Exploratory Data Analysis On Diwali Sales - దీపావళి అమ్మాకాలు ఎలా ఉన్నాయో, మన భారతదేశం లో, విశ్లేషణ

Data Analytics
Data Visualizations
EDA
Python
Author

Sumendar

Published

August 18, 2023

మన భారతదేశంలో దీపావళి అమ్మకాలు ఎలా ఉండాలని, దీపావళి సేల్స్‌ను ప్రభావితం చేసే కొన్ని అంశాలు పరిగిణించుకుంటూ అన్వేషణాత్మక డేటా విశ్లేషణ

# import python packages which we need for this project

import numpy as np 
import pandas as pd 
import matplotlib.pyplot as plt # visualizing data
%matplotlib inline
import seaborn as sns
# import csv file for this
df = pd.read_csv('Diwali Sales Data.csv', encoding= 'unicode_escape')
df.shape
(11251, 15)
df.head()
User_ID Cust_name Product_ID Gender Age Group Age Marital_Status State Zone Occupation Product_Category Orders Amount Status unnamed1
0 1002903 Sanskriti P00125942 F 26-35 28 0 Maharashtra Western Healthcare Auto 1 23952.0 NaN NaN
1 1000732 Kartik P00110942 F 26-35 35 1 Andhra Pradesh Southern Govt Auto 3 23934.0 NaN NaN
2 1001990 Bindu P00118542 F 26-35 35 1 Uttar Pradesh Central Automobile Auto 3 23924.0 NaN NaN
3 1001425 Sudevi P00237842 M 0-17 16 0 Karnataka Southern Construction Auto 2 23912.0 NaN NaN
4 1000588 Joni P00057942 M 26-35 28 1 Gujarat Western Food Processing Auto 2 23877.0 NaN NaN 
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 11251 entries, 0 to 11250
Data columns (total 15 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   User_ID           11251 non-null  int64  
 1   Cust_name         11251 non-null  object 
 2   Product_ID        11251 non-null  object 
 3   Gender            11251 non-null  object 
 4   Age Group         11251 non-null  object 
 5   Age               11251 non-null  int64  
 6   Marital_Status    11251 non-null  int64  
 7   State             11251 non-null  object 
 8   Zone              11251 non-null  object 
 9   Occupation        11251 non-null  object 
 10  Product_Category  11251 non-null  object 
 11  Orders            11251 non-null  int64  
 12  Amount            11239 non-null  float64
 13  Status            0 non-null      float64
 14  unnamed1          0 non-null      float64
dtypes: float64(3), int64(4), object(8)
memory usage: 1.3+ MB
#drop unrelated/blank columns
df.drop(['Status', 'unnamed1'], axis=1, inplace=True)
#check for null values
pd.isnull(df).sum()
User_ID              0
Cust_name            0
Product_ID           0
Gender               0
Age Group            0
Age                  0
Marital_Status       0
State                0
Zone                 0
Occupation           0
Product_Category     0
Orders               0
Amount              12
dtype: int64
# drop null values
df.dropna(inplace=True)
# change data type
df['Amount'] = df['Amount'].astype('int')
df['Amount'].dtypes
dtype('int32')
df.columns
Index(['User_ID', 'Cust_name', 'Product_ID', 'Gender', 'Age Group', 'Age',
       'Marital_Status', 'State', 'Zone', 'Occupation', 'Product_Category',
       'Orders', 'Amount'],
      dtype='object')
#rename column
df.rename(columns= {'Marital_Status':'Shaadi'})
User_ID Cust_name Product_ID Gender Age Group Age Shaadi State Zone Occupation Product_Category Orders Amount
0 1002903 Sanskriti P00125942 F 26-35 28 0 Maharashtra Western Healthcare Auto 1 23952
1 1000732 Kartik P00110942 F 26-35 35 1 Andhra Pradesh Southern Govt Auto 3 23934
2 1001990 Bindu P00118542 F 26-35 35 1 Uttar Pradesh Central Automobile Auto 3 23924
3 1001425 Sudevi P00237842 M 0-17 16 0 Karnataka Southern Construction Auto 2 23912
4 1000588 Joni P00057942 M 26-35 28 1 Gujarat Western Food Processing Auto 2 23877
... ... ... ... ... ... ... ... ... ... ... ... ... ...
11246 1000695 Manning P00296942 M 18-25 19 1 Maharashtra Western Chemical Office 4 370
11247 1004089 Reichenbach P00171342 M 26-35 33 0 Haryana Northern Healthcare Veterinary 3 367
11248 1001209 Oshin P00201342 F 36-45 40 0 Madhya Pradesh Central Textile Office 4 213
11249 1004023 Noonan P00059442 M 36-45 37 0 Karnataka Southern Agriculture Office 3 206
11250 1002744 Brumley P00281742 F 18-25 19 0 Maharashtra Western Healthcare Office 3 188

11239 rows × 13 columns

# describe() method returns description of the data in the DataFrame (i.e. count, mean, std, etc)
df.describe()
User_ID Age Marital_Status Orders Amount
count 1.123900e+04 11239.000000 11239.000000 11239.000000 11239.000000
mean 1.003004e+06 35.410357 0.420055 2.489634 9453.610553
std 1.716039e+03 12.753866 0.493589 1.114967 5222.355168
min 1.000001e+06 12.000000 0.000000 1.000000 188.000000
25% 1.001492e+06 27.000000 0.000000 2.000000 5443.000000
50% 1.003064e+06 33.000000 0.000000 2.000000 8109.000000
75% 1.004426e+06 43.000000 1.000000 3.000000 12675.000000
max 1.006040e+06 92.000000 1.000000 4.000000 23952.000000 
# use describe() for specific columns
df[['Age', 'Orders', 'Amount']].describe()
Age Orders Amount
count 11239.000000 11239.000000 11239.000000
mean 35.410357 2.489634 9453.610553
std 12.753866 1.114967 5222.355168
min 12.000000 1.000000 188.000000
25% 27.000000 2.000000 5443.000000
50% 33.000000 2.000000 8109.000000
75% 43.000000 3.000000 12675.000000
max 92.000000 4.000000 23952.000000

