consistent formatting

tdhopper · tdhopper · commit e8a20bbb9c60 · 2017-06-22T06:42:48.000-04:00
diff --git a/INTRO.md b/INTRO.md
@@ -16,9 +16,9 @@ Many excellent plotting tools are built on top of Matplotlib.
 
 [Pandas plots](https://pandas.pydata.org/pandas-docs/stable/visualization.html "pandas documentation") provides the "basics to easily create decent looking plots" from data frames. It provides about 70% of what I want to do day-to-day. Importantly, it lacks robust faceting capabilities.
 
-[plotnine](https://plotnine.readthedocs.io/en/stable/ "plotnine: A grammar of graphics for Python"): "plotnine is an implementation of a grammar of graphics in Python, it is based on ggplot2." __plotnine__ is a recent attempt to directly translate ggplot2 to Python; despite some quirks and bugs, it works very well for a young product.[^ggpy]
+"[plotnine](https://plotnine.readthedocs.io/en/stable/") is an implementation of a grammar of graphics in Python, it is based on ggplot2." plotnine is a recent attempt to directly translate ggplot2 to Python; despite some quirks and bugs, it works very well for a young product.[^ggpy]
 
-"[Seaborn](https://seaborn.pydata.org/ "Seaborn: statistical data visualization") is a Python visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics." __Seaborn__ makes beautiful plots but is geared toward specific statistical plots, not general purpose plotting. It does have a powerful [faceting utility function](http://seaborn.pydata.org/tutorial/axis_grids.html) that I use regularly.
+"[Seaborn](https://seaborn.pydata.org/ "Seaborn: statistical data visualization") is a Python visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics." Seaborn makes beautiful plots but is geared toward specific statistical plots, not general purpose plotting. It does have a powerful [faceting utility function](http://seaborn.pydata.org/tutorial/axis_grids.html) that I use regularly.
 
 [^ggpy]: There is also [ggpy](https://github.com/yhat/ggpy "ggplot port for python"), a ggplot2 port, from the company formerly known as yhat. It appears to be abandoned.
 
@@ -34,11 +34,11 @@ Altair is new on the scene and offers a lot of promise; it will have significant
 
 There are several tools that can make the kinds of plots described here. At present, I have little experience with them. If anyone would like to help add examples, please [get in touch](https://github.com/tdhopper/ggplot_vs_python_vis).
 
-"[__Bokeh__](http://bokeh.pydata.org/en/latest/ "Python interactive visualization library") is a Python interactive visualization library that targets modern web browsers for presentation."
+"[Bokeh](http://bokeh.pydata.org/en/latest/ "Python interactive visualization library") is a Python interactive visualization library that targets modern web browsers for presentation."
 
-"[__bqplot__](https://github.com/bloomberg/bqplot) is a Grammar of Graphics-based interactive plotting framework for the Jupyter notebook."
+"[bqplot](https://github.com/bloomberg/bqplot) is a Grammar of Graphics-based interactive plotting framework for the Jupyter notebook."
 
-"[__plotly__](https://plot.ly/ "Plotly - Make charts and dashboards online")'s Python graphing library makes interactive, publication-quality graphs online. Examples of how to make line plots, scatter plots, area charts, bar charts, error bars, box plots, histograms, heatmaps, subplots, multiple-axes, polar charts, and bubble charts."
+"[plotly](https://plot.ly/ "Plotly - Make charts and dashboards online")'s Python graphing library makes interactive, publication-quality graphs online. Examples of how to make line plots, scatter plots, area charts, bar charts, error bars, box plots, histograms, heatmaps, subplots, multiple-axes, polar charts, and bubble charts."
 
 ### The Python Plotting Landscape
 
