Some data related to the question:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 38223 entries, 0 to 38222
Data columns (total 16 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 trans_id 38223 non-null int64
1 prod_upc 38223 non-null int64
2 cust_id 38223 non-null int64
3 trans_timestamp 38223 non-null object
4 trans_year 38223 non-null int64
5 trans_month 38223 non-null int64
6 trans_day 38223 non-null int64
7 trans_hour 38223 non-null int64
8 trans_quantity 38223 non-null int64
9 cust_age 38223 non-null int64
10 cust_state 38223 non-null object
11 prod_price 38223 non-null float64
12 prod_title 38223 non-null object
13 prod_category 38223 non-null object
14 prod_animal_type 38223 non-null object
15 total_sales 38223 non-null float64
dtypes: float64(2), int64(9), object(5)
memory usage: 4.7+ MB
prod_title
Reddy Beddy 6583
Yum Fish-Dish 4298
Kitty Climber 3329
Feline Fix Mix 3262
Tuna Tasties 3102
Chewie Dental 2579
Purrfect Puree 2453
Whole Chemistry Recipe 2410
Cat Cave 2408
Snoozer Hammock 2311
All Veggie Yummies 2296
Chomp-a Plush 2114
Snoozer Essentails 2058
Fetch Blaster 1898
Ball and String 1885
Purr Mix 1876
Foozy Mouse 1863
Scratchy Post 1833
Snack-em Fish 1585
The New Bone 1234
Tug-a-Back 1152
Name: trans_quantity, dtype: int64
prod_title
Reddy Beddy 408023.09
Cat Cave 175759.92
Kitty Climber 119810.71
Snoozer Hammock 106282.89
Snoozer Essentails 100739.10
Yum Fish-Dish 81447.10
Scratchy Post 65951.34
Feline Fix Mix 65207.38
Foozy Mouse 61460.37
Tuna Tasties 58782.90
Ball and String 53628.25
Fetch Blaster 47355.10
Purrfect Puree 46484.35
Chomp-a Plush 46402.30
Whole Chemistry Recipe 31257.70
Chewie Dental 28291.63
Tug-a-Back 26484.48
Snack-em Fish 25344.15
Purr Mix 24369.24
All Veggie Yummies 22845.20
The New Bone 13561.66
Name: total_sales, dtype: float64
Question 1: What are the top ten product titles by the total number of items sold for that product? Display in descending order. Store in variable top_num_sales.
Question 2: What are the top ten product titles by total dollar amount made? Display in descending order. Store in variable top_tot_sales.
Answer for Q 1:
Code: "product_sales = df.groupby('prod_title')\['trans_quantity'\].sum()
top_num_sales = product_sales.sort_values(ascending=False)\[:10\].index.tolist()"
Output:
Your answer for the `top_tot_sales` isn't quite right.Looks like you have 1 or more products that aren't correct. You might want to check the order of your answer.
Take a closer look at your code to see what you can change.
Answer for Q 2:
Code: "
df_cleaned\['total_sales'\] = df_cleaned\['trans_quantity'\] \* df_cleaned\['prod_price'\]
top_tot_sales = df_cleaned.groupby('prod_title')\['total_sales'\].sum().sort_values(ascending=False).head(10)
print(top_tot_sales)`
Output:
Your answer for the `top_tot_sales` isn't quite right.Looks like you have 1 or more products that aren't correct. You might want to check the order of your answer.
Take a closer look at your code to see what you can change.
