This project focused on creating data frames, filtering data, grouping data, merging, and displaying data. Furthermore, it also includes creating new columns in which specific conditions can be applied. The data is used to solve business problems within a superstore. The first problem statement is determining the prizes taken from the Top 5 products from the Mobiles & Tablet Category. Second, the data is processed to fulfill the requirement to check whether there is a decrease in the sales of the Others Category in 2022. The task also requires the display of the top 20 products that have the highest decrease. Third, I utilize the data to process the Customer ID and Registered Data of the consumers who have checked out but have not yet made payment. Fourth, the data is sorted and analyzed to compare the average daily sales on the weekends and those on the weekdays in the time range of 3 months.

1. Python Final Project

2. TOOL

3. About the Dataset
Data analyzed in this task are collected from Tokopedia (not the original data). The dataset is:
Variable Data Type Description
id object the unique number of order/id_order
customer_id object the unique number of customer
order_date object date when the transaction is carried out
sku_id object the unique number of a product (sku is stock keeping unit)
price int64 the amount of money given in payment for something
qty_ordered int64 the number of items purchased by customers
before_discount float64 the total price value of products
discount_amount float64 the discount value of the total product
after_discount float64 the value of the total price after aggregated by the discount
is_gross int64 shows that customers have not yet paid the orders
is_valid int64 shows that customers have paid the orders
is_net int64 shows that the transaction is finished
payment_id int64 the unique number of payment method
Variable Data Type Description
id object the unique number of a product (it can be used as a key for
joining)
sku_name object the name of the product
base_price float64 the price that is shown in the tagging
cogs int64 cost of selling one product
category object product category
order_detail:
sku_detail:
Variable Data Type Description
id object the unique number of a customer
registered_date object the date when a customer sign up as a member
customer_detail:
payment_detail:
Variable Data Type Description
id int64 the unique number of a payment
payment_method object the method of payment applied during transaction

4. Importing Libraries

5. Reviewing order_detail Table

6. Reviewing sku_detail Table

7. Reviewing customer_detail Table

8. Reviewing payment_detail Table

9. Pre-Processing the Data
We have 4 sets of tables. However, we need to join them by implementing SQL in Colab
so that we can carry out further analysis. In this case, the LEFT JOIN function is used.
1. Implement SQL in Google Colab 2. Write the SQL queries for combining 4 tables

10. Pre-Processing the Data
Reviewing the joined datasets by checking the first 10 rows

11. Pre-Processing the Data
Checking the data type from each column. This is carried out to check whether the data type is correct or not.

12. Pre-Processing the Data
The types of data in some columns need to be changed to make them easier to process. The data in
the number column is changed into integers. The data in the format column is transformed by giving
the new format so that the date is ordered.
Queries
Result

13. Pre-Processing the Data
Change the date type to date time

14. Problem Statements
1. Dear Data Analyst,
At the end of this year, the company will provide prizes for the consumers who win
the Year-End Festival Competition. The Marketing Team needs help to determine the
prizes which will be given to the winners. The prizes will be taken from the Top 5
products from Mobiles and Tablets Category in 2022, with the highest order quantity
(valid = 1).
Please help send the data before the end of this month to the Marketing Team. We
appreciate your help. Thank you.
Kindly regards,
Marketing Team

15. Explanation:
We need to filter the data. First of all, we need to filter the category as this column consists of different types of
categories and we only need Mobiles and Tablets. Next, we filter the order date based on transactions in 2022. We
specify the date by writing >= ‘2022-01-01 (this is the date when any transaction begins in 2022) and <= ‘2022-12-31’
(this is the date when any transaction ends in 2022). After we obtain the transaction data, we need to add ‘is_valid’
function to ensure all the data is correct.
Second, we aggregate the data based on product and total quantity. Therefore, we use groupby function as we need to
group the data. Use sku_name to query the name of the product and qty_order to query the quantity order. We need
to calculate the quantity order. Therefore, we use sum function. We also need to reset the header and sort the data in
ascending. Write reset_index and sort_values by “qty_ordered”. Write ascending=False to order the data from the
highest to the lowest.
Third, we display the top 5 products. Therefore, write df_filter.head ()

