PRODUCT SALES ANALYSIs¶

Cleaning and analyzing data using Python¶

I have 12 months sales data broken down by month, prodcut type, cost, address etc. In this analysis I am using Jupyter Notebook.

PROBLEMS

I will try to answer some questions written below:

  1. What was the best month for sales? How much was earned that month?
  2. What city sold the most product?
  3. What time should we display advertisements to maximize likelihood of customer’s buying products?
  4. What Products are most often sold together?
  5. What product sold the most? Why?</a>

Import pandas and Numpy¶

In [17]:
import pandas as pd
import numpy as np

Checking data¶

In [18]:
data = pd.read_csv("sales_data.csv")
In [19]:
data.head()
Out[19]:
Order ID Product Quantity Ordered Price Each Order Date Purchase Address
0 176558 USB-C Charging Cable 2 11.95 04/19/19 08:46 917 1st St, Dallas, TX 75001
1 NaN NaN NaN NaN NaN NaN
2 176559 Bose SoundSport Headphones 1 99.99 04/07/19 22:30 682 Chestnut St, Boston, MA 02215
3 176560 Google Phone 1 600 04/12/19 14:38 669 Spruce St, Los Angeles, CA 90001
4 176560 Wired Headphones 1 11.99 04/12/19 14:38 669 Spruce St, Los Angeles, CA 90001
In [20]:
data.dtypes
Out[20]:
Order ID            object
Product             object
Quantity Ordered    object
Price Each          object
Order Date          object
Purchase Address    object
dtype: object
In [21]:
data.shape
Out[21]:
(186850, 6)
|As we can see dataset contain 186850 rows, and 6 columns. All the columns are object data type, maybe we need to change data type. we will do it later. Now, there are some null values. In order to get most accurate answers we need to clean null values. Lets check how many null we have.
In [22]:
null = data[data.isnull().any(axis=1)]
null.head()
Out[22]:
Order ID Product Quantity Ordered Price Each Order Date Purchase Address
1 NaN NaN NaN NaN NaN NaN
356 NaN NaN NaN NaN NaN NaN
735 NaN NaN NaN NaN NaN NaN
1433 NaN NaN NaN NaN NaN NaN
1553 NaN NaN NaN NaN NaN NaN

Removing null¶

In [23]:
data = data.dropna(how="all")
In [24]:
data.shape
Out[24]:
(186305, 6)
|As we can see null values are removed.

lets attemp questions:¶

PROBLEMS

  1. What was the best month for sales? How much was earned that month?

To answer the question I need month and sales column. But As we see above data we don't have any column named month or sales. But, from "order date" column we can extract month and sales from quantity ordered and price column. Let's create them.

In [25]:
data['Month'] = data['Order Date'].str[0:2]
In [26]:
data['Month'] = data['Month'].astype('int32')
data.head()

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_3908\4101063803.py in <module>
----> 1 data['Month'] = data['Month'].astype('int32')
      2 data.head()

~\anaconda3\lib\site-packages\pandas\core\generic.py in astype(self, dtype, copy, errors)
   5910         else:
   5911             # else, only a single dtype is given
-> 5912             new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors)
   5913             return self._constructor(new_data).__finalize__(self, method="astype")
   5914 

~\anaconda3\lib\site-packages\pandas\core\internals\managers.py in astype(self, dtype, copy, errors)
    417 
    418     def astype(self: T, dtype, copy: bool = False, errors: str = "raise") -> T:
--> 419         return self.apply("astype", dtype=dtype, copy=copy, errors=errors)
    420 
    421     def convert(

~\anaconda3\lib\site-packages\pandas\core\internals\managers.py in apply(self, f, align_keys, ignore_failures, **kwargs)
    302                     applied = b.apply(f, **kwargs)
    303                 else:
--> 304                     applied = getattr(b, f)(**kwargs)
    305             except (TypeError, NotImplementedError):
    306                 if not ignore_failures:

~\anaconda3\lib\site-packages\pandas\core\internals\blocks.py in astype(self, dtype, copy, errors)
    578         values = self.values
    579 
--> 580         new_values = astype_array_safe(values, dtype, copy=copy, errors=errors)
    581 
    582         new_values = maybe_coerce_values(new_values)

~\anaconda3\lib\site-packages\pandas\core\dtypes\cast.py in astype_array_safe(values, dtype, copy, errors)
   1290 
   1291     try:
-> 1292         new_values = astype_array(values, dtype, copy=copy)
   1293     except (ValueError, TypeError):
   1294         # e.g. astype_nansafe can fail on object-dtype of strings

~\anaconda3\lib\site-packages\pandas\core\dtypes\cast.py in astype_array(values, dtype, copy)
   1235 
   1236     else:
-> 1237         values = astype_nansafe(values, dtype, copy=copy)
   1238 
   1239     # in pandas we don't store numpy str dtypes, so convert to object

~\anaconda3\lib\site-packages\pandas\core\dtypes\cast.py in astype_nansafe(arr, dtype, copy, skipna)
   1152         # work around NumPy brokenness, #1987
   1153         if np.issubdtype(dtype.type, np.integer):
-> 1154             return lib.astype_intsafe(arr, dtype)
   1155 
   1156         # if we have a datetime/timedelta array of objects

~\anaconda3\lib\site-packages\pandas\_libs\lib.pyx in pandas._libs.lib.astype_intsafe()

ValueError: invalid literal for int() with base 10: 'Or'
Error: Here, I get issues. There are values 'Or' in data. let's check it.
In [27]:
checkingOr = data[data['Order Date'].str[0:2]=='Or']
checkingOr.head()
Out[27]:
Order ID Product Quantity Ordered Price Each Order Date Purchase Address Month
519 Order ID Product Quantity Ordered Price Each Order Date Purchase Address Or
1149 Order ID Product Quantity Ordered Price Each Order Date Purchase Address Or
1155 Order ID Product Quantity Ordered Price Each Order Date Purchase Address Or
2878 Order ID Product Quantity Ordered Price Each Order Date Purchase Address Or
2893 Order ID Product Quantity Ordered Price Each Order Date Purchase Address Or 
So, Or is coming from Month column. let's change it just by changing equal sign to not equal.
In [28]:
data = data[data['Order Date'].str[0:2]!='Or']


