missing_data.rst

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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<cookbook.missing_data> 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.

python

df = pd.DataFrame(np.random.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

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:

python

df2['one'] pd.isna(df2['one']) df2['four'].notna() df2.isna()

Warning

One has to be mindful that in Python (and NumPy), the nan's don't compare equal, but None's do. Note that pandas/NumPy uses the fact that np.nan != np.nan, and treats None like np.nan.

python

None == None # noqa: E711 np.nan == np.nan

So as compared to above, a scalar equality comparison versus a None/np.nan doesn't provide useful information.

python

df2['one'] == np.nan

Integer Dtypes and Missing Data

Because NaN is a float, a column of integers with even one missing values is cast to floating-point dtype (see gotchas.intna for more). Pandas provides a nullable integer array, which can be used by explicitly requesting the dtype:

python

pd.Series([1, 2, np.nan, 4], dtype=pd.Int64Dtype())

Alternatively, the string alias dtype='Int64' (note the capital "I") can be used.

See integer_na for more.

Datetimes

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

python

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

Inserting missing data

You can insert missing values by simply assigning to containers. The actual missing value used will be chosen based on the dtype.

For example, numeric containers will always use NaN regardless of the missing value type chosen:

python

s = pd.Series([1, 2, 3]) s.loc[0] = None s

Likewise, datetime containers will always use NaT.

For object containers, pandas will use the value given:

python

s = pd.Series(["a", "b", "c"]) s.loc[0] = None s.loc[1] = np.nan s

Calculations with missing data

Missing values propagate naturally through arithmetic operations between pandas objects.

python

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

python

a b a + b

The descriptive statistics and computational methods discussed in the data structure overview <basics.stats> (and listed here <api.series.stats> and 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 0.
• Cumulative methods like ~DataFrame.cumsum and ~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.

python

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

Sum/Prod of Empties/Nans

Warning

This behavior is now standard as of v0.22.0 and is consistent with the default in numpy; previously sum/prod of all-NA or empty Series/DataFrames would return NaN. See v0.22.0 whatsnew <whatsnew_0220> for more.

The sum of an empty or all-NA Series or column of a DataFrame is 0.

python

pd.Series([np.nan]).sum()

pd.Series([]).sum()

The product of an empty or all-NA Series or column of a DataFrame is 1.

python

pd.Series([np.nan]).prod()

pd.Series([]).prod()

NA values in GroupBy

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

python

df df.groupby('one').mean()

See the groupby section here <groupby.missing> for more information.

Cleaning / filling missing data

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

Filling missing values: fillna

~DataFrame.fillna can "fill in" NA values with non-NA data in a couple of ways, which we illustrate:

Replace NA with a scalar value

python

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

Fill gaps forward or backward

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

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:

python

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

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.

~DataFrame.ffill is equivalent to fillna(method='ffill') and ~DataFrame.bfill is equivalent to fillna(method='bfill')

Filling with a PandasObject

You can also fillna using a dict or Series that is alignable. The labels of the dict or index of the Series must match the columns of the frame you wish to fill. The use case of this is to fill a DataFrame with the mean of that column.

python

dff = pd.DataFrame(np.random.randn(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()) dff.fillna(dff.mean()['B':'C'])

Same result as above, but is aligning the 'fill' value which is a Series in this case.

python

dff.where(pd.notna(dff), dff.mean(), axis='columns')

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 ~DataFrame.dropna:

python

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

python

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

An equivalent ~Series.dropna is available for Series. DataFrame.dropna has considerably more options than Series.dropna, which can be examined in the API <api.dataframe.missing>.

Interpolation

0.21.0

The limit_area keyword argument was added.

Both Series and DataFrame objects have ~DataFrame.interpolate that, by default, performs linear interpolation at missing data points.

python

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

python

ts ts.count() ts.interpolate().count()

@savefig series_interpolate.png ts.interpolate().plot()

Index aware interpolation is available via the method keyword:

python

ts2 = ts[[0, 1, 30, 60, 99]]

python

ts2 ts2.interpolate() ts2.interpolate(method='time')

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

python

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

python

ser ser.interpolate() ser.interpolate(method='values')

You can also interpolate with a DataFrame:

python

df = pd.DataFrame({'A': [1, 2.1, np.nan, 4.7, 5.6, 6.8],

'B': [.25, np.nan, np.nan, 4, 12.2, 14.4]})

df df.interpolate()

The method argument gives access to fancier interpolation methods. If you have scipy installed, you can pass the name of a 1-d interpolation routine to method. You'll want to consult the full scipy interpolation documentation and reference guide for details. The appropriate interpolation method will depend on the type of data you are working with.

• If you are dealing with a time series that is growing at an increasing rate, method='quadratic' may be appropriate.
• If you have values approximating a cumulative distribution function, then method='pchip' should work well.
• To fill missing values with goal of smooth plotting, consider method='akima'.