Exploratory Data Analysis

Gender

# plotting a bar chart for Gender and it's count

ax = sns.countplot(x = 'Gender',data = df)

for bars in ax.containers:
    ax.bar_label(bars)

# plotting a bar chart for gender vs total amount

sales_gen = df.groupby(['Gender'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False)

sns.barplot(x = 'Gender',y= 'Amount' ,data = sales_gen)
<AxesSubplot:xlabel='Gender', ylabel='Amount'>

From above graphs we can see that most of the buyers are females and even the purchasing power of females are greater than men

Age

ax = sns.countplot(data = df, x = 'Age Group', hue = 'Gender')

for bars in ax.containers:
    ax.bar_label(bars)

# Total Amount vs Age Group
sales_age = df.groupby(['Age Group'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False)

sns.barplot(x = 'Age Group',y= 'Amount' ,data = sales_age)
<AxesSubplot:xlabel='Age Group', ylabel='Amount'>

From above graphs we can see that most of the buyers are of age group between 26-35 yrs female

State

# total number of orders from top 10 states

sales_state = df.groupby(['State'], as_index=False)['Orders'].sum().sort_values(by='Orders', ascending=False).head(10)

sns.set(rc={'figure.figsize':(15,5)})
sns.barplot(data = sales_state, x = 'State',y= 'Orders')
<AxesSubplot:xlabel='State', ylabel='Orders'>

# total amount/sales from top 10 states

sales_state = df.groupby(['State'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False).head(10)

sns.set(rc={'figure.figsize':(15,5)})
sns.barplot(data = sales_state, x = 'State',y= 'Amount')
<AxesSubplot:xlabel='State', ylabel='Amount'>

From above graphs we can see that most of the orders & total sales/amount are from Uttar Pradesh, Maharashtra and Karnataka respectively

Marital Status

ax = sns.countplot(data = df, x = 'Marital_Status')

sns.set(rc={'figure.figsize':(7,5)})
for bars in ax.containers:
    ax.bar_label(bars)

sales_state = df.groupby(['Marital_Status', 'Gender'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False)

sns.set(rc={'figure.figsize':(6,5)})
sns.barplot(data = sales_state, x = 'Marital_Status',y= 'Amount', hue='Gender')
<AxesSubplot:xlabel='Marital_Status', ylabel='Amount'>

From above graphs we can see that most of the buyers are married (women) and they have high purchasing power

Occupation

sns.set(rc={'figure.figsize':(20,5)})
ax = sns.countplot(data = df, x = 'Occupation')

for bars in ax.containers:
    ax.bar_label(bars)

sales_state = df.groupby(['Occupation'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False)

sns.set(rc={'figure.figsize':(20,5)})
sns.barplot(data = sales_state, x = 'Occupation',y= 'Amount')
<AxesSubplot:xlabel='Occupation', ylabel='Amount'>

From above graphs we can see that most of the buyers are working in IT, Healthcare and Aviation sector

Product Category

sns.set(rc={'figure.figsize':(20,5)})
ax = sns.countplot(data = df, x = 'Product_Category')

for bars in ax.containers:
    ax.bar_label(bars)

sales_state = df.groupby(['Product_Category'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False).head(10)

sns.set(rc={'figure.figsize':(20,5)})
sns.barplot(data = sales_state, x = 'Product_Category',y= 'Amount')
<AxesSubplot:xlabel='Product_Category', ylabel='Amount'>

From above graphs we can see that most of the sold products are from Food, Clothing and Electronics category

sales_state = df.groupby(['Product_ID'], as_index=False)['Orders'].sum().sort_values(by='Orders', ascending=False).head(10)

sns.set(rc={'figure.figsize':(20,5)})
sns.barplot(data = sales_state, x = 'Product_ID',y= 'Orders')
<AxesSubplot:xlabel='Product_ID', ylabel='Orders'>

# top 10 most sold products (same thing as above)

fig1, ax1 = plt.subplots(figsize=(12,7))
df.groupby('Product_ID')['Orders'].sum().nlargest(10).sort_values(ascending=False).plot(kind='bar')
<AxesSubplot:xlabel='Product_ID'>

Conclusion:

Married women in the 26-35 age range from the states of Uttar Pradesh, Maharashtra, and Karnataka who work in the IT, healthcare, and aviation industries are more likely to purchase items from the food, clothing, and electronics categories.

IT, హెల్త్‌కేర్ మరియు విమానయాన పరిశ్రమలలో పనిచేసే ఉత్తరప్రదేశ్, మహారాష్ట్ర మరియు కర్ణాటక రాష్ట్రాల నుండి 26-35 సంవత్సరాల వయస్సు గల వివాహిత మహిళలు ఆహారం, దుస్తులు మరియు ఎలక్ట్రానిక్స్ వర్గాల నుండి వస్తువులను కొనుగోలు చేసే అవకాశం ఉంది.

Thank you!

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