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other:python:jyp_steps [2019/06/03 16:12]
jypeter [Useful matplotlib reference pages] Added animations
other:python:jyp_steps [2019/08/09 09:42]
jypeter Improved colors/colorbars
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 The matplotlib documentation is good, but not always easy to use. <wrap hi>A good way to start with matplotlib</​wrap>​ is to quickly read the following, practice, and read this section again The matplotlib documentation is good, but not always easy to use. <wrap hi>A good way to start with matplotlib</​wrap>​ is to quickly read the following, practice, and read this section again
   - Have a quick look at the [[https://​matplotlib.org/​gallery/​index.html|matplotlib gallery]] to get an idea of all you can do with matplotlib. Later, when you need to plot something, go back to the gallery to find some examples that are close to what you need and click on them to view their source code   - Have a quick look at the [[https://​matplotlib.org/​gallery/​index.html|matplotlib gallery]] to get an idea of all you can do with matplotlib. Later, when you need to plot something, go back to the gallery to find some examples that are close to what you need and click on them to view their source code
-    * some examples are more //​pythonic//​ (ie object oriented) than others, some example ​mix different styles of coding, ​all this can be confusing. Try to [[http://​matplotlib.org/​faq/​usage_faq.html#​coding-styles|use an object oriented way of doing things]]!+    * some examples are more //​pythonic//​ (ie object oriented) than others, ​and some examples ​mix different styles of coding, ​which can be quite confusing. Try to [[http://​matplotlib.org/​faq/​usage_faq.html#​coding-styles|use an object oriented way of doing things]]!
   - Use the free hints provided by JY!   - Use the free hints provided by JY!
     - You will usually **initialize matplotlib** with: ''​import matplotlib.pyplot as plt''​     - You will usually **initialize matplotlib** with: ''​import matplotlib.pyplot as plt''​
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       * later, you may need other matplotlib related modules, for advanced usage       * later, you may need other matplotlib related modules, for advanced usage
     - You need to know some **matplotlib specific vocabulary**:​     - You need to know some **matplotlib specific vocabulary**:​
-      * a Matplotlib **//​Figure//​** (or //canvas//) is a graphical window in which you create your plots...+      * a Matplotlib **//​Figure//​** (or //canvas//) is a **graphical window** in which you create your plots...
         * example: ''​my_page = plt.figure()''​         * example: ''​my_page = plt.figure()''​
-        * if you need several display windows at the same time, create several figures+        * if you need several display windows at the same time, create several figures!\\ <​code>​win_1 = plt.figure() 
 +win_2 = plt.figure()</​code>​
         * the [[http://​matplotlib.org/​faq/​usage_faq.html#​parts-of-a-figure|parts of a figure]] are often positioned in //​normalized coordinates//:​ ''​(0,​ 0)''​ is the bottom left of the figure, and ''​(1,​ 1)''​ the top right         * the [[http://​matplotlib.org/​faq/​usage_faq.html#​parts-of-a-figure|parts of a figure]] are often positioned in //​normalized coordinates//:​ ''​(0,​ 0)''​ is the bottom left of the figure, and ''​(1,​ 1)''​ the top right
         * You don't really specify the **page orientation** (//​portrait//​ or //​landscape//​) of a plot. If you want a portrait plot, it's up to you to create a plot that will look higher than it is large. The idea is not to worry about this and just check the final resulting plot: create a plot, save it, display the resulting png/pdf and then adjust the creation script         * You don't really specify the **page orientation** (//​portrait//​ or //​landscape//​) of a plot. If you want a portrait plot, it's up to you to create a plot that will look higher than it is large. The idea is not to worry about this and just check the final resulting plot: create a plot, save it, display the resulting png/pdf and then adjust the creation script
-          * If you have an idea of the layout of what you want to plot, it may be easier to explicitly specify the figure size/ratio at creation time, and try to //fill// the figure+          * If you do have an idea of the layout of what you want to plot, it may be easier to explicitly specify the figure size/ratio at creation time, and then try to //​fill// ​the normalized coordinates space of the figure
           * ''​my_page = plt.figure()'':​ the ratio of the default figure is ''​landscape'',​ because it is 33% larger than it is high. Creating a default figure will be OK most of the time!           * ''​my_page = plt.figure()'':​ the ratio of the default figure is ''​landscape'',​ because it is 33% larger than it is high. Creating a default figure will be OK most of the time!