data['Month'] = data['Order Date'].str[0:2]
data['Month'] = data['Month'].astype('int32')
data.head()
Out[28]:
Order ID Product Quantity Ordered Price Each Order Date Purchase Address Month
0 176558 USB-C Charging Cable 2 11.95 04/19/19 08:46 917 1st St, Dallas, TX 75001 4
2 176559 Bose SoundSport Headphones 1 99.99 04/07/19 22:30 682 Chestnut St, Boston, MA 02215 4
3 176560 Google Phone 1 600 04/12/19 14:38 669 Spruce St, Los Angeles, CA 90001 4
4 176560 Wired Headphones 1 11.99 04/12/19 14:38 669 Spruce St, Los Angeles, CA 90001 4
5 176561 Wired Headphones 1 11.99 04/30/19 09:27 333 8th St, Los Angeles, CA 90001 4 
Alright! month column is created. In the second step I will create Sales column. But, before going ahead lets check data another time:
In [29]:
data.dtypes
Out[29]:
Order ID            object
Product             object
Quantity Ordered    object
Price Each          object
Order Date          object
Purchase Address    object
Month                int32
dtype: object
As we can see Quantity Orders and Price Each are string value. We can not multiply strings in order to get Sales. So, we need to convert data types first.
In [30]:
data['Quantity Ordered'] = pd.to_numeric(data['Quantity Ordered'])
data['Price Each'] = pd.to_numeric(data['Price Each'])
In [31]:
data.dtypes
Out[31]:
Order ID             object
Product              object
Quantity Ordered      int64
Price Each          float64
Order Date           object
Purchase Address     object
Month                 int32
dtype: object
Alright! Data type has been changed to numeric value as int and float. Lets create Sales column.
In [32]:
data['Sales'] = data['Quantity Ordered']*data['Price Each']
data.head()
Out[32]:
Order ID Product Quantity Ordered Price Each Order Date Purchase Address Month Sales
0 176558 USB-C Charging Cable 2 11.95 04/19/19 08:46 917 1st St, Dallas, TX 75001 4 23.90
2 176559 Bose SoundSport Headphones 1 99.99 04/07/19 22:30 682 Chestnut St, Boston, MA 02215 4 99.99
3 176560 Google Phone 1 600.00 04/12/19 14:38 669 Spruce St, Los Angeles, CA 90001 4 600.00
4 176560 Wired Headphones 1 11.99 04/12/19 14:38 669 Spruce St, Los Angeles, CA 90001 4 11.99
5 176561 Wired Headphones 1 11.99 04/30/19 09:27 333 8th St, Los Angeles, CA 90001 4 11.99 
Sales column is successfully created. let's, find out the answer of first question.
In [33]:
Monthly_sales = data.groupby('Month').sum().sort_values(by='Sales', ascending=False)
Monthly_sales.head(12)
Out[33]:
Quantity Ordered Price Each Sales
Month
12 28114 4588415.41 4613443.34
10 22703 3715554.83 3736726.88
4 20558 3367671.02 3390670.24
11 19798 3180600.68 3199603.20
5 18667 3135125.13 3152606.75
3 17005 2791207.83 2807100.38
7 16072 2632539.56 2647775.76
6 15253 2562025.61 2577802.26
8 13448 2230345.42 2244467.88
2 13449 2188884.72 2202022.42
9 13109 2084992.09 2097560.13
1 10903 1811768.38 1822256.73 
Clearly, Month 12 Which is december is the highest sales with nearly $4614000. we need to visualize it to make people easier to understand.

Import Matplotlib and plotly¶

In [34]:
import matplotlib.pyplot as matlib
import plotly.express as px
In [35]:
months=range(1,13) #x axes
result=data.groupby('Month').sum()
matlib.bar(months, result['Sales'])
label, location = matlib.yticks()
matlib.yticks(label,(label/1000000).astype(int))
matlib.xlabel('Month in Number')
matlib.ylabel('Sales in Million USD')

matlib.show()
In [36]:
px.bar(Monthly_sales, y= 'Sales', color='Quantity Ordered')
Visualize: Here, not only the highest sales, but we can also get the lowest sales just by looking at the bar for a few seconds. December is the highest selling month and january is the least.

PROBLEMS

  1. What city sold the most product?
In [37]:
data.head()
Out[37]:
Order ID Product Quantity Ordered Price Each Order Date Purchase Address Month Sales
0 176558 USB-C Charging Cable 2 11.95 04/19/19 08:46 917 1st St, Dallas, TX 75001 4 23.90
2 176559 Bose SoundSport Headphones 1 99.99 04/07/19 22:30 682 Chestnut St, Boston, MA 02215 4 99.99
3 176560 Google Phone 1 600.00 04/12/19 14:38 669 Spruce St, Los Angeles, CA 90001 4 600.00
4 176560 Wired Headphones 1 11.99 04/12/19 14:38 669 Spruce St, Los Angeles, CA 90001 4 11.99
5 176561 Wired Headphones 1 11.99 04/30/19 09:27 333 8th St, Los Angeles, CA 90001 4 11.99
In [38]:
data['City'] = data['Purchase Address'].apply(lambda x: x.split(',')[1])+ " " +data['Purchase Address'].apply(lambda x: x.split(',')[2].split(' ')[1])
data.head()
Out[38]:
Order ID Product Quantity Ordered Price Each Order Date Purchase Address Month Sales City
0 176558 USB-C Charging Cable 2 11.95 04/19/19 08:46 917 1st St, Dallas, TX 75001 4 23.90 Dallas TX
2 176559 Bose SoundSport Headphones 1 99.99 04/07/19 22:30 682 Chestnut St, Boston, MA 02215 4 99.99 Boston MA
3 176560 Google Phone 1 600.00 04/12/19 14:38 669 Spruce St, Los Angeles, CA 90001 4 600.00 Los Angeles CA
4 176560 Wired Headphones 1 11.99 04/12/19 14:38 669 Spruce St, Los Angeles, CA 90001 4 11.99 Los Angeles CA
5 176561 Wired Headphones 1 11.99 04/30/19 09:27 333 8th St, Los Angeles, CA 90001 4 11.99 Los Angeles CA
In [39]:
sales_by_city = data.groupby(['City']).agg({'Sales':'sum'}).reset_index()
sales_by_city
Out[39]:
City Sales
0 Atlanta GA 2795498.58
1 Austin TX 1819581.75
2 Boston MA 3661642.01
3 Dallas TX 2767975.40
4 Los Angeles CA 5452570.80
5 New York City NY 4664317.43
6 Portland ME 449758.27
7 Portland OR 1870732.34
8 San Francisco CA 8262203.91
9 Seattle WA 2747755.48
In [40]:
sales_by_city.sort_values('Sales', ascending=False)
Out[40]:
City Sales
8 San Francisco CA 8262203.91
4 Los Angeles CA 5452570.80
5 New York City NY 4664317.43
2 Boston MA 3661642.01
0 Atlanta GA 2795498.58
3 Dallas TX 2767975.40
9 Seattle WA 2747755.48
7 Portland OR 1870732.34
1 Austin TX 1819581.75
6 Portland ME 449758.27
In [41]:
px.bar(sales_by_city, x='City', y='Sales')
Visualize: Here, san fransisco is the most selling city compare to other cities with approximately $82,00,000.

