Pandas DataFrame.fillna() Syntax

Syntax: DataFrame.fillna(value=None, method=None, axis=None, inplace=False, limit=None, downcast=None, **kwargs)

Parameters: 

• value : Static, dictionary, array, series or dataframe to fill instead of NaN.
• method : Method is used if user doesn’t pass any value. Pandas have different methods like bfill, backfill, or ffill which fills the place with value in the Forward index or Previous/Back respectively.
• axis: axis takes int or string value for rows/columns. Input can be 0 or 1 for Integer and ‘index’ or ‘columns’ for String
• inplace: It is a boolean which makes the changes in data frame itself if True.
• limit : This is an integer value which specifies maximum number of consecutive forward/backward NaN value fills.
• downcast : It takes a dict which specifies what dtype to downcast to which one. Like Float64 to int64.
• **kwargs : Any other Keyword arguments

Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas is one of those packages and makes importing and analyzing data much easier. Sometimes csv file has null values, which are later displayed as NaN in Data Frame. Just like the pandas dropna() method manages and remove Null values from a data frame, fillna() manages and let the user replace NaN values with some value of their own.