.. _missing_data:
{{ header }}
*************************
Working with missing data
*************************
Values considered "missing"
~~~~~~~~~~~~~~~~~~~~~~~~~~~
pandas uses different sentinel values to represent a missing (also referred to as NA)
depending on the data type.
``numpy.nan`` for NumPy data types. The disadvantage of using NumPy data types
is that the original data type will be coerced to ``np.float64`` or ``object``.
.. ipython:: python
pd.Series([1, 2], dtype=np.int64).reindex([0, 1, 2])
pd.Series([True, False], dtype=np.bool_).reindex([0, 1, 2])
:class:`NaT` for NumPy ``np.datetime64``, ``np.timedelta64``, and :class:`PeriodDtype`. For typing applications,
use :class:`api.types.NaTType`.
.. ipython:: python
pd.Series([1, 2], dtype=np.dtype("timedelta64[ns]")).reindex([0, 1, 2])
pd.Series([1, 2], dtype=np.dtype("datetime64[ns]")).reindex([0, 1, 2])
pd.Series(["2020", "2020"], dtype=pd.PeriodDtype("D")).reindex([0, 1, 2])
:class:`NA` for :class:`StringDtype`, :class:`Int64Dtype` (and other bit widths),
:class:`Float64Dtype`(and other bit widths), :class:`BooleanDtype` and :class:`ArrowDtype`.
These types will maintain the original data type of the data.
For typing applications, use :class:`api.types.NAType`.
.. ipython:: python
pd.Series([1, 2], dtype="Int64").reindex([0, 1, 2])
pd.Series([True, False], dtype="boolean[pyarrow]").reindex([0, 1, 2])
To detect these missing value, use the :func:`isna` or :func:`notna` methods.
.. ipython:: python
ser = pd.Series([pd.Timestamp("2020-01-01"), pd.NaT])
ser
pd.isna(ser)
.. note::
:func:`isna` or :func:`notna` will also consider ``None`` a missing value.
.. ipython:: python
ser = pd.Series([1, None], dtype=object)
ser
pd.isna(ser)
.. warning::
Equality compaisons between ``np.nan``, :class:`NaT`, and :class:`NA`
do not act like ``None``
.. ipython:: python
None == None # noqa: E711
np.nan == np.nan
pd.NaT == pd.NaT
pd.NA == pd.NA
Therefore, an equality comparison between a :class:`DataFrame` or :class:`Series`
with one of these missing values does not provide the same information as
:func:`isna` or :func:`notna`.
.. ipython:: python
ser = pd.Series([True, None], dtype="boolean[pyarrow]")
ser == pd.NA
pd.isna(ser)
.. _missing_data.NA:
:class:`NA` semantics
~~~~~~~~~~~~~~~~~~~~~
.. warning::
Experimental: the behaviour of :class:`NA`` can still change without warning.
Starting from pandas 1.0, an experimental :class:`NA` value (singleton) is
available to represent scalar missing values. The goal of :class:`NA` is provide a
"missing" indicator that can be used consistently across data types
(instead of ``np.nan``, ``None`` or ``pd.NaT`` depending on the data type).
For example, when having missing values in a :class:`Series` with the nullable integer
dtype, it will use :class:`NA`:
.. ipython:: python
s = pd.Series([1, 2, None], dtype="Int64")
s
s[2]
s[2] is pd.NA
Currently, pandas does not yet use those data types using :class:`NA` by default
a :class:`DataFrame` or :class:`Series`, so you need to specify
the dtype explicitly. An easy way to convert to those dtypes is explained in the
:ref:`conversion section <missing_data.NA.conversion>`.
Propagation in arithmetic and comparison operations
---------------------------------------------------
In general, missing values *propagate* in operations involving :class:`NA`. When
one of the operands is unknown, the outcome of the operation is also unknown.
For example, :class:`NA` propagates in arithmetic operations, similarly to
``np.nan``:
.. ipython:: python
pd.NA + 1
"a" * pd.NA
There are a few special cases when the result is known, even when one of the
operands is ``NA``.
.. ipython:: python
pd.NA ** 0
1 ** pd.NA
In equality and comparison operations, :class:`NA` also propagates. This deviates
from the behaviour of ``np.nan``, where comparisons with ``np.nan`` always
return ``False``.
.. ipython:: python
pd.NA == 1
pd.NA == pd.NA
pd.NA < 2.5
To check if a value is equal to :class:`NA`, use :func:`isna`
.. ipython:: python
pd.isna(pd.NA)
.. note::
An exception on this basic propagation rule are *reductions* (such as the
mean or the minimum), where pandas defaults to skipping missing values. See the
:ref:`calculation section <missing_data.calculations>` for more.