PROBLEMS

  1. What Time Should We Display Advertisements to Maximize Likelihood of Customer’s Buying Product?
To Do This: Grouping data by hours and counting all the orders should work. Hour is in the Order Date column. I will now extract it.
In [42]:
data.head()
Out[42]:
Order ID Product Quantity Ordered Price Each Order Date Purchase Address Month Sales City
0 176558 USB-C Charging Cable 2 11.95 04/19/19 08:46 917 1st St, Dallas, TX 75001 4 23.90 Dallas TX
2 176559 Bose SoundSport Headphones 1 99.99 04/07/19 22:30 682 Chestnut St, Boston, MA 02215 4 99.99 Boston MA
3 176560 Google Phone 1 600.00 04/12/19 14:38 669 Spruce St, Los Angeles, CA 90001 4 600.00 Los Angeles CA
4 176560 Wired Headphones 1 11.99 04/12/19 14:38 669 Spruce St, Los Angeles, CA 90001 4 11.99 Los Angeles CA
5 176561 Wired Headphones 1 11.99 04/30/19 09:27 333 8th St, Los Angeles, CA 90001 4 11.99 Los Angeles CA
In [43]:
data['Order Date_DTO']=pd.to_datetime(data['Order Date'])
data.head()
Out[43]:
Order ID Product Quantity Ordered Price Each Order Date Purchase Address Month Sales City Order Date_DTO
0 176558 USB-C Charging Cable 2 11.95 04/19/19 08:46 917 1st St, Dallas, TX 75001 4 23.90 Dallas TX 2019-04-19 08:46:00
2 176559 Bose SoundSport Headphones 1 99.99 04/07/19 22:30 682 Chestnut St, Boston, MA 02215 4 99.99 Boston MA 2019-04-07 22:30:00
3 176560 Google Phone 1 600.00 04/12/19 14:38 669 Spruce St, Los Angeles, CA 90001 4 600.00 Los Angeles CA 2019-04-12 14:38:00
4 176560 Wired Headphones 1 11.99 04/12/19 14:38 669 Spruce St, Los Angeles, CA 90001 4 11.99 Los Angeles CA 2019-04-12 14:38:00
5 176561 Wired Headphones 1 11.99 04/30/19 09:27 333 8th St, Los Angeles, CA 90001 4 11.99 Los Angeles CA 2019-04-30 09:27:00
In [44]:
data['Hour'] = data['Order Date_DTO'].dt.hour
data.head()
Out[44]:
Order ID Product Quantity Ordered Price Each Order Date Purchase Address Month Sales City Order Date_DTO Hour
0 176558 USB-C Charging Cable 2 11.95 04/19/19 08:46 917 1st St, Dallas, TX 75001 4 23.90 Dallas TX 2019-04-19 08:46:00 8
2 176559 Bose SoundSport Headphones 1 99.99 04/07/19 22:30 682 Chestnut St, Boston, MA 02215 4 99.99 Boston MA 2019-04-07 22:30:00 22
3 176560 Google Phone 1 600.00 04/12/19 14:38 669 Spruce St, Los Angeles, CA 90001 4 600.00 Los Angeles CA 2019-04-12 14:38:00 14
4 176560 Wired Headphones 1 11.99 04/12/19 14:38 669 Spruce St, Los Angeles, CA 90001 4 11.99 Los Angeles CA 2019-04-12 14:38:00 14
5 176561 Wired Headphones 1 11.99 04/30/19 09:27 333 8th St, Los Angeles, CA 90001 4 11.99 Los Angeles CA 2019-04-30 09:27:00 9
In [45]:
hours_count = data.groupby(['Hour']).count().reset_index()
hours_count.head()
Out[45]:
Hour Order ID Product Quantity Ordered Price Each Order Date Purchase Address Month Sales City Order Date_DTO
0 0 3910 3910 3910 3910 3910 3910 3910 3910 3910 3910
1 1 2350 2350 2350 2350 2350 2350 2350 2350 2350 2350
2 2 1243 1243 1243 1243 1243 1243 1243 1243 1243 1243
3 3 831 831 831 831 831 831 831 831 831 831
4 4 854 854 854 854 854 854 854 854 854 854
In [46]:
px.line(hours_count, x='Hour', y='Quantity Ordered')
Visualize: As we can see from line chart, there are approximately 2 peaks at the data - 12 (12 PM) and 19 (7 PM). So, we can say most people shopping during this hour. From this data, we can suggest to our bussiness partner to advertise their product right before 12 PM and/or 7 PM.
It could be 11.30 AM and/or 6.30 PM.

PROBLEMS

  1. What Products are most often sold together?
In [47]:
data.head()
Out[47]:
Order ID Product Quantity Ordered Price Each Order Date Purchase Address Month Sales City Order Date_DTO Hour
0 176558 USB-C Charging Cable 2 11.95 04/19/19 08:46 917 1st St, Dallas, TX 75001 4 23.90 Dallas TX 2019-04-19 08:46:00 8
2 176559 Bose SoundSport Headphones 1 99.99 04/07/19 22:30 682 Chestnut St, Boston, MA 02215 4 99.99 Boston MA 2019-04-07 22:30:00 22
3 176560 Google Phone 1 600.00 04/12/19 14:38 669 Spruce St, Los Angeles, CA 90001 4 600.00 Los Angeles CA 2019-04-12 14:38:00 14
4 176560 Wired Headphones 1 11.99 04/12/19 14:38 669 Spruce St, Los Angeles, CA 90001 4 11.99 Los Angeles CA 2019-04-12 14:38:00 14
5 176561 Wired Headphones 1 11.99 04/30/19 09:27 333 8th St, Los Angeles, CA 90001 4 11.99 Los Angeles CA 2019-04-30 09:27:00 9
To Do This: By seeing dataset we can say that “Order ID” indicate transaction. So by grouping the product by the Order ID, we are able to know what products are often sold together.
In [48]:
new_data = data[data['Order ID'].duplicated(keep=False)]

