Landclearing is a major issue for Australia, due the changes it creates in hydrology, storage of carbon, water quality, ecology and possible other interactions with climate. As a result, the Queensland government has an extensive program to track changes in landcover that first reported every three years, then every two years and finally every year. The results are tabulated by Natural Resource Management (NRM) region, by period and by the resulting land use (that is what landuse resulted from the clearing). We have added an additional column to indicate the length of the period. Using the quiz questions, you will write code to analyse data to identify regions and find out which type of landuse results after land clearing.

The file QldLandclearingdata.csv contains the landclearing data as a comma delimited file, so each of the fields is separated by a comma, rather than a space, as in the Module 1 population data. The data was downloaded from the Qld government website. In particular, the file contains:

Period: the specific period for which the data are collected
NRM_Region: the Queensland Natural Resource Management (NRM) Region to which the data applies
Replacement_Class: the landuse class that results after clearing
Clearing_Rate_ha_yr: the rate of clearing in hectare (ha) per year We have added an additional column:
length: the length of the period
This question consists of three parts – your program should perform each of the three parts in order.

Part 1
Write Python code that reads the file and saves the data into a Pandas data frame. Make sure you load the required packages. Your code should also print the descriptive statistics that summarise the data, such as the mean, the standard deviation etc.

Part 2
The column labelled Clearing_Rate_ha_yr describes area cleared per year. Find the Period, NRM_Region, and Replacement_Class which had the highest clearing rate.

Part 3
Create a subsample containing all the data of the Replacement_Class with the highest clearing rate. What is the total area cleared over the overall period for this replacement class? Note that the data is for different length periods, but the rate is per year. So you need to multiply the column length by the column Clearing_Rate_per_year.