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Calculating the Number of Missing Values | Preprocessing Data
Advanced Techniques in pandas
course content

Course Content

Advanced Techniques in pandas

Advanced Techniques in pandas

1. Getting Familiar With Indexing and Selecting Data
2. Dealing With Conditions
3. Extracting Data
4. Aggregating Data
5. Preprocessing Data

bookCalculating 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.

Task

  1. Calculate the number of missing values for the dataset using one of the mentioned functions.
  2. Output the result.

Try to draw your own conclusions.

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Section 5. Chapter 2
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bookCalculating 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.

Task

  1. Calculate the number of missing values for the dataset using one of the mentioned functions.
  2. Output the result.

Try to draw your own conclusions.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 5. Chapter 2
toggle bottom row

bookCalculating 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.

Task

  1. Calculate the number of missing values for the dataset using one of the mentioned functions.
  2. Output the result.

Try to draw your own conclusions.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

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.

Task

  1. Calculate the number of missing values for the dataset using one of the mentioned functions.
  2. Output the result.

Try to draw your own conclusions.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 5. Chapter 2
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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