Python Pandas – How to use Pandas DataFrame Property: shape

The shape property in Pandas DataFrame returns a tuple containing the number of rows and columns. It's essential for understanding your dataset dimensions before performing data analysis operations.

DataFrame.shape Property

The shape property returns (rows, columns) as a tuple. You can access individual values using indexing ?

# Basic syntax
df.shape        # Returns (rows, columns)
df.shape[0]     # Number of rows
df.shape[1]     # Number of columns

Creating Sample Data

Let's create a sample products dataset to demonstrate the shape property ?

import pandas as pd

# Create sample products data
data = {
    'id': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
    'product': ['Bike', 'Car', 'Truck', 'Bus', 'Car', 'Car', 'Bike', 'Truck', 'Car', 'Bus'],
    'engine': ['Gas', 'Diesel', 'Diesel', 'Diesel', 'Gas', 'Gas', 'Gas', 'Diesel', 'Diesel', 'Gas'],
    'avgmileage': [45, 21, 12, 8, 18, 19, 42, 15, 23, 10],
    'price': [8500, 16500, 35000, 45000, 17450, 15250, 9200, 38000, 16925, 42000],
    'height_mm': [1200, 1530, 2800, 3200, 1530, 1530, 1150, 2750, 1530, 3100],
    'width_mm': [800, 1735, 2400, 2500, 1780, 1790, 750, 2350, 1800, 2450],
    'productionYear': [2019, 2020, 2021, 2018, 2018, 2019, 2020, 2019, 2018, 2017]
}

df = pd.DataFrame(data)
print("Dataset shape:", df.shape)
print("Rows:", df.shape[0], "Columns:", df.shape[1])

Dataset shape: (10, 8)
Rows: 10 Columns: 8

Method 1: Using iloc with Column Index

Filter the first ten rows and find cars using column index position ?

import pandas as pd

# Create the same sample data
data = {
    'id': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
    'product': ['Bike', 'Car', 'Truck', 'Bus', 'Car', 'Car', 'Bike', 'Truck', 'Car', 'Bus'],
    'engine': ['Gas', 'Diesel', 'Diesel', 'Diesel', 'Gas', 'Gas', 'Gas', 'Diesel', 'Diesel', 'Gas'],
    'avgmileage': [45, 21, 12, 8, 18, 19, 42, 15, 23, 10],
    'price': [8500, 16500, 35000, 45000, 17450, 15250, 9200, 38000, 16925, 42000],
    'height_mm': [1200, 1530, 2800, 3200, 1530, 1530, 1150, 2750, 1530, 3100],
    'width_mm': [800, 1735, 2400, 2500, 1780, 1790, 750, 2350, 1800, 2450],
    'productionYear': [2019, 2020, 2021, 2018, 2018, 2019, 2020, 2019, 2018, 2017]
}

df = pd.DataFrame(data)
print("Rows:", df.shape[0], "Columns:", df.shape[1])

# Get first 10 rows (all rows in this case)
df1 = df.iloc[0:10, :]

# Filter cars using column index (product column is at index 1)
cars_data = df1[df1.iloc[:, 1] == 'Car']
print(cars_data)

Rows: 10 Columns: 8
   id product  engine  avgmileage  price  height_mm  width_mm  productionYear
1   2     Car  Diesel          21  16500       1530      1735            2020
4   5     Car     Gas          18  17450       1530      1780            2018
5   6     Car     Gas          19  15250       1530      1790            2019
8   9     Car  Diesel          23  16925       1530      1800            2018

Method 2: Using head() with Column Name

Use head() method and filter by column name for more readable code ?

import pandas as pd

# Create the same sample data
data = {
    'id': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
    'product': ['Bike', 'Car', 'Truck', 'Bus', 'Car', 'Car', 'Bike', 'Truck', 'Car', 'Bus'],
    'engine': ['Gas', 'Diesel', 'Diesel', 'Diesel', 'Gas', 'Gas', 'Gas', 'Diesel', 'Diesel', 'Gas'],
    'avgmileage': [45, 21, 12, 8, 18, 19, 42, 15, 23, 10],
    'price': [8500, 16500, 35000, 45000, 17450, 15250, 9200, 38000, 16925, 42000],
    'height_mm': [1200, 1530, 2800, 3200, 1530, 1530, 1150, 2750, 1530, 3100],
    'width_mm': [800, 1735, 2400, 2500, 1780, 1790, 750, 2350, 1800, 2450],
    'productionYear': [2019, 2020, 2021, 2018, 2018, 2019, 2020, 2019, 2018, 2017]
}

df = pd.DataFrame(data)
print("Rows:", df.shape[0], "Columns:", df.shape[1])

# Get first 10 rows using head()
df1 = df.head(10)

# Filter cars using column name
cars_data = df1[df1['product'] == 'Car']
print(cars_data)

Rows: 10 Columns: 8
   id product  engine  avgmileage  price  height_mm  width_mm  productionYear
1   2     Car  Diesel          21  16500       1530      1735            2020
4   5     Car     Gas          18  17450       1530      1780            2018
5   6     Car     Gas          19  15250       1530      1790            2019
8   9     Car  Diesel          23  16925       1530      1800            2018

Comparison

Conclusion

The shape property is essential for understanding DataFrame dimensions. Use df.shape[0] for rows and df.shape[1] for columns. Method 2 with column names is more readable and maintainable.