16. Result
The sales data of Mobiles & Tablets in 2022 shows that IDROID_BALRX7-Gold is the most
popular product as its quantity order reaches 2000. It is followed by RS_Coconut Bites with a
quantity order of 300. There is a large gap in quantity order between IDROID_BALRX7-Gold
and RS_Coconut Bites. The gap may be caused by the better features of IDROID_BALRX7-
Gold and the different marketing strategies for these two products.
Analysis:

17. Problem Statements
2. Dear Data Analyst,
Following up on the meeting with the Warehouse Team and Marketing Team, we found out
that there was plenty of product stock with Others Category available at the end of 2022.
1. We kindly ask for your help to check the sales data for this category in 2021 based on
sales quantity. Our temporary assumption is that there was a decrease in the sales
quantity in 2022 compared to 2021. Please also display the data of the 15 products
from the category.
2. If there is a decrease in the sales quantity of Others Category, we ask for your help to
present the data of the TOP 20 products that have the highest decrease in 2022
compared with 2021. Please send the result no later than 4 days from today We will
use the result for the next meeting. We appreciate your help. Thank you
Kindly regards,
WarehouseTeam

18. 2.1
Explanation:
First of all, we need to filter the transaction data in 2021. Use is_valid function to ensure the data is valid. We specify the date by writing >=
‘2022-01-01 (this is the date when any transaction begins in 2021) and <= ‘2022-12-31’ (this is the date when any transaction ends in 2021).
Second, use groupby function as we need to group the 2021 data. Use category to query the product category and ‘qty_order’ to query the
quantity order. We need to calculate the quantity order. Therefore, we use sum function. We also need to reset the header andsort the data in
ascending. Write reset_index and sort_values by “qty_ordered”. Write ascending=False to order the data from the highest to the lowest.
Apply the same query to filter the 2022 data and create a group for the 2022 data.

19. Explanation:
2.1
After sorting the data and creating a group each for 2021 and 2022, we need to merge two tables by using the merge function
on ‘category’. Subsequently, we renamed the column ‘qty_ordered_x’ became ‘Quantity 2021’ and column ‘qty_ordered_y’
became ‘Quantity 2022’. Next, add a new column by calculating ‘Quantity_2022’ minus ‘Quantity 2021’

20. 2.1
Explanation:
Furthermore, we also need to create a new column in which the condition can be applied. In this case, we use df_merged[] =
df_merged[].apply(condition) function. Then, display the merged data from the highest to the lowest by using ascending. As
we only need 15 data, add head(15).
We also need to create a parameter to determine whether the sales data can be considered as an increase or decrease. Add
the if value function that will process any value that is larger than 0, considered as an increase, and if not, then the value falls
under the decrease category.

21. Result
Analysis:
The categories that show a decrease in sales in
2022 are Others, Soghaat, Men Fashion, and
Beauty & Grooming. Other is the category that
experienced the biggest downturn as its sales
quantity reduced by 147 from 2021 to 2022.

22. 2.2
Explanation:
The query for 2.2 is quite similar to 2.1. However, when filtering the 2021 and 2022 data, add a new column which is the
category. This column is utilized to filter the new table based on Others Category. The symbol shows that the query is still in
the same line.

23. 2.2
Explanation:
After sorting the data and creating a group each for 2021 and 2022, we need to merge two tables by using the merge function
on ‘sku_name’. Subsequently, we renamed the column ‘qty_ordered_x’ became ‘Quantity 2021’ and column ‘qty_ordered_y’
became ‘Quantity 2022’. Next, add a new column by calculating ‘Quantity_2022’ minus ‘Quantity 2021’

24. 2.2
We also need to create a parameter to determine whether the sales data can be considered as an increase or decrease. Add
the if value function that will process any value that is larger than 0, considered as an increase, and if not, then the value falls
under the decrease category.
Explanation:
Create a new column in which the condition can be applied. In this case, we use df_merged[] =
df_merged[].apply(condition) function. Then, display the merged data from the highest to the lowest by using ascending. As
we need 20 data, add head(20).

