A cheat sheet on Pandas DataFrame Attributes

This post summarises a range of commonly used attributes of a pandas DataFrame object.
Using this DataFrame as an example:

import pandas as pd 
df = pd.DataFrame({
    'name': ["A", "B"],
    'value': [1,2]
})

1. Core Attributes

Attribute	Description	Output
.shape	Returns a tuple representing the dimensionality of the DataFrame in the form (number_of_rows, number_of_columns).	print(df.shape) # Output: (2,2)
.columns	Returns an Index object containing the column labels of the DataFrame	print(df.columns) # Output: Index(['name', 'value'], dtype='object')
.index	Represents the row labels of the DataFrame, encapsulated in an Index object.	print(df.index) # Output: RangeIndex(start=0, stop=2, step=1)
.dtypes	Provides the data type of each column in the DataFrame.	print(df.dtypes) # Output name object value int64 dtype: object

2. Data Inspection Attributes

Attribute	Description	Output
.values	Returns a NumPy array representation of the DataFrame's data.	print(df.values) # Output: [['A' 1] ['B' 2]]
.size	Returns the total number of elements in the DataFrame (calculated as rows x columns).	print(df.size) # Output: 4
.ndim	Returns the number of dimensions of the DataFrame. For DataFrames, this is always 2.	print(df.ndim) # Output: 2
.empty	Returns True if the DataFrame is empty (ie. has no elements), otherwise False.	print(pd.DataFrame().empty) # Output: True

3. Others

Attribute	Description	Output
.T (Transpose)	Returns the transpose of the DataFrame, swapping rows with columns.	print(df.values) name A B value 1 2