User Tools

Site Tools


other:python:jyp_steps

Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revision Previous revision
Next revision
Previous revision
other:python:jyp_steps [2018/10/18 17:56]
jypeter [Matplotlib] Added background color trick
other:python:jyp_steps [2019/07/11 17:28] (current)
jypeter [Pandas] re-organized the cheat sheets and tutorials
Line 180: Line 180:
 Help on //stack overflow//: [[https://​stackoverflow.com/​questions/​tagged/​matplotlib|matplotlib help]] Help on //stack overflow//: [[https://​stackoverflow.com/​questions/​tagged/​matplotlib|matplotlib help]]
  
-The documentation is good, but not always easy to use. <wrap hi>A good way to start with matplotlib</​wrap>​ is to: +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 
-  - Look at the [[http://​matplotlib.org/​gallery.html|matplotlib gallery]] to get an idea of all you can do with matplotlib. Later, when you need to plot something, ​come back to the gallery to find some examples that are close to what you need and click on them to get the sources+  - 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, 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!
-    - a Matplotlib //Figure// is a graphical window in which you make your plots...  +    - You will usually **initialize matplotlib** with: ''​import matplotlib.pyplot as plt''​ 
-    - a Matplotlib //Axis// is a plot inside a Figure... [[http://​matplotlib.org/​faq/​usage_faq.html#​parts-of-a-figure|More details]] +      * in some cases you may also need: ''​import matplotlib as mpl''​ 
-    - some examples are more //pythonic// (ie object orientedthan otherssome example mix different styles ​of codingall this can be confusingTry to [[http://​matplotlib.org/​faq/usage_faq.html#coding-styles|use an object oriented way of doing things]]! +      * later, you may need other matplotlib related modules, for advanced usage 
-    - it may be hard to (remember how to) work with colors. Some examples from the [[http://​matplotlib.org/​gallery.html|Gallery]] can help you!+    - You need to know some **matplotlib specific vocabulary**:​ 
 +      * a Matplotlib ​**//Figure//** (or //​canvas//​) ​is a **graphical window** in which you create ​your plots... 
 +        * example: ''​my_page = plt.figure()''​ 
 +        * 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 
 +        * 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 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(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) 
 +      * 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:​ 
 +          * ''​my_plot = my_page.add_subplot(1,​ 1, 1)'':​ syntax is ''​add_subplot(nrows,​ ncols, index)''​ 
 +          * ''​my_plot = my_page.subplot**s**()''​ 
 +        * create **3 plots on 1 column** (each plot uses the full width of the figure): 
 +          * <​code>​top_plot = my_page.add_subplot(3,​ 1, 1) 
 +middle_plot = my_page.add_subplot(3,​ 1, 2) 
 +bottom_plot = my_page.add_subplot(3,​ 1, 3)</​code>​ 
 +          * the following method is more efficient than add_subplot when there are lots of plots on a page<​code>​plot_array = my_page.subplots(3,​ 1) 
 +top_plot = plot_array[0] 
 +middle_plot = plot_array[1] 
 +bottom_plot = plot_array[2]<​/code> 
 +          * 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([leftbottom, width, height])''​ 
 +      * a Matplotlib **//​Artist//​** or //Patch// is //​something//​ (e.g a line, a group of markerstext, the legend...) plotted ​ on the Figure/​Axis 
 +      * **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_plot.clear()''​ or ''​my_plot.cla()'':​ clear the (current) axis 
 +    - some resources for having multiple plots on the same figure 
 +      * [[https://​matplotlib.org/​gallery/​recipes/​create_subplots.html#​sphx-glr-gallery-recipes-create-subplots-py|Easily creating subplots]] 
 +        * [[https://​matplotlib.org/​api/​_as_gen/​matplotlib.figure.Figure.html#​matplotlib.figure.Figure.add_subplot|fig.add_subplot(...)]] 
 +        * [[https://​matplotlib.org/​api/​_as_gen/​matplotlib.figure.Figure.html#​matplotlib.figure.Figure.add_axes|fig.add_axes(...)]] 
 +        * [[https://​matplotlib.org/​api/​_as_gen/​matplotlib.pyplot.subplot.html|plt.subplot(...)]] 
 +        * [[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)''​ 
 +          * ''​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/​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]] 
 +    - use [[https://​matplotlib.org/​api/​_as_gen/​matplotlib.pyplot.savefig.html|my_page.savefig(...)]] to save a figure 
 +      *  <wrap hi>​savefig(...) must be called **before** plt.show()!</​wrap>​ 
 +      * ''​my_page.savefig('​my_plot.pdf'​)'':​ save the figure to a pdf file 
 +      * ''​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()''​ 
 +    - it may be hard to (remember how to) **work with colors**. Some examples from the [[https://​matplotlib.org/​gallery/index.html]] can help you!
       * [[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/​examples/​pylab_examples/​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)
Line 192: Line 244:
       * [[https://​matplotlib.org/​examples/​color/​named_colors.html|named_colors.py]]:​ named colors       * [[https://​matplotlib.org/​examples/​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 the colors below, in the [[#​graphics_related_resources|Resources section]]
-    - sometimes the results of the python/​matplolib commands are displayed ​directly, 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 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 
 +      * 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 
 +    - 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 
 +    - 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. 
 +      * Check how you can [[https://​matplotlib.org/​faq/​howto_faq.html?​highlight=web#​generate-images-without-having-a-window-appear|generate images offline]]
     - the documentation may mention [[http://​matplotlib.org/​faq/​usage_faq.html#​what-is-a-backend|backends]]. What?? Basically, you use python commands to create a plot, and the backend is the //thing// that will render your plot on the screen or in a file (png, pdf, etc...)     - the documentation may mention [[http://​matplotlib.org/​faq/​usage_faq.html#​what-is-a-backend|backends]]. What?? Basically, you use python commands to create a plot, and the backend is the //thing// that will render your plot on the screen or in a file (png, pdf, etc...)
-    ​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 +  ​Read the [[https://github.com/rougier/matplotlib-tutorial|Matplotlib tutorial by Nicolas Rougier]] 
-  ​Read the [[http://​www.labri.fr/​perso/​nrougier/​teaching/​matplotlib/​|Matplotlib tutorial by Nicolas Rougier]] +  - Download the [[http://​matplotlib.org/​contents.html|pdf version of the manual]]. **Do not print** the 2300+ pages of the manual! Read the beginner'​s guide (Chapter //FIVE// of //Part II//) and have a super quick look at the table of contents of the whole document.
-  - Download the [[http://​matplotlib.org/​contents.html|pdf version of the manual]]. **Do not print** the 2800+ pages of the manual! Read the beginner'​s guide (Chapter //FIVE// of //Part II//) and have a super quick look at the table of contents of the whole document.+
  
