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.. currentmodule:: pandas

Working with missing data

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

.. ipython:: python
   :suppress:

   import numpy as np; randn = np.random.randn; randint =np.random.randint
   from pandas import *
   import matplotlib.pyplot as plt

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, :mod:`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.

Missing data basics

When / why does data become missing?

Some might quibble over our usage of missing. By "missing" we simply mean null 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

.. ipython:: python

   df = DataFrame(randn(5, 3), index=['a', 'c', 'e', 'f', 'h'],
                  columns=['one', 'two', 'three'])
   df['four'] = 'bar'
   df['five'] = df['one'] > 0
   df
   df2 = df.reindex(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])
   df2

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 "null".

Until recently, for legacy reasons inf and -inf were also considered to be "null" in computations. This is no longer the case by default; use the mode.use_inf_as_null option to recover it.

To make detecting missing values easier (and across different array dtypes), pandas provides the :func:`~pandas.core.common.isnull` and :func:`~pandas.core.common.notnull` functions, which are also methods on Series objects:

.. ipython:: python

   df2['one']
   isnull(df2['one'])
   df2['four'].notnull()

Summary: NaN and None (in object arrays) are considered missing by the isnull and notnull functions. inf and -inf are no longer considered missing by default.

Datetimes

For datetime64[ns] types, NaT represents missing values. This is a pseudo-native sentinal value that can be represented by numpy in a singular dtype (datetime64[ns]). Pandas objects provide intercompatibility between NaT and NaN.

.. ipython:: python

   df2 = df.copy()
   df2['timestamp'] = Timestamp('20120101')
   df2
   df2.ix[['a','c','h'],['one','timestamp']] = np.nan
   df2
   df2.get_dtype_counts()

Calculations with missing data

Missing values propagate naturally through arithmetic operations between pandas objects.

.. ipython:: python
   :suppress:

   df = df2.ix[:, ['one', 'two', 'three']]
   a = df2.ix[:5, ['one', 'two']].fillna(method='pad')
   b = df2.ix[:5, ['one', 'two', 'three']]


.. ipython:: python

   a
   b
   a + b

The descriptive statistics and computational methods discussed in the :ref:`data structure overview <basics.stats>` (and listed :ref:`here <api.series.stats>` and :ref:`here <api.dataframe.stats>`) are all written to account for missing data. For example:

  • When summing data, NA (missing) values will be treated as zero
  • If the data are all NA, the result will be NA
  • Methods like cumsum and cumprod ignore NA values, but preserve them in the resulting arrays
.. ipython:: python

   df
   df['one'].sum()
   df.mean(1)
   df.cumsum()

NA values in GroupBy

NA groups in GroupBy are automatically excluded. This behavior is consistent with R, for example.

Cleaning / filling missing data

pandas objects are equipped with various data manipulation methods for dealing with missing data.

Filling missing values: fillna

The fillna function can "fill in" NA values with non-null data in a couple of ways, which we illustrate:

Replace NA with a scalar value

.. ipython:: python

   df2
   df2.fillna(0)
   df2['four'].fillna('missing')

Fill gaps forward or backward

Using the same filling arguments as :ref:`reindexing <basics.reindexing>`, we can propagate non-null values forward or backward:

.. ipython:: python

   df
   df.fillna(method='pad')

Limit the amount of filling

If we only want consecutive gaps filled up to a certain number of data points, we can use the limit keyword:

.. ipython:: python
   :suppress:

   df.ix[2:4, :] = np.nan


.. ipython:: python

   df
   df.fillna(method='pad', limit=1)

To remind you, these are the available filling methods:

Method Action
pad / ffill Fill values forward
bfill / backfill Fill values backward

With time series data, using pad/ffill is extremely common so that the "last known value" is available at every time point.

Dropping axis labels with missing data: dropna

You may wish to simply exclude labels from a data set which refer to missing data. To do this, use the dropna method:

.. ipython:: python
   :suppress:

   df['two'] = df['two'].fillna(0)
   df['three'] = df['three'].fillna(0)


.. ipython:: python

   df
   df.dropna(axis=0)
   df.dropna(axis=1)
   df['one'].dropna()

dropna is presently only implemented for Series and DataFrame, but will be eventually added to Panel. Series.dropna is a simpler method as it only has one axis to consider. DataFrame.dropna has considerably more options, which can be examined :ref:`in the API <api.dataframe.missing>`.

Interpolation

A linear interpolate method has been implemented on Series. The default interpolation assumes equally spaced points.

.. ipython:: python
   :suppress:

   np.random.seed(123456)
   idx = date_range('1/1/2000', periods=100, freq='BM')
   ts = Series(randn(100), index=idx)
   ts[1:20] = np.nan
   ts[60:80] = np.nan
   ts = ts.cumsum()


.. ipython:: python

   ts.count()

   ts.head()

   ts.interpolate().count()

   ts.interpolate().head()

   @savefig series_interpolate.png width=6in
   ts.interpolate().plot()

Index aware interpolation is available via the method keyword:

.. ipython:: python
   :suppress:

   ts = ts[[0, 1, 30, 60, 99]]


.. ipython:: python

   ts

   ts.interpolate()

   ts.interpolate(method='time')

For a floating-point index, use method='values':

.. ipython:: python
   :suppress:

   idx = [0., 1., 10.]
   ser = Series([0., np.nan, 10.], idx)


.. ipython:: python

   ser

   ser.interpolate()

   ser.interpolate(method='values')

Replacing Generic Values

Often times we want to replace arbitrary values with other values. New in v0.8 is the replace method in Series/DataFrame that provides an efficient yet flexible way to perform such replacements.

For a Series, you can replace a single value or a list of values by another value:

.. ipython:: python

   ser = Series([0., 1., 2., 3., 4.])

   ser.replace(0, 5)

You can replace a list of values by a list of other values:

.. ipython:: python

   ser.replace([0, 1, 2, 3, 4], [4, 3, 2, 1, 0])

You can also specify a mapping dict:

.. ipython:: python

   ser.replace({0: 10, 1: 100})

For a DataFrame, you can specify individual values by column:

.. ipython:: python

   df = DataFrame({'a': [0, 1, 2, 3, 4], 'b': [5, 6, 7, 8, 9]})

   df.replace({'a': 0, 'b': 5}, 100)

Instead of replacing with specified values, you can treat all given values as missing and interpolate over them:

.. ipython:: python

   ser.replace([1, 2, 3], method='pad')

Missing data casting rules and indexing

While pandas supports storing arrays of integer and boolean type, these types are not capable of storing missing data. Until we can switch to using a native NA type in NumPy, we've established some "casting rules" when reindexing will cause missing data to be introduced into, say, a Series or DataFrame. Here they are:

data type Cast to
integer float
boolean object
float no cast
object no cast

For example:

.. ipython:: python

   s = Series(randn(5), index=[0, 2, 4, 6, 7])
   s > 0
   (s > 0).dtype
   crit = (s > 0).reindex(range(8))
   crit
   crit.dtype

Ordinarily NumPy will complain if you try to use an object array (even if it contains boolean values) instead of a boolean array to get or set values from an ndarray (e.g. selecting values based on some criteria). If a boolean vector contains NAs, an exception will be generated:

.. ipython:: python
   :okexcept:

   reindexed = s.reindex(range(8)).fillna(0)
   reindexed[crit]

However, these can be filled in using fillna and it will work fine:

.. ipython:: python

   reindexed[crit.fillna(False)]
   reindexed[crit.fillna(True)]