Matplotlib: Difference between revisions
Jump to navigation
Jump to search
| (20 intermediate revisions by the same user not shown) | |||
| Line 4: | Line 4: | ||
=Internal= | =Internal= | ||
* [[ | * [[Python_for_Data_Analysis#Overview|Python for Data Analysis]] | ||
=Overview= | =Overview= | ||
= | |||
matplotlib is the most popular Python library for producing plots and other two-dimensional data visualizations. | |||
=Install= | |||
<syntaxhighlight lang='bash'> | |||
pip3 install matplotlib | |||
</syntaxhighlight> | |||
=Import Convention= | |||
<syntaxhighlight lang='py'> | |||
import matplotlib.pyplot as plt | |||
</syntaxhighlight> | |||
=Visualization Types= | |||
{{External|https://matplotlib.org/stable/plot_types/index.html}} | {{External|https://matplotlib.org/stable/plot_types/index.html}} | ||
==Plot== | ==Plot== | ||
{{Internal|matplotlib Plot#Overview|Plot}} | |||
==Scatter== | ==Scatter== | ||
{{Internal|matplotlib Scatter|Scatter}} | |||
==Difference between Plot and Scatter== | |||
A "plot" expects two X and Y vectors as argument, which are expected to have the same length, and it creates a 2D line that includes the (X,Y) points. The points are connected by line segments. | |||
=Visualization Style= | |||
The visualization style can be set globally with: | |||
<syntaxhighlight lang='py'> | |||
import matplotlib.pyplot as plt | |||
plt.style.use(<style-name>) | |||
</syntaxhighlight> | |||
where available style names can be obtained with: | |||
<syntaxhighlight lang='py'> | |||
import matplotlib.pyplot as plt | |||
print(plt.style.available) | |||
</syntaxhighlight> | |||
<syntaxhighlight lang='text'> | |||
Solarize_Light2 | |||
_classic_test_patch | |||
_mpl-gallery | |||
_mpl-gallery-nogrid | |||
bmh | |||
classic | |||
dark_background | |||
fast | |||
fivethirtyeight | |||
ggplot | |||
grayscale | |||
seaborn-v0_8 | |||
seaborn-v0_8-bright | |||
seaborn-v0_8-colorblind | |||
seaborn-v0_8-dark | |||
seaborn-v0_8-dark-palette | |||
seaborn-v0_8-darkgrid | |||
seaborn-v0_8-deep | |||
seaborn-v0_8-muted | |||
seaborn-v0_8-notebook | |||
seaborn-v0_8-paper | |||
seaborn-v0_8-pastel | |||
seaborn-v0_8-poster | |||
seaborn-v0_8-talk | |||
seaborn-v0_8-ticks | |||
seaborn-v0_8-white | |||
seaborn-v0_8-whitegrid | |||
tableau-colorblind10 | |||
</syntaxhighlight> | |||
Interesting styles: | |||
* seaborn-v0_8-whitegrid | |||
Otherwise, style elements like axes, gridlines, line color and width can be set up individually. | |||
==Color Palette== | |||
A list of colors that can be used to change the colors in plots, so they don't look the same. This line pulls the default colors from the [[Matplotlib#Visualization_Style|style]]. | |||
<syntaxhighlight lang='py'> | |||
color_pal = plt.rcParams["axes.prop_cycle"].by_key()["color"] | |||
</syntaxhighlight> | |||
Latest revision as of 00:02, 15 May 2024
External
Internal
Overview
matplotlib is the most popular Python library for producing plots and other two-dimensional data visualizations.
Install
pip3 install matplotlib
Import Convention
import matplotlib.pyplot as plt
Visualization Types
Plot
Scatter
Difference between Plot and Scatter
A "plot" expects two X and Y vectors as argument, which are expected to have the same length, and it creates a 2D line that includes the (X,Y) points. The points are connected by line segments.
Visualization Style
The visualization style can be set globally with:
import matplotlib.pyplot as plt
plt.style.use(<style-name>)
where available style names can be obtained with:
import matplotlib.pyplot as plt
print(plt.style.available)
Solarize_Light2
_classic_test_patch
_mpl-gallery
_mpl-gallery-nogrid
bmh
classic
dark_background
fast
fivethirtyeight
ggplot
grayscale
seaborn-v0_8
seaborn-v0_8-bright
seaborn-v0_8-colorblind
seaborn-v0_8-dark
seaborn-v0_8-dark-palette
seaborn-v0_8-darkgrid
seaborn-v0_8-deep
seaborn-v0_8-muted
seaborn-v0_8-notebook
seaborn-v0_8-paper
seaborn-v0_8-pastel
seaborn-v0_8-poster
seaborn-v0_8-talk
seaborn-v0_8-ticks
seaborn-v0_8-white
seaborn-v0_8-whitegrid
tableau-colorblind10
Interesting styles:
- seaborn-v0_8-whitegrid
Otherwise, style elements like axes, gridlines, line color and width can be set up individually.
Color Palette
A list of colors that can be used to change the colors in plots, so they don't look the same. This line pulls the default colors from the style.
color_pal = plt.rcParams["axes.prop_cycle"].by_key()["color"]