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# 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`.

    In [1]: pd.Series([1, 2], dtype=np.int64).reindex([0, 1, 2])
    Out[1]:
    0    1.0
    1    2.0
    2    NaN
    dtype: float64

    In [2]: pd.Series([True, False], dtype=np.bool_).reindex([0, 1, 2])
    Out[2]:
    0     True
    1    False
    2      NaN
    dtype: object

`NaT` for NumPy `np.datetime64`, `np.timedelta64`, and `PeriodDtype`. For typing applications, use `api.types.NaTType`.

    In [3]: pd.Series([1, 2], dtype=np.dtype("timedelta64[ns]")).reindex([0, 1, 2])
    Out[3]:
    0   0 days 00:00:00.000000001
    1   0 days 00:00:00.000000002
    2                         NaT
    dtype: timedelta64[ns]

    In [4]: pd.Series([1, 2], dtype=np.dtype("datetime64[ns]")).reindex([0, 1, 2])
    Out[4]:
    0   1970-01-01 00:00:00.000000001
    1   1970-01-01 00:00:00.000000002
    2                             NaT
    dtype: datetime64[ns]

    In [5]: pd.Series(["2020", "2020"], dtype=pd.PeriodDtype("D")).reindex([0, 1, 2])
    Out[5]:
    0    2020-01-01
    1    2020-01-01
    2           NaT
    dtype: period[D]

`NA` for `StringDtype`, `Int64Dtype` (and other bit widths), `Float64Dtype`(and other bit widths), :class:`BooleanDtype` and `ArrowDtype`. These types will maintain the original data type of the data. For typing applications, use `api.types.NAType`.

    In [6]: pd.Series([1, 2], dtype="Int64").reindex([0, 1, 2])
    Out[6]:
    0       1
    1       2
    2    <NA>
    dtype: Int64

    In [7]: pd.Series([True, False], dtype="boolean[pyarrow]").reindex([0, 1, 2])
    Out[7]:
    0     True
    1    False
    2     <NA>
    dtype: bool[pyarrow]

To detect these missing value, use the `isna()` or `notna()` methods.

    In [8]: ser = pd.Series([pd.Timestamp("2020-01-01"), pd.NaT])

    In [9]: ser
    Out[9]:
    0   2020-01-01
    1          NaT
    dtype: datetime64[ns]

    In [10]: pd.isna(ser)
    Out[10]:
    0    False
    1     True
    dtype: bool

Note

`isna()` or `notna()` will also consider `None` a missing value.

    In [11]: ser = pd.Series([1, None], dtype=object)

    In [12]: ser
    Out[12]:
    0       1
    1    None
    dtype: object

    In [13]: pd.isna(ser)
    Out[13]:
    0    False
    1     True
    dtype: bool

Warning

Equality compaisons between `np.nan`, `NaT`, and `NA` do not act like `None`

    In [14]: None == None  # noqa: E711
    Out[14]: True

    In [15]: np.nan == np.nan
    Out[15]: False

    In [16]: pd.NaT == pd.NaT
    Out[16]: False

    In [17]: pd.NA == pd.NA
    Out[17]: <NA>

Therefore, an equality comparison between a `DataFrame` or `Series` with one of these missing values does not provide the same information as `isna()` or `notna()`.

    In [18]: ser = pd.Series([True, None], dtype="boolean[pyarrow]")

    In [19]: ser == pd.NA
    Out[19]:
    0    <NA>
    1    <NA>
    dtype: bool[pyarrow]

    In [20]: pd.isna(ser)
    Out[20]:
    0    False
    1     True
    dtype: bool

## `NA` semantics

Warning

Experimental: the behaviour of `NA`` can still change without warning.

Starting from pandas 1.0, an experimental `NA` value (singleton) is available to represent scalar missing values. The goal of `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 `Series` with the nullable integer dtype, it will use `NA`:

    In [21]: s = pd.Series([1, 2, None], dtype="Int64")

    In [22]: s
    Out[22]:
    0       1
    1       2
    2    <NA>
    dtype: Int64

    In [23]: s[2]
    Out[23]: <NA>

    In [24]: s[2] is pd.NA
    Out[24]: True

Currently, pandas does not yet use those data types using `NA` by default a `DataFrame` or `Series`, so you need to specify the dtype explicitly. An easy way to convert to those dtypes is explained in the conversion section.

