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# Essential basic functionality

Here we discuss a lot of the essential functionality common to the pandas data structures. To begin, let’s create some example objects like we did in the 10 minutes to pandas section:

    In [1]: index = pd.date_range("1/1/2000", periods=8)

    In [2]: s = pd.Series(np.random.randn(5), index=["a", "b", "c", "d", "e"])

    In [3]: df = pd.DataFrame(np.random.randn(8, 3), index=index, columns=["A", "B", "C"])

## Head and tail

To view a small sample of a Series or DataFrame object, use the `head()` and `tail()` methods. The default number of elements to display is five, but you may pass a custom number.

    In [4]: long_series = pd.Series(np.random.randn(1000))

    In [5]: long_series.head()
    Out[5]:
    0   -1.157892
    1   -1.344312
    2    0.844885
    3    1.075770
    4   -0.109050
    dtype: float64

    In [6]: long_series.tail(3)
    Out[6]:
    997   -0.289388
    998   -1.020544
    999    0.589993
    dtype: float64

## Attributes and underlying data

pandas objects have a number of attributes enabling you to access the metadata.

- shape: gives the axis dimensions of the object, consistent with ndarray

- Axis labels

    - Series: index (only axis)

    - DataFrame: index (rows) and columns

Note, these attributes can be safely assigned to!

    In [7]: df[:2]
    Out[7]:
                       A         B         C
    2000-01-01 -0.173215  0.119209 -1.044236
    2000-01-02 -0.861849 -2.104569 -0.494929

    In [8]: df.columns = [x.lower() for x in df.columns]

    In [9]: df
    Out[9]:
                       a         b         c
    2000-01-01 -0.173215  0.119209 -1.044236
    2000-01-02 -0.861849 -2.104569 -0.494929
    2000-01-03  1.071804  0.721555 -0.706771
    2000-01-04 -1.039575  0.271860 -0.424972
    2000-01-05  0.567020  0.276232 -1.087401
    2000-01-06 -0.673690  0.113648 -1.478427
    2000-01-07  0.524988  0.404705  0.577046
    2000-01-08 -1.715002 -1.039268 -0.370647

pandas objects (`Index`, `Series`, `DataFrame`) can be thought of as containers for arrays, which hold the actual data and do the actual computation. For many types, the underlying array is a `numpy.ndarray`. However, pandas and 3rd party libraries may extend NumPy’s type system to add support for custom arrays (see dtypes).

To get the actual data inside a `Index` or `Series`, use the `.array` property.

    In [10]: s.array
    Out[10]:
    <NumpyExtensionArray>
    [ 0.4691122999071863, -0.2828633443286633, -1.5090585031735124,
     -1.1356323710171934,  1.2121120250208506]
    Length: 5, dtype: float64

    In [11]: s.index.array
    Out[11]:
    <ArrowStringArray>
    ['a', 'b', 'c', 'd', 'e']
    Length: 5, dtype: str

`array` will always be an `ExtensionArray`. The exact details of what an `ExtensionArray` is and why pandas uses them are a bit beyond the scope of this introduction. See dtypes for more.

If you know you need a NumPy array, use `to_numpy()` or `numpy.asarray()`.

    In [12]: s.to_numpy()
    Out[12]: array([ 0.4691, -0.2829, -1.5091, -1.1356,  1.2121])

    In [13]: np.asarray(s)
    Out[13]: array([ 0.4691, -0.2829, -1.5091, -1.1356,  1.2121])

When the Series or Index is backed by an `ExtensionArray`, `to_numpy()` may involve copying data and coercing values. See dtypes for more.

`to_numpy()` gives some control over the `dtype` of the resulting `numpy.ndarray`. For example, consider datetimes with timezones. NumPy doesn’t have a dtype to represent timezone-aware datetimes, so there are two possibly useful representations:

1. An object-dtype `numpy.ndarray` with `Timestamp` objects, each with the correct `tz`.

2. A `datetime64[ns]` -dtype `numpy.ndarray`, where the values have been converted to UTC and the timezone discarded.

Timezones may be preserved with `dtype=object`:

    In [14]: ser = pd.Series(pd.date_range("2000", periods=2, tz="CET"))

    In [15]: ser.to_numpy(dtype=object)
    Out[15]:
    array([Timestamp('2000-01-01 00:00:00+0100', tz='CET'),
           Timestamp('2000-01-02 00:00:00+0100', tz='CET')], dtype=object)

Or thrown away with `dtype='datetime64[ns]'`:

    In [16]: ser.to_numpy(dtype="datetime64[ns]")
    Out[16]:
    array(['1999-12-31T23:00:00.000000000', '2000-01-01T23:00:00.000000000'],
          dtype='datetime64[ns]')

Getting the “raw data” inside a `DataFrame` is possibly a bit more complex. When your `DataFrame` only has a single data type for all the columns, `DataFrame.to_numpy()` will return the underlying data:

    In [17]: df.to_numpy()
    Out[17]:
    array([[-0.1732,  0.1192, -1.0442],
           [-0.8618, -2.1046, -0.4949],
           [ 1.0718,  0.7216, -0.7068],
           [-1.0396,  0.2719, -0.425 ],
           [ 0.567 ,  0.2762, -1.0874],
           [-0.6737,  0.1136, -1.4784],
           [ 0.525 ,  0.4047,  0.577 ],
           [-1.715 , -1.0393, -0.3706]])

If a DataFrame contains homogeneously-typed data, the ndarray can actually be modified in-place, and the changes will be reflected in the data structure. For heterogeneous data (e.g. some of the DataFrame’s columns are not all the same dtype), this will not be the case. The values attribute itself, unlike the axis labels, cannot be assigned to.

Note

When working with heterogeneous data, the dtype of the resulting ndarray will be chosen to accommodate all of the data involved. For example, if strings are involved, the result will be of object dtype. If there are only floats and integers, the resulting array will be of float dtype.

In the past, pandas recommended `Series.values` or `DataFrame.values` for extracting the data from a Series or DataFrame. You’ll still find references to these in old code bases and online. Going forward, we recommend avoiding `.values` and using `.array` or `.to_numpy()`. `.values` has the following drawbacks:

1. When your Series contains an extension type, it’s unclear whether `Series.values` returns a NumPy array or the extension array. `Series.array` will always return an `ExtensionArray`, and will never copy data. `Series.to_numpy()` will always return a NumPy array, potentially at the cost of copying / coercing values.

2. When your DataFrame contains a mixture of data types, `DataFrame.values` may involve copying data and coercing values to a common dtype, a relatively expensive operation. `DataFrame.to_numpy()`, being a method, makes it clearer that the returned NumPy array may not be a view on the same data in the DataFrame.

## Accelerated operations

pandas has support for accelerating certain types of binary numerical and boolean operations using the `numexpr` library and the `bottleneck` libraries.

These libraries are especially useful when dealing with large data sets, and provide large speedups. `numexpr` uses smart chunking, caching, and multiple cores. `bottleneck` is a set of specialized cython routines that are especially fast when dealing with arrays that have `nans`.

You are highly encouraged to install both libraries. See the section Recommended Dependencies for more installation info.

These are both enabled to be used by default, you can control this by setting the options:

    pd.set_option("compute.use_bottleneck", False)
    pd.set_option("compute.use_numexpr", False)

## Flexible binary operations

With binary operations between pandas data structures, there are two key points of interest:

- Broadcasting behavior between higher- (e.g. DataFrame) and lower-dimensional (e.g. Series) objects.

- Missing data in computations.

We will demonstrate how to manage these issues independently, though they can be handled simultaneously.

### Matching / broadcasting behavior

DataFrame has the methods `add()`, `sub()`, `mul()`, `div()` and related functions `radd()`, `rsub()`, … for carrying out binary operations. For broadcasting behavior, Series input is of primary interest. Using these functions, you can use to either match on the index or columns via the axis keyword:

    In [18]: df = pd.DataFrame(
       ....:     {
       ....:         "one": pd.Series(np.random.randn(3), index=["a", "b", "c"]),
       ....:         "two": pd.Series(np.random.randn(4), index=["a", "b", "c", "d"]),
       ....:         "three": pd.Series(np.random.randn(3), index=["b", "c", "d"]),
       ....:     }
       ....: )
       ....:

    In [19]: df
    Out[19]:
            one       two     three
    a  1.394981  1.772517       NaN
    b  0.343054  1.912123 -0.050390
    c  0.695246  1.478369  1.227435
    d       NaN  0.279344 -0.613172

    In [20]: row = df.iloc[1]

    In [21]: column = df["two"]

    In [22]: df.sub(row, axis="columns")
    Out[22]:
            one       two     three
    a  1.051928 -0.139606       NaN
    b  0.000000  0.000000  0.000000
    c  0.352192 -0.433754  1.277825
    d       NaN -1.632779 -0.562782

    In [23]: df.sub(row, axis=1)
    Out[23]:
            one       two     three
    a  1.051928 -0.139606       NaN
    b  0.000000  0.000000  0.000000
    c  0.352192 -0.433754  1.277825
    d       NaN -1.632779 -0.562782

    In [24]: df.sub(column, axis="index")
    Out[24]:
            one  two     three
    a -0.377535  0.0       NaN
    b -1.569069  0.0 -1.962513
    c -0.783123  0.0 -0.250933
    d       NaN  0.0 -0.892516

    In [25]: df.sub(column, axis=0)
    Out[25]:
            one  two     three
    a -0.377535  0.0       NaN
    b -1.569069  0.0 -1.962513
    c -0.783123  0.0 -0.250933
    d       NaN  0.0 -0.892516

Furthermore you can align a level of a MultiIndexed DataFrame with a Series.

    In [26]: dfmi = df.copy()

    In [27]: dfmi.index = pd.MultiIndex.from_tuples(
       ....:     [(1, "a"), (1, "b"), (1, "c"), (2, "a")], names=["first", "second"]
       ....: )
       ....:

    In [28]: dfmi.sub(column, axis=0, level="second")
    Out[28]:
                       one       two     three
    first second
    1     a      -0.377535  0.000000       NaN
          b      -1.569069  0.000000 -1.962513
          c      -0.783123  0.000000 -0.250933
    2     a            NaN -1.493173 -2.385688

When operating with a plain Python list, pandas aligns it to the columns of the DataFrame (not the rows); to broadcast row-wise, pass `axis=0` to the explicit method, e.g. `df.add([1, 2, 3], axis=0)`.

Series and Index also support the `divmod()` builtin. This function takes the floor division and modulo operation at the same time returning a two-tuple of the same type as the left hand side. For example:

    In [29]: s = pd.Series(np.arange(10))

    In [30]: s
    Out[30]:
    0    0
    1    1
    2    2
    3    3
    4    4
    5    5
    6    6
    7    7
    8    8
    9    9
    dtype: int64

    In [31]: div, rem = divmod(s, 3)

    In [32]: div
    Out[32]:
    0    0
    1    0
    2    0
    3    1
    4    1
    5    1
    6    2
    7    2
    8    2
    9    3
    dtype: int64

    In [33]: rem
    Out[33]:
    0    0
    1    1
    2    2
    3    0
    4    1
    5    2
    6    0
    7    1
    8    2
    9    0
    dtype: int64

    In [34]: idx = pd.Index(np.arange(10))

    In [35]: idx
    Out[35]: Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype='int64')

    In [36]: div, rem = divmod(idx, 3)

    In [37]: div
    Out[37]: Index([0, 0, 0, 1, 1, 1, 2, 2, 2, 3], dtype='int64')

    In [38]: rem
    Out[38]: Index([0, 1, 2, 0, 1, 2, 0, 1, 2, 0], dtype='int64')

We can also do elementwise `divmod()`:

    In [39]: div, rem = divmod(s, [2, 2, 3, 3, 4, 4, 5, 5, 6, 6])

    In [40]: div
    Out[40]:
    0    0
    1    0
    2    0
    3    1
    4    1
    5    1
    6    1
    7    1
    8    1
    9    1
    dtype: int64

    In [41]: rem
    Out[41]:
    0    0
    1    1
    2    2
    3    0
    4    0
    5    1
    6    1
    7    2
    8    2
    9    3
    dtype: int64

### Missing data / operations with fill values

In Series and DataFrame, the arithmetic functions have the option of inputting a fill_value, namely a value to substitute when at most one of the values at a location are missing. For example, when adding two DataFrame objects, you may wish to treat NaN as 0 unless both DataFrames are missing that value, in which case the result will be NaN (you can later replace NaN with some other value using `fillna` if you wish).

    In [42]: df2 = df.copy()

    In [43]: df2.loc["a", "three"] = 1.0

    In [44]: df
    Out[44]:
            one       two     three
    a  1.394981  1.772517       NaN
    b  0.343054  1.912123 -0.050390
    c  0.695246  1.478369  1.227435
    d       NaN  0.279344 -0.613172

    In [45]: df2
    Out[45]:
            one       two     three
    a  1.394981  1.772517  1.000000
    b  0.343054  1.912123 -0.050390
    c  0.695246  1.478369  1.227435
    d       NaN  0.279344 -0.613172

    In [46]: df + df2
    Out[46]:
            one       two     three
    a  2.789963  3.545034       NaN
    b  0.686107  3.824246 -0.100780
    c  1.390491  2.956737  2.454870
    d       NaN  0.558688 -1.226343

    In [47]: df.add(df2, fill_value=0)
    Out[47]:
            one       two     three
    a  2.789963  3.545034  1.000000
    b  0.686107  3.824246 -0.100780
    c  1.390491  2.956737  2.454870
    d       NaN  0.558688 -1.226343

### Flexible comparisons

Series and DataFrame have the binary comparison methods `eq`, `ne`, `lt`, `gt`, `le`, and `ge` whose behavior is analogous to the binary arithmetic operations described above:

    In [48]: df.gt(df2)
    Out[48]:
         one    two  three
    a  False  False  False
    b  False  False  False
    c  False  False  False
    d  False  False  False

    In [49]: df2.ne(df)
    Out[49]:
         one    two  three
    a  False  False   True
    b  False  False  False
    c  False  False  False
    d   True  False  False

These operations produce a pandas object of the same type as the left-hand-side input that is of dtype `bool`. These `boolean` objects can be used in indexing operations, see the section on Boolean indexing.

### Boolean reductions

You can apply the reductions: `empty`, `any()`, `all()`.

    In [50]: (df > 0).all()
    Out[50]:
    one      False
    two       True
    three    False
    dtype: bool

    In [51]: (df > 0).any()
    Out[51]:
    one      True
    two      True
    three    True
    dtype: bool

You can reduce to a final boolean value.

    In [52]: (df > 0).any().any()
    Out[52]: np.True_

You can test if a pandas object is empty, via the `empty` property.

    In [53]: df.empty
    Out[53]: False

    In [54]: pd.DataFrame(columns=list("ABC")).empty
    Out[54]: True

Warning

Asserting the truthiness of a pandas object will raise an error, as the testing of the emptiness or values is ambiguous.

    In [55]: if df:
       ....:     print(True)
       ....:
    ---------------------------------------------------------------------------
    ValueError                                Traceback (most recent call last)
    Cell In[55], line 1
    ----> 1 if df:
          2     print(True)

    File ~/work/pandas/pandas/pandas/core/generic.py:1547, in NDFrame.__bool__(self)
       1545     @final
       1546     def __bool__(self) -> NoReturn:
    -> 1547         raise ValueError(
       1548             f"The truth value of a {type(self).__name__} is ambiguous. "
       1549             "Use a.empty, a.item(), a.any() or a.all()."
       1550         )

    ValueError: The truth value of a DataFrame is ambiguous. Use a.empty, a.item(), a.any() or a.all().

    In [56]: df and df2
    ---------------------------------------------------------------------------
    ValueError                                Traceback (most recent call last)
    Cell In[56], line 1
    ----> 1 df and df2

    File ~/work/pandas/pandas/pandas/core/generic.py:1547, in NDFrame.__bool__(self)
       1545     @final
       1546     def __bool__(self) -> NoReturn:
    -> 1547         raise ValueError(
       1548             f"The truth value of a {type(self).__name__} is ambiguous. "
       1549             "Use a.empty, a.item(), a.any() or a.all()."
       1550         )

    ValueError: The truth value of a DataFrame is ambiguous. Use a.empty, a.item(), a.any() or a.all().

See gotchas for a more detailed discussion.

### Comparing if objects are equivalent

Often you may find that there is more than one way to compute the same result. As a simple example, consider `df + df` and `df * 2`. To test that these two computations produce the same result, given the tools shown above, you might imagine using `(df + df == df * 2).all()`. But in fact, this expression is False:

    In [57]: df + df == df * 2
    Out[57]:
         one   two  three
    a   True  True  False
    b   True  True   True
    c   True  True   True
    d  False  True   True

    In [58]: (df + df == df * 2).all()
    Out[58]:
    one      False
    two       True
    three    False
    dtype: bool

Notice that the boolean DataFrame `df + df == df * 2` contains some False values! This is because NaNs do not compare as equals:

    In [59]: np.nan == np.nan
    Out[59]: False

So, NDFrames (such as Series and DataFrames) have an `equals()` method for testing equality, with NaNs in corresponding locations treated as equal.

    In [60]: (df + df).equals(df * 2)
    Out[60]: True

Note that the Series or DataFrame index needs to be in the same order for equality to be True:

    In [61]: df1 = pd.DataFrame({"col": ["foo", 0, np.nan]})

    In [62]: df2 = pd.DataFrame({"col": [np.nan, 0, "foo"]}, index=[2, 1, 0])

    In [63]: df1.equals(df2)
    Out[63]: False

    In [64]: df1.equals(df2.sort_index())
    Out[64]: True

### Comparing array-like objects

You can conveniently perform element-wise comparisons when comparing a pandas data structure with a scalar value:

    In [65]: pd.Series(["foo", "bar", "baz"]) == "foo"
    Out[65]:
    0     True
    1    False
    2    False
    dtype: bool

    In [66]: pd.Index(["foo", "bar", "baz"]) == "foo"
    Out[66]: array([ True, False, False])

pandas also handles element-wise comparisons between different array-like objects of the same length:

    In [67]: pd.Series(["foo", "bar", "baz"]) == pd.Index(["foo", "bar", "qux"])
    Out[67]:
    0     True
    1     True
    2    False
    dtype: bool

    In [68]: pd.Series(["foo", "bar", "baz"]) == np.array(["foo", "bar", "qux"])
    Out[68]:
    0     True
    1     True
    2    False
    dtype: bool

Trying to compare `Index` or `Series` objects of different lengths will raise a ValueError:

    In [69]: pd.Series(['foo', 'bar', 'baz']) == pd.Series(['foo', 'bar'])
    ---------------------------------------------------------------------------
    ValueError                                Traceback (most recent call last)
    Cell In[69], line 1
    ----> 1 pd.Series(['foo', 'bar', 'baz']) == pd.Series(['foo', 'bar'])

    File ~/work/pandas/pandas/pandas/core/ops/common.py:125, in _unpack_zerodim_and_defer.<locals>.new_method(self, other)
        122     other = sanitize_array(other, None)
        123     other = ensure_wrapped_if_datetimelike(other)
    --> 125 return method(self, other)

    File ~/work/pandas/pandas/pandas/core/arraylike.py:44, in OpsMixin.__eq__(self, other)
         42 @unpack_zerodim_and_defer("__eq__")
         43 def __eq__(self, other) -> Self:  # type: ignore[override]
    ---> 44     return self._cmp_method(other, operator.eq)

    File ~/work/pandas/pandas/pandas/core/series.py:7207, in Series._cmp_method(self, other, op)
       7203     def _cmp_method(self, other, op) -> Series:
       7204         res_name = ops.get_op_result_name(self, other)
       7205
       7206         if isinstance(other, Series) and not self._indexed_same(other):
    -> 7207             raise ValueError("Can only compare identically-labeled Series objects")
       7208
       7209         lvalues = self._values
       7210         if not isinstance(lvalues, ExtensionArray) and lvalues.dtype != object:

    ValueError: Can only compare identically-labeled Series objects

    In [70]: pd.Series(['foo', 'bar', 'baz']) == pd.Series(['foo'])
    ---------------------------------------------------------------------------
    ValueError                                Traceback (most recent call last)
    Cell In[70], line 1
    ----> 1 pd.Series(['foo', 'bar', 'baz']) == pd.Series(['foo'])

    File ~/work/pandas/pandas/pandas/core/ops/common.py:125, in _unpack_zerodim_and_defer.<locals>.new_method(self, other)
        122     other = sanitize_array(other, None)
        123     other = ensure_wrapped_if_datetimelike(other)
    --> 125 return method(self, other)

    File ~/work/pandas/pandas/pandas/core/arraylike.py:44, in OpsMixin.__eq__(self, other)
         42 @unpack_zerodim_and_defer("__eq__")
         43 def __eq__(self, other) -> Self:  # type: ignore[override]
    ---> 44     return self._cmp_method(other, operator.eq)

    File ~/work/pandas/pandas/pandas/core/series.py:7207, in Series._cmp_method(self, other, op)
       7203     def _cmp_method(self, other, op) -> Series:
       7204         res_name = ops.get_op_result_name(self, other)
       7205
       7206         if isinstance(other, Series) and not self._indexed_same(other):
    -> 7207             raise ValueError("Can only compare identically-labeled Series objects")
       7208
       7209         lvalues = self._values
       7210         if not isinstance(lvalues, ExtensionArray) and lvalues.dtype != object:

    ValueError: Can only compare identically-labeled Series objects

### Combining overlapping data sets

A problem occasionally arising is the combination of two similar data sets where values in one are preferred over the other. An example would be two data series representing a particular economic indicator where one is considered to be of “higher quality”. However, the lower quality series might extend further back in history or have more complete data coverage. As such, we would like to combine two DataFrame objects where missing values in one DataFrame are conditionally filled with like-labeled values from the other DataFrame. The function implementing this operation is `combine_first()`, which we illustrate:

    In [71]: df1 = pd.DataFrame(
       ....:     {"A": [1.0, np.nan, 3.0, 5.0, np.nan], "B": [np.nan, 2.0, 3.0, np.nan, 6.0]}
       ....: )
       ....:

    In [72]: df2 = pd.DataFrame(
       ....:     {
       ....:         "A": [5.0, 2.0, 4.0, np.nan, 3.0, 7.0],
       ....:         "B": [np.nan, np.nan, 3.0, 4.0, 6.0, 8.0],
       ....:     }
       ....: )
       ....:

    In [73]: df1
    Out[73]:
         A    B
    0  1.0  NaN
    1  NaN  2.0
    2  3.0  3.0
    3  5.0  NaN
    4  NaN  6.0

    In [74]: df2
    Out[74]:
         A    B
    0  5.0  NaN
    1  2.0  NaN
    2  4.0  3.0
    3  NaN  4.0
    4  3.0  6.0
    5  7.0  8.0

    In [75]: df1.combine_first(df2)
    Out[75]:
         A    B
    0  1.0  NaN
    1  2.0  2.0
    2  3.0  3.0
    3  5.0  4.0
    4  3.0  6.0
    5  7.0  8.0

### General DataFrame combine

The `combine_first()` method above calls the more general `DataFrame.combine()`. This method takes another DataFrame and a combiner function, aligns the input DataFrame and then passes the combiner function pairs of Series (i.e., columns whose names are the same).

So, for instance, to reproduce `combine_first()` as above:

    In [76]: def combiner(x, y):
       ....:     return np.where(pd.isna(x), y, x)
       ....:

    In [77]: df1.combine(df2, combiner)
    Out[77]:
         A    B
    0  1.0  NaN
    1  2.0  2.0
    2  3.0  3.0
    3  5.0  4.0
    4  3.0  6.0
    5  7.0  8.0

## Descriptive statistics

There exists a large number of methods for computing descriptive statistics and other related operations on Series, DataFrame. Most of these are aggregations (hence producing a lower-dimensional result) like `sum()`, `mean()`, and `quantile()`, but some of them, like `cumsum()` and `cumprod()`, produce an object of the same size. Generally speaking, these methods take an axis argument, just like ndarray.{sum, std, …}, but the axis can be specified by name or integer:

- Series: no axis argument needed

- DataFrame: “index” (axis=0, default), “columns” (axis=1)

For example:

    In [78]: df
    Out[78]:
            one       two     three
    a  1.394981  1.772517       NaN
    b  0.343054  1.912123 -0.050390
    c  0.695246  1.478369  1.227435
    d       NaN  0.279344 -0.613172

    In [79]: df.mean(axis=0)
    Out[79]:
    one      0.811094
    two      1.360588
    three    0.187958
    dtype: float64

    In [80]: df.mean(axis=1)
    Out[80]:
    a    1.583749
    b    0.734929
    c    1.133683
    d   -0.166914
    dtype: float64

All such methods have a `skipna` option signaling whether to exclude missing data (`True` by default):

    In [81]: df.sum(axis=0, skipna=False)
    Out[81]:
    one           NaN
    two      5.442353
    three         NaN
    dtype: float64

    In [82]: df.sum(axis=1, skipna=True)
    Out[82]:
    a    3.167498
    b    2.204786
    c    3.401050
    d   -0.333828
    dtype: float64

Combined with the broadcasting / arithmetic behavior, one can describe various statistical procedures, like standardization (rendering data zero mean and standard deviation of 1), very concisely:

    In [83]: ts_stand = (df - df.mean()) / df.std()

    In [84]: ts_stand.std()
    Out[84]:
    one      1.0
    two      1.0
    three    1.0
    dtype: float64

    In [85]: xs_stand = df.sub(df.mean(axis=1), axis=0).div(df.std(axis=1), axis=0)

    In [86]: xs_stand.std(axis=1)
    Out[86]:
    a    1.0
    b    1.0
    c    1.0
    d    1.0
    dtype: float64

Note that methods like `cumsum()` and `cumprod()` preserve the location of `NaN` values. This is somewhat different from `expanding()` and `rolling()` since `NaN` behavior is furthermore dictated by a `min_periods` parameter.

    In [87]: df.cumsum()
    Out[87]:
            one       two     three
    a  1.394981  1.772517       NaN
    b  1.738035  3.684640 -0.050390
    c  2.433281  5.163008  1.177045
    d       NaN  5.442353  0.563873

Here is a quick reference summary table of common functions. Each also takes an optional `level` parameter which applies only if the object has a hierarchical index.

Function | Description
--- | ---
`count` | Number of non-NA observations
`sum` | Sum of values
`mean` | Mean of values
`median` | Arithmetic median of values
`min` | Minimum
`max` | Maximum
`mode` | Mode
`abs` | Absolute Value
`prod` | Product of values
`std` | Bessel-corrected sample standard deviation
`var` | Unbiased variance
`sem` | Standard error of the mean
`skew` | Sample skewness (3rd moment)
`kurt` | Sample kurtosis (4th moment)
`quantile` | Sample quantile (value at %)
`cumsum` | Cumulative sum
`cumprod` | Cumulative product
`cummax` | Cumulative maximum
`cummin` | Cumulative minimum

Note that by chance some NumPy methods, like `mean`, `std`, and `sum`, will exclude NAs on Series input by default:

    In [88]: np.mean(df["one"])
    Out[88]: np.float64(0.8110935116651192)

    In [89]: np.mean(df["one"].to_numpy())
    Out[89]: np.float64(nan)

`Series.nunique()` will return the number of unique non-NA values in a Series:

    In [90]: series = pd.Series(np.random.randn(500))

    In [91]: series[20:500] = np.nan

    In [92]: series[10:20] = 5

    In [93]: series.nunique()
    Out[93]: 11

### Summarizing data: describe

There is a convenient `describe()` function which computes a variety of summary statistics about a Series or the columns of a DataFrame (excluding NAs of course):

    In [94]: series = pd.Series(np.random.randn(1000))

    In [95]: series[::2] = np.nan

    In [96]: series.describe()
    Out[96]:
    count    500.000000
    mean      -0.021292
    std        1.015906
    min       -2.683763
    25%       -0.699070
    50%       -0.069718
    75%        0.714483
    max        3.160915
    dtype: float64

    In [97]: frame = pd.DataFrame(np.random.randn(1000, 5), columns=["a", "b", "c", "d", "e"])

    In [98]: frame.iloc[::2] = np.nan

    In [99]: frame.describe()
    Out[99]:
                    a           b           c           d           e
    count  500.000000  500.000000  500.000000  500.000000  500.000000
    mean     0.033387    0.030045   -0.043719   -0.051686    0.005979
    std      1.017152    0.978743    1.025270    1.015988    1.006695
    min     -3.000951   -2.637901   -3.303099   -3.159200   -3.188821
    25%     -0.647623   -0.576449   -0.712369   -0.691338   -0.691115
    50%      0.047578   -0.021499   -0.023888   -0.032652   -0.025363
    75%      0.729907    0.775880    0.618896    0.670047    0.649748
    max      2.740139    2.752332    3.004229    2.728702    3.240991

You can select specific percentiles to include in the output:

    In [100]: series.describe(percentiles=[0.05, 0.25, 0.75, 0.95])
    Out[100]:
    count    500.000000
    mean      -0.021292
    std        1.015906
    min       -2.683763
    5%        -1.645423
    25%       -0.699070
    75%        0.714483
    95%        1.711409
    max        3.160915
    dtype: float64

By default, the median is always included.

For a non-numerical Series object, `describe()` will give a simple summary of the number of unique values and most frequently occurring values:

    In [101]: s = pd.Series(["a", "a", "b", "b", "a", "a", np.nan, "c", "d", "a"])

    In [102]: s.describe()
    Out[102]:
    count     9
    unique    4
    top       a
    freq      5
    dtype: object

Note that on a mixed-type DataFrame object, `describe()` will restrict the summary to include only numerical columns or, if none are, only categorical columns:

    In [103]: frame = pd.DataFrame({"a": ["Yes", "Yes", "No", "No"], "b": range(4)})

    In [104]: frame.describe()
    Out[104]:
                  b
    count  4.000000
    mean   1.500000
    std    1.290994
    min    0.000000
    25%    0.750000
    50%    1.500000
    75%    2.250000
    max    3.000000

This behavior can be controlled by providing a list of types as `include`/`exclude` arguments. The special value `all` can also be used:

    In [105]: frame.describe(include=["str"])
    Out[105]:
              a
    count     4
    unique    2
    top     Yes
    freq      2

    In [106]: frame.describe(include=["number"])
    Out[106]:
                  b
    count  4.000000
    mean   1.500000
    std    1.290994
    min    0.000000
    25%    0.750000
    50%    1.500000
    75%    2.250000
    max    3.000000

    In [107]: frame.describe(include="all")
    Out[107]:
              a         b
    count     4  4.000000
    unique    2       NaN
    top     Yes       NaN
    freq      2       NaN
    mean    NaN  1.500000
    std     NaN  1.290994
    min     NaN  0.000000
    25%     NaN  0.750000
    50%     NaN  1.500000
    75%     NaN  2.250000
    max     NaN  3.000000

That feature relies on select_dtypes. Refer to there for details about accepted inputs.

### Index of min/max values

The `idxmin()` and `idxmax()` functions on Series and DataFrame compute the index labels with the minimum and maximum corresponding values:

    In [108]: s1 = pd.Series(np.random.randn(5))

    In [109]: s1
    Out[109]:
    0    1.118076
    1   -0.352051
    2   -1.242883
    3   -1.277155
    4   -0.641184
    dtype: float64

    In [110]: s1.idxmin(), s1.idxmax()
    Out[110]: (3, 0)

    In [111]: df1 = pd.DataFrame(np.random.randn(5, 3), columns=["A", "B", "C"])

    In [112]: df1
    Out[112]:
              A         B         C
    0 -0.327863 -0.946180 -0.137570
    1 -0.186235 -0.257213 -0.486567
    2 -0.507027 -0.871259 -0.111110
    3  2.000339 -2.430505  0.089759
    4 -0.321434 -0.033695  0.096271

    In [113]: df1.idxmin(axis=0)
    Out[113]:
    A    2
    B    3
    C    1
    dtype: int64

    In [114]: df1.idxmax(axis=1)
    Out[114]:
    0    C
    1    A
    2    C
    3    A
    4    C
    dtype: str

When there are multiple rows (or columns) matching the minimum or maximum value, `idxmin()` and `idxmax()` return the first matching index:

    In [115]: df3 = pd.DataFrame([2, 1, 1, 3, np.nan], columns=["A"], index=list("edcba"))

    In [116]: df3
    Out[116]:
         A
    e  2.0
    d  1.0
    c  1.0
    b  3.0
    a  NaN

    In [117]: df3["A"].idxmin()
    Out[117]: 'd'

Note

`idxmin` and `idxmax` are called `argmin` and `argmax` in NumPy.

### Value counts (histogramming) / mode

The `value_counts()` Series method computes a histogram of a 1D array of values. It can also be used as a function on regular arrays:

    In [118]: data = np.random.randint(0, 7, size=50)

    In [119]: data
    Out[119]:
    array([6, 6, 2, 3, 5, 3, 2, 5, 4, 5, 4, 3, 4, 5, 0, 2, 0, 4, 2, 0, 3, 2,
           2, 5, 6, 5, 3, 4, 6, 4, 3, 5, 6, 4, 3, 6, 2, 6, 6, 2, 3, 4, 2, 1,
           6, 2, 6, 1, 5, 4])

    In [120]: s = pd.Series(data)

    In [121]: s.value_counts()
    Out[121]:
    6    10
    2    10
    4     9
    3     8
    5     8
    0     3
    1     2
    Name: count, dtype: int64

The `value_counts()` method can be used to count combinations across multiple columns. By default all columns are used but a subset can be selected using the `subset` argument.

    In [122]: data = {"a": [1, 2, 3, 4], "b": ["x", "x", "y", "y"]}

    In [123]: frame = pd.DataFrame(data)

    In [124]: frame.value_counts()
    Out[124]:
    a  b
    1  x    1
    2  x    1
    3  y    1
    4  y    1
    Name: count, dtype: int64

Similarly, you can get the most frequently occurring value(s), i.e. the mode, of the values in a Series or DataFrame:

    In [125]: s5 = pd.Series([1, 1, 3, 3, 3, 5, 5, 7, 7, 7])

    In [126]: s5.mode()
    Out[126]:
    0    3
    1    7
    dtype: int64

    In [127]: df5 = pd.DataFrame(
       .....:     {
       .....:         "A": np.random.randint(0, 7, size=50),
       .....:         "B": np.random.randint(-10, 15, size=50),
       .....:     }
       .....: )
       .....:

    In [128]: df5.mode()
    Out[128]:
         A   B
    0  1.0  -9
    1  NaN  10
    2  NaN  13

### Discretization and quantiling

Continuous values can be discretized using the `cut()` (bins based on values) and `qcut()` (bins based on sample quantiles) functions:

    In [129]: arr = np.random.randn(20)

    In [130]: factor = pd.cut(arr, 4)

    In [131]: factor
    Out[131]:
    [(-0.251, 0.464], (-0.968, -0.251], (0.464, 1.179], (-0.251, 0.464],
     (-0.968, -0.251], ..., (-0.251, 0.464], (-0.968, -0.251], (-0.968, -0.251],
     (-0.968, -0.251], (-0.968, -0.251]]
    Length: 20
    Categories (4, interval[float64, right]): [(-0.968, -0.251] < (-0.251, 0.464] <
                                               (0.464, 1.179] < (1.179, 1.893]]

    In [132]: factor = pd.cut(arr, [-5, -1, 0, 1, 5])

    In [133]: factor
    Out[133]:
    [(0, 1], (-1, 0], (0, 1], (0, 1], (-1, 0], ..., (-1, 0], (-1, 0], (-1, 0],
     (-1, 0], (-1, 0]]
    Length: 20
    Categories (4, interval[int64, right]): [(-5, -1] < (-1, 0] < (0, 1] < (1, 5]]

`qcut()` computes sample quantiles. For example, we could slice up some normally distributed data into equal-size quartiles like so:

    In [134]: arr = np.random.randn(30)

    In [135]: factor = pd.qcut(arr, [0, 0.25, 0.5, 0.75, 1])

    In [136]: factor
    Out[136]:
    [(0.569, 1.184], (-2.278, -0.301], (-2.278, -0.301], (0.569, 1.184],
     (0.569, 1.184], ..., (-0.301, 0.569], (1.184, 2.346], (1.184, 2.346],
     (-0.301, 0.569], (-2.278, -0.301]]
    Length: 30
    Categories (4, interval[float64, right]): [(-2.278, -0.301] < (-0.301, 0.569] <
                                               (0.569, 1.184] < (1.184, 2.346]]

We can also pass infinite values to define the bins:

    In [137]: arr = np.random.randn(20)

    In [138]: factor = pd.cut(arr, [-np.inf, 0, np.inf])

    In [139]: factor
    Out[139]:
    [(-inf, 0.0], (0.0, inf], (0.0, inf], (-inf, 0.0], (-inf, 0.0], ..., (-inf, 0.0],
     (-inf, 0.0], (-inf, 0.0], (0.0, inf], (0.0, inf]]
    Length: 20
    Categories (2, interval[float64, right]): [(-inf, 0.0] < (0.0, inf]]

### Floating-point precision

pandas stores real-valued data using 64-bit floating point by default. Floating-point numbers can represent only a finite set of values: a decimal number like `0.03` is stored as the nearest representable binary fraction, and the result of every arithmetic operation is rounded to the nearest representable value. As a consequence, computations that would give identical results in exact arithmetic can differ, typically around the 16th significant digit, depending on how they are carried out:

    In [140]: ser = pd.Series(0.03, index=range(60))

    In [141]: ser.head(20).mean() == ser.mean()
    Out[141]: np.False_

Both means display as `0.03` by default, but they are not identical:

    In [142]: means = pd.Series([ser.head(20).mean(), ser.mean()])

    In [143]: with pd.option_context("display.precision", 20):
       .....:     print(means)
       .....:
    0    0.03000000000000000583
    1    0.03000000000000000236
    dtype: float64

This is not specific to pandas: it is inherent to IEEE 754 floating-point arithmetic, which is used by virtually all modern hardware and software. See the Python tutorial for an accessible introduction.

For this reason, avoid relying on exact equality of computed floating-point values. In particular, using computed floats as labels — for example grouping on a column produced by a `mean` — can split what displays as a single value into multiple groups:

    In [144]: means.nunique()
    Out[144]: 2

If you need to compare or group on computed floats, round them first with `round()`, compare with `numpy.isclose()`, or bin the values with `cut()`:

    In [145]: means.round(10).nunique()
    Out[145]: 1

## Function application

To apply your own or another library’s functions to pandas objects, you should be aware of the three methods below. The appropriate method to use depends on whether your function expects to operate on an entire `DataFrame` or `Series`, row- or column-wise, or elementwise.

1. Tablewise Function Application: `pipe()`

2. Row or Column-wise Function Application: `apply()`

3. Aggregation API: `agg()` and `transform()`

4. Applying Elementwise Functions: `map()`

### Tablewise function application

`DataFrames` and `Series` can be passed into functions. However, if the function needs to be called in a chain, consider using the `pipe()` method.

First some setup:

    In [146]: def extract_city_name(df):
       .....:     """
       .....:     Chicago, IL -> Chicago for city_name column
       .....:     """
       .....:     df["city_name"] = df["city_and_code"].str.split(",").str.get(0)
       .....:     return df
       .....:

    In [147]: def add_country_name(df, country_name=None):
       .....:     """
       .....:     Chicago -> Chicago-US for city_name column
       .....:     """
       .....:     col = "city_name"
       .....:     df["city_and_country"] = df[col] + country_name
       .....:     return df
       .....:

    In [148]: df_p = pd.DataFrame({"city_and_code": ["Chicago, IL"]})

`extract_city_name` and `add_country_name` are functions taking and returning `DataFrames`.

Now compare the following:

    In [149]: add_country_name(extract_city_name(df_p), country_name="US")
    Out[149]:
      city_and_code city_name city_and_country
    0   Chicago, IL   Chicago        ChicagoUS

Is equivalent to:

    In [150]: df_p.pipe(extract_city_name).pipe(add_country_name, country_name="US")
    Out[150]:
      city_and_code city_name city_and_country
    0   Chicago, IL   Chicago        ChicagoUS

pandas encourages the second style, which is known as method chaining. `pipe` makes it easy to use your own or another library’s functions in method chains, alongside pandas’ methods.

In the example above, the functions `extract_city_name` and `add_country_name` each expected a `DataFrame` as the first positional argument. What if the function you wish to apply takes its data as, say, the second argument? In this case, provide `pipe` with a tuple of `(callable, data_keyword)`. `.pipe` will route the `DataFrame` to the argument specified in the tuple.

For example, we can fit a regression using statsmodels. Their API expects a formula first and a `DataFrame` as the second argument, `data`. We pass in the function, keyword pair `(sm.ols, 'data')` to `pipe`:

    In [147]: import statsmodels.formula.api as sm

    In [148]: bb = pd.read_csv("data/baseball.csv", index_col="id")

    In [149]: (
       .....:     bb.query("h > 0")
       .....:     .assign(ln_h=lambda df: np.log(df.h))
       .....:     .pipe((sm.ols, "data"), "hr ~ ln_h + year + g + C(lg)")
       .....:     .fit()
       .....:     .summary()
       .....: )
       .....:
    Out[149]:
    <class 'statsmodels.iolib.summary.Summary'>
    """
                               OLS Regression Results
    ==============================================================================
    Dep. Variable:                     hr   R-squared:                       0.685
    Model:                            OLS   Adj. R-squared:                  0.665
    Method:                 Least Squares   F-statistic:                     34.28
    Date:                Tue, 22 Nov 2022   Prob (F-statistic):           3.48e-15
    Time:                        05:34:17   Log-Likelihood:                -205.92
    No. Observations:                  68   AIC:                             421.8
    Df Residuals:                      63   BIC:                             432.9
    Df Model:                           4
    Covariance Type:            nonrobust
    ===============================================================================
                      coef    std err          t      P>|t|      [0.025      0.975]
    -------------------------------------------------------------------------------
    Intercept   -8484.7720   4664.146     -1.819      0.074   -1.78e+04     835.780
    C(lg)[T.NL]    -2.2736      1.325     -1.716      0.091      -4.922       0.375
    ln_h           -1.3542      0.875     -1.547      0.127      -3.103       0.395
    year            4.2277      2.324      1.819      0.074      -0.417       8.872
    g               0.1841      0.029      6.258      0.000       0.125       0.243
    ==============================================================================
    Omnibus:                       10.875   Durbin-Watson:                   1.999
    Prob(Omnibus):                  0.004   Jarque-Bera (JB):               17.298
    Skew:                           0.537   Prob(JB):                     0.000175
    Kurtosis:                       5.225   Cond. No.                     1.49e+07
    ==============================================================================

    Notes:
    [1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
    [2] The condition number is large, 1.49e+07. This might indicate that there are
    strong multicollinearity or other numerical problems.
    """

The pipe method is inspired by unix pipes and more recently dplyr and magrittr, which have introduced the popular `(%>%)` (read pipe) operator for R. The implementation of `pipe` here is quite clean and feels right at home in Python. We encourage you to view the source code of `pipe()`.

### Row or column-wise function application

Arbitrary functions can be applied along the axes of a DataFrame using the `apply()` method, which, like the descriptive statistics methods, takes an optional `axis` argument:

    In [151]: df.apply(lambda x: np.mean(x))
    Out[151]:
    one      0.811094
    two      1.360588
    three    0.187958
    dtype: float64

    In [152]: df.apply(lambda x: np.mean(x), axis=1)
    Out[152]:
    a    1.583749
    b    0.734929
    c    1.133683
    d   -0.166914
    dtype: float64

    In [153]: df.apply(lambda x: x.max() - x.min())
    Out[153]:
    one      1.051928
    two      1.632779
    three    1.840607
    dtype: float64

    In [154]: df.apply(np.cumsum)
    Out[154]:
            one       two     three
    a  1.394981  1.772517       NaN
    b  1.738035  3.684640 -0.050390
    c  2.433281  5.163008  1.177045
    d       NaN  5.442353  0.563873

    In [155]: df.apply(np.exp)
    Out[155]:
            one       two     three
    a  4.034899  5.885648       NaN
    b  1.409244  6.767440  0.950858
    c  2.004201  4.385785  3.412466
    d       NaN  1.322262  0.541630

Warning

For a general Python callable, `apply()` invokes it once per column (or once per row with `axis=1`) in a Python-level loop. It is a tool for flexibility, not speed: when a vectorized equivalent exists, it will generally be much faster. Every example above can be written more directly without `apply`:

    In [156]: df.mean()            # df.apply(lambda x: np.mean(x))
    Out[156]:
    one      0.811094
    two      1.360588
    three    0.187958
    dtype: float64

    In [157]: df.mean(axis=1)      # df.apply(lambda x: np.mean(x), axis=1)
    Out[157]:
    a    1.583749
    b    0.734929
    c    1.133683
    d   -0.166914
    dtype: float64

    In [158]: df.max() - df.min()  # df.apply(lambda x: x.max() - x.min())
    Out[158]:
    one      1.051928
    two      1.632779
    three    1.840607
    dtype: float64

    In [159]: df.cumsum()          # df.apply(np.cumsum)
    Out[159]:
            one       two     three
    a  1.394981  1.772517       NaN
    b  1.738035  3.684640 -0.050390
    c  2.433281  5.163008  1.177045
    d       NaN  5.442353  0.563873

    In [160]: np.exp(df)           # df.apply(np.exp)
    Out[160]:
            one       two     three
    a  4.034899  5.885648       NaN
    b  1.409244  6.767440  0.950858
    c  2.004201  4.385785  3.412466
    d       NaN  1.322262  0.541630

The first four avoid the per-column loop and are several times faster on a wide frame. The last is a NumPy ufunc, which `apply` recognizes and applies to the whole frame at once, so the two are equally fast; the direct spelling is simply clearer.

