readme

Author: Christopher Doris

README.md

@@ -1,5 +1,5 @@
-![PythonCall.jl logo](https://raw.githubusercontent.com/cjdoris/PythonCall.jl/master/logo-text.svg)
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+<h1><img src="https://raw.githubusercontent.com/cjdoris/PythonCall.jl/master/docs/src/assets/logo.svg" alt="PythonCall.jl logo" style="max-width: 100px;"><br>PythonCall &amp;&nbsp;JuliaCall</h1>
+
 [![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)
 [![Stable Documentation](https://img.shields.io/badge/docs-stable-blue.svg)](https://cjdoris.github.io/PythonCall.jl/stable)
 [![Dev Documentation](https://img.shields.io/badge/docs-dev-blue.svg)](https://cjdoris.github.io/PythonCall.jl/dev)
@@ -21,7 +21,7 @@ To get started, read the [documentation](https://cjdoris.github.io/PythonCall.jl
 
 ## Example 1: Calling Python from Julia
 
-In this example, we use PythonCall from a [Pluto](https://github.com/fonsp/Pluto.jl) notebook to inspect the Iris dataset:
+In this example, we use the Julia module PythonCall from a [Pluto](https://github.com/fonsp/Pluto.jl) notebook to inspect the Iris dataset:
 - We load the Iris dataset as a Julia [DataFrame](https://dataframes.juliadata.org/stable/) using [RDatasets](https://github.com/JuliaStats/RDatasets.jl).
 - We use `pytable(df)` to convert it to a Python [Pandas DataFrame](https://pandas.pydata.org/).
 - We use the Python package [Seaborn](https://seaborn.pydata.org/) to produce a pair-plot, which is automatically displayed.
@@ -39,7 +39,7 @@ In this example we use the Python module JuliaCall from an IPython notebook to t
 
 ## What about PyCall?
 
-The existing package [`PyCall`](https://github.com/JuliaPy/PyCall.jl) is another similar interface to Python. Here we note some key differences, but a more detailed comparison is in the documentation.
+The existing package [PyCall](https://github.com/JuliaPy/PyCall.jl) is another similar interface to Python. Here we note some key differences, but a more detailed comparison is in the documentation.
 - PythonCall supports a wider range of conversions between Julia and Python, and the conversion mechanism is extensible.
 - PythonCall by default never copies mutable objects when converting, but instead directly wraps the mutable object. This means that modifying the converted object modifies the original, and conversion is faster.
 - PythonCall does not usually automatically convert results to Julia values, but leaves them as Python objects. This makes it easier to do Pythonic things with these objects (e.g. accessing methods) and is type-stable.