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)
Output
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)
Output
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)
Output
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)
Output
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)
Output
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’.
Get a specific row in a given Pandas DataFrame – FAQs
How to Extract Specific Rows from DataFrame in Pandas
To extract specific rows from a DataFrame based on a condition, you can use boolean indexing. This involves specifying a condition that rows must meet to be included in the output.
import pandas as pd
# Sample DataFrame
df = pd.DataFrame({ 'Name': ['Alice', 'Bob', 'Charlie'],'Age': [25, 30, 35],'City': ['New York', 'Los Angeles', 'Chicago'])
# Extract rows where Age is greater than 28
specific_rows = df[df['Age'] > 28]
print(specific_rows)
How Do I Select a Specific Row in a Pandas DataFrame?
To select a specific row by its integer location, you can use the
.iloc[]indexer, which accesses rows based on integer indexing.# Select the second row
specific_row = df.iloc[1]
print(specific_row)
How to Access Rows in a DataFrame
Rows in a DataFrame can be accessed using the
.loc[]and.iloc[]indexers:
.loc[]accesses rows by the index label..iloc[]accesses rows by the integer index.# Access using .loc if the DataFrame has a custom index or if you know the index label
row_by_label = df.loc[0]
# Access using .iloc for position-based indexing
row_by_position = df.iloc[0]
print(row_by_label)
print(row_by_position)
How Do You Copy Specific Rows in Pandas DataFrame?
To copy specific rows from a DataFrame and ensure that modifications do not affect the original DataFrame, use the
.copy()method after selecting the rows.# Copy rows where City is 'Chicago'
copied_rows = df[df['City'] == 'Chicago'].copy()
print(copied_rows)
How to Fetch Specific Data from DataFrame in Pandas
To fetch specific data from a DataFrame, you can combine column and row selectors. Here’s an example of selecting specific rows based on a condition and retrieving a subset of columns:
# Fetch specific data: Names of people over 28
specific_data = df.loc[df['Age'] > 28, 'Name']
print(specific_data)These methods allow you to navigate and manipulate DataFrames effectively, providing powerful tools for data analysis and manipulation in Python using Pandas.
