandas/.
stats writer (2024) 'How can I drop rows by index in Pandas?', PSYCHOLOGICAL SCALES. Available at: https://scales.arabpsychology.com/stats/how-can-i-drop-rows-by-index-in-pandas/.
[1] stats writer, "How can I drop rows by index in Pandas?," PSYCHOLOGICAL SCALES, vol. X, no. Y, ص Z-Z, April, 2024.
stats writer. How can I drop rows by index in Pandas?. PSYCHOLOGICAL SCALES. 2024;vol(issue):pages.