-==== Misc numpy tricks ====+==== Useful matplotlib reference pages ==== 
 + 
 +  * Some plot types: 
 +    * [[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 
 +    * [[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 
 +    * ''​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]] 
 +    * Default marker size and edge width: 
 +      * ''​mpl.rcParams['​lines.markersize'​] %%**%% 2''​ => 36 
 +      * ''​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/​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]] 
 +    * 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''​) 
 +    * [[https://​matplotlib.org/​api/​text_api.html#​matplotlib.text.Text|all the text properties]] 
 +  * [[https://​matplotlib.org/​api/​pyplot_api.html#​matplotlib.pyplot.legend|legend(...)]] ([[https://​matplotlib.org/​examples/​pylab_examples/​legend_demo3.html|legend demo]], [[https://​matplotlib.org/​users/​legend_guide.html|advanced legend guide]]) 
 +    * The legend will //show// the lines (or other objects) that were associated with a //label// with the ''​label=''​ keyword when creating/​updating a plot 
 +      * If there are some elements of a plot that you do not want to associate with a legend (e.g. there are several lines with the same color and markers, but you want to plot the legend only once), do not specify a ''​label=''​ keyword for these elements, or add a ''​_''​ at the front of the label strings 
 +    * The legend is positioned somewhere (that can be specified) **inside** the plot. In order to place a legend **outside** the plot, use the ''​bbox_to_anchor''​ parameter 
 +      * the parameters of ''​bbox_to_anchor''​ are in normalized coordinates of the current (sub)plot:​ 
 +        * ''​(0,​ 0)''​ is the lower left corner of the plot, and ''​(1,​ 1)''​ the upper right corner 
 +        * ''​legend(... bbox_to_anchor=(1.05,​ 1.), loc='​upper left', ...)''​ will put the upper left corner of the legend slightly right (''​(1.05,​ 1.)''​) of the upper right corner (''​(1,​ 1)''​) of the plot 
 +      * if the legend is outside of the plot, you have to **explicitly provide enough space for the legend on the page** 
 +        * e.g. with [[https://​matplotlib.org/​api/​_as_gen/​matplotlib.pyplot.subplots_adjust.html|subplots_adjust]],​ ''​plt.subplots_adjust(right=0.75)''​ will make all the plots use 75% on the left of the page, and leave 25% on the right for the legend 
 +  * 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 
 +  * [[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]''​) 
 +    * current settings'​ file:  ''​mpl.matplotlib_fname()''​ 
 +  * [[https://​matplotlib.org/​api/​animation_api.html|Animations]] ([[https://​matplotlib.org/​gallery/​index.html#​animation|demo]]) 
 + 
 +==== Misc Matplotlib ​tricks ====
  
