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from zoneinfo import ZoneInfo # zoneinfo (default when using Olson strings) rng\_zi = pd.date\_range("3/6/2012 00:00", periods=3, freq="D", tz="Europe/London") rng\_zi.tz # dateutil rng\_dateutil = pd.date\_range("3/6/2012 00:00", periods=3, freq="D") rng\_dateutil = rng\_dateutil.tz\_localize("dateutil/Europe/London")... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/timeseries.rst | main | pandas | [
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10, 27, 1, 30, 0, 0), tz="dateutil/Europe/London", fold=0, ) pd.Timestamp( year=2019, month=10, day=27, hour=1, minute=30, tz="dateutil/Europe/London", fold=1, ) .. \_timeseries.timezone\_ambiguous: Ambiguous times when localizing ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ``tz\_localize`` may not be able to determine the UTC off... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/timeseries.rst | main | pandas | [
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python pd.Series(s\_aware.to\_numpy(dtype=object)) | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/timeseries.rst | main | pandas | [
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.. \_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: .. ... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/basics.rst | main | pandas | [
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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 ... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/basics.rst | main | pandas | [
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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 w... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/basics.rst | main | pandas | [
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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 ... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/basics.rst | main | pandas | [
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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): .. ipyt... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/basics.rst | main | pandas | [
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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... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/basics.rst | main | pandas | [
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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... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/basics.rst | main | pandas | [
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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 whic... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/basics.rst | main | pandas | [
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"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 functi... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/basics.rst | main | pandas | [
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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 op... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/basics.rst | main | pandas | [
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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": n... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/basics.rst | main | pandas | [
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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 ... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/basics.rst | main | pandas | [
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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 .. ipyt... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/basics.rst | main | pandas | [
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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 -------... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/basics.rst | main | pandas | [
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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, ... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/basics.rst | main | pandas | [
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``[]`` 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... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/basics.rst | main | pandas | [
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= 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:`Dat... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/basics.rst | main | pandas | [
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.. \_cookbook: {{ header }} \*\*\*\*\*\*\*\* Cookbook \*\*\*\*\*\*\*\* This is a repository for \*short and sweet\* examples and links for useful pandas recipes. Simplified, condensed, new-user friendly, in-line examples have been inserted where possible to augment the Stack-Overflow and GitHub links. Many of the links... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/cookbook.rst | main | pandas | [
