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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

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