Checking for Missing Values

I'm happy to see you in the last section of the course. Here, you will process data on the passengers of the Titanic. First, let's examine it:

The first step of our learning is finding missing values. By the way, sometimes it is difficult or even impossible to fill all the values of the column; some of them may be missing. Such cases can spoil your result. In the dataset, they always look like this: NaN. First, let's find out if your data set contains missing values.

Pandas has two functions you can apply to the dataset to find missing values. Both of them will put False if the dataset values aren't missing, and True otherwise.

data.isna()
# Or
data.isnull()

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Sektion 5. Kapitel 1

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Kursusindhold

Advanced Techniques in pandas

1. Getting Familiar With Indexing and Selecting Data

2. Dealing With Conditions

Setting Condition Selecting Data Based on Condition Dealing With Several Conditions Making Your Code Beautiful Conditions Quiz

3. Extracting Data

Is Data in ...?Combining Your Knowledge Between Method Extracting Specific Data Finding the Smallest Values of a Column Finding the Largest Values of a Column Finding the Correlation

4. Aggregating Data

Getting Familiar With the .groupby() Method Grouping by Several Columns Complicated Grouping Advanced Grouping Dealing With Pivot Tables Creating a Pivot Table