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copied!<p>Try passing columns of the <code>DataFrame</code> directly to matplotlib, as in the examples below, instead of extracting them as numpy arrays.</p> <pre><code>df = pd.DataFrame(np.random.randn(10,2), columns=['col1','col2']) df['col3'] = np.arange(len(df))**2 * 100 + 100 In [5]: df Out[5]: col1 col2 col3 0 -1.000075 -0.759910 100 1 0.510382 0.972615 200 2 1.872067 -0.731010 500 3 0.131612 1.075142 1000 4 1.497820 0.237024 1700 </code></pre> <h3>Vary scatter point size based on another column</h3> <pre><code>plt.scatter(df.col1, df.col2, s=df.col3) # OR (with pandas 0.13 and up) df.plot(kind='scatter', x='col1', y='col2', s=df.col3) </code></pre> <p><img src="https://i.stack.imgur.com/FA5KP.png" alt="enter image description here"></p> <h3>Vary scatter point color based on another column</h3> <pre><code>colors = np.where(df.col3 > 300, 'r', 'k') plt.scatter(df.col1, df.col2, s=120, c=colors) # OR (with pandas 0.13 and up) df.plot(kind='scatter', x='col1', y='col2', s=120, c=colors) </code></pre> <p><img src="https://i.stack.imgur.com/rghSv.png" alt="enter image description here"></p> <h3>Scatter plot with legend</h3> <p>However, the easiest way I've found to create a scatter plot with legend is to call <code>plt.scatter</code> once for each point type.</p> <pre><code>cond = df.col3 > 300 subset_a = df[cond].dropna() subset_b = df[~cond].dropna() plt.scatter(subset_a.col1, subset_a.col2, s=120, c='b', label='col3 > 300') plt.scatter(subset_b.col1, subset_b.col2, s=60, c='r', label='col3 <= 300') plt.legend() </code></pre> <p><img src="https://i.stack.imgur.com/tlibK.png" alt="enter image description here"></p> <h3><em>Update</em></h3> <p>From what I can tell, matplotlib simply skips points with NA x/y coordinates or NA style settings (e.g., color/size). To find points skipped due to NA, try the <code>isnull</code> method: <code>df[df.col3.isnull()]</code></p> <p>To split a list of points into many types, take a look at <a href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.select.html" rel="noreferrer">numpy <code>select</code></a>, which is a vectorized if-then-else implementation and accepts an optional default value. For example:</p> <pre><code>df['subset'] = np.select([df.col3 < 150, df.col3 < 400, df.col3 < 600], [0, 1, 2], -1) for color, label in zip('bgrm', [0, 1, 2, -1]): subset = df[df.subset == label] plt.scatter(subset.col1, subset.col2, s=120, c=color, label=str(label)) plt.legend() </code></pre> <p><img src="https://i.stack.imgur.com/CtA9s.png" alt="enter image description here"></p>

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