           * ''​my_page = plt.figure(figsize=(width,​ height))'':​ create a figure with a custom ratio (sizes are considered to be in inches)           * ''​my_page = plt.figure(figsize=(width,​ height))'':​ create a figure with a custom ratio (sizes are considered to be in inches)
-          ​* ''​my_page = plt.figure(figsize=(8.3,​ 11.7))'':​ create a figure that will theoretically fill an A4 size page in portrait mode (check [[https://​www.papersizes.org/​a-paper-sizes.htm|Dimensions Of A Series Paper Sizes]] if you need more size details) +            ​* ''​my_page = plt.figure(figsize=(8.3,​ 11.7))'':​ create a figure that will theoretically fill an A4 size page in portrait mode (check [[https://​www.papersizes.org/​a-paper-sizes.htm|Dimensions Of A Series Paper Sizes]] if you need more size details) 
-      * a Matplotlib **//​Axis//​** is a plot inside a Figure... [[http://​matplotlib.org/​faq/​usage_faq.html#​parts-of-a-figure|More details]]+      * a Matplotlib **//​Axis//​** is a **plot** inside a Figure... [[http://​matplotlib.org/​faq/​usage_faq.html#​parts-of-a-figure|More details]]
         * reserve space for **one plot** that will use most of the available area of the figure/​page:​         * reserve space for **one plot** that will use most of the available area of the figure/​page:​
           * ''​my_plot = my_page.add_subplot(1,​ 1, 1)'':​ syntax is ''​add_subplot(nrows,​ ncols, index)''​           * ''​my_plot = my_page.add_subplot(1,​ 1, 1)'':​ syntax is ''​add_subplot(nrows,​ ncols, index)''​
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 bottom_plot = plot_array[2]</​code>​ bottom_plot = plot_array[2]</​code>​
           * creating a figure and axes with a single line: ''​my_page,​ plot_array = **plt**.subplots(3,​ 1)''​           * creating a figure and axes with a single line: ''​my_page,​ plot_array = **plt**.subplots(3,​ 1)''​
 +        * use [[https://​matplotlib.org/​api/​_as_gen/​matplotlib.figure.Figure.html#​matplotlib.figure.Figure.add_axes|my_page.add_axes(...)]] to add an axis in an arbirary location of the page\\ ''​my_page.add_axes([left,​ bottom, width, height])''​
       * a Matplotlib **//​Artist//​** or //Patch// is //​something//​ (e.g a line, a group of markers, text, the legend...) plotted ​ on the Figure/Axis       * a Matplotlib **//​Artist//​** or //Patch// is //​something//​ (e.g a line, a group of markers, text, the legend...) plotted ​ on the Figure/Axis
-      * **clearing** the //page// (or part of it): you probably won't need that+      * **clearing** the //page// (or part of it): you probably won't need that...