### Propagation in arithmetic and comparison operations

In general, missing values propagate in operations involving `NA`. When one of the operands is unknown, the outcome of the operation is also unknown.

For example, `NA` propagates in arithmetic operations, similarly to `np.nan`:

    In [25]: pd.NA + 1
    Out[25]: <NA>

    In [26]: "a" * pd.NA
    Out[26]: <NA>

There are a few special cases when the result is known, even when one of the operands is `NA`.

    In [27]: pd.NA ** 0
    Out[27]: 1

    In [28]: 1 ** pd.NA
    Out[28]: 1

In equality and comparison operations, `NA` also propagates. This deviates from the behaviour of `np.nan`, where comparisons with `np.nan` always return `False`.

    In [29]: pd.NA == 1
    Out[29]: <NA>

    In [30]: pd.NA == pd.NA
    Out[30]: <NA>

    In [31]: pd.NA < 2.5
    Out[31]: <NA>

To check if a value is equal to `NA`, use `isna()`

    In [32]: pd.isna(pd.NA)
    Out[32]: True

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 calculation section for more.

### Logical operations

For logical operations, `NA` follows the rules of the 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, `NA` does not propagate:

    In [33]: True | False
    Out[33]: True

    In [34]: True | pd.NA
    Out[34]: True

    In [35]: pd.NA | True
    Out[35]: 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 `NA` propagates:

    In [36]: False | True
    Out[36]: True

    In [37]: False | False
    Out[37]: False

    In [38]: False | pd.NA
    Out[38]: <NA>

The behaviour of the logical “and” operation (`&`) can be derived using similar logic (where now `NA` will not propagate if one of the operands is already `False`):

    In [39]: False & True
    Out[39]: False

    In [40]: False & False
    Out[40]: False

    In [41]: False & pd.NA
    Out[41]: False

    In [42]: True & True
    Out[42]: True

    In [43]: True & False
    Out[43]: False

    In [44]: True & pd.NA
    Out[44]: <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.

    In [45]: bool(pd.NA)
    ---------------------------------------------------------------------------
    TypeError                                 Traceback (most recent call last)
    Cell In[45], line 1
    ----> 1 bool(pd.NA)

    File ~/work/pandas/pandas/pandas/_libs/missing.pyx:392, in pandas._libs.missing.NAType.__bool__()

    TypeError: boolean value of NA is ambiguous

This also means that `NA` cannot be used in a context where it is evaluated to a boolean, such as `if condition: ...` where `condition` can potentially be `NA`. In such cases, `isna()` can be used to check for `NA` or `condition` being `NA` can be avoided, for example by filling missing values beforehand.

A similar situation occurs when using `Series` or `DataFrame` objects in `if` statements, see Using if/truth statements with pandas.

### NumPy ufuncs

`pandas.NA` implements NumPy’s `__array_ufunc__` protocol. Most ufuncs work with `NA`, and generally return `NA`:

    In [46]: np.log(pd.NA)
    Out[46]: <NA>

    In [47]: np.add(pd.NA, 1)
    Out[47]: <NA>

Warning

Currently, ufuncs involving an ndarray and `NA` will return an object-dtype filled with NA values.

    In [48]: a = np.array([1, 2, 3])

    In [49]: np.greater(a, pd.NA)
    Out[49]: array([<NA>, <NA>, <NA>], dtype=object)

The return type here may change to return a different array type in the future.

See DataFrame interoperability with NumPy functions for more on ufuncs.

#### Conversion

If you have a `DataFrame` or `Series` using `np.nan`, `Series.convert_dtypes()` and `DataFrame.convert_dtypes()` in `DataFrame` that can convert data to use the data types that use `NA` such as `Int64Dtype` or `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.

    In [50]: import io

    In [51]: data = io.StringIO("a,b\n,True\n2,")

    In [52]: df = pd.read_csv(data)

    In [53]: df.dtypes
    Out[53]:
    a    float64
    b     object
    dtype: object

    In [54]: df_conv = df.convert_dtypes()

    In [55]: df_conv
    Out[55]:
          a     b
    0  <NA>  True
    1     2  <NA>

    In [56]: df_conv.dtypes
    Out[56]:
    a      Int64
    b    boolean
    dtype: object

## Inserting missing data

You can insert missing values by simply assigning to a `Series` or `DataFrame`. The missing value sentinel used will be chosen based on the dtype.

