.. _basics:

{{ header }}

==============================
 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 :ref:`10 minutes to pandas <10min>` section:

.. ipython:: python

   index = pd.date_range("1/1/2000", periods=8)
   s = pd.Series(np.random.randn(5), index=["a", "b", "c", "d", "e"])
   df = pd.DataFrame(np.random.randn(8, 3), index=index, columns=["A", "B", "C"])

.. _basics.head_tail:

Head and tail
-------------

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

.. ipython:: python

   long_series = pd.Series(np.random.randn(1000))
   long_series.head()
   long_series.tail(3)

.. _basics.attrs:

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**!

.. ipython:: python

   df[:2]
   df.columns = [x.lower() for x in df.columns]
   df

pandas objects (:class:`Index`, :class:`Series`, :class:`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
:class:`numpy.ndarray`. However, pandas and 3rd party libraries may *extend*
NumPy's type system to add support for custom arrays
(see :ref:`basics.dtypes`).

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

.. ipython:: python

   s.array
   s.index.array

:attr:`~Series.array` will always be an :class:`~pandas.api.extensions.ExtensionArray`.
The exact details of what an :class:`~pandas.api.extensions.ExtensionArray` is and why pandas uses them are a bit
beyond the scope of this introduction. See :ref:`basics.dtypes` for more.

If you know you need a NumPy array, use :meth:`~Series.to_numpy`
or :meth:`numpy.asarray`.

.. ipython:: python

   s.to_numpy()
   np.asarray(s)

When the Series or Index is backed by
an :class:`~pandas.api.extensions.ExtensionArray`, :meth:`~Series.to_numpy`
may involve copying data and coercing values. See :ref:`basics.dtypes` for more.

:meth:`~Series.to_numpy` gives some control over the ``dtype`` of the
resulting :class:`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 :class:`numpy.ndarray` with :class:`Timestamp` objects, each
   with the correct ``tz``
2. A ``datetime64[ns]`` -dtype :class:`numpy.ndarray`, where the values have
   been converted to UTC and the timezone discarded

Timezones may be preserved with ``dtype=object``

.. ipython:: python

   ser = pd.Series(pd.date_range("2000", periods=2, tz="CET"))
   ser.to_numpy(dtype=object)

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

.. ipython:: python

   ser.to_numpy(dtype="datetime64[ns]")

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

.. ipython:: python

   df.to_numpy()

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 :attr:`Series.values` or :attr:`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 :ref:`extension type <extending.extension-types>`, it's
   unclear whether :attr:`Series.values` returns a NumPy array or the extension array.
   :attr:`Series.array` will always return an :class:`~pandas.api.extensions.ExtensionArray`, and will never
   copy data. :meth:`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, :attr:`DataFrame.values` may
   involve copying data and coercing values to a common dtype, a relatively expensive
   operation. :meth:`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.

.. _basics.accelerate:

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``.

Here is a sample (using 100 column x 100,000 row ``DataFrames``):

.. csv-table::
    :header: "Operation", "0.11.0 (ms)", "Prior Version (ms)", "Ratio to Prior"
    :widths: 25, 25, 25, 25
    :delim: ;

    ``df1 > df2``; 13.32; 125.35;  0.1063
    ``df1 * df2``; 21.71;  36.63;  0.5928
    ``df1 + df2``; 22.04;  36.50;  0.6039

You are highly encouraged to install both libraries. See the section
:ref:`Recommended Dependencies <install.recommended_dependencies>` for more installation info.

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

.. code-block:: python

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

.. _basics.binop:

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 :meth:`~DataFrame.add`, :meth:`~DataFrame.sub`,
:meth:`~DataFrame.mul`, :meth:`~DataFrame.div` and related functions
:meth:`~DataFrame.radd`, :meth:`~DataFrame.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:

.. ipython:: python

   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"]),
       }
   )
   df
   row = df.iloc[1]
   column = df["two"]

   df.sub(row, axis="columns")
   df.sub(row, axis=1)

   df.sub(column, axis="index")
   df.sub(column, axis=0)

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

.. ipython:: python

   dfmi = df.copy()
   dfmi.index = pd.MultiIndex.from_tuples(
       [(1, "a"), (1, "b"), (1, "c"), (2, "a")], names=["first", "second"]
   )
   dfmi.sub(column, axis=0, level="second")

Series and Index also support the :func:`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:

.. ipython:: python

   s = pd.Series(np.arange(10))
   s
   div, rem = divmod(s, 3)
   div
   rem

   idx = pd.Index(np.arange(10))
   idx
   div, rem = divmod(idx, 3)
   div
   rem

We can also do elementwise :func:`divmod`:

.. ipython:: python

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

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).

.. ipython:: python

   df2 = df.copy()
   df2["three"]["a"] = 1.0
   df
   df2
   df + df2
   df.add(df2, fill_value=0)

.. _basics.compare:

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:

.. ipython:: python

   df.gt(df2)
   df2.ne(df)

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 :ref:`Boolean indexing<indexing.boolean>`.

