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Select Single Column in Pandas DataFrame 

October 8, 2025October 8, 2025|

In this article, you’ll learn the different methods to extract a single column and how each affects the result.

# Create sample DataFrame
import pandas as pd

df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35],
    'City': ['New York', 'Los Angeles', 'Chicago']
})

Method 1: Using Bracket Notation (Recommended)

This is the most common and robust method to select a single column:

# Select single column 'Name'
name_column = df['Name']
print(name_column)

This returns a Pandas Series, not a DataFrame.

0     Alice
1       Bob
2    Charlie
Name: Name, dtype: object

Note: This method works even if column names have spaces or special characters.

Method 2: Using Dot Notation

# Select single column using dot notation
name_column = df.Name
print(name_column)

Caution: This only works if:

  • The column name contains no spaces or special characters
  • The name does not conflict with existing DataFrame attributes or methods (like count, index, etc.)

It’s less safe than bracket notation but more concise.

Method 3: Using loc[] for Label-Based Selection

# Select column using loc
name_column = df.loc[:, 'Name']
print(name_column)

df.loc[:, 'Name'] means: select all rows and only the column labeled ‘Name’. It returns a Series.

Tip: loc is useful when selecting rows and columns at the same time.

Method 4: Using iloc[] for Index-Based Selection

# Select the first column using its index (0)
name_column = df.iloc[:, 0]
print(name_column)

iloc is helpful when you know the column’s index but not its name, such as in dynamic column selection scenarios.

How to Return the Output as a DataFrame Instead of Series

By default, all above methods return a Series (1D). If you want to keep the output as a DataFrame (2D), wrap the column name in a list:

# Keep output as DataFrame
name_df = df[['Name']]
print(name_df)

Output:

Name
0    Alice
1      Bob
2  Charlie

Note: Double brackets return a DataFrame, single brackets return a Series.

Best Practices

  • Always use bracket notation when working with user-generated or unpredictable column names.
  • Use df[['column']] if downstream operations require the result to be a DataFrame.
  • Prefer loc and iloc when performing both row and column filtering together.
  • Use dot notation only for quick scripts with simple column names.

Frequently Asked Questions — Select Single Column in Pandas DataFrame

How do I select a single column in a Pandas DataFrame?

Use bracket notation with the column name: df['col_name']. This returns a Series.

import pandas as pd df = pd.DataFrame({'A': [1,2], 'B':[3,4]}) col = df['A'] # Series: values from column A

How do I select a column and keep it as a DataFrame (not Series)?

Use a list of one column: df[['col_name']] — returns a single-column DataFrame.

df_single_df = df[['A']] # DataFrame with only column A

Can I select a column by integer position?

Yes — use df.iloc[:, pos] (returns Series) or df.iloc[:, pos:pos+1] (returns DataFrame).

df.iloc[:, 0] # first column as Series df.iloc[:, 0:1] # first column as DataFrame

How do I select a column using dot notation (df.col)?

Dot notation (df.col_name) works for simple names (no spaces/special chars) but is less safe—prefer df['col_name'].

How to select a column and convert it to a NumPy array?

Use the .to_numpy() or .values methods: arr = df['A'].to_numpy().

How do I check if a column exists before selecting it?

Use in on df.columns or .get() with a default: if 'A' in df.columns: df['A'].

How do I select a column and rename it in one step?

Use df[['old_name']].rename(columns={'old_name':'new_name'}) or assign a new column: df['new'] = df['old'].

How to select a column and filter rows at the same time?

Combine boolean indexing with column selection: df.loc[df['A'] > 3, 'B'] returns column B values for matching rows.

Why am I getting a KeyError when selecting a column?

KeyError usually means the column name doesn’t exist or has extra whitespace/casing—check df.columns.tolist() for exact names.

Which method is best for performance when selecting a single column?

Bracket notation (df['col']) is idiomatic and efficient. For repeated heavy operations, consider working with NumPy arrays (.to_numpy()).

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