         * ''​my_page.clear()''​ or ''​my_page.clf()''​ or ''​plt.clf()'':​ clear the (current) figure         * ''​my_page.clear()''​ or ''​my_page.clf()''​ or ''​plt.clf()'':​ clear the (current) figure
         * ''​my_plot.clear()''​ or ''​my_plot.cla()'':​ clear the (current) axis         * ''​my_plot.clear()''​ or ''​my_plot.cla()'':​ clear the (current) axis
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         * [[https://​matplotlib.org/​api/​_as_gen/​matplotlib.pyplot.subplots.html|plt.subplots(...)]] with an **s** at the end ([[https://​matplotlib.org/​gallery/​subplots_axes_and_figures/​subplots_demo.html|demo]])         * [[https://​matplotlib.org/​api/​_as_gen/​matplotlib.pyplot.subplots.html|plt.subplots(...)]] with an **s** at the end ([[https://​matplotlib.org/​gallery/​subplots_axes_and_figures/​subplots_demo.html|demo]])
         * [[https://​matplotlib.org/​api/​_as_gen/​matplotlib.pyplot.subplots_adjust.html|subplots_adjust]] can be used to change the overall boundaries of the subplots on the figure, and the spacing between the subplots\\ ''​plt.subplots_adjust(left=None,​ bottom=None,​ right=None, top=None, wspace=None,​ hspace=None)''​\\ or ''​my_page.subplots_adjust(left=None,​ bottom=None,​ right=None, top=None, wspace=None,​ hspace=None)''​         * [[https://​matplotlib.org/​api/​_as_gen/​matplotlib.pyplot.subplots_adjust.html|subplots_adjust]] can be used to change the overall boundaries of the subplots on the figure, and the spacing between the subplots\\ ''​plt.subplots_adjust(left=None,​ bottom=None,​ right=None, top=None, wspace=None,​ hspace=None)''​\\ or ''​my_page.subplots_adjust(left=None,​ bottom=None,​ right=None, top=None, wspace=None,​ hspace=None)''​
 +          * ''​hspace''/''​wspace''​ is the amount of height/​width between the subplots
 +            * ''​hspace=0.1''​ is enough for just displaying the ticks and the labels, without the axis name
 +            * use ''​hspace=0''​ to stick the plots together vertically
 +              * do not forget to disable the ticks where there is no space to plot them: ''​my_plot.set_xticks([])''​
 +          * ''​my_page.subplots_adjust(right=0.75)''​ will leave 25% on the right of the page for adding a legend outside of a plot
 +        * You can also **resize an existing (sub)plot** the following way:
 +          - Get the current size information:​ ''​pl_x_bottomleft,​ pl_y_bottomleft,​ pl_width, pl_height = my_plot.get_position().bounds''​
 +          - Set the new size: e.g reduce the height with ''​my_plot.set_position( (pl_x_bottomleft,​ pl_y_bottomleft,​ pl_width, pl_height ​ * 0.5) )''​
       * [[https://​matplotlib.org/​gallery/​index.html#​subplots-axes-and-figures|Subplots,​ axes and figures]] gallery       * [[https://​matplotlib.org/​gallery/​index.html#​subplots-axes-and-figures|Subplots,​ axes and figures]] gallery
       * [[https://​matplotlib.org/​tutorials/​intermediate/​gridspec.html#​sphx-glr-tutorials-intermediate-gridspec-py|Customizing Figure Layouts Using GridSpec and Other Functions]],​ [[https://​matplotlib.org/​tutorials/​intermediate/​constrainedlayout_guide.html|constrained layout]] and [[https://​matplotlib.org/​tutorials/​intermediate/​tight_layout_guide.html|tight layout]]       * [[https://​matplotlib.org/​tutorials/​intermediate/​gridspec.html#​sphx-glr-tutorials-intermediate-gridspec-py|Customizing Figure Layouts Using GridSpec and Other Functions]],​ [[https://​matplotlib.org/​tutorials/​intermediate/​constrainedlayout_guide.html|constrained layout]] and [[https://​matplotlib.org/​tutorials/​intermediate/​tight_layout_guide.html|tight layout]]
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       * ''​my_page.savefig('​my_plot.png',​ dpi=200, transparent=True,​ bbox_inches='​tight'​)'':​ save the figure to a png file at a higher resolution than the default (default is 100 dots per inch), with a transparent background and no extra space around the figure       * ''​my_page.savefig('​my_plot.png',​ dpi=200, transparent=True,​ bbox_inches='​tight'​)'':​ save the figure to a png file at a higher resolution than the default (default is 100 dots per inch), with a transparent background and no extra space around the figure
     - **display** the figure and its plots, and **start interacting** (zooming, panning...) with them:\\ ''​plt.show()''​     - **display** the figure and its plots, and **start interacting** (zooming, panning...) with them:\\ ''​plt.show()''​
