Of course! In Python, when working with the popular pandas library, the term "len" on a DataFrame refers to its length. The length of a DataFrame is the number of rows it contains.

Here’s a complete guide covering the primary ways to get the length of a DataFrame, along with explanations and examples.

The Short Answer: len(df)

The most common and Pythonic way to get the number of rows in a DataFrame is to use the built-in len() function.

import pandas as pd
# Create a sample DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie', 'David'],
        'Age': [25, 30, 35, 28],
        'City': ['New York', 'Los Angeles', 'Chicago', 'Houston']}
df = pd.DataFrame(data)
# Get the number of rows
number_of_rows = len(df)
print(f"The DataFrame has {number_of_rows} rows.")
print(f"Using len(df): {len(df)}")

Output:

The DataFrame has 4 rows.
Using len(df): 4

Detailed Explanation and Other Methods

While len(df) is the most common, it's good to know what's happening under the hood and what other options are available.

len(df) (Recommended)

This is the preferred method for its simplicity and readability. It directly calls the __len__() special method of the DataFrame object, which is optimized to return the number of rows.

Pros:

• Readable: len(df) clearly states its intent.
• Fast: It's a highly optimized operation.
• Pythonic: It uses a standard Python built-in function.

df.shape

The .shape attribute of a DataFrame returns a tuple representing the dimensions of the DataFrame: (number_of_rows, number_of_columns).

To get just the number of rows, you need to access the first element of the tuple (at index 0).

# Get the dimensions (rows, columns)
dimensions = df.shape
print(f"df.shape returns: {dimensions}")
# Get the number of rows from the shape
rows_from_shape = df.shape[0]
print(f"Number of rows from df.shape[0]: {rows_from_shape}")

Output:

df.shape returns: (4, 3)
Number of rows from df.shape[0]: 4

Pros:

• Useful when you need both dimensions: If you also need the number of columns, this is the most efficient way to get both at once.
• Unambiguous: There is no confusion about what it returns.

Cons:

• Slightly more verbose: df.shape[0] is a bit longer than len(df) if you only need the row count.

len(df.index)

A DataFrame has two main indexable components: its index (for rows) and its columns. You can explicitly get the length of the row index.

# Get the length of the DataFrame's index
rows_from_index = len(df.index)
print(f"Number of rows from len(df.index): {rows_from_index}")

Output:

Number of rows from len(df.index): 4

Pros:

• Very explicit: It clearly states that you are measuring the row axis.

Cons:

• Redundant: It's functionally identical to len(df) but less concise.

Comparison Table

Important Consideration: Empty DataFrames

What happens if your DataFrame is empty? All the methods above handle this gracefully and correctly return 0.

# Create an empty DataFrame
empty_df = pd.DataFrame()
print(f"len(empty_df): {len(empty_df)}")
print(f"empty_df.shape: {empty_df.shape}")
print(f"len(empty_df.index): {len(empty_df.index)}")

Output:

len(empty_df): 0
empty_df.shape: (0, 0)
len(empty_df.index): 0

What About the Number of Columns?

If you're looking for the number of columns, you can use df.shape[1] or len(df.columns).

# Get the number of columns
num_cols_shape = df.shape[1]
num_cols_cols = len(df.columns)
print(f"Number of columns (from shape): {num_cols_shape}")
print(f"Number of columns (from columns): {num_cols_cols}")

Output:

Number of columns (from shape): 3
Number of columns (from columns): 3