donard20/simple-sales-data-analysis

A simple data analysis project from DICT (Analyze Data with Python - Module 4 data Analysis in the Real World) using jupyter notebook, numpy, pandas, matplotlib and seaborn to answer the questions need by the client.

📜 Data Analysis in the Real World

📖 Table Of Contents

• 🔥 Aim for this lesson
• 🛠️ Problem
• 🚀 Solutions
• 🔥 Learned

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🔥 Aim for this lesson

Using a real world data, I am going to cleaned the data and analyze what my data needs to be answered using what I learned in Python.

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🛠️ Problem and Solution

Define the question/s

1. What is the yearly sales trend?
2. Which product sold the most in 2016?
3. Among the three product categories, which one had the lowest sale in 2018?
4. What is the yearl sales trend of the three customer groups?
5. Are we getting more corporate customers?

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Sales Data Analysis

Data Conclusion

Q1. What is the yearly sales trend?

Ans. There's an increase of sales from 2015 to 2018

Q2. Which product sold the most in 2016?

Ans. The product that sold the most in 2018 is Canon imageCLASS 2200 Advanced Copier with 14000 sales

Q3. Among the three product categories, which one had the lowest sale in 2018?

Ans. The Furniture Category had the lowest sale in 2018

Q4. What is the yearly sales trend of the three customer groups?

Ans. All of the three customer groups exhibited an upward trend starting 2016, but the Corporate customers had a quite a big leap in 2017

Q5. Are we getting more corporate customers?

Ans. We can say that the number of corporate customers is increasing yearly

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🔥 Learned:

• Analyze the Data
  • Clean the Data
  • Applied the methods learned from the previous lesson/s
  • fillna() method - to fill null values into non null
  • to_datetime() method - to convert object into date data type
  • astype method() - to convert a object into string readable
• Working with DataFrame
  • dropna(), isnull, and isna() nethods - for null vaues in data
  • describe() method - information of dataframe
  • simple filtering with conditionals
  • arranging with sort_values
  • aggregate method
• Modelling the Data
  • More about Data Analysis Steps
  • More about Pandas basic methods
  • barplot() - for making barcharts from matplotlib.pyplot packages
  • lineplot() - for making line charts from matplotlib.pyplot packages
  • label() - for labeling the axes
  • title() - for making title in the charts
  • show() - displaying the charts

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© 2022 Donard Azura