20casedenco.py

# -*- coding: utf-8 -*-
import pandas as pd

pd.set_option('display.max_columns',15)
pd.set_option('display.max_rows',100)
#others - max_rows, width, precision, height, date_dayfirst, date_yearfirst

import numpy as np

#read data

df = pd.read_csv('20denco.csv')

df.columns

df['region'].value_counts()
df['region'].value_counts().plot(kind='bar')


df1 = df[df['region']=='02-Central']
df1
df2 = df[df['region']=='01-East']
df2

df1
df2


df.head(10)
df.tail(10)

'df.columns


len(df)

df.dtypes

df['partnum'] =(df['partnum']).astype('str')

df.dtypes

df.describe()

df.shape


'''
df['region'].unique()
df['region'] = df['region'].astype('category')
df['region'].unique()
'''

df.region.value_counts()

df.region.value_counts().plot(kind='bar')

#do summary actions
#Who are the most loyal Customers - 
#Improve repeated sales, Target customers with low sales Volumes
#count by customers, sort, pick top 5 


#numpy method of counts
df.custname.value_counts()
df.custname.value_counts().sort_values(ascending=False)
df.custname.value_counts().sort_values(ascending=False).head(10)
df.custname.value_counts().sort_values(ascending=False).head(5).plot(kind='bar')


#pandas way
df.groupby('custname').size()
df.groupby('custname').size().sort_values(ascending=False)
#pickup first 5
df.groupby('custname').size().sort_values(ascending=False).head(5)
#these are most loyal customers
df.groupby('custname').size().sort_values(ascending=False).head(5).plot(kind='bar')


#extra- ??
'''
df.groupby(['custname'])['margin'].nlargest(3)


df.sort_values(['revenue'], ascending= True).groupby( 'region' ).mean()
'''

#Which customers contribute the most to their revenue - How do I 
#retain these customers & target incentives
#find total revenue of each customer, sort in descending.

df.groupby('custname').aggregate({'revenue':np.sum})
df.groupby('custname').aggregate({'revenue':np.sum}).sort_values(by='revenue', ascending=False)
df.groupby('custname').aggregate({'revenue':np.sum}).sort_values(by='revenue', ascending=False).head(5)

#these are top 5 customers who gave revnue to Denco

#extras ???

'''
df.groupby('custname').aggregate({'revenue':[np.sum, 'size'] }).sort_values(by='revenue', ascending=False) #error

df[['revenue','custname']].groupby('custname').agg['size'] 
.sort_values(by='revenue', ascending=False) #error
'''




#What part numbers bring in to significant portion of revenue - 
#Maximise revenue from high value parts

#grouby partnum, find value of revenue, sell these more
'''
df[['partnum','revenue']].sort_values(by='revenue', ascending=False)
df[['partnum','revenue']].sort_values(by='revenue', ascending=False).head(5)
'''

#these are top parts which give max revenue
#if total sales has to be considered
df[['partnum','revenue']].groupby('partnum').sum()
df[['partnum','revenue']].groupby('partnum').sum().sort_values(by='revenue', ascending=False).head(5)
#this total revenue value giving items


df.dtypes

#What parts have the highest profit margin - What parts are driving profits & 
#what parts need to build further
#check for margin value, their individual margin and total sales margin like revenue

df[['partnum','margin']].sort_values(by='margin', ascending=False)
df[['partnum','margin']].sort_values(by='margin', ascending=False).head(5)
df[['partnum','margin']].sort_values(by='margin', ascending=False).tail(5)

#these are top parts which give max margins
#if total sales has to be considered


pd.set_option('display.max_columns',1000)


df[['partnum','margin']].groupby('partnum').sum()
df1= df[['partnum','margin']].groupby('partnum').sum().sort_values(by='margin', ascending=False).head(5)

df1

df1.to_csv('aa.csv')



#this total revenue value giving items
#Most sold items

df.groupby('partnum').size().sort_values(ascending=False).head(5)


#which regions gave max revenue
df2 = df[['revenue', 'region']].groupby( 'region').sum().sort_values(by='revenue', ascending=False).head(5)
df2