Foundations of Data Science

Get Started With Python

Translate Numbers into Insights

The Power of Statistics

Here's a mock dataset representing a fictional e-commerce company's sales records:

Copy code
order_id,customer_id,product_category,order_date,order_value
1,C1001,Electronics,2023-05-01,1200
2,C1002,Clothing,2023-05-02,500
3,C1003,Electronics,2023-05-02,800
4,C1004,Beauty,2023-05-03,150
5,C1001,Books,2023-05-04,250
6,C1005,Electronics,2023-05-05,900
7,C1002,Clothing,2023-05-06,700
8,C1006,Electronics,2023-05-06,1000
9,C1007,Beauty,2023-05-07,300
10,C1008,Books,2023-05-07,200

Now, let's dive into some exploratory data analysis questions that you can practice with:

1. How many records are there in the dataset?

   df.count()
   	#10 in each column, 10 records
   df.size()
   	#total size 50, five columns, 10 records
   len(df.columns) #for column amount
   

2. What are the different product categories available?

   df2 = df.groupby("product_category").size().reset_index(name = "count")
   print(df2)
   
   output = product_category  count
   0           Beauty      2
   1            Books      2
   2         Clothing      2
   3      Electronics      4
   #four different categories available
   

3. How many unique customers made purchases?

   distinct_values = df['customer_id'].unique()
   print(distinct_values)
   #gives unique customer ids
   len(distinct_values)
   #gives the number of ids, 8
   

4. What is the range of dates covered in the dataset?

   df['order_date'] = pd.to_datetime(df['order_date'])
   date_range = []
   date_range.append(df['order_date'].min()) 
   date_range.append(df['order_date'].max())
   date_range
   
   #[Timestamp('2023-05-01 00:00:00'), Timestamp('2023-05-07 00:00:00')]
   

5. What is the average order value?

   orderVal_average =df['order_value'].mean()
   orderVal_average
   #600
   

6. What is the minimum and maximum order value?

   minmax_orderVal = []
   minmax_orderVal.append(df['order_value'].min())
   minmax_orderVal.append(df['order_value'].max())
   minmax_orderVal
   

7. Which product category has the highest total sales value?

   df2 = df.groupby('product_category')['order_value'].sum()
   df2
   largest_category = df2.idxmax()
   
   print("Product category with the largest order value sum:", largest_category)
   

8. How many orders were placed per product category?

   df3 = df.groupby('product_category')['order_id'].count()
   df3
   

9. Can you calculate the average order value per product category?

   df4 = df.groupby('product_category')['order_value'].mean()
   df4
   

10. What is the distribution of order values? Can you plot a histogram?

    plt.hist(df['order_value'], bins=5, color='skyblue', edgecolor='black')
    plt.xlabel('Order Value')
    plt.ylabel('Number of Orders')
    plt.title('Order Value Distribution')
    plt.grid(True)
    plt.show()