#join product with same order ID
new_data['Sold_together'] = new_data.groupby('Order ID')['Product'].transform(lambda x: ','.join(x))
new_data.head()

C:\Users\HP\AppData\Local\Temp\ipykernel_3908\4287307258.py:4: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
Out[48]:
Order ID Product Quantity Ordered Price Each Order Date Purchase Address Month Sales City Order Date_DTO Hour Sold_together
3 176560 Google Phone 1 600.00 04/12/19 14:38 669 Spruce St, Los Angeles, CA 90001 4 600.00 Los Angeles CA 2019-04-12 14:38:00 14 Google Phone,Wired Headphones
4 176560 Wired Headphones 1 11.99 04/12/19 14:38 669 Spruce St, Los Angeles, CA 90001 4 11.99 Los Angeles CA 2019-04-12 14:38:00 14 Google Phone,Wired Headphones
18 176574 Google Phone 1 600.00 04/03/19 19:42 20 Hill St, Los Angeles, CA 90001 4 600.00 Los Angeles CA 2019-04-03 19:42:00 19 Google Phone,USB-C Charging Cable
19 176574 USB-C Charging Cable 1 11.95 04/03/19 19:42 20 Hill St, Los Angeles, CA 90001 4 11.95 Los Angeles CA 2019-04-03 19:42:00 19 Google Phone,USB-C Charging Cable
30 176585 Bose SoundSport Headphones 1 99.99 04/07/19 11:31 823 Highland St, Boston, MA 02215 4 99.99 Boston MA 2019-04-07 11:31:00 11 Bose SoundSport Headphones,Bose SoundSport Hea...
Warning: I get a warning here. Its because some paird data are encountered twice. That's mean I need to remove duplicate data to get accurate answer. lets, remove duplicate.
In [49]:
#dropping duplicate values
new_data = new_data[['Order ID', 'Sold_together']].drop_duplicates()
new_data.head()
Out[49]:
Order ID Sold_together
3 176560 Google Phone,Wired Headphones
18 176574 Google Phone,USB-C Charging Cable
30 176585 Bose SoundSport Headphones,Bose SoundSport Hea...
32 176586 AAA Batteries (4-pack),Google Phone
119 176672 Lightning Charging Cable,USB-C Charging Cable
Info: To find the answer of the question I will count the pair of products. To do that, new lybraries will be needed to count the combinations to Sold_together. I will use itertools and collections libraries

import libraries¶

In [50]:
from itertools import combinations
from collections import Counter
In [51]:
count=Counter()
for row in new_data['Sold_together']:
    row_list = row.split(',')
    count.update(Counter(combinations(row_list,2)))
print(count)

