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This article mainly introduces "how to use Python and Tableau to do Singles Day operation data analysis for supermarket chains". In daily operation, it is believed that many people have doubts about how to use Python and Tableau to do Singles Day operation data analysis for supermarket chains. The editor consulted all kinds of materials and sorted out simple and useful operation methods. I hope it will be helpful for you to answer the question of "how to use Python and Tableau to do Singles Day operation data analysis for supermarket chains"! Next, please follow the editor to study!

1. Load data

My friend provided a dataset of a file in CSV format, and now I use pandas to load it into the virtual table of Python.

Preview the data source:

Import pandas as pdtengyulong1 = pd.read_csv ("C:\ Users\ QDM\ Desktop\ fruit supermarket .csv", parse_dates = ["date of sale"]) tengyulong1

Preview the table:

two。 Analyze which items sell well (large sales quantity)

First, the aggregate operation of "sales quantity" is carried out according to "commodity number" and "commodity name", and the total sales quantity of different items is calculated respectively.

Tengyulong2 = tengyulong1.groupby (["Commodity number", "Commodity name"]) ["sales quantity"] .sum () .reset_index () tengyulong2

two。 Analyze which items sell well (large sales quantity)

2.1 first, aggregate the "sales quantity" according to the "commodity number" and "commodity name", and calculate the total sales quantity of different items respectively.

2.2 then sort the order in descending order to see what are the items of the sales quantity TOP10

2.3 use Tableau to summarize the proportion of total sales volume of different goods:

As can be seen from the above, imported blueberries, Thai coconut green, imported bananas, Xinjiang Korla fragrant pears, honey oranges, Shaanxi Fuji, Bingtang pears, Ehime fruit frozen oranges, Nanfeng tangerines, and Bingtang tangerines sell well. So next year's Singles Day holiday can consider more stock. Although the other products with low sales volume failed to support the sales report, they virtually played a role of drainage to meet the tastes and choices of different customers. That is, it has the value of its existence, so it can not be off the shelf blindly.

3. Analyze the proportion of sales in different channels:

Calculate the total sales amount of each item and add it to DataFrame.

3.2 make a group sum according to the "sales channel":

3.3 draw a circular chart to take a look at the sales share of different channels:

Render effect:

Obviously, the supermarket to "offline stores" as the main sales channel, while not forgetting to keep up with the Internet trend, expand relevant e-commerce sales channels, a two-pronged approach, to pull as many performance reports as possible. Although the e-commerce channel only accounts for about 1x4 of the performance contribution on Singles' Day, it should not be underestimated. Usually after the success of appropriate trial and error, we should also actively expand the e-commerce business.

5. Analysis of the high-score peak period of supermarket passenger flow

5.1 it is necessary to understand the peak periods of passenger flow to help supermarkets determine the most appropriate time to carry out promotional activities.

Note: because it is virtual data, please do not substitute the results into the actual scene.

5.2 obviously, the result of this calculation is not very intuitive, so I draw it as a column chart and a line chart divided by point in time.

5.3.Because the result of the sales data is too large, export the result to Excel for simple cleaning first, and convert it into "ten thousand" units in the auxiliary column, so as to avoid the data label being too long and dazzling in the next visualization graph.

Then, Python is used to visualize the results after cleaning reasonably.

5.5 as can be found from the above picture, around 12:00 and 6pm, that is, before and after meals are the peak sales of the day, so the promotion effect will be better in these two periods of time.

The early morning time is the bedtime, the passenger flow is low, so do not need to invest too much manpower cost to guard.

The highest sales are 13:00 in the afternoon, which can be guessed as a result of people going out for a walk or online shopping after lunch or after work. In addition, the average total sales per hour is about 14.8923 million.

At this point, the study on "how to use Python and Tableau to do Singles Day operation data analysis for supermarket chains" is over. I hope to be able to solve your doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!

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