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2, 4]))] Use loc for label-oriented slicing and iloc positional slicing :issue:`2904` .. ipython:: python df = pd.DataFrame( {"AAA": [4, 5, 6, 7], "BBB": [10, 20, 30, 40], "CCC": [100, 50, -30, -50]}, index=["foo", "bar", "boo", "kar"], ) There are 2 explicit slicing methods, with a third general case 1. Positional-ori... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/cookbook.rst | main | pandas | [
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level and 1st axis the index: .. ipython:: python # Note : level and axis are optional, and default to zero df.xs("BB", level=0, axis=0) ...and now the 2nd level of the 1st axis. .. ipython:: python df.xs("six", level=1, axis=0) `Slicing a MultiIndex with xs, method #2 `\_\_ .. ipython:: python import itertools index =... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/cookbook.rst | main | pandas | [
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in a column based on the index `\_\_ .. ipython:: python df = pd.DataFrame( {"line\_race": [10, 10, 8, 10, 10, 8], "beyer": [99, 102, 103, 103, 88, 100]}, index=[ "Last Gunfighter", "Last Gunfighter", "Last Gunfighter", "Paynter", "Paynter", "Paynter", ], ) df df["beyer\_shifted"] = df.groupby(level=0)["beyer"].shift(1... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/cookbook.rst | main | pandas | [
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s = pd.Series( { df.index[i]: gm(df.iloc[i: min(i + 51, len(df) - 1)], 5) for i in range(len(df) - 50) } ) s `Rolling apply with a DataFrame returning a Scalar `\_\_ Rolling Apply to multiple columns where function returns a Scalar (Volume Weighted Average Price) .. ipython:: python rng = pd.date\_range(start="2014-01-... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/cookbook.rst | main | pandas | [
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`\_\_ `Using searchsorted to merge based on values inside a range `\_\_ .. \_cookbook.plotting: Plotting -------- The :ref:`Plotting ` docs. `Make Matplotlib look like R `\_\_ `Setting x-axis major and minor labels `\_\_ `Plotting multiple charts in an IPython Jupyter notebook `\_\_ `Creating a multi-line plot `\_\_ `P... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/cookbook.rst | main | pandas | [
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visible sheets :issue:`19842#issuecomment-892150745` .. \_cookbook.html: HTML \*\*\*\* `Reading HTML tables from a server that cannot handle the default request header `\_\_ .. \_cookbook.hdf: HDFStore \*\*\*\*\*\*\*\* The :ref:`HDFStores ` docs `Simple queries with a Timestamp Index `\_\_ Managing heterogeneous data u... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/cookbook.rst | main | pandas | [
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\*\*\*\*\*\*\*\*\*\*\* Often it's useful to obtain the lower (or upper) triangular form of a correlation matrix calculated from :func:`DataFrame.corr`. This can be achieved by passing a boolean mask to ``where`` as follows: .. ipython:: python df = pd.DataFrame(np.random.random(size=(100, 5))) corr\_mat = df.corr() mas... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/cookbook.rst | main | pandas | [
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.. \_text: {{ header }} ###################### Working with text data ###################### .. versionchanged:: 3.0 The inference and behavior of strings changed significantly in pandas 3.0. See the :ref:`string\_migration\_guide`. .. \_text.types: Text data types =============== There are two ways to store text data ... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/text.rst | main | pandas | [
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can then be used to clean up the columns as needed. Here we are removing leading and trailing whitespaces, lower casing all names, and replacing any remaining whitespaces with underscores: .. ipython:: python df.columns = df.columns.str.strip().str.lower().str.replace(" ", "\_") df .. note:: If you have a ``Series`` wh... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/text.rst | main | pandas | [
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The ``replace`` method also accepts a compiled regular expression object from :func:`re.compile` as a pattern. All flags should be included in the compiled regular expression object. .. ipython:: python import re regex\_pat = re.compile(r"^.a|dog", flags=re.IGNORECASE) s3.str.replace(regex\_pat, "XX-XX ", regex=True) I... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/text.rst | main | pandas | [
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the union of these indexes will be used as the basis for the final concatenation: .. ipython:: python u.loc[[3]] v.loc[[-1, 0]] s.str.cat([u.loc[[3]], v.loc[[-1, 0]]], join="right", na\_rep="-") Indexing with ``.str`` ====================== .. \_text.indexing: You can use ``[]`` notation to directly index by position l... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/text.rst | main | pandas | [
