Data Analysis Using Python

Introduction Before diving into any of the practicals in this series — creating DataFrames, cleaning data, building charts, or running […]

Introduction In real-world projects, data is rarely clean. You’ll routinely encounter the same underlying value written in multiple inconsistent ways:

Introduction Many real-world datasets involve dates and time: sales by day, website traffic by month, stock prices, weather measurements, customer

Introduction In the previous chapter, we calculated overall statistics like mean, median, and correlation. Now we’ll learn to answer more

Introduction Descriptive statistics helps us summarize and understand data using numbers, so we don’t have to examine every individual value

Introduction Exploratory Data Analysis (EDA) is the process of investigating a dataset thoroughly before drawing conclusions or building models. It’s

Introduction Seaborn is a Python library built on top of Matplotlib. It makes statistical visualization easier and produces attractive, informative

Introduction Data visualization means representing data using charts and graphs. Instead of only looking at raw numbers, visualization helps us

Introduction Data cleaning is one of the most important — and most time-consuming — steps in data analysis. Real-world data

Introduction In the previous chapter, we created data manually inside Python. Now we’ll work with a real CSV file —