.. _10min_tut_08_combine:
{{ header }}
.. ipython:: python
import pandas as pd
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Data used for this tutorial:
Air quality Nitrate data
For this tutorial, air quality data about :math:`NO_2` is used, made available by
`OpenAQ `__ and downloaded using the
`py-openaq `__ package.
The ``air_quality_no2_long.csv`` data set provides :math:`NO_2`
values for the measurement stations *FR04014*, *BETR801* and *London
Westminster* in respectively Paris, Antwerp and London.
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For this tutorial, air quality data about Particulate
matter less than 2.5 micrometers is used, made available by
`OpenAQ `__ and downloaded using the
`py-openaq `__ package.
The ``air_quality_pm25_long.csv`` data set provides :math:`PM_{25}`
values for the measurement stations *FR04014*, *BETR801* and *London
Westminster* in respectively Paris, Antwerp and London.
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How to combine data from multiple tables
----------------------------------------
Concatenating objects
~~~~~~~~~~~~~~~~~~~~~
.. image:: ../../_static/schemas/08_concat_row.svg
:align: center
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I want to combine the measurements of :math:`NO_2` and :math:`PM_{25}`, two tables with a similar structure, in a single table.
.. ipython:: python
air_quality = pd.concat([air_quality_pm25, air_quality_no2], axis=0)
air_quality.head()
The :func:`~pandas.concat` function performs concatenation operations of multiple
tables along one of the axes (row-wise or column-wise).
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By default concatenation is along axis 0, so the resulting table combines the rows
of the input tables. Let’s check the shape of the original and the
concatenated tables to verify the operation:
.. ipython:: python
print('Shape of the ``air_quality_pm25`` table: ', air_quality_pm25.shape)
print('Shape of the ``air_quality_no2`` table: ', air_quality_no2.shape)
print('Shape of the resulting ``air_quality`` table: ', air_quality.shape)
Hence, the resulting table has 3178 = 1110 + 2068 rows.
.. note::
The **axis** argument will return in a number of pandas
methods that can be applied **along an axis**. A ``DataFrame`` has two
corresponding axes: the first running vertically downwards across rows
(axis 0), and the second running horizontally across columns (axis 1).
Most operations like concatenation or summary statistics are by default
across rows (axis 0), but can be applied across columns as well.
Sorting the table on the datetime information also illustrates the
combination of both tables, with the ``parameter`` column defining the
origin of the table (either ``no2`` from table ``air_quality_no2`` or
``pm25`` from table ``air_quality_pm25``):
.. ipython:: python
air_quality = air_quality.sort_values("date.utc")
air_quality.head()
In this specific example, the ``parameter`` column provided by the data
ensures that each of the original tables can be identified. This is not
always the case. The ``concat`` function provides a convenient solution
with the ``keys`` argument, adding an additional (hierarchical) row
index. For example:
.. ipython:: python
air_quality_ = pd.concat([air_quality_pm25, air_quality_no2], keys=["PM25", "NO2"])
air_quality_.head()
.. note::
The existence of multiple row/column indices at the same time
has not been mentioned within these tutorials. *Hierarchical indexing*
or *MultiIndex* is an advanced and powerful pandas feature to analyze
higher dimensional data.
Multi-indexing is out of scope for this pandas introduction. For the
moment, remember that the function ``reset_index`` can be used to
convert any level of an index to a column, e.g.
``air_quality.reset_index(level=0)``
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To user guide
Feel free to dive into the world of multi-indexing at the user guide section on :ref:`advanced indexing `.
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To user guide
More options on table concatenation (row and column
wise) and how ``concat`` can be used to define the logic (union or
intersection) of the indexes on the other axes is provided at the section on
:ref:`object concatenation `.
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Join tables using a common identifier
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. image:: ../../_static/schemas/08_merge_left.svg
:align: center
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Add the station coordinates, provided by the stations metadata table, to the corresponding rows in the measurements table.
.. warning::
The air quality measurement station coordinates are stored in a data
file ``air_quality_stations.csv``, downloaded using the
`py-openaq `__ package.
.. ipython:: python
stations_coord = pd.read_csv("data/air_quality_stations.csv")
stations_coord.head()
.. note::
The stations used in this example (FR04014, BETR801 and London
Westminster) are just three entries enlisted in the metadata table. We
only want to add the coordinates of these three to the measurements
table, each on the corresponding rows of the ``air_quality`` table.
.. ipython:: python
air_quality.head()
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air_quality = pd.merge(air_quality, stations_coord, how="left", on="location")
air_quality.head()
Using the :meth:`~pandas.merge` function, for each of the rows in the
``air_quality`` table, the corresponding coordinates are added from the
``air_quality_stations_coord`` table. Both tables have the column
``location`` in common which is used as a key to combine the
information. By choosing the ``left`` join, only the locations available
in the ``air_quality`` (left) table, i.e. FR04014, BETR801 and London
Westminster, end up in the resulting table. The ``merge`` function
supports multiple join options similar to database-style operations.
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Add the parameters' full description and name, provided by the parameters metadata table, to the measurements table.
.. warning::
The air quality parameters metadata are stored in a data file
``air_quality_parameters.csv``, downloaded using the
`py-openaq `__ package.
.. ipython:: python
air_quality_parameters = pd.read_csv("data/air_quality_parameters.csv")
air_quality_parameters.head()
.. ipython:: python
air_quality = pd.merge(air_quality, air_quality_parameters,
how='left', left_on='parameter', right_on='id')
air_quality.head()
Compared to the previous example, there is no common column name.
However, the ``parameter`` column in the ``air_quality`` table and the
``id`` column in the ``air_quality_parameters_name`` both provide the
measured variable in a common format. The ``left_on`` and ``right_on``
arguments are used here (instead of just ``on``) to make the link
between the two tables.
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To user guide
pandas also supports inner, outer, and right joins.
More information on join/merge of tables is provided in the user guide section on
:ref:`database style merging of tables `. Or have a look at the
:ref:`comparison with SQL` page.
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REMEMBER
- Multiple tables can be concatenated column-wise or row-wise using
the ``concat`` function.
- For database-like merging/joining of tables, use the ``merge``
function.
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To user guide
See the user guide for a full description of the various :ref:`facilities to combine data tables `.
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