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(starts from 0). .. ipython:: python pd.Index(["a1a2", "b1", "c1"]).str.extractall(two\_groups) pd.Series(["a1a2", "b1", "c1"], dtype="str").str.extractall(two\_groups) Testing for strings that match or contain a pattern =================================================== You can check whether elements contain a patter... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/text.rst | main | pandas | [
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variants ====================================== There are four :class:`StringDtype` variants that are available to users, controlled by the ``storage`` and ``na\_value`` parameters of :class:`StringDtype`. At runtime, these can be checked via the :attr:`StringDtype.storage` and :attr:`StringDtype.na\_value` attributes.... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/text.rst | main | pandas | [
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each regex capture group :meth:`~Series.str.extractall`,Call ``re.findall`` on each element returning DataFrame with one row for each match and one column for each regex capture group :meth:`~Series.str.len`,Compute string lengths :meth:`~Series.str.strip`,Equivalent to ``str.strip`` :meth:`~Series.str.rstrip`,Equivale... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/text.rst | main | pandas | [
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.. \_advanced: {{ header }} \*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\* MultiIndex / advanced indexing \*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\* This section covers :ref:`indexing with a MultiIndex ` and :ref:`other advanced indexing features `. See the :ref:`Indexing and Selecting D... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/advanced.rst | main | pandas | [
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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 i... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/advanced.rst | main | pandas | [
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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 pa... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/advanced.rst | main | pandas | [
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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", ax... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/advanced.rst | main | pandas | [
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"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 ``Perf... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/advanced.rst | main | pandas | [
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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 ``Categoric... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/advanced.rst | main | pandas | [
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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 re... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/advanced.rst | main | pandas | [
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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:... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/advanced.rst | main | pandas | [
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.. \_visualization: {{ header }} \*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\* Chart visualization \*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\* .. note:: The examples below assume that you're using `Jupyter `\_. This section demonstrates visualization through charting. For information on visualization of tabular data please see the... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/visualization.rst | main | pandas | [
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"c", "d"]) @savefig bar\_plot\_multi\_ex.png df2.plot.bar(); To produce a stacked bar plot, pass ``stacked=True``: .. ipython:: python :suppress: plt.close("all") plt.figure() .. ipython:: python @savefig bar\_plot\_stacked\_ex.png df2.plot.bar(stacked=True); To get horizontal bar plots, use the ``barh`` method: .. ipy... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/visualization.rst | main | pandas | [
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= pd.DataFrame(np.random.rand(10, 5)) plt.figure(); @savefig box\_plot\_ex.png bp = df.boxplot() You can create a stratified boxplot using the ``by`` keyword argument to create groupings. For instance, .. ipython:: python :suppress: plt.close("all") np.random.seed(123456) .. ipython:: python :okwarning: df = pd.DataFra... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/visualization.rst | main | pandas | [
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method specifying target ``ax``. It is recommended to specify ``color`` and ``label`` keywords to distinguish each groups. .. ipython:: python :okwarning: ax = df.plot.scatter(x="a", y="b", color="DarkBlue", label="Group 1") @savefig scatter\_plot\_repeated.png df.plot.scatter(x="c", y="d", color="DarkGreen", label="Gr... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/visualization.rst | main | pandas | [