25. Result
Analysis:
The product from Others Category that has the
biggest decrease is RB-Dettol Germ Busting
Kit_bf. Its downturn reaches as much as 155,
followed by Telemail_MM-DR-HB-L, which
sales quantity dropped by 21. The drop may be
caused by poor marketing. Therefore, the
company should give attention to these two
products to overcome the sales decrease.

26. Problem Statements
3. Dear Data Analyst,
Following the company’s anniversary in the next 2 months, the Digital Marketing Team will
provide promotional information to customers at the end of this month. The customer
criteria that we need are those who have checked out but have not yet made a payment
(is_gross = 1) in 2022. The data we need are the Customer ID and Registered Date.
Please send the data before the end of this month to the Digital Marketing Team. Thank you
for your help.
Kindly Regards,
Digital Marketing Team

27. Explanation:
We need to filter the data. We look for customers who have checked
out but have not yet made a payment. Therefore, we need to filter
the data based on is_gross, is_net, and order_date. We add the
is_valid function to ensure the data is correct. Then display the data
by using df function.
Explanation:
The Digital Marketing Team needs the data
analyst to send the data. To download the
data, we need to import the files from
Google Colab and convert them to csv
format. Then write files. download.

28. Result

29. Result

30. Problem Statements
4. Dear Data Analyst,
From October to December 2022, we will carry out campaigns every Saturday and Sunday.
We will assess whether the campaign has an impact on the sales increase (before_discount).
We kindly ask for your help to display the data:
1. The average daily weekend sales (Saturday and Sunday) vs average daily weekday
sales (Monday-Friday) per month. Is there an increase of sales in each month?
2. The average daily weekend sales (Saturday and Sunday) vs average daily weekday
sales (Monday-Friday) for 3 months.
Please send the data no later than next week. We appreciate your help. Thank you
Kindly Regards,
Digital Marketing Team

31. 4.1
Explanation:
Create new columns that consist of day, month, month_num, and year.

32. 4.1
Explanation:
We need to create two tables: one that consists of weekend data and another that consists of weekday data. First, filter the weekend
data based on is_valid = 1, month = October, November, and December, day = Saturday and Sunday, and year = 2022. Use the isin
function to check the value inside the data frame and return the data frame.
Second, we group the data based on the month and the sales increase. Use ‘month’ to query the months and “before_discount” to
query the sales increase. As we need to aggregate the average daily sales, we use sum function. We also need to reset the header and
sort the data in ascending. Write reset_index and sort_values by “before_discount”. Write ascending = False to order the data from
the highest to the lowest.
Use the same syntax for the weekday data.

33. 4.1
Explanation:
Merge the data as we need to see the data from two tables. The function that is used in this step is merge.

34. 4.1
Explanation:
We also need to create a parameter to determine whether the sales data can be considered as an increase
or decrease. Add the if value function that will process any value that is larger than 0, considered as an
increase, and if not, then the value falls under the decrease category.
Create a new column in which the condition can be applied. In this case, we use df_merged[] =
df_merged[].apply(condition) function. Then, display the merged data from the highest to the
lowest by using ascending.

35. 4.2
Explanation:
Create a bar chart to ease the users in comparing the sales on the weekends and the weekdays.
Use plot function to make the bar chart.

36. Result
Analysis:
From the processed data, it is
apparent that the average sales
were the highest in October 2022.
Within three months, the average
sales on the weekdays were higher
than the sales on the weekends.
There was a great drop in the
average weekday sales in November.
However, it rocketed again in
December. On the other hand, the
weekend sales continually decrease
from October to December.
Therefore, the company needs to
give more attention to the weekend
sales. There may be a need to
change the campaign strategy to
improve the average sales on the
weekend.

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Bootcamp Data Analysis
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