In this section, we will discuss missing (also referred to as NA) values in pandas.
The choice of using
NaN internally to denote missing data was largely for simplicity and performance reasons. It differs from the MaskedArray approach of, for example,
scikits.timeseries. We are hopeful that NumPy will soon be able to provide a native NA type solution (similar to R) performant enough to be used in pandas.
See the cookbook for some advanced strategies.
Some might quibble over our usage of missing. By “missing” we simply mean NA (“not available”) or “not present for whatever reason”. Many data sets simply arrive with missing data, either because it exists and was not collected or it never existed. For example, in a collection of financial time series, some of the time series might start on different dates. Thus, values prior to the start date would generally be marked as missing.
In pandas, one of the most common ways that missing data is introduced into a data set is by reindexing. For example:
In : df = pd.DataFrame(np.random.randn(5, 3), index=['a', 'c', 'e', 'f', 'h'], ...: columns=['one', 'two', 'three']) ...: In : df['four'] = 'bar' In : df['five'] = df['one'] > 0 In : df Out: one two three four five a -0.166778 0.501113 -0.355322 bar False c -0.337890 0.580967 0.983801 bar False e 0.057802 0.761948 -0.712964 bar True f -0.443160 -0.974602 1.047704 bar False h -0.717852 -1.053898 -0.019369 bar False In : df2 = df.reindex(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h']) In : df2 Out: one two three four five a -0.166778 0.501113 -0.355322 bar False b NaN NaN NaN NaN NaN c -0.337890 0.580967 0.983801 bar False d NaN NaN NaN NaN NaN e 0.057802 0.761948 -0.712964 bar True f -0.443160 -0.974602 1.047704 bar False g NaN NaN NaN NaN NaN h -0.717852 -1.053898 -0.019369 bar False
As data comes in many shapes and forms, pandas aims to be flexible with regard to handling missing data. While
NaN is the default missing value marker for reasons of computational speed and convenience, we need to be able to easily detect this value with data of different types: floating point, integer, boolean, and general object. In many cases, however, the Python
None will arise and we wish to also consider that “missing” or “not available” or “NA”.
If you want to consider
-inf to be “NA” in computations, you can set
pandas.options.mode.use_inf_as_na = True.
In : df2['one'] Out: a -0.166778 b NaN c -0.337890 d NaN e 0.057802 f -0.443160 g NaN h -0.717852 Name: one, dtype: float64 In : pd.isna(df2['one'])
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