    In [57]: ser = pd.Series([1., 2., 3.])

    In [58]: ser.loc[0] = None

    In [59]: ser
    Out[59]:
    0    NaN
    1    2.0
    2    3.0
    dtype: float64

    In [60]: ser = pd.Series([pd.Timestamp("2021"), pd.Timestamp("2021")])

    In [61]: ser.iloc[0] = np.nan

    In [62]: ser
    Out[62]:
    0          NaT
    1   2021-01-01
    dtype: datetime64[ns]

    In [63]: ser = pd.Series([True, False], dtype="boolean[pyarrow]")

    In [64]: ser.iloc[0] = None

    In [65]: ser
    Out[65]:
    0     <NA>
    1    False
    dtype: bool[pyarrow]

For `object` types, pandas will use the value given:

    In [66]: s = pd.Series(["a", "b", "c"], dtype=object)

    In [67]: s.loc[0] = None

    In [68]: s.loc[1] = np.nan

    In [69]: s
    Out[69]:
    0    None
    1     NaN
    2       c
    dtype: object

## Calculations with missing data

Missing values propagate through arithmetic operations between pandas objects.

    In [70]: ser1 = pd.Series([np.nan, np.nan, 2, 3])

    In [71]: ser2 = pd.Series([np.nan, 1, np.nan, 4])

    In [72]: ser1
    Out[72]:
    0    NaN
    1    NaN
    2    2.0
    3    3.0
    dtype: float64

    In [73]: ser2
    Out[73]:
    0    NaN
    1    1.0
    2    NaN
    3    4.0
    dtype: float64

    In [74]: ser1 + ser2
    Out[74]:
    0    NaN
    1    NaN
    2    NaN
    3    7.0
    dtype: float64

The descriptive statistics and computational methods discussed in the data structure overview (and listed here and here) are all account for missing data.

When summing data, NA values or empty data will be treated as zero.

    In [75]: pd.Series([np.nan]).sum()
    Out[75]: 0.0

    In [76]: pd.Series([], dtype="float64").sum()
    Out[76]: 0.0

When taking the product, NA values or empty data will be treated as 1.

    In [77]: pd.Series([np.nan]).prod()
    Out[77]: 1.0

    In [78]: pd.Series([], dtype="float64").prod()
    Out[78]: 1.0

Cumulative methods like `cumsum()` and `cumprod()` ignore NA values by default preserve them in the result. This behavior can be changed with `skipna`

- Cumulative methods like `cumsum()` and `cumprod()` ignore NA values by default, but preserve them in the resulting arrays. To override this behaviour and include NA values, use `skipna=False`.

    In [79]: ser = pd.Series([1, np.nan, 3, np.nan])

    In [80]: ser
    Out[80]:
    0    1.0
    1    NaN
    2    3.0
    3    NaN
    dtype: float64

    In [81]: ser.cumsum()
    Out[81]:
    0    1.0
    1    NaN
    2    4.0
    3    NaN
    dtype: float64

    In [82]: ser.cumsum(skipna=False)
    Out[82]:
    0    1.0
    1    NaN
    2    NaN
    3    NaN
    dtype: float64

## Dropping missing data

`dropna()` dropa rows or columns with missing data.

    In [83]: df = pd.DataFrame([[np.nan, 1, 2], [1, 2, np.nan], [1, 2, 3]])

    In [84]: df
    Out[84]:
         0  1    2
    0  NaN  1  2.0
    1  1.0  2  NaN
    2  1.0  2  3.0

    In [85]: df.dropna()
    Out[85]:
         0  1    2
    2  1.0  2  3.0

    In [86]: df.dropna(axis=1)
    Out[86]:
       1
    0  1
    1  2
    2  2

    In [87]: ser = pd.Series([1, pd.NA], dtype="int64[pyarrow]")

    In [88]: ser.dropna()
    Out[88]:
    0    1
    dtype: int64[pyarrow]

## Filling missing data

### Filling by value

`fillna()` replaces NA values with non-NA data.

