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Update README.md (#36772)
* Update README.md * Update README.md
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@@ -32,32 +32,32 @@ its way towards this goal.
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Here are just a few of the things that pandas does well:
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- Easy handling of [**missing data**][missing-data] (represented as
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`NaN`, `NA`, or `NaT`) in floating point as well as non-floating point data
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`NaN`, `NA`, or `NaT`) in floating point as well as non-floating point data;
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- Size mutability: columns can be [**inserted and
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deleted**][insertion-deletion] from DataFrame and higher dimensional
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objects
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objects;
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- Automatic and explicit [**data alignment**][alignment]: objects can
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be explicitly aligned to a set of labels, or the user can simply
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ignore the labels and let `Series`, `DataFrame`, etc. automatically
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align the data for you in computations
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align the data for you in computations;
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- Powerful, flexible [**group by**][groupby] functionality to perform
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split-apply-combine operations on data sets, for both aggregating
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and transforming data
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and transforming data;
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- Make it [**easy to convert**][conversion] ragged,
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differently-indexed data in other Python and NumPy data structures
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into DataFrame objects
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into DataFrame objects;
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- Intelligent label-based [**slicing**][slicing], [**fancy
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indexing**][fancy-indexing], and [**subsetting**][subsetting] of
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large data sets
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large data sets;
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- Intuitive [**merging**][merging] and [**joining**][joining] data
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sets
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sets;
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- Flexible [**reshaping**][reshape] and [**pivoting**][pivot-table] of
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data sets
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data sets;
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- [**Hierarchical**][mi] labeling of axes (possible to have multiple
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labels per tick)
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labels per tick);
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- Robust IO tools for loading data from [**flat files**][flat-files]
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(CSV and delimited), [**Excel files**][excel], [**databases**][db],
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and saving/loading data from the ultrafast [**HDF5 format**][hdfstore]
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and saving/loading data from the ultrafast [**HDF5 format**][hdfstore];
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- [**Time series**][timeseries]-specific functionality: date range
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generation and frequency conversion, moving window statistics,
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date shifting and lagging.

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