diff --git a/doc/source/getting_started/index.rst b/doc/source/getting_started/index.rst index d9cb1de14aded..9f29f7f4f4406 100644 --- a/doc/source/getting_started/index.rst +++ b/doc/source/getting_started/index.rst @@ -134,8 +134,8 @@ to explore, clean, and process your data. In pandas, a data table is called a :c
-pandas supports the integration with many file formats or data sources out of the box (csv, excel, sql, json, parquet,…). Importing data from each of these -data sources is provided by function with the prefix ``read_*``. Similarly, the ``to_*`` methods are used to store data. +pandas supports the integration with many file formats or data sources out of the box (csv, excel, sql, json, parquet,…). The ability to import data from each of these +data sources is provided by functions with the prefix, ``read_*``. Similarly, the ``to_*`` methods are used to store data. .. image:: ../_static/schemas/02_io_readwrite.svg :align: center @@ -181,7 +181,7 @@ data sources is provided by function with the prefix ``read_*``. Similarly, the
-Selecting or filtering specific rows and/or columns? Filtering the data on a condition? Methods for slicing, selecting, and extracting the +Selecting or filtering specific rows and/or columns? Filtering the data on a particular condition? Methods for slicing, selecting, and extracting the data you need are available in pandas. .. image:: ../_static/schemas/03_subset_columns_rows.svg @@ -228,7 +228,7 @@ data you need are available in pandas.
-pandas provides plotting your data out of the box, using the power of Matplotlib. You can pick the plot type (scatter, bar, boxplot,...) +pandas provides plotting for your data right out of the box with the power of Matplotlib. Simply pick the plot type (scatter, bar, boxplot,...) corresponding to your data. .. image:: ../_static/schemas/04_plot_overview.svg @@ -275,7 +275,7 @@ corresponding to your data.
-There is no need to loop over all rows of your data table to do calculations. Data manipulations on a column work elementwise. +There's no need to loop over all rows of your data table to do calculations. Column data manipulations work elementwise in pandas. Adding a column to a :class:`DataFrame` based on existing data in other columns is straightforward. .. image:: ../_static/schemas/05_newcolumn_2.svg @@ -322,7 +322,7 @@ Adding a column to a :class:`DataFrame` based on existing data in other columns
-Basic statistics (mean, median, min, max, counts...) are easily calculable. These or custom aggregations can be applied on the entire +Basic statistics (mean, median, min, max, counts...) are easily calculable across data frames. These, or even custom aggregations, can be applied on the entire data set, a sliding window of the data, or grouped by categories. The latter is also known as the split-apply-combine approach. .. image:: ../_static/schemas/06_groupby.svg @@ -369,8 +369,8 @@ data set, a sliding window of the data, or grouped by categories. The latter is
-Change the structure of your data table in multiple ways. You can :func:`~pandas.melt` your data table from wide to long/tidy form or :func:`~pandas.pivot` -from long to wide format. With aggregations built-in, a pivot table is created with a single command. +Change the structure of your data table in a variety of ways. You can use :func:`~pandas.melt` to reshape your data from a wide format to a long and tidy one. Use :func:`~pandas.pivot` + to go from long to wide format. With aggregations built-in, a pivot table can be created with a single command. .. image:: ../_static/schemas/07_melt.svg :align: center @@ -416,7 +416,7 @@ from long to wide format. With aggregations built-in, a pivot table is created w
-Multiple tables can be concatenated both column wise and row wise as database-like join/merge operations are provided to combine multiple tables of data. +Multiple tables can be concatenated column wise or row wise with pandas' database-like join and merge operations. .. image:: ../_static/schemas/08_concat_row.svg :align: center @@ -505,7 +505,7 @@ pandas has great support for time series and has an extensive set of tools for w
-Data sets do not only contain numerical data. pandas provides a wide range of functions to clean textual data and extract useful information from it. +Data sets often contain more than just numerical data. pandas provides a wide range of functions to clean textual data and extract useful information from it. .. raw:: html @@ -551,9 +551,9 @@ the pandas-equivalent operations compared to software you already know: :class-card: comparison-card :shadow: md - The `R programming language `__ provides the - ``data.frame`` data structure and multiple packages, such as - `tidyverse `__ use and extend ``data.frame`` + The `R programming language `__ provides a + ``data.frame`` data structure as well as packages like + `tidyverse `__ which use and extend ``data.frame`` for convenient data handling functionalities similar to pandas. +++ @@ -572,8 +572,8 @@ the pandas-equivalent operations compared to software you already know: :class-card: comparison-card :shadow: md - Already familiar to ``SELECT``, ``GROUP BY``, ``JOIN``, etc.? - Most of these SQL manipulations do have equivalents in pandas. + Already familiar with ``SELECT``, ``GROUP BY``, ``JOIN``, etc.? + Many SQL manipulations have equivalents in pandas. +++ @@ -631,10 +631,10 @@ the pandas-equivalent operations compared to software you already know: :class-card: comparison-card :shadow: md - The `SAS `__ statistical software suite - also provides the ``data set`` corresponding to the pandas ``DataFrame``. - Also SAS vectorized operations, filtering, string processing operations, - and more have similar functions in pandas. + `SAS `__, the statistical software suite, + uses the ``data set`` structure, which closely corresponds pandas' ``DataFrame``. + Also SAS vectorized operations such as filtering or string processing operations + have similar functions in pandas. +++