Pandas Access Rows

Rows in a Pandas DataFrame represent individual records or observations and accessing them efficiently is key to data manipulation.

Accessing rows in a Pandas DataFrame is fundamental for data manipulation and analysis. The most basic approach of accessing rows is using iloc function. The iloc method is used for positional indexing, allowing us to access rows by their integer position.  To access a single row, use its integer index:

import pandas as pd
from io import StringIO

data = """Name,Age,Gender,Salary
John,25,Male,50000
Alice,30,Female,55000
Bob,22,Male,40000
Eve,35,Female,70000
Charlie,28,Male,48000"""

df = pd.read_csv(StringIO(data))

row1 = df.iloc[0]
print(row1)

Name       John
Age          25
Gender     Male
Salary    50000
Name: 0, dtype: object

This method allows you to easily access a single column of data.

Now, let's select multiple rows using iloc with the help of slicing:

# Access rows 1 to 3 (index 1 to 3)
rows = df.iloc[1:4]
print(rows)

    Name  Age  Gender  Salary
1  Alice   30  Female   55000
2    Bob   22    Male   40000
3    Eve   35  Female   70000

This approach enables us to select and manipulate multiple columns simultaneously.

In addition to the this method, there are several other methods to access rows in a Pandas DataFrame:

Accessing Rows Using loc

The loc method is used for label-based indexing. It allows you to access rows by their index labels (the row names). This method is useful when we know the row labels but not their integer position.

# Access the row with label 2
row_label_2 = df.loc[2]
print(row_label_2)

Name        Bob
Age          22
Gender     Male
Salary    40000
Name: 2, dtype: object

Accessing Rows Using Conditions

Pandas allows us to access rows based on a condition or filter. This is useful when we want to retrieve rows that meet specific criteria.

# Access rows where 'Age' is greater than 25
row_f = df[df['Age'] > 25]
print(row_f)

      Name  Age  Gender  Salary
1    Alice   30  Female   55000
3      Eve   35  Female   70000
4  Charlie   28    Male   48000

Accessing Specific Rows Using query

The query() method allows us to filter rows using a SQL-like syntax. This method is useful for complex queries and makes the code more readable when dealing with conditions on multiple columns.

# Access rows where 'Age' is greater than 25 and 'Salary' is less than 60000
query_f = df.query('Age > 25 and Salary < 60000')
print(query_f)

      Name  Age  Gender  Salary
1    Alice   30  Female   55000
4  Charlie   28    Male   48000

Accessing Rows Using head and tail

The head() and tail() methods allow us to quickly access the first or last few rows of a DataFrame, respectively. These methods are useful when we want to inspect the top or bottom records in a dataset.

# Access the first 2 rows
r1 = df.head(2)
print(r1)

# Access the last 2 rows
r2 = df.tail(2)
print(r2)

    Name  Age  Gender  Salary
0   John   25    Male   50000
1  Alice   30  Female   55000
      Name  Age  Gender  Salary
3      Eve   35  Female   70000
4  Charlie   28    Male   48000

Accessing Rows Using iloc with Conditions

We can combine iloc with conditions to access specific rows by position after applying a filter. This allows us to select rows based on criteria and then access them by their position in the filtered DataFrame.

# Access the first row where 'Age' is greater than 25
row_f = df[df['Age'] > 25].iloc[0]
print(row_f)

Name       Alice
Age           30
Gender    Female
Salary     55000
Name: 1, dtype: object

For more information refer to below:

• Get a specific row in a given Pandas DataFrame
• Python | Pandas Extracting rows using .loc[]
• Indexing and Selecting Data in Pandas