Hands-On Exploratory Data Analysis with Python

Reshaping and pivoting

During EDA, we often need to rearrange data in a dataframe in some consistent manner. This can be done with hierarchical indexing using two actions:

• Stacking: Stack rotates from any particular column in the data to the rows.
• Unstacking: Unstack rotates from the rows into the column.

We will look at the following example:

1. Let's create a dataframe that records the rainfall, humidity, and wind conditions of five different counties in Norway:

data = np.arange(15).reshape((3,5))indexers = ['Rainfall', 'Humidity', 'Wind']dframe1 = pd.DataFrame(data, index=indexers, columns=['Bergen', 'Oslo', 'Trondheim', 'Stavanger', 'Kristiansand'])dframe1

The output of the preceding snippet is as follows:

2. Now, using the stack() ...