Data analytics workflow

Data analysis example workbook

pandas
seaborn
matplotlib
numpy
Author

Kunal Khurana

Published

March 16, 2024

#!pip install calmap
!pip install ydata-profiling
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1. Libraries

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings as wrn

wrn.filterwarnings('ignore', category = DeprecationWarning) 
wrn.filterwarnings('ignore', category = FutureWarning) 
wrn.filterwarnings('ignore', category = UserWarning) 
#from pandas_profiling import ProfileReport

Context

  1. Invoice ID: A unique identifier for each invoice or transaction.

  2. Branch: The branch or location where the transaction occurred.

  3. City: The city where the branch is located.

  4. Customer Type: Indicates whether the customer is a regular or new customer.

  5. Gender: The gender of the customer.

  6. Product Line: The category or type of product purchased.

  7. Unit Price: The price of a single unit of the product.

  8. Quantity: The number of units of the product purchased.

  9. Tax 5%: The amount of tax (5% of the total cost) applied to the transaction.

  10. Total: The total cost of the transaction, including tax.

  11. Date: The date when the transaction took place.

  12. Time: The time of day when the transaction occurred.

  13. Payment: The payment method used (e.g., credit card, cash).

  14. COGS (Cost of Goods Sold): The direct costs associated with producing or purchasing the products sold.

  15. Gross Margin Percentage: The profit margin percentage for the transaction.

  16. Gross Income: The total profit earned from the transaction.

  17. Rating: Customer satisfaction rating or feedback on the transaction.

For instance, if you were interested in predicting customer satisfaction, Rating might be a suitable label. If you were trying to predict sales or revenue, Total or Gross Income could be a potential label.

2. Initial Data Exploration

df = pd.read_csv("/kaggle/input/super-market-sales/supermarket_sales.csv")
df.head(10)
Invoice ID Branch City Customer type Gender Product line Unit price Quantity Tax 5% Total Date Time Payment cogs gross margin percentage gross income Rating
0 750-67-8428 A Yangon Member Female Health and beauty 74.69 7 26.1415 548.9715 1/5/2019 13:08 Ewallet 522.83 4.761905 26.1415 9.1
1 226-31-3081 C Naypyitaw Normal Female Electronic accessories 15.28 5 3.8200 80.2200 3/8/2019 10:29 Cash 76.40 4.761905 3.8200 9.6
2 631-41-3108 A Yangon Normal Male Home and lifestyle 46.33 7 16.2155 340.5255 3/3/2019 13:23 Credit card 324.31 4.761905 16.2155 7.4
3 123-19-1176 A Yangon Member Male Health and beauty 58.22 8 23.2880 489.0480 1/27/2019 20:33 Ewallet 465.76 4.761905 23.2880 8.4
4 373-73-7910 A Yangon Normal Male Sports and travel 86.31 7 30.2085 634.3785 2/8/2019 10:37 Ewallet 604.17 4.761905 30.2085 5.3
5 699-14-3026 C Naypyitaw Normal Male Electronic accessories 85.39 7 29.8865 627.6165 3/25/2019 18:30 Ewallet 597.73 4.761905 29.8865 4.1
6 355-53-5943 A Yangon Member Female Electronic accessories 68.84 6 20.6520 433.6920 2/25/2019 14:36 Ewallet 413.04 4.761905 20.6520 5.8
7 315-22-5665 C Naypyitaw Normal Female Home and lifestyle 73.56 10 36.7800 772.3800 2/24/2019 11:38 Ewallet 735.60 4.761905 36.7800 8.0
8 665-32-9167 A Yangon Member Female Health and beauty 36.26 2 3.6260 76.1460 1/10/2019 17:15 Credit card 72.52 4.761905 3.6260 7.2
9 692-92-5582 B Mandalay Member Female Food and beverages 54.84 3 8.2260 172.7460 2/20/2019 13:27 Credit card 164.52 4.761905 8.2260 5.9 
df.columns
Index(['Invoice ID', 'Branch', 'City', 'Customer type', 'Gender',
       'Product line', 'Unit price', 'Quantity', 'Tax 5%', 'Total', 'Date',
       'Time', 'Payment', 'cogs', 'gross margin percentage', 'gross income',
       'Rating'],
      dtype='object')
df.dtypes
Invoice ID                  object
Branch                      object
City                        object
Customer type               object
Gender                      object
Product line                object
Unit price                 float64
Quantity                     int64
Tax 5%                     float64
Total                      float64
Date                        object
Time                        object
Payment                     object
cogs                       float64
gross margin percentage    float64
gross income               float64
Rating                     float64
dtype: object
df['Date'] = pd.to_datetime(df['Date'])
df.dtypes
Invoice ID                         object
Branch                             object
City                               object
Customer type                      object
Gender                             object
Product line                       object
Unit price                        float64
Quantity                            int64
Tax 5%                            float64
Total                             float64
Date                       datetime64[ns]
Time                               object
Payment                            object
cogs                              float64
gross margin percentage           float64
gross income                      float64
Rating                            float64
dtype: object
df.set_index("Date", inplace=True)
df.describe()
Unit price Quantity Tax 5% Total cogs gross margin percentage gross income Rating
count 1000.000000 1000.000000 1000.000000 1000.000000 1000.00000 1000.000000 1000.000000 1000.00000
mean 55.672130 5.510000 15.379369 322.966749 307.58738 4.761905 15.379369 6.97270
std 26.494628 2.923431 11.708825 245.885335 234.17651 0.000000 11.708825 1.71858
min 10.080000 1.000000 0.508500 10.678500 10.17000 4.761905 0.508500 4.00000
25% 32.875000 3.000000 5.924875 124.422375 118.49750 4.761905 5.924875 5.50000
50% 55.230000 5.000000 12.088000 253.848000 241.76000 4.761905 12.088000 7.00000
75% 77.935000 8.000000 22.445250 471.350250 448.90500 4.761905 22.445250 8.50000
max 99.960000 10.000000 49.650000 1042.650000 993.00000 4.761905 49.650000 10.00000