Replace NA with a scalar value

    In [89]: data = {"np": [1.0, np.nan, np.nan, 2], "arrow": pd.array([1.0, pd.NA, pd.NA, 2], dtype="float64[pyarrow]")}

    In [90]: df = pd.DataFrame(data)

    In [91]: df
    Out[91]:
        np  arrow
    0  1.0    1.0
    1  NaN   <NA>
    2  NaN   <NA>
    3  2.0    2.0

    In [92]: df.fillna(0)
    Out[92]:
        np  arrow
    0  1.0    1.0
    1  0.0    0.0
    2  0.0    0.0
    3  2.0    2.0

Fill gaps forward or backward

    In [93]: df.ffill()
    Out[93]:
        np  arrow
    0  1.0    1.0
    1  1.0    1.0
    2  1.0    1.0
    3  2.0    2.0

    In [94]: df.bfill()
    Out[94]:
        np  arrow
    0  1.0    1.0
    1  2.0    2.0
    2  2.0    2.0
    3  2.0    2.0

Limit the number of NA values filled

    In [95]: df.ffill(limit=1)
    Out[95]:
        np  arrow
    0  1.0    1.0
    1  1.0    1.0
    2  NaN   <NA>
    3  2.0    2.0

NA values can be replaced with corresponding value from a `Series` or `DataFrame` where the index and column aligns between the original object and the filled object.

    In [96]: dff = pd.DataFrame(np.arange(30, dtype=np.float64).reshape(10, 3), columns=list("ABC"))

    In [97]: dff.iloc[3:5, 0] = np.nan

    In [98]: dff.iloc[4:6, 1] = np.nan

    In [99]: dff.iloc[5:8, 2] = np.nan

    In [100]: dff
    Out[100]:
          A     B     C
    0   0.0   1.0   2.0
    1   3.0   4.0   5.0
    2   6.0   7.0   8.0
    3   NaN  10.0  11.0
    4   NaN   NaN  14.0
    5  15.0   NaN   NaN
    6  18.0  19.0   NaN
    7  21.0  22.0   NaN
    8  24.0  25.0  26.0
    9  27.0  28.0  29.0

    In [101]: dff.fillna(dff.mean())
    Out[101]:
           A     B          C
    0   0.00   1.0   2.000000
    1   3.00   4.0   5.000000
    2   6.00   7.0   8.000000
    3  14.25  10.0  11.000000
    4  14.25  14.5  14.000000
    5  15.00  14.5  13.571429
    6  18.00  19.0  13.571429
    7  21.00  22.0  13.571429
    8  24.00  25.0  26.000000
    9  27.00  28.0  29.000000

Note

`DataFrame.where()` can also be used to fill NA values.Same result as above.

    In [102]: dff.where(pd.notna(dff), dff.mean(), axis="columns")
    Out[102]:
           A     B          C
    0   0.00   1.0   2.000000
    1   3.00   4.0   5.000000
    2   6.00   7.0   8.000000
    3  14.25  10.0  11.000000
    4  14.25  14.5  14.000000
    5  15.00  14.5  13.571429
    6  18.00  19.0  13.571429
    7  21.00  22.0  13.571429
    8  24.00  25.0  26.000000
    9  27.00  28.0  29.000000

### Interpolation

`DataFrame.interpolate()` and `Series.interpolate()` fills NA values using various interpolation methods.

    In [103]: 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],
       .....:     }
       .....: )
       .....:

    In [104]: df
    Out[104]:
         A      B
    0  1.0   0.25
    1  2.1    NaN
    2  NaN    NaN
    3  4.7   4.00
    4  5.6  12.20
    5  6.8  14.40

    In [105]: df.interpolate()
    Out[105]:
         A      B
    0  1.0   0.25
    1  2.1   1.50
    2  3.4   2.75
    3  4.7   4.00
    4  5.6  12.20
    5  6.8  14.40

    In [106]: idx = pd.date_range("2020-01-01", periods=10, freq="D")

    In [107]: data = np.random.default_rng(2).integers(0, 10, 10).astype(np.float64)

    In [108]: ts = pd.Series(data, index=idx)

