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In [1]:

    
import numpy as np
import pandas as pd

df = pd.read_csv('E:/Unilever/data1.csv')



In [2]:

    
import plotly.graph_objs as go
from plotly.offline import iplot



In [3]:

    
fig = go.Figure([
                    go.Bar(x = df["Customer Group"].unique(),
                           y = df.groupby(["Customer Group"])["Sales Org Code"].count(),
                           name = "Customer Group",
                           marker = dict(color = 'rgba(255, 174, 255)',
                                         line=dict(color='rgb(0,0,0)',width=1.5)))
                ])
fig.update_layout(title=go.layout.Title(text="Customer Groups Distribution"))
fig.show()

This graph depicts the Customer Group variations wherein Distributors are the maximum in the given data!



In [4]:

    
fig = go.Figure([
                    go.Bar(x = df["Customer Group new"].unique(),
                           y = df.groupby(["Customer Group new"])["Sales Org Code"].count(),
                           name = "Customer Group",
                           marker = dict(color = 'rgba(255, 174, 255)',
                                         line=dict(color='rgb(0,0,0)',width=1.5)))
                ])
fig.update_layout(title=go.layout.Title(text="New Customer Groups Distribution"))
fig.show()

A graph on 'New Customer Group' variations wherein Distr are the maximum again!



In [5]:

    
fig = go.Figure([
                    go.Bar(x = df["Customer Group by Directors"].unique(),
                           y = df.groupby(["Customer Group by Directors"])["Sales Org Code"].count(),
                           name = "Customer Group",
                           marker = dict(color = 'rgba(255, 174, 255)',
                                         line=dict(color='rgb(0,0,0)',width=1.5)))
                ])
fig.update_layout(title=go.layout.Title(text="Customer Group by Directors Distribution"))
fig.show()

A graph on 'Director Group' variations wherein DT Director is the max!

Considering just one customer for ease of understanding!



In [6]:

    
df1 = df[df["Master Customer Name"] == "IVANOVA ELENA NIKOLAEVNA"]

'''Considering just one customer for the ease of representation here!'''

fig = go.Figure([
                    go.Bar(
                        x=df1["Category"].unique(),
                        y=df1.groupby(["Category"])["Sal QTY"].sum(),
                        name = "Packages sold",
                        marker = dict(color = 'rgba(16, 112, 2, 0.8)'))
                ])
fig.update_layout(title=go.layout.Title(text="Category Wise Packages Sold for a Customer"))
fig.show()



In [7]:

    
fig = go.Figure([
                    go.Bar(
                        x=df1["Category"].unique(),
                        y=df1.groupby(["Category"])["Sal GSV"].sum(),
                        name = "Total Sales GSV",
                        marker = dict(color = 'rgba(16, 112, 2, 0.8)'))
                ])
fig.update_layout(title=go.layout.Title(text="Category Wise Sales GSV for a Customer"))
fig.show()



In [8]:

    
fig = go.Figure([
                    go.Bar(
                        x=df1["Category"].unique(),
                        y=df1.groupby(["Category"])["Sal NIV"].sum(),
                        name = "Total Sales GSV",
                        marker = dict(color = 'rgba(16, 112, 2, 0.8)'))
                ])
fig.update_layout(title=go.layout.Title(text="Category Wise Sales NIV for a Customer"))
fig.show()

In general we can say that Skin Cleansing product is the maximum sold and high GSV returning product domain.