Advanced Techniques in pandas

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Calculating 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:


python

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

Oppgave

Swipe to start coding

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

Try to draw your own conclusions.

Løsning

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Calculating 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:


python

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

Oppgave

Swipe to start coding

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

Try to draw your own conclusions.

Løsning

Bytt til skrivebordet for virkelighetspraksisFortsett der du er med et av alternativene nedenfor

Alt var klart?

Takk for tilbakemeldingene dine!

Sveip for å vise menyen

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