demo + utils venv
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d3m1g0d
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import numpy as np
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import pandas.compat as compat
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import pandas as pd
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class TablePlotter(object):
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"""
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Layout some DataFrames in vertical/horizontal layout for explanation.
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Used in merging.rst
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"""
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def __init__(self, cell_width=0.37, cell_height=0.25, font_size=7.5):
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self.cell_width = cell_width
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self.cell_height = cell_height
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self.font_size = font_size
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def _shape(self, df):
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"""
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Calculate table chape considering index levels.
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"""
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row, col = df.shape
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return row + df.columns.nlevels, col + df.index.nlevels
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def _get_cells(self, left, right, vertical):
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"""
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Calculate appropriate figure size based on left and right data.
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"""
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if vertical:
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# calculate required number of cells
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vcells = max(sum(self._shape(l)[0] for l in left),
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self._shape(right)[0])
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hcells = (max(self._shape(l)[1] for l in left) +
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self._shape(right)[1])
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else:
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vcells = max([self._shape(l)[0] for l in left] +
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[self._shape(right)[0]])
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hcells = sum([self._shape(l)[1] for l in left] +
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[self._shape(right)[1]])
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return hcells, vcells
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def plot(self, left, right, labels=None, vertical=True):
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"""
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Plot left / right DataFrames in specified layout.
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Parameters
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----------
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left : list of DataFrames before operation is applied
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right : DataFrame of operation result
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labels : list of str to be drawn as titles of left DataFrames
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vertical : bool
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If True, use vertical layout. If False, use horizontal layout.
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"""
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import matplotlib.pyplot as plt
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import matplotlib.gridspec as gridspec
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if not isinstance(left, list):
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left = [left]
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left = [self._conv(l) for l in left]
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right = self._conv(right)
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hcells, vcells = self._get_cells(left, right, vertical)
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if vertical:
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figsize = self.cell_width * hcells, self.cell_height * vcells
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else:
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# include margin for titles
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figsize = self.cell_width * hcells, self.cell_height * vcells
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fig = plt.figure(figsize=figsize)
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if vertical:
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gs = gridspec.GridSpec(len(left), hcells)
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# left
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max_left_cols = max(self._shape(l)[1] for l in left)
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max_left_rows = max(self._shape(l)[0] for l in left)
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for i, (l, label) in enumerate(zip(left, labels)):
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ax = fig.add_subplot(gs[i, 0:max_left_cols])
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self._make_table(ax, l, title=label,
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height=1.0 / max_left_rows)
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# right
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ax = plt.subplot(gs[:, max_left_cols:])
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self._make_table(ax, right, title='Result', height=1.05 / vcells)
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fig.subplots_adjust(top=0.9, bottom=0.05, left=0.05, right=0.95)
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else:
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max_rows = max(self._shape(df)[0] for df in left + [right])
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height = 1.0 / np.max(max_rows)
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gs = gridspec.GridSpec(1, hcells)
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# left
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i = 0
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for l, label in zip(left, labels):
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sp = self._shape(l)
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ax = fig.add_subplot(gs[0, i:i + sp[1]])
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self._make_table(ax, l, title=label, height=height)
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i += sp[1]
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# right
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ax = plt.subplot(gs[0, i:])
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self._make_table(ax, right, title='Result', height=height)
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fig.subplots_adjust(top=0.85, bottom=0.05, left=0.05, right=0.95)
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return fig
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def _conv(self, data):
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"""Convert each input to appropriate for table outplot"""
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if isinstance(data, pd.Series):
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if data.name is None:
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data = data.to_frame(name='')
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else:
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data = data.to_frame()
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data = data.fillna('NaN')
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return data
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def _insert_index(self, data):
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# insert is destructive
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data = data.copy()
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idx_nlevels = data.index.nlevels
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if idx_nlevels == 1:
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data.insert(0, 'Index', data.index)
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else:
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for i in range(idx_nlevels):
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data.insert(i, 'Index{0}'.format(i),
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data.index._get_level_values(i))
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col_nlevels = data.columns.nlevels
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if col_nlevels > 1:
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col = data.columns._get_level_values(0)
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values = [data.columns._get_level_values(i).values
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for i in range(1, col_nlevels)]
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col_df = pd.DataFrame(values)
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data.columns = col_df.columns
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data = pd.concat([col_df, data])
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data.columns = col
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return data
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def _make_table(self, ax, df, title, height=None):
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if df is None:
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ax.set_visible(False)
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return
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import pandas.plotting as plotting
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idx_nlevels = df.index.nlevels
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col_nlevels = df.columns.nlevels
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# must be convert here to get index levels for colorization
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df = self._insert_index(df)
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tb = plotting.table(ax, df, loc=9)
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tb.set_fontsize(self.font_size)
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if height is None:
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height = 1.0 / (len(df) + 1)
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props = tb.properties()
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for (r, c), cell in compat.iteritems(props['celld']):
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if c == -1:
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cell.set_visible(False)
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elif r < col_nlevels and c < idx_nlevels:
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cell.set_visible(False)
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elif r < col_nlevels or c < idx_nlevels:
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cell.set_facecolor('#AAAAAA')
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cell.set_height(height)
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ax.set_title(title, size=self.font_size)
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ax.axis('off')
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class _WritableDoc(type):
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# Remove this when Python2 support is dropped
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# __doc__ is not mutable for new-style classes in Python2, which means
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# we can't use @Appender to share class docstrings. This can be used
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# with `add_metaclass` to make cls.__doc__ mutable.
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pass
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if __name__ == "__main__":
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import matplotlib.pyplot as plt
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p = TablePlotter()
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df1 = pd.DataFrame({'A': [10, 11, 12],
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'B': [20, 21, 22],
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'C': [30, 31, 32]})
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df2 = pd.DataFrame({'A': [10, 12],
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'C': [30, 32]})
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p.plot([df1, df2], pd.concat([df1, df2]),
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labels=['df1', 'df2'], vertical=True)
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plt.show()
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df3 = pd.DataFrame({'X': [10, 12],
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'Z': [30, 32]})
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p.plot([df1, df3], pd.concat([df1, df3], axis=1),
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labels=['df1', 'df2'], vertical=False)
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plt.show()
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idx = pd.MultiIndex.from_tuples([(1, 'A'), (1, 'B'), (1, 'C'),
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(2, 'A'), (2, 'B'), (2, 'C')])
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col = pd.MultiIndex.from_tuples([(1, 'A'), (1, 'B')])
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df3 = pd.DataFrame({'v1': [1, 2, 3, 4, 5, 6],
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'v2': [5, 6, 7, 8, 9, 10]},
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index=idx)
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df3.columns = col
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p.plot(df3, df3, labels=['df3'])
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plt.show()
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