Exploring Data with Pivot Tables in Pandas: A Practical Guide with Examples from the Titanic Dataset”

Pandas is a popular data analysis library in Python that provides tools for data manipulation, analysis, and visualization. One of the most useful features of Pandas is the ability to create pivot tables. Pivot tables are an essential tool for data analysis and can help summarize and analyze large datasets quickly.

In this blog, we will discuss pivot tables in Pandas and demonstrate how to create and manipulate them.

What is a Pivot Table?

A pivot table is a data summarization tool that allows you to reorganize and summarize data in a tabular format. Pivot tables can help you quickly analyze and explore large datasets, identify patterns and trends, and derive insights.

In a pivot table, you can summarize data by grouping and aggregating them according to one or more categorical variables. You can also compute summary statistics such as the mean, median, standard deviation, and count for each group.

Pandas Pivot Table Function

Pandas provides a powerful pivot_table() function that allows you to create pivot tables from Pandas data frames. The pivot_table() function takes several arguments, including:

• data: The data frame that contains the data to be pivoted.
• index: The column or columns to use as the row labels in the resulting pivot table.
• columns: The column or columns to use as the column labels in the resulting pivot table.
• values: The column or columns to use as the values in the resulting pivot table.
• aggfunc: The aggregation function to use when aggregating the data. By default, this is set to ‘mean’.
• fill_value: The value to use when replacing missing values in the pivot table. By default, this is set to None.

Creating a Pivot Table

Let’s create a simple pivot table to illustrate how it works. We will use the Titanic dataset as an example.

• We can take a quick look at the dataset to get a sense of its structure:

Now, let’s create our first pivot table. We will use the pivot_table method of the Pandas DataFrame to summarize the survival rates by sex and class:

We can also use the pivot_table method to calculate multiple statistics at once. For example, let's say we want to calculate the mean fare and the survival rate by sex and class:

We can also use the aggfunc parameter to specify a different aggregation function for each value we want to summarize. For example, let's say we want to calculate the maximum fare and the minimum fare by sex and class:

We can also use the margins parameter to calculate subtotals for each row and column. For example, let's say we want to calculate the survival rate by sex and class, and also calculate subtotals for each row and column:

In this example, we can see that the margins=True parameter has added subtotals for each row (‘All’) and each column (‘All’). The ‘All’ row and column represent the overall survival rate for each gender and a grand total for all passengers. The resulting pivot table shows that 74.2% of female passengers survived, compared to only 18.9% of male passengers. The overall survival rate for all passengers was 38.4%.

When creating a pivot table in Pandas, we can use the fill_value parameter to fill any missing values in the pivot table with a specific value.

We can clearly see the age column got 177 null values and we can use the fill_value parameter to fill any missing values in the pivot table with a specific value.

In conclusion, the pivot_table method is powerful tools in the Pandas library for summarizing and analyzing data. By using these functions, we can quickly and easily create pivot tables that show the relationships between different variables in our data.