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each pie plots by default; specify ``legend=False`` to hide it. .. ipython:: python :suppress: np.random.seed(123456) plt.figure() .. ipython:: python df = pd.DataFrame( 3 \* np.random.rand(4, 2), index=["a", "b", "c", "d"], columns=["x", "y"] ) @savefig df\_pie\_plot.png df.plot.pie(subplots=True, figsize=(8, 4)); .. ... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/visualization.rst | main | pandas | [
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andrews\_curves.png andrews\_curves(data, "Name"); .. \_visualization.parallel\_coordinates: Parallel coordinates ~~~~~~~~~~~~~~~~~~~~ Parallel coordinates is a plotting technique for plotting multivariate data, see the `Wikipedia entry `\_\_ for an introduction. Parallel coordinates allows one to see clusters in data ... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/visualization.rst | main | pandas | [
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package `Radviz `\_\_ for more information. \*\*Note\*\*: The "Iris" dataset is available `here `\_\_. .. ipython:: python from pandas.plotting import radviz data = pd.read\_csv("data/iris.data") plt.figure(); @savefig radviz.png radviz(data, "Name"); .. ipython:: python :suppress: plt.close("all") .. \_visualization.f... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/visualization.rst | main | pandas | [
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purposes. Here is the default behavior, notice how the x-axis tick labeling is performed: .. ipython:: python plt.figure(); @savefig ser\_plot\_suppress.png df["A"].plot(); .. ipython:: python :suppress: plt.close("all") Using the ``x\_compat`` parameter, you can suppress this behavior: .. ipython:: python plt.figure()... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/visualization.rst | main | pandas | [
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of errors with column names matching the ``columns`` attribute of the plotting :class:`DataFrame` or matching the ``name`` attribute of the :class:`Series`. \* As a ``str`` indicating which of the columns of plotting :class:`DataFrame` contain the error values. \* As raw values (``list``, ``tuple``, or ``np.ndarray``).... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/visualization.rst | main | pandas | [
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can be difficult to distinguish some series due to repetition in the default colors. To remedy this, ``DataFrame`` plotting supports the use of the ``colormap`` argument, which accepts either a Matplotlib `colormap `\_\_ or a string that is a name of a colormap registered with Matplotlib. A visualization of the default... | https://github.com/pandas-dev/pandas/blob/main/doc/source/user_guide/visualization.rst | main | pandas | [
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# What to do when things go wrong First, have a look at the common problems listed below. If you can figure it out from these notes, it will be quicker than asking for help. Check that you have the latest version of any packages that look relevant. Unfortunately it's not always easy to figure out what packages are rele... | https://github.com/jupyter/notebook/blob/main/docs/source/troubleshooting.md | main | jupyter | [
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In these situations, take a close look at your environment structure and ensure all packages required by your notebook's code are installed in the correct environment. If you need to run the kernel from different environments than your Notebook server, check out [IPython's documentation](https://ipython.readthedocs.io/... | https://github.com/jupyter/notebook/blob/main/docs/source/troubleshooting.md | main | jupyter | [
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extension - typically resulting in `FileNotFoundError` exceptions. ## This Worked An Hour Ago The Jupyter stack is very complex and rightfully so, there's a lot going on. On occasion you might find the system working perfectly well, then, suddenly, you can't get past a certain cell due to `import` failures. In these si... | https://github.com/jupyter/notebook/blob/main/docs/source/troubleshooting.md | main | jupyter | [
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# Migrating \_Updated 2023-05-17\_ ```{warning} Version 7 of the Jupyter Notebook application might break your extensions or other customizations. Please read this page to find out if you need to take any actions to ensure a smooth, uninterrupted experience. ``` A major upgrade to the Jupyter Notebook interface is comi... | https://github.com/jupyter/notebook/blob/main/docs/source/migrate_to_notebook7.md | main | jupyter | [
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# User interface components When opening bug reports or sending emails to the Jupyter mailing list, it is useful to know the names of different UI components so that other developers and users have an easier time helping you diagnose your problems. This section will familiarize you with the names of UI elements within ... | https://github.com/jupyter/notebook/blob/main/docs/source/ui_components.md | main | jupyter | [
