:orphan:
.. _chart_gallery.line:
==========
Line Plots
==========
.. Necessary Code Elements
.. ipython:: python
:suppress:
import vastorbit as vo
data = vo.VastFrame({
"date": [1900, 1950, 2000],
"Asia": [947, 1402, 3634],
"Africa": [133, 221, 767],
"Europe": [408, 547, 729],
"America": [156, 339, 818],
"Oceania": [6, 13, 30],
})
General
-------
Let's begin by importing ``vastorbit``.
.. ipython:: python
import vastorbit as vo
Let's generate a dataset using the following data.
.. code-block:: python
data = vo.VastFrame({
"date": [1900, 1950, 2000],
"Asia": [947, 1402, 3634],
"Africa": [133, 221, 767],
"Europe": [408, 547, 729],
"America": [156, 339, 818],
"Oceania": [6, 13, 30],
})
In the context of data visualization, we have the flexibility to harness multiple plotting libraries to craft a wide range of graphical representations. vastorbit, as a versatile tool, provides support for several graphic libraries, such as Matplotlib and Plotly. Each of these libraries offers unique features and capabilities, allowing us to choose the most suitable one for our specific data visualization needs.
.. image:: ../../docs/source/_static/plotting_libs.png
:width: 80%
:align: center
.. note::
To select the desired plotting library, we simply need to use the :py:func:`~vastorbit.set_option` function. vastorbit offers the flexibility to smoothly transition between different plotting libraries. In instances where a particular graphic is not supported by the chosen library or is not supported within the vastorbit framework, the tool will automatically generate a warning and then switch to an alternative library where the graphic can be created.
Please click on the tabs to view the various graphics generated by the different plotting libraries.
.. ipython:: python
:suppress:
import vastorbit as vo
.. tab:: Plotly
.. ipython:: python
:suppress:
vo.set_option("plotting_lib", "plotly")
We can switch to using the ``plotly`` module.
.. code-block:: python
vo.set_option("plotting_lib", "plotly")
In vastorbit, creating one or multiple line charts within a single graphic is a straightforward and flexible process. This feature enables you to efficiently visualize and compare multiple datasets or trends, providing you with a powerful tool for gaining insights from your data.
.. tab:: Single
.. code-block:: python
data["Asia"].plot(ts = "date")
.. ipython:: python
:suppress:
fig = data["Asia"].plot(ts = "date")
fig.write_html("figures/plotting_plotly_line_single.html")
.. raw:: html
:file: /Users/badr.ouali/Documents/VastOrbit-master/docs/figures/plotting_plotly_line_single.html
**Drawing a Spline plot**
.. code-block:: python
data["Asia"].plot(ts = "date", kind = "spline")
.. ipython:: python
:suppress:
fig = data["Asia"].plot(ts = "date", kind = "spline")
fig.write_html("figures/plotting_plotly_line_single_spline.html")
.. raw:: html
:file: /Users/badr.ouali/Documents/VastOrbit-master/docs/figures/plotting_plotly_line_single_spline.html
**Drawing a step plot**
.. code-block:: python
data["Asia"].plot(ts = "date", kind = "step")
.. ipython:: python
:suppress:
fig = data["Asia"].plot(ts = "date", kind = "step")
fig.write_html("figures/plotting_plotly_line_single_step.html")
.. raw:: html
:file: /Users/badr.ouali/Documents/VastOrbit-master/docs/figures/plotting_plotly_line_single_step.html
.. tab:: Multi
.. code-block:: python
data.plot(columns = ["Asia", "Africa", "Europe", "America", "Oceania"], ts = "date")
.. ipython:: python
:suppress:
fig = data.plot(columns = ["Asia", "Africa", "Europe", "America", "Oceania"], ts = "date")
fig.write_html("figures/plotting_plotly_line_multi.html")
.. raw:: html
:file: /Users/badr.ouali/Documents/VastOrbit-master/docs/figures/plotting_plotly_line_multi.html
**Drawing a Spline plot**
.. code-block:: python
data.plot(columns = ["Asia", "Africa", "Europe", "America", "Oceania"], ts = "date", kind = "spline")
.. ipython:: python
:suppress:
fig = data.plot(columns = ["Asia", "Africa", "Europe", "America", "Oceania"], ts = "date", kind = "spline")
fig.write_html("figures/plotting_plotly_line_multi_spline.html")
.. raw:: html
:file: /Users/badr.ouali/Documents/VastOrbit-master/docs/figures/plotting_plotly_line_multi_spline.html
**Drawing a step plot**
.. code-block:: python
