[amp_mcq option1=”Remove duplicates” option2=”Group data” option3=”Sort data” option4=”Fill missing values” correct=”option4″]<\/p>\n
The correct answer is D. Fill missing values.<\/p>\n
The The For example, the following code fills in the missing values in the The following code fills in the missing values in the The following code fills in the missing values in the The [amp_mcq option1=”Remove duplicates” option2=”Group data” option3=”Sort data” option4=”Fill missing values” correct=”option4″]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[737],"tags":[],"class_list":["post-58589","post","type-post","status-publish","format-standard","hentry","category-data-analysis-with-python","no-featured-image-padding"],"yoast_head":"\nfillna<\/code> method in Pandas is used to fill missing values in a DataFrame. It can be used to fill missing values with a constant value, the mean of the column, or the median of the column. It can also be used to fill missing values with the value from another column.<\/p>\nfillna<\/code> method takes two arguments: the value to fill in the missing values, and the axis to fill in the missing values. The axis can be either 0<\/code> for the rows or 1<\/code> for the columns.<\/p>\nDataFrame<\/code> df<\/code> with the value 0<\/code>:<\/p>\ndf = df.fillna(0)<\/code><\/p>\nDataFrame<\/code> df<\/code> with the mean of the column A<\/code>:<\/p>\ndf = df.fillna(df['A'].mean())<\/code><\/p>\nDataFrame<\/code> df<\/code> with the value from the column B<\/code>:<\/p>\ndf = df.fillna(df['B'])<\/code><\/p>\nfillna<\/code> method is a very powerful tool that can be used to fill in missing values in a DataFrame. It is important to note that the fillna<\/code> method does not change the original DataFrame. It creates a new DataFrame with the missing values filled in.<\/p>\n","protected":false},"excerpt":{"rendered":"