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This section covers :ref:`indexing with a MultiIndex <advanced.hierarchical>` and :ref:`other advanced indexing features <advanced.index_types>`.
See the :ref:`Indexing and Selecting Data <indexing>` for general indexing documentation.
See the :ref:`cookbook<cookbook.selection>` for some advanced strategies.
Hierarchical / Multi-level indexing is very exciting as it opens the door to some
quite sophisticated data analysis and manipulation, especially for working with
higher dimensional data. In essence, it enables you to store and manipulate
data with an arbitrary number of dimensions in lower dimensional data
structures like Series (1d) and DataFrame (2d).
In this section, we will show what exactly we mean by "hierarchical" indexing and how it integrates with all of the pandas indexing functionality described above and in prior sections. Later, when discussing :ref:`group by <groupby>` and :ref:`pivoting and reshaping data <reshaping>`, we'll show non-trivial applications to illustrate how it aids in structuring data for analysis.
See the :ref:`cookbook<cookbook.multi_index>` for some advanced strategies.
The :class:`MultiIndex` object is the hierarchical analogue of the standard
:class:`Index` object which typically stores the axis labels in pandas objects. You
can think of MultiIndex as an array of tuples where each tuple is unique. A
MultiIndex can be created from a list of arrays (using
:meth:`MultiIndex.from_arrays`), an array of tuples (using
:meth:`MultiIndex.from_tuples`), a crossed set of iterables (using
:meth:`MultiIndex.from_product`), or a :class:`DataFrame` (using
:meth:`MultiIndex.from_frame`). The Index constructor will attempt to return
a MultiIndex when it is passed a list of tuples. The following examples
demonstrate different ways to initialize MultiIndexes.
.. ipython:: python
arrays = [
["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"],
["one", "two", "one", "two", "one", "two", "one", "two"],
]
tuples = list(zip(*arrays))
tuples
index = pd.MultiIndex.from_tuples(tuples, names=["first", "second"])
index
s = pd.Series(np.random.randn(8), index=index)
s
When you want every pairing of the elements in two iterables, it can be easier to use the :meth:`MultiIndex.from_product` method:
.. ipython:: python iterables = [["bar", "baz", "foo", "qux"], ["one", "two"]] pd.MultiIndex.from_product(iterables, names=["first", "second"])
You can also construct a MultiIndex from a DataFrame directly, using
the method :meth:`MultiIndex.from_frame`. This is a complementary method to
:meth:`MultiIndex.to_frame`.
.. ipython:: python
df = pd.DataFrame(
[["bar", "one"], ["bar", "two"], ["foo", "one"], ["foo", "two"]],
columns=["first", "second"],
)
pd.MultiIndex.from_frame(df)
As a convenience, you can pass a list of arrays directly into Series or
DataFrame to construct a MultiIndex automatically:
.. ipython:: python
arrays = [
np.array(["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"]),
np.array(["one", "two", "one", "two", "one", "two", "one", "two"]),
]
s = pd.Series(np.random.randn(8), index=arrays)
s
df = pd.DataFrame(np.random.randn(8, 4), index=arrays)
df
All of the MultiIndex constructors accept a names argument which stores
string names for the levels themselves. If no names are provided, None will
be assigned:
.. ipython:: python df.index.names
This index can back any axis of a pandas object, and the number of levels of the index is up to you:
.. ipython:: python df = pd.DataFrame(np.random.randn(3, 8), index=["A", "B", "C"], columns=index) df pd.DataFrame(np.random.randn(6, 6), index=index[:6], columns=index[:6])
We've "sparsified" the higher levels of the indexes to make the console output a
bit easier on the eyes. Note that how the index is displayed can be controlled using the
multi_sparse option in pandas.set_options():
.. ipython:: python
with pd.option_context("display.multi_sparse", False):
df
It's worth keeping in mind that there's nothing preventing you from using tuples as atomic labels on an axis:
.. ipython:: python pd.Series(np.random.randn(8), index=tuples)
The reason that the MultiIndex matters is that it can allow you to do
grouping, selection, and reshaping operations as we will describe below and in
subsequent areas of the documentation. As you will see in later sections, you
can find yourself working with hierarchically-indexed data without creating a
MultiIndex explicitly yourself. However, when loading data from a file, you
may wish to generate your own MultiIndex when preparing the data set.
