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general:datavis [2022/11/21 10:14] – [Creating custom color palettes] felixgeneral:datavis [2022/11/21 10:14] (current) – [Data Visualization] felix
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 <figure plottype> {{  :general:images:diverse:choosing_a_good_chart-1.png  }}<caption> **Choosing the right plot for your data.**  </caption> </figure> <figure plottype> {{  :general:images:diverse:choosing_a_good_chart-1.png  }}<caption> **Choosing the right plot for your data.**  </caption> </figure>
  
-<WRAP info> If you want to get a short primer on data visualization, check out these slides:+If you want to get a short primer on data visualization, check out these slides:
  
   * {{:general:documentation:00_data_vis_concepts.pdf|Data visualization slides by R. Burke Squires (NIH)}}   * {{:general:documentation:00_data_vis_concepts.pdf|Data visualization slides by R. Burke Squires (NIH)}}
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 visualisation]] visualisation]]
  
-</WRAP> 
  
 ===== Programming free Data Visualization ===== ===== Programming free Data Visualization =====
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 Packages like seaborn come with a [[https://seaborn.pydata.org/tutorial/color_palettes.html|variety of color palettes]] for all kinds of data (continues, categorical, divergent etc.). Sometimes you might want to create your own color scheme. In this case you can use the webtool [[https://coolors.co/|Coloors]] to quickly generate your own set of colorations. Packages like seaborn come with a [[https://seaborn.pydata.org/tutorial/color_palettes.html|variety of color palettes]] for all kinds of data (continues, categorical, divergent etc.). Sometimes you might want to create your own color scheme. In this case you can use the webtool [[https://coolors.co/|Coloors]] to quickly generate your own set of colorations.
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 +----
  
 <WRAP tabs> <WRAP tabs>