Pandas Slice Rows
Last Updated :
17 Jan, 2025
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Simplest way to select rows is by using the iloc method which allows to access rows by their integer position. For instance, to slice a single row you can specify its index.
import pandas as pd
data = {'Name': ['John', 'Alice', 'Bob', 'Eve'],
'Age': [25, 30, 22, 35],
'Gender': ['Male', 'Female', 'Male', 'Female'],
'Salary': [50000, 55000, 40000, 70000]}
df = pd.DataFrame(data)
# Slice rows from position 1 to 3 (exclusive of 3)
sliced_rows = df.iloc[1:3]
print(sliced_rows)
Output
Name Age Gender Salary 1 Alice 30 Female 55000 2 Bob 22 Male 40000
In addition to the this method, there are other methods to do the same:
Methid 1: Slicing Rows Using loc with Conditions
The loc[] method allows you to slice rows based on conditions or index labels. This is useful when you want to filter rows that meet specific criteria.
# Slice rows where Age is greater than 25
filtered_rows = df.loc[df['Age'] > 25]
print(filtered_rows)
Output
Name Age Gender Salary 1 Alice 30 Female 55000 3 Eve 35 Female 70000
Method 2: Slicing Rows Using head and tail
The head() and tail() methods provide a quick way to slice the first or last few rows of a DataFrame. This is useful when you want to inspect the top or bottom portion of your data.
# Get the first 3 rows
top_rows = df.head(3)
print(top_rows)
Output
Name Age Gender Salary 0 John 25 Male 50000 1 Alice 30 Female 55000 2 Bob 22 Male 40000
# Get the last 2 rows
bottom_rows = df.tail(2)
print(bottom_rows)
Output
Name Age Gender Salary 2 Bob 22 Male 40000 3 Eve 35 Female 70000
