The data.csv contains more than eighty thousand earthquake events in Indonesia. The features

or columns in the original file of data.csv are date, time, latitude, longitude, depth, and

magnitude. Save the data.csv into a data frame variable of dfRawXXXX. Before doing the data

analysis and data visualisation to give insight into the dataset, add date, month, year, earthquake

category and depth category features to the dataset by using the following code:

a$)$ Date, month and year features

dfRawXXXX$[\text{'}$date$\text{'}]=$ pd$.$to$\_$datetime$($dfRaw$[\text{'}$date$\text{'}],$ format$=\text{'}\%$Y$-\%$m$-\%$d$\text{'})$

dfRawXXXX$[\text{'}$month$\text{'}]=$ dfRaw$[\text{'}$date$\text{'}].$dt$.$month

dfRawXXXX$[\text{'}$year$\text{'}]=$ dfRaw$[\text{'}$date$\text{'}].$dt$.$year

b$)$ Earthquake category

tempList $=[]$

for i in dfRawXXXX$[\text{'}$magnitude$\text{'}].$tolist$()$:

tempVar $=\text{'}\text{'}$

if $($i$<3)$:

tempVar $=$ 'Micro'

elif $($i$<4)$:

tempVar $=$ 'Minor'

elif $($i$<5)$:

tempVar $=$ 'Light'

elif $($i$<6)$:

tempVar $=$ 'Moderate'

elif $($i$<7)$:

tempVar $=$ 'Strong'

elif $($i$<8)$:

tempVar $=$ 'Major'

else:

tempVar $=$ 'Great'

tempList.append$($tempVar$)$

dfRawXXXX$[\text{'}$earthquakeCategory$\text{'}]=$ tempList

c$)$ Depth category

tempList $=[]$

for i in dfRaw$[\text{'}$depth$\text{'}].$tolist$()$:

tempVar $=\text{'}\text{'}$

if $($i$<71)$:

tempVar $=$ 'Shallow'

elif $($i$<301)$:

tempVar $=$ 'Intermediate'

else:

tempVar $=$ 'Deep'

tempList.append$($tempVar$)$

dfRaw$[\text{'}$depthCategory$\text{'}]=$ tempList

Visualise the data using as many data visualisation techniques as possible in Python by

importing the seaborn module and the matplotlib.pyplot module to get the insight.