    In [109]: ts.iloc[[1, 2, 5, 6, 9]] = np.nan

    In [110]: ts
    Out[110]:
    2020-01-01    8.0
    2020-01-02    NaN
    2020-01-03    NaN
    2020-01-04    2.0
    2020-01-05    4.0
    2020-01-06    NaN
    2020-01-07    NaN
    2020-01-08    0.0
    2020-01-09    3.0
    2020-01-10    NaN
    Freq: D, dtype: float64

    In [111]: ts.plot()
    Out[111]: <Axes: >

    In [112]: ts.interpolate()
    Out[112]:
    2020-01-01    8.000000
    2020-01-02    6.000000
    2020-01-03    4.000000
    2020-01-04    2.000000
    2020-01-05    4.000000
    2020-01-06    2.666667
    2020-01-07    1.333333
    2020-01-08    0.000000
    2020-01-09    3.000000
    2020-01-10    3.000000
    Freq: D, dtype: float64

    In [113]: ts.interpolate().plot()
    Out[113]: <Axes: >

Interpolation relative to a `Timestamp` in the `DatetimeIndex` is available by setting `method="time"`

    In [114]: ts2 = ts.iloc[[0, 1, 3, 7, 9]]

    In [115]: ts2
    Out[115]:
    2020-01-01    8.0
    2020-01-02    NaN
    2020-01-04    2.0
    2020-01-08    0.0
    2020-01-10    NaN
    dtype: float64

    In [116]: ts2.interpolate()
    Out[116]:
    2020-01-01    8.0
    2020-01-02    5.0
    2020-01-04    2.0
    2020-01-08    0.0
    2020-01-10    0.0
    dtype: float64

    In [117]: ts2.interpolate(method="time")
    Out[117]:
    2020-01-01    8.0
    2020-01-02    6.0
    2020-01-04    2.0
    2020-01-08    0.0
    2020-01-10    0.0
    dtype: float64

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

    In [118]: idx = [0.0, 1.0, 10.0]

    In [119]: ser = pd.Series([0.0, np.nan, 10.0], idx)

    In [120]: ser
    Out[120]:
    0.0      0.0
    1.0      NaN
    10.0    10.0
    dtype: float64

    In [121]: ser.interpolate()
    Out[121]:
    0.0      0.0
    1.0      5.0
    10.0    10.0
    dtype: float64

    In [122]: ser.interpolate(method="values")
    Out[122]:
    0.0      0.0
    1.0      1.0
    10.0    10.0
    dtype: float64

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'`.

    In [123]: 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],
       .....:    }
       .....: )
       .....:

    In [124]: df
    Out[124]:
         A      B
    0  1.0   0.25
    1  2.1    NaN
    2  NaN    NaN
    3  4.7   4.00
    4  5.6  12.20
    5  6.8  14.40

    In [125]: df.interpolate(method="barycentric")
    Out[125]:
          A       B
    0  1.00   0.250
    1  2.10  -7.660
    2  3.53  -4.515
    3  4.70   4.000
    4  5.60  12.200
    5  6.80  14.400

    In [126]: df.interpolate(method="pchip")
    Out[126]:
             A          B
    0  1.00000   0.250000
    1  2.10000   0.672808
    2  3.43454   1.928950
    3  4.70000   4.000000
    4  5.60000  12.200000
    5  6.80000  14.400000

    In [127]: df.interpolate(method="akima")
    Out[127]:
              A          B
    0  1.000000   0.250000
    1  2.100000  -0.873316
    2  3.406667   0.320034
    3  4.700000   4.000000
    4  5.600000  12.200000
    5  6.800000  14.400000

When interpolating via a polynomial or spline approximation, you must also specify the degree or order of the approximation:

    In [128]: df.interpolate(method="spline", order=2)
    Out[128]:
              A          B
    0  1.000000   0.250000
    1  2.100000  -0.428598
    2  3.404545   1.206900
    3  4.700000   4.000000
    4  5.600000  12.200000
    5  6.800000  14.400000

    In [129]: df.interpolate(method="polynomial", order=2)
    Out[129]:
              A          B
    0  1.000000   0.250000
    1  2.100000  -2.703846
    2  3.451351  -1.453846
    3  4.700000   4.000000
    4  5.600000  12.200000
    5  6.800000  14.400000

Comparing several methods.