Logical operations
------------------
For logical operations, :class:`NA` follows the rules of the
`three-valued logic <https://en.wikipedia.org/wiki/Three-valued_logic>`__ (or
*Kleene logic*, similarly to R, SQL and Julia). This logic means to only
propagate missing values when it is logically required.
For example, for the logical "or" operation (``|``), if one of the operands
is ``True``, we already know the result will be ``True``, regardless of the
other value (so regardless the missing value would be ``True`` or ``False``).
In this case, :class:`NA` does not propagate:
.. ipython:: python
True | False
True | pd.NA
pd.NA | True
On the other hand, if one of the operands is ``False``, the result depends
on the value of the other operand. Therefore, in this case :class:`NA`
propagates:
.. ipython:: python
False | True
False | False
False | pd.NA
The behaviour of the logical "and" operation (``&``) can be derived using
similar logic (where now :class:`NA` will not propagate if one of the operands
is already ``False``):
.. ipython:: python
False & True
False & False
False & pd.NA
.. ipython:: python
True & True
True & False
True & pd.NA
``NA`` in a boolean context
---------------------------
Since the actual value of an NA is unknown, it is ambiguous to convert NA
to a boolean value.
.. ipython:: python
:okexcept:
bool(pd.NA)
This also means that :class:`NA` cannot be used in a context where it is
evaluated to a boolean, such as ``if condition: ...`` where ``condition`` can
potentially be :class:`NA`. In such cases, :func:`isna` can be used to check
for :class:`NA` or ``condition`` being :class:`NA` can be avoided, for example by
filling missing values beforehand.
A similar situation occurs when using :class:`Series` or :class:`DataFrame` objects in ``if``
statements, see :ref:`gotchas.truth`.
NumPy ufuncs
------------
:attr:`pandas.NA` implements NumPy's ``__array_ufunc__`` protocol. Most ufuncs
work with ``NA``, and generally return ``NA``:
.. ipython:: python
np.log(pd.NA)
np.add(pd.NA, 1)
.. warning::
Currently, ufuncs involving an ndarray and ``NA`` will return an
object-dtype filled with NA values.
.. ipython:: python
a = np.array([1, 2, 3])
np.greater(a, pd.NA)
The return type here may change to return a different array type
in the future.
See :ref:`dsintro.numpy_interop` for more on ufuncs.
.. _missing_data.NA.conversion:
Conversion
^^^^^^^^^^
If you have a :class:`DataFrame` or :class:`Series` using ``np.nan``,
:meth:`Series.convert_dtypes` and :meth:`DataFrame.convert_dtypes`
in :class:`DataFrame` that can convert data to use the data types that use :class:`NA`
such as :class:`Int64Dtype` or :class:`ArrowDtype`. This is especially helpful after reading
in data sets from IO methods where data types were inferred.
In this example, while the dtypes of all columns are changed, we show the results for
the first 10 columns.
.. ipython:: python
import io
data = io.StringIO("a,b\n,True\n2,")
df = pd.read_csv(data)
df.dtypes
df_conv = df.convert_dtypes()
df_conv
df_conv.dtypes
.. _missing.inserting:
Inserting missing data
~~~~~~~~~~~~~~~~~~~~~~
You can insert missing values by simply assigning to a :class:`Series` or :class:`DataFrame`.
The missing value sentinel used will be chosen based on the dtype.
.. ipython:: python
ser = pd.Series([1., 2., 3.])
ser.loc[0] = None
ser
ser = pd.Series([pd.Timestamp("2021"), pd.Timestamp("2021")])
ser.iloc[0] = np.nan
ser
ser = pd.Series([True, False], dtype="boolean[pyarrow]")
ser.iloc[0] = None
ser
For ``object`` types, pandas will use the value given:
.. ipython:: python
s = pd.Series(["a", "b", "c"], dtype=object)
s.loc[0] = None
s.loc[1] = np.nan
s
.. _missing_data.calculations:
Calculations with missing data
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Missing values propagate through arithmetic operations between pandas objects.
.. ipython:: python
ser1 = pd.Series([np.nan, np.nan, 2, 3])
ser2 = pd.Series([np.nan, 1, np.nan, 4])
ser1
ser2
ser1 + ser2
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
account for missing data.
When summing data, NA values or empty data will be treated as zero.
.. ipython:: python
pd.Series([np.nan]).sum()
pd.Series([], dtype="float64").sum()
When taking the product, NA values or empty data will be treated as 1.
.. ipython:: python
pd.Series([np.nan]).prod()
pd.Series([], dtype="float64").prod()
Cumulative methods like :meth:`~DataFrame.cumsum` and :meth:`~DataFrame.cumprod`
ignore NA values by default preserve them in the result. This behavior can be changed
with ``skipna``
* Cumulative methods like :meth:`~DataFrame.cumsum` and :meth:`~DataFrame.cumprod` ignore NA values by default, but preserve them in the resulting arrays. To override this behaviour and include NA values, use ``skipna=False``.
