For a pandas dataframe df, state which of the following statements is correct and which is not?
--Not answered-- Correct Not correct --Empty-- 1- df.isnull() will find and replace with 0 all Nan values in the dataframe
--Not answered-- Correct Not correct --Empty-- 2- df[:].isnull().any() will display the names of all columns that have null in them
--Not answered-- Correct Not correct --Empty-- 3- df['col1'].describe().iloc[1] will return the mean of column ‘col1’
--Not answered-- Correct Not correct --Empty-- 4- df[:].isnull().any(axis=1).sum() will count how many rows have null values in them
--Not answered-- Correct Not correct --Empty-- 5- df.dropna(axis='rows', thresh=4 ) will remove the rows that have less than four non-missing values
--Not answered-- Correct Not correct --Empty-- 6- df = df[ df.isnull().any(axis=1) == False ] will remove the rows with missing values
--Not answered-- Correct Not correct --Empty-- 7- df.dropna(axis=1) is equivalent to df[ df.isnull().any(axis=1 ) == False ]
--Not answered-- Correct Not correct --Empty-- 8- df[ 'col1' ].fillna(1) will replace all nan values of column ‘col1’ with value 0
--Not answered-- Correct Not correct --Empty-- 9- df[ df.notnull().all(axis=1) ] is equivalent to df.dropna()
--Not answered-- Correct Not correct --Empty-- 10- df.describe( ) will list df's columns and their data types
For a pandas dataframe df, state which of the following statements is correct and which is not?
--Not answered-- Correct Not correct --Empty-- 1- df.isnull() will find and replace with 0 all Nan values in the dataframe
--Not answered-- Correct Not correct --Empty-- 2- df[:].isnull().any() will display the names of all columns that have null in them
--Not answered-- Correct Not correct --Empty-- 3- df['col1'].describe().iloc[1] will return the mean of column ‘col1’
--Not answered-- Correct Not correct --Empty-- 4- df[:].isnull().any(axis=1).sum() will count how many rows have null values in them
--Not answered-- Correct Not correct --Empty-- 5- df.dropna(axis='rows', thresh=4 ) will remove the rows that have less than four non-missing values
--Not answered-- Correct Not correct --Empty-- 6- df = df[ df.isnull().any(axis=1) == False ] will remove the rows with missing values
--Not answered-- Correct Not correct --Empty-- 7- df.dropna(axis=1) is equivalent to df[ df.isnull().any(axis=1 ) == False ]
--Not answered-- Correct Not correct --Empty-- 8- df[ 'col1' ].fillna(1) will replace all nan values of column ‘col1’ with value 0
--Not answered-- Correct Not correct --Empty-- 9- df[ df.notnull().all(axis=1) ] is equivalent to df.dropna()
--Not answered-- Correct Not correct --Empty-- 10- df.describe( ) will list df's columns and their data types