CHAPTER 3 LEARN EXPLORATORY DATA ANALYSIS (EDA)

 

LEARN PYTHON THROUGH SERIES OF ROBOT'S ACTIVITIES 

 

Sales Forecast -An Example: Blessy is an analyst robot. She works as a waitress in a sales hall. Her boss lady learns a lot of information from her every day. Specifically, it includes details such as revenue for a day, list of best-selling items, list of least-selling items, number of employees, salary, and sales details of new items. Today was the first day of the Month, and Blessy began to struggle with her work. It was an amazing process to extract vast amounts of new data from her calculations.

For example, details such as, which products a certain age group buys and how many of the same products are the most purchased on the list.

Now she tells her master about today's information.

 

Blessy, the analyst robot, has to read the files which contain the business data. Python libraries are mostly supported for the EDA.

[In]import pandas as pd

[In]import numpy as np

[In]import matplotlib as plt

[In]import seaborn as sns

[In]import csv

[In]from pandas import read_csv

[In]path=r"c:\Users\ELCOT\Documents\MLP ENGINEERS\ML SHEET12.csv"

#File location must be provided. To get the document - excel sheet: https://drive.google.com/file/d/1yWXPmUbh-1j8_ceMuovkvcXw92ZQJzJE/view?usp=share_link

 

[In]data=read_csv(path)

[In]print(data.shape)

[Out](17, 9)

    [In]data.head()

    [In]print(data[:3])

[Out]

    s.n        date cus_name products  price count order_price       base_price 

    0-1  04-05-2022    cus 1    pro 1     10      5           50           4  

    1-2  05-05-2022    cus 2    pro 2     20      3           60           2  

    2-3  06-05-2022    cus 3    pro 3     30     34         1020          33  

[Out]profit 

    0      10 

    1      20 

    2      30

[In]len(data)

[Out]17

    [In]a=data["price"].isna().sum()

    [In]print(a)

 [Out]0

    [In]a=data['products'].isna().sum()

    [In]print(a)

      [Out]0

    [In]print(data.shape)

   [Out] (17, 9)

        [In]print(data[:3])

   [Out]

       s.n        date cus_name products  price  count  order_price  base_price  \

    0    1  04-05-2022    cus 1    pro 1     10      5           50           4  

    1    2  05-05-2022    cus 2    pro 2     20      3           60           2  

    2    3  06-05-2022    cus 3    pro 3     30     34         1020          33   

   

       profit 

    0      10 

    1      20 

    2      30