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(htmlnotebook)= # The Jupyter Notebook ## Introduction \*\*Jupyter Notebook\*\* is a notebook authoring application, under the [Project Jupyter](https://docs.jupyter.org/en/latest/) umbrella. Built on the power of the [computational notebook format](https://docs.jupyter.org/en/latest/#what-is-a-notebook), \*\*Jupyter N... | https://github.com/jupyter/notebook/blob/main/docs/source/notebook.md | main | jupyter | [
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your data anywhere else—and as it's open source, other people can check that we're being honest about this. You can also use Jupyter remotely: your company or university might run the server for you, for instance. If you want to work with sensitive data in those cases, talk to your IT or data protection staff about it.... | https://github.com/jupyter/notebook/blob/main/docs/source/notebook.md | main | jupyter | [
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the notebook uses the IPython kernel, you can also see this connection data by running the `%connect\_info` {ref}`magic `, which will print the same ID information along with other details. You can then, for example, manually start a Qt console connected to the \_same\_ kernel from the command line, by passing a portio... | https://github.com/jupyter/notebook/blob/main/docs/source/notebook.md | main | jupyter | [
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specify which parts of the text should be emphasized (italics), bold, form lists, etc. If you want to provide structure for your document, you can use markdown headings. Markdown headings consist of 1 to 6 hash # signs `#` followed by a space and the title of your section. The markdown heading will be converted to a cl... | https://github.com/jupyter/notebook/blob/main/docs/source/notebook.md | main | jupyter | [
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: In command mode, you can navigate around the notebook using keyboard shortcuts. - {kbd}`Enter`: Edit mode : In edit mode, you can edit text in cells. For the full list of available shortcuts, click {guilabel}`Help`, {guilabel}`Keyboard Shortcuts` in the notebook menus. ## Searching Jupyter Notebook has an advanced bu... | https://github.com/jupyter/notebook/blob/main/docs/source/notebook.md | main | jupyter | [
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# Jupyter Notebook Documentation Welcome to the \*\*Jupyter Notebook\*\* documentation site. \*\*Jupyter Notebook\*\* is a simplified notebook authoring application, and is a part of [Project Jupyter](https://docs.jupyter.org/en/latest/), a large umbrella project centered around the goal of providing tools (and [standa... | https://github.com/jupyter/notebook/blob/main/docs/source/index.md | main | jupyter | [
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# New features in Notebook 7 This document describes the new features in Notebook 7 as originally mentioned in the related Jupyter Enhancement Proposal [JEP 79][jep 79]. ```{contents} Table of Contents :depth: 3 :local: ``` ## Debugger Notebook 7 includes a new debugger that allows you to step through your code cell by... | https://github.com/jupyter/notebook/blob/main/docs/source/notebook_7_features.md | main | jupyter | [
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you can use the following commands: ```bash pip install jupyter-lsp ``` ```bash conda install -c conda-forge jupyter-lsp ``` Popular extensions like `nbgrader` and `RISE` have already been ported to work with Notebook 7. ### nbgrader ```{note} The nbgrader extension is still under active development and a version compa... | https://github.com/jupyter/notebook/blob/main/docs/source/notebook_7_features.md | main | jupyter | [
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# Applying Custom CSS To apply custom CSS, you can add a `/custom/custom.css` file in the jupyter `config` directory. You can find the path, `~/.jupyter`, to this directory by running `jupyter --paths`. There you can create a folder named `custom` and create a `custom.css` file within the folder. ## Jupyter Styling You... | https://github.com/jupyter/notebook/blob/main/docs/source/custom_css.md | main | jupyter | [
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# Simultaneous usage of different versions of Notebook 7 and the Classic Notebook UI With the release of Notebook 7, the classic Notebook UI is now available as a Jupyter Server extension, NbClassic. This means that NbClassic can be installed independently of Notebook 7, and can be also installed alongside Notebook 7. ... | https://github.com/jupyter/notebook/blob/main/docs/source/migrating/multiple-interfaces.md | main | jupyter | [
