Working with missing data¶
In this section, we will discuss missing (also referred to as NA) values in pandas.
Note
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.
Values considered “missing”¶
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”.
Note
If you want to consider inf
and -inf
to be “NA” in computations,
you can set pandas.options.mode.use_inf_as_na = True
.
In [1]: df = pd.DataFrame(np.random.randn(5, 3), index=['a', 'c', 'e', 'f', 'h'],
...: columns=['one', 'two', 'three'])
...:
In [2]: df['four'] = 'bar'
In [3]: df['five'] = df['one'] > 0
In [4]: df
Out[4]:
one two three four five
a 0.469112 -0.282863 -1.509059 bar True
c -1.135632 1.212112 -0.173215 bar False
e 0.119209 -1.044236 -0.861849 bar True
f -2.104569 -0.494929 1.071804 bar False
h 0.721555 -0.706771 -1.039575 bar True
In [5]: df2 = df.reindex(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])
In [6]: df2
Out[6]:
one two three four five
a 0.469112 -0.282863 -1.509059 bar True
b NaN NaN NaN NaN NaN
c -1.135632 1.212112 -0.173215 bar False
d NaN NaN NaN NaN NaN
e 0.119209 -1.044236 -0.861849 bar True
f -2.104569 -0.494929 1.071804 bar False
g NaN NaN NaN NaN NaN
h 0.721555 -0.706771 -1.039575 bar True
To make detecting missing values easier (and across different array dtypes),
pandas provides the isna()
and
notna()
functions, which are also methods on
Series and DataFrame objects:
In [7]: df2['one']
Out[7]:
a 0.469112
b NaN
c -1.135632
d NaN
e 0.119209
f -2.104569
g NaN
h 0.721555
Name: one, dtype: float64
In [8]: pd.isna(df2['one'])