[amp_mcq option1=”groupby()” option2=”drop_duplicates()” option3=”dropna()” option4=”fillna()” correct=”option3″]<\/p>\n
The correct answer is C. dropna().<\/p>\n
The dropna() method is used to remove missing values from a DataFrame. It can be used to remove all missing values, or to remove only missing values from a specific column or columns.<\/p>\n
The syntax for the dropna() method is as follows:<\/p>\n
This will remove all rows from the DataFrame df that have missing values in either the column1 or column2 columns. The how parameter can be set to ‘any’ to remove rows with missing values in any of the specified columns, or to ‘all’ to remove rows with missing values in all of the specified columns.<\/p>\n The groupby() method is used to group rows in a DataFrame by a common value. The drop_duplicates() method is used to remove duplicate rows from a DataFrame. The fillna() method is used to fill in missing values in a DataFrame.<\/p>\n Here is an example of how to use the dropna() method:<\/p>\n “` df = pd.DataFrame({‘A’: [1, 2, np.nan, 4], ‘B’: [3, 5, 6, 7]})<\/p>\n print(df)<\/p>\n A B df.dropna(subset=[‘A’], how=’any’)<\/p>\n print(df)<\/p>\n A B As you can see, the dropna() method has removed the row with the NaN value in the A column.<\/p>\n","protected":false},"excerpt":{"rendered":" [amp_mcq option1=”groupby()” option2=”drop_duplicates()” option3=”dropna()” option4=”fillna()” correct=”option3″]<\/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-43247","post","type-post","status-publish","format-standard","hentry","category-data-analysis-with-python","no-featured-image-padding"],"yoast_head":"\ndf.dropna(subset=['column1', 'column2'], how='any')<\/code><\/p>\n
\nimport pandas as pd<\/p>\n
\n0 1 3
\n1 2 5
\n2 NaN 6
\n3 4 7<\/p>\n
\n0 1 3
\n1 2 5
\n3 4 7
\n“`<\/p>\n