Get a specific row in a given Pandas DataFrame

In the Pandas Dataframe, we can find the specified row value with the function iloc(). In this function, we pass the row number as a parameter. The core idea behind this is simple: you access the rows by using their index or position. In this article, we'll explore different ways to get a row from a Pandas DataFrame and highlight which method works best in different situations. Below is a quick example to help you understand the concept right away:

Method 1. Using iloc for Integer-Location-Based Indexing

The iloc method is used for integer-location-based indexing, which means you select rows and columns based on their integer positions. This is significant because it allows you to access rows and columns by their numerical indices, starting from 0.

import pandas as pd
data = {'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 35], 'City': ['NY', 'LA', 'SF']}
df = pd.DataFrame(data)

# Select the second row
specific_row = df.iloc[1]
print(specific_row)

Name    Bob
Age      30
City     LA
Name: 1, dtype: object

This example shows how to extract the second row using the .iloc[] method. Now, let's dive deeper into various methods for selecting rows. .iloc[] is highly versatile for positional indexing, especially when working with large datasets where row labels are unknown or irrelevant.

Method 2. Using loc for Label-Based Indexing

The loc method is used for label-based indexing, which means you select rows and columns based on their labels. This is particularly useful when your DataFrame has custom index labels. .loc[] allows both label-based indexing and conditional filtering, making it more intuitive for labeled datasets.

import pandas as pd
data = {'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 35], 'City': ['NY', 'LA', 'SF']}
df = pd.DataFrame(data)
# Select rows where Age is greater than 25
filtered_rows = df.loc[df['Age'] > 25]
print(filtered_rows)

      Name  Age City
1      Bob   30   LA
2  Charlie   35   SF

Method 3. Using Slicing for Specific Range of rows

You can use slicing to select specific ranges of rows. This method is straightforward but limited to numerical indices. It’s simple and effective for extracting contiguous rows quickly without additional syntax.

import pandas as pd
data = {'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 35], 'City': ['NY', 'LA', 'SF']}
df = pd.DataFrame(data)
# Select the first two rows
rows = df[:2]
print(rows)

    Name  Age City
0  Alice   25   NY
1    Bob   30   LA

Method 4. Combining .iloc[] with Column Selection

You can combine .iloc[] with column selection to extract specific cells or subsets of data. This method provides fine-grained control over both rows and columns, making it ideal for targeted data extraction.

import pandas as pd
data = {'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 35], 'City': ['NY', 'LA', 'SF']}
df = pd.DataFrame(data)
# Select the 'Name' and 'City' columns for the second row
subset = df[['Name', 'City']].iloc[1]
print(subset)

Name    Bob
City     LA
Name: 1, dtype: object

Method 5. Using Boolean Indexing

Boolean indexing allows to filter rows based on conditions applied to one or more columns. Instead of manually selecting rows by index numbers, you can use logical conditions (such as greater than, less than, or equal to) to automatically identify and select the rows that meet those criteria.

import pandas as pd
data = {'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 35], 'City': ['NY', 'LA', 'SF']}
df = pd.DataFrame(data)
# Select rows where City is 'NY'
ny_rows = df[df['City'] == 'NY']
print(ny_rows)

    Name  Age City
0  Alice   25   NY

In this example, df['City'] == 'New York' creates a Boolean Series where each entry is either True or False based on whether the condition is met. By passing this Boolean Series into df[], Pandas filters the rows that correspond to True values, effectively returning all rows where the 'City' column equals 'New York'.