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***Use`'print(<S>.to_string())'`to print a Series that has more than 60 items.**
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***Indexing objects can't be tuples because `'obj[x, y]'` is converted to `'obj[(x, y)]'`.**
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***Pandas uses NumPy types like `'np.int64'`. Series is converted to `'float64'` if we assign np.nan to any item. Use `'<S>.astype(<str/type>)'` to get converted Series.**
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***Series will silently overflow if we run `'pd.Series([100], dtype="int8") + 100'`!**
<li><strong>Use <codeclass="python hljs"><spanclass="hljs-string">'fill=<color>'</span></code> to set the primary color.</strong></li>
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<li><strong>Use <codeclass="python hljs"><spanclass="hljs-string">'width=<int>'</span></code> to set the width of lines or contours.</strong></li>
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<li><strong>Use <codeclass="python hljs"><spanclass="hljs-string">'outline=<color>'</span></code> to set the color of the contours.</strong></li>
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<li><strong>Color can be an int, tuple, <codeclass="python hljs"><spanclass="hljs-string">'#rrggbb[aa]'</span></code>string or a color name.</strong></li>
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<li><strong>Color can be an int, tuple, <codeclass="python hljs"><spanclass="hljs-string">'#rrggbb[aa]'</span></code> or a color name.</strong></li>
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</ul>
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<div><h2id="animation"><ahref="#animation" name="animation">#</a>Animation</h2><div><h4id="createsagifofabouncingball">Creates a GIF of a bouncing ball:</h4><pre><codeclass="python language-python hljs"><spanclass="hljs-comment"># $ pip3 install imageio</span>
<li><strong>Use<codeclass="python hljs"><spanclass="hljs-string">'print(<S>.to_string())'</span></code>to print a Series that has more than 60 items.</strong></li>
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<li><strong>Indexing objects can't be tuples because <codeclass="python hljs"><spanclass="hljs-string">'obj[x, y]'</span></code> is converted to <codeclass="python hljs"><spanclass="hljs-string">'obj[(x, y)]'</span></code>.</strong></li>
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<li><strong>Pandas uses NumPy types like <codeclass="python hljs"><spanclass="hljs-string">'np.int64'</span></code>. Series is converted to <codeclass="python hljs"><spanclass="hljs-string">'float64'</span></code> if we assign np.nan to any item. Use <codeclass="python hljs"><spanclass="hljs-string">'<S>.astype(<str/type>)'</span></code> to get converted Series.</strong></li>
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<li><strong>Series will silently overflow if we run <codeclass="python hljs"><spanclass="hljs-string">'pd.Series([100], dtype="int8") + 100'</span></code>!</strong></li>
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