-    - it may be hard to (remember how to) **work with colors**. Some examples from the [[https://​matplotlib.org/​gallery/​index.html]] can help you! +    - it may be hard to (remember how to) **work with colors ​//and colorbars//**. Some examples from the [[https://​matplotlib.org/​gallery/​index.html|matplotlib Gallery]] can help you!\\ Note: A **reversed version of each colormap** is available by appending ''​_r''​ to the name, e.g., ''​viridis_r''​ 
-      * [[https://​matplotlib.org/​examples/pylab_examples/​leftventricle_bulleye.html|leftventricle_bulleye.py]]:​ associating different types of colormaps to a plot and colorbar+      * [[https://​matplotlib.org/​gallery/specialty_plots/​leftventricle_bulleye.html|leftventricle_bulleye.py]]:​ associating different types of colormaps to a plot and colorbar
       * [[https://​matplotlib.org/​examples/​api/​colorbar_only.html|colorbar_only.py]]:​ the different types of colorbars (or plotting only a colorbar)       * [[https://​matplotlib.org/​examples/​api/​colorbar_only.html|colorbar_only.py]]:​ the different types of colorbars (or plotting only a colorbar)
-      * [[https://​matplotlib.org/​examples/color/colormaps_reference.html|colormaps_reference.py]]:​ pre-defined colormaps +      * [[https://​matplotlib.org/​gallery/color/colormap_reference.html|colormaps_reference.py]]:​ pre-defined colormaps 
-      * [[https://​matplotlib.org/​examples/​color/​named_colors.html|named_colors.py]]:​ named colors +      * [[https://​matplotlib.org/​gallery/​color/​named_colors.html|named_colors.py]]:​ named colors 
-      * More details about the colors below, in the [[#graphics_related_resources|Resources ​section]] +      * More details about colors ​and colorbars ​below, in the [[#useful_matplotlib_reference_pages|Useful matplotlib reference pages]] ​section ​and the [[#​graphics_related_resources|Graphics related resources]] section 
-    - if you don't see a part of what you have plotted, maybe it's hidden behind other elements! Use the [[https://​matplotlib.org/​examples/​pylab_examples/​zorder_demo.html|zorder parameter]] to explicitly specify the plotting order/​layers+    - if you don't see a part of what you have plotted, maybe it's hidden behind other elements! Use the [[https://​matplotlib.org/​examples/​pylab_examples/​zorder_demo.html|zorder parameter]] to explicitly ​**specify the plotting order/​layers/depth**
       * things should automatically work //as expected// if //zorder// is not explicitly specified       * things should automatically work //as expected// if //zorder// is not explicitly specified
       * Use the ''​zorder=NN''​ parameter when creating objects. ''​NN''​ is an integer where 0 is the lowest value (the farthest from the eye), and objects are plotted above objects with a lower //zorder// value       * Use the ''​zorder=NN''​ parameter when creating objects. ''​NN''​ is an integer where 0 is the lowest value (the farthest from the eye), and objects are plotted above objects with a lower //zorder// value
       * Use ''​matplotlib_object.set_order(NN)''​ to change the order after an object has been created       * Use ''​matplotlib_object.set_order(NN)''​ to change the order after an object has been created
 +    - you can use **transparency** to partially show what is behind some markers or other objects. Many //artists// accept the ''​alpha''​ parameter where ''​0.0''​ means that the object is completely transparent,​ and ''​1.0''​ means completely opaque\\ e.g. ''​my_plot.scatter(...,​ alpha=0.7)''​
     - sometimes the results of the python/​matplolib commands are displayed immediately,​ sometimes not. It depends if you are in [[http://​matplotlib.org/​faq/​usage_faq.html#​what-is-interactive-mode|interactive or non-interactive]] mode     - sometimes the results of the python/​matplolib commands are displayed immediately,​ sometimes not. It depends if you are in [[http://​matplotlib.org/​faq/​usage_faq.html#​what-is-interactive-mode|interactive or non-interactive]] mode
     - if your matplotlib is executed in a batch script, it will generate an error when trying to create (''​show()''​) a plot, because matplotlib expects to be able to display the figure on a screen by default.     - if your matplotlib is executed in a batch script, it will generate an error when trying to create (''​show()''​) a plot, because matplotlib expects to be able to display the figure on a screen by default.
