Pandas DataFrame.loc[] Method

Last Updated : 22 Sep, 2026

The loc[] accessor in Pandas is used to select rows and columns from a DataFrame using labels, slices, or Boolean conditions. It can be used to access individual values, select specific rows or columns, and filter data based on conditions.

Python 
import pandas as pd

df = pd.DataFrame({
    "Name": ["Alice", "Bob", "Charlie"],
    "Age": [20, 25, 22]
})

print(df.loc[1])

Output
Name    Bob
Age      25
Name: 1, dtype: object

Syntax

The loc[] accessor has the following syntax:

DataFrame.loc[row_labels, column_labels]

Parameters:

  • row_labels: Specifies the row labels to select.
  • column_labels: Specifies the column labels to select.

Returns: Scalar, Series, or DataFrame depending on the selection.

Example 1: Select a Single Value Using loc[]

The loc[] accessor can be used to select a specific value by providing its row and column labels.

Python 
import pandas as pd

df = pd.DataFrame({
    'Weight': [45, 88, 56, 15, 71],
    'Name': ['Sam', 'Andrea', 'Alex', 'Robin', 'Kia'],
    'Age': [14, 25, 55, 8, 21]
})

df.index = ['Row_1', 'Row_2', 'Row_3', 'Row_4', 'Row_5']

print("Original DataFrame:")
print(df)

result = df.loc['Row_2', 'Name']

print("\nSelected Value:")
print(result)

Output
Original DataFrame:
       Weight    Name  Age
Row_1      45     Sam   14
Row_2      88  Andrea   25
Row_3      56    Alex   55
Row_4      15   Robin    8
Row_5      71     Kia   21

Selected Value:
A...

Explanation:

  • df.loc['Row_2', 'Name'] selects the value at Row_2 and the Name column.
  • loc[] uses labels rather than integer positions.
  • The selected value is Andrea.

Example 2: Select Multiple Rows and Columns

We can use loc[] to select multiple columns while keeping all rows.

Python 
import pandas as pd

df = pd.DataFrame({
    "A": [12, 4, 5, None, 1],
    "B": [7, 2, 54, 3, None],
    "C": [20, 16, 11, 3, 8],
    "D": [14, 3, None, 2, 6]
})

df.index = ['Row_1', 'Row_2', 'Row_3', 'Row_4', 'Row_5']
print("Original DataFrame:")
print(df)
result = df.loc[:, ['A', 'D']]
print("\nSelected Columns:")
print(result)

Output
Original DataFrame:
          A     B   C     D
Row_1  12.0   7.0  20  14.0
Row_2   4.0   2.0  16   3.0
Row_3   5.0  54.0  11   NaN
Row_4   NaN   3.0   3   2.0
Row_5   1.0   NaN   8   6.0

Selected Co...

Explanation:

  • : selects all rows.
  • ['A', 'D'] selects the A and D columns.
  • loc[] returns a DataFrame containing the selected columns.

Example 3: Select Rows and Columns by Label

loc[] can select a range of rows and columns using their labels.

Python 
import pandas as pd

df = pd.DataFrame({
    "A": [12, 4, 5, None, 1],
    "B": [7, 2, 54, 3, None],
    "C": [20, 16, 11, 3, 8],
    "D": [14, 3, None, 2, 6]
})

df.index = ['Row_1', 'Row_2', 'Row_3', 'Row_4', 'Row_5']

selected_data = df.loc['Row_2':'Row_4', 'B':'D']

print(selected_data)

Output
          B   C    D
Row_2   2.0  16  3.0
Row_3  54.0  11  NaN
Row_4   3.0   3  2.0

Explanation:

  • 'Row_2':'Row_4' selects rows from Row_2 through Row_4.
  • 'B':'D' selects columns from B through D.
  • Label-based slicing with loc[] includes the ending label.

Example 4: Select Rows Using a Condition

loc[] can filter rows by applying a Boolean condition to a column.

Python 
import pandas as pd

df = pd.DataFrame({
    "A": [12, 4, 5, None, 1],
    "B": [7, 2, 54, 3, None],
    "C": [20, 16, 11, 3, 8],
    "D": [14, 3, None, 2, 6]
})

df.index = ['Row_1', 'Row_2', 'Row_3', 'Row_4', 'Row_5']

result = df.loc[df['A'] > 5]

print(result)

Output
          A    B   C     D
Row_1  12.0  7.0  20  14.0

Explanation:

  • df['A'] > 5 creates a Boolean condition.
  • loc[] selects the rows where the condition is True.
  • Only Row_1 has a value greater than 5 in column A.

Example 5: Using Conditions with Pandas loc

The loc[] accessor can filter rows based on conditions applied to DataFrame columns. We can use it to select rows where a column meets a specific condition or contains non-null values.

Python 
import pandas as pd

df = pd.DataFrame({
    "A": [12, 4, 5, None, 1],
    "B": [7, 2, 54, 3, None],
    "C": [20, 16, 11, 3, 8],
    "D": [14, 3, None, 2, 6]
})

df.index = ['Row_1', 'Row_2', 'Row_3', 'Row_4', 'Row_5']
print("Original DataFrame:")
print(df)
selected_rows = df.loc[df['A'] > 5]
print("\nRows where column 'A' is greater than 5:")
print(selected_rows)
non_null_rows = df.loc[df['B'].notnull()]
print("\nRows where column 'B' is not null:")
print(non_null_rows)

Output
Original DataFrame:
          A     B   C     D
Row_1  12.0   7.0  20  14.0
Row_2   4.0   2.0  16   3.0
Row_3   5.0  54.0  11   NaN
Row_4   NaN   3.0   3   2.0
Row_5   1.0   NaN   8   6.0

Rows where ...

Explanation:

  • df['A'] > 5 checks which rows have a value greater than 5 in column A.
  • df.loc[df['A'] > 5] returns only the rows that satisfy this condition.
  • df['B'].notnull() checks whether column B contains a non-null value.
  • df.loc[df['B'].notnull()] returns the rows where column B is not null.
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