Python Learning Journey

Introduction

Today I learned how professional data analysts inspect datasets before cleaning, transforming, or visualizing data. Understanding the structure of data is the first step of every analytics project.

Sample Retail Dataset

Date	Product	Quantity	Price
2023-12-02	Tea	4	40
2025-05-20	Coffee	2	40
2024-10-03	Tea	3	30
2022-11-01	Coffee	5	100
2026-01-05	Tea	10	100

Create DataFrame

import pandas as pd

sales_data = {
 'date':['2023-12-02','2025-05-20','2024-10-03','2022-11-01','2026-01-05'],
 'product':['tea','coffee','tea','coffee','tea'],
 'quantity':[4,2,3,5,10],
 'price':[40,40,30,100,100]
}

df = pd.DataFrame(sales_data)

Functions Learned

df.info()

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
date       5 non-null object
product    5 non-null object
quantity   5 non-null int64
price      5 non-null int64

df.shape

df.shape
(5,4)

df.columns

Index(['date','product','quantity','price'])

df.dtypes

date object
product object
quantity int64
price int64

df.head()

df.head(2)

df.tail()

df.tail(2)

df.describe()

Returns count, mean, std, min, max and quartiles.

unique() & nunique()

df['product'].unique()
df['product'].nunique()

Real World Use Case

Suppose a company sends a sales file containing 50,000 rows. Before building a Power BI dashboard or performing SQL analysis, a data analyst first checks:

• Dataset shape
• Column names
• Data types
• Missing values
• Summary statistics
• Unique categories

This process helps identify data quality issues early and saves hours of debugging later.

Key Takeaways

✅ Understanding DataFrame structure

✅ Inspecting rows and columns

✅ Checking datatypes

✅ Using describe() for summaries

✅ Finding unique values

✅ Building the foundation for future data analysis