Counter({('iPhone', 'Lightning Charging Cable'): 1005, ('Google Phone', 'USB-C Charging Cable'): 987, ('iPhone', 'Wired Headphones'): 447, ('Google Phone', 'Wired Headphones'): 414, ('Vareebadd Phone', 'USB-C Charging Cable'): 361, ('iPhone', 'Apple Airpods Headphones'): 360, ('Google Phone', 'Bose SoundSport Headphones'): 220, ('USB-C Charging Cable', 'Wired Headphones'): 160, ('Vareebadd Phone', 'Wired Headphones'): 143, ('Lightning Charging Cable', 'Wired Headphones'): 92, ('Lightning Charging Cable', 'Apple Airpods Headphones'): 81, ('Vareebadd Phone', 'Bose SoundSport Headphones'): 80, ('USB-C Charging Cable', 'Bose SoundSport Headphones'): 77, ('Apple Airpods Headphones', 'Wired Headphones'): 69, ('Lightning Charging Cable', 'USB-C Charging Cable'): 58, ('Lightning Charging Cable', 'AA Batteries (4-pack)'): 55, ('Lightning Charging Cable', 'Lightning Charging Cable'): 54, ('Bose SoundSport Headphones', 'Wired Headphones'): 53, ('AA Batteries (4-pack)', 'Lightning Charging Cable'): 51, ('AAA Batteries (4-pack)', 'USB-C Charging Cable'): 50, ('Apple Airpods Headphones', 'AAA Batteries (4-pack)'): 48, ('AA Batteries (4-pack)', 'AAA Batteries (4-pack)'): 48, ('USB-C Charging Cable', 'USB-C Charging Cable'): 48, ('AAA Batteries (4-pack)', 'AAA Batteries (4-pack)'): 48, ('USB-C Charging Cable', 'AAA Batteries (4-pack)'): 45, ('Wired Headphones', 'USB-C Charging Cable'): 45, ('AA Batteries (4-pack)', 'Wired Headphones'): 44, ('AAA Batteries (4-pack)', 'Lightning Charging Cable'): 44, ('AAA Batteries (4-pack)', 'Wired Headphones'): 43, ('Wired Headphones', 'AAA Batteries (4-pack)'): 43, ('USB-C Charging Cable', 'Lightning Charging Cable'): 42, ('AA Batteries (4-pack)', 'Apple Airpods Headphones'): 41, ('AAA Batteries (4-pack)', 'AA Batteries (4-pack)'): 39, ('Wired Headphones', 'AA Batteries (4-pack)'): 39, ('Lightning Charging Cable', 'Bose SoundSport Headphones'): 39, ('USB-C Charging Cable', 'AA Batteries (4-pack)'): 38, ('Bose SoundSport Headphones', 'AAA Batteries (4-pack)'): 37, ('AA Batteries (4-pack)', 'USB-C Charging Cable'): 37, ('Wired Headphones', 'Lightning Charging Cable'): 37, ('Lightning Charging Cable', 'AAA Batteries (4-pack)'): 36, ('Apple Airpods Headphones', 'Lightning Charging Cable'): 35, ('Wired Headphones', 'Wired Headphones'): 35, ('AA Batteries (4-pack)', 'AA Batteries (4-pack)'): 35, ('USB-C Charging Cable', 'Apple Airpods Headphones'): 34, ('Bose SoundSport Headphones', 'Lightning Charging Cable'): 33, ('AAA Batteries (4-pack)', 'Apple Airpods Headphones'): 33, ('Apple Airpods Headphones', 'Bose SoundSport Headphones'): 32, ('Wired Headphones', 'Apple Airpods Headphones'): 31, ('USB-C Charging Cable', '27in FHD Monitor'): 31, ('Apple Airpods Headphones', 'USB-C Charging Cable'): 29, ('Apple Airpods Headphones', 'AA Batteries (4-pack)'): 29, ('AA Batteries (4-pack)', 'Bose SoundSport Headphones'): 28, ('Bose SoundSport Headphones', 'Bose SoundSport Headphones'): 27, ('Bose SoundSport Headphones', 'AA Batteries (4-pack)'): 27, ('Bose SoundSport Headphones', 'USB-C Charging Cable'): 25, ('Apple Airpods Headphones', 'Apple Airpods Headphones'): 24, ('AAA Batteries (4-pack)', '27in FHD Monitor'): 22, ('27in FHD Monitor', 'AAA Batteries (4-pack)'): 21, ('Wired Headphones', 'Bose SoundSport Headphones'): 21, ('AAA Batteries (4-pack)', 'Bose SoundSport Headphones'): 20, ('34in Ultrawide Monitor', 'AA Batteries (4-pack)'): 19, ('Lightning Charging Cable', '27in 4K Gaming Monitor'): 18, ('AA Batteries (4-pack)', 'iPhone'): 18, ('27in FHD Monitor', 'Lightning Charging Cable'): 18, ('Lightning Charging Cable', '27in FHD Monitor'): 18, ('34in Ultrawide Monitor', 'Lightning Charging Cable'): 18, ('Wired Headphones', '27in 4K Gaming Monitor'): 18, ('Bose SoundSport Headphones', 'Apple Airpods Headphones'): 18, ('iPhone', 'AAA Batteries (4-pack)'): 17, ('Wired Headphones', '34in Ultrawide Monitor'): 17, ('ThinkPad Laptop', 'AAA Batteries (4-pack)'): 16, ('Lightning Charging Cable', 'Google Phone'): 16, ('27in 4K Gaming Monitor', 'Lightning Charging Cable'): 16, ('34in Ultrawide Monitor', 'USB-C Charging Cable'): 15, ('27in FHD Monitor', 'AA Batteries (4-pack)'): 15, ('Wired Headphones', 'iPhone'): 15, ('AAA Batteries (4-pack)', '27in 4K Gaming Monitor'): 15, ('iPhone', 'USB-C Charging Cable'): 15, ('20in Monitor', 'USB-C Charging Cable'): 15, ('Lightning Charging Cable', '20in Monitor'): 15, ('27in 4K Gaming Monitor', 'AAA Batteries (4-pack)'): 15, ('Lightning Charging Cable', '34in Ultrawide Monitor'): 15, ('Google Phone', 'AA Batteries (4-pack)'): 14, ('Apple Airpods Headphones', 'Google Phone'): 14, ('USB-C Charging Cable', 'iPhone'): 14, ('Bose SoundSport Headphones', '27in FHD Monitor'): 14, ('AA Batteries (4-pack)', '27in 4K Gaming Monitor'): 14, ('AAA Batteries (4-pack)', 'iPhone'): 14, ('iPhone', 'AA Batteries (4-pack)'): 14, ('AA Batteries (4-pack)', 'Flatscreen TV'): 13, ('AA Batteries (4-pack)', '34in Ultrawide Monitor'): 13, ('AAA Batteries (4-pack)', '34in Ultrawide Monitor'): 13, ('Apple Airpods Headphones', 'iPhone'): 13, ('Wired Headphones', 'Macbook Pro Laptop'): 13, ('Apple Airpods Headphones', '27in 4K Gaming Monitor'): 12, ('Apple Airpods Headphones', '27in FHD Monitor'): 12, ('27in FHD Monitor', 'Bose SoundSport Headphones'): 12, ('27in FHD Monitor', 'USB-C Charging Cable'): 12, ('Google Phone', 'Lightning Charging Cable'): 12, ('Apple Airpods Headphones', 'Macbook Pro Laptop'): 12, ('27in 4K Gaming Monitor', 'USB-C Charging Cable'): 12, ('Macbook Pro Laptop', 'USB-C Charging Cable'): 12, ('Wired