.. _basics.reductions:

Boolean reductions
~~~~~~~~~~~~~~~~~~

You can apply the reductions: :attr:`~DataFrame.empty`, :meth:`~DataFrame.any`,
:meth:`~DataFrame.all`, and :meth:`~DataFrame.bool` to provide a
way to summarize a boolean result.

.. ipython:: python

   (df > 0).all()
   (df > 0).any()

You can reduce to a final boolean value.

.. ipython:: python

   (df > 0).any().any()

You can test if a pandas object is empty, via the :attr:`~DataFrame.empty` property.

.. ipython:: python

   df.empty
   pd.DataFrame(columns=list("ABC")).empty

.. warning::

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

   .. ipython:: python
      :okexcept:

      if df:
          print(True)

   .. ipython:: python
      :okexcept:

      df and df2

   See :ref:`gotchas<gotchas.truth>` for a more detailed discussion.

.. _basics.equals:

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:

.. ipython:: python

   df + df == df * 2
   (df + df == df * 2).all()

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

.. ipython:: python

   np.nan == np.nan

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

.. ipython:: python

   (df + df).equals(df * 2)

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

.. ipython:: python

   df1 = pd.DataFrame({"col": ["foo", 0, np.nan]})
   df2 = pd.DataFrame({"col": [np.nan, 0, "foo"]}, index=[2, 1, 0])
   df1.equals(df2)
   df1.equals(df2.sort_index())

Comparing array-like objects
~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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

.. ipython:: python

   pd.Series(["foo", "bar", "baz"]) == "foo"
   pd.Index(["foo", "bar", "baz"]) == "foo"

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

.. ipython:: python

    pd.Series(["foo", "bar", "baz"]) == pd.Index(["foo", "bar", "qux"])
    pd.Series(["foo", "bar", "baz"]) == np.array(["foo", "bar", "qux"])

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

.. ipython:: python
   :okexcept:

    pd.Series(['foo', 'bar', 'baz']) == pd.Series(['foo', 'bar'])

    pd.Series(['foo', 'bar', 'baz']) == pd.Series(['foo'])

Note that this is different from the NumPy behavior where a comparison can
be broadcast:

.. ipython:: python

    np.array([1, 2, 3]) == np.array([2])

or it can return False if broadcasting can not be done:

.. ipython:: python
   :okwarning:

    np.array([1, 2, 3]) == np.array([1, 2])

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 :meth:`~DataFrame.combine_first`,
which we illustrate:

.. ipython:: python

   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]}
   )
   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],
       }
   )
   df1
   df2
   df1.combine_first(df2)

General DataFrame combine
~~~~~~~~~~~~~~~~~~~~~~~~~

The :meth:`~DataFrame.combine_first` method above calls the more general
:meth:`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 :meth:`~DataFrame.combine_first` as above:

.. ipython:: python

   def combiner(x, y):
       return np.where(pd.isna(x), y, x)


   df1.combine(df2, combiner)

.. _basics.stats:

Descriptive statistics
----------------------

There exists a large number of methods for computing descriptive statistics and
other related operations on :ref:`Series <api.series.stats>`, :ref:`DataFrame
<api.dataframe.stats>`. Most of these
are aggregations (hence producing a lower-dimensional result) like
:meth:`~DataFrame.sum`, :meth:`~DataFrame.mean`, and :meth:`~DataFrame.quantile`,
but some of them, like :meth:`~DataFrame.cumsum` and :meth:`~DataFrame.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:

.. ipython:: python

   df
   df.mean(0)
   df.mean(1)

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

.. ipython:: python

   df.sum(0, skipna=False)
   df.sum(axis=1, skipna=True)

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:

.. ipython:: python

   ts_stand = (df - df.mean()) / df.std()
   ts_stand.std()
   xs_stand = df.sub(df.mean(1), axis=0).div(df.std(1), axis=0)
   xs_stand.std(1)

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

.. ipython:: python

   df.cumsum()

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
:ref:`hierarchical index<advanced.hierarchical>`.

.. csv-table::
    :header: "Function", "Description"
    :widths: 20, 80

    ``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:

.. ipython:: python

   np.mean(df["one"])
   np.mean(df["one"].to_numpy())

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

.. ipython:: python

   series = pd.Series(np.random.randn(500))
   series[20:500] = np.nan
   series[10:20] = 5
   series.nunique()

.. _basics.describe:

Summarizing data: describe
~~~~~~~~~~~~~~~~~~~~~~~~~~

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

.. ipython:: python

    series = pd.Series(np.random.randn(1000))
    series[::2] = np.nan
    series.describe()
    frame = pd.DataFrame(np.random.randn(1000, 5), columns=["a", "b", "c", "d", "e"])
    frame.iloc[::2] = np.nan
    frame.describe()

You can select specific percentiles to include in the output:

.. ipython:: python

    series.describe(percentiles=[0.05, 0.25, 0.75, 0.95])

By default, the median is always included.

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

.. ipython:: python

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

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

.. ipython:: python

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

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

.. ipython:: python

    frame.describe(include=["object"])
    frame.describe(include=["number"])
    frame.describe(include="all")

That feature relies on :ref:`select_dtypes <basics.selectdtypes>`. Refer to
there for details about accepted inputs.

.. _basics.idxmin:

Index of min/max values
~~~~~~~~~~~~~~~~~~~~~~~

