From aa4135f72404853347d80c95e36085eeae8c1a10 Mon Sep 17 00:00:00 2001 From: Marco Gorelli Date: Thu, 1 Oct 2020 18:22:01 +0100 Subject: [PATCH] Revert "Update README.md (#36772)" This reverts commit 0f6fc8dffc5e0bdfca18becd8f8b83730ccd2efd. --- README.md | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index da8487d76f4a1..a2f2f1c04442a 100644 --- a/README.md +++ b/README.md @@ -32,32 +32,32 @@ its way towards this goal. Here are just a few of the things that pandas does well: - Easy handling of [**missing data**][missing-data] (represented as - `NaN`, `NA`, or `NaT`) in floating point as well as non-floating point data; + `NaN`, `NA`, or `NaT`) in floating point as well as non-floating point data - Size mutability: columns can be [**inserted and deleted**][insertion-deletion] from DataFrame and higher dimensional - objects; + objects - Automatic and explicit [**data alignment**][alignment]: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let `Series`, `DataFrame`, etc. automatically - align the data for you in computations; + align the data for you in computations - Powerful, flexible [**group by**][groupby] functionality to perform split-apply-combine operations on data sets, for both aggregating - and transforming data; + and transforming data - Make it [**easy to convert**][conversion] ragged, differently-indexed data in other Python and NumPy data structures - into DataFrame objects; + into DataFrame objects - Intelligent label-based [**slicing**][slicing], [**fancy indexing**][fancy-indexing], and [**subsetting**][subsetting] of - large data sets; + large data sets - Intuitive [**merging**][merging] and [**joining**][joining] data - sets; + sets - Flexible [**reshaping**][reshape] and [**pivoting**][pivot-table] of - data sets; + data sets - [**Hierarchical**][mi] labeling of axes (possible to have multiple - labels per tick); + labels per tick) - Robust IO tools for loading data from [**flat files**][flat-files] (CSV and delimited), [**Excel files**][excel], [**databases**][db], - and saving/loading data from the ultrafast [**HDF5 format**][hdfstore]; + and saving/loading data from the ultrafast [**HDF5 format**][hdfstore] - [**Time series**][timeseries]-specific functionality: date range generation and frequency conversion, moving window statistics, date shifting and lagging.