Data Analysis with Pandas in Python
Learn Pandas for data analysis in Python! A beginner-friendly guide to DataFrames, filtering data, grouping, and handling messy missing data just like Excel.
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How it works
Pandas is the absolute king of Python data analysis! If you've ever used Excel or Google Sheets, you already know how to use Pandas—you just don't know the code yet! 🐼
The Pandas Dictionary
1. Series: Think of this as a single column in a spreadsheet.
2. DataFrame: This is your entire spreadsheet! It has multiple columns, and each column can be a different type of data (like names, ages, and salaries).
Awesome Things We Can Do
df.describe(), Pandas instantly does all the math to tell you the average, minimum, maximum, and percentiles for every single numeric column in your dataset. It's magic!df[df['Age'] > 28]. It's that easy! You can even chain conditions using & (AND) or | (OR).groupby() lets you quickly categorize your data. Want to know the average salary in each city? Group by City, select the Salary column, and ask for the mean()!NaNs - Not a Number) and fill them in with an average, or drop them entirely using fillna() or dropna().Run the code to see a virtual spreadsheet come to life and crunch some serious numbers!
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