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    copied!<p>The matplotlib documentation says </p> <blockquote> <p>Add a table to the current axes. Returns a matplotlib.table.Table instance. For finer grained control over tables, use the Table class and add it to the axes with add_table().</p> </blockquote> <p>You could do is the following, look at the properties of your table (it's and object belonging to that class Table):</p> <pre><code>print the_table.properties() # hint it's a dictionary do: type(the_table.properties() &lt;type 'dict'&gt; </code></pre> <p>edit that dictionary the way you see right, and the update your table, with:</p> <pre><code>the_table.update(giveHereYourDictionary) </code></pre> <p>Hint: if you work with IPython or interactive shell it's enough to do help(objectName), e.g. help(the_table) to see all the object's methods. This should, hopefully, work.</p> <p>OK, I'm adding here a walk through of how to to that kind of stuff. I admit, it's not trivial, but I am using matplotlib for 3.5 years now, so ...</p> <p>Do your code in IPython (I said it before, but I must emphasize again), it really helps to examine all the properties that objects have (type object name and then the key):</p> <pre><code>In [95]: prop=the_table.properties() In [96]: prop #This is a dictionary, it's not so trivial, but never the less one can understand how dictionaries work... Out[96]: {'agg_filter': None, 'alpha': None, 'animated': False, 'axes': &lt;matplotlib.axes.AxesSubplot at 0x9eba34c&gt;, 'celld': {(0, -1): &lt;matplotlib.table.Cell at 0xa0cf5ec&gt;, (0, 0): &lt;matplotlib.table.Cell at 0xa0c2d0c&gt;, (0, 1): &lt;matplotlib.table.Cell at 0xa0c2dec&gt;, (0, 2): &lt;matplotlib.table.Cell at 0xa0c2ecc&gt;, (1, -1): &lt;matplotlib.table.Cell at 0xa0cf72c&gt;, (1, 0): &lt;matplotlib.table.Cell at 0xa0c2fac&gt;, (1, 1): &lt;matplotlib.table.Cell at 0xa0cf08c&gt;, (1, 2): &lt;matplotlib.table.Cell at 0xa0cf18c&gt;, (2, -1): &lt;matplotlib.table.Cell at 0xa0cf84c&gt;, (2, 0): &lt;matplotlib.table.Cell at 0xa0cf28c&gt;, (2, 1): &lt;matplotlib.table.Cell at 0xa0cf3ac&gt;, (2, 2): &lt;matplotlib.table.Cell at 0xa0cf4cc&gt;}, 'child_artists': [&lt;matplotlib.table.Cell at 0xa0c2dec&gt;, &lt;matplotlib.table.Cell at 0xa0cf18c&gt;, &lt;matplotlib.table.Cell at 0xa0c2d0c&gt;, &lt;matplotlib.table.Cell at 0xa0cf84c&gt;, &lt;matplotlib.table.Cell at 0xa0cf3ac&gt;, &lt;matplotlib.table.Cell at 0xa0cf08c&gt;, &lt;matplotlib.table.Cell at 0xa0cf28c&gt;, &lt;matplotlib.table.Cell at 0xa0cf4cc&gt;, &lt;matplotlib.table.Cell at 0xa0cf5ec&gt;, &lt;matplotlib.table.Cell at 0xa0c2fac&gt;, &lt;matplotlib.table.Cell at 0xa0cf72c&gt;, &lt;matplotlib.table.Cell at 0xa0c2ecc&gt;], 'children': [&lt;matplotlib.table.Cell at 0xa0c2dec&gt;, &lt;matplotlib.table.Cell at 0xa0cf18c&gt;, ...snip snap ... &lt;matplotlib.table.Cell at 0xa0cf72c&gt;, &lt;matplotlib.table.Cell at 0xa0c2ecc&gt;], 'clip_box': TransformedBbox(Bbox(array([[ 0., 0.], [ 1., 1.]])), CompositeAffine2D(BboxTransformTo(Bbox(array([[ 0., 0.], [ 1., 1.]]))), BboxTransformTo(TransformedBbox(Bbox(array([[ 0.25, 0.3 ], [ 0.95, 0.8 ]])), BboxTransformTo(TransformedBbox(Bbox(array([[ 0., 0.], [ 8., 6.]])), Affine2D(array([[ 80., 0., 0.], [ 0., 80., 0.], [ 0., 0., 1.]])))))))), 'clip_on': True, 'clip_path': None, 'contains': None, 'figure': &lt;matplotlib.figure.Figure at 0x9eaf56c&gt;, 'gid': None, 'label': '', 'picker': None, 'rasterized': None, 'snap': None, 'transform': BboxTransformTo(TransformedBbox(Bbox(array([[ 0.25, 0.3 ], [ 0.95, 0.8 ]])), BboxTransformTo(TransformedBbox(Bbox(array([[ 0., 0.], [ 8., 6.]])), Affine2D(array([[ 80., 0., 0.], [ 0., 80., 0.], [ 0., 0., 1.]])))))), 'transformed_clip_path_and_affine': (None, None), 'url': None, 'visible': True, 'zorder': 0} # we now get all the cells ... [97]: cells = prop['child_artists'] In [98]: cells Out[98]: [&lt;matplotlib.table.Cell at 0xa0c2dec&gt;, &lt;matplotlib.table.Cell at 0xa0cf18c&gt;, ... snip snap... &lt;matplotlib.table.Cell at 0xa0cf72c&gt;, &lt;matplotlib.table.Cell at 0xa0c2ecc&gt;] In [99]:cell=cells[0] In [100]: cell # press tab here to see cell's attributes Display all 122 possibilities? (y or n) cell.PAD cell.add_callback ...snip snap ... cell.draw cell.eventson cell.figure ...snip snap ... In [100]: cell.set_h cell.set_hatch cell.set_height # this looks promising no? Hell, I love python ;-) wait, let's examine something first ... In [100]: cell.get_height() Out[100]: 0.055555555555555552 In [101]: cell.set_height(0.1) # we just 'doubled' the height... In [103]: pyplot.show() </code></pre> <p>and TA DA:</p> <p><img src="https://i.stack.imgur.com/W2xaG.png" alt="Table with modified height for one cell"></p> <p>Now, I challege you to change the height of all the cells, using a for loop. Should not be so hard. Would be nice to win that bounty ;-)</p>
 

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