The :meth:`~DataFrame.idxmin` and :meth:`~DataFrame.idxmax` functions on Series
and DataFrame compute the index labels with the minimum and maximum
corresponding values:

.. ipython:: python

   s1 = pd.Series(np.random.randn(5))
   s1
   s1.idxmin(), s1.idxmax()

   df1 = pd.DataFrame(np.random.randn(5, 3), columns=["A", "B", "C"])
   df1
   df1.idxmin(axis=0)
   df1.idxmax(axis=1)

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

.. ipython:: python

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

.. note::

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

.. _basics.discretization:

Value counts (histogramming) / mode
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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

.. ipython:: python

   data = np.random.randint(0, 7, size=50)
   data
   s = pd.Series(data)
   s.value_counts()

The :meth:`~DataFrame.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.

.. ipython:: python

    data = {"a": [1, 2, 3, 4], "b": ["x", "x", "y", "y"]}
    frame = pd.DataFrame(data)
    frame.value_counts()

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

.. ipython:: python

    s5 = pd.Series([1, 1, 3, 3, 3, 5, 5, 7, 7, 7])
    s5.mode()
    df5 = pd.DataFrame(
        {
            "A": np.random.randint(0, 7, size=50),
            "B": np.random.randint(-10, 15, size=50),
        }
    )
    df5.mode()


Discretization and quantiling
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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

.. ipython:: python

   arr = np.random.randn(20)
   factor = pd.cut(arr, 4)
   factor

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

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

.. ipython:: python

   arr = np.random.randn(30)
   factor = pd.qcut(arr, [0, 0.25, 0.5, 0.75, 1])
   factor

We can also pass infinite values to define the bins:

.. ipython:: python

   arr = np.random.randn(20)
   factor = pd.cut(arr, [-np.inf, 0, np.inf])
   factor

.. _basics.apply:

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`_: :meth:`~DataFrame.pipe`
2. `Row or Column-wise Function Application`_: :meth:`~DataFrame.apply`
3. `Aggregation API`_: :meth:`~DataFrame.agg` and :meth:`~DataFrame.transform`
4. `Applying Elementwise Functions`_: :meth:`~DataFrame.map`

.. _basics.pipe:

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 :meth:`~DataFrame.pipe` method.

First some setup:

.. ipython:: python

    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


    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


    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:

.. ipython:: python

    add_country_name(extract_city_name(df_p), country_name="US")

Is equivalent to:

.. ipython:: python

    df_p.pipe(extract_city_name).pipe(add_country_name, country_name="US")

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``:

.. code-block:: ipython

   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 :meth:`~DataFrame.pipe`.

.. _dplyr: https://github.com/tidyverse/dplyr
.. _magrittr: https://github.com/tidyverse/magrittr
.. _R: https://www.r-project.org


Row or column-wise function application
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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

.. ipython:: python

   df.apply(lambda x: np.mean(x))
   df.apply(lambda x: np.mean(x), axis=1)
   df.apply(lambda x: x.max() - x.min())
   df.apply(np.cumsum)
   df.apply(np.exp)

The :meth:`~DataFrame.apply` method will also dispatch on a string method name.

.. ipython:: python

   df.apply("mean")
   df.apply("mean", axis=1)

The return type of the function passed to :meth:`~DataFrame.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``.

:meth:`~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:

.. ipython:: python

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

You may also pass additional arguments and keyword arguments to the :meth:`~DataFrame.apply`
method.

.. ipython:: python

   def subtract_and_divide(x, sub, divide=1):
       return (x - sub) / divide

   df_udf = pd.DataFrame(np.ones((2, 2)))
   df_udf.apply(subtract_and_divide, args=(5,), divide=3)

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

.. ipython:: python

   tsdf = pd.DataFrame(
       np.random.randn(10, 3),
       columns=["A", "B", "C"],
       index=pd.date_range("1/1/2000", periods=10),
   )
   tsdf.iloc[3:7] = np.nan
   tsdf
   tsdf.apply(pd.Series.interpolate)


Finally, :meth:`~DataFrame.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.

.. _basics.aggregate:

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 :ref:`groupby API <groupby.aggregate>`, the
:ref:`window API <window.overview>`, and the :ref:`resample API <timeseries.aggregate>`.
The entry point for aggregation is :meth:`DataFrame.aggregate`, or the alias
:meth:`DataFrame.agg`.

We will use a similar starting frame from above:

.. ipython:: python

   tsdf = pd.DataFrame(
       np.random.randn(10, 3),
       columns=["A", "B", "C"],
       index=pd.date_range("1/1/2000", periods=10),
   )
   tsdf.iloc[3:7] = np.nan
   tsdf

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

.. ipython:: python

   tsdf.agg(lambda x: np.sum(x))

   tsdf.agg("sum")

   # these are equivalent to a ``.sum()`` because we are aggregating
   # on a single function
   tsdf.sum()

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

.. ipython:: python

   tsdf["A"].agg("sum")


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.

