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Advanced Techniques in pandas

1. Get Familiar With Indexing and Selecting Data

Output Columns by Title Output Rows by Index Select Specific Rows and Columns Learn More About Indexation Get Familiar With lambda Functions Expand Functionality of the .iloc[] Function

2. Dealing With Conditions

Set Condition Select Data Based on Condition Dealing With Several Conditions Make Your Code Beautiful Conditions Quiz

3. Extract Data

Is Data in ...?Combine Your Knowledge Between Function Extract Specific Data Find the Smallest Values of a Column Find the Largest Values of a Column Find the Correlation

4. Aggregating Data

Get Familiar With the .groupby() Method Group by Several Columns Complicated Grouping Group Dealing With Pivot Tables Create a Pivot Table

5. Preprocessing Data

Check for Missing Values Calculate the Number of Missing Values What Will We Do With the NaN Values?How to Delete Only NaN Values?Fill In the Missing Values Manage Categorical Variables Check the Column Type Manage an Incorrect Column Rename the Column

Advanced Techniques in pandas

Calculate the Number of Missing Values

It should be noted that it isn't convenient to check each value of the dataset for the NaN. It is more convenient to see the number of missing values to conclude columns where we have NaNs. As you remember, we have two functions to check for the missing values. To calculate the sum, just use the .sum() function. Thus, in general, we have 2 options for outputting the number of NaNs for each column:

Okay, nothing complicated. Let's move on the task.