Sales Analysis Project

Project:

In this project, we are going to analyze 12 months of data of sales. We will learn how to clean, manage and analyze the dataset to find some meaningful information.

We start by cleaning our data. Tasks during this section include:

• Drop NaN values from DataFrame

• Removing rows based on a condition

• Change the type of columns (to_numeric, to_datetime, astype)

Once we have cleaned up our data a bit, we move the data exploration section. In this section we explore 5 high level business questions related to our data:

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

• What city sold the most product?

• What time should we display advertisemens to maximize the likelihood of customer’s buying product?

• What products are most often sold together?

• What product sold the most? Why do you think it sold the most?

Download Dataset:

2MB

SalesData.zip

archive

Once you have downloaded the dataset , extract and save it somewhere.

Import Necessary Libraries

import os
import pandas as pd

Assign Path Variable to Path of Data

base_path = r'C:\Users\abhis\Downloads\SalesData (1)'

Merging 12 months of sales data in a single file:

To Calculate and analyze Company Sales Data, We must merge all 12 CSV files into one.

Let us go through one-month data first , to know things better.

one_month = pd.read_csv(fr"{base_path}\Sales_April_2019.csv")
one_month.head(3)

Order ID	Product	Quantity Ordered	Price Each	Order Date	Purchase Address
176558	USB-C Charging Cable	2	11.95	04/19/19 08:46	917 1st St, Dallas, TX 75001
176559	Bose SoundSport Headphones	1	99.99	04/07/19 22:30	682 Chestnut St, Boston, MA 02215
176560	Google Phone	1	600	04/12/19 14:38	669 Spruce St, Los Angeles, CA 90001

This is how our one month data looks like. Can we get all files from the directory ?

List all files from the directory

To get all files from a specific directory , we can use listdir() method.

files = os.listdir(path)
files

Output :

['Sales_April_2019.csv',
 'Sales_August_2019.csv',
 'Sales_December_2019.csv',
 'Sales_February_2019.csv',
 'Sales_January_2019.csv',
 'Sales_July_2019.csv',
 'Sales_June_2019.csv',
 'Sales_March_2019.csv',
 'Sales_May_2019.csv',
 'Sales_November_2019.csv',
 'Sales_October_2019.csv',
 'Sales_September_2019.csv']

Now we need to concat all these file in a single file, for that we need to create an empty DataFrame and concat all sales file.

Concatenate all files :

all_month_data = pd.DataFrame()  #Empty DataFrame

for file in files:    #Iteration In Sales Files
    monthly_data = pd.read_csv(fr"{path}\{file}")    #Read File
    all_month_data = pd.concat((all_month_data,monthly_data))  #Concatenate file
    
all_month_data

Order ID	Product	Quantity Ordered	Price Each	Order Date	Purchase Address
176558	USB-C Charging Cable	2	11.95	04/19/19 08:46	917 1st St, Dallas, TX 75001
176559	Bose SoundSport Headphones	1	99.99	04/07/19 22:30	682 Chestnut St, Boston, MA 02215
176560	Google Phone	1	600	04/12/19 14:38	669 Spruce St, Los Angeles, CA 90001
176560	Wired Headphones	1	11.99	04/12/19 14:38	669 Spruce St, Los Angeles, CA 90001
176561	Wired Headphones	1	11.99	04/30/19 09:27	333 8th St, Los Angeles, CA 90001
...	...	...	...	...	...
259353	AAA Batteries (4-pack)	3	2.99	09/17/19 20:56	840 Highland St, Los Angeles, CA 90001
259354	iPhone	1	700	09/01/19 16:00	216 Dogwood St, San Francisco, CA 94016
259355	iPhone	1	700	09/23/19 07:39	220 12th St, San Francisco, CA 94016
259356	34in Ultrawide Monitor	1	379.99	09/19/19 17:30	511 Forest St, San Francisco, CA 94016
259357	USB-C Charging Cable	1	11.95	09/30/19 00:18	250 Meadow St, San Francisco, CA 94016

186305 rows × 6 columns

Now that we have a DataFrame with all sales Files , let us convert it into a CSV File.

Convert To CSV

all_month_data.to_csv(fr'{path}\{"Sales.csv"}',index=False)

Let us read out Data From Updated DataFrame.

