Python Pandas – Count the rows and columns in a DataFrame

To count the rows and columns in a DataFrame, use the shape property. This returns a tuple where the first value is the number of rows and the second value is the number of columns.

What is the shape Property?

The shape property returns the dimensions of a DataFrame as a tuple (rows, columns). It's a quick way to understand the size of your dataset ?

import pandas as pd

# Create a sample DataFrame
data = {'Car': ['Audi', 'Porsche', 'BMW', 'Mercedes'],
        'Place': ['Bangalore', 'Mumbai', 'Delhi', 'Hyderabad'],
        'UnitsSold': [80, 110, 95, 80]}

df = pd.DataFrame(data)
print("DataFrame:")
print(df)
print("\nShape:", df.shape)

DataFrame:
       Car      Place  UnitsSold
0     Audi  Bangalore         80
1  Porsche     Mumbai        110
2      BMW      Delhi         95
3 Mercedes  Hyderabad         80

Shape: (4, 3)

Getting Individual Counts

You can also get the row and column counts separately using indexing ?

import pandas as pd

data = {'Car': ['Audi', 'Porsche', 'BMW', 'Mercedes', 'Lamborghini'],
        'Place': ['Bangalore', 'Mumbai', 'Delhi', 'Hyderabad', 'Pune'],
        'UnitsSold': [80, 110, 95, 80, 100]}

df = pd.DataFrame(data)

# Get total dimensions
rows, columns = df.shape
print(f"Total rows: {rows}")
print(f"Total columns: {columns}")

# Alternative methods
print(f"Rows using len(): {len(df)}")
print(f"Columns using len(): {len(df.columns)}")

Total rows: 5
Total columns: 3
Rows using len(): 5
Columns using len(): 3

Practical Example with Data Analysis

Here's a complete example showing how to use shape for data analysis ?

import pandas as pd

# Create a larger dataset
car_data = {
    'Car': ['Audi', 'Porsche', 'RollsRoyce', 'BMW', 'Mercedes', 
            'Lamborghini', 'Audi', 'Mercedes', 'Lamborghini'],
    'Place': ['Bangalore', 'Mumbai', 'Pune', 'Delhi', 'Hyderabad',
              'Chandigarh', 'Mumbai', 'Pune', 'Delhi'],
    'UnitsSold': [80, 110, 100, 95, 80, 80, 100, 120, 100]
}

df = pd.DataFrame(car_data)

print("Complete DataFrame:")
print(df)

print(f"\nDataset dimensions: {df.shape}")
print(f"Total data points: {df.shape[0] * df.shape[1]}")

# Show first 5 rows
print(f"\nFirst 5 rows:")
print(df.head(5))

Complete DataFrame:
          Car       Place  UnitsSold
0        Audi   Bangalore         80
1     Porsche      Mumbai        110
2  RollsRoyce        Pune        100
3         BMW       Delhi         95
4    Mercedes   Hyderabad         80
5 Lamborghini  Chandigarh         80
6        Audi      Mumbai        100
7    Mercedes        Pune        120
8 Lamborghini       Delhi        100

Dataset dimensions: (9, 3)
Total data points: 27

First 5 rows:
          Car      Place  UnitsSold
0        Audi  Bangalore         80
1     Porsche     Mumbai        110
2  RollsRoyce       Pune        100
3         BMW      Delhi         95
4    Mercedes  Hyderabad         80

Alternative Methods

Besides shape, you can use other methods to get DataFrame dimensions ?

import pandas as pd

data = {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]}
df = pd.DataFrame(data)

print("Using different methods:")
print(f"shape: {df.shape}")
print(f"len(df): {len(df)}")
print(f"df.index.size: {df.index.size}")
print(f"len(df.columns): {len(df.columns)}")
print(f"df.size (total elements): {df.size}")

Using different methods:
shape: (3, 3)
len(df): 3
df.index.size: 3
len(df.columns): 3
df.size (total elements): 9

Conclusion

The shape property is the most efficient way to get DataFrame dimensions, returning (rows, columns). Use len(df) for just row count and len(df.columns) for column count when needed separately.