Beginner Machine Learning | Pandas Python Library | Exercise: Summary Functions and Maps

📊 "Kaggle Pandas Exercise: Summary Functions & Maps - Deep Dive into Data Insights!" 📊

🔍 We're back with Kaggle's Pandas course, tackling the "Summary Functions and Maps" exercise. Let's solidify our understanding of data aggregation and transformation!

📌 Setting Up the Environment:

import pandas as pd: Importing the Pandas library.

📊 Exercise 1: Median of the "points" Column:

We'll find the median value of the "points" column.

✏️ Exercise 2: Unique Countries:

We'll identify the unique countries represented in the dataset.

🌍 Exercise 3: Reviews per Country:

We'll count how many times each country appears in the dataset.

📈 Exercise 4: Centered Price:

We'll create a new Series centered_price with the mean price subtracted from each price.

🤖 Exercise 5: Best Bargain Wine:

We'll find the title of the wine with the highest points-to-price ratio.

🔥 Exercise 6: Descriptor Counts:

We'll count the occurrences of "tropical" and "fruity" in the "description" column (case-insensitive).
Creating a Pandas Series descriptor_counts.

🧠 Exercise 7: Star Ratings:

We'll create a Series star_ratings based on the "points" and "country" columns.
Defining a function stars(row) with conditional logic for star assignment.

🔗 Moving Forward:

We've successfully completed the "Summary Functions and Maps" exercise.
We're now moving on to "Grouping and Sorting."
Let's explore how to group and order our data for further analysis!

#KagglePandas #SummaryFunctions #DataMapping #PythonPandas #DataTransformation #PandasTutorial #DataAnalysis #LearnPandas 📊✏️🌍📈🤖🔥🧠🔗

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