.. ipython:: python
ser = pd.Series([1, np.nan, 3, np.nan])
ser
ser.cumsum()
ser.cumsum(skipna=False)
.. _missing_data.dropna:
Dropping missing data
~~~~~~~~~~~~~~~~~~~~~
:meth:`~DataFrame.dropna` dropa rows or columns with missing data.
.. ipython:: python
df = pd.DataFrame([[np.nan, 1, 2], [1, 2, np.nan], [1, 2, 3]])
df
df.dropna()
df.dropna(axis=1)
ser = pd.Series([1, pd.NA], dtype="int64[pyarrow]")
ser.dropna()
Filling missing data
~~~~~~~~~~~~~~~~~~~~
.. _missing_data.fillna:
Filling by value
----------------
:meth:`~DataFrame.fillna` replaces NA values with non-NA data.
Replace NA with a scalar value
.. ipython:: python
data = {"np": [1.0, np.nan, np.nan, 2], "arrow": pd.array([1.0, pd.NA, pd.NA, 2], dtype="float64[pyarrow]")}
df = pd.DataFrame(data)
df
df.fillna(0)
Fill gaps forward or backward
.. ipython:: python
df.ffill()
df.bfill()
.. _missing_data.fillna.limit:
Limit the number of NA values filled
.. ipython:: python
df.ffill(limit=1)
NA values can be replaced with corresponding value from a :class:`Series` or :class:`DataFrame`
where the index and column aligns between the original object and the filled object.
.. ipython:: python
dff = pd.DataFrame(np.arange(30, dtype=np.float64).reshape(10, 3), columns=list("ABC"))
dff.iloc[3:5, 0] = np.nan
dff.iloc[4:6, 1] = np.nan
dff.iloc[5:8, 2] = np.nan
dff
dff.fillna(dff.mean())
.. note::
:meth:`DataFrame.where` can also be used to fill NA values.Same result as above.
.. ipython:: python
dff.where(pd.notna(dff), dff.mean(), axis="columns")
.. _missing_data.interpolate:
Interpolation
-------------
:meth:`DataFrame.interpolate` and :meth:`Series.interpolate` fills NA values
using various interpolation methods.
.. ipython:: python
df = pd.DataFrame(
{
"A": [1, 2.1, np.nan, 4.7, 5.6, 6.8],
"B": [0.25, np.nan, np.nan, 4, 12.2, 14.4],
}
)
df
df.interpolate()
idx = pd.date_range("2020-01-01", periods=10, freq="D")
data = np.random.default_rng(2).integers(0, 10, 10).astype(np.float64)
ts = pd.Series(data, index=idx)
ts.iloc[[1, 2, 5, 6, 9]] = np.nan
ts
@savefig series_before_interpolate.png
ts.plot()
.. ipython:: python
ts.interpolate()
@savefig series_interpolate.png
ts.interpolate().plot()
Interpolation relative to a :class:`Timestamp` in the :class:`DatetimeIndex`
is available by setting ``method="time"``
.. ipython:: python
ts2 = ts.iloc[[0, 1, 3, 7, 9]]
ts2
ts2.interpolate()
ts2.interpolate(method="time")
For a floating-point index, use ``method='values'``:
.. ipython:: python
idx = [0.0, 1.0, 10.0]
ser = pd.Series([0.0, np.nan, 10.0], idx)
ser
ser.interpolate()
ser.interpolate(method="values")
If you have scipy_ installed, you can pass the name of a 1-d interpolation routine to ``method``.
as specified in the scipy interpolation documentation_ and reference guide_.
The appropriate interpolation method will depend on the data type.
.. tip::
If you are dealing with a time series that is growing at an increasing rate,
use ``method='barycentric'``.
If you have values approximating a cumulative distribution function,
use ``method='pchip'``.
To fill missing values with goal of smooth plotting use ``method='akima'``.
.. ipython:: python
df = pd.DataFrame(
{
"A": [1, 2.1, np.nan, 4.7, 5.6, 6.8],
"B": [0.25, np.nan, np.nan, 4, 12.2, 14.4],
}
)
df
df.interpolate(method="barycentric")
df.interpolate(method="pchip")
df.interpolate(method="akima")
When interpolating via a polynomial or spline approximation, you must also specify
the degree or order of the approximation:
.. ipython:: python
df.interpolate(method="spline", order=2)
df.interpolate(method="polynomial", order=2)
Comparing several methods.
.. ipython:: python
np.random.seed(2)
ser = pd.Series(np.arange(1, 10.1, 0.25) ** 2 + np.random.randn(37))
missing = np.array([4, 13, 14, 15, 16, 17, 18, 20, 29])
ser.iloc[missing] = np.nan
methods = ["linear", "quadratic", "cubic"]
df = pd.DataFrame({m: ser.interpolate(method=m) for m in methods})
@savefig compare_interpolations.png
df.plot()
Interpolating new observations from expanding data with :meth:`Series.reindex`.