The method :meth:`~MultiIndex.get_level_values` will return a vector of the labels for each location at a particular level:
.. ipython:: python
index.get_level_values(0)
index.get_level_values("second")
One of the important features of hierarchical indexing is that you can select data by a "partial" label identifying a subgroup in the data. Partial selection "drops" levels of the hierarchical index in the result in a completely analogous way to selecting a column in a regular DataFrame:
.. ipython:: python df["bar"] df["bar", "one"] df["bar"]["one"] s["qux"]
See :ref:`Cross-section with hierarchical index <advanced.xs>` for how to select on a deeper level.
The :class:`MultiIndex` keeps all the defined levels of an index, even if they are not actually used. When slicing an index, you may notice this. For example:
.. ipython:: python df.columns.levels # original MultiIndex df[["foo","qux"]].columns.levels # sliced
This is done to avoid a recomputation of the levels in order to make slicing highly performant. If you want to see only the used levels, you can use the :meth:`~MultiIndex.get_level_values` method.
.. ipython:: python df[["foo", "qux"]].columns.to_numpy() # for a specific level df[["foo", "qux"]].columns.get_level_values(0)
To reconstruct the MultiIndex with only the used levels, the
:meth:`~MultiIndex.remove_unused_levels` method may be used.
.. ipython:: python new_mi = df[["foo", "qux"]].columns.remove_unused_levels() new_mi.levels
Operations between differently-indexed objects having MultiIndex on the
axes will work as you expect; data alignment will work the same as an Index of
tuples:
.. ipython:: python s + s[:-2] s + s[::2]
The :meth:`~DataFrame.reindex` method of Series/DataFrames can be
called with another MultiIndex, or even a list or array of tuples:
.. ipython:: python
s.reindex(index[:3])
s.reindex([("foo", "two"), ("bar", "one"), ("qux", "one"), ("baz", "one")])
Syntactically integrating MultiIndex in advanced indexing with .loc is a
bit challenging, but we've made every effort to do so. In general, MultiIndex
keys take the form of tuples. For example, the following works as you would expect:
.. ipython:: python
df = df.T
df
df.loc[("bar", "two")]
Note that df.loc['bar', 'two'] would also work in this example, but this shorthand
notation can lead to ambiguity in general.
If you also want to index a specific column with .loc, you must use a tuple
like this:
.. ipython:: python
df.loc[("bar", "two"), "A"]
You don't have to specify all levels of the MultiIndex by passing only the
first elements of the tuple. For example, you can use "partial" indexing to
get all elements with bar in the first level as follows:
.. ipython:: python df.loc["bar"]
This is a shortcut for the slightly more verbose notation df.loc[('bar',),] (equivalent
to df.loc['bar',] in this example).
"Partial" slicing also works quite nicely.
.. ipython:: python df.loc["baz":"foo"]
You can slice with a 'range' of values, by providing a slice of tuples.
.. ipython:: python
df.loc[("baz", "two"):("qux", "one")]
df.loc[("baz", "two"):"foo"]
Passing a list of labels or tuples works similar to reindexing:
.. ipython:: python
df.loc[[("bar", "two"), ("qux", "one")]]
Note
It is important to note that tuples and lists are not treated identically in pandas when it comes to indexing. Whereas a tuple is interpreted as one multi-level key, a list is used to specify several keys. Or in other words, tuples go horizontally (traversing levels), lists go vertically (scanning levels).
Importantly, a list of tuples indexes several complete MultiIndex keys,
whereas a tuple of lists refer to several values within a level:
.. ipython:: python
s = pd.Series(
[1, 2, 3, 4, 5, 6],
index=pd.MultiIndex.from_product([["A", "B"], ["c", "d", "e"]]),
)
s.loc[[("A", "c"), ("B", "d")]] # list of tuples
s.loc[(["A", "B"], ["c", "d"])] # tuple of lists
You can slice a MultiIndex by providing multiple indexers.
You can provide any of the selectors as if you are indexing by label, see :ref:`Selection by Label <indexing.label>`, including slices, lists of labels, labels, and boolean indexers.
