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    <h3>Edit 1 year later:</h3> <p>With <code>seaborn</code>, the example below becomes:</p> <pre><code>import numpy as np import matplotlib.pyplot as plt import seaborn seaborn.set(style='ticks') # Data to be represented X = np.random.randn(256) # Actual plotting fig = plt.figure(figsize=(8,6), dpi=72, facecolor="white") axes = plt.subplot(111) heights, positions, patches = axes.hist(X, color='white') seaborn.despine(ax=axes, offset=10, trim=True) fig.tight_layout() plt.show() </code></pre> <p>Pretty dang easy.</p> <h3>Original post:</h3> <p>This blog post is the best I've seen so far. <a href="http://messymind.net/making-matplotlib-look-like-ggplot/" rel="noreferrer">http://messymind.net/making-matplotlib-look-like-ggplot/</a></p> <p>It doesn't focus on your standard R plots like you see in most of the "getting started"-type examples. Instead it tries to emulate the style of ggplot2, which seems to be nearly universally heralded as stylish and well-designed. </p> <p>To get the axis spines like you see the in bar plot, try to follow one of the first few examples here: <a href="http://www.loria.fr/~rougier/coding/gallery/" rel="noreferrer">http://www.loria.fr/~rougier/coding/gallery/</a></p> <p>Lastly, to get the axis tick marks pointing outward, you can edit your <code>matplotlibrc</code> files to say <code>xtick.direction : out</code> and <code>ytick.direction : out</code>.</p> <p>Combining these concepts together we get something like this:</p> <pre><code>import numpy as np import matplotlib import matplotlib.pyplot as plt # Data to be represented X = np.random.randn(256) # Actual plotting fig = plt.figure(figsize=(8,6), dpi=72, facecolor="white") axes = plt.subplot(111) heights, positions, patches = axes.hist(X, color='white') axes.spines['right'].set_color('none') axes.spines['top'].set_color('none') axes.xaxis.set_ticks_position('bottom') # was: axes.spines['bottom'].set_position(('data',1.1*X.min())) axes.spines['bottom'].set_position(('axes', -0.05)) axes.yaxis.set_ticks_position('left') axes.spines['left'].set_position(('axes', -0.05)) axes.set_xlim([np.floor(positions.min()), np.ceil(positions.max())]) axes.set_ylim([0,70]) axes.xaxis.grid(False) axes.yaxis.grid(False) fig.tight_layout() plt.show() </code></pre> <p>The position of the spines can be specified a number of ways. If you run the code above in IPython, you can then do <code>axes.spines['bottom'].set_position?</code> to see all of your options.</p> <p><img src="https://i.stack.imgur.com/f2zrz.png" alt="R-style bar plot in python"></p> <p>So yeah. It's not exactly trivial, but you can get close.</p>
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    1. POmaking matplotlib graphs look like R by default?
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    1. COThat does the grid but pretty much doesn't do any of the axes things outlines above... its ticks also look funny.
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    2. COThe 3 folks I know who use R, make figures using ggplot2, so it seems relevant to me. To get the spines down, check out the first few examples here: http://www.loria.fr/~rougier/coding/gallery/. They should at least set you down the right path.
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    3. COggplot2 is nice but I am referring mainly to the R base. While ggplot2 looks stylish, you can argue that this background colored grids lead to a large waste of ink when printed, etc. Also, many people who use R regularly use only the R base without ggplot2, so my goal is to make matplotlib look like R base which more universal than ggplot2.
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