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 ==== Useful matplotlib reference pages ==== ==== Useful matplotlib reference pages ====
  
-  * [[https://​matplotlib.org/​api/​_as_gen/​matplotlib.pyplot.plot.html|plot(...)]]:​ Plot y versus x as lines and/or markers +  ​* Some plot types: 
-  * [[https://​matplotlib.org/​api/​_as_gen/​matplotlib.pyplot.scatter.html|scatter(...)]]:​ A scatter plot of y vs x with varying marker size and/or color+    ​* [[https://​matplotlib.org/​api/​_as_gen/​matplotlib.pyplot.plot.html|plot(...)]]:​ Plot y versus x as lines and/or markers 
 +    * [[https://​matplotlib.org/​api/​_as_gen/​matplotlib.pyplot.scatter.html|scatter(...)]]:​ A scatter plot of y vs x with varying marker size and/or color
     * The ''​plot''​ function will be faster for scatterplots where markers don't vary in size or color     * The ''​plot''​ function will be faster for scatterplots where markers don't vary in size or color
 +    * [[https://​matplotlib.org/​api/​_as_gen/​matplotlib.axes.Axes.contourf.html|contour(...) and contourf(...)]]:​ draw contour lines and filled contours
 +  * X and Y axes parameters
 +    * Axis range: ''​my_plot.set_xlim(x_leftmost_value,​ x_rightmost_value)''​
 +      * Use the leftmost and rightmost values to specify the orientation of the axis (i.e the rightmost value can be smaller than the leftmost)
 +    * Axis label: ''​my_plot.set_xlabel(x_label_string,​ fontsize=axis_label_fontsize)''​
 +      * Use the extra labelpad parameter to move the label closer (negative value) to the axis or farther (positive value): e.g. ''​my_plot.set_xlabel('​A closer label',​ labelpad=-20''​
 +    * Major (and minor) tick marks location: ''​my_plot.set_xticks(x_ticks_values,​ minor=False)''​
 +      * Use an empty list if you don't want tick marks: ''​my_plot.set_xticks([])''​
 +    * Tick labels (if you don't want the default values): ''​my_plot.set_xticklabels(x_ticks_labels,​ minor=False,​ fontsize=ticklabels_fontsize)''​
 +      * ''​x_ticks_labels''​ is a list of strings that has the same length as ''​x_ticks_values''​. Use an empty string in the positions where you don't want a label
 +      * Many more options for ticks, labels, orientation,​ ...
   * [[https://​matplotlib.org/​api/​_as_gen/​matplotlib.lines.Line2D.html|line]] parameters   * [[https://​matplotlib.org/​api/​_as_gen/​matplotlib.lines.Line2D.html|line]] parameters
     * ''​linestyle'':​ ''​solid'',​ ''​None'',​ [[https://​matplotlib.org/​api/​_as_gen/​matplotlib.lines.Line2D.html#​matplotlib.lines.Line2D.set_linestyle|other]] ([[https://​matplotlib.org/​examples/​lines_bars_and_markers/​line_styles_reference.html|default styles example]], [[https://​matplotlib.org/​examples/​lines_bars_and_markers/​linestyles.html|custom styles example]])     * ''​linestyle'':​ ''​solid'',​ ''​None'',​ [[https://​matplotlib.org/​api/​_as_gen/​matplotlib.lines.Line2D.html#​matplotlib.lines.Line2D.set_linestyle|other]] ([[https://​matplotlib.org/​examples/​lines_bars_and_markers/​line_styles_reference.html|default styles example]], [[https://​matplotlib.org/​examples/​lines_bars_and_markers/​linestyles.html|custom styles example]])
   * [[https://​matplotlib.org/​api/​markers_api.html|marker types]]   * [[https://​matplotlib.org/​api/​markers_api.html|marker types]]
-    * ''​fillstyle'':​ ''​full'',​ ''​None'',​ [[https://​matplotlib.org/​gallery/​lines_bars_and_markers/​marker_fillstyle_reference.html|other]] +    ​* Default marker size and edge width: 