.. ipython:: python

   tsdf.agg(["sum"])

Multiple functions yield multiple rows:

.. ipython:: python

   tsdf.agg(["sum", "mean"])

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

.. ipython:: python

   tsdf["A"].agg(["sum", "mean"])

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

.. ipython:: python

   tsdf["A"].agg(["sum", lambda x: x.mean()])

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

.. ipython:: python

   def mymean(x):
       return x.mean()


   tsdf["A"].agg(["sum", mymean])

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.

.. ipython:: python

   tsdf.agg({"A": "mean", "B": "sum"})

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``:

.. ipython:: python

   tsdf.agg({"A": ["mean", "min"], "B": "sum"})

.. _basics.aggregation.custom_describe:

Custom describe
+++++++++++++++

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

.. ipython:: python

   from functools import partial

   q_25 = partial(pd.Series.quantile, q=0.25)
   q_25.__name__ = "25%"
   q_75 = partial(pd.Series.quantile, q=0.75)
   q_75.__name__ = "75%"

   tsdf.agg(["count", "mean", "std", "min", q_25, "median", q_75, "max"])

.. _basics.transform:

Transform API
~~~~~~~~~~~~~

The :meth:`~DataFrame.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.

.. ipython:: python

   tsdf = pd.DataFrame(
       np.random.randn(10, 3),
       columns=["A", "B", "C"],
       index=pd.date_range("1/1/2000", periods=10),
   )
   tsdf.iloc[3:7] = np.nan
   tsdf

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

.. ipython:: python
   :okwarning:

   tsdf.transform(np.abs)
   tsdf.transform("abs")
   tsdf.transform(lambda x: x.abs())

Here :meth:`~DataFrame.transform` received a single function; this is equivalent to a `ufunc
<https://numpy.org/doc/stable/reference/ufuncs.html>`__ application.

.. ipython:: python

   np.abs(tsdf)

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

.. ipython:: python

   tsdf["A"].transform(np.abs)


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.

.. ipython:: python

   tsdf.transform([np.abs, lambda x: x + 1])

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

.. ipython:: python

   tsdf["A"].transform([np.abs, lambda x: x + 1])


Transforming with a dict
++++++++++++++++++++++++


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

.. ipython:: python

   tsdf.transform({"A": np.abs, "B": lambda x: x + 1})

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

.. ipython:: python
   :okwarning:

   tsdf.transform({"A": np.abs, "B": [lambda x: x + 1, "sqrt"]})

.. _basics.elementwise:

Applying elementwise functions
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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

.. ipython:: python

   df4 = df.copy()
   df4

   def f(x):
       return len(str(x))

   df4["one"].map(f)
   df4.map(f)

:meth:`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 :ref:`merging/joining functionality <merging>`:

.. ipython:: python

   s = pd.Series(
       ["six", "seven", "six", "seven", "six"], index=["a", "b", "c", "d", "e"]
   )
   t = pd.Series({"six": 6.0, "seven": 7.0})
   s
   s.map(t)


.. _basics.reindexing:

Reindexing and altering labels
------------------------------

:meth:`~Series.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:

.. ipython:: python

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

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:

.. ipython:: python

   df
   df.reindex(index=["c", "f", "b"], columns=["three", "two", "one"])

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:

.. ipython:: python

   rs = s.reindex(df.index)
   rs
   rs.index is df.index

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

:meth:`DataFrame.reindex` also supports an "axis-style" calling convention,
where you specify a single ``labels`` argument and the ``axis`` it applies to.

.. ipython:: python

   df.reindex(["c", "f", "b"], axis="index")
   df.reindex(["three", "two", "one"], axis="columns")

.. seealso::

   :ref:`MultiIndex / Advanced Indexing <advanced>` 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.

.. _basics.reindex_like:

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 :meth:`~DataFrame.reindex_like` method is
available to make this simpler:

.. ipython:: python

   df2 = df.reindex(["a", "b", "c"], columns=["one", "two"])
   df3 = df2 - df2.mean()
   df2
   df3
   df.reindex_like(df2)

.. _basics.align:

Aligning objects with each other with ``align``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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

.. ipython:: python

   s = pd.Series(np.random.randn(5), index=["a", "b", "c", "d", "e"])
   s1 = s[:4]
   s2 = s[1:]
   s1.align(s2)
   s1.align(s2, join="inner")
   s1.align(s2, join="left")

.. _basics.df_join:

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

.. ipython:: python

   df.align(df2, join="inner")

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

.. ipython:: python

   df.align(df2, join="inner", axis=0)

.. _basics.align.frame.series:

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

.. ipython:: python

   df.align(df2.iloc[0], axis=1)

.. _basics.reindex_fill:

Filling while reindexing
~~~~~~~~~~~~~~~~~~~~~~~~

:meth:`~Series.reindex` takes an optional parameter ``method`` which is a
filling method chosen from the following table:

.. csv-table::
    :header: "Method", "Action"
    :widths: 30, 50

    pad / ffill, Fill values forward
    bfill / backfill, Fill values backward
    nearest, Fill from the nearest index value

We illustrate these fill methods on a simple Series:

.. ipython:: python

   rng = pd.date_range("1/3/2000", periods=8)
   ts = pd.Series(np.random.randn(8), index=rng)
   ts2 = ts.iloc[[0, 3, 6]]
   ts
   ts2

   ts2.reindex(ts.index)
   ts2.reindex(ts.index, method="ffill")
   ts2.reindex(ts.index, method="bfill")
   ts2.reindex(ts.index, method="nearest")

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

Note that the same result could have been achieved using
:ref:`ffill <missing_data.fillna>` (except for ``method='nearest'``) or
:ref:`interpolate <missing_data.interpolate>`:

.. ipython:: python

   ts2.reindex(ts.index).ffill()

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

.. _basics.limits_on_reindex_fill:

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:

.. ipython:: python

   ts2.reindex(ts.index, method="ffill", limit=1)

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

.. ipython:: python

   ts2.reindex(ts.index, method="ffill", tolerance="1 day")

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.