You can use slice(None) to select all the contents of that level. You do not need to specify all the
deeper levels, they will be implied as slice(None).
As usual, both sides of the slicers are included as this is label indexing.
Warning
You should specify all axes in the .loc specifier, meaning the indexer for the index and
for the columns. There are some ambiguous cases where the passed indexer could be misinterpreted
as indexing both axes, rather than into say the MultiIndex for the rows.
You should do this:
df.loc[(slice("A1", "A3"), ...), :] # noqa: E999You should not do this:
df.loc[(slice("A1", "A3"), ...)] # noqa: E999.. ipython:: python
def mklbl(prefix, n):
return ["%s%s" % (prefix, i) for i in range(n)]
miindex = pd.MultiIndex.from_product(
[mklbl("A", 4), mklbl("B", 2), mklbl("C", 4), mklbl("D", 2)]
)
micolumns = pd.MultiIndex.from_tuples(
[("a", "foo"), ("a", "bar"), ("b", "foo"), ("b", "bah")], names=["lvl0", "lvl1"]
)
dfmi = (
pd.DataFrame(
np.arange(len(miindex) * len(micolumns)).reshape(
(len(miindex), len(micolumns))
),
index=miindex,
columns=micolumns,
)
.sort_index()
.sort_index(axis=1)
)
dfmi
Basic MultiIndex slicing using slices, lists, and labels.
.. ipython:: python
dfmi.loc[(slice("A1", "A3"), slice(None), ["C1", "C3"]), :]
You can use :class:`pandas.IndexSlice` to facilitate a more natural syntax
using :, rather than using slice(None).
.. ipython:: python idx = pd.IndexSlice dfmi.loc[idx[:, :, ["C1", "C3"]], idx[:, "foo"]]
It is possible to perform quite complicated selections using this method on multiple axes at the same time.
.. ipython:: python dfmi.loc["A1", (slice(None), "foo")] dfmi.loc[idx[:, :, ["C1", "C3"]], idx[:, "foo"]]
Using a boolean indexer you can provide selection related to the values.
.. ipython:: python
mask = dfmi[("a", "foo")] > 200
dfmi.loc[idx[mask, :, ["C1", "C3"]], idx[:, "foo"]]
You can also specify the axis argument to .loc to interpret the passed
slicers on a single axis.
.. ipython:: python dfmi.loc(axis=0)[:, :, ["C1", "C3"]]
Furthermore, you can set the values using the following methods.
.. ipython:: python :okwarning: df2 = dfmi.copy() df2.loc(axis=0)[:, :, ["C1", "C3"]] = -10 df2
You can use a right-hand-side of an alignable object as well.
.. ipython:: python df2 = dfmi.copy() df2.loc[idx[:, :, ["C1", "C3"]], :] = df2 * 1000 df2
The :meth:`~DataFrame.xs` method of DataFrame additionally takes a level argument to make
selecting data at a particular level of a MultiIndex easier.
.. ipython:: python
df
df.xs("one", level="second")
.. ipython:: python # using the slicers df.loc[(slice(None), "one"), :]
You can also select on the columns with xs, by
providing the axis argument.
.. ipython:: python
df = df.T
df.xs("one", level="second", axis=1)
.. ipython:: python # using the slicers df.loc[:, (slice(None), "one")]
xs also allows selection with multiple keys.
.. ipython:: python
df.xs(("one", "bar"), level=("second", "first"), axis=1)
.. ipython:: python
# using the slicers
df.loc[:, ("bar", "one")]
You can pass drop_level=False to xs to retain
the level that was selected.
.. ipython:: python
df.xs("one", level="second", axis=1, drop_level=False)
Compare the above with the result using drop_level=True (the default value).