-    Other attributes: ''​markersize'',​ ''​markerfacecolor''​ (and ''​markerfacecoloralt'' ​for dual color markers), ''​markeredgecolor'',​ ''​markeredgewidth''​ +      ​* ''​mpl.rcParams['lines.markersize'] %%**%% 2''​ => 36 
-  * [[https://​matplotlib.org/​api/​colors_api.html|colors]]+      * ''​mpl.rcParams['​lines.linewidth'​]''​ => 1.5 
 +    * Other marker attributes. For ''​plot'',​ all the markers have the same attributes, and for ''​scatter''​ the attributes can be the same, or specified for each marker 
 +      * [[https://​matplotlib.org/​api/​_as_gen/​matplotlib.pyplot.plot.html|plot(...)]]:​ //fmt// (see documentation) or ''​marker''​ and ''​markerfacecolor''/''​mfc''​ (and ''​markerfacecoloralt''/''​mfcalt''​ for dual color markers), ''​markersize'',​ ''​markeredgewidth''/''​mew'',​ ''​markeredgecolor'',​ ''​fillstyle''​ (''​full'',​ ''​None'',​ [[https://​matplotlib.org/​gallery/​lines_bars_and_markers/​marker_fillstyle_reference.html|other]]) 
 +      [[https://​matplotlib.org/​api/​_as_gen/​matplotlib.pyplot.scatter.html|scatter(...)]]: ''​marker'' ​(marker type), ''​c''​ (color), ​''​s'' ​(size), ''​linewidths'' ​(linewidth of the marker edges), ''​edgecolors''​ 
 +  * [[https://​matplotlib.org/​api/​colors_api.html|colors]] ​and colormaps
     * [[https://​matplotlib.org/​gallery/​color/​color_demo.html|color demo]]     * [[https://​matplotlib.org/​gallery/​color/​color_demo.html|color demo]]
     * [[https://​matplotlib.org/​examples/​color/​named_colors.html|named colors]]     * [[https://​matplotlib.org/​examples/​color/​named_colors.html|named colors]]
 +    * Reverting the colors: add ''​_r''​ at the end of the colormap name
 +    * Special colormap colors
 +      * ''​cmap.set_bad(color='​k'​)'':​ color to be used for masked values
 +      * ''​cmap.set_over(color='​k'​)'':​ color to be used for high out-of-range values
 +      * ''​cmap.set_under(color='​k'​)'':​ color to be used for low out-of-range values
 +  * [[https://​matplotlib.org/​api/​_as_gen/​matplotlib.figure.Figure.html#​matplotlib.figure.Figure.colorbar|colorbar]] and ([[https://​matplotlib.org/​gallery/​images_contours_and_fields/​contourf_demo.html|contourf + colorbar demo]])
   * [[https://​matplotlib.org/​api/​_as_gen/​matplotlib.pyplot.text.html|text(...)]] and [[https://​matplotlib.org/​tutorials/​text/​annotations.html|annotations]]   * [[https://​matplotlib.org/​api/​_as_gen/​matplotlib.pyplot.text.html|text(...)]] and [[https://​matplotlib.org/​tutorials/​text/​annotations.html|annotations]]
 +    * Some titles:
 +      * [[https://​matplotlib.org/​api/​_as_gen/​matplotlib.figure.Figure.html#​matplotlib.figure.Figure.suptitle|Figure title]]: ''​my_figure.suptitle('​Figure title',​ ...)''​
 +      * [[https://​matplotlib.org/​api/​axes_api.html#​axis-labels-title-and-legend|Axis Labels, title, and legend]]: ''​my_plot.set_title('​Plot title',​ ...)''​
     * ''​fontsize'':​ size in points, or (better!) string specifying a relative size (''​xx-small'',​ ''​x-small'',​ ''​small'',​ ''​medium'',​ ''​large'',​ ''​x-large'',​ ''​xx-large''​)     * ''​fontsize'':​ size in points, or (better!) string specifying a relative size (''​xx-small'',​ ''​x-small'',​ ''​small'',​ ''​medium'',​ ''​large'',​ ''​x-large'',​ ''​xx-large''​)
     * [[https://​matplotlib.org/​api/​text_api.html#​matplotlib.text.Text|all the text properties]]     * [[https://​matplotlib.org/​api/​text_api.html#​matplotlib.text.Text|all the text properties]]
