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added checks for standardized pandas
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‎ci/code_checks.sh

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@@ -80,6 +80,13 @@ if [[ -z "$CHECK" || "$CHECK" == "lint" ]]; then
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flake8-rst doc/source --filename=*.rst --format="$FLAKE8_FORMAT"
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RET=$(($RET + $?)) ; echo $MSG "DONE"
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# Check if pandas is referenced as pandas, not *pandas* or Pandas
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MSG='Checking if pandas reference is standardized or not' ; echo $MSG
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grep -nr '*pandas*|Pandas' doc/*
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grep -nr '*pandas*|Pandas' web/*
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RET=$(($RET + $?)) ; echo $MSG "DONE"
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# Check that cython casting is of the form `<type>obj` as opposed to `<type> obj`;
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# it doesn't make a difference, but we want to be internally consistent.
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# Note: this grep pattern is (intended to be) equivalent to the python

‎doc/source/getting_started/intro_tutorials/01_table_oriented.rst

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.. _10min_tut_01_tableoriented:
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{{ header }}
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What kind of data does pandas handle?
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=====================================
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.. raw:: html
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<ul class="task-bullet">
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<li>
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I want to start using pandas
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.. ipython:: python
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import pandas as pd
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To load the pandas package and start working with it, import the
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package. The community agreed alias for pandas is ``pd``, so loading
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pandas as ``pd`` is assumed standard practice for all of the pandas
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documentation.
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.. raw:: html
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</li>
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</ul>
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Pandas data table representation
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. image:: ../../_static/schemas/01_table_dataframe.svg
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:align: center
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.. raw:: html
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<ul class="task-bullet">
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<li>
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I want to store passenger data of the Titanic. For a number of passengers, I know the name (characters), age (integers) and sex (male/female) data.
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.. ipython:: python
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df = pd.DataFrame({
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"Name": ["Braund, Mr. Owen Harris",
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"Allen, Mr. William Henry",
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"Bonnell, Miss. Elizabeth"],
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"Age": [22, 35, 58],
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"Sex": ["male", "male", "female"]}
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)
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df
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To manually store data in a table, create a ``DataFrame``. When using a Python dictionary of lists, the dictionary keys will be used as column headers and
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the values in each list as rows of the ``DataFrame``.
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.. raw:: html
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</li>
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</ul>
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A :class:`DataFrame` is a 2-dimensional data structure that can store data of
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different types (including characters, integers, floating point values,
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categorical data and more) in columns. It is similar to a spreadsheet, a
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SQL table or the ``data.frame`` in R.
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- The table has 3 columns, each of them with a column label. The column
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labels are respectively ``Name``, ``Age`` and ``Sex``.
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- The column ``Name`` consists of textual data with each value a
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string, the column ``Age`` are numbers and the column ``Sex`` is
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textual data.
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In spreadsheet software, the table representation of our data would look
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very similar:
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.. image:: ../../_static/schemas/01_table_spreadsheet.png
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:align: center
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Each column in a ``DataFrame`` is a ``Series``
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. image:: ../../_static/schemas/01_table_series.svg
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:align: center
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.. raw:: html
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<ul class="task-bullet">
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<li>
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I’m just interested in working with the data in the column ``Age``
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.. ipython:: python
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df["Age"]
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When selecting a single column of a pandas :class:`DataFrame`, the result is
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a pandas :class:`Series`. To select the column, use the column label in
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between square brackets ``[]``.
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.. raw:: html
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</li>
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</ul>
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.. note::
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If you are familiar to Python
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:ref:`dictionaries <python:tut-dictionaries>`, the selection of a
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single column is very similar to selection of dictionary values based on
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the key.
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You can create a ``Series`` from scratch as well:
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.. ipython:: python
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ages = pd.Series([22, 35, 58], name="Age")
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ages
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A pandas ``Series`` has no column labels, as it is just a single column
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of a ``DataFrame``. A Series does have row labels.
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Do something with a DataFrame or Series
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. raw:: html
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<ul class="task-bullet">
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<li>
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I want to know the maximum Age of the passengers
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We can do this on the ``DataFrame`` by selecting the ``Age`` column and
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applying ``max()``:
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.. ipython:: python
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df["Age"].max()
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Or to the ``Series``:
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.. ipython:: python
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ages.max()
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.. raw:: html
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</li>
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</ul>
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As illustrated by the ``max()`` method, you can *do* things with a
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``DataFrame`` or ``Series``. pandas provides a lot of functionalities,
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each of them a *method* you can apply to a ``DataFrame`` or ``Series``.
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As methods are functions, do not forget to use parentheses ``()``.
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.. raw:: html
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<ul class="task-bullet">
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<li>
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I’m interested in some basic statistics of the numerical data of my data table
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.. ipython:: python
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df.describe()
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The :func:`~DataFrame.describe` method provides a quick overview of the numerical data in
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a ``DataFrame``. As the ``Name`` and ``Sex`` columns are textual data,
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these are by default not taken into account by the :func:`~DataFrame.describe` method.
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.. raw:: html
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</li>
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</ul>
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Many pandas operations return a ``DataFrame`` or a ``Series``. The
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:func:`~DataFrame.describe` method is an example of a pandas operation returning a
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pandas ``Series``.
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.. raw:: html
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<div class="d-flex flex-row gs-torefguide">
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<span class="badge badge-info">To user guide</span>
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Check more options on ``describe`` in the user guide section about :ref:`aggregations with describe <basics.describe>`
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.. raw:: html
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</div>
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.. note::
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This is just a starting point. Similar to spreadsheet
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software, pandas represents data as a table with columns and rows. Apart
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from the representation, also the data manipulations and calculations
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you would do in spreadsheet software are supported by pandas. Continue
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reading the next tutorials to get started!
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.. raw:: html
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<div class="shadow gs-callout gs-callout-remember">
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<h4>REMEMBER</h4>
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- Import the package, aka ``import pandas as pd``
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- A table of data is stored as a pandas ``DataFrame``
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- Each column in a ``DataFrame`` is a ``Series``
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- You can do things by applying a method to a ``DataFrame`` or ``Series``
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.. raw:: html
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</div>
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.. raw:: html
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<div class="d-flex flex-row gs-torefguide">
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<span class="badge badge-info">To user guide</span>
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A more extended explanation to ``DataFrame`` and ``Series`` is provided in the :ref:`introduction to data structures <dsintro>`.
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.. raw:: html
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</div>

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