.. ipython:: python
df.xs("one", level="second", axis=1, drop_level=True)
Using the parameter level in the :meth:`~DataFrame.reindex` and
:meth:`~DataFrame.align` methods of pandas objects is useful to broadcast
values across a level. For instance:
.. ipython:: python
midx = pd.MultiIndex(
levels=[["zero", "one"], ["x", "y"]], codes=[[1, 1, 0, 0], [1, 0, 1, 0]]
)
df = pd.DataFrame(np.random.randn(4, 2), index=midx)
df
df2 = df.groupby(level=0).mean()
df2
df2.reindex(df.index, level=0)
# aligning
df_aligned, df2_aligned = df.align(df2, level=0)
df_aligned
df2_aligned
The :meth:`~MultiIndex.swaplevel` method can switch the order of two levels:
.. ipython:: python df[:5] df[:5].swaplevel(0, 1, axis=0)
The :meth:`~MultiIndex.reorder_levels` method generalizes the swaplevel
method, allowing you to permute the hierarchical index levels in one step:
.. ipython:: python df[:5].reorder_levels([1, 0], axis=0)
The :meth:`~DataFrame.rename` method is used to rename the labels of a
MultiIndex, and is typically used to rename the columns of a DataFrame.
The columns argument of rename allows a dictionary to be specified
that includes only the columns you wish to rename.
.. ipython:: python
df.rename(columns={0: "col0", 1: "col1"})
This method can also be used to rename specific labels of the main index
of the DataFrame.
.. ipython:: python
df.rename(index={"one": "two", "y": "z"})
The :meth:`~DataFrame.rename_axis` method is used to rename the name of a
Index or MultiIndex. In particular, the names of the levels of a
MultiIndex can be specified, which is useful if reset_index() is later
used to move the values from the MultiIndex to a column.
.. ipython:: python df.rename_axis(index=["abc", "def"])
Note that the columns of a DataFrame are an index, so that using
rename_axis with the columns argument will change the name of that
index.
.. ipython:: python df.rename_axis(columns="Cols").columns
Both rename and rename_axis support specifying a dictionary,
Series or a mapping function to map labels/names to new values.
When working with an Index object directly, rather than via a DataFrame,
:meth:`Index.set_names` can be used to change the names.
.. ipython:: python
mi = pd.MultiIndex.from_product([[1, 2], ["a", "b"]], names=["x", "y"])
mi.names
mi2 = mi.rename("new name", level=0)
mi2
You cannot set the names of the MultiIndex via a level.
.. ipython:: python :okexcept: mi.levels[0].name = "name via level"
Use :meth:`Index.set_names` instead.
For :class:`MultiIndex`-ed objects to be indexed and sliced effectively, they need to be sorted. As with any index, you can use :meth:`~DataFrame.sort_index`.
.. ipython:: python import random random.shuffle(tuples) s = pd.Series(np.random.randn(8), index=pd.MultiIndex.from_tuples(tuples)) s s.sort_index() s.sort_index(level=0) s.sort_index(level=1)
You may also pass a level name to sort_index if the MultiIndex levels
are named.
.. ipython:: python s.index = s.index.set_names(["L1", "L2"]) s.sort_index(level="L1") s.sort_index(level="L2")
On higher dimensional objects, you can sort any of the other axes by level if
they have a MultiIndex:
.. ipython:: python df.T.sort_index(level=1, axis=1)
Indexing will work even if the data are not sorted, but will be rather
inefficient (and show a PerformanceWarning). It will also
return a copy of the data rather than a view:
.. ipython:: python
:okwarning:
dfm = pd.DataFrame(
{"jim": [0, 0, 1, 1], "joe": ["x", "x", "z", "y"], "jolie": np.random.rand(4)}
)
dfm = dfm.set_index(["jim", "joe"])
dfm
dfm.loc[(1, 'z')]
Furthermore, if you try to index something that is not fully lexsorted, this can raise:
.. ipython:: python :okexcept: dfm.loc[(0, 'y'):(1, 'z')]
The :meth:`~MultiIndex.is_monotonic_increasing` method on a MultiIndex shows if the
index is sorted:
.. ipython:: python dfm.index.is_monotonic_increasing
.. ipython:: python dfm = dfm.sort_index() dfm dfm.index.is_monotonic_increasing
And now selection works as expected.
.. ipython:: python dfm.loc[(0, "y"):(1, "z")]
Similar to NumPy ndarrays, pandas Index, Series, and DataFrame also provides
the :meth:`~DataFrame.take` method that retrieves elements along a given axis at the given
indices. The given indices must be either a list or an ndarray of integer
index positions. take will also accept negative integers as relative positions to the end of the object.
