DataFrame Attributes

Just like a Series, a DataFrame carries built-in attributes that describe it — its labels, its shape, its types and more. They're facts about the table, so you read them without brackets.

1Click through the attributes

Here's a small DataFrame df. Click any attribute to see what it returns.

DataFrame attribute inspector

Attributes describe a DataFrame (no brackets — they aren't functions). Click one to see what it returns.

	Name	English	IP	Maths
ID 1	Rinku	65	89	72
ID 2	Ritu	77	83	69
ID 3	Pankaj	72	95	87

DataFrame: df

# (rows, columns)

df.shape

(3, 4)

2The attributes at a glance

index — the row labels.
columns — the column labels.
axes — a list of both axes: the index and the columns.
dtypes — the dtype of each column (they can differ!).
shape — a (rows, columns) tuple.
size — total number of cells (rows × columns).
ndim — number of dimensions; always 2.
values — the data as a NumPy array.
empty — True if the DataFrame has no data.
T — the transpose (rows become columns and vice-versa).

3See them all at once

df_attributes.py

import pandas as pd

df = pd.DataFrame({
    'Name':    ['Rinku', 'Ritu', 'Pankaj'],
    'English': [65, 77, 72],
    'IP':      [89, 83, 95],
    'Maths':   [72, 69, 87],
}, index=['ID 1', 'ID 2', 'ID 3'])
print(df)

print("\nindex:  ", df.index.tolist())
print("columns:", df.columns.tolist())
print("shape:  ", df.shape)
print("size:   ", df.size)
print("ndim:   ", df.ndim)
print("empty:  ", df.empty)
print("\ndtypes:")
print(df.dtypes)

import pandas as pd

df = pd.DataFrame({
    'Name':    ['Rinku', 'Ritu', 'Pankaj'],
    'English': [65, 77, 72],
    'IP':      [89, 83, 95],
    'Maths':   [72, 69, 87],
}, index=['ID 1', 'ID 2', 'ID 3'])
print(df)

print("\nindex:  ", df.index.tolist())
print("columns:", df.columns.tolist())
print("shape:  ", df.shape)
print("size:   ", df.size)
print("ndim:   ", df.ndim)
print("empty:  ", df.empty)
print("\ndtypes:")
print(df.dtypes)

Watch Out

Attributes take no brackets: it's df.shape, not df.shape(). Notice df.dtypes lists a type per column— that's the big difference from a Series, which has just one dtype.

Quick Check

For a DataFrame with 4 rows and 3 columns, what is df.shape and df.size?

Quick Check

Which attribute can return DIFFERENT types for different columns?

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🧱 Creating a DataFrame

Selecting Columns 📁