Python pandas.plotting.table() Examples

The following are 21 code examples of pandas.plotting.table(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module pandas.plotting , or try the search function .
Example #1
Source File: _doctools.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up 
def _conv(self, data):
        """Convert each input to appropriate for table outplot"""
        if isinstance(data, pd.Series):
            if data.name is None:
                data = data.to_frame(name='')
            else:
                data = data.to_frame()
        data = data.fillna('NaN')
        return data
Example #2
Source File: test_frame.py    From twitter-stock-recommendation with MIT License 5 votes vote down vote up 
def test_table(self):
        df = DataFrame(np.random.rand(10, 3),
                       index=list(string.ascii_letters[:10]))
        _check_plot_works(df.plot, table=True)
        _check_plot_works(df.plot, table=df)

        ax = df.plot()
        assert len(ax.tables) == 0
        plotting.table(ax, df.T)
        assert len(ax.tables) == 1
Example #3
Source File: test_frame.py    From coffeegrindsize with MIT License 5 votes vote down vote up 
def test_table(self):
        df = DataFrame(np.random.rand(10, 3),
                       index=list(string.ascii_letters[:10]))
        _check_plot_works(df.plot, table=True)
        _check_plot_works(df.plot, table=df)

        ax = df.plot()
        assert len(ax.tables) == 0
        plotting.table(ax, df.T)
        assert len(ax.tables) == 1
Example #4
Source File: _doctools.py    From elasticintel with GNU General Public License v3.0 5 votes vote down vote up 
def _make_table(self, ax, df, title, height=None):
        if df is None:
            ax.set_visible(False)
            return

        import pandas.plotting as plotting

        idx_nlevels = df.index.nlevels
        col_nlevels = df.columns.nlevels
        # must be convert here to get index levels for colorization
        df = self._insert_index(df)
        tb = plotting.table(ax, df, loc=9)
        tb.set_fontsize(self.font_size)

        if height is None:
            height = 1.0 / (len(df) + 1)

        props = tb.properties()
        for (r, c), cell in compat.iteritems(props['celld']):
            if c == -1:
                cell.set_visible(False)
            elif r < col_nlevels and c < idx_nlevels:
                cell.set_visible(False)
            elif r < col_nlevels or c < idx_nlevels:
                cell.set_facecolor('#AAAAAA')
            cell.set_height(height)

        ax.set_title(title, size=self.font_size)
        ax.axis('off')
Example #5
Source File: _doctools.py    From elasticintel with GNU General Public License v3.0 5 votes vote down vote up 
def _conv(self, data):
        """Convert each input to appropriate for table outplot"""
        if isinstance(data, pd.Series):
            if data.name is None:
                data = data.to_frame(name='')
            else:
                data = data.to_frame()
        data = data.fillna('NaN')
        return data
Example #6
Source File: _doctools.py    From elasticintel with GNU General Public License v3.0 5 votes vote down vote up 
def _shape(self, df):
        """
        Calculate table chape considering index levels.
        """

        row, col = df.shape
        return row + df.columns.nlevels, col + df.index.nlevels
Example #7
Source File: test_frame.py    From elasticintel with GNU General Public License v3.0 5 votes vote down vote up 
def test_table(self):
        df = DataFrame(np.random.rand(10, 3),
                       index=list(string.ascii_letters[:10]))
        _check_plot_works(df.plot, table=True)
        _check_plot_works(df.plot, table=df)

        ax = df.plot()
        assert len(ax.tables) == 0
        plotting.table(ax, df.T)
        assert len(ax.tables) == 1
Example #8
Source File: _doctools.py    From Splunking-Crime with GNU Affero General Public License v3.0 5 votes vote down vote up 
def _make_table(self, ax, df, title, height=None):
        if df is None:
            ax.set_visible(False)
            return

        import pandas.plotting as plotting

        idx_nlevels = df.index.nlevels
        col_nlevels = df.columns.nlevels
        # must be convert here to get index levels for colorization
        df = self._insert_index(df)
        tb = plotting.table(ax, df, loc=9)
        tb.set_fontsize(self.font_size)

        if height is None:
            height = 1.0 / (len(df) + 1)

        props = tb.properties()
        for (r, c), cell in compat.iteritems(props['celld']):
            if c == -1:
                cell.set_visible(False)
            elif r < col_nlevels and c < idx_nlevels:
                cell.set_visible(False)
            elif r < col_nlevels or c < idx_nlevels:
                cell.set_facecolor('#AAAAAA')
            cell.set_height(height)

        ax.set_title(title, size=self.font_size)
        ax.axis('off')
Example #9
Source File: _doctools.py    From Splunking-Crime with GNU Affero General Public License v3.0 5 votes vote down vote up 
def _conv(self, data):
        """Convert each input to appropriate for table outplot"""
        if isinstance(data, pd.Series):
            if data.name is None:
                data = data.to_frame(name='')
            else:
                data = data.to_frame()
        data = data.fillna('NaN')
        return data
Example #10
Source File: _doctools.py    From Splunking-Crime with GNU Affero General Public License v3.0 5 votes vote down vote up 
def _shape(self, df):
        """
        Calculate table chape considering index levels.
        """

        row, col = df.shape
        return row + df.columns.nlevels, col + df.index.nlevels
Example #11
Source File: _doctools.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up 
def _make_table(self, ax, df, title, height=None):
        if df is None:
            ax.set_visible(False)
            return

        import pandas.plotting as plotting

        idx_nlevels = df.index.nlevels
        col_nlevels = df.columns.nlevels
        # must be convert here to get index levels for colorization
        df = self._insert_index(df)
        tb = plotting.table(ax, df, loc=9)
        tb.set_fontsize(self.font_size)

        if height is None:
            height = 1.0 / (len(df) + 1)

        props = tb.properties()
        for (r, c), cell in compat.iteritems(props['celld']):
            if c == -1:
                cell.set_visible(False)
            elif r < col_nlevels and c < idx_nlevels:
                cell.set_visible(False)
            elif r < col_nlevels or c < idx_nlevels:
                cell.set_facecolor('#AAAAAA')
            cell.set_height(height)

        ax.set_title(title, size=self.font_size)
        ax.axis('off')
Example #12
Source File: test_frame.py    From recruit with Apache License 2.0 5 votes vote down vote up 
def test_table(self):
        df = DataFrame(np.random.rand(10, 3),
                       index=list(string.ascii_letters[:10]))
        _check_plot_works(df.plot, table=True)
        _check_plot_works(df.plot, table=df)

        ax = df.plot()
        assert len(ax.tables) == 0
        plotting.table(ax, df.T)
        assert len(ax.tables) == 1
Example #13
Source File: _doctools.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up 
def _shape(self, df):
        """
        Calculate table chape considering index levels.
        """

        row, col = df.shape
        return row + df.columns.nlevels, col + df.index.nlevels
Example #14
Source File: test_frame.py    From predictive-maintenance-using-machine-learning with Apache License 2.0 5 votes vote down vote up 
def test_table(self):
        df = DataFrame(np.random.rand(10, 3),
                       index=list(string.ascii_letters[:10]))
        _check_plot_works(df.plot, table=True)
        _check