Handling Missing Data - p.10 Data Analysis with Python and Pandas Tutorial

TL;DR

This tutorial discusses the different options for handling missing data in Python and Pandas, including ignoring it, deleting it, filling it, or replacing it with a static value.

Transcript

what is going on everybody welcome to part 10 of our data analysis with Python and pandas tutorial series in this part what we're going to be talking about is handling missing data you're going to see this usually as any N or nan which means not a number but generally all missing data will be called not a number regardless of whether or not it's an... Read More

Key Insights

• 🎟️ Missing data in Python and Pandas is often represented as NaN or NA.
• 🎟️ Ignoring missing data, deleting rows with missing data, filling missing data, and replacing missing data with a static value are the four major choices for handling missing data.
• 🤨 The dropna() function is used to delete rows with missing data, while the fillna() function is used to fill missing data.
• ▶️ The fillna() function can be used with forward fill (ffill) or backward fill (bfill) methods to fill missing data.
• ⛔ The limit parameter in fillna() can be used to limit the number of missing values filled.

Questions & Answers

Q: What are the four major choices for handling missing data?

The four major choices are ignoring the missing data, deleting the rows with missing data, filling the missing data with forward or backward fill, or replacing the missing data with a static value.

Q: How can we delete rows with missing data using Pandas?

We can use the dropna() function in Pandas to delete rows with missing data. By default, it drops any row that contains any amount of missing data. We can also use the how parameter to specify "all" to drop rows where every value is missing.

Q: How can we fill missing data with forward or backward fill in Pandas?

We can use the fillna() function in Pandas to fill missing data. By using forward fill (ffill), the missing values are filled with previous values. By using backward fill (bfill), the missing values are filled with future values.

Q: Can we fill missing data with a specific value in Pandas?

Yes, we can use the fillna() function with the value parameter to fill missing data with a specific value. For example, we can fill missing data with a value of -99999.

Summary & Key Takeaways

• The tutorial covers the four major choices for handling missing data: ignoring it, deleting it, filling it, or replacing it with a static value.

• The tutorial explains how to use the dropna() function to delete rows with missing data and the fillna() function to fill missing data with forward or backward fill.

• The tutorial also shows how to set a threshold for dropping rows based on the number of non-null values and how to limit the number of missing values filled using the limit parameter in fillna().