Headphones', '27in FHD Monitor'): 12, ('20in Monitor', 'Wired Headphones'): 12, ('Lightning Charging Cable', 'Flatscreen TV'): 12, ('27in FHD Monitor', 'Apple Airpods Headphones'): 12, ('USB-C Charging Cable', 'Google Phone'): 12, ('27in 4K Gaming Monitor', 'AA Batteries (4-pack)'): 12, ('34in Ultrawide Monitor', 'AAA Batteries (4-pack)'): 12, ('AAA Batteries (4-pack)', 'Google Phone'): 11, ('AAA Batteries (4-pack)', 'Macbook Pro Laptop'): 11, ('USB-C Charging Cable', '27in 4K Gaming Monitor'): 11, ('USB-C Charging Cable', 'ThinkPad Laptop'): 11, ('34in Ultrawide Monitor', 'Wired Headphones'): 11, ('20in Monitor', 'Lightning Charging Cable'): 11, ('AA Batteries (4-pack)', '27in FHD Monitor'): 11, ('Bose SoundSport Headphones', '34in Ultrawide Monitor'): 11, ('ThinkPad Laptop', 'Lightning Charging Cable'): 11, ('Google Phone', 'AAA Batteries (4-pack)'): 11, ('USB-C Charging Cable', '34in Ultrawide Monitor'): 11, ('Macbook Pro Laptop', 'Lightning Charging Cable'): 11, ('AA Batteries (4-pack)', 'Google Phone'): 11, ('AAA Batteries (4-pack)', 'ThinkPad Laptop'): 11, ('Macbook Pro Laptop', 'Bose SoundSport Headphones'): 11, ('27in 4K Gaming Monitor', 'Wired Headphones'): 11, ('Flatscreen TV', 'AAA Batteries (4-pack)'): 11, ('Flatscreen TV', 'Lightning Charging Cable'): 10, ('Wired Headphones', 'ThinkPad Laptop'): 10, ('USB-C Charging Cable', '20in Monitor'): 10, ('27in 4K Gaming Monitor', 'Apple Airpods Headphones'): 10, ('USB-C Charging Cable', 'Flatscreen TV'): 10, ('27in FHD Monitor', 'Wired Headphones'): 10, ('AA Batteries (4-pack)', '20in Monitor'): 10, ('AAA Batteries (4-pack)', 'Flatscreen TV'): 10, ('Lightning Charging Cable', 'iPhone'): 10, ('Bose SoundSport Headphones', 'Flatscreen TV'): 10, ('Lightning Charging Cable', 'Macbook Pro Laptop'): 10, ('Bose SoundSport Headphones', '27in 4K Gaming Monitor'): 10, ('Apple Airpods Headphones', 'ThinkPad Laptop'): 9, ('Wired Headphones', 'Google Phone'): 9, ('27in 4K Gaming Monitor', 'Bose SoundSport Headphones'): 9, ('20in Monitor', 'Bose SoundSport Headphones'): 9, ('Macbook Pro Laptop', 'AA Batteries (4-pack)'): 9, ('ThinkPad Laptop', 'USB-C Charging Cable'): 9, ('ThinkPad Laptop', 'Bose SoundSport Headphones'): 9, ('Vareebadd Phone', 'AA Batteries (4-pack)'): 9, ('USB-C Charging Cable', 'Macbook Pro Laptop'): 9, ('27in FHD Monitor', '27in FHD Monitor'): 9, ('AA Batteries (4-pack)', 'ThinkPad Laptop'): 9, ('Lightning Charging Cable', 'ThinkPad Laptop'): 9, ('AA Batteries (4-pack)', 'Macbook Pro Laptop'): 8, ('Flatscreen TV', 'AA Batteries (4-pack)'): 8, ('Apple Airpods Headphones', 'Flatscreen TV'): 8, ('ThinkPad Laptop', 'AA Batteries (4-pack)'): 8, ('AAA Batteries (4-pack)', '20in Monitor'): 8, ('34in Ultrawide Monitor', 'Apple Airpods Headphones'): 8, ('Bose SoundSport Headphones', 'Google Phone'): 8, ('20in Monitor', 'Apple Airpods Headphones'): 7, ('Macbook Pro Laptop', 'Apple Airpods Headphones'): 7, ('Wired Headphones', 'Flatscreen TV'): 7, ('Wired Headphones', '20in Monitor'): 7, ('Macbook Pro Laptop', 'Wired Headphones'): 7, ('USB-C Charging Cable', 'Vareebadd Phone'): 7, ('Google Phone', '27in FHD Monitor'): 7, ('Macbook Pro Laptop', 'AAA Batteries (4-pack)'): 7, ('34in Ultrawide Monitor', 'iPhone'): 7, ('34in Ultrawide Monitor', '34in Ultrawide Monitor'): 7, ('Flatscreen TV', 'USB-C Charging Cable'): 7, ('Bose SoundSport Headphones', 'iPhone'): 7, ('ThinkPad Laptop', 'Apple Airpods Headphones'): 7, ('Google Phone', 'Apple Airpods Headphones'): 7, ('Macbook Pro Laptop', '27in 4K Gaming Monitor'): 7, ('iPhone', '27in 4K Gaming Monitor'): 6, ('Flatscreen TV', 'Flatscreen TV'): 6, ('Apple Airpods Headphones', '34in Ultrawide Monitor'): 6, ('iPhone', '34in Ultrawide Monitor'): 6, ('Vareebadd Phone', 'Apple Airpods Headphones'): 6, ('27in 4K Gaming Monitor', '34in Ultrawide Monitor'): 6, ('27in 4K Gaming Monitor', 'Macbook Pro Laptop'): 6, ('Bose SoundSport Headphones', '20in Monitor'): 6, ('iPhone', 'Flatscreen TV'): 6, ('Apple Airpods Headphones', '20in Monitor'): 6, ('Apple Airpods Headphones', 'Vareebadd Phone'): 6, ('Wired Headphones', 'Vareebadd Phone'): 6, ('34in Ultrawide Monitor', 'Bose SoundSport Headphones'): 6, ('Google Phone', 'iPhone'): 6, ('27in FHD Monitor', 'Macbook Pro Laptop'): 6, ('20in Monitor', 'AA Batteries (4-pack)'): 6, ('iPhone', 'Bose SoundSport Headphones'): 5, ('27in 4K Gaming Monitor', '27in 4K Gaming Monitor'): 5, ('Flatscreen TV', '34in Ultrawide Monitor'): 5, ('27in 4K Gaming Monitor', 'Google Phone'): 5, ('27in FHD Monitor', '34in Ultrawide Monitor'): 5, ('Flatscreen TV', 'Apple Airpods Headphones'): 5, ('34in Ultrawide Monitor', '27in FHD Monitor'): 5, ('Macbook Pro Laptop', '34in Ultrawide Monitor'): 4, ('iPhone', 'Vareebadd Phone'): 4, ('Bose SoundSport Headphones', 'ThinkPad Laptop'): 4, ('20in Monitor', 'Macbook Pro Laptop'): 4, ('Vareebadd Phone', '34in Ultrawide Monitor'): 4, ('Flatscreen TV', 'Wired Headphones'): 4, ('Flatscreen TV', '27in FHD Monitor'): 4, ('LG Dryer', 'AA Batteries (4-pack)'): 4, ('Flatscreen TV', 'Macbook Pro Laptop'): 4, ('27in FHD Monitor', '27in 4K Gaming Monitor'): 4, ('ThinkPad Laptop', 'Flatscreen TV'): 4, ('Flatscreen TV', 'iPhone'): 4, ('27in 4K Gaming Monitor', 'ThinkPad Laptop'): 4, ('Vareebadd Phone', 'Google Phone'): 4, ('Macbook Pro Laptop', 'Google Phone'): 4, ('27in 4K Gaming Monitor', '27in FHD Monitor'): 4, ('Lightning Charging Cable', 'LG Washing Machine'): 4, ('27in FHD Monitor', 'ThinkPad Laptop'): 4, ('ThinkPad Laptop', 'Wired Headphones'): 4, ('iPhone', 'ThinkPad Laptop'): 4, ('Bose