Let’s discuss how to count the number of columns of a Pandas DataFrame. Lets first make a dataframe.
Example:
Python3
# Import Required Librariesimport pandas as pdimport numpy as np # Create a dictionary for the dataframedict = {'Name': ['Sukritin', 'Sumit Tyagi', 'Akriti Goel', 'Sanskriti', 'Abhishek Jain'], 'Age': [22, 20, np.inf, -np.inf, 22], 'Marks': [90, 84, 33, 87, 82]} # Converting Dictionary to Pandas Dataframedf = pd.DataFrame(dict) # Print Dataframedf |
Output:
Method 1: Using shape property
Shape property returns the tuple representing the shape of the DataFrame. The first index consists of the number of rows and the second index consist of the number of columns.
Python3
# Getting shape of the dfshape = df.shape # Printing Number of columnsprint('Number of columns :', shape[1]) |
Output:
Method 2: Using columns property
The columns property of the Pandas DataFrame return the list of columns and calculating the length of the list of columns, we can get the number of columns in the df.
Python3
# Getting the list of columnscol = df.columns # Printing Number of columnsprint('Number of columns :', len(col)) |
Output:
Method 3: Casting DataFrame to list
Like the columns property, typecasting DataFrame to the list returns the list of the name of the columns.
Python3
# Typecasting df to listdf_list = list(df) # Printing Number of columnsprint('Number of columns :', len(df_list)) |
Output:
Method 4: Using info() method of DataFrame
This methods prints a concise summary of the DataFrame. info() method prints information about the DataFrame including dtypes of columns and index, memory usage, number of columns, etc.
Python3
# Printing info of dfdf.info() |
Output:
