Practical activity: Qualitative statistics using Python libraries

Dataset

We are going to use the same set of data that we used in the previous module. Here is a recap of the introduction to the scenario and the dataset:

Scenario

With the alarming rise in the number of forest fires recorded in Brazil alone, authorities and people in the country, and across the world, are worried. Hence, understanding the frequency of the forest fires and finding other patterns using data analysis can help with taking proactive actions to prevent them.

In the exercise, you are exclusively going to use the Forest Fire Data that has been made available by Brazilian Forest Departments. [1]

For this exercise, the data has been pre-formatted as comma-separated values and translated into English where applicable.

The data set includes the following information:

• Year: when the fire occurred

• State: where the fire was reported

• Month: when the fire occurred

• Number of fires: frequency reported

• Date reported: when the fire was reported.

Download the CSV file of the dataset and get started with the activities in hand at the end of each module on the already provided Jupyter Notebooks.

Download: Brazilian-fire-dataset.csv

Download the file and save it under the file path as shown below:

dataset/brazilianfire.csv

Exercise

In this section, you will be writing the Python code to perform certain activities as instructed in the notebook.

Download: Quantitative-Statistics-using-NumPy-and-Pandas-Exercise

You will see that there are cells with the heading TO DO – Activity. You are required to write the code in the code cells after the activity markdown cells.

Solution

Once you finish adding your codes and running them on the notebook, download the solution notebook to compare and contrast with the expert copy of the notebook provided to you.

Download: Quantitative-Statistics-using-NumPy-and-Pandas-solution.ipynb

All the best for this exercise!

Share your experience with this exercise in the comments and let others know how you plan to use your learning from this to frame your data pipeline for the final assessment.