SoundSport Headphones', 'Macbook Pro Laptop'): 4, ('AAA Batteries (4-pack)', 'Vareebadd Phone'): 4, ('LG Washing Machine', 'AAA Batteries (4-pack)'): 4, ('Macbook Pro Laptop', 'ThinkPad Laptop'): 3, ('ThinkPad Laptop', 'Google Phone'): 3, ('34in Ultrawide Monitor', 'Macbook Pro Laptop'): 3, ('Lightning Charging Cable', 'Vareebadd Phone'): 3, ('Google Phone', 'ThinkPad Laptop'): 3, ('20in Monitor', '20in Monitor'): 3, ('ThinkPad Laptop', 'iPhone'): 3, ('Vareebadd Phone', 'Flatscreen TV'): 3, ('34in Ultrawide Monitor', 'Flatscreen TV'): 3, ('Macbook Pro Laptop', 'Macbook Pro Laptop'): 3, ('34in Ultrawide Monitor', 'ThinkPad Laptop'): 3, ('Macbook Pro Laptop', 'iPhone'): 3, ('Vareebadd Phone', 'iPhone'): 3, ('Wired Headphones', 'LG Washing Machine'): 3, ('Google Phone', '34in Ultrawide Monitor'): 3, ('Macbook Pro Laptop', '27in FHD Monitor'): 3, ('Flatscreen TV', 'Bose SoundSport Headphones'): 3, ('AA Batteries (4-pack)', 'Vareebadd Phone'): 3, ('27in FHD Monitor', '20in Monitor'): 3, ('iPhone', 'Google Phone'): 3, ('27in 4K Gaming Monitor', 'iPhone'): 3, ('Google Phone', 'Google Phone'): 3, ('Flatscreen TV', 'Google Phone'): 3, ('Google Phone', 'Macbook Pro Laptop'): 3, ('27in 4K Gaming Monitor', 'Flatscreen TV'): 3, ('Apple Airpods Headphones', 'LG Dryer'): 3, ('20in Monitor', 'AAA Batteries (4-pack)'): 3, ('iPhone', 'Macbook Pro Laptop'): 3, ('34in Ultrawide Monitor', 'Google Phone'): 2, ('Macbook Pro Laptop', '20in Monitor'): 2, ('Lightning Charging Cable', 'LG Dryer'): 2, ('Flatscreen TV', '27in 4K Gaming Monitor'): 2, ('ThinkPad Laptop', 'Macbook Pro Laptop'): 2, ('Macbook Pro Laptop', 'LG Washing Machine'): 2, ('20in Monitor', '27in FHD Monitor'): 2, ('ThinkPad Laptop', 'ThinkPad Laptop'): 2, ('Bose SoundSport Headphones', 'Vareebadd Phone'): 2, ('Vareebadd Phone', 'ThinkPad Laptop'): 2, ('20in Monitor', 'ThinkPad Laptop'): 2, ('iPhone', 'iPhone'): 2, ('27in FHD Monitor', 'LG Dryer'): 2, ('Vareebadd Phone', '27in 4K Gaming Monitor'): 2, ('27in 4K Gaming Monitor', '20in Monitor'): 2, ('LG Washing Machine', 'Lightning Charging Cable'): 2, ('LG Washing Machine', 'Bose SoundSport Headphones'): 2, ('AA Batteries (4-pack)', 'LG Dryer'): 2, ('Vareebadd Phone', 'AAA Batteries (4-pack)'): 2, ('iPhone', '20in Monitor'): 2, ('20in Monitor', 'Google Phone'): 2, ('Flatscreen TV', 'ThinkPad Laptop'): 2, ('ThinkPad Laptop', '27in FHD Monitor'): 2, ('27in FHD Monitor', 'Flatscreen TV'): 2, ('Google Phone', '20in Monitor'): 2, ('27in 4K Gaming Monitor', 'Vareebadd Phone'): 1, ('27in FHD Monitor', 'iPhone'): 1, ('Vareebadd Phone', 'Lightning Charging Cable'): 1, ('Google Phone', 'Vareebadd Phone'): 1, ('20in Monitor', 'iPhone'): 1, ('LG Dryer', 'Vareebadd Phone'): 1, ('Macbook Pro Laptop', 'Flatscreen TV'): 1, ('ThinkPad Laptop', 'Vareebadd Phone'): 1, ('Google Phone', 'Flatscreen TV'): 1, ('LG Washing Machine', 'Google Phone'): 1, ('LG Washing Machine', 'Wired Headphones'): 1, ('LG Dryer', 'Flatscreen TV'): 1, ('27in FHD Monitor', 'LG Washing Machine'): 1, ('LG Dryer', '27in FHD Monitor'): 1, ('20in Monitor', '34in Ultrawide Monitor'): 1, ('34in Ultrawide Monitor', '20in Monitor'): 1, ('34in Ultrawide Monitor', 'LG Washing Machine'): 1, ('Google Phone', '27in 4K Gaming Monitor'): 1, ('LG Washing Machine', 'iPhone'): 1, ('LG Dryer', 'Wired Headphones'): 1, ('27in FHD Monitor', 'Vareebadd Phone'): 1, ('LG Washing Machine', '27in 4K Gaming Monitor'): 1, ('LG Washing Machine', 'Apple Airpods Headphones'): 1, ('27in 4K Gaming Monitor', 'LG Dryer'): 1, ('20in Monitor', 'LG Washing Machine'): 1, ('LG Dryer', 'Google Phone'): 1, ('Vareebadd Phone', '27in FHD Monitor'): 1, ('ThinkPad Laptop', '27in 4K Gaming Monitor'): 1, ('20in Monitor', 'Flatscreen TV'): 1, ('USB-C Charging Cable', 'LG Dryer'): 1, ('LG Washing Machine', '20in Monitor'): 1, ('Flatscreen TV', '20in Monitor'): 1, ('27in FHD Monitor', 'Google Phone'): 1, ('iPhone', '27in FHD Monitor'): 1, ('LG Dryer', 'AAA Batteries (4-pack)'): 1, ('ThinkPad Laptop', '34in Ultrawide Monitor'): 1, ('iPhone', 'LG Washing Machine'): 1, ('AAA Batteries (4-pack)', 'LG Dryer'): 1, ('LG Dryer', '27in 4K Gaming Monitor'): 1, ('LG Dryer', 'Lightning Charging Cable'): 1, ('ThinkPad Laptop', 'LG Dryer'): 1, ('LG Washing Machine', 'AA Batteries (4-pack)'): 1})
In [52]:
count.most_common(10)
Out[52]:
[(('iPhone', 'Lightning Charging Cable'), 1005),
 (('Google Phone', 'USB-C Charging Cable'), 987),
 (('iPhone', 'Wired Headphones'), 447),
 (('Google Phone', 'Wired Headphones'), 414),
 (('Vareebadd Phone', 'USB-C Charging Cable'), 361),
 (('iPhone', 'Apple Airpods Headphones'), 360),
 (('Google Phone', 'Bose SoundSport Headphones'), 220),
 (('USB-C Charging Cable', 'Wired Headphones'), 160),
 (('Vareebadd Phone', 'Wired Headphones'), 143),
 (('Lightning Charging Cable', 'Wired Headphones'), 92)]
In [53]:
px.bar(new_data, y='Sold_together',width=1000,height=800)
In [54]:
px.treemap(new_data , path = ['Sold_together'] , values = 'Order ID')
In [55]:
px.sunburst(new_data, path=['Sold_together'], values = 'Order ID')
Visualize: What did I find out? Well, We can see the most often sold products together are iPhone and lightning Charging Cable, Google phone and USB-C charging cable, iphone and Wired Headphones. From treemap we are getting betting understanding What would we do with this data? Well, we could offer a smart deal to the customer that buy iPhone, and you will get charging cable with discount and also for other pairs. That’s one of the possibility and stackholder can bundle the remaining products if need to.