Warning

These methods require scipy.

python

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:

python

df.interpolate(method='spline', order=2)

df.interpolate(method='polynomial', order=2)

Compare several methods:

python

np.random.seed(2)

ser = pd.Series(np.arange(1, 10.1, .25)**2 + np.random.randn(37)) bad = np.array([4, 13, 14, 15, 16, 17, 18, 20, 29]) ser[bad] = np.nan methods = ['linear', 'quadratic', 'cubic']

df = pd.DataFrame({m: ser.interpolate(method=m) for m in methods}) @savefig compare_interpolations.png df.plot()

Another use case is interpolation at new values. Suppose you have 100 observations from some distribution. And let's suppose that you're particularly interested in what's happening around the middle. You can mix pandas' reindex and interpolate methods to interpolate at the new values.

python

ser = pd.Series(np.sort(np.random.uniform(size=100)))

# interpolate at new_index new_index = ser.index | 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[49:51]

Interpolation Limits

Like other pandas fill methods, ~DataFrame.interpolate accepts a limit keyword argument. Use this argument to limit the number of consecutive NaN values filled since the last valid observation:

python

ser = pd.Series([np.nan, np.nan, 5, np.nan, np.nan,

np.nan, 13, np.nan, np.nan])

# fill all consecutive values in a forward direction ser.interpolate()

# fill one consecutive value in a forward direction 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.

python

# fill one consecutive value backwards ser.interpolate(limit=1, limit_direction='backward')

# fill one consecutive value in both directions ser.interpolate(limit=1, limit_direction='both')

# fill all consecutive values in both directions ser.interpolate(limit_direction='both')

By default, NaN values are filled whether they are inside (surrounded by) existing valid values, or outside existing valid values. Introduced in v0.23 the limit_area parameter restricts filling to either inside or outside values.

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')

Replacing Generic Values

Often times we want to replace arbitrary values with other values.

~Series.replace in Series and ~DataFrame.replace in DataFrame 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:

python

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

ser.replace(0, 5)

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

python

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

You can also specify a mapping dict:

python

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

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

python

df = pd.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:

python

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

String/Regular Expression Replacement

Note

Python strings prefixed with the r character such as r'hello world' are so-called "raw" strings. 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'\' == '\\'. You should read about them if this is unclear.

Replace the '.' with NaN (str -> str):

python

d = {'a': list(range(4)), 'b': list('ab..'), 'c': ['a', 'b', np.nan, 'd']} df = pd.DataFrame(d) df.replace('.', np.nan)

Now do it with a regular expression that removes surrounding whitespace (regex -> regex):

python

df.replace(r's.s', np.nan, regex=True)

Replace a few different values (list -> list):

python

df.replace(['a', '.'], ['b', np.nan])

list of regex -> list of regex:

python

df.replace([r'.', r'(a)'], ['dot', '1stuff'], regex=True)

Only search in column 'b' (dict -> dict):

python

df.replace({'b': '.'}, {'b': np.nan})

Same as the previous example, but use a regular expression for searching instead (dict of regex -> dict):

python

df.replace({'b': r's.s'}, {'b': np.nan}, regex=True)

You can pass nested dictionaries of regular expressions that use regex=True:

python

df.replace({'b': {'b': r''}}, regex=True)

Alternatively, you can pass the nested dictionary like so:

python

df.replace(regex={'b': {r's.s': np.nan}})

You can also use the group of a regular expression match when replacing (dict of regex -> dict of regex), this works for lists as well.

python

df.replace({'b': r's(.)s'}, {'b': r'1ty'}, regex=True)

You can pass a list of regular expressions, of which those that match will be replaced with a scalar (list of regex -> regex).

python

df.replace([r's.s', r'a|b'], np.nan, 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. The previous example, in this case, would then be:

python

df.replace(regex=[r's.s', r'a|b'], value=np.nan)

This can be convenient if you do not want to pass regex=True every time you want to use a regular expression.

Note

Anywhere in the above replace examples that you see a regular expression a compiled regular expression is valid as well.

Numeric Replacement

~DataFrame.replace is similar to ~DataFrame.fillna.

python

df = pd.DataFrame(np.random.randn(10, 2)) df[np.random.rand(df.shape[0]) > 0.5] = 1.5 df.replace(1.5, np.nan)

Replacing more than one value is possible by passing a list.

python

df00 = df.iloc[0, 0] df.replace([1.5, df00], [np.nan, 'a']) df[1].dtype

You can also operate on the DataFrame in place:

python

df.replace(1.5, np.nan, inplace=True)

Warning

When replacing multiple bool or datetime64 objects, the first argument to replace (to_replace) must match the type of the value being replaced. For example,

>>> s = pd.Series([True, False, True])
>>> s.replace({'a string': 'new value', True: False})  # raises
TypeError: Cannot compare types 'ndarray(dtype=bool)' and 'str'

will raise a TypeError because one of the dict keys is not of the correct type for replacement.

However, when replacing a single object such as,

python

s = pd.Series([True, False, True]) s.replace('a string', 'another string')

the original NDFrame object will be returned untouched. We're working on unifying this API, but for backwards compatibility reasons we cannot break the latter behavior. See 6354 for more details.

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 a reindexing operation introduces missing data, the Series will be cast according to the rules introduced in the table below.

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

For example:

python

s = pd.Series(np.random.randn(5), index=[0, 2, 4, 6, 7]) s > 0 (s > 0).dtype crit = (s > 0).reindex(list(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:

python

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

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

python

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

Pandas provides a nullable integer dtype, but you must explicitly request it when creating the series or column. Notice that we use a capital "I" in the dtype="Int64".

python

s = pd.Series([0, 1, np.nan, 3, 4], dtype="Int64") s

See integer_na for more.