.. _basics.drop:

Dropping labels from an axis
~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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

.. ipython:: python

   df
   df.drop(["a", "d"], axis=0)
   df.drop(["one"], axis=1)

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

.. ipython:: python

   df.reindex(df.index.difference(["a", "d"]))

.. _basics.rename:

Renaming / mapping labels
~~~~~~~~~~~~~~~~~~~~~~~~~

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

.. ipython:: python

   s
   s.rename(str.upper)

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:

.. ipython:: python

   df.rename(
       columns={"one": "foo", "two": "bar"},
       index={"a": "apple", "b": "banana", "d": "durian"},
   )

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.

:meth:`DataFrame.rename` also supports an "axis-style" calling convention, where
you specify a single ``mapper`` and the ``axis`` to apply that mapping to.

.. ipython:: python

   df.rename({"one": "foo", "two": "bar"}, axis="columns")
   df.rename({"a": "apple", "b": "banana", "d": "durian"}, axis="index")

Finally, :meth:`~Series.rename` also accepts a scalar or list-like
for altering the ``Series.name`` attribute.

.. ipython:: python

   s.rename("scalar-name")

.. _basics.rename_axis:

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

.. ipython:: python

   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"]
       ),
   )
   df
   df.rename_axis(index={"let": "abc"})
   df.rename_axis(index=str.upper)

.. _basics.iteration:

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:

.. ipython:: python

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

   for col in df:
       print(col)


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

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

* :meth:`~DataFrame.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.
* :meth:`~DataFrame.itertuples`: Iterate over the rows of a DataFrame
  as namedtuples of the values.  This is a lot faster than
  :meth:`~DataFrame.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 :meth:`~DataFrame.apply` instead of iterating
    over the values. See the docs on :ref:`function application <basics.apply>`.

  * 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 :ref:`enhancing performance <enhancingperf>` 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:

  .. ipython:: python

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

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

    df

items
~~~~~

Consistent with the dict-like interface, :meth:`~DataFrame.items` iterates
through key-value pairs:

* **Series**: (index, scalar value) pairs
* **DataFrame**: (column, Series) pairs

For example:

.. ipython:: python

   for label, ser in df.items():
       print(label)
       print(ser)

.. _basics.iterrows:

iterrows
~~~~~~~~

:meth:`~DataFrame.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:

.. ipython:: python

   for row_index, row in df.iterrows():
       print(row_index, row, sep="\n")

.. note::

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

   .. ipython:: python

      df_orig = pd.DataFrame([[1, 1.5]], columns=["int", "float"])
      df_orig.dtypes
      row = next(df_orig.iterrows())[1]
      row

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

   .. ipython:: python

      row["int"].dtype
      df_orig["int"].dtype

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

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

.. ipython:: python

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

   df2_t = pd.DataFrame({idx: values for idx, values in df2.iterrows()})
   print(df2_t)

itertuples
~~~~~~~~~~

The :meth:`~DataFrame.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:

.. ipython:: python

   for row in df.itertuples():
       print(row)

This method does not convert the row to a Series object; it merely
returns the values inside a namedtuple. Therefore,
:meth:`~DataFrame.itertuples` preserves the data type of the values
and is generally faster as :meth:`~DataFrame.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.

.. _basics.dt_accessors:

.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.

.. ipython:: python

   # datetime
   s = pd.Series(pd.date_range("20130101 09:10:12", periods=4))
   s
   s.dt.hour
   s.dt.second
   s.dt.day

This enables nice expressions like this:

.. ipython:: python

   s[s.dt.day == 2]

You can easily produces tz aware transformations:

.. ipython:: python

   stz = s.dt.tz_localize("US/Eastern")
   stz
   stz.dt.tz

You can also chain these types of operations:

.. ipython:: python

   s.dt.tz_localize("UTC").dt.tz_convert("US/Eastern")

You can also format datetime values as strings with :meth:`Series.dt.strftime` which
supports the same format as the standard :meth:`~datetime.datetime.strftime`.