Read From Updated DataFrame

all_data = pd.read_csv(fr'{path}\{"Sales.csv"}')
all_data.head()

Order ID	Product	Quantity Ordered	Price Each	Order Date	Purchase Address
176558	USB-C Charging Cable	2	11.95	04/19/19 08:46	917 1st St, Dallas, TX 75001
176559	Bose SoundSport Headphones	1	99.99	04-07-2019 22:30	682 Chestnut St, Boston, MA 02215
176560	Google Phone	1	600	04-12-2019 14:38	669 Spruce St, Los Angeles, CA 90001
176560	Wired Headphones	1	11.99	04-12-2019 14:38	669 Spruce St, Los Angeles, CA 90001
176561	Wired Headphones	1	11.99	04/30/19 09:27	333 8th St, Los Angeles, CA 90001

Augment Data With Additional Columns

Clean up the data :

The first step in this is figuring out what we need to clean. I have found in practice, that you find things you need to clean as you perform operations and get errors. Based on the error, you decide how you should go about cleaning the data.

Display All rows of NAN:

all_data[all_data.isna().any(axis=1)]

Convert Order Date in Datetime

To convert Order Date in Datetime, first thing that needs to be done is to bring all date in a single format.

all_data['Order Date'] = all_data['Order Date'].str.replace('/','-')
all_data['Order Date'] = pd.to_datetime(all_data['Order Date'])

Output :

File "C:\Users\abhis\.virtualenvs\python\lib\site-packages\dateutil\parser\_parser.py", line 643, in parse
    raise ParserError("Unknown string format: %s", timestr)
dateutil.parser._parser.ParserError: Unknown string format: Order Date

Reason Behind Error :

---> There is some unknown string named as 'Order Date' in our Dataset.

Let us see where this 'Order Date' is by using conditional formatting.

temp = all_data.loc[all_data['Order Date'] == 'Order Date']
temp

Order ID	Product	Quantity Ordered	Price Each	Order Date	Purchase Address
517	Order ID	Product	Quantity Ordered	Price Each	Order Date	Purchase Address
1146	Order ID	Product	Quantity Ordered	Price Each	Order Date	Purchase Address
1152	Order ID	Product	Quantity Ordered	Price Each	Order Date	Purchase Address
2869	Order ID	Product	Quantity Ordered	Price Each	Order Date	Purchase Address
2884	Order ID	Product	Quantity Ordered	Price Each	Order Date	Purchase Address
...	...	...	...	...	...	...
2234519	Order ID	Product	Quantity Ordered	Price Each	Order Date	Purchase Address
2234906	Order ID	Product	Quantity Ordered	Price Each	Order Date	Purchase Address
2235918	Order ID	Product	Quantity Ordered	Price Each	Order Date	Purchase Address
2235987	Order ID	Product	Quantity Ordered	Price Each	Order Date	Purchase Address
2236093	Order ID	Product	Quantity Ordered	Price Each	Order Date	Purchase Address

4260 rows × 6 columns

Let us remove all these 4260 rows from our Dataframe.

all_data = all_data.loc[all_data['Order Date']!='Order Date']
all_data.head()

Now let us try to convert Order Date again.

all_data['Order Date'] = all_data['Order Date'].str.replace('/','-')
all_data['Order Date'] = pd.to_datetime(all_data['Order Date'])

Add Month Column:

To add month column , all we need to do is to use date time methods.

all_data['Month'] = all_data['Order Date'].dt.month_name()
all_data['Month']

Output:

0              April
1              April
2              April
3              April
4              April
             ...    
4285010    September
4285011    September
4285012    September
4285013    September
4285014    September
Name: Month, Length: 4276850, dtype: object

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

In Our DataFrame , There are Two columns Price Each and Quantity Ordered. But we do not have Total Sale Value (TSV), We should also add a calculated column Total Sale Value. Let's do it first.

Now Price Each , Quantity Ordered is object data type , so we need to convert it.

Convert Price Each and Quantity Ordered in Float and Calculate TSV.

#Conversion To Float
all_data['Quantity Ordered'] = all_data['Quantity Ordered'].astype('float')
all_data['Price Each'] = all_data['Price Each'].astype('float')

#Calculated Column
all_data['TSV'] = all_data['Quantity Ordered'] * all_data['Price Each']
all_data.head()

Now we can easily use groupby method to find maximum sales value.

all_data.groupby('month')['TSV'].sum()

month
1     3644513.46
2     4404044.84
3     5614200.76
4     6781340.48
5     6305213.50
6     5155604.52
7     5295551.52
8     4488935.76
9     4195120.26
10    7473453.76
11    6399206.40
12    9226886.68
Name: TSV, dtype: float64

Best Month For Sales : December

Total Sale Value In December : 9226886.68

Plot the Data:

months = range(1,13)
plt.bar(months,results)
plt.xticks(months)
plt.xlabel('Month')
plt.ylabel('Sales')
plt.show()

December is a Festival Month , the biggest thing is December is Christmas, So people spend money and maybe that is the reason for december is best month for sales.