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a Jupyter Server extension, indicated Jupyter Server will be available. This provides an additional way to view the NbClassic frontend. You would be able to manually enable the extension when running an instance of Jupyter Server, `> jupyter server --ServerApp.jpserver\_extensions="nbclassic=True"`, which will provide ... | https://github.com/jupyter/notebook/blob/main/docs/source/migrating/multiple-interfaces.md | main | jupyter | [
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# Custom themes in Notebook 7 In Notebook 7, the way to create custom themes has changed. This means that custom themes developed for Notebook 6 or earlier will not work with Notebook 7 and upwards. This is for example the case for community contributed themes such as [jupyter-themes](https://github.com/dunovank/jupyte... | https://github.com/jupyter/notebook/blob/main/docs/source/migrating/custom-themes.md | main | jupyter | [
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# Server Extensions in Notebook 7 Notebook 7 is now based on Jupyter Server, which is a new server application that allows to run multiple Jupyter applications (e.g. Notebook, JupyterLab, NBClassic, etc.) on the same server. This means that Notebook 7 is able to reuse many of the existing server extensions from the Jup... | https://github.com/jupyter/notebook/blob/main/docs/source/migrating/server-extensions.md | main | jupyter | [
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# Server Imports in Notebook 7 Notebook 7 is now based on Jupyter Server, which lets users run multiple Jupyter frontends (e.g. Notebook, JupyterLab, NBClassic, etc.) on the same server. Prior to Notebook 7, the Classic Notebook server included the server modules in the `notebook` package. This means it was possible to... | https://github.com/jupyter/notebook/blob/main/docs/source/migrating/server-imports.md | main | jupyter | [
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# Frontend Extensions in Notebook 7 ```{warning} Any extension developed for Notebook \< 7 or NbClassic will not be compatible with Notebook 7 and upwards. Some extensions like nbgrader have already been ported. We invite you to check if the extensions you are using have already been ported. ``` You can check the follo... | https://github.com/jupyter/notebook/blob/main/docs/source/migrating/frontend-extensions.md | main | jupyter | [
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(frontend-extensions)= # Custom front-end extensions This describes the basic steps to write a TypeScript extension for the Jupyter notebook front-end. This allows you to customize the behaviour of the various pages like the dashboard, the notebook, or the text editor. Starting with Notebook 7, front-end extensions for... | https://github.com/jupyter/notebook/blob/main/docs/source/extending/frontend_extensions.md | main | jupyter | [
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# Extending the Notebook ```{warning} Please note that the extension system for Notebook 7 is radically different from the one used in Notebook 6.5.x and earlier. If you are looking for information on how to extend the classic Notebook, please refer to the [documentation for NbClassic](https://nbclassic.readthedocs.io/... | https://github.com/jupyter/notebook/blob/main/docs/source/extending/index.md | main | jupyter | [
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(configuration-overview)= # Configuration Overview Beyond the default configuration settings, you can configure a rich array of options to suit your workflow. Here are areas that are commonly configured when using Jupyter Notebook: > - {ref}`Jupyter's common configuration system ` > - {ref}`Jupyter Server ` > - {ref}`N... | https://github.com/jupyter/notebook/blob/main/docs/source/configuring/config_overview.md | main | jupyter | [
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# Managing plugins Notebook 7 uses the same extension system as JupyterLab. An extension can provide multiple plugins. ```{note} See the [JupyterLab documentation](https://jupyterlab.readthedocs.io/en/latest/user/extensions.html) to learn more about the extension system. ``` ## Examples ### Disabling the download butto... | https://github.com/jupyter/notebook/blob/main/docs/source/configuring/plugins.md | main | jupyter | [
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# Interface Customization Multiple elements in the Notebook interface can be customized via the Settings Editor. ## Layout By default some widgets are displayed in pre-defined parts of the user interface, which are often called "areas" or "regions". For example the table of contents will be displayed in the `left` area... | https://github.com/jupyter/notebook/blob/main/docs/source/configuring/interface_customization.md | main | jupyter | [
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## Installing & building ### System requirements | Requirement | Details | | --------------------------- | --------------------------------------------------------------- | | Operating systems | macOS 12+, Ubuntu 20.04+/Debian 10+, or Windows 11 \*\*via WSL2\*\* | | Git (optional, recommended) | 2.23+ for built-in PR h... | https://github.com/openai/codex/blob/main/docs/install.md | main | openai-codex | [
