kaggle dataset marketing data analysis of a coffee shop

Since I'm still in the learning phase of data analysis (wild Python scholar), I found two datasets at the site kaggle to give myself a practice run.

preliminary

import pandas as pd
import os
import  as plt
import numpy as np
from random import choice

Getting data

Here I downloaded two datasets the first one is about coffee sales and the second one is about the 1000 most popular bloggers on the site Instagram.

I'll start with the table of coffee sales, because I looked at the second table and really didn't have any eyebrows to go for it

# Read the files in the directory
directory = r'C:\Users\Admin\Desktop\demo\exercise'
files = (directory)
print(files)

['coffee_result.csv', '']

# Storage of documents
files_list = []
for file in files:
    if ('.csv'):
        directory_file = fr'{directory}\{file}'
        files_list.append(directory_file)
print(files_list)

['C:\\Users\\Admin\\Desktop\\demo\\practice\\coffee_result.csv', 'C:\\Users\\Admin\\Desktop\\demo\\practice\\']

# Read the needed files
df = pd.read_csv(files_list[0])

View some essential information

()
df

<class ''>
RangeIndex: 1464 entries, 0 to 1463
Data columns (total 6 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   date         1464 non-null   object 
 1   datetime     1464 non-null   object 
 2   cash_type    1464 non-null   object 
 3   card         1375 non-null   object 
 4   money        1464 non-null   float64
 5   coffee_name  1464 non-null   object 
dtypes: float64(1), object(5)
memory usage: 68.8+ KB

print(df['cash_type'].unique().tolist(),'\n', 
len(df['card'].unique().tolist()),'\n', 
df['coffee_name'].unique().tolist(),'\n',
len(df['coffee_name'].unique().tolist()))

['card', 'cash'] 
 589 
 ['Latte', 'Hot Chocolate', 'Americano', 'Americano with Milk', 'Cocoa', 'Cortado', 'Espresso', 'Cappuccino'] 
 8

The information returned by info shows that there are some nulls in the card column, so I'll take care of the nulls.

df[df['card'].isnull()]

The null value is generated by the row corresponding to the column whose payment type is cash payment

df['card'] = df['card'].fillna("-1")
df['card'].isnull().any()

np.False_

Processing of data

In the information returned by info, I see that the value type of the date column is an object, so I'll turn it into a date type to facilitate my own subsequent operations.

print(type([1,'date']),type([1,'datetime']))
[1,'date']

<class 'str'> <class 'str'>
'2024-03-01'

# Adjust the date format to extract the month of each row of data
df['date'] = pd.to_datetime(df['date'])
df['datetime'] = pd.to_datetime(df['datetime'])
df['month'] = df['date'].
print(len(df['month'].unique()))

7

View monthly sales

Since there were only 5 days of data in September this month was not included in the analysis

# View monthly sales and amounts
df_six = df[df['month']!=9].copy()
month = df_six['month'].unique() # Separate out the months
month_sales = df_six.groupby('month')['money'].count()
month_sum = df_six.groupby('month')['money'].sum()

figure,axes = (1,2,figsize=[16,8])
("Month sales and sum",size=20)
ax1 = axes[0].bar(month,month_sales)
axes[0].set_xlabel('Month',size=16)
axes[0].set_ylabel('Count',size=16)

ax2 = axes[1].bar(month,month_sum)
axes[1].set_xlabel('Month',size=16)
axes[1].set_ylabel('Sum',size=16)

axes[0].bar_label(ax1,fmt="%d",label_type="center")
axes[1].bar_label(ax2,fmt="%d",label_type="center")
plt.subplots_adjust(wspace=0.5)

Statistics on the marketing of each coffee

Monthly marketing per coffee

nrows,ncols = 2,4
figure3,axes = (nrows,ncols,figsize=[16,8],sharex=True,sharey=True)

coffee_month_sales = df_six.groupby(['month','coffee_name'])['money'].sum().reset_index(name='sum')
coffee_names = coffee_month_sales['coffee_name'].unique().tolist()

for idx,coffee_name in enumerate(coffee_names):
    x,y = divmod(idx,ncols)
    coffee_data = coffee_month_sales[coffee_month_sales['coffee_name']==coffee_name]
    bars = axes[x,y].bar(coffee_data['month'],coffee_data['sum'])
    axes[x,y].bar_label(bars,fmt="%d",label_type="center")
    subtitle = f"{coffee_name} {int(coffee_data['sum'].sum())}"
    axes[x,y].set_title(subtitle)
    axes[x,y].set_xlabel('month',size=16)
    axes[x,y].set_ylabel('sum',size=16)
    
('coffee month sales',size=20)
plt.tight_layout()
plt.subplots_adjust(wspace=0.5)

See how many people are reached by different coffees and what percentage of the audience they represent

stati = df_six.groupby('coffee_name')['money'].count().reset_index(name='buyers')
stati.sort_values(by='buyers',ascending=True,inplace=True,ignore_index=True)

figure2,axes = (1,2,figsize=(16,8))
("Coffee audience number and proportion",size=20)
ax1 = axes[0].barh([:,0],[:,1])
axes[0].bar_label(ax1,fmt="%d",label_type="center")
axes[0].set_ylabel("Kind",size=16)
axes[0].set_xlabel("Sum",size=16)

axes[1].pie([:,1],labels=[:,0],autopct='%0.1f')
plt.subplots_adjust(wspace=0.5)

Statistics on the actual consumption of customers

cardholder = df_six[df_six['card']!='-1'].copy()
cardholder['tag'] = 1
(columns=['date','datetime','cash_type'],inplace=True)
cardholder['month_sum'] = ('card')['tag'].transform('sum')

active_buyer = ('card')['month_sum'].max().reset_index(name='buys')
active_buyer.sort_values(by='buys',inplace=True,ignore_index=True,ascending=False)

cardholder['money_sum'] = ('card')['money'].transform('sum')
money_sum = cardholder.drop_duplicates(subset='card',ignore_index=True).copy()
money_sum.drop(columns=['money','coffee_name','month','tag','month_sum'],inplace=True)
money_sum.sort_values(by='money_sum',inplace=True,ignore_index=True,ascending=False)

result = (active_buyer,money_sum)
print('Average of total amount spent:',result['money_sum'].mean(),'\n','
      (10))

Average total amount spent: 75.29034111310592
                   card buys money_sum
0 anon-0000-0000-0012 96 2772.44
1 anon-0000-0000-0009 67 2343.98
2 anon-0000-0000-0141 44 1101.08
3 anon-0000-0000-0097 38 1189.34
4 anon-0000-0000-0040 30 910.12
5 anon-0000-0000-0003 27 744.04
6 anon-0000-0000-0001 17 646.14
7 anon-0000-0000-0134 13 470.76
8 anon-0000-0000-0024 12 422.26
9 anon-0000-0000-0059 12 337.00

As you can see by the printout this is considered to be the most active group of users

That's roughly the extent to do this, thanks for watching, and if there's any good ways to do this, comment in the comments section as well!