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general:datavis [2022/11/21 10:14] – [Programming free Data Visualization] 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.
  
 +----
 +
 +<WRAP tabs>
 +   * [[ecoevo_molevol:course_introduction|Back to EcoEvo course]]
 +   * [[pbioc_basics:start|Back to PBioC course]]
 +   * [[digikomp_bio:course_introduction|Back to DigiKomp course]]
 +</WRAP>