Python Practical $4$: Acceptance rate by date

The Problem Statement

The goal is to calculate the overall friend acceptance rate by date.

To do this, we need to merge the df$\_$sent and df$\_$accepted dataframes using the user IDs as the merge keys.

Then, we group the merged dataframe by the date, count the number of accepted actions per date, and calculate the acceptance rate.

Finally, we create a new dataframe with the date and acceptance rate, sorted by date.

Given a table of purchases by date, calculate the month$-$over$-$month percentage change in revenue.

The output should include the year$-$month date $($YYYY$-$MM$)$ and percentage change,

rounded to the $2$nd decimal point,

and sorted from the beginning of the year to the end of the year.

The percentage change column will be populated from the $2$nd month forward and can be calculated as

$(($this month's revenue $-$ last month's revenue$)/$ last month's revenue$)*100.$

HINT $1$:

To calculate the overall friend acceptance rate by date, we need to merge two dataframes :

one containing the friend requests that were sent $($action $=$ 'sent'$)$ and another containing the

friend requests that were accepted $($action $=$ 'accepted'$).$

We can then group the merged dataframe by date and count the number of

accepted requests for each date.

HINT $2$:

To merge the two dataframes, we can use the pandas merge function.

First, create two new dataframes: one for the sent requests and another

for the accepted requests.

Use the condition fb$\_$friend$\_$requests.action $==$ 'sent' to filter the rows for the sent requests,

and fb$\_$friend$\_$requests.action $==$ 'accepted' to filter the rows for the accepted requests.

Then, merge the two dataframes using the merge function, specifying the columns to

merge on $($user$\_$id$\_$sender and user$\_$id$\_$receiver$)$ and the merge type $($how$=$'left'$).$