Pandas df.size, df.shape and df.ndim Methods

Understanding structure of our data is an important step in data analysis and Pandas helps in making this easy with its df.size, df.shape and df.ndim functions. They allow us to identify the size, shape and dimensions of our DataFrame. In this article, we will see how to implement these functions in python.

We can use any dataset for its understanding but here we are using NBA dataset which contains data from NBA players. You can download it from here.

1. Pandas Size Function(df.size)

df.size is used to return the total number of elements in a DataFrame or Series. If we're working with a DataFrame it gives the product of rows and columns or if we're working with a Series it just returns the number of elements (rows).

Syntax: dataframe.size

Return : Size of dataframe/series.

Here we use pandas library read nba.csv and prints the total number of elements (size) in the DataFrame.

import pandas as pd data = pd.read_csv("/content/nba.csv") size = data.size print("Size = {}".format(size)) 

Output:

Size = 4122

2. Pandas Shape Function(df.shape)

The df.shape function returns a tuple representing dimensions of the DataFrame. It provides the number of rows (records) and columns (attributes) in the DataFrame.

Syntax: dataframe.shape

Return : A tuple in the form of (rows, columns).

Here we use pandas library to read and then it prints the shape of the DataFrame which includes the number of rows and columns.

import pandas as pd data = pd.read_csv("/content/nba.csv") shape = data.shape print("Shape = {}".format(shape)) 

Output:

Shape = (458, 9)

3. Pandas ndim Function(df.ndim)

The df.ndim function returns number of dimensions (or axes) in the DataFrame or Series. A DataFrame is always two-dimensional (rows and columns) so it returns 2. A Series is one-dimensional so it returns 1.

Syntax: dataframe.ndim

Return:

• 1 for a Series (one-dimensional).
• 2 for a DataFrame (two-dimensional).

Here we use pandas library to read and then it prints number of dimensions (ndim) for both the DataFrame and a specific column ("Salary") treated as a Series within the DataFrame.

import pandas as pd data = pd.read_csv("/content/nba.csv") df_ndim = data.ndim series_ndim = data["Salary"].ndim print("ndim of dataframe = {}\nndim of series ={}". format(df_ndim, series_ndim)) 

Output:

ndim of dataframe = 2
ndim of series =1

These methods help us to quickly understand the structure of our data and helps in providing more efficient and effective data analysis with Pandas.