.. ipython:: python
ser = pd.Series(np.sort(np.random.uniform(size=100)))
# interpolate at new_index
new_index = ser.index.union(pd.Index([49.25, 49.5, 49.75, 50.25, 50.5, 50.75]))
interp_s = ser.reindex(new_index).interpolate(method="pchip")
interp_s.loc[49:51]
.. _scipy: https://scipy.org/
.. _documentation: https://docs.scipy.org/doc/scipy/reference/interpolate.html#univariate-interpolation
.. _guide: https://docs.scipy.org/doc/scipy/tutorial/interpolate.html
.. _missing_data.interp_limits:
Interpolation limits
^^^^^^^^^^^^^^^^^^^^
:meth:`~DataFrame.interpolate` accepts a ``limit`` keyword
argument to limit the number of consecutive ``NaN`` values
filled since the last valid observation
.. ipython:: python
ser = pd.Series([np.nan, np.nan, 5, np.nan, np.nan, np.nan, 13, np.nan, np.nan])
ser
ser.interpolate()
ser.interpolate(limit=1)
By default, ``NaN`` values are filled in a ``forward`` direction. Use
``limit_direction`` parameter to fill ``backward`` or from ``both`` directions.
.. ipython:: python
ser.interpolate(limit=1, limit_direction="backward")
ser.interpolate(limit=1, limit_direction="both")
ser.interpolate(limit_direction="both")
By default, ``NaN`` values are filled whether they are surrounded by
existing valid values or outside existing valid values. The ``limit_area``
parameter restricts filling to either inside or outside values.
.. ipython:: python
# fill one consecutive inside value in both directions
ser.interpolate(limit_direction="both", limit_area="inside", limit=1)
# fill all consecutive outside values backward
ser.interpolate(limit_direction="backward", limit_area="outside")
# fill all consecutive outside values in both directions
ser.interpolate(limit_direction="both", limit_area="outside")
.. _missing_data.replace:
Replacing values
----------------
:meth:`Series.replace` and :meth:`DataFrame.replace` can be used similar to
:meth:`Series.fillna` and :meth:`DataFrame.fillna` to replace or insert missing values.
.. ipython:: python
df = pd.DataFrame(np.eye(3))
df
df_missing = df.replace(0, np.nan)
df_missing
df_filled = df_missing.replace(np.nan, 2)
df_filled
Replacing more than one value is possible by passing a list.
.. ipython:: python
df_filled.replace([1, 44], [2, 28])
Replacing using a mapping dict.
.. ipython:: python
df_filled.replace({1: 44, 2: 28})
.. _missing_data.replace_expression:
Regular expression replacement
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. note::
Python strings prefixed with the ``r`` character such as ``r'hello world'``
are `"raw" strings <https://docs.python.org/3/reference/lexical_analysis.html#string-and-bytes-literals>`_.
They have different semantics regarding backslashes than strings without this prefix.
Backslashes in raw strings will be interpreted as an escaped backslash, e.g., ``r'\' == '\\'``.
Replace the '.' with ``NaN``
.. ipython:: python
d = {"a": list(range(4)), "b": list("ab.."), "c": ["a", "b", np.nan, "d"]}
df = pd.DataFrame(d)
df.replace(".", np.nan)
Replace the '.' with ``NaN`` with regular expression that removes surrounding whitespace
.. ipython:: python
df.replace(r"\s*\.\s*", np.nan, regex=True)
Replace with a list of regexes.
.. ipython:: python
df.replace([r"\.", r"(a)"], ["dot", r"\1stuff"], regex=True)
Replace with a regex in a mapping dict.
.. ipython:: python
df.replace({"b": r"\s*\.\s*"}, {"b": np.nan}, regex=True)
Pass nested dictionaries of regular expressions that use the ``regex`` keyword.
.. ipython:: python
df.replace({"b": {"b": r""}}, regex=True)
df.replace(regex={"b": {r"\s*\.\s*": np.nan}})
df.replace({"b": r"\s*(\.)\s*"}, {"b": r"\1ty"}, regex=True)
Pass a list of regular expressions that will replace matches with a scalar.
.. ipython:: python
df.replace([r"\s*\.\s*", r"a|b"], "placeholder", regex=True)
All of the regular expression examples can also be passed with the
``to_replace`` argument as the ``regex`` argument. In this case the ``value``
argument must be passed explicitly by name or ``regex`` must be a nested
dictionary.
.. ipython:: python
df.replace(regex=[r"\s*\.\s*", r"a|b"], value="placeholder")
.. note::
A regular expression object from ``re.compile`` is a valid input as well.