.. ipython:: python index = pd.Index(np.random.randint(0, 1000, 10)) index positions = [0, 9, 3] index[positions] index.take(positions) ser = pd.Series(np.random.randn(10)) ser.iloc[positions] ser.take(positions)
For DataFrames, the given indices should be a 1d list or ndarray that specifies row or column positions.
.. ipython:: python frm = pd.DataFrame(np.random.randn(5, 3)) frm.take([1, 4, 3]) frm.take([0, 2], axis=1)
It is important to note that the take method on pandas objects are not
intended to work on boolean indices and may return unexpected results.
.. ipython:: python arr = np.random.randn(10) arr.take([False, False, True, True]) arr[[0, 1]] ser = pd.Series(np.random.randn(10)) ser.take([False, False, True, True]) ser.iloc[[0, 1]]
Finally, as a small note on performance, because the take method handles
a narrower range of inputs, it can offer performance that is a good deal
faster than fancy indexing.
.. ipython:: python arr = np.random.randn(10000, 5) indexer = np.arange(10000) random.shuffle(indexer) %timeit arr[indexer] %timeit arr.take(indexer, axis=0)
.. ipython:: python ser = pd.Series(arr[:, 0]) %timeit ser.iloc[indexer] %timeit ser.take(indexer)
We have discussed MultiIndex in the previous sections pretty extensively.
Documentation about DatetimeIndex and PeriodIndex are shown :ref:`here <timeseries.overview>`,
and documentation about TimedeltaIndex is found :ref:`here <timedeltas.index>`.
In the following sub-sections we will highlight some other index types.
:class:`CategoricalIndex` is a type of index that is useful for supporting indexing with duplicates. This is a container around a :class:`Categorical` and allows efficient indexing and storage of an index with a large number of duplicated elements.
.. ipython:: python
from pandas.api.types import CategoricalDtype
df = pd.DataFrame({"A": np.arange(6), "B": list("aabbca")})
df["B"] = df["B"].astype(CategoricalDtype(list("cab")))
df
df.dtypes
df["B"].cat.categories
Setting the index will create a CategoricalIndex.
.. ipython:: python
df2 = df.set_index("B")
df2.index
Indexing with __getitem__/.iloc/.loc works similarly to an Index with duplicates.
The indexers must be in the category or the operation will raise a KeyError.
.. ipython:: python df2.loc["a"]
The CategoricalIndex is preserved after indexing:
.. ipython:: python df2.loc["a"].index
Sorting the index will sort by the order of the categories (recall that we
created the index with CategoricalDtype(list('cab')), so the sorted
order is cab).
.. ipython:: python df2.sort_index()
Groupby operations on the index will preserve the index nature as well.
.. ipython:: python df2.groupby(level=0, observed=True).sum() df2.groupby(level=0, observed=True).sum().index
Reindexing operations will return a resulting index based on the type of the passed
indexer. Passing a list will return a plain-old Index; indexing with
a Categorical will return a CategoricalIndex, indexed according to the categories
of the passed Categorical dtype. This allows one to arbitrarily index these even with
values not in the categories, similarly to how you can reindex any pandas index.
.. ipython:: python
df3 = pd.DataFrame(
{"A": np.arange(3), "B": pd.Series(list("abc")).astype("category")}
)
df3 = df3.set_index("B")
df3
.. ipython:: python
df3.reindex(["a", "e"])
df3.reindex(["a", "e"]).index
df3.reindex(pd.Categorical(["a", "e"], categories=list("abe")))
df3.reindex(pd.Categorical(["a", "e"], categories=list("abe"))).index
Warning
Reshaping and Comparison operations on a CategoricalIndex must have the same categories
or a TypeError will be raised.
.. ipython:: python
df4 = pd.DataFrame({"A": np.arange(2), "B": list("ba")})
df4["B"] = df4["B"].astype(CategoricalDtype(list("ab")))
df4 = df4.set_index("B")
df4.index
df5 = pd.DataFrame({"A": np.arange(2), "B": list("bc")})
df5["B"] = df5["B"].astype(CategoricalDtype(list("bc")))
df5 = df5.set_index("B")
df5.index
.. ipython:: python :okexcept: pd.concat([df4, df5])
:class:`RangeIndex` is a sub-class of :class:`Index` that provides the default index for all :class:`DataFrame` and :class:`Series` objects.
RangeIndex is an optimized version of Index that can represent a monotonic ordered set. These are analogous to Python range types.
A RangeIndex will always have an int64 dtype.
.. ipython:: python idx = pd.RangeIndex(5) idx
RangeIndex is the default index for all :class:`DataFrame` and :class:`Series` objects:
.. ipython:: python ser = pd.Series([1, 2, 3]) ser.index df = pd.DataFrame([[1, 2], [3, 4]]) df.index df.columns
A RangeIndex will behave similarly to a :class:`Index` with an int64 dtype and operations on a RangeIndex,
whose result cannot be represented by a RangeIndex, but should have an integer dtype, will be converted to an Index with int64.
For example:
.. ipython:: python idx[[0, 2]]
:class:`IntervalIndex` together with its own dtype, :class:`~pandas.api.types.IntervalDtype` as well as the :class:`Interval` scalar type, allow first-class support in pandas for interval notation.
The IntervalIndex allows some unique indexing and is also used as a
return type for the categories in :func:`cut` and :func:`qcut`.
An IntervalIndex can be used in Series and in DataFrame as the index.
.. ipython:: python
df = pd.DataFrame(
{"A": [1, 2, 3, 4]}, index=pd.IntervalIndex.from_breaks([0, 1, 2, 3, 4])
)
df
Label based indexing via .loc along the edges of an interval works as you would expect,
selecting that particular interval.
.. ipython:: python df.loc[2] df.loc[[2, 3]]
If you select a label contained within an interval, this will also select the interval.
.. ipython:: python df.loc[2.5] df.loc[[2.5, 3.5]]
Selecting using an Interval will only return exact matches.
.. ipython:: python df.loc[pd.Interval(1, 2)]
Trying to select an Interval that is not exactly contained in the IntervalIndex will raise a KeyError.
.. ipython:: python :okexcept: df.loc[pd.Interval(0.5, 2.5)]
Selecting all Intervals that overlap a given Interval can be performed using the
:meth:`~IntervalIndex.overlaps` method to create a boolean indexer.
.. ipython:: python idxr = df.index.overlaps(pd.Interval(0.5, 2.5)) idxr df[idxr]
:func:`cut` and :func:`qcut` both return a Categorical object, and the bins they
create are stored as an IntervalIndex in its .categories attribute.
.. ipython:: python c = pd.cut(range(4), bins=2) c c.categories
:func:`cut` also accepts an IntervalIndex for its bins argument, which enables
a useful pandas idiom. First, We call :func:`cut` with some data and bins set to a
fixed number, to generate the bins. Then, we pass the values of .categories as the
bins argument in subsequent calls to :func:`cut`, supplying new data which will be
binned into the same bins.
.. ipython:: python pd.cut([0, 3, 5, 1], bins=c.categories)
Any value which falls outside all bins will be assigned a NaN value.
If we need intervals on a regular frequency, we can use the :func:`interval_range` function
to create an IntervalIndex using various combinations of start, end, and periods.
The default frequency for interval_range is a 1 for numeric intervals, and calendar day for
datetime-like intervals:
.. ipython:: python
pd.interval_range(start=0, end=5)
pd.interval_range(start=pd.Timestamp("2017-01-01"), periods=4)
pd.interval_range(end=pd.Timedelta("3 days"), periods=3)
The freq parameter can used to specify non-default frequencies, and can utilize a variety
of :ref:`frequency aliases <timeseries.offset_aliases>` with datetime-like intervals:
.. ipython:: python
pd.interval_range(start=0, periods=5, freq=1.5)
pd.interval_range(start=pd.Timestamp("2017-01-01"), periods=4, freq="W")
pd.interval_range(start=pd.Timedelta("0 days"), periods=3, freq="9h")
Additionally, the closed parameter can be used to specify which side(s) the intervals
are closed on. Intervals are closed on the right side by default.
.. ipython:: python pd.interval_range(start=0, end=4, closed="both") pd.interval_range(start=0, end=4, closed="neither")
Specifying start, end, and periods will generate a range of evenly spaced
intervals from start to end inclusively, with periods number of elements
in the resulting IntervalIndex:
.. ipython:: python
pd.interval_range(start=0, end=6, periods=4)
pd.interval_range(pd.Timestamp("2018-01-01"), pd.Timestamp("2018-02-28"), periods=3)
Label-based indexing with integer axis labels is a thorny topic. It has been
discussed heavily on mailing lists and among various members of the scientific
Python community. In pandas, our general viewpoint is that labels matter more
than integer locations. Therefore, with an integer axis index only
label-based indexing is possible with the standard tools like .loc. The
following code will generate exceptions:
.. ipython:: python :okexcept: s = pd.Series(range(5)) s[-1] df = pd.DataFrame(np.random.randn(5, 4)) df df.loc[-2:]
This deliberate decision was made to prevent ambiguities and subtle bugs (many users reported finding bugs when the API change was made to stop "falling back" on position-based indexing).
If the index of a Series or DataFrame is monotonically increasing or decreasing, then the bounds
of a label-based slice can be outside the range of the index, much like slice indexing a
normal Python list. Monotonicity of an index can be tested with the :meth:`~Index.is_monotonic_increasing` and
:meth:`~Index.is_monotonic_decreasing` attributes.
.. ipython:: python
df = pd.DataFrame(index=[2, 3, 3, 4, 5], columns=["data"], data=list(range(5)))
df.index.is_monotonic_increasing
# no rows 0 or 1, but still returns rows 2, 3 (both of them), and 4:
df.loc[0:4, :]
# slice is are outside the index, so empty DataFrame is returned
df.loc[13:15, :]
On the other hand, if the index is not monotonic, then both slice bounds must be unique members of the index.
.. ipython:: python
df = pd.DataFrame(index=[2, 3, 1, 4, 3, 5], columns=["data"], data=list(range(6)))
df.index.is_monotonic_increasing
# OK because 2 and 4 are in the index
df.loc[2:4, :]
.. ipython:: python
:okexcept:
# 0 is not in the index
df.loc[0:4, :]
# 3 is not a unique label
df.loc[2:3, :]
Index.is_monotonic_increasing and Index.is_monotonic_decreasing only check that
an index is weakly monotonic. To check for strict monotonicity, you can combine one of those with
the :meth:`~Index.is_unique` attribute.
.. ipython:: python weakly_monotonic = pd.Index(["a", "b", "c", "c"]) weakly_monotonic weakly_monotonic.is_monotonic_increasing weakly_monotonic.is_monotonic_increasing & weakly_monotonic.is_unique
Compared with standard Python sequence slicing in which the slice endpoint is
not inclusive, label-based slicing in pandas is inclusive. The primary
reason for this is that it is often not possible to easily determine the
"successor" or next element after a particular label in an index. For example,
consider the following Series:
.. ipython:: python
s = pd.Series(np.random.randn(6), index=list("abcdef"))
s
Suppose we wished to slice from c to e, using integers this would be
accomplished as such:
.. ipython:: python s[2:5]
However, if you only had c and e, determining the next element in the
index can be somewhat complicated. For example, the following does not work:
.. ipython:: python
:okexcept:
s.loc['c':'e' + 1]
A very common use case is to limit a time series to start and end at two specific dates. To enable this, we made the design choice to make label-based slicing include both endpoints:
.. ipython:: python
s.loc["c":"e"]
This is most definitely a "practicality beats purity" sort of thing, but it is something to watch out for if you expect label-based slicing to behave exactly in the way that standard Python integer slicing works.
The different indexing operation can potentially change the dtype of a Series.
.. ipython:: python series1 = pd.Series([1, 2, 3]) series1.dtype res = series1.reindex([0, 4]) res.dtype res
.. ipython:: python series2 = pd.Series([True]) series2.dtype res = series2.reindex_like(series1) res.dtype res
This is because the (re)indexing operations above silently inserts NaNs and the dtype
changes accordingly. This can cause some issues when using numpy ufuncs
such as numpy.logical_and.
See the :issue:`2388` for a more detailed discussion.