The descriptive statistics and computational methods discussed in the data structure overview (and listed here and here) are all account for missing data. When summing data, NA values or empty data …

Working with missing data # In this section, we will discuss missing (also referred to as NA) values in pandas.

Remove missing values. See the User Guide for more on which values are considered missing, and how to work with missing data. Parameters: axis{0 or ‘index’, 1 or ‘columns’}, default 0 Determine if rows …

If method is specified, this is the maximum number of consecutive NaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be …

These methods use the numerical values of the index. Both ‘polynomial’ and ‘spline’ require that you also specify an order (int), e.g. df.interpolate(method='polynomial', order=5).

If method is specified, this is the maximum number of consecutive NaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be …

See also Series.isna Indicate missing values. Series.notna Indicate existing (non-missing) values. Series.fillna Replace missing values. DataFrame.dropna Drop rows or columns which contain NA …

If method is specified, this is the maximum number of consecutive NaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be …

Missing values # NA is the way to represent missing values for nullable dtypes (see below):

The descriptive statistics and computational methods discussed in the data structure overview (and listed here and here) all account for missing data. When summing data, NA values or empty data will …