.. ipython:: python

   # DatetimeIndex
   s = pd.Series(pd.date_range("20130101", periods=4))
   s
   s.dt.strftime("%Y/%m/%d")

.. ipython:: python

   # PeriodIndex
   s = pd.Series(pd.period_range("20130101", periods=4))
   s
   s.dt.strftime("%Y/%m/%d")

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

.. ipython:: python

   # period
   s = pd.Series(pd.period_range("20130101", periods=4, freq="D"))
   s
   s.dt.year
   s.dt.day

.. ipython:: python

   # timedelta
   s = pd.Series(pd.timedelta_range("1 day 00:00:05", periods=4, freq="s"))
   s
   s.dt.days
   s.dt.seconds
   s.dt.components

.. 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:

 .. ipython:: python

  s = pd.Series(
      ["A", "B", "C", "Aaba", "Baca", np.nan, "CABA", "dog", "cat"], dtype="string"
  )
  s.str.lower()

Powerful pattern-matching methods are provided as well, but note that
pattern-matching generally uses `regular expressions
<https://docs.python.org/3/library/re.html>`__ 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 :class:`StringDtype` which is dedicated
   to strings. See :ref:`text.types` for more.

Please see :ref:`Vectorized String Methods <text.string_methods>` for a complete
description.

.. _basics.sorting:

Sorting
-------

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

.. _basics.sort_index:

By index
~~~~~~~~

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

.. ipython:: python

   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"]),
       }
   )

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

   # DataFrame
   unsorted_df.sort_index()
   unsorted_df.sort_index(ascending=False)
   unsorted_df.sort_index(axis=1)

   # Series
   unsorted_df["three"].sort_index()

.. _basics.sort_index_key:

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``.

.. ipython:: python

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

.. ipython:: python

   s1.sort_index(level="a")
   s1.sort_index(level="a", key=lambda idx: idx.str.lower())

For information on key sorting by value, see :ref:`value sorting
<basics.sort_value_key>`.

.. _basics.sort_values:

By values
~~~~~~~~~

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

.. ipython:: python

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

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

.. ipython:: python

   df1[["one", "two", "three"]].sort_values(by=["one", "two"])

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

.. ipython:: python

   s[2] = np.nan
   s.sort_values()
   s.sort_values(na_position="first")

.. _basics.sort_value_key:

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

.. ipython:: python

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

.. ipython:: python

   s1.sort_values()
   s1.sort_values(key=lambda x: x.str.lower())

``key`` will be given the :class:`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.

.. ipython:: python

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

.. ipython:: python

   df.sort_values(by="a")
   df.sort_values(by="a", key=lambda col: col.str.lower())

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

.. _basics.sort_indexes_and_values:

By indexes and values
~~~~~~~~~~~~~~~~~~~~~

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

.. ipython:: python

   # Build MultiIndex
   idx = pd.MultiIndex.from_tuples(
       [("a", 1), ("a", 2), ("a", 2), ("b", 2), ("b", 1), ("b", 1)]
   )
   idx.names = ["first", "second"]

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

Sort by 'second' (index) and 'A' (column)

.. ipython:: python

   df_multi.sort_values(by=["second", "A"])

.. 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.

.. _basics.searchsorted:

searchsorted
~~~~~~~~~~~~

Series has the :meth:`~Series.searchsorted` method, which works similarly to
:meth:`numpy.ndarray.searchsorted`.

.. ipython:: python

   ser = pd.Series([1, 2, 3])
   ser.searchsorted([0, 3])
   ser.searchsorted([0, 4])
   ser.searchsorted([1, 3], side="right")
   ser.searchsorted([1, 3], side="left")
   ser = pd.Series([3, 1, 2])
   ser.searchsorted([0, 3], sorter=np.argsort(ser))

.. _basics.nsorted:

