Page 76 - Informatics_Practices_Fliipbook_Class12
P. 76
<class 'tuple'> 2 Food <class 'pandas.core.frame.DataFrame'>
<class 'tuple'> 2 Household <class 'pandas.core.frame.DataFrame'>
<class 'tuple'> 2 Hygiene <class 'pandas.core.frame.DataFrame'>
Now we are ready to display the information contained in the grouped DataFrame groceryGroupedDF group by
group:
>>> for records in groceryGroupedDF:
print("\nCategory:", records[0])
print("Items Purchased")
print(records[1])
Category: Food
Items Purchased
Product Category Price Quantity Total Price
0 Bread Food 20 2 40
1 Milk Food 60 5 300
2 Biscuit Food 20 2 40
3 Bourn-Vita Food 70 1 70
Category: Household
Items Purchased
Product Category Price Quantity Total Price
6 Detergent Household 80 1 80
Category: Hygiene
Items Purchased
Product Category Price Quantity Total Price
4 Soap Hygiene 40 4 160
5 Brush Hygiene 30 2 60
7 Tissues Hygiene 30 5 150
Before closing our discussion of Pandas, we would like to introduce our readers to the Pandas tutor available at https://
pandastutor.com/vis.html. Pandas tutor lets us visualizes how Pandas code transforms DataFrames on sample dataset.
The tool provides some insightful visualization of data, even though it has some limitations such as:
1. Pandastutor is limited to handling small code files (up to 5000 bytes) and can only visualize small datasets.
2. Data must be encoded within the code itself, as external resource reading (such as csv or txt files) is not
supported.
3. It offers limited Pandas methods support and allows visualization only on the last line.
Below, we provide the pandas tutor visualization for the grouped records:
Product Category Price Quantity Total Price
0 Bread Food 20 2 40
1 Milk Food 60 5 300
2 Biscuit Food 20 2 40
3 Bourn-Vita Food 70 1 70
6 Detergent Household 80 1 80
4 Soap Hygiene 40 4 160
5 Brush Hygiene 30 2 60
7 Tissues Hygiene 30 5 150
2.10.2 Aggregation
As we have grouped the products based on their category, we can compute the total price paid for all the items taken
together that have the same Category value using the GroupBy object groceryGroupedDF created above and
applying the aggregate method sum() on the column Total Price as shown below:
>>> #Total expenditure per category
>>> groceryGroupedDF['Total Price'].sum()
62 Touchpad Informatics Practices-XII