What city sold the most product?

There is no City Column in our Dataset, But you can see Purchase Address ,where City is mentioned in middle, we can use Purchase address and create a City Column.

all_data['City'] = all_data['Purchase Address'].str.split(',',expand=True)[1]
all_data

Now we can groupby City and Calculate Total Quantity Ordered.

all_data.groupby('City')['Quantity Ordered'].sum().sort_values()

Output :

City
 Austin            22306.0
 Portland          28106.0
 Seattle           33106.0
 Atlanta           33204.0
 Dallas            33460.0
 Boston            45056.0
 New York City     55864.0
 Los Angeles       66578.0
 San Francisco    100478.0
Name: Quantity Ordered, dtype: float64

City Sold Most Product : San Francisco

Plot the Data:

import matplotlib.pyplot as plt

keys = [city for city, df in all_data.groupby(['City'])]

plt.bar(keys,all_data.groupby(['City']).sum()['TSV'])
plt.ylabel('Sales in USD ($)')
plt.xlabel('Month number')
plt.xticks(keys, rotation='vertical', size=8)
plt.show()

What time should we display advertisements to maximize the likelihood of customer’s buying product?

Because we need to work on time, let's create Hour and Minute Column.

all_data['Hour'] = all_data['Order Date'].dt.hour
all_data['Minute'] = all_data['Order Date'].dt.minute
all_data.head()

Let us see what time most people bought from this Company .

all_data.groupby('Hour')['TSV'].sum().sort_values(ascending=False)

Hour
19    4825877.08
12    4633642.68
11    4601220.48
20    4563432.48
18    4438696.60
13    4310779.60
17    4258723.22
14    4167345.46
21    4084001.72
10    3888573.54
15    3883099.20
16    3809202.62
9     3278061.16
22    3215098.42
8     2384697.94
23    2358608.88
7     1489708.24
0     1427442.54
1      921733.76
6      896226.00
2      469702.88
5      461359.64
4      325322.02
3      291515.78
Name: TSV, dtype: float64

Plot the Data :

keys = [pair for pair, df in all_data.groupby(['Hour'])]

plt.plot(keys, all_data.groupby(['Hour']).count())
plt.xticks(keys)
plt.grid()
plt.show()

What Products are most often sold together ?

This is going to be a tricky business. Before diving into the problem, let us understand the problem.

What Products are most often sold together gives a very important insight that helps company lure customers into spending more and generate more profit.

You must have seen Amazon's Customer Who Bought this item also bought this Item. We are going to do something like this.

Let's try to solve this problem Step by step :

Duplicate Order ID :

Same Order ID for different products suggests customer bought different products Together in Our Data. Let us look for Duplicate OrderId Products:

# All Duplicate Order ID 
df = all_data[all_data['Order ID'].duplicated(keep=False)]
df

Group Different Products With Same Order ID :

Let's create a new column that contains different Products with the same Order ID. We can do this with the help of transform method.

df['Grouped'] = df.groupby('Order ID')['Product'].transform(lambda x : ','.join(x))
df.head(10)

Counting "unique pairs" of numbers into a python dictionary?Stack Overflow

from itertools import combinations
from collections import Counter

count = Counter()
row_list = []
for row in df['Grouped']:
    row_list = row.split(',')
    count.update(Counter(combinations(row_list, 3)))

count.most_common(10)

Output :

[(('Google Phone', 'USB-C Charging Cable', 'Wired Headphones'), 86),
 (('iPhone', 'Lightning Charging Cable', 'Wired Headphones'), 62),
 (('iPhone', 'Lightning Charging Cable', 'Apple Airpods Headphones'), 47),
 (('Google Phone', 'USB-C Charging Cable', 'Bose SoundSport Headphones'), 35),
 (('Vareebadd Phone', 'USB-C Charging Cable', 'Wired Headphones'), 33),
 (('iPhone', 'Apple Airpods Headphones', 'Wired Headphones'), 27),
 (('Google Phone', 'Bose SoundSport Headphones', 'Wired Headphones'), 24),
 (('Vareebadd Phone', 'USB-C Charging Cable', 'Bose SoundSport Headphones'),
  16),
 (('USB-C Charging Cable', 'Bose SoundSport Headphones', 'Wired Headphones'),
  5),
 (('Vareebadd Phone', 'Bose SoundSport Headphones', 'Wired Headphones'), 5)]

What product sold the most? Why do you think it sold the most?

This is pretty simple , we will group all product and calculate quantity ordered.

all_data.groupby('Product')['Quantity Ordered'].sum().sort_values().plot(kind='bar')