smallest / largest values
~~~~~~~~~~~~~~~~~~~~~~~~~

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

.. ipython:: python

   s = pd.Series(np.random.permutation(10))
   s
   s.sort_values()
   s.nsmallest(3)
   s.nlargest(3)

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

.. ipython:: python

   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],
       }
   )
   df.nlargest(3, "a")
   df.nlargest(5, ["a", "c"])
   df.nsmallest(3, "a")
   df.nsmallest(5, ["a", "c"])


.. _basics.multiindex_sorting:

Sorting by a MultiIndex column
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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

.. ipython:: python

   df1.columns = pd.MultiIndex.from_tuples(
       [("a", "one"), ("a", "two"), ("b", "three")]
   )
   df1.sort_values(by=("a", "two"))


Copying
-------

The :meth:`~DataFrame.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.

.. _basics.dtypes:

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 :ref:`extending.extension-types` for how to write your own extension that
works with pandas. See `the ecosystem page <https://pandas.pydata.org/community/ecosystem.html>`_ 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                         |
+=================================================+===============+===========+========+===========+===============================+========================================+
| :ref:`tz-aware datetime <timeseries.timezone>`  | :class:`DatetimeTZDtype`  | :class:`Timestamp` | :class:`arrays.DatetimeArray` | ``'datetime64[ns, <tz>]'``             |
|                                                 |                           |                    |                               |                                        |
+-------------------------------------------------+---------------+-----------+--------------------+-------------------------------+----------------------------------------+
| :ref:`Categorical <categorical>`                | :class:`CategoricalDtype` | (none)             | :class:`Categorical`          | ``'category'``                         |
+-------------------------------------------------+---------------------------+--------------------+-------------------------------+----------------------------------------+
| :ref:`period (time spans) <timeseries.periods>` | :class:`PeriodDtype`      | :class:`Period`    | :class:`arrays.PeriodArray`   | ``'period[<freq>]'``,                  |
|                                                 |                           |                    | ``'Period[<freq>]'``          |                                        |
+-------------------------------------------------+---------------------------+--------------------+-------------------------------+----------------------------------------+
| :ref:`sparse <sparse>`                          | :class:`SparseDtype`      | (none)             | :class:`arrays.SparseArray`   | ``'Sparse'``, ``'Sparse[int]'``,       |
|                                                 |                           |                    |                               | ``'Sparse[float]'``                    |
+-------------------------------------------------+---------------------------+--------------------+-------------------------------+----------------------------------------+
| :ref:`intervals <advanced.intervalindex>`       | :class:`IntervalDtype`    | :class:`Interval`  | :class:`arrays.IntervalArray` | ``'interval'``, ``'Interval'``,        |
|                                                 |                           |                    |                               | ``'Interval[<numpy_dtype>]'``,         |
|                                                 |                           |                    |                               | ``'Interval[datetime64[ns, <tz>]]'``,  |
|                                                 |                           |                    |                               | ``'Interval[timedelta64[<freq>]]'``    |
+-------------------------------------------------+---------------------------+--------------------+-------------------------------+----------------------------------------+
| :ref:`nullable integer <integer_na>`            | :class:`Int64Dtype`, ...  | (none)             | :class:`arrays.IntegerArray`  | ``'Int8'``, ``'Int16'``, ``'Int32'``,  |
|                                                 |                           |                    |                               | ``'Int64'``, ``'UInt8'``, ``'UInt16'``,|
|                                                 |                           |                    |                               | ``'UInt32'``, ``'UInt64'``             |
+-------------------------------------------------+---------------------------+--------------------+-------------------------------+----------------------------------------+
| ``nullable float``                              | :class:`Float64Dtype`, ...| (none)             | :class:`arrays.FloatingArray` | ``'Float32'``, ``'Float64'``           |
+-------------------------------------------------+---------------------------+--------------------+-------------------------------+----------------------------------------+
| :ref:`Strings <text>`                           | :class:`StringDtype`      | :class:`str`       | :class:`arrays.StringArray`   | ``'string'``                           |
+-------------------------------------------------+---------------------------+--------------------+-------------------------------+----------------------------------------+
| :ref:`Boolean (with NA) <api.arrays.bool>`      | :class:`BooleanDtype`     | :class:`bool`      | :class:`arrays.BooleanArray`  | ``'boolean'``                          |
+-------------------------------------------------+---------------------------+--------------------+-------------------------------+----------------------------------------+

pandas has two ways to store strings.

1. ``object`` dtype, which can hold any Python object, including strings.
2. :class:`StringDtype`, which is dedicated to strings.

Generally, we recommend using :class:`StringDtype`. See :ref:`text.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 :ref:`basics.object_conversion`).

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

.. ipython:: python

   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"),
       }
   )
   dft
   dft.dtypes

On a ``Series`` object, use the :attr:`~Series.dtype` attribute.

.. ipython:: python

   dft["A"].dtype

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).

.. ipython:: python

   # these ints are coerced to floats
   pd.Series([1, 2, 3, 4, 5, 6.0])

   # string data forces an ``object`` dtype
   pd.Series([1, 2, 3, 6.0, "foo"])

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

.. ipython:: python

   dft.dtypes.value_counts()

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.

.. ipython:: python

   df1 = pd.DataFrame(np.random.randn(8, 1), columns=["A"], dtype="float32")
   df1
   df1.dtypes
   df2 = pd.DataFrame(
       {
           "A": pd.Series(np.random.randn(8), dtype="float16"),
           "B": pd.Series(np.random.randn(8)),
           "C": pd.Series(np.random.randint(0, 255, size=8), dtype="uint8"),  # [0,255] (range of uint8)
       }
   )
   df2
   df2.dtypes

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.

.. ipython:: python

   pd.DataFrame([1, 2], columns=["a"]).dtypes
   pd.DataFrame({"a": [1, 2]}).dtypes
   pd.DataFrame({"a": 1}, index=list(range(2))).dtypes

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

.. ipython:: python

   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``).

.. ipython:: python

   df3 = df1.reindex_like(df2).fillna(value=0.0) + df2
   df3
   df3.dtypes

:meth:`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*.

.. ipython:: python

   df3.to_numpy().dtype

astype
~~~~~~

.. _basics.cast:

You can use the :meth:`~DataFrame.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.

.. ipython:: python

   df3
   df3.dtypes

   # conversion of dtypes
   df3.astype("float32").dtypes


Convert a subset of columns to a specified type using :meth:`~DataFrame.astype`.

.. ipython:: python

   dft = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]})
   dft[["a", "b"]] = dft[["a", "b"]].astype(np.uint8)
   dft
   dft.dtypes

Convert certain columns to a specific dtype by passing a dict to :meth:`~DataFrame.astype`.

