{"media":{"type":{"params":{"contentName":"Image"},"library":"H5P.Image 1.1","subContentId":"1e868e13-dbe7-456a-bd22-4924e6d4c8fc","metadata":{"contentType":"Image","license":"U","title":"Untitled Image"}},"disableImageZooming":false},"answers":[{"correct":false,"tipsAndFeedback":{"tip":"","chosenFeedback":"","notChosenFeedback":""},"text":"

df.isnull(axis = 0).sum()<\/div>\n"},{"correct":false,"tipsAndFeedback":{"tip":"","chosenFeedback":"","notChosenFeedback":""},"text":"

df.isnull(axis = 1).sum()<\/div>\n"},{"correct":false,"tipsAndFeedback":{"tip":"","chosenFeedback":"","notChosenFeedback":""},"text":"

df.isnull().sum(axis = 0)<\/div>\n"},{"correct":true,"tipsAndFeedback":{"tip":"","chosenFeedback":"

\nThe df.isnull() function gives the null values in the whole dataframe. And the sum() function counts the total number. By default, it counts the missing values along the columns, i.e. axis = 0. If you want the row-wise NA count, just specify the axis as 1. \n<\/div>\n","notChosenFeedback":""},"text":"

df.isnull().sum(axis = 1)<\/div>\n"}],"overallFeedback":[{"from":0,"to":100}],"behaviour":{"enableRetry":true,"enableSolutionsButton":true,"enableCheckButton":true,"type":"auto","singlePoint":false,"randomAnswers":true,"showSolutionsRequiresInput":true,"confirmCheckDialog":false,"confirmRetryDialog":false,"autoCheck":false,"passPercentage":100,"showScorePoints":true},"UI":{"checkAnswerButton":"Check","showSolutionButton":"Show solution","tryAgainButton":"Retry","tipsLabel":"Show tip","scoreBarLabel":"You got :num out of :total points","tipAvailable":"Tip available","feedbackAvailable":"Feedback available","readFeedback":"Read feedback","wrongAnswer":"Wrong answer","correctAnswer":"Correct answer","shouldCheck":"Should have been checked","shouldNotCheck":"Should not have been checked","noInput":"Please answer before viewing the solution","a11yCheck":"Check the answers. The responses will be marked as correct, incorrect, or unanswered.","a11yShowSolution":"Show the solution. The task will be marked with its correct solution.","a11yRetry":"Retry the task. Reset all responses and start the task over again."},"confirmCheck":{"header":"Finish ?","body":"Are you sure you wish to finish ?","cancelLabel":"Cancel","confirmLabel":"Finish"},"confirmRetry":{"header":"Retry ?","body":"Are you sure you wish to retry ?","cancelLabel":"Cancel","confirmLabel":"Confirm"},"question":"

.<\/p>\n"}

Which of these commands will give you the row-wise missing value count for a dataframe 'df'?

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