Before reaching for `apply`, look for a vectorized alternative: a DataFrame or Series method, an arithmetic or comparison operation (see Flexible binary operations), or an accessor method such as the vectorized string methods under `.str` or the datetime properties under `.dt`. Reserve `apply` for logic with no vectorized equivalent, such as a function from a third-party library that operates on one row or column at a time.

The `apply()` method will also dispatch on a string method name.

    In [161]: df.apply("mean")
    Out[161]:
    one      0.811094
    two      1.360588
    three    0.187958
    dtype: float64

    In [162]: df.apply("mean", axis=1)
    Out[162]:
    a    1.583749
    b    0.734929
    c    1.133683
    d   -0.166914
    dtype: float64

The return type of the function passed to `apply()` affects the type of the final output from `DataFrame.apply` for the default behaviour:

- If the applied function returns a `Series`, the final output is a `DataFrame`. The columns match the index of the `Series` returned by the applied function.

- If the applied function returns any other type, the final output is a `Series`.

This default behaviour can be overridden using the `result_type`, which accepts three options: `reduce`, `broadcast`, and `expand`. These will determine how list-likes return values expand (or not) to a `DataFrame`.

`apply()` combined with some cleverness can be used to answer many questions about a data set. For example, suppose we wanted to extract the date where the maximum value for each column occurred:

    In [163]: tsdf = pd.DataFrame(
       .....:     np.random.randn(1000, 3),
       .....:     columns=["A", "B", "C"],
       .....:     index=pd.date_range("1/1/2000", periods=1000),
       .....: )
       .....:

    In [164]: tsdf.apply(lambda x: x.idxmax())
    Out[164]:
    A   2000-08-06
    B   2001-01-18
    C   2001-07-18
    dtype: datetime64[us]

(In this case the built-in `tsdf.idxmax()` gives the same result more simply; `apply` is shown for illustration.)

You may also pass additional arguments and keyword arguments to the `apply()` method.

    In [165]: def subtract_and_divide(x, sub, divide=1):
       .....:     return (x - sub) / divide
       .....:

    In [166]: df_udf = pd.DataFrame(np.ones((2, 2)))

    In [167]: df_udf.apply(subtract_and_divide, args=(5,), divide=3)
    Out[167]:
              0         1
    0 -1.333333 -1.333333
    1 -1.333333 -1.333333

Another useful feature is the ability to pass Series methods to carry out some Series operation on each column or row:

    In [168]: tsdf = pd.DataFrame(
       .....:     np.random.randn(10, 3),
       .....:     columns=["A", "B", "C"],
       .....:     index=pd.date_range("1/1/2000", periods=10),
       .....: )
       .....:

    In [169]: tsdf.iloc[3:7] = np.nan

    In [170]: tsdf
    Out[170]:
                       A         B         C
    2000-01-01 -0.158131 -0.232466  0.321604
    2000-01-02 -1.810340 -3.105758  0.433834
    2000-01-03 -1.209847 -1.156793 -0.136794
    2000-01-04       NaN       NaN       NaN
    2000-01-05       NaN       NaN       NaN
    2000-01-06       NaN       NaN       NaN
    2000-01-07       NaN       NaN       NaN
    2000-01-08 -0.653602  0.178875  1.008298
    2000-01-09  1.007996  0.462824  0.254472
    2000-01-10  0.307473  0.600337  1.643950

    In [171]: tsdf.apply(pd.Series.interpolate)
    Out[171]:
                       A         B         C
    2000-01-01 -0.158131 -0.232466  0.321604
    2000-01-02 -1.810340 -3.105758  0.433834
    2000-01-03 -1.209847 -1.156793 -0.136794
    2000-01-04 -1.098598 -0.889659  0.092225
    2000-01-05 -0.987349 -0.622526  0.321243
    2000-01-06 -0.876100 -0.355392  0.550262
    2000-01-07 -0.764851 -0.088259  0.779280
    2000-01-08 -0.653602  0.178875  1.008298
    2000-01-09  1.007996  0.462824  0.254472
    2000-01-10  0.307473  0.600337  1.643950

(Here too, `tsdf.interpolate()` is the idiomatic spelling.)

Finally, `apply()` takes an argument `raw` which is False by default, which converts each row or column into a Series before applying the function. When set to True, the passed function will instead receive an ndarray object, which has positive performance implications if you do not need the indexing functionality.

### Aggregation API

The aggregation API allows one to express possibly multiple aggregation operations in a single concise way. This API is similar across pandas objects, see groupby API, the window API, and the resample API. The entry point for aggregation is `DataFrame.aggregate()`, or the alias `DataFrame.agg()`.

We will use a similar starting frame from above:

    In [172]: tsdf = pd.DataFrame(
       .....:     np.random.randn(10, 3),
       .....:     columns=["A", "B", "C"],
       .....:     index=pd.date_range("1/1/2000", periods=10),
       .....: )
       .....:

    In [173]: tsdf.iloc[3:7] = np.nan

    In [174]: tsdf
    Out[174]:
                       A         B         C
    2000-01-01  1.257606  1.004194  0.167574
    2000-01-02 -0.749892  0.288112 -0.757304
    2000-01-03 -0.207550 -0.298599  0.116018
    2000-01-04       NaN       NaN       NaN
    2000-01-05       NaN       NaN       NaN
    2000-01-06       NaN       NaN       NaN
    2000-01-07       NaN       NaN       NaN
    2000-01-08  0.814347 -0.257623  0.869226
    2000-01-09 -0.250663 -1.206601  0.896839
    2000-01-10  2.169758 -1.333363  0.283157

Using a single function is equivalent to `apply()`. You can also pass named methods as strings. These will return a `Series` of the aggregated output:

    In [175]: tsdf.agg(lambda x: np.sum(x))
    Out[175]:
    A    3.033606
    B   -1.803879
    C    1.575510
    dtype: float64

    In [176]: tsdf.agg("sum")
    Out[176]:
    A    3.033606
    B   -1.803879
    C    1.575510
    dtype: float64

    # these are equivalent to a ``.sum()`` because we are aggregating
    # on a single function
    In [177]: tsdf.sum()
    Out[177]:
    A    3.033606
    B   -1.803879
    C    1.575510
    dtype: float64

Single aggregations on a `Series` this will return a scalar value:

    In [178]: tsdf["A"].agg("sum")
    Out[178]: np.float64(3.033606102414146)

#### Aggregating with multiple functions

You can pass multiple aggregation arguments as a list. The results of each of the passed functions will be a row in the resulting `DataFrame`. These are naturally named from the aggregation function.

    In [179]: tsdf.agg(["sum"])
    Out[179]:
                A         B        C
    sum  3.033606 -1.803879  1.57551

Multiple functions yield multiple rows:

    In [180]: tsdf.agg(["sum", "mean"])
    Out[180]:
                 A         B         C
    sum   3.033606 -1.803879  1.575510
    mean  0.505601 -0.300647  0.262585

On a `Series`, multiple functions return a `Series`, indexed by the function names:

    In [181]: tsdf["A"].agg(["sum", "mean"])
    Out[181]:
    sum     3.033606
    mean    0.505601
    Name: A, dtype: float64

Passing a `lambda` function will yield a `<lambda>` named row:

    In [182]: tsdf["A"].agg(["sum", lambda x: x.mean()])
    Out[182]:
    sum         3.033606
    <lambda>    0.505601
    Name: A, dtype: float64

Passing a named function will yield that name for the row:

    In [183]: def mymean(x):
       .....:     return x.mean()
       .....:

    In [184]: tsdf["A"].agg(["sum", mymean])
    Out[184]:
    sum       3.033606
    mymean    0.505601
    Name: A, dtype: float64

#### Aggregating with a dict

Passing a dictionary of column names to a scalar or a list of scalars, to `DataFrame.agg` allows you to customize which functions are applied to which columns. Note that the results are not in any particular order, you can use an `OrderedDict` instead to guarantee ordering.

    In [185]: tsdf.agg({"A": "mean", "B": "sum"})
    Out[185]:
    A    0.505601
    B   -1.803879
    dtype: float64

Passing a list-like will generate a `DataFrame` output. You will get a matrix-like output of all of the aggregators. The output will consist of all unique functions. Those that are not noted for a particular column will be `NaN`:

    In [186]: tsdf.agg({"A": ["mean", "min"], "B": "sum"})
    Out[186]:
                 A         B
    mean  0.505601       NaN
    min  -0.749892       NaN
    sum        NaN -1.803879

#### Custom describe

With `.agg()` it is possible to easily create a custom describe function, similar to the built in describe function.

    In [187]: from functools import partial

    In [188]: q_25 = partial(pd.Series.quantile, q=0.25)

    In [189]: q_25.__name__ = "25%"

    In [190]: q_75 = partial(pd.Series.quantile, q=0.75)

    In [191]: q_75.__name__ = "75%"

    In [192]: tsdf.agg(["count", "mean", "std", "min", q_25, "median", q_75, "max"])
    Out[192]:
                   A         B         C
    count   6.000000  6.000000  6.000000
    mean    0.505601 -0.300647  0.262585
    std     1.103362  0.887508  0.606860
    min    -0.749892 -1.333363 -0.757304
    25%    -0.239885 -0.979600  0.128907
    median  0.303398 -0.278111  0.225365
    75%     1.146791  0.151678  0.722709
    max     2.169758  1.004194  0.896839

### Transform API

The `transform()` method returns an object that is indexed the same (same size) as the original. This API allows you to provide multiple operations at the same time rather than one-by-one. Its API is quite similar to the `.agg` API.

We create a frame similar to the one used in the above sections.

    In [193]: tsdf = pd.DataFrame(
       .....:     np.random.randn(10, 3),
       .....:     columns=["A", "B", "C"],
       .....:     index=pd.date_range("1/1/2000", periods=10),
       .....: )
       .....:

    In [194]: tsdf.iloc[3:7] = np.nan

    In [195]: tsdf
    Out[195]:
                       A         B         C
    2000-01-01 -0.428759 -0.864890 -0.675341
    2000-01-02 -0.168731  1.338144 -1.279321
    2000-01-03 -1.621034  0.438107  0.903794
    2000-01-04       NaN       NaN       NaN
    2000-01-05       NaN       NaN       NaN
    2000-01-06       NaN       NaN       NaN
    2000-01-07       NaN       NaN       NaN
    2000-01-08  0.254374 -1.240447 -0.201052
    2000-01-09 -0.157795  0.791197 -1.144209
    2000-01-10 -0.030876  0.371900  0.061932

Transform the entire frame. `.transform()` allows input functions as: a NumPy function, a string function name or a user defined function.

    In [196]: tsdf.transform(np.abs)
    Out[196]:
                       A         B         C
    2000-01-01  0.428759  0.864890  0.675341
    2000-01-02  0.168731  1.338144  1.279321
    2000-01-03  1.621034  0.438107  0.903794
    2000-01-04       NaN       NaN       NaN
    2000-01-05       NaN       NaN       NaN
    2000-01-06       NaN       NaN       NaN
    2000-01-07       NaN       NaN       NaN
    2000-01-08  0.254374  1.240447  0.201052
    2000-01-09  0.157795  0.791197  1.144209
    2000-01-10  0.030876  0.371900  0.061932

    In [197]: tsdf.transform("abs")
    Out[197]:
                       A         B         C
    2000-01-01  0.428759  0.864890  0.675341
    2000-01-02  0.168731  1.338144  1.279321
    2000-01-03  1.621034  0.438107  0.903794
    2000-01-04       NaN       NaN       NaN
    2000-01-05       NaN       NaN       NaN
    2000-01-06       NaN       NaN       NaN
    2000-01-07       NaN       NaN       NaN
    2000-01-08  0.254374  1.240447  0.201052
    2000-01-09  0.157795  0.791197  1.144209
    2000-01-10  0.030876  0.371900  0.061932

    In [198]: tsdf.transform(lambda x: x.abs())
    Out[198]:
                       A         B         C
    2000-01-01  0.428759  0.864890  0.675341
    2000-01-02  0.168731  1.338144  1.279321
    2000-01-03  1.621034  0.438107  0.903794
    2000-01-04       NaN       NaN       NaN
    2000-01-05       NaN       NaN       NaN
    2000-01-06       NaN       NaN       NaN
    2000-01-07       NaN       NaN       NaN
    2000-01-08  0.254374  1.240447  0.201052
    2000-01-09  0.157795  0.791197  1.144209
    2000-01-10  0.030876  0.371900  0.061932

Here `transform()` received a single function; this is equivalent to a ufunc application.

    In [199]: np.abs(tsdf)
    Out[199]:
                       A         B         C
    2000-01-01  0.428759  0.864890  0.675341
    2000-01-02  0.168731  1.338144  1.279321
    2000-01-03  1.621034  0.438107  0.903794
    2000-01-04       NaN       NaN       NaN
    2000-01-05       NaN       NaN       NaN
    2000-01-06       NaN       NaN       NaN
    2000-01-07       NaN       NaN       NaN
    2000-01-08  0.254374  1.240447  0.201052
    2000-01-09  0.157795  0.791197  1.144209
    2000-01-10  0.030876  0.371900  0.061932

Passing a single function to `.transform()` with a `Series` will yield a single `Series` in return.

    In [200]: tsdf["A"].transform(np.abs)
    Out[200]:
    2000-01-01    0.428759
    2000-01-02    0.168731
    2000-01-03    1.621034
    2000-01-04         NaN
    2000-01-05         NaN
    2000-01-06         NaN
    2000-01-07         NaN
    2000-01-08    0.254374
    2000-01-09    0.157795
    2000-01-10    0.030876
    Freq: D, Name: A, dtype: float64

#### Transform with multiple functions

Passing multiple functions will yield a column MultiIndexed DataFrame. The first level will be the original frame column names; the second level will be the names of the transforming functions.

    In [201]: tsdf.transform([np.abs, lambda x: x + 1])
    Out[201]:
                       A                   B                   C
                absolute  <lambda>  absolute  <lambda>  absolute  <lambda>
    2000-01-01  0.428759  0.571241  0.864890  0.135110  0.675341  0.324659
    2000-01-02  0.168731  0.831269  1.338144  2.338144  1.279321 -0.279321
    2000-01-03  1.621034 -0.621034  0.438107  1.438107  0.903794  1.903794
    2000-01-04       NaN       NaN       NaN       NaN       NaN       NaN
    2000-01-05       NaN       NaN       NaN       NaN       NaN       NaN
    2000-01-06       NaN       NaN       NaN       NaN       NaN       NaN
    2000-01-07       NaN       NaN       NaN       NaN       NaN       NaN
    2000-01-08  0.254374  1.254374  1.240447 -0.240447  0.201052  0.798948
    2000-01-09  0.157795  0.842205  0.791197  1.791197  1.144209 -0.144209
    2000-01-10  0.030876  0.969124  0.371900  1.371900  0.061932  1.061932

Passing multiple functions to a Series will yield a DataFrame. The resulting column names will be the transforming functions.

    In [202]: tsdf["A"].transform([np.abs, lambda x: x + 1])
    Out[202]:
                absolute  <lambda>
    2000-01-01  0.428759  0.571241
    2000-01-02  0.168731  0.831269
    2000-01-03  1.621034 -0.621034
    2000-01-04       NaN       NaN
    2000-01-05       NaN       NaN
    2000-01-06       NaN       NaN
    2000-01-07       NaN       NaN
    2000-01-08  0.254374  1.254374
    2000-01-09  0.157795  0.842205
    2000-01-10  0.030876  0.969124

#### Transforming with a dict

Passing a dict of functions will allow selective transforming per column.

    In [203]: tsdf.transform({"A": np.abs, "B": lambda x: x + 1})
    Out[203]:
                       A         B
    2000-01-01  0.428759  0.135110
    2000-01-02  0.168731  2.338144
    2000-01-03  1.621034  1.438107
    2000-01-04       NaN       NaN
    2000-01-05       NaN       NaN
    2000-01-06       NaN       NaN
    2000-01-07       NaN       NaN
    2000-01-08  0.254374 -0.240447
    2000-01-09  0.157795  1.791197
    2000-01-10  0.030876  1.371900

Passing a dict of lists will generate a MultiIndexed DataFrame with these selective transforms.

    In [204]: tsdf.transform({"A": np.abs, "B": [lambda x: x + 1, "sqrt"]})
    Out[204]:
                       A         B
                absolute  <lambda>      sqrt
    2000-01-01  0.428759  0.135110       NaN
    2000-01-02  0.168731  2.338144  1.156782
    2000-01-03  1.621034  1.438107  0.661897
    2000-01-04       NaN       NaN       NaN
    2000-01-05       NaN       NaN       NaN
    2000-01-06       NaN       NaN       NaN
    2000-01-07       NaN       NaN       NaN
    2000-01-08  0.254374 -0.240447       NaN
    2000-01-09  0.157795  1.791197  0.889493
    2000-01-10  0.030876  1.371900  0.609836

### Applying elementwise functions

Since not all functions can be vectorized (accept NumPy arrays and return another array or value), the methods `map()` on DataFrame and analogously `map()` on Series accept any Python function taking a single value and returning a single value. For example:

    In [205]: df4 = df.copy()

    In [206]: df4
    Out[206]:
            one       two     three
    a  1.394981  1.772517       NaN
    b  0.343054  1.912123 -0.050390
    c  0.695246  1.478369  1.227435
    d       NaN  0.279344 -0.613172

    In [207]: def f(x):
       .....:     return len(str(x))
       .....:

    In [208]: df4["one"].map(f)
    Out[208]:
    a    18
    b    19
    c    18
    d     3
    Name: one, dtype: int64

    In [209]: df4.map(f)
    Out[209]:
       one  two  three
    a   18   17      3
    b   19   18     20
    c   18   18     16
    d    3   19     19

`Series.map()` has an additional feature; it can be used to easily “link” or “map” values defined by a secondary series. This is closely related to merging/joining functionality:

    In [210]: s = pd.Series(
       .....:     ["six", "seven", "six", "seven", "six"], index=["a", "b", "c", "d", "e"]
       .....: )
       .....:

    In [211]: t = pd.Series({"six": 6.0, "seven": 7.0})

    In [212]: s
    Out[212]:
    a      six
    b    seven
    c      six
    d    seven
    e      six
    dtype: str

    In [213]: s.map(t)
    Out[213]:
    a    6.0
    b    7.0
    c    6.0
    d    7.0
    e    6.0
    dtype: float64

## Reindexing and altering labels

`reindex()` is the fundamental data alignment method in pandas. It is used to implement nearly all other features relying on label-alignment functionality. To reindex means to conform the data to match a given set of labels along a particular axis. This accomplishes several things:

- Reorders the existing data to match a new set of labels

- Inserts missing value (NA) markers in label locations where no data for that label existed

- If specified, fill data for missing labels using logic (highly relevant to working with time series data)

Here is a simple example:

    In [214]: s = pd.Series(np.random.randn(5), index=["a", "b", "c", "d", "e"])

    In [215]: s
    Out[215]:
    a    1.695148
    b    1.328614
    c    1.234686
    d   -0.385845
    e   -1.326508
    dtype: float64

    In [216]: s.reindex(["e", "b", "f", "d"])
    Out[216]:
    e   -1.326508
    b    1.328614
    f         NaN
    d   -0.385845
    dtype: float64

Here, the `f` label was not contained in the Series and hence appears as `NaN` in the result.

With a DataFrame, you can simultaneously reindex the index and columns:

    In [217]: df
    Out[217]:
            one       two     three
    a  1.394981  1.772517       NaN
    b  0.343054  1.912123 -0.050390
    c  0.695246  1.478369  1.227435
    d       NaN  0.279344 -0.613172

    In [218]: df.reindex(index=["c", "f", "b"], columns=["three", "two", "one"])
    Out[218]:
          three       two       one
    c  1.227435  1.478369  0.695246
    f       NaN       NaN       NaN
    b -0.050390  1.912123  0.343054

Note that the `Index` objects containing the actual axis labels can be shared between objects. So if we have a Series and a DataFrame, the following can be done:

    In [219]: rs = s.reindex(df.index)

    In [220]: rs
    Out[220]:
    a    1.695148
    b    1.328614
    c    1.234686
    d   -0.385845
    dtype: float64

    In [221]: rs.index is df.index
    Out[221]: True

This means that the reindexed Series’s index is the same Python object as the DataFrame’s index.

`DataFrame.reindex()` also supports an “axis-style” calling convention, where you specify a single `labels` argument and the `axis` it applies to.

    In [222]: df.reindex(["c", "f", "b"], axis="index")
    Out[222]:
            one       two     three
    c  0.695246  1.478369  1.227435
    f       NaN       NaN       NaN
    b  0.343054  1.912123 -0.050390

    In [223]: df.reindex(["three", "two", "one"], axis="columns")
    Out[223]:
          three       two       one
    a       NaN  1.772517  1.394981
    b -0.050390  1.912123  0.343054
    c  1.227435  1.478369  0.695246
    d -0.613172  0.279344       NaN

See also

MultiIndex / Advanced Indexing is an even more concise way of doing reindexing.

Note

When writing performance-sensitive code, there is a good reason to spend some time becoming a reindexing ninja: many operations are faster on pre-aligned data. Adding two unaligned DataFrames internally triggers a reindexing step. For exploratory analysis you will hardly notice the difference (because `reindex` has been heavily optimized), but when CPU cycles matter sprinkling a few explicit `reindex` calls here and there can have an impact.

### Reindexing to align with another object

You may wish to take an object and reindex its axes to be labeled the same as another object. While the syntax for this is straightforward albeit verbose, it is a common enough operation that the `reindex_like()` method is available to make this simpler:

    In [224]: df2 = df.reindex(["a", "b", "c"], columns=["one", "two"])

    In [225]: df3 = df2 - df2.mean()

    In [226]: df2
    Out[226]:
            one       two
    a  1.394981  1.772517
    b  0.343054  1.912123
    c  0.695246  1.478369

    In [227]: df3
    Out[227]:
            one       two
    a  0.583888  0.051514
    b -0.468040  0.191120
    c -0.115848 -0.242634

    In [228]: df.reindex_like(df2)
    Out[228]:
            one       two
    a  1.394981  1.772517
    b  0.343054  1.912123
    c  0.695246  1.478369

### Aligning objects with each other with `align`

The `align()` method is the fastest way to simultaneously align two objects. It supports a `join` argument (related to joining and merging):

> - `join='outer'`: take the union of the indexes (default)

> - `join='left'`: use the calling object’s index

> - `join='right'`: use the passed object’s index

> - `join='inner'`: intersect the indexes

It returns a tuple with both of the reindexed Series:

    In [229]: s = pd.Series(np.random.randn(5), index=["a", "b", "c", "d", "e"])

    In [230]: s1 = s[:4]

    In [231]: s2 = s[1:]

    In [232]: s1.align(s2)
    Out[232]:
    (a   -0.186646
     b   -1.692424
     c   -0.303893
     d   -1.425662
     e         NaN
     dtype: float64,
     a         NaN
     b   -1.692424
     c   -0.303893
     d   -1.425662
     e    1.114285
     dtype: float64)

    In [233]: s1.align(s2, join="inner")
    Out[233]:
    (b   -1.692424
     c   -0.303893
     d   -1.425662
     dtype: float64,
     b   -1.692424
     c   -0.303893
     d   -1.425662
     dtype: float64)

    In [234]: s1.align(s2, join="left")
    Out[234]:
    (a   -0.186646
     b   -1.692424
     c   -0.303893
     d   -1.425662
     dtype: float64,
     a         NaN
     b   -1.692424
     c   -0.303893
     d   -1.425662
     dtype: float64)

For DataFrames, the join method will be applied to both the index and the columns by default:

    In [235]: df.align(df2, join="inner")
    Out[235]:
    (        one       two
     a  1.394981  1.772517
     b  0.343054  1.912123
     c  0.695246  1.478369,
             one       two
     a  1.394981  1.772517
     b  0.343054  1.912123
     c  0.695246  1.478369)

You can also pass an `axis` option to only align on the specified axis:

    In [236]: df.align(df2, join="inner", axis=0)
    Out[236]:
    (        one       two     three
     a  1.394981  1.772517       NaN
     b  0.343054  1.912123 -0.050390
     c  0.695246  1.478369  1.227435,
             one       two
     a  1.394981  1.772517
     b  0.343054  1.912123
     c  0.695246  1.478369)

If you pass a Series to `DataFrame.align()`, you can choose to align both objects either on the DataFrame’s index or columns using the `axis` argument:

    In [237]: df.align(df2.iloc[0], axis=1)
    Out[237]:
    (        one     three       two
     a  1.394981       NaN  1.772517
     b  0.343054 -0.050390  1.912123
     c  0.695246  1.227435  1.478369
     d       NaN -0.613172  0.279344,
     one      1.394981
     three         NaN
     two      1.772517
     Name: a, dtype: float64)

### Filling while reindexing

`reindex()` takes an optional parameter `method` which is a filling method chosen from the following table:

Method | Action
--- | ---
ffill | Fill values forward
bfill | Fill values backward
nearest | Fill from the nearest index value

We illustrate these fill methods on a simple Series:

    In [238]: rng = pd.date_range("1/3/2000", periods=8)

    In [239]: ts = pd.Series(np.random.randn(8), index=rng)

    In [240]: ts2 = ts.iloc[[0, 3, 6]]

    In [241]: ts
    Out[241]:
    2000-01-03    0.183051
    2000-01-04    0.400528
    2000-01-05   -0.015083
    2000-01-06    2.395489
    2000-01-07    1.414806
    2000-01-08    0.118428
    2000-01-09    0.733639
    2000-01-10   -0.936077
    Freq: D, dtype: float64

    In [242]: ts2
    Out[242]:
    2000-01-03    0.183051
    2000-01-06    2.395489
    2000-01-09    0.733639
    Freq: 3D, dtype: float64

    In [243]: ts2.reindex(ts.index)
    Out[243]:
    2000-01-03    0.183051
    2000-01-04         NaN
    2000-01-05         NaN
    2000-01-06    2.395489
    2000-01-07         NaN
    2000-01-08         NaN
    2000-01-09    0.733639
    2000-01-10         NaN
    Freq: D, dtype: float64

    In [244]: ts2.reindex(ts.index, method="ffill")
    Out[244]:
    2000-01-03    0.183051
    2000-01-04    0.183051
    2000-01-05    0.183051
    2000-01-06    2.395489
    2000-01-07    2.395489
    2000-01-08    2.395489
    2000-01-09    0.733639
    2000-01-10    0.733639
    Freq: D, dtype: float64

    In [245]: ts2.reindex(ts.index, method="bfill")
    Out[245]:
    2000-01-03    0.183051
    2000-01-04    2.395489
    2000-01-05    2.395489
    2000-01-06    2.395489
    2000-01-07    0.733639
    2000-01-08    0.733639
    2000-01-09    0.733639
    2000-01-10         NaN
    Freq: D, dtype: float64

    In [246]: ts2.reindex(ts.index, method="nearest")
    Out[246]:
    2000-01-03    0.183051
    2000-01-04    0.183051
    2000-01-05    2.395489
    2000-01-06    2.395489
    2000-01-07    2.395489
    2000-01-08    0.733639
    2000-01-09    0.733639
    2000-01-10    0.733639
    Freq: D, dtype: float64

These methods require that the indexes are ordered increasing or decreasing.

Note that the same result could have been achieved using ffill (except for `method='nearest'`) or interpolate:

    In [247]: ts2.reindex(ts.index).ffill()
    Out[247]:
    2000-01-03    0.183051
    2000-01-04    0.183051
    2000-01-05    0.183051
    2000-01-06    2.395489
    2000-01-07    2.395489
    2000-01-08    2.395489
    2000-01-09    0.733639
    2000-01-10    0.733639
    Freq: D, dtype: float64

`reindex()` will raise a ValueError if the index is not monotonically increasing or decreasing. `fillna()` and `interpolate()` will not perform any checks on the order of the index.

### Limits on filling while reindexing

The `limit` and `tolerance` arguments provide additional control over filling while reindexing. Limit specifies the maximum count of consecutive matches:

    In [248]: ts2.reindex(ts.index, method="ffill", limit=1)
    Out[248]:
    2000-01-03    0.183051
    2000-01-04    0.183051
    2000-01-05         NaN
    2000-01-06    2.395489
    2000-01-07    2.395489
    2000-01-08         NaN
    2000-01-09    0.733639
    2000-01-10    0.733639
    Freq: D, dtype: float64

In contrast, tolerance specifies the maximum distance between the index and indexer values:

    In [249]: ts2.reindex(ts.index, method="ffill", tolerance="1 day")
    Out[249]:
    2000-01-03    0.183051
    2000-01-04    0.183051
    2000-01-05         NaN
    2000-01-06    2.395489
    2000-01-07    2.395489
    2000-01-08         NaN
    2000-01-09    0.733639
    2000-01-10    0.733639
    Freq: D, dtype: float64

Notice that when used on a `DatetimeIndex`, `TimedeltaIndex` or `PeriodIndex`, `tolerance` will coerced into a `Timedelta` if possible. This allows you to specify tolerance with appropriate strings.

### Dropping labels from an axis

A method closely related to `reindex` is the `drop()` function. It removes a set of labels from an axis:

    In [250]: df
    Out[250]:
            one       two     three
    a  1.394981  1.772517       NaN
    b  0.343054  1.912123 -0.050390
    c  0.695246  1.478369  1.227435
    d       NaN  0.279344 -0.613172

    In [251]: df.drop(["a", "d"], axis=0)
    Out[251]:
            one       two     three
    b  0.343054  1.912123 -0.050390
    c  0.695246  1.478369  1.227435

    In [252]: df.drop(["one"], axis=1)
    Out[252]:
            two     three
    a  1.772517       NaN
    b  1.912123 -0.050390
    c  1.478369  1.227435
    d  0.279344 -0.613172

Note that the following also works, but is a bit less obvious / clean:

    In [253]: df.reindex(df.index.difference(["a", "d"]))
    Out[253]:
            one       two     three
    b  0.343054  1.912123 -0.050390
    c  0.695246  1.478369  1.227435

### Renaming / mapping labels

The `rename()` method allows you to relabel an axis based on some mapping (a dict or Series) or an arbitrary function.

    In [254]: s
    Out[254]:
    a   -0.186646
    b   -1.692424
    c   -0.303893
    d   -1.425662
    e    1.114285
    dtype: float64

    In [255]: s.rename(str.upper)
    Out[255]:
    A   -0.186646
    B   -1.692424
    C   -0.303893
    D   -1.425662
    E    1.114285
    dtype: float64

If you pass a function, it must return a value when called with any of the labels (and must produce a set of unique values). A dict or Series can also be used:

    In [256]: df.rename(
       .....:     columns={"one": "foo", "two": "bar"},
       .....:     index={"a": "apple", "b": "banana", "d": "durian"},
       .....: )
       .....:
    Out[256]:
                 foo       bar     three
    apple   1.394981  1.772517       NaN
    banana  0.343054  1.912123 -0.050390
    c       0.695246  1.478369  1.227435
    durian       NaN  0.279344 -0.613172

If the mapping doesn’t include a column/index label, it isn’t renamed. Note that extra labels in the mapping don’t throw an error.

`DataFrame.rename()` also supports an “axis-style” calling convention, where you specify a single `mapper` and the `axis` to apply that mapping to.

    In [257]: df.rename({"one": "foo", "two": "bar"}, axis="columns")
    Out[257]:
            foo       bar     three
    a  1.394981  1.772517       NaN
    b  0.343054  1.912123 -0.050390
    c  0.695246  1.478369  1.227435
    d       NaN  0.279344 -0.613172

    In [258]: df.rename({"a": "apple", "b": "banana", "d": "durian"}, axis="index")
    Out[258]:
                 one       two     three
    apple   1.394981  1.772517       NaN
    banana  0.343054  1.912123 -0.050390
    c       0.695246  1.478369  1.227435
    durian       NaN  0.279344 -0.613172

Finally, `rename()` also accepts a scalar or list-like for altering the `Series.name` attribute.

    In [259]: s.rename("scalar-name")
    Out[259]:
    a   -0.186646
    b   -1.692424
    c   -0.303893
    d   -1.425662
    e    1.114285
    Name: scalar-name, dtype: float64

The methods `DataFrame.rename_axis()` and `Series.rename_axis()` allow specific names of a `MultiIndex` to be changed (as opposed to the labels).

    In [260]: df = pd.DataFrame(
       .....:     {"x": [1, 2, 3, 4, 5, 6], "y": [10, 20, 30, 40, 50, 60]},
       .....:     index=pd.MultiIndex.from_product(
       .....:         [["a", "b", "c"], [1, 2]], names=["let", "num"]
       .....:     ),
       .....: )
       .....:

    In [261]: df
    Out[261]:
             x   y
    let num
    a   1    1  10
        2    2  20
    b   1    3  30
        2    4  40
    c   1    5  50
        2    6  60

    In [262]: df.rename_axis(index={"let": "abc"})
    Out[262]:
             x   y
    abc num
    a   1    1  10
        2    2  20
    b   1    3  30
        2    4  40
    c   1    5  50
        2    6  60

    In [263]: df.rename_axis(index=str.upper)
    Out[263]:
             x   y
    LET NUM
    a   1    1  10
        2    2  20
    b   1    3  30
        2    4  40
    c   1    5  50
        2    6  60

## Iteration

The behavior of basic iteration over pandas objects depends on the type. When iterating over a Series, it is regarded as array-like, and basic iteration produces the values. DataFrames follow the dict-like convention of iterating over the “keys” of the objects.

In short, basic iteration (`for i in object`) produces:

- Series: values

- DataFrame: column labels

Thus, for example, iterating over a DataFrame gives you the column names:

    In [264]: df = pd.DataFrame(
       .....:     {"col1": np.random.randn(3), "col2": np.random.randn(3)}, index=["a", "b", "c"]
       .....: )
       .....:

    In [265]: for col in df:
       .....:     print(col)
       .....:
    col1
    col2

pandas objects also have the dict-like `items()` method to iterate over the (key, value) pairs.

To iterate over the rows of a DataFrame, you can use the following methods:

- `iterrows()`: Iterate over the rows of a DataFrame as (index, Series) pairs. This converts the rows to Series objects, which can change the dtypes and has some performance implications.

- `itertuples()`: Iterate over the rows of a DataFrame as namedtuples of the values. This is a lot faster than `iterrows()`, and is in most cases preferable to use to iterate over the values of a DataFrame.

Warning

Iterating through pandas objects is generally slow. In many cases, iterating manually over the rows is not needed and can be avoided with one of the following approaches:

- Look for a vectorized solution: many operations can be performed using built-in methods or NumPy functions, (boolean) indexing, …

- When you have a function that cannot work on the full DataFrame/Series at once, it is better to use `apply()` instead of iterating over the values. See the docs on function application.

- If you need to do iterative manipulations on the values but performance is important, consider writing the inner loop with cython or numba. See the enhancing performance section for some examples of this approach.

Warning

You should never modify something you are iterating over. This is not guaranteed to work in all cases. Depending on the data types, the iterator returns a copy and not a view, and writing to it will have no effect!

For example, in the following case setting the value has no effect:

    In [266]: df = pd.DataFrame({"a": [1, 2, 3], "b": ["a", "b", "c"]})

    In [267]: for index, row in df.iterrows():
       .....:     row["a"] = 10
       .....:

    In [268]: df
    Out[268]:
       a  b
    0  1  a
    1  2  b
    2  3  c

### items

Consistent with the dict-like interface, `items()` iterates through key-value pairs:

- Series: (index, scalar value) pairs

- DataFrame: (column, Series) pairs

For example:

    In [269]: for label, ser in df.items():
       .....:     print(label)
       .....:     print(ser)
       .....:
    a
    0    1
    1    2
    2    3
    Name: a, dtype: int64
    b
    0    a
    1    b
    2    c
    Name: b, dtype: str

### iterrows

`iterrows()` allows you to iterate through the rows of a DataFrame as Series objects. It returns an iterator yielding each index value along with a Series containing the data in each row:

    In [270]: for row_index, row in df.iterrows():
       .....:     print(row_index, row, sep="\n")
       .....:
    0
    a    1
    b    a
    Name: 0, dtype: object
    1
    a    2
    b    b
    Name: 1, dtype: object
    2
    a    3
    b    c
    Name: 2, dtype: object

Note

Because `iterrows()` returns a Series for each row, it does not preserve dtypes across the rows (dtypes are preserved across columns for DataFrames). For example,

    In [271]: df_orig = pd.DataFrame([[1, 1.5]], columns=["int", "float"])

    In [272]: df_orig.dtypes
    Out[272]:
    int        int64
    float    float64
    dtype: object

    In [273]: row = next(df_orig.iterrows())[1]

    In [274]: row
    Out[274]:
    int      1.0
    float    1.5
    Name: 0, dtype: float64

All values in `row`, returned as a Series, are now upcasted to floats, also the original integer value in column `x`:

    In [275]: row["int"].dtype
    Out[275]: dtype('float64')

    In [276]: df_orig["int"].dtype
    Out[276]: dtype('int64')

To preserve dtypes while iterating over the rows, it is better to use `itertuples()` which returns namedtuples of the values and which is generally much faster than `iterrows()`.

For instance, a contrived way to transpose the DataFrame would be:

    In [277]: df2 = pd.DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})

    In [278]: print(df2)
       x  y
    0  1  4
    1  2  5
    2  3  6

    In [279]: print(df2.T)
       0  1  2
    x  1  2  3
    y  4  5  6

    In [280]: df2_t = pd.DataFrame({idx: values for idx, values in df2.iterrows()})

    In [281]: print(df2_t)
       0  1  2
    x  1  2  3
    y  4  5  6

### itertuples

The `itertuples()` method will return an iterator yielding a namedtuple for each row in the DataFrame. The first element of the tuple will be the row’s corresponding index value, while the remaining values are the row values.

For instance:

    In [282]: for row in df.itertuples():
       .....:     print(row)
       .....:
    Pandas(Index=0, a=1, b='a')
    Pandas(Index=1, a=2, b='b')
    Pandas(Index=2, a=3, b='c')

This method does not convert the row to a Series object; it merely returns the values inside a namedtuple. Therefore, `itertuples()` preserves the data type of the values and is generally faster than `iterrows()`.

Note

The column names will be renamed to positional names if they are invalid Python identifiers, repeated, or start with an underscore. With a large number of columns (>255), regular tuples are returned.

## .dt accessor

`Series` has an accessor to succinctly return datetime like properties for the values of the Series, if it is a datetime/period like Series. This will return a Series, indexed like the existing Series.

    # datetime
    In [283]: s = pd.Series(pd.date_range("20130101 09:10:12", periods=4))

    In [284]: s
    Out[284]:
    0   2013-01-01 09:10:12
    1   2013-01-02 09:10:12
    2   2013-01-03 09:10:12
    3   2013-01-04 09:10:12
    dtype: datetime64[us]

    In [285]: s.dt.hour
    Out[285]:
    0    9
    1    9
    2    9
    3    9
    dtype: int32

    In [286]: s.dt.second
    Out[286]:
    0    12
    1    12
    2    12
    3    12
    dtype: int32

    In [287]: s.dt.day
    Out[287]:
    0    1
    1    2
    2    3
    3    4
    dtype: int32

This enables nice expressions like this:

    In [288]: s[s.dt.day == 2]
    Out[288]:
    1   2013-01-02 09:10:12
    dtype: datetime64[us]

You can easily produce tz aware transformations:

    In [289]: stz = s.dt.tz_localize("US/Eastern")

    In [290]: stz
    Out[290]:
    0   2013-01-01 09:10:12-05:00
    1   2013-01-02 09:10:12-05:00
    2   2013-01-03 09:10:12-05:00
    3   2013-01-04 09:10:12-05:00
    dtype: datetime64[us, US/Eastern]

    In [291]: stz.dt.tz
    Out[291]: zoneinfo.ZoneInfo(key='US/Eastern')

You can also chain these types of operations:

    In [292]: s.dt.tz_localize("UTC").dt.tz_convert("US/Eastern")
    Out[292]:
    0   2013-01-01 04:10:12-05:00
    1   2013-01-02 04:10:12-05:00
    2   2013-01-03 04:10:12-05:00
    3   2013-01-04 04:10:12-05:00
    dtype: datetime64[us, US/Eastern]

You can also format datetime values as strings with `Series.dt.strftime()` which supports the same format as the standard `strftime()`.

    # DatetimeIndex
    In [293]: s = pd.Series(pd.date_range("20130101", periods=4))

    In [294]: s
    Out[294]:
    0   2013-01-01
    1   2013-01-02
    2   2013-01-03
    3   2013-01-04
    dtype: datetime64[us]

    In [295]: s.dt.strftime("%Y/%m/%d")
    Out[295]:
    0    2013/01/01
    1    2013/01/02
    2    2013/01/03
    3    2013/01/04
    dtype: str

    # PeriodIndex
    In [296]: s = pd.Series(pd.period_range("20130101", periods=4))

    In [297]: s
    Out[297]:
    0    2013-01-01
    1    2013-01-02
    2    2013-01-03
    3    2013-01-04
    dtype: period[D]

    In [298]: s.dt.strftime("%Y/%m/%d")
    Out[298]:
    0    2013/01/01
    1    2013/01/02
    2    2013/01/03
    3    2013/01/04
    dtype: str

The `.dt` accessor works for period and timedelta dtypes.

    # period
    In [299]: s = pd.Series(pd.period_range("20130101", periods=4, freq="D"))

    In [300]: s
    Out[300]:
    0    2013-01-01
    1    2013-01-02
    2    2013-01-03
    3    2013-01-04
    dtype: period[D]

    In [301]: s.dt.year
    Out[301]:
    0    2013
    1    2013
    2    2013
    3    2013
    dtype: int64

    In [302]: s.dt.day
    Out[302]:
    0    1
    1    2
    2    3
    3    4
    dtype: int64

    # timedelta
    In [303]: s = pd.Series(pd.timedelta_range("1 day 00:00:05", periods=4, freq="s"))

    In [304]: s
    Out[304]:
    0   1 days 00:00:05
    1   1 days 00:00:06
    2   1 days 00:00:07
    3   1 days 00:00:08
    dtype: timedelta64[us]

    In [305]: s.dt.days
    Out[305]:
    0    1
    1    1
    2    1
    3    1
    dtype: int64

    In [306]: s.dt.seconds
    Out[306]:
    0    5
    1    6
    2    7
    3    8
    dtype: int32

    In [307]: s.dt.components
    Out[307]:
       days  hours  minutes  seconds  milliseconds  microseconds  nanoseconds
    0     1      0        0        5             0             0            0
    1     1      0        0        6             0             0            0
    2     1      0        0        7             0             0            0
    3     1      0        0        8             0             0            0

Note

`Series.dt` will raise a `TypeError` if you access with a non-datetime-like values.

## Vectorized string methods

Series is equipped with a set of string processing methods that make it easy to operate on each element of the array. Perhaps most importantly, these methods exclude missing/NA values automatically. These are accessed via the Series’s `str` attribute and generally have names matching the equivalent (scalar) built-in string methods. For example:

>     In [308]: s = pd.Series(
>        .....:     ["A", "B", "C", "Aaba", "Baca", np.nan, "CABA", "dog", "cat"], dtype="string"
>        .....: )
>        .....:

>     In [309]: s.str.lower()
>     Out[309]:
>     0       a
>     1       b
>     2       c
>     3    aaba
>     4    baca
>     5    <NA>
>     6    caba
>     7     dog
>     8     cat
>     dtype: string

Powerful pattern-matching methods are provided as well, but note that pattern-matching generally uses regular expressions by default (and in some cases always uses them).

Note

Prior to pandas 1.0, string methods were only available on `object` -dtype `Series`. pandas 1.0 added the `StringDtype` which is dedicated to strings. See Text data types for more.

Please see Vectorized String Methods for a complete description.

## Sorting

pandas supports three kinds of sorting: sorting by index labels, sorting by column values, and sorting by a combination of both.

### By index

The `Series.sort_index()` and `DataFrame.sort_index()` methods are used to sort a pandas object by its index levels.

    In [310]: df = pd.DataFrame(
       .....:     {
       .....:         "one": pd.Series(np.random.randn(3), index=["a", "b", "c"]),
       .....:         "two": pd.Series(np.random.randn(4), index=["a", "b", "c", "d"]),
       .....:         "three": pd.Series(np.random.randn(3), index=["b", "c", "d"]),
       .....:     }
       .....: )
       .....:

    In [311]: unsorted_df = df.reindex(
       .....:     index=["a", "d", "c", "b"], columns=["three", "two", "one"]
       .....: )
       .....:

    In [312]: unsorted_df
    Out[312]:
          three       two       one
    a       NaN -1.152244  0.562973
    d -0.252916 -0.109597       NaN
    c  1.273388 -0.167123  0.640382
    b -0.098217  0.009797 -1.299504

    # DataFrame
    In [313]: unsorted_df.sort_index()
    Out[313]:
          three       two       one
    a       NaN -1.152244  0.562973
    b -0.098217  0.009797 -1.299504
    c  1.273388 -0.167123  0.640382
    d -0.252916 -0.109597       NaN

    In [314]: unsorted_df.sort_index(ascending=False)
    Out[314]:
          three       two       one
    d -0.252916 -0.109597       NaN
    c  1.273388 -0.167123  0.640382
    b -0.098217  0.009797 -1.299504
    a       NaN -1.152244  0.562973

    In [315]: unsorted_df.sort_index(axis=1)
    Out[315]:
            one     three       two
    a  0.562973       NaN -1.152244
    d       NaN -0.252916 -0.109597
    c  0.640382  1.273388 -0.167123
    b -1.299504 -0.098217  0.009797

    # Series
    In [316]: unsorted_df["three"].sort_index()
    Out[316]:
    a         NaN
    b   -0.098217
    c    1.273388
    d   -0.252916
    Name: three, dtype: float64

Sorting by index also supports a `key` parameter that takes a callable function to apply to the index being sorted. For `MultiIndex` objects, the key is applied per-level to the levels specified by `level`.

    In [317]: s1 = pd.DataFrame({"a": ["B", "a", "C"], "b": [1, 2, 3], "c": [2, 3, 4]}).set_index(
       .....:     list("ab")
       .....: )
       .....:

    In [318]: s1
    Out[318]:
         c
    a b
    B 1  2
    a 2  3
    C 3  4

    In [319]: s1.sort_index(level="a")
    Out[319]:
         c
    a b
    B 1  2
    C 3  4
    a 2  3

    In [320]: s1.sort_index(level="a", key=lambda idx: idx.str.lower())
    Out[320]:
         c
    a b
    a 2  3
    B 1  2
    C 3  4

For information on key sorting by value, see value sorting.

### By values

The `Series.sort_values()` method is used to sort a `Series` by its values. The `DataFrame.sort_values()` method is used to sort a `DataFrame` by its column or row values. The optional `by` parameter to `DataFrame.sort_values()` may used to specify one or more columns to use to determine the sorted order.

    In [321]: df1 = pd.DataFrame(
       .....:     {"one": [2, 1, 1, 1], "two": [1, 3, 2, 4], "three": [5, 4, 3, 2]}
       .....: )
       .....:

    In [322]: df1.sort_values(by="two")
    Out[322]:
       one  two  three
    0    2    1      5
    2    1    2      3
    1    1    3      4
    3    1    4      2

The `by` parameter can take a list of column names, e.g.:

    In [323]: df1[["one", "two", "three"]].sort_values(by=["one", "two"])
    Out[323]:
       one  two  three
    2    1    2      3
    1    1    3      4
    3    1    4      2
    0    2    1      5

These methods have special treatment of NA values via the `na_position` argument:

    In [324]: s[2] = np.nan

    In [325]: s.sort_values()
    Out[325]:
    0       A
    3    Aaba
    1       B
    4    Baca
    6    CABA
    8     cat
    7     dog
    2    <NA>
    5    <NA>
    dtype: string

    In [326]: s.sort_values(na_position="first")
    Out[326]:
    2    <NA>
    5    <NA>
    0       A
    3    Aaba
    1       B
    4    Baca
    6    CABA
    8     cat
    7     dog
    dtype: string

Sorting also supports a `key` parameter that takes a callable function to apply to the values being sorted.

    In [327]: s1 = pd.Series(["B", "a", "C"])

    In [328]: s1.sort_values()
    Out[328]:
    0    B
    2    C
    1    a
    dtype: str

    In [329]: s1.sort_values(key=lambda x: x.str.lower())
    Out[329]:
    1    a
    0    B
    2    C
    dtype: str

`key` will be given the `Series` of values and should return a `Series` or array of the same shape with the transformed values. For `DataFrame` objects, the key is applied per column, so the key should still expect a Series and return a Series, e.g.

    In [330]: df = pd.DataFrame({"a": ["B", "a", "C"], "b": [1, 2, 3]})

    In [331]: df.sort_values(by="a")
    Out[331]:
       a  b
    0  B  1
    2  C  3
    1  a  2

    In [332]: df.sort_values(by="a", key=lambda col: col.str.lower())
    Out[332]:
       a  b
    1  a  2
    0  B  1
    2  C  3

The name or type of each column can be used to apply different functions to different columns.

### By indexes and values

Strings passed as the `by` parameter to `DataFrame.sort_values()` may refer to either columns or index level names.

    # Build MultiIndex
    In [333]: idx = pd.MultiIndex.from_tuples(
       .....:     [("a", 1), ("a", 2), ("a", 2), ("b", 2), ("b", 1), ("b", 1)]
       .....: )
       .....:

    In [334]: idx.names = ["first", "second"]

    # Build DataFrame
    In [335]: df_multi = pd.DataFrame({"A": np.arange(6, 0, -1)}, index=idx)

    In [336]: df_multi
    Out[336]:
                  A
    first second
    a     1       6
          2       5
          2       4
    b     2       3
          1       2
          1       1

Sort by ‘second’ (index) and ‘A’ (column)

    In [337]: df_multi.sort_values(by=["second", "A"])
    Out[337]:
                  A
    first second
    b     1       1
          1       2
    a     1       6
    b     2       3
    a     2       4
          2       5

Note

If a string matches both a column name and an index level name then a warning is issued and the column takes precedence. This will result in an ambiguity error in a future version.

### searchsorted

Series has the `searchsorted()` method, which works similarly to `numpy.ndarray.searchsorted()`.

    In [338]: ser = pd.Series([1, 2, 3])

    In [339]: ser.searchsorted([0, 3])
    Out[339]: array([0, 2])

    In [340]: ser.searchsorted([0, 4])
    Out[340]: array([0, 3])

    In [341]: ser.searchsorted([1, 3], side="right")
    Out[341]: array([1, 3])

    In [342]: ser.searchsorted([1, 3], side="left")
    Out[342]: array([0, 2])

    In [343]: ser = pd.Series([3, 1, 2])

    In [344]: ser.searchsorted([0, 3], sorter=np.argsort(ser))
    Out[344]: array([0, 2])

### smallest / largest values

`Series` has the `nsmallest()` and `nlargest()` methods which return the smallest or largest \(n\) values. For a large `Series` this can be much faster than sorting the entire Series and calling `head(n)` on the result.

    In [345]: s = pd.Series(np.random.permutation(10))

    In [346]: s
    Out[346]:
    0    2
    1    0
    2    3
    3    7
    4    1
    5    5
    6    9
    7    6
    8    8
    9    4
    dtype: int64

    In [347]: s.sort_values()
    Out[347]:
    1    0
    4    1
    0    2
    2    3
    9    4
    5    5
    7    6
    3    7
    8    8
    6    9
    dtype: int64

    In [348]: s.nsmallest(3)
    Out[348]:
    1    0
    4    1
    0    2
    dtype: int64

    In [349]: s.nlargest(3)
    Out[349]:
    6    9
    8    8
    3    7
    dtype: int64

`DataFrame` also has the `nlargest` and `nsmallest` methods.

    In [350]: df = pd.DataFrame(
       .....:     {
       .....:         "a": [-2, -1, 1, 10, 8, 11, -1],
       .....:         "b": list("abdceff"),
       .....:         "c": [1.0, 2.0, 4.0, 3.2, np.nan, 3.0, 4.0],
       .....:     }
       .....: )
       .....:

    In [351]: df.nlargest(3, "a")
    Out[351]:
        a  b    c
    5  11  f  3.0
    3  10  c  3.2
    4   8  e  NaN

    In [352]: df.nlargest(5, ["a", "c"])
    Out[352]:
        a  b    c
    5  11  f  3.0
    3  10  c  3.2
    4   8  e  NaN
    2   1  d  4.0
    6  -1  f  4.0

    In [353]: df.nsmallest(3, "a")
    Out[353]:
       a  b    c
    0 -2  a  1.0
    1 -1  b  2.0
    6 -1  f  4.0

    In [354]: df.nsmallest(5, ["a", "c"])
    Out[354]:
       a  b    c
    0 -2  a  1.0
    1 -1  b  2.0
    6 -1  f  4.0
    2  1  d  4.0
    4  8  e  NaN

### Sorting by a MultiIndex column

You must be explicit about sorting when the column is a MultiIndex, and fully specify all levels to `by`.

    In [355]: df1.columns = pd.MultiIndex.from_tuples(
       .....:     [("a", "one"), ("a", "two"), ("b", "three")]
       .....: )
       .....:

    In [356]: df1.sort_values(by=("a", "two"))
    Out[356]:
        a         b
      one two three
    0   2   1     5
    2   1   2     3
    1   1   3     4
    3   1   4     2

## Copying

The `copy()` method on pandas objects copies the underlying data (though not the axis indexes, since they are immutable) and returns a new object. Note that it is seldom necessary to copy objects. For example, there are only a handful of ways to alter a DataFrame in-place:

- Inserting, deleting, or modifying a column.

- Assigning to the `index` or `columns` attributes.

- For homogeneous data, directly modifying the values via the `values` attribute or advanced indexing.

To be clear, no pandas method has the side effect of modifying your data; almost every method returns a new object, leaving the original object untouched. If the data is modified, it is because you did so explicitly.

## dtypes

For the most part, pandas uses NumPy arrays and dtypes for Series or individual columns of a DataFrame. NumPy provides support for `float`, `int`, `bool`, `timedelta64[ns]` and `datetime64[ns]` (note that NumPy does not support timezone-aware datetimes).

pandas and third-party libraries extend NumPy’s type system in a few places. This section describes the extensions pandas has made internally. See Extension types for how to write your own extension that works with pandas. See the ecosystem page for a list of third-party libraries that have implemented an extension.

The following table lists all of pandas extension types. For methods requiring `dtype` arguments, strings can be specified as indicated. See the respective documentation sections for more on each type.

Kind of Data | Data Type | Scalar | Array | String Aliases
--- | --- | --- | --- | ---
tz-aware datetime | `DatetimeTZDtype` | `Timestamp` | `arrays.DatetimeArray` | `'datetime64[ns, <tz>]'`
Categorical | `CategoricalDtype` | (none) | `Categorical` | `'category'`
period (time spans) | `PeriodDtype` | `Period` | `arrays.PeriodArray` `'Period[<freq>]'` | `'period[<freq>]'`,
sparse | `SparseDtype` | (none) | `arrays.SparseArray` | `'Sparse'`, `'Sparse[int]'`, `'Sparse[float]'`
intervals | `IntervalDtype` | `Interval` | `arrays.IntervalArray` | `'interval'`, `'Interval'`, `'Interval[<numpy_dtype>]'`, `'Interval[datetime64[ns, <tz>]]'`, `'Interval[timedelta64[<freq>]]'`
nullable integer | `Int64Dtype`, … | (none) | `arrays.IntegerArray` | `'Int8'`, `'Int16'`, `'Int32'`, `'Int64'`, `'UInt8'`, `'UInt16'`, `'UInt32'`, `'UInt64'`
nullable float | `Float64Dtype`, … | (none) | `arrays.FloatingArray` | `'Float32'`, `'Float64'`
Strings | `StringDtype` | `str` | `arrays.StringArray` | `'string'`
Boolean (with NA) | `BooleanDtype` | `bool` | `arrays.BooleanArray` | `'boolean'`

pandas has two ways to store strings.

1. `object` dtype, which can hold any Python object, including strings.

2. `StringDtype`, which is dedicated to strings.

Generally, we recommend using `StringDtype`. See Text data types for more.

Finally, arbitrary objects may be stored using the `object` dtype, but should be avoided to the extent possible (for performance and interoperability with other libraries and methods. See object conversion).

A convenient `dtypes` attribute for DataFrame returns a Series with the data type of each column.

    In [357]: dft = pd.DataFrame(
       .....:     {
       .....:         "A": np.random.rand(3),
       .....:         "B": 1,
       .....:         "C": "foo",
       .....:         "D": pd.Timestamp("20010102"),
       .....:         "E": pd.Series([1.0] * 3).astype("float32"),
       .....:         "F": False,
       .....:         "G": pd.Series([1] * 3, dtype="int8"),
       .....:     }
       .....: )
       .....:

    In [358]: dft
    Out[358]:
              A  B    C          D    E      F  G
    0  0.035962  1  foo 2001-01-02  1.0  False  1
    1  0.701379  1  foo 2001-01-02  1.0  False  1
    2  0.281885  1  foo 2001-01-02  1.0  False  1

    In [359]: dft.dtypes
    Out[359]:
    A           float64
    B             int64
    C               str
    D    datetime64[us]
    E           float32
    F              bool
    G              int8
    dtype: object

On a `Series` object, use the `dtype` attribute.

    In [360]: dft["A"].dtype
    Out[360]: dtype('float64')

If a pandas object contains data with multiple dtypes in a single column, the dtype of the column will be chosen to accommodate all of the data types (`object` is the most general).

    # these ints are coerced to floats
    In [361]: pd.Series([1, 2, 3, 4, 5, 6.0])
    Out[361]:
    0    1.0
    1    2.0
    2    3.0
    3    4.0
    4    5.0
    5    6.0
    dtype: float64

    # string data forces an ``object`` dtype
    In [362]: pd.Series([1, 2, 3, 6.0, "foo"])
    Out[362]:
    0      1
    1      2
    2      3
    3    6.0
    4    foo
    dtype: object

The number of columns of each type in a `DataFrame` can be found by calling `DataFrame.dtypes.value_counts()`.

    In [363]: dft.dtypes.value_counts()
    Out[363]:
    float64           1
    int64             1
    str               1
    datetime64[us]    1
    float32           1
    bool              1
    int8              1
    Name: count, dtype: int64

Numeric dtypes will propagate and can coexist in DataFrames. If a dtype is passed (either directly via the `dtype` keyword, a passed `ndarray`, or a passed `Series`), then it will be preserved in DataFrame operations. Furthermore, different numeric dtypes will NOT be combined. The following example will give you a taste.

    In [364]: df1 = pd.DataFrame(np.random.randn(8, 1), columns=["A"], dtype="float64")

    In [365]: df1
    Out[365]:
              A
    0  0.224364
    1  1.890546
    2  0.182879
    3  0.787847
    4 -0.188449
    5  0.667715
    6 -0.011736
    7 -0.399073

    In [366]: df1.dtypes
    Out[366]:
    A    float64
    dtype: object

    In [367]: df2 = pd.DataFrame(
       .....:     {
       .....:         "A": pd.Series(np.random.randn(8), dtype="float32"),
       .....:         "B": pd.Series(np.random.randn(8)),
       .....:         "C": pd.Series(np.random.randint(0, 255, size=8), dtype="uint8"),  # [0,255] (range of uint8)
       .....:     }
       .....: )
       .....:

    In [368]: df2
    Out[368]:
              A         B    C
    0  0.823270  0.256090   26
    1  1.607861  1.426469   86
    2 -0.333669 -0.416203   46
    3 -0.063467  1.139976  212
    4 -1.014637 -1.193477   26
    5  0.678758  0.096706    7
    6 -0.040865 -1.956850  184
    7 -0.357346 -0.714337  206

    In [369]: df2.dtypes
    Out[369]:
    A    float32
    B    float64
    C      uint8
    dtype: object

### defaults

By default integer types are `int64` and float types are `float64`, regardless of platform (32-bit or 64-bit). The following will all result in `int64` dtypes.

    In [370]: pd.DataFrame([1, 2], columns=["a"]).dtypes
    Out[370]:
    a    int64
    dtype: object

    In [371]: pd.DataFrame({"a": [1, 2]}).dtypes
    Out[371]:
    a    int64
    dtype: object

    In [372]: pd.DataFrame({"a": 1}, index=list(range(2))).dtypes
    Out[372]:
    a    int64
    dtype: object

Note that Numpy will choose platform-dependent types when creating arrays. The following WILL result in `int32` on 32-bit platform.

    In [373]: frame = pd.DataFrame(np.array([1, 2]))

### upcasting

Types can potentially be upcasted when combined with other types, meaning they are promoted from the current type (e.g. `int` to `float`).

    In [374]: df3 = df1.reindex_like(df2).fillna(value=0.0) + df2

    In [375]: df3
    Out[375]:
              A         B      C
    0  1.047634  0.256090   26.0
    1  3.498407  1.426469   86.0
    2 -0.150790 -0.416203   46.0
    3  0.724380  1.139976  212.0
    4 -1.203087 -1.193477   26.0
    5  1.346474  0.096706    7.0
    6 -0.052601 -1.956850  184.0
    7 -0.756419 -0.714337  206.0

    In [376]: df3.dtypes
    Out[376]:
    A    float64
    B    float64
    C    float64
    dtype: object

`DataFrame.to_numpy()` will return the lower-common-denominator of the dtypes, meaning the dtype that can accommodate ALL of the types in the resulting homogeneous dtyped NumPy array. This can force some upcasting.

    In [377]: df3.to_numpy().dtype
    Out[377]: dtype('float64')

### astype

You can use the `astype()` method to explicitly convert dtypes from one to another. These will by default return a copy, even if the dtype was unchanged (pass `copy=False` to change this behavior). In addition, they will raise an exception if the astype operation is invalid.

Upcasting is always according to the NumPy rules. If two different dtypes are involved in an operation, then the more general one will be used as the result of the operation.

    In [378]: df3
    Out[378]:
              A         B      C
    0  1.047634  0.256090   26.0
    1  3.498407  1.426469   86.0
    2 -0.150790 -0.416203   46.0
    3  0.724380  1.139976  212.0
    4 -1.203087 -1.193477   26.0
    5  1.346474  0.096706    7.0
    6 -0.052601 -1.956850  184.0
    7 -0.756419 -0.714337  206.0

    In [379]: df3.dtypes
    Out[379]:
    A    float64
    B    float64
    C    float64
    dtype: object

    # conversion of dtypes
    In [380]: df3.astype("float32").dtypes
    Out[380]:
    A    float32
    B    float32
    C    float32
    dtype: object

Convert a subset of columns to a specified type using `astype()`.

    In [381]: dft = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]})

    In [382]: dft[["a", "b"]] = dft[["a", "b"]].astype(np.uint8)

    In [383]: dft
    Out[383]:
       a  b  c
    0  1  4  7
    1  2  5  8
    2  3  6  9

    In [384]: dft.dtypes
    Out[384]:
    a    uint8
    b    uint8
    c    int64
    dtype: object

Convert certain columns to a specific dtype by passing a dict to `astype()`.

    In [385]: dft1 = pd.DataFrame({"a": [1, 0, 1], "b": [4, 5, 6], "c": [7, 8, 9]})

    In [386]: dft1 = dft1.astype({"a": np.bool_, "c": np.float64})

    In [387]: dft1
    Out[387]:
           a  b    c
    0   True  4  7.0
    1  False  5  8.0
    2   True  6  9.0

    In [388]: dft1.dtypes
    Out[388]:
    a       bool
    b      int64
    c    float64
    dtype: object

Note

When trying to convert a subset of columns to a specified type using `astype()` and `loc()`, upcasting occurs.

`loc()` tries to fit in what we are assigning to the current dtypes, while `[]` will overwrite them taking the dtype from the right hand side. Therefore the following piece of code produces the unintended result.

    In [389]: dft = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]})

    In [390]: dft.loc[:, ["a", "b"]].astype(np.uint8).dtypes
    Out[390]:
    a    uint8
    b    uint8
    dtype: object

    In [391]: dft.loc[:, ["a", "b"]] = dft.loc[:, ["a", "b"]].astype(np.uint8)

    In [392]: dft.dtypes
    Out[392]:
    a    int64
    b    int64
    c    int64
    dtype: object

### object conversion

pandas offers various functions to try to force conversion of types from the `object` dtype to other types. In cases where the data is already of the correct type, but stored in an `object` array, the `DataFrame.infer_objects()` and `Series.infer_objects()` methods can be used to soft convert to the correct type.

>     In [393]: import datetime

>     In [394]: df = pd.DataFrame(
>        .....:     [
>        .....:         [1, 2],
>        .....:         ["a", "b"],
>        .....:         [datetime.datetime(2016, 3, 2), datetime.datetime(2016, 3, 2)],
>        .....:     ]
>        .....: )
>        .....:

>     In [395]: df = df.T

>     In [396]: df
>     Out[396]:
>        0  1                    2
>     0  1  a  2016-03-02 00:00:00
>     1  2  b  2016-03-02 00:00:00

>     In [397]: df.dtypes
>     Out[397]:
>     0    object
>     1    object
>     2    object
>     dtype: object

Because the data was transposed the original inference stored all columns as object, which `infer_objects` will correct.

>     In [398]: df.infer_objects().dtypes
>     Out[398]:
>     0             int64
>     1               str
>     2    datetime64[us]
>     dtype: object

The following functions are available for one dimensional object arrays or scalars to perform hard conversion of objects to a specified type:

- `to_numeric()` (conversion to numeric dtypes)

      In [399]: m = ["1.1", 2, 3]

      In [400]: pd.to_numeric(m)
      Out[400]: array([1.1, 2. , 3. ])

- `to_datetime()` (conversion to datetime objects)

      In [401]: import datetime

      In [402]: m = ["2016-07-09", datetime.datetime(2016, 3, 2)]

      In [403]: pd.to_datetime(m)
      Out[403]: DatetimeIndex(['2016-07-09', '2016-03-02'], dtype='datetime64[us]', freq=None)

- `to_timedelta()` (conversion to timedelta objects)

      In [404]: m = ["5us", pd.Timedelta("1day")]

      In [405]: pd.to_timedelta(m)
      Out[405]:
      TimedeltaIndex(['0 days 00:00:00.000005', '1 days 00:00:00.000000'],
                     dtype='timedelta64[us]', freq=None)

To force a conversion, we can pass in an `errors` argument, which specifies how pandas should deal with elements that cannot be converted to desired dtype or object. By default, `errors='raise'`, meaning that any errors encountered will be raised during the conversion process. However, if `errors='coerce'`, these errors will be ignored and pandas will convert problematic elements to `pd.NaT` (for datetime and timedelta) or `np.nan` (for numeric). This might be useful if you are reading in data which is mostly of the desired dtype (e.g. numeric, datetime), but occasionally has non-conforming elements intermixed that you want to represent as missing:

    In [406]: import datetime

    In [407]: m = ["apple", datetime.datetime(2016, 3, 2)]

    In [408]: pd.to_datetime(m, errors="coerce")
    Out[408]: DatetimeIndex(['NaT', '2016-03-02'], dtype='datetime64[us]', freq=None)

    In [409]: m = ["apple", 2, 3]

    In [410]: pd.to_numeric(m, errors="coerce")
    Out[410]: array([nan,  2.,  3.])

    In [411]: m = ["apple", pd.Timedelta("1day")]

    In [412]: pd.to_timedelta(m, errors="coerce")
    Out[412]: TimedeltaIndex([NaT, '1 days'], dtype='timedelta64[us]', freq=None)

In addition to object conversion, `to_numeric()` provides another argument `downcast`, which gives the option of downcasting the newly (or already) numeric data to a smaller dtype, which can conserve memory:

    In [413]: m = ["1", 2, 3]

    In [414]: pd.to_numeric(m, downcast="integer")  # smallest signed int dtype
    Out[414]: array([1, 2, 3], dtype=int8)

    In [415]: pd.to_numeric(m, downcast="signed")  # same as 'integer'
    Out[415]: array([1, 2, 3], dtype=int8)

    In [416]: pd.to_numeric(m, downcast="unsigned")  # smallest unsigned int dtype
    Out[416]: array([1, 2, 3], dtype=uint8)

    In [417]: pd.to_numeric(m, downcast="float")  # smallest float dtype
    Out[417]: array([1., 2., 3.], dtype=float32)

As these methods apply only to one-dimensional arrays, lists or scalars; they cannot be used directly on multi-dimensional objects such as DataFrames. However, with `apply()`, we can “apply” the function over each column efficiently:

    In [418]: import datetime

    In [419]: df = pd.DataFrame([["2016-07-09", datetime.datetime(2016, 3, 2)]] * 2, dtype="O")

    In [420]: df
    Out[420]:
                0                    1
    0  2016-07-09  2016-03-02 00:00:00
    1  2016-07-09  2016-03-02 00:00:00

    In [421]: df.apply(pd.to_datetime)
    Out[421]:
               0          1
    0 2016-07-09 2016-03-02
    1 2016-07-09 2016-03-02

    In [422]: df = pd.DataFrame([["1.1", 2, 3]] * 2, dtype="O")

    In [423]: df
    Out[423]:
         0  1  2
    0  1.1  2  3
    1  1.1  2  3

    In [424]: df.apply(pd.to_numeric)
    Out[424]:
         0  1  2
    0  1.1  2  3
    1  1.1  2  3

    In [425]: df = pd.DataFrame([["5us", pd.Timedelta("1day")]] * 2, dtype="O")

    In [426]: df
    Out[426]:
         0                1
    0  5us  1 days 00:00:00
    1  5us  1 days 00:00:00

    In [427]: df.apply(pd.to_timedelta)
    Out[427]:
                           0      1
    0 0 days 00:00:00.000005 1 days
    1 0 days 00:00:00.000005 1 days

### gotchas

Performing selection operations on `integer` type data can easily upcast the data to `floating`. The dtype of the input data will be preserved in cases where `nans` are not introduced. See also Support for integer NA.

    In [428]: dfi = df3.astype("int32")

    In [429]: dfi["E"] = 1

    In [430]: dfi
    Out[430]:
       A  B    C  E
    0  1  0   26  1
    1  3  1   86  1
    2  0  0   46  1
    3  0  1  212  1
    4 -1 -1   26  1
    5  1  0    7  1
    6  0 -1  184  1
    7  0  0  206  1

    In [431]: dfi.dtypes
    Out[431]:
    A    int32
    B    int32
    C    int32
    E    int64
    dtype: object

    In [432]: casted = dfi[dfi > 0]

    In [433]: casted
    Out[433]:
         A    B    C  E
    0  1.0  NaN   26  1
    1  3.0  1.0   86  1
    2  NaN  NaN   46  1
    3  NaN  1.0  212  1
    4  NaN  NaN   26  1
    5  1.0  NaN    7  1
    6  NaN  NaN  184  1
    7  NaN  NaN  206  1

    In [434]: casted.dtypes
    Out[434]:
    A    float64
    B    float64
    C      int32
    E      int64
    dtype: object

While float dtypes are unchanged.

    In [435]: dfa = df3.copy()

    In [436]: dfa["A"] = dfa["A"].astype("float32")

    In [437]: dfa.dtypes
    Out[437]:
    A    float32
    B    float64
    C    float64
    dtype: object

    In [438]: casted = dfa[df2 > 0]

    In [439]: casted
    Out[439]:
              A         B      C
    0  1.047634  0.256090   26.0
    1  3.498407  1.426469   86.0
    2       NaN       NaN   46.0
    3       NaN  1.139976  212.0
    4       NaN       NaN   26.0
    5  1.346474  0.096706    7.0
    6       NaN       NaN  184.0
    7       NaN       NaN  206.0

    In [440]: casted.dtypes
    Out[440]:
    A    float32
    B    float64
    C    float64
    dtype: object

## Selecting columns based on `dtype`

The `select_dtypes()` method implements subsetting of columns based on their `dtype`.

First, let’s create a `DataFrame` with a slew of different dtypes:

    In [441]: df = pd.DataFrame(
       .....:     {
       .....:         "string": list("abc"),
       .....:         "int64": list(range(1, 4)),
       .....:         "uint8": np.arange(3, 6).astype("u1"),
       .....:         "float64": np.arange(4.0, 7.0),
       .....:         "bool1": [True, False, True],
       .....:         "bool2": [False, True, False],
       .....:         "dates": pd.date_range("now", periods=3),
       .....:         "category": pd.Series(list("ABC")).astype("category"),
       .....:     }
       .....: )
       .....:

    In [442]: df["tdeltas"] = df.dates.diff()

    In [443]: df["uint64"] = np.arange(3, 6).astype("u8")

    In [444]: df["other_dates"] = pd.date_range("20130101", periods=3)

    In [445]: df["tz_aware_dates"] = pd.date_range("20130101", periods=3, tz="US/Eastern")

    In [446]: df
    Out[446]:
      string  int64  uint8  ...  uint64 other_dates            tz_aware_dates
    0      a      1      3  ...       3  2013-01-01 2013-01-01 00:00:00-05:00
    1      b      2      4  ...       4  2013-01-02 2013-01-02 00:00:00-05:00
    2      c      3      5  ...       5  2013-01-03 2013-01-03 00:00:00-05:00

    [3 rows x 12 columns]

And the dtypes:

    In [447]: df.dtypes
    Out[447]:
    string                                   str
    int64                                  int64
    uint8                                  uint8
    float64                              float64
    bool1                                   bool
    bool2                                   bool
    dates                         datetime64[us]
    category                            category
    tdeltas                      timedelta64[us]
    uint64                                uint64
    other_dates                   datetime64[us]
    tz_aware_dates    datetime64[us, US/Eastern]
    dtype: object

`select_dtypes()` has two parameters `include` and `exclude` that allow you to say “give me the columns with these dtypes” (`include`) and/or “give the columns without these dtypes” (`exclude`).

For example, to select `bool` columns:

    In [448]: df.select_dtypes(include=[bool])
    Out[448]:
       bool1  bool2
    0   True  False
    1  False   True
    2   True  False

You can also pass the name of a dtype in the NumPy dtype hierarchy:

    In [449]: df.select_dtypes(include=["bool"])
    Out[449]:
       bool1  bool2
    0   True  False
    1  False   True
    2   True  False

`select_dtypes()` also works with generic dtypes.

For example, to select all numeric and boolean columns while excluding unsigned integers:

    In [450]: df.select_dtypes(include=["number", "bool"], exclude=["unsignedinteger"])
    Out[450]:
       int64  float64  bool1  bool2 tdeltas
    0      1      4.0   True  False     NaT
    1      2      5.0  False   True  1 days
    2      3      6.0   True  False  1 days

To select string columns include `str`:

    In [451]: df.select_dtypes(include=[str])
    Out[451]:
      string
    0      a
    1      b
    2      c

Note

This is a change in pandas 3.0. Previously strings were stored in `object` dtype columns, so would be selected with `include=[object]`. See the migration guide for details on how to write code that works with both versions.

To see all the child dtypes of a generic `dtype` like `numpy.number` you can define a function that returns a tree of child dtypes:

    In [452]: def subdtypes(dtype):
       .....:     subs = dtype.__subclasses__()
       .....:     if not subs:
       .....:         return dtype
       .....:     return [dtype, [subdtypes(dt) for dt in subs]]
       .....:

All NumPy dtypes are subclasses of `numpy.generic`:

    In [453]: subdtypes(np.generic)
    Out[453]:
    [numpy.generic,
     [[numpy.number,
       [[numpy.integer,
         [[numpy.signedinteger,
           [numpy.int8,
            numpy.int16,
            numpy.int32,
            numpy.int64,
            numpy.longlong,
            numpy.timedelta64]],
          [numpy.unsignedinteger,
           [numpy.uint8,
            numpy.uint16,
            numpy.uint32,
            numpy.uint64,
            numpy.ulonglong]]]],
        [numpy.inexact,
         [[numpy.floating,
           [numpy.float16, numpy.float32, numpy.float64, numpy.longdouble]],
          [numpy.complexfloating,
           [numpy.complex64, numpy.complex128, numpy.clongdouble]]]]]],
      [numpy.flexible,
       [[numpy.character, [numpy.bytes_, numpy.str_]],
        [numpy.void, [numpy.record]]]],
      numpy.bool,
      numpy.datetime64,
      numpy.object_]]

Note

pandas also defines the types `category`, and `datetime64[ns, tz]`, which are not integrated into the normal NumPy hierarchy and won’t show up with the above function.