Python | Pandas dataframe.notnull ()

The Pandas dataframe.notnull() function detects existing / non-missing values ​​in a dataframe. The function returns a boolean that has the same size as the object to which it is applied, indicating whether each individual value is a na value or not. All non-missing values ​​are displayed as true, and missing values ​​are displayed as false.

Note: Characters such as empty strings "or numpy.inf are not considered NA values. (unless you set pandas.options.mode.use_inf_as_na = True).

Syntax: DataFrame.notnull ()

Returns: Mask of bool values ​​for each element in DataFrame that indicates whether an element is not an NA value.

Example # 1: Use notnull () to find all non-missing values ​​in the data frame.

# import pandas as pd import pandas as pd    # Create first data frame df = pd.DataFrame ({ "A" : [ 14 , 4 , 5 , 4 , 1 ], "B" : [ "Sam" , "olivia" , " terica " , " megan " , "amanda" ], "C" : [ 20 + 5j , 20 + 3j , 7 , 3 , 8 ], "D" : [ 14 , 3 , 6 , 2 , 6 ]})   # Print the data frame df

Let’s use the dataframe.notnull () function to find all non-missing values ​​in the frame data.

# find non-values ​​ df.notnull ()

Output:

As we can see in the output, everything is not missing values ​​in the data frame have been matched against true. There is no false value since there is no missing value in the data frame

Example # 2: Use notnull () to find non-missing values ​​when in notnull () there are missing values.

# import pandas as pd import pandas as pd   # Create data frame df = pd.DataFrame ({ "A" : [ " Sandy " , " alex " , "brook" , "kelly" , np.nan],  "B" : [np.nan, "olivia" , "terica" ​​ , " ", " amanda"], "C" : [ 20 + 5j , 20 + 3j , 7 , None , 8 ],   "D" : [ 14.8 , 3 , None , 2.3 , 6 ]})   # find not missing values ​​ df.notnull ()

Output:

Note that an empty string is also displayed as true, indicating that it is not a NaN value.