What is happiness?
While there are a number of ways to express and define if,
one particular form is challenge your brain get the work done and prove yourself
that you are good at work.
Well, I was struck at
the middle of a python program for a logic.
Supposing we have a data for analysing the website visit
data as given below
|
|
|
Repeated
visit date
|
||||||||
|
first
Visit date
|
Customers
|
08-12-2019
|
09-12-2019
|
10-12-2019
|
11-12-2019
|
16-12-2019
|
17-12-2019
|
19-12-2019
|
20-12-2019
|
21-12-2019
|
|
08-12-2019
|
15
|
2
|
12
|
3
|
4
|
7
|
8
|
1
|
1
|
2
|
|
09-12-2019
|
25
|
4
|
11
|
2
|
3
|
6
|
7
|
0
|
0
|
1
|
|
15-12-2019
|
30
|
5
|
13
|
4
|
5
|
8
|
9
|
2
|
2
|
3
|
|
16-12-2019
|
42
|
7
|
8
|
1
|
1
|
2
|
3
|
4
|
7
|
3
|
|
17-12-2019
|
54
|
21
|
7
|
0
|
0
|
1
|
2
|
3
|
6
|
2
|
|
19-12-2019
|
22
|
6
|
9
|
2
|
2
|
3
|
4
|
5
|
8
|
4
|
|
20-12-2019
|
12
|
8
|
8
|
1
|
3
|
8
|
1
|
1
|
2
|
4
|
The business problem is : How many times the customers acquired
daily ,revisit in the first week of their visit ?
We have thousands of
data for months together. Missing days
cannot be added manually for sure.
Now let us look at the data, the first visit date is not
continuous. The re visit date is also not continuous.
What is needed ? say on 8th Dec, we got 15
customers, and they visited ,2+12+3+4= 21 times. Like wise we need to identify
for all the customers and aggregate.
If the data is in such a way that the rows start at 8th
Dec and continues till 20th Dec without a break and similarly for
the column, the logic is simple . It is the sum of 7 numbers.
Here it is different. The first problem to address is to
have a square matrix- Data frame. Find
the logic, the most important thing.
1.
Get the min date of first visit and the max date
of the revisit
2.
Get a pd.series with date.
3.
Convert to data frame and then, left join it
with the current data frame
4.
Delete the additional column that is
automatically created and replace NaN with 0.
5.
The rows now will have the continuous dates,
what about the columns ? Transpose and repeat the process.
6.
Transpose again to get the original matrix[ df ]
and now it is complete
Wow ! we got the result. But cost
me good hours to get the logic, patting on my back- the result of work- Happiness..
Now the compute the problem’s
solution.
Logic:- for 1st
row-> sum of 2nd column
till 8th column
For 2nd row -> sum of 3rd column till 9th
column
Now we get the catch , use 2 for
loops and fix. Enjoy coding and analytical solutions..