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# Configuration For basic configuration instructions, see [this documentation](https://developers.openai.com/codex/config-basic). For advanced configuration instructions, see [this documentation](https://developers.openai.com/codex/config-advanced). For a full configuration reference, see [this documentation](https://d... | https://github.com/openai/codex/blob/main/docs/config.md | main | openai-codex | [
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# Individual Contributor License Agreement (v1.0, OpenAI) \_Based on the Apache Software Foundation Individual CLA v 2.2.\_ By commenting \*\*“I have read the CLA Document and I hereby sign the CLA”\*\* on a Pull Request, \*\*you (“Contributor”) agree to the following terms\*\* for any past and future “Contributions” s... | https://github.com/openai/codex/blob/main/docs/CLA.md | main | openai-codex | [
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## Contributing \*\*External contributions are by invitation only\*\* At this time, the Codex team does not accept unsolicited code contributions. If you would like to propose a new feature or a change in behavior, please open an issue describing the proposal or upvote an existing enhancement request. We prioritize new... | https://github.com/openai/codex/blob/main/docs/contributing.md | main | openai-codex | [
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compatibility defaults in mind: omitted `input\_modalities` currently implies text + image support. - Ensure client surfaces that accept images (for example, TUI paste/attach) consume the same capability signal. - Add/update tests that cover unsupported-image behavior and warning paths. ### Opening a pull request (by i... | https://github.com/openai/codex/blob/main/docs/contributing.md | main | openai-codex | [
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[[setup]] = Set up Elasticsearch [partintro] -- This section includes information on how to setup Elasticsearch and get it running, including: \* Downloading \* Installing \* Starting \* Configuring [[supported-platforms]] [float] == Supported platforms The matrix of officially supported operating systems and JVMs is a... | https://github.com/elastic/elasticsearch/blob/6.2/docs/reference/setup.asciidoc | 6.2 | elasticsearch-6-2 | [
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[[analysis]] = Analysis [partintro] -- \_Analysis\_ is the process of converting text, like the body of any email, into \_tokens\_ or \_terms\_ which are added to the inverted index for searching. Analysis is performed by an <> which can be either a built-in analyzer or a <> analyzer defined per index. [float] == Index... | https://github.com/elastic/elasticsearch/blob/6.2/docs/reference/analysis.asciidoc | 6.2 | elasticsearch-6-2 | [
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[[index-modules]] = Index Modules [partintro] -- Index Modules are modules created per index and control all aspects related to an index. [float] [[index-modules-settings]] == Index Settings Index level settings can be set per-index. Settings may be: \_static\_:: They can only be set at index creation time or on a <>. ... | https://github.com/elastic/elasticsearch/blob/6.2/docs/reference/index-modules.asciidoc | 6.2 | elasticsearch-6-2 | [
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memory and time proportional to `from + size` and this limits that memory. See <> or <> for a more efficient alternative to raising this. `index.max\_inner\_result\_window`:: The maximum value of `from + size` for inner hits definition and top hits aggregations to this index. Defaults to `100`. Inner hits and top hits ... | https://github.com/elastic/elasticsearch/blob/6.2/docs/reference/index-modules.asciidoc | 6.2 | elasticsearch-6-2 | [
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are logged. <>:: Configure the type of filesystem used to access shard data. <>:: Control over the transaction log and background flush operations. -- include::index-modules/analysis.asciidoc[] include::index-modules/allocation.asciidoc[] include::index-modules/mapper.asciidoc[] include::index-modules/merge.asciidoc[] ... | https://github.com/elastic/elasticsearch/blob/6.2/docs/reference/index-modules.asciidoc | 6.2 | elasticsearch-6-2 | [
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[[query-dsl]] = Query DSL [partintro] -- Elasticsearch provides a full Query DSL (Domain Specific Language) based on JSON to define queries. Think of the Query DSL as an AST (Abstract Syntax Tree) of queries, consisting of two types of clauses: Leaf query clauses:: Leaf query clauses look for a particular value in a pa... | https://github.com/elastic/elasticsearch/blob/6.2/docs/reference/query-dsl.asciidoc | 6.2 | elasticsearch-6-2 | [