.. ipython:: python

   dft1 = pd.DataFrame({"a": [1, 0, 1], "b": [4, 5, 6], "c": [7, 8, 9]})
   dft1 = dft1.astype({"a": np.bool_, "c": np.float64})
   dft1
   dft1.dtypes

.. note::

    When trying to convert a subset of columns to a specified type using :meth:`~DataFrame.astype`  and :meth:`~DataFrame.loc`, upcasting occurs.

    :meth:`~DataFrame.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.

    .. ipython:: python

       dft = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]})
       dft.loc[:, ["a", "b"]].astype(np.uint8).dtypes
       dft.loc[:, ["a", "b"]] = dft.loc[:, ["a", "b"]].astype(np.uint8)
       dft.dtypes

.. _basics.object_conversion:

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
:meth:`DataFrame.infer_objects` and :meth:`Series.infer_objects` methods can be used to soft convert
to the correct type.

  .. ipython:: python

     import datetime

     df = pd.DataFrame(
         [
             [1, 2],
             ["a", "b"],
             [datetime.datetime(2016, 3, 2), datetime.datetime(2016, 3, 2)],
         ]
     )
     df = df.T
     df
     df.dtypes

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

  .. ipython:: python

     df.infer_objects().dtypes

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

* :meth:`~pandas.to_numeric` (conversion to numeric dtypes)

  .. ipython:: python

     m = ["1.1", 2, 3]
     pd.to_numeric(m)

* :meth:`~pandas.to_datetime` (conversion to datetime objects)

  .. ipython:: python

     import datetime

     m = ["2016-07-09", datetime.datetime(2016, 3, 2)]
     pd.to_datetime(m)

* :meth:`~pandas.to_timedelta` (conversion to timedelta objects)

  .. ipython:: python

     m = ["5us", pd.Timedelta("1day")]
     pd.to_timedelta(m)

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:

.. ipython:: python
   :okwarning:

    import datetime

    m = ["apple", datetime.datetime(2016, 3, 2)]
    pd.to_datetime(m, errors="coerce")

    m = ["apple", 2, 3]
    pd.to_numeric(m, errors="coerce")

    m = ["apple", pd.Timedelta("1day")]
    pd.to_timedelta(m, errors="coerce")

The ``errors`` parameter has a third option of ``errors='ignore'``, which will simply return the passed in data if it
encounters any errors with the conversion to a desired data type:

.. ipython:: python
    :okwarning:

    import datetime

    m = ["apple", datetime.datetime(2016, 3, 2)]
    pd.to_datetime(m, errors="ignore")

    m = ["apple", 2, 3]
    pd.to_numeric(m, errors="ignore")

    m = ["apple", pd.Timedelta("1day")]
    pd.to_timedelta(m, errors="ignore")

In addition to object conversion, :meth:`~pandas.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:

.. ipython:: python

    m = ["1", 2, 3]
    pd.to_numeric(m, downcast="integer")  # smallest signed int dtype
    pd.to_numeric(m, downcast="signed")  # same as 'integer'
    pd.to_numeric(m, downcast="unsigned")  # smallest unsigned int dtype
    pd.to_numeric(m, downcast="float")  # smallest float dtype

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 :meth:`~pandas.DataFrame.apply`, we can "apply" the function over each column efficiently:

.. ipython:: python

    import datetime

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

    df = pd.DataFrame([["1.1", 2, 3]] * 2, dtype="O")
    df
    df.apply(pd.to_numeric)

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

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 :ref:`Support for integer NA <gotchas.intna>`.

.. ipython:: python

   dfi = df3.astype("int32")
   dfi["E"] = 1
   dfi
   dfi.dtypes

   casted = dfi[dfi > 0]
   casted
   casted.dtypes

While float dtypes are unchanged.

.. ipython:: python

   dfa = df3.copy()
   dfa["A"] = dfa["A"].astype("float32")
   dfa.dtypes

   casted = dfa[df2 > 0]
   casted
   casted.dtypes

Selecting columns based on ``dtype``
------------------------------------

.. _basics.selectdtypes:

The :meth:`~DataFrame.select_dtypes` method implements subsetting of columns
based on their ``dtype``.

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

.. ipython:: python

   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"),
       }
   )
   df["tdeltas"] = df.dates.diff()
   df["uint64"] = np.arange(3, 6).astype("u8")
   df["other_dates"] = pd.date_range("20130101", periods=3)
   df["tz_aware_dates"] = pd.date_range("20130101", periods=3, tz="US/Eastern")
   df

And the dtypes:

.. ipython:: python

   df.dtypes

:meth:`~DataFrame.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:

.. ipython:: python

   df.select_dtypes(include=[bool])

You can also pass the name of a dtype in the `NumPy dtype hierarchy
<https://numpy.org/doc/stable/reference/arrays.scalars.html>`__:

.. ipython:: python

   df.select_dtypes(include=["bool"])

:meth:`~pandas.DataFrame.select_dtypes` also works with generic dtypes as well.

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

.. ipython:: python

   df.select_dtypes(include=["number", "bool"], exclude=["unsignedinteger"])

To select string columns you must use the ``object`` dtype:

.. ipython:: python

   df.select_dtypes(include=["object"])

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:

.. ipython:: python

   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``:

.. ipython:: python

    subdtypes(np.generic)

.. 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.