PROBLEMS

  1. What product sold the most? Why?
In [56]:
data.head()
Out[56]:
Order ID Product Quantity Ordered Price Each Order Date Purchase Address Month Sales City Order Date_DTO Hour
0 176558 USB-C Charging Cable 2 11.95 04/19/19 08:46 917 1st St, Dallas, TX 75001 4 23.90 Dallas TX 2019-04-19 08:46:00 8
2 176559 Bose SoundSport Headphones 1 99.99 04/07/19 22:30 682 Chestnut St, Boston, MA 02215 4 99.99 Boston MA 2019-04-07 22:30:00 22
3 176560 Google Phone 1 600.00 04/12/19 14:38 669 Spruce St, Los Angeles, CA 90001 4 600.00 Los Angeles CA 2019-04-12 14:38:00 14
4 176560 Wired Headphones 1 11.99 04/12/19 14:38 669 Spruce St, Los Angeles, CA 90001 4 11.99 Los Angeles CA 2019-04-12 14:38:00 14
5 176561 Wired Headphones 1 11.99 04/30/19 09:27 333 8th St, Los Angeles, CA 90001 4 11.99 Los Angeles CA 2019-04-30 09:27:00 9
In [57]:
product_group = data.groupby('Product')
product_group.sum()
Out[57]:
Quantity Ordered Price Each Month Sales Hour
Product
20in Monitor 4129 451068.99 29336 454148.71 58764
27in 4K Gaming Monitor 6244 2429637.70 44440 2435097.56 90916
27in FHD Monitor 7550 1125974.93 52558 1132424.50 107540
34in Ultrawide Monitor 6199 2348718.19 43304 2355558.01 89076
AA Batteries (4-pack) 27635 79015.68 145558 106118.40 298342
AAA Batteries (4-pack) 31017 61716.59 146370 92740.83 297332
Apple Airpods Headphones 15661 2332350.00 109477 2349150.00 223304
Bose SoundSport Headphones 13457 1332366.75 94113 1345565.43 192445
Flatscreen TV 4819 1440000.00 34224 1445700.00 68815
Google Phone 5532 3315000.00 38305 3319200.00 79479
LG Dryer 646 387600.00 4383 387600.00 9326
LG Washing Machine 666 399600.00 4523 399600.00 9785
Lightning Charging Cable 23217 323787.10 153092 347094.15 312529
Macbook Pro Laptop 4728 8030800.00 33548 8037600.00 68261
ThinkPad Laptop 4130 4127958.72 28950 4129958.70 59746
USB-C Charging Cable 23975 261740.85 154819 286501.25 314645
Vareebadd Phone 2068 826000.00 14309 827200.00 29472
Wired Headphones 20557 226395.18 133397 246478.43 271720
iPhone 6849 4789400.00 47941 4794300.00 98657
In [58]:
quantity_ordered = product_group.sum()['Quantity Ordered'].reset_index()
quantity_ordered
Out[58]:
Product Quantity Ordered
0 20in Monitor 4129
1 27in 4K Gaming Monitor 6244
2 27in FHD Monitor 7550
3 34in Ultrawide Monitor 6199
4 AA Batteries (4-pack) 27635
5 AAA Batteries (4-pack) 31017
6 Apple Airpods Headphones 15661
7 Bose SoundSport Headphones 13457
8 Flatscreen TV 4819
9 Google Phone 5532
10 LG Dryer 646
11 LG Washing Machine 666
12 Lightning Charging Cable 23217
13 Macbook Pro Laptop 4728
14 ThinkPad Laptop 4130
15 USB-C Charging Cable 23975
16 Vareebadd Phone 2068
17 Wired Headphones 20557
18 iPhone 6849
In [59]:
px.bar(quantity_ordered, y='Quantity Ordered', x='Product')

Now we can see what product sold the most, it’s AAA Batteries(4 pack). We can also see that AA Batteries (4 pack), Lightning Charging Cable, USB-C Charging Cable, and Wired Headphones are sold more than other products. But, Why are they sold the most?

The first impression is that they are cheaper than other products. As a data analyst, let’s prove this hypothesis.

In [60]:
prices = data.groupby('Product').min()['Price Each']
prices
Out[60]:
Product
20in Monitor                   109.99
27in 4K Gaming Monitor         389.99
27in FHD Monitor               149.99
34in Ultrawide Monitor         379.99
AA Batteries (4-pack)            3.84
AAA Batteries (4-pack)           2.99
Apple Airpods Headphones       150.00
Bose SoundSport Headphones      99.99
Flatscreen TV                  300.00
Google Phone                   600.00
LG Dryer                       600.00
LG Washing Machine             600.00
Lightning Charging Cable        14.95
Macbook Pro Laptop            1700.00
ThinkPad Laptop                999.99
USB-C Charging Cable            11.95
Vareebadd Phone                400.00
Wired Headphones                11.99
iPhone                         700.00
Name: Price Each, dtype: float64
In [61]:
px.line(prices,y='Price Each')
Visualize: Now we will interprate our results. Our hypothesis is true if the high sold products have low price. From the graph we can see it is the case for AAA Batteries and all products except the Macbook Pro Laptop and ThinkPad Laptop. They have decent orders eventhough they are expensive. We can say that there are many people in the world need laptops. So the laptops are the exception because the laptops have high demand.

FINAL FINDINGS:

  1. What was the best month for sales? How much was earned that month?

    The best month for sales is December. The company earned approximately $ 4,61,400.

  1. What city sold the most product?

    San Fransisco is the city with the highest sales.

  1. What time should we display advertisements to maximize likelihood of customer’s buying products?

    We can suggest to advertise the products right before 12 PM and/or 7 PM. It could be 11.30 AM and/or 6.30 PM.

  1. What Products are most often sold together?

    The most often products sold together are iPhone and Lightning Charging Cable with 1005 transactions.

  1. What product sold the most? Why?

    AAA Batteries(4 pack) is the most sold product. Because it’s cheaper than other products and has high demand. </a>

THANK YOU¶