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[[search]] = Search APIs [partintro] -- Most search APIs are <>, with the exception of the <> endpoints. [float] [[search-routing]] == Routing When executing a search, it will be broadcast to all the index/indices shards (round robin between replicas). Which shards will be searched on can be controlled by providing the... | https://github.com/elastic/elasticsearch/blob/6.2/docs/reference/search.asciidoc | 6.2 | elasticsearch-6-2 | [
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that start after the change is made. [float] [[search-concurrency-and-parallelism]] == Search concurrency and parallelism By default Elasticsearch doesn't reject any search requests based on the number of shards the request hits. While Elasticsearch will optimize the search execution on the coordinating node a large nu... | https://github.com/elastic/elasticsearch/blob/6.2/docs/reference/search.asciidoc | 6.2 | elasticsearch-6-2 | [
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[[setup-upgrade]] = Upgrade Elasticsearch [partintro] -- Elasticsearch can usually be upgraded using a <> process so upgrading does not interrupt service. However, you might need to <> indices created in older versions. Upgrades across major versions prior to 6.0 require a <>. When upgrading to a new version of Elastic... | https://github.com/elastic/elasticsearch/blob/6.2/docs/reference/upgrade.asciidoc | 6.2 | elasticsearch-6-2 | [
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["appendix",role="exclude",id="redirects"] = Deleted pages The following pages have moved or been deleted. [role="exclude",id="cluster-nodes-shutdown"] === Nodes shutdown The `\_shutdown` API has been removed. Instead, setup Elasticsearch to run as a service (see <>, <>, or <>) or use the `-p` command line option to <>... | https://github.com/elastic/elasticsearch/blob/6.2/docs/reference/redirects.asciidoc | 6.2 | elasticsearch-6-2 | [
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a query in ``query context'' and as a filter in ``filter context'' (see <>). [role="exclude",id="query-dsl-has-child-filter"] === Has Child Filter The `has\_child` filter has been replaced by the <>. It behaves as a query in ``query context'' and as a filter in ``filter context'' (see <>). [role="exclude",id="query-dsl... | https://github.com/elastic/elasticsearch/blob/6.2/docs/reference/redirects.asciidoc | 6.2 | elasticsearch-6-2 | [
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later allowing to narrow the navigation to a specific partition. In Elasticsearch, `facets` are also the name of a feature that allowed to compute these summaries. `facets` have been replaced by <> in Elasticsearch 1.0, which are a superset of facets. [role="exclude",id="search-facets-filter-facet"] === Filter Facet Fa... | https://github.com/elastic/elasticsearch/blob/6.2/docs/reference/redirects.asciidoc | 6.2 | elasticsearch-6-2 | [
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Reference See the {painless}/painless-api-reference.html[Painless API Reference] in the guide to the {painless}/index.html[Painless Scripting Language]. | https://github.com/elastic/elasticsearch/blob/6.2/docs/reference/redirects.asciidoc | 6.2 | elasticsearch-6-2 | [
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[[cat]] = cat APIs [partintro] -- ["float",id="intro"] == Introduction JSON is great... for computers. Even if it's pretty-printed, trying to find relationships in the data is tedious. Human eyes, especially when looking at an ssh terminal, need compact and aligned text. The cat API aims to meet this need. All the cat ... | https://github.com/elastic/elasticsearch/blob/6.2/docs/reference/cat.asciidoc | 6.2 | elasticsearch-6-2 | [
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"docs.count": "0", "docs.deleted": "0", "store.size": "650b" } ] -------------------------------------------------- // NOTCONSOLE [float] [[sort]] === Sort Each of the commands accepts a query string parameter `s` which sorts the table by the columns specified as the parameter value. Columns are specified either by nam... | https://github.com/elastic/elasticsearch/blob/6.2/docs/reference/cat.asciidoc | 6.2 | elasticsearch-6-2 | [
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[[mapping]] = Mapping [partintro] -- Mapping is the process of defining how a document, and the fields it contains, are stored and indexed. For instance, use mappings to define: \* which string fields should be treated as full text fields. \* which fields contain numbers, dates, or geolocations. \* whether the values o... | https://github.com/elastic/elasticsearch/blob/6.2/docs/reference/mapping.asciidoc | 6.2 | elasticsearch-6-2 | [
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<> rules can be configured to customise the mapping that is used for new fields. [float] == Explicit mappings You know more about your data than Elasticsearch can guess, so while dynamic mapping can be useful to get started, at some point you will want to specify your own explicit mappings. You can create field mapping... | https://github.com/elastic/elasticsearch/blob/6.2/docs/reference/mapping.asciidoc | 6.2 | elasticsearch-6-2 | [
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