   * Specifying the background color of a plot (e.g. when plotting a masked variable and you don't want the masked areas to be white)   * Specifying the background color of a plot (e.g. when plotting a masked variable and you don't want the masked areas to be white)
Line 207: Line 322:
  
   * [[http://​journals.plos.org/​ploscompbiol/​article?​id=10.1371/​journal.pcbi.1003833|Ten Simple Rules for Better Figures]]   * [[http://​journals.plos.org/​ploscompbiol/​article?​id=10.1371/​journal.pcbi.1003833|Ten Simple Rules for Better Figures]]
 +  * [[https://​www.machinelearningplus.com/​plots/​top-50-matplotlib-visualizations-the-master-plots-python/​|Top 50 matplotlib Visualizations]]
   * [[http://​seaborn.pydata.org/​|Seaborn]] is a library for making attractive and informative statistical graphics in Python, built on top of matplotlib   * [[http://​seaborn.pydata.org/​|Seaborn]] is a library for making attractive and informative statistical graphics in Python, built on top of matplotlib
     * See also: [[https://​www.datacamp.com/​community/​tutorials/​seaborn-python-tutorial|     * See also: [[https://​www.datacamp.com/​community/​tutorials/​seaborn-python-tutorial|
Line 305: Line 421:
 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 =====
Line 318: Line 439:
 This is **a really nice and useful document** that is regularly updated and used for the [[https://​www.euroscipy.org/​|EuroScipy]] tutorials. You will learn more things about python, numpy and matplotlib, debugging and optimizing scripts, and also learn about using python for statistics, image processing, machine learning, washing dishes (this is just to check if you have read this page), etc... This is **a really nice and useful document** that is regularly updated and used for the [[https://​www.euroscipy.org/​|EuroScipy]] tutorials. You will learn more things about python, numpy and matplotlib, debugging and optimizing scripts, and also learn about using python for statistics, image processing, machine learning, washing dishes (this is just to check if you have read this page), etc...
  
-===== Quick Reference =====+===== Quick Reference ​and cheat sheets ​=====
  
   * The nice and convenient Python 2.7 Quick Reference: [[http://​rgruet.free.fr/​PQR27/​PQR2.7_printing_a4.pdf|pdf]] - [[http://​rgruet.free.fr/​PQR27/​PQR2.7.html|html]]   * The nice and convenient Python 2.7 Quick Reference: [[http://​rgruet.free.fr/​PQR27/​PQR2.7_printing_a4.pdf|pdf]] - [[http://​rgruet.free.fr/​PQR27/​PQR2.7.html|html]]
Line 324: Line 445:
  
   * Python 3 [[https://​perso.limsi.fr/​pointal/​python:​abrege|Quick reference]] and [[https://​perso.limsi.fr/​pointal/​python:​memento|Cheat sheet]]   * Python 3 [[https://​perso.limsi.fr/​pointal/​python:​abrege|Quick reference]] and [[https://​perso.limsi.fr/​pointal/​python:​memento|Cheat sheet]]
 +
 +  * [[https://​www.cheatography.com/​weidadeyue/​cheat-sheets/​jupyter-notebook/​pdf_bw/​|Jupyter Notebook Keyboard Shortcuts]]
  
 ===== Misc tutorials ===== ===== Misc tutorials =====
Line 364: Line 487:
   * [[https://​www.datacamp.com/​community/​tutorials/​data-science-python-ide|Top 5 Python IDEs For Data Science]]   * [[https://​www.datacamp.com/​community/​tutorials/​data-science-python-ide|Top 5 Python IDEs For Data Science]]
   * [[http://​noeticforce.com/​best-python-ide-for-programmers-windows-and-mac|Python IDE: The10 Best IDEs for Python Programmers]]   * [[http://​noeticforce.com/​best-python-ide-for-programmers-windows-and-mac|Python IDE: The10 Best IDEs for Python Programmers]]
 +  * [[https://​www.techbeamers.com/​best-python-ide-python-programming/​|Get the Best Python IDE]]
   * [[https://​wiki.python.org/​moin/​IntegratedDevelopmentEnvironments]]   * [[https://​wiki.python.org/​moin/​IntegratedDevelopmentEnvironments]]
  
other/python/jyp_steps.1539878219.txt.gz · Last modified: 2018/10/18 17:56 by jypeter