data.plot(columns = ["Asia", "Africa", "Europe", "America", "Oceania"], ts = "date", kind = "step")
.. ipython:: python
:suppress:
fig = data.plot(columns = ["Asia", "Africa", "Europe", "America", "Oceania"], ts = "date", kind = "step")
fig.write_html("figures/plotting_plotly_line_multi_step.html")
.. raw:: html
:file: /Users/badr.ouali/Documents/VastOrbit-master/docs/figures/plotting_plotly_line_multi_step.html
.. hint::
You can achieve the same graphic in vastorbit by employing the "GROUP BY" functionality, made possible through the "by" parameter. Consider a dataset comprising three columns: a timestamp, a categorical column, and a value. Utilizing the "by" parameter in conjunction with this dataset allows for efficient grouping and visualization. This capability enables you to effectively analyze and present data trends over time, across categories, or based on specific values within a single graph, enhancing your ability to extract meaningful insights from your data.
.. tab:: Matplotlib
.. ipython:: python
:suppress:
vo.set_option("plotting_lib", "matplotlib")
We can switch to using the ``matplotlib`` module.
.. code-block:: python
vo.set_option("plotting_lib", "matplotlib")
In vastorbit, creating one or multiple line charts within a single graphic is a straightforward and flexible process. This feature enables you to efficiently visualize and compare multiple datasets or trends, providing you with a powerful tool for gaining insights from your data.
.. tab:: Single
.. ipython:: python
:okwarning:
@savefig plotting_matplotlib_line_single.png
data["Asia"].plot(ts = "date")
**Drawing a step plot**
.. ipython:: python
:okwarning:
@savefig plotting_matplotlib_line_single_step.png
data["Asia"].plot(ts = "date", kind = "step")
.. tab:: Multi
.. ipython:: python
:okwarning:
@savefig plotting_matplotlib_line_multi.png
data.plot(columns = ["Asia", "Africa", "Europe", "America", "Oceania"], ts = "date")
**Drawing a step plot**
.. ipython:: python
:okwarning:
@savefig plotting_matplotlib_line_multi_step.png
data.plot(columns = ["Asia", "Africa", "Europe", "America", "Oceania"], ts = "date", kind = "step")
.. hint::
You can achieve the same graphic in vastorbit by employing the "GROUP BY" functionality, made possible through the "by" parameter. Consider a dataset comprising three columns: a timestamp, a categorical column, and a value. Utilizing the "by" parameter in conjunction with this dataset allows for efficient grouping and visualization. This capability enables you to effectively analyze and present data trends over time, across categories, or based on specific values within a single graph, enhancing your ability to extract meaningful insights from your data.
___________________
Chart Customization
-------------------
vastorbit empowers users with a high degree of flexibility when it comes to tailoring the visual aspects of their plots.
This customization extends to essential elements such as **color schemes**, **text labels**, and **plot sizes**, as well as a wide range of other attributes that can be fine-tuned to align with specific design preferences and analytical requirements. Whether you want to make your visualizations more visually appealing or need to convey specific insights with precision, vastorbit's customization options enable you to craft graphics that suit your exact needs.
.. Important:: Different customization parameters are available for Plotly and Matplotlib.
For a comprehensive list of customization features, please consult the documentation of the respective
libraries: `plotly `__, `matplotlib `__.
Colors
~~~~~~
.. tab:: Plotly
.. ipython:: python
:suppress:
vo.set_option("plotting_lib", "plotly")
**Custom colors**
.. code-block:: python
fig = data["Asia"].plot(ts = "date")
fig.update_traces(marker = dict(color="red"))
.. ipython:: python
:suppress:
fig = data["Asia"].plot(ts = "date", colors = "red")
fig.write_html("figures/plotting_plotly_line_custom_color_1.html")
.. raw:: html
:file: /Users/badr.ouali/Documents/VastOrbit-master/docs/figures/plotting_plotly_line_custom_color_1.html
**Custom colors mapping for categories**
.. note:: You can leverage all the capabilities of the Plotly object, including functions like `update_trace`.
