shape for dimensions in Pandas

Introduction

The shape tells you the size of your data in rows and columns. It helps you understand how big your table is.

When you want to know how many rows and columns your data has.

Before cleaning data, to check if any rows or columns are missing.

To confirm the size of data after filtering or selecting parts of it.

When you want to prepare data for machine learning and need to know input size.

Syntax

Pandas

dataframe.shape

shape returns a tuple: (number_of_rows, number_of_columns).

You can use it on pandas DataFrames or Series (Series shape is (number_of_rows,)).

Examples

This shows the shape of a DataFrame with 2 rows and 2 columns.

Pandas

import pandas as pd

df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
print(df.shape)

This shows the shape of a Series, which only has rows (3,) because it has no columns.

Pandas

import pandas as pd

s = pd.Series([10, 20, 30])
print(s.shape)

Sample Program

This program creates a table with 3 rows and 4 columns, then prints its shape.

Pandas

import pandas as pd

# Create a DataFrame with 3 rows and 4 columns
data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, 30, 35],
        'City': ['NY', 'LA', 'Chicago'],
        'Score': [85, 90, 95]}
df = pd.DataFrame(data)

# Print the shape of the DataFrame
print(df.shape)

Important Notes

The first number in shape is always the number of rows (data entries).

The second number is the number of columns (features or variables).

You can use shape to quickly check if your data loaded correctly.

Summary

shape tells you the size of your data as (rows, columns).

Use it to understand how much data you have before analysis.

Works on both DataFrames and Series in pandas.

Practice

1. What does the shape attribute of a pandas DataFrame tell you?

easy

A. The data types of each column

B. The summary statistics of the data

C. The first few rows of the DataFrame

D. The number of rows and columns as a tuple (rows, columns)

Solution

1. Step 1: Understand what shape returns

   The shape attribute gives the size of the DataFrame as a tuple showing rows and columns.

2. Step 2: Compare options with this definition

   Only The number of rows and columns as a tuple (rows, columns) correctly describes shape as (rows, columns).

3. Final Answer:

   The number of rows and columns as a tuple (rows, columns) -> Option D

4. Quick Check:

   shape = (rows, columns) [OK]

Hint: Remember shape shows (rows, columns) size [OK]

Common Mistakes:

• Confusing shape with data types
• Thinking shape shows data preview
• Mixing shape with summary statistics

2. Which of the following is the correct way to get the shape of a pandas DataFrame named df?

easy

A. df.shape()

B. df.shape

C. df.get_shape()

D. shape(df)

Solution

1. Step 1: Recall how to access attributes in pandas

   The shape is an attribute, not a method, so no parentheses are used.

2. Step 2: Check each option

   df.shape uses df.shape correctly without parentheses. Others are incorrect syntax.

3. Final Answer:

   df.shape -> Option B

4. Quick Check:

   Use attribute without () for shape [OK]

Hint: Use attribute without parentheses for shape [OK]

Common Mistakes:

• Adding parentheses like a function call
• Using non-existent methods like get_shape()
• Trying to call shape as a standalone function

3. Given the following code:

import pandas as pd
data = {'A': [1, 2, 3], 'B': [4, 5, 6]}
df = pd.DataFrame(data)
print(df.shape)

What will be the output?

medium

A. (3, 2)

B. (2, 3)

C. [3, 2]

D. Error

Solution

1. Step 1: Understand the DataFrame structure

   The dictionary has 2 keys (columns) and each key has 3 values (rows).

2. Step 2: Determine the shape

   So the DataFrame has 3 rows and 2 columns, shape is (3, 2).

3. Final Answer:

   (3, 2) -> Option A

4. Quick Check:

   Rows = 3, Columns = 2 [OK]

Hint: Count rows then columns for shape tuple [OK]

Common Mistakes:

• Swapping rows and columns order
• Using list brackets instead of tuple
• Expecting an error from correct code

4. You run this code:

import pandas as pd
df = pd.DataFrame({'X': [1, 2], 'Y': [3, 4]})
print(df.shape())

What is the problem?

medium

A. Column names are invalid

B. DataFrame is empty

C. Using parentheses with shape causes an error

D. Missing import statement

Solution

1. Step 1: Identify how shape is accessed

   shape is an attribute, not a method, so it should not have parentheses.

2. Step 2: Understand the error cause

   Calling df.shape() tries to call a tuple as a function, causing a TypeError.

3. Final Answer:

   Using parentheses with shape causes an error -> Option C

4. Quick Check:

   Access shape without () to avoid error [OK]

Hint: Don't add () after shape attribute [OK]

Common Mistakes:

• Treating shape like a function
• Assuming empty DataFrame causes error
• Ignoring import errors

5. You have a pandas Series s = pd.Series([10, 20, 30, 40]). What will s.shape return?

hard

A. (4,)

B. (4, 1)

C. 4

D. Error

Solution

1. Step 1: Understand Series shape

   A pandas Series is one-dimensional, so its shape is a tuple with one element: the number of items.

2. Step 2: Check the length of the Series

   There are 4 elements, so shape is (4,).

3. Final Answer:

   (4,) -> Option A

4. Quick Check:

   Series shape is single-element tuple (length,) [OK]

Hint: Series shape is a one-item tuple with length [OK]

Common Mistakes:

• Expecting a 2D shape tuple
• Returning just an integer instead of tuple
• Thinking shape causes error on Series