3. Univariate Analysis

Q1 What does the disribution of customer rating looks like? Is it skewed?

sns.displot(df["Rating"])
plt.axvline(x=np.mean(df["Rating"]), c='red', ls= "--")
plt.axvline(x=np.percentile(df["Rating"],25), c='green', ls= "--")
plt.axvline(x=np.percentile(df["Rating"],75), c='green', ls= "--")
<matplotlib.lines.Line2D at 0x7fa762ae94b0>

df.hist(figsize=(10,10))
array([[<Axes: title={'center': 'Unit price'}>,
        <Axes: title={'center': 'Quantity'}>,
        <Axes: title={'center': 'Tax 5%'}>],
       [<Axes: title={'center': 'Total'}>,
        <Axes: title={'center': 'cogs'}>,
        <Axes: title={'center': 'gross margin percentage'}>],
       [<Axes: title={'center': 'gross income'}>,
        <Axes: title={'center': 'Rating'}>, <Axes: >]], dtype=object)

df['Branch'].value_counts()
Branch
A    340
B    332
C    328
Name: count, dtype: int64

4. Bivariate analysis

#sns.countplot(df['Payment'])
# comparison between two columns
sns.scatterplot(df['Rating'])
<Axes: xlabel='Date', ylabel='Rating'>

Q2: is there a noticiable time trend in gross income?

sns.boxplot(df, x='Branch', y='gross income')
<Axes: xlabel='Branch', ylabel='gross income'>

sns.boxplot(df, x="Gender", y="gross income")
<Axes: xlabel='Gender', ylabel='gross income'>

df.groupby(by='gross income')
<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7fa75e5eb910>
sns.pairplot(df)

5. Dealing with duplicate rows and missing values

df.duplicated()
Date
2019-01-05    False
2019-03-08    False
2019-03-03    False
2019-01-27    False
2019-02-08    False
              ...  
2019-01-29    False
2019-03-02    False
2019-02-09    False
2019-02-22    False
2019-02-18    False
Length: 1000, dtype: bool
df.duplicated().sum()
0
df.isna().sum()
Invoice ID                 0
Branch                     0
City                       0
Customer type              0
Gender                     0
Product line               0
Unit price                 0
Quantity                   0
Tax 5%                     0
Total                      0
Time                       0
Payment                    0
cogs                       0
gross margin percentage    0
gross income               0
Rating                     0
dtype: int64
sns.heatmap(df.isnull())
<Axes: ylabel='Date'>

df.mode()
Invoice ID Branch City Customer type Gender Product line Unit price Quantity Tax 5% Total Time Payment cogs gross margin percentage gross income Rating
0 101-17-6199 A Yangon Member Female Fashion accessories 83.77 10.0 4.1540 87.2340 14:42 Ewallet 83.08 4.761905 4.1540 6.0
1 101-81-4070 NaN NaN NaN NaN NaN NaN NaN 4.4640 93.7440 19:48 NaN 89.28 NaN 4.4640 NaN
2 102-06-2002 NaN NaN NaN NaN NaN NaN NaN 8.3770 175.9170 NaN NaN 167.54 NaN 8.3770 NaN
3 102-77-2261 NaN NaN NaN NaN NaN NaN NaN 9.0045 189.0945 NaN NaN 180.09 NaN 9.0045 NaN
4 105-10-6182 NaN NaN NaN NaN NaN NaN NaN 10.3260 216.8460 NaN NaN 206.52 NaN 10.3260 NaN
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
995 894-41-5205 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
996 895-03-6665 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
997 895-66-0685 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
998 896-34-0956 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
999 898-04-2717 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

1000 rows × 16 columns

df.mode().iloc[0]
Invoice ID                         101-17-6199
Branch                                       A
City                                    Yangon
Customer type                           Member
Gender                                  Female
Product line               Fashion accessories
Unit price                               83.77
Quantity                                  10.0
Tax 5%                                   4.154
Total                                   87.234
Time                                     14:42
Payment                                Ewallet
cogs                                     83.08
gross margin percentage               4.761905
gross income                             4.154
Rating                                     6.0
Name: 0, dtype: object

6. Correlation analysis

np.corrcoef(df["gross income"], df['Rating'])
array([[ 1.       , -0.0364417],
       [-0.0364417,  1.       ]])
np.corrcoef(df["gross income"], df['Rating'])[1][0]
-0.03644170499701839
# rounding off
round(np.corrcoef(df['gross income'], df['Rating'])[1][0],2)
-0.04

7. Profiling

dataset = pd.read_csv("/kaggle/input/super-market-sales/supermarket_sales.csv")

from ydata_profiling import ProfileReport
profile = ProfileReport(dataset, title='Profiling Report')
profile

8. Resources

  1. https://www.data-to-viz.com/
  2. https://seaborn.pydata.org/examples/index.html
  3. https://pypi.org/project/pandas-profiling/