Kaggle Pandas Exercise: Data Types & Missing Values

🧠 "Kaggle Pandas Exercise: Data Types & Missing Values - Cleaning Your Data!" 🛠️

🔍 We're back with Kaggle's Pandas course, tackling the "Data Types and Missing Values" exercise to master data cleaning!

📌 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.
from learntools.core import binder: Importing tools for answer checking.
reviews.head(): Displaying the first few rows of the DataFrame.

📊 Exercise 1: Data Type of the "points" Column:

We'll find the data type of the "points" column.
dtype_of_points = reviews.points.dtype: Accessing the .dtype attribute.

✏️ Exercise 2: Converting "points" to Strings:

We'll create a Series with "points" values converted to strings.
point_strings = reviews.points.astype(str): Using the .astype(str) method.

🌍 Exercise 3: Missing Prices:

We'll count the number of reviews with missing "price" values.
missing_price_count = reviews.price.isnull().sum(): Using .isnull() to create a boolean mask and .sum() to count True values.

📈 Exercise 4: Most Common Wine Producing Regions (Handling Missing Data):

We'll find the most common "region_1," filling missing values with "Unknown."

🔗 Moving Forward:

We've successfully completed the "Data Types and Missing Values" exercise.
We're now moving on to "Renaming and Combining."
Let's learn how to reshape and combine our DataFrames!

#KagglePandas #DataTypes #MissingValues #NaN #DataCleaning #PythonPandas #PandasTutorial #DataScience #LearnPandas 🧠🛠️🌍📈🔗

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