    In [130]: np.random.seed(2)

    In [131]: ser = pd.Series(np.arange(1, 10.1, 0.25) ** 2 + np.random.randn(37))

    In [132]: missing = np.array([4, 13, 14, 15, 16, 17, 18, 20, 29])

    In [133]: ser.iloc[missing] = np.nan

    In [134]: methods = ["linear", "quadratic", "cubic"]

    In [135]: df = pd.DataFrame({m: ser.interpolate(method=m) for m in methods})

    In [136]: df.plot()
    Out[136]: <Axes: >

Interpolating new observations from expanding data with `Series.reindex()`.

    In [137]: ser = pd.Series(np.sort(np.random.uniform(size=100)))

    # interpolate at new_index
    In [138]: new_index = ser.index.union(pd.Index([49.25, 49.5, 49.75, 50.25, 50.5, 50.75]))

    In [139]: interp_s = ser.reindex(new_index).interpolate(method="pchip")

    In [140]: interp_s.loc[49:51]
    Out[140]:
    49.00    0.471410
    49.25    0.476841
    49.50    0.481780
    49.75    0.485998
    50.00    0.489266
    50.25    0.491814
    50.50    0.493995
    50.75    0.495763
    51.00    0.497074
    dtype: float64

#### Interpolation limits

`interpolate()` accepts a `limit` keyword argument to limit the number of consecutive `NaN` values filled since the last valid observation

    In [141]: ser = pd.Series([np.nan, np.nan, 5, np.nan, np.nan, np.nan, 13, np.nan, np.nan])

    In [142]: ser
    Out[142]:
    0     NaN
    1     NaN
    2     5.0
    3     NaN
    4     NaN
    5     NaN
    6    13.0
    7     NaN
    8     NaN
    dtype: float64

    In [143]: ser.interpolate()
    Out[143]:
    0     NaN
    1     NaN
    2     5.0
    3     7.0
    4     9.0
    5    11.0
    6    13.0
    7    13.0
    8    13.0
    dtype: float64

    In [144]: ser.interpolate(limit=1)
    Out[144]:
    0     NaN
    1     NaN
    2     5.0
    3     7.0
    4     NaN
    5     NaN
    6    13.0
    7    13.0
    8     NaN
    dtype: float64

By default, `NaN` values are filled in a `forward` direction. Use `limit_direction` parameter to fill `backward` or from `both` directions.

    In [145]: ser.interpolate(limit=1, limit_direction="backward")
    Out[145]:
    0     NaN
    1     5.0
    2     5.0
    3     NaN
    4     NaN
    5    11.0
    6    13.0
    7     NaN
    8     NaN
    dtype: float64

    In [146]: ser.interpolate(limit=1, limit_direction="both")
    Out[146]:
    0     NaN
    1     5.0
    2     5.0
    3     7.0
    4     NaN
    5    11.0
    6    13.0
    7    13.0
    8     NaN
    dtype: float64

    In [147]: ser.interpolate(limit_direction="both")
    Out[147]:
    0     5.0
    1     5.0
    2     5.0
    3     7.0
    4     9.0
    5    11.0
    6    13.0
    7    13.0
    8    13.0
    dtype: float64

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.

    # fill one consecutive inside value in both directions
    In [148]: ser.interpolate(limit_direction="both", limit_area="inside", limit=1)
    Out[148]:
    0     NaN
    1     NaN
    2     5.0
    3     7.0
    4     NaN
    5    11.0
    6    13.0
    7     NaN
    8     NaN
    dtype: float64

    # fill all consecutive outside values backward
    In [149]: ser.interpolate(limit_direction="backward", limit_area="outside")
    Out[149]:
    0     5.0
    1     5.0
    2     5.0
    3     NaN
    4     NaN
    5     NaN
    6    13.0
    7     NaN
    8     NaN
    dtype: float64

    # fill all consecutive outside values in both directions
    In [150]: ser.interpolate(limit_direction="both", limit_area="outside")
    Out[150]:
    0     5.0
    1     5.0
    2     5.0
    3     NaN
    4     NaN
    5     NaN
    6    13.0
    7    13.0
    8    13.0
    dtype: float64

### Replacing values

`Series.replace()` and `DataFrame.replace()` can be used similar to `Series.fillna()` and `DataFrame.fillna()` to replace or insert missing values.

