Beginner Machine Learning | Pandas Python Library | Exercise: Indexing, Selecting, & Assigning
📊 "Kaggle Pandas Exercise: Indexing, Selecting, & Assigning - Data Wrangling Mastery!" 📊
🔍 We're tackling the Kaggle Pandas exercise on Indexing, Selecting, and Assigning. Let's put our data selection skills to the test!
📌 Setting Up the Environment:
import pandas as pd: Importing the Pandas library.
reviews = pd.read_csv(...): Loading the wine reviews dataset with index_col=0.
pd.set_option('display.max_rows', 5): Limiting displayed rows for brevity.
from learntools.core import binder: Importing tools for answer checking.
📊 Exercise 1: Selecting the "description" Column:
We'll select the "description" column and assign it to the variable desc.
reviews.description or reviews['description']: Both are valid.
We'll determine the type of desc (Pandas Series).
✏️ Exercise 2: Selecting the First Value:
We'll select the first value from the "description" column and assign it to first_description.
desc[0] or reviews.description.iloc[0]: Both work, but iloc is preferred for clarity.
🌍 Exercise 3: Selecting the First Row:
We'll select the first row of the DataFrame and assign it to first_row.
reviews.iloc[0]: Selecting the first row using iloc.
📈 Exercise 4: Selecting the First 10 Values:
We'll select the first 10 values from the "description" column and assign it to first_descriptions.
reviews.description.iloc[:10]: Selecting the first 10 values using iloc.
🤖 Exercise 5: Selecting Specific Records:
We'll select records with index labels 1, 2, 3, 5, and 8 and assign them to sample_reviews.
reviews.iloc[[1, 2, 3, 5, 8]]: Selecting specific rows using iloc and a list.
🔥 Exercise 6: Selecting Specific Columns and Rows:
We'll create a DataFrame df with "country," "province," "region_1," and "region_2" columns for rows 0, 1, 10, and 100.
reviews.loc[[0, 1, 10, 100], ['country', 'province', 'region_1', 'region_2']]: Selecting rows and columns using loc.
🧠 Exercise 7: Selecting Country and Variety:
We'll create a DataFrame df with "country" and "variety" columns for the first 100 records.
reviews.loc[:99, ['country', 'variety']] or reviews.iloc[:100, [0, 11]]: Selecting rows and columns using loc or iloc.
🚀 Exercise 8: Italian Wines DataFrame:
We'll create a DataFrame italian_wines containing reviews of wines made in Italy.
reviews.loc[reviews.country == 'Italy']: Selecting rows based on a condition.
🔗 Exercise 9: Top Ocean Wines DataFrame:
We'll create a DataFrame top_ocean_wines with reviews of wines from Australia or New Zealand with at least 95 points.
reviews.loc[((reviews.country == 'Australia') | (reviews.country == 'New Zealand')) & (reviews.points greater than 95)]: Selecting rows with multiple conditions.
🏁 Moving Forward:
We've successfully completed the "Indexing, Selecting, and Assigning" exercise.
We're now moving on to "Summary Functions and Maps."
Let's continue our Pandas journey!
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