.. code-block:: python
fig = data.plot(columns = ["Asia", "Africa", "Europe"], ts = "date", colors = ["red", "orange","green"])
.. ipython:: python
:suppress:
fig = data.plot(columns = ["Asia", "Africa", "Europe"], ts = "date", colors = ["red", "orange","green"])
fig.write_html("figures/plotting_plotly_line_custom_color_2.html")
.. raw:: html
:file: /Users/badr.ouali/Documents/VastOrbit-master/docs/figures/plotting_plotly_line_custom_color_2.html
.. tab:: Matplolib
.. ipython:: python
:suppress:
vo.set_option("plotting_lib", "matplotlib")
**Custom colors**
.. ipython:: python
:okwarning:
@savefig plotting_matplotlib_line_custom_color_1.png
data["Asia"].plot(ts = "date", colors = ["red"])
**Custom colors mapping for categories**
.. ipython:: python
:okwarning:
@savefig plotting_matplotlib_line_custom_color_2.png
data.plot(columns = ["Asia", "Africa", "Europe"], ts = "date", colors = ["red", "orange", "green"])
____
Size
~~~~
.. tab:: Plotly
.. ipython:: python
:suppress:
vo.set_option("plotting_lib", "plotly")
Custom Width and Height.
.. code-block:: python
data["Asia"].plot(ts = "date", width = 300, height = 300)
.. ipython:: python
:suppress:
fig = data["Asia"].plot(ts = "date", width = 300, height = 300)
fig.write_html("figures/plotting_plotly_line_custom_size.html")
.. raw:: html
:file: /Users/badr.ouali/Documents/VastOrbit-master/docs/figures/plotting_plotly_line_custom_size.html
.. tab:: Matplolib
.. ipython:: python
:suppress:
vo.set_option("plotting_lib", "matplotlib")
Custom Width and Height.
.. ipython:: python
:okwarning:
@savefig plotting_matplotlib_line_single_custom_size.png
data["Asia"].plot(ts = "date", width = 6, height = 3)
_____
Text
~~~~
.. tab:: Plotly
.. ipython:: python
:suppress:
vo.set_option("plotting_lib", "plotly")
**Custom Title**
.. code-block:: python
data["Asia"].plot(ts = "date").update_layout(title_text = "Custom Title")
.. ipython:: python
:suppress:
fig = data["Asia"].plot(ts = "date").update_layout(title_text = "Custom Title")
fig.write_html("figures/plotting_plotly_line_custom_main_title.html")
.. raw:: html
:file: /Users/badr.ouali/Documents/VastOrbit-master/docs/figures/plotting_plotly_line_custom_main_title.html
**Custom Axis Titles**
.. code-block:: python
data["Asia"].plot(ts = "date", yaxis_title = "Custom Y-Axis Title")
.. ipython:: python
:suppress:
fig = data["Asia"].plot(ts = "date", yaxis_title = "Custom Y-Axis Title")
fig.write_html("figures/plotting_plotly_line_custom_y_title.html")
.. raw:: html
:file: /Users/badr.ouali/Documents/VastOrbit-master/docs/figures/plotting_plotly_line_custom_y_title.html
.. tab:: Matplolib
.. ipython:: python
:suppress:
vo.set_option("plotting_lib", "matplotlib")
**Custom Title Text**
.. ipython:: python
:okwarning:
@savefig plotting_matplotlib_line_custom_title_label.png
data["Asia"].plot(ts = "date").set_title("Custom Title")
**Custom Axis Titles**
.. ipython:: python
:okwarning:
@savefig plotting_matplotlib_line_custom_yaxis_label.png
data["Asia"].plot(ts = "date").set_ylabel("Custom Y Axis")
.. ipython:: python
:suppress:
from vastorbit._utils._sql._sys import purge_memory
purge_memory()