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   * The [[https://​matplotlib.org/​api/​_as_gen/​matplotlib.figure.Figure.html|figure(...)]] and the associated methods   * The [[https://​matplotlib.org/​api/​_as_gen/​matplotlib.figure.Figure.html|figure(...)]] and the associated methods
   * The [[https://​matplotlib.org/​api/​axes_api.html|axes]] and the associated methods   * The [[https://​matplotlib.org/​api/​axes_api.html|axes]] and the associated methods
-  * [[https://​matplotlib.org/​tutorials/​introductory/​customizing.html#​matplotlib-rcparams|matplotlib default settings]] can be queried and updated+  * [[https://​matplotlib.org/​tutorials/​introductory/​customizing.html#​matplotlib-rcparams|matplotlib default ​config/settings]] can be queried and updated
     * example: the default figure size (inches) is ''​mpl.rcParams['​figure.figsize'​]''​ (''​[6.4,​ 4.8]''​)     * example: the default figure size (inches) is ''​mpl.rcParams['​figure.figsize'​]''​ (''​[6.4,​ 4.8]''​)
     * current settings'​ file:  ''​mpl.matplotlib_fname()''​     * current settings'​ file:  ''​mpl.matplotlib_fname()''​
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 Where: [[http://​pandas.pydata.org|Pandas web site]] Where: [[http://​pandas.pydata.org|Pandas web site]]
  
-JYP's comment: pandas is supposed to be quite good for loading, processing and plotting time series, without writing custom code. You should at least have a quick look at: +JYP's comment: pandas is supposed to be quite good for loading, processing and plotting time series, without writing custom code. It is **very convenient for processing tables in xlsx files** (or csv, etc...). You should at least have a quick look at: 
-  * The [[http://www.scipy-lectures.org/packages/​statistics/​index.html|Statistics in Python]] tutorial that combines Pandas, ​[[http://statsmodels.sourceforge.net/|Statsmodels]] and [[http://seaborn.pydata.org/|Seaborn]] + 
-  ​* ​the cheat sheet on the [[https://​www.enthought.com/​services/​training/​pandas-mastery-workshop/​|Enthought workshops advertising page]] +  * Some //Cheat Sheets// (in the following order): 
-  * the cheat sheet on the [[https://github.com/​pandas-dev/pandas/tree/master/doc/cheatsheet|github ​Pandas ​doc page]]+    - Basics: ​[[http://datacamp-community-prod.s3.amazonaws.com/dbed353d-2757-4617-8206-8767ab379ab3|Pandas basics]] (associated with the [[https://www.datacamp.com/community/​blog/​python-pandas-cheat-sheet|Pandas Cheat Sheet for Data Science in Python]] pandas introduction page) 
 +    - Intermediate: ​[[https://github.com/pandas-dev/​pandas/​tree/​master/​doc/​cheatsheet|github Pandas doc page]] 
 +    - Advanced: ​the cheat sheet on the [[https://​www.enthought.com/​services/​training/​pandas-mastery-workshop/​|Enthought workshops advertising page]] 
 +  * Some tutorials:​ 
 +    * [[https://www.datacamp.com/community/​blog/​python-pandas-cheat-sheet|Pandas Cheat Sheet for Data Science in Python]] ​pandas ​introduction page 
 +    * The [[http://www.scipy-lectures.org/packages/statistics/​index.html|Statistics in Python]] tutorial that combines ​Pandas, [[http://​statsmodels.sourceforge.net/​|Statsmodels]] and [[http://​seaborn.pydata.org/​|Seaborn]]
  
 ===== Scipy Lecture Notes ===== ===== Scipy Lecture Notes =====
other/python/jyp_steps.txt · Last modified: 2024/03/07 10:15 by jypeter