amazone_sales.py

#!/usr/bin/env python
# coding: utf-8

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

sales = pd.read_csv('Amazon Sale Report.csv')


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sales.shape


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sales.describe()


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sales.info


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sales.head()


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sales.tail()


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sales.info()


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# Data Cleaning


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# Drop Unrelated / black Columns

sales.drop(['New' , 'PendingS'], axis = 1, inplace =True)


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sales.info()


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# Check Null Values
pd.isnull(sales)


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# Sum will Give Total Values of Null Values
pd.isnull(sales).sum()


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sales.shape


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#  Drop null Values
sales.dropna(inplace =True)


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sales.shape


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sales.columns


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sales.info()


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# Change Data type 

sales['ship-postal-code'] = sales['ship-postal-code'].astype('int')


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# Checking whether the data type or not

sales['ship-postal-code'].dtype


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#  To change the date type of date columns (object to date)
sales['Date'] = pd.to_datetime(sales['Date'])


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sales['Date'].dtype


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sales.info()


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#  Rename the Column
sales.rename(columns={'Qty':'Quantity'})


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# Describe() method return description of the data in the DateFrame(i.e count,mean , std,min ...etc)
sales.describe()


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#  if you need to apply a describe method on object column data type the used include keyword
sales.describe(include = 'object')


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# if you need to apply any specified column then 
sales[['Qty','Amount']].describe()


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# Exploratory Data Analysis


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sales.columns


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# Size

ax =sns.countplot(x = 'Size' , data = sales)


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# give label on the bar graphs
ax = sns.countplot(x='Size' , data= sales)
for bars in ax.containers:
    ax.bar_label(bars)


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# Note : From Above the graph we can see that most of the people buys M-size


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#  Group By 
# The grooupby() function in pandas is used to group data based on one or more columns in a dataFrame


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sales.groupby(['Size'],as_index = False)['Qty'].sum().sort_values(by ='Qty',ascending =False)


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sales_Qty = sales.groupby(['Size'], as_index = False)['Qty'].sum().sort_values(by='Qty',ascending=False)
sns.barplot(x='Size',y = 'Qty',data =sales_Qty)


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#  Note : From above Graph we can see that most of the Qunatity buy m -size in the sales


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# Courier Status


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sns.countplot(data = sales , x = 'Courier Status' , hue ='Status')


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plt.figure(figsize = (9,5))
ax=sns.countplot(data =sales,x ='Courier Status',hue ='Status')


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# Note : from above the graph the majority of the orders are shipped through the courier


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# Histogram Diagram 


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sales['Size'].hist()


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# Catogory
sales['Category'] = sales['Category'].astype(str)
column_data = sales['Category']
plt.figure(figsize=(10,5))
plt.hist(column_data,bins = 30 , edgecolor = 'Black')
plt.xticks(rotation = 90)
plt.show()


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# note  : from above Gragh you can see that most of the buyers are buy T-shirt


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# Check B2B Data By using pie Chart

b2b_sales = sales['B2B'].value_counts()

plt.pie(b2b_sales, labels =b2b_sales.index, autopct ='%1.1f%%')
plt.show()


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#  Note : From Above the chart we can see the maximum i.e 99.3% of the buyer are retailers and 0.8% are B2b buyers


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#  Scatter plot


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x_data = sales['Category']
y_data = sales['Size']

plt.scatter(x_data , y_data)
plt.xlabel('Category')
plt.ylabel('Size')
plt.title('Scatter plot')
plt.show()


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# plot count of cities by state

plt.figure(figsize =(15,6))
sns.countplot(data = sales,x ='ship-state')
plt.xlabel('ship-state')
plt.ylabel('count')
plt.title('Distribution of state')
plt.xticks(rotation =90)
plt.show()


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# top 10 state 
top_state = sales['ship-state'].value_counts().head(10)
plt.figure(figsize=(12,6))
sns.countplot(data =sales[sales['ship-state'].isin(top_state.index)],x = 'ship-state')
plt.xlabel('ship-state')
plt.ylabel('count')
plt.title('Distrubution of state')
plt.xticks(rotation = 90)
plt.show()


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#  Note from above Graph we can see that most of the buyers from the maharashtra state


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# # Conclusion

# the data analysis reveals that the business has a siginficant customer base in maharshtra state mainly serves retailer , fulfills order through 
# amazone , exoeriences high demand for T-shirt and size is M-size as the preferred choice among buyers


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