    In [151]: df = pd.DataFrame(np.eye(3))

    In [152]: df
    Out[152]:
         0    1    2
    0  1.0  0.0  0.0
    1  0.0  1.0  0.0
    2  0.0  0.0  1.0

    In [153]: df_missing = df.replace(0, np.nan)

    In [154]: df_missing
    Out[154]:
         0    1    2
    0  1.0  NaN  NaN
    1  NaN  1.0  NaN
    2  NaN  NaN  1.0

    In [155]: df_filled = df_missing.replace(np.nan, 2)

    In [156]: df_filled
    Out[156]:
         0    1    2
    0  1.0  2.0  2.0
    1  2.0  1.0  2.0
    2  2.0  2.0  1.0

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

    In [157]: df_filled.replace([1, 44], [2, 28])
    Out[157]:
         0    1    2
    0  2.0  2.0  2.0
    1  2.0  2.0  2.0
    2  2.0  2.0  2.0

Replacing using a mapping dict.

    In [158]: df_filled.replace({1: 44, 2: 28})
    Out[158]:
          0     1     2
    0  44.0  28.0  28.0
    1  28.0  44.0  28.0
    2  28.0  28.0  44.0

#### Regular expression replacement

Note

Python strings prefixed with the `r` character such as `r'hello world'` are “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'\' == '\\'`.

Replace the ‘.’ with `NaN`

    In [159]: d = {"a": list(range(4)), "b": list("ab.."), "c": ["a", "b", np.nan, "d"]}

    In [160]: df = pd.DataFrame(d)

    In [161]: df.replace(".", np.nan)
    Out[161]:
       a    b    c
    0  0    a    a
    1  1    b    b
    2  2  NaN  NaN
    3  3  NaN    d

Replace the ‘.’ with `NaN` with regular expression that removes surrounding whitespace

    In [162]: df.replace(r"\s*\.\s*", np.nan, regex=True)
    Out[162]:
       a    b    c
    0  0    a    a
    1  1    b    b
    2  2  NaN  NaN
    3  3  NaN    d

Replace with a list of regexes.

    In [163]: df.replace([r"\.", r"(a)"], ["dot", r"\1stuff"], regex=True)
    Out[163]:
       a       b       c
    0  0  astuff  astuff
    1  1       b       b
    2  2     dot     NaN
    3  3     dot       d

Replace with a regex in a mapping dict.

    In [164]: df.replace({"b": r"\s*\.\s*"}, {"b": np.nan}, regex=True)
    Out[164]:
       a    b    c
    0  0    a    a
    1  1    b    b
    2  2  NaN  NaN
    3  3  NaN    d

Pass nested dictionaries of regular expressions that use the `regex` keyword.

    In [165]: df.replace({"b": {"b": r""}}, regex=True)
    Out[165]:
       a  b    c
    0  0  a    a
    1  1       b
    2  2  .  NaN
    3  3  .    d

    In [166]: df.replace(regex={"b": {r"\s*\.\s*": np.nan}})
    Out[166]:
       a    b    c
    0  0    a    a
    1  1    b    b
    2  2  NaN  NaN
    3  3  NaN    d

    In [167]: df.replace({"b": r"\s*(\.)\s*"}, {"b": r"\1ty"}, regex=True)
    Out[167]:
       a    b    c
    0  0    a    a
    1  1    b    b
    2  2  .ty  NaN
    3  3  .ty    d

Pass a list of regular expressions that will replace matches with a scalar.

    In [168]: df.replace([r"\s*\.\s*", r"a|b"], "placeholder", regex=True)
    Out[168]:
       a            b            c
    0  0  placeholder  placeholder
    1  1  placeholder  placeholder
    2  2  placeholder          NaN
    3  3  placeholder            d

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.

    In [169]: df.replace(regex=[r"\s*\.\s*", r"a|b"], value="placeholder")
    Out[169]:
       a            b            c
    0  0  placeholder  placeholder
    1  1  placeholder  placeholder
    2  2  placeholder          NaN
    3  3  placeholder            d

Note

A regular expression object from `re.compile` is a valid input as well.