A Data Science Project For Beginners (Exploratory Data Analysis (EDA))
By: Saicharan Kr
Tags: Data Science Projects, Data Analysis, Data Science, Walmart Sales Analysis, Pandas Dataframe

We will now start our analysis .

STEP 1 :- Start by importing required Libraries .

import pandas as pd

from datetime import date

import numpy as np

import matplotlib.pyplot as plt

import seaborn as sb

STEP 2:- Import Data Set

data = pd.read_csv('Datasets/Walmart_Store_sales.csv')

NOTE:- read_csv is the function in Pandas used to import your dataset which is in csv format

Syntax :- pandas.read_csv(‘Your Dataset Location’)

STEP 3:- Understand dataset .

data.head() #default displays the First Five rows from the dataset

1st Five rows

Basic information about our dataset

data.info() #gives the basics information about our dataset like dimension, No. of nulls , datatype of the columns.

Dataset information

data.max() #Finds the Maximum value in each column

Max Values in each column

Above Steps-1,2,3 are common for Analysis of any Data ,Now we will Start with our Analysis tasks.

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QUESTION 1 :- Which store has maximum sales in this dataset?

Now we are going to find answer for this from our data .

data.loc[data['Weekly_Sales'] == data['Weekly_Sales'].max()] # used to find the row meeting the specific condition, Here we are checking in column Weekly_Sales which row or store in particular is having the maximum Weekly_Sales.

Maximum weekly sales

From above Figure we see that Store 14 has the maximum weekly sales.

QUESTION 2 :- Which store has maximum standard deviation i.e., the sales vary a lot. Also, find out the coefficient of variance (C0V)

In simple terms CoV is ratio of standard deviation to the mean.

Checkout more What is CoV from Below Wikipedia link.

#Here i am grouping by store and finding the standard deviation and mean of each store.

maxstd=pd.DataFrame(data.groupby('Store').agg({'Weekly_Sales':['std','mean']})) #Just resetting the index.

maxstd = maxstd.reset_index() #Now we know that CoV is std/ mean we are doing this for each store.

maxstd['CoV'] =(maxstd[('Weekly_Sales','std')]/maxstd[('Weekly_Sales','mean')]) *100 #finding the store with maximum standard deviation.

maxstd.loc[maxstd[('Weekly_Sales','std')]==maxstd[('Weekly_Sales','std')].max()]

Store With maximum standard deviation

From above Figure we can conclude that sales in store 14 vary a lot

QUESTION 3 :- Which store/s has good quarterly growth rate in Q3’2012.

#Converting the data type of date column to dateTime

data['Date'] = pd.to_datetime(data['Date']) #defining the start and end date of Q3 and Q2

Q3_date_from = pd.Timestamp(date(2012,7,1))

Q3_date_to = pd.Timestamp(date(2012,9,30)) Q2_date_from = pd.Timestamp(date(2012,4,1))

Q2_date_to = pd.Timestamp(date(2012,6,30)) #Collecting the data of Q3 and Q2 from original dataset.

Q2data=data[(data['Date'] > Q2_date_from) & (data['Date'] < Q2_date_to)] Q3data=data[(data['Date'] > Q3_date_from) & (data['Date'] < Q3_date_to)] #finding the sum weekly sales of each store in Q2

Q2 = pd.DataFrame(Q2data.groupby('Store')['Weekly_Sales'].sum())

Q2.reset_index(inplace=True)

Q2.rename(columns={'Weekly_Sales': 'Q2_Weekly_Sales'},inplace=True) #finding the sum weekly sales of each store in Q2

Q3 = pd.DataFrame(Q3data.groupby('Store')['Weekly_Sales'].sum())

Q3.reset_index(inplace=True)

Q3.rename(columns={'Weekly_Sales': 'Q3_Weekly_Sales'},inplace=True) #mergeing Q2 and Q3 data on Store as a common column

Q3_Growth= Q2.merge(Q3,how='inner',on='Store')

Growth rate formula is defined as the ratio of difference in present value to past value by past value whole multiplied with 100 (since it is in percentage)

((Present value — Past value )/Past value )*100

#Calculating Growth rate of each Store and collecting it into a dataframe

Q3_Growth['Growth_Rate'] =(Q3_Growth['Q3_Weekly_Sales'] - Q3_Growth['Q2_Weekly_Sales'])/Q3_Growth['Q2_Weekly_Sales'] Q3_Growth['Growth_Rate']=round(Q3_Growth['Growth_Rate'],2)

Q3_Growth.sort_values('Growth_Rate',ascending=False).head(1)

Store with min loss in growth rate.

Q3_Growth.sort_values('Growth_Rate',ascending=False).tail(1)

Store with max loss in growth rate

From above tables we can infer that Q3 growth rate is in losses .

the Store 16 has the least loss of 3% compared the other stores and store 14 has highest loss of 18%.

QUESTION 4:- Some holidays have a negative impact on sales. Find out holidays which have higher sales than the mean sales in non-holiday season for all stores together.

#finding the mean sales of non holiday and holiday

data.groupby('Holiday_Flag')['Weekly_Sales'].mean()

#marking the holiday dates

Christmas1 = pd.Timestamp(date(2010,12,31) )

Christmas2 = pd.Timestamp(date(2011,12,30) )

Christmas3 = pd.Timestamp(date(2012,12,28) )

Christmas4 = pd.Timestamp(date(2013,12,27) )



Thanksgiving1=pd.Timestamp(date(2010,11,26) )

Thanksgiving2=pd.Timestamp(date(2011,11,25) )

Thanksgiving3=pd.Timestamp(date(2012,11,23) )

Thanksgiving4=pd.Timestamp(date(2013,11,29) )



LabourDay1=pd.Timestamp(date(2010,2,10) )

LabourDay2=pd.Timestamp(date(2011,2,9) )

LabourDay3=pd.Timestamp(date(2012,2,7) )

LabourDay4=pd.Timestamp(date(2013,2,6) )



SuperBowl1=pd.Timestamp(date(2010,9,12) )

SuperBowl2=pd.Timestamp(date(2011,9,11) )

SuperBowl3=pd.Timestamp(date(2012,9,10) )

SuperBowl4=pd.Timestamp(date(2013,9,8) )



#Calculating the mean sales during the holidays

Christmas_mean_sales=data[(data['Date'] == Christmas1) | (data['Date'] == Christmas2) | (data['Date'] == Christmas3) | (data['Date'] == Christmas4)]

Thanksgiving_mean_sales=data[(data['Date'] == Thanksgiving1) | (data['Date'] == Thanksgiving2) | (data['Date'] == Thanksgiving3) | (data['Date'] == Thanksgiving4)]

LabourDay_mean_sales=data[(data['Date'] == LabourDay1) | (data['Date'] == LabourDay2) | (data['Date'] == LabourDay3) | (data['Date'] == LabourDay4)]

SuperBowl_mean_sales=data[(data['Date'] == SuperBowl1) | (data['Date'] == SuperBowl2) | (data['Date'] == SuperBowl3) | (data['Date'] == SuperBowl4)] #

list_of_mean_sales = {'Christmas_mean_sales' : round(Christmas_mean_sales['Weekly_Sales'].mean(),2), 'Thanksgiving_mean_sales': round(Thanksgiving_mean_sales['Weekly_Sales'].mean(),2), 'LabourDay_mean_sales' : round(LabourDay_mean_sales['Weekly_Sales'].mean(),2), 'SuperBowl_mean_sales':round(SuperBowl_mean_sales['Weekly_Sales'].mean(),2), 'Non holiday weekly sales' : data[data['Holiday_Flag'] == 0 ]['Weekly_Sales'].mean()} list_of_mean_sales

From above Figure we can infer that the mean sales of thanks giving is more than the non holiday weekly sales .

QUESTION 5 :-Provide a Monthly,Quaterly and Semester view of sales in units and give insights.

#Monthly sales

monthly = data.groupby(pd.Grouper(key='Date', freq='1M')).sum()# groupby each 1 month

monthly=monthly.reset_index()

fig, ax = plt.subplots(figsize=(10,8))

X = monthly['Date']

Y = monthly['Weekly_Sales']

plt.plot(X,Y)

plt.title('Month Wise Sales')

plt.xlabel('Monthly')

plt.ylabel('Weekly_Sales') #Quaterly Sales

Quaterly = data.groupby(pd.Grouper(key='Date', freq='3M')).sum()

Quaterly = Quaterly.reset_index()

fig, ax = plt.subplots(figsize=(10,8))

X = Quaterly['Date']

Y = Quaterly['Weekly_Sales']

plt.plot(X,Y)

plt.title('Quaterly Wise Sales')

plt.xlabel('Quaterly')

plt.ylabel('Weekly_Sales') #Semester Sales

Semester = data.groupby(pd.Grouper(key='Date', freq='6M')).sum()

Semester = Semester.reset_index()

fig, ax = plt.subplots(figsize=(10,8))

X = Semester['Date']

Y = Semester['Weekly_Sales']

plt.plot(X,Y)

plt.title('Semester Wise Sales')

plt.xlabel('Semester')

plt.ylabel('Weekly_Sales')

1. We can observe from the Monthly Sales Graph that highest sum of sales is recorded in between jan-2011 to march-2011.

2. We can observe from the Quarterly Sales Graph that higest sum of sales is recorded in Q1 of 2011 and 2012.

3. We can Observe from Semester Sales graph that at beginning of 1st sem of 2010 and 1st sem of 2013 sales are lowest .