Using Python for data analysis (10) pandas basics: processing missing data, pythonpandas

Using Python for data analysis (10) pandas basics: processing missing data, pythonpandas
Incomplete Data is common in data analysis. Pandas uses the floating-point value NaN to indicate missing data in floating-point and non-floating-point groups. Pandas uses the isnull () and notnull () functions to determine the missing condition.
The general processing method for missing data isFilter outOrFill.
Filter out missing dataFor a Series, the dropna () function returns a Series containing non-null data and index values, for example, filling in missing data.
If you do not want to discard the missing data but want to use the default value to fill these holes, you can use the fillna () function: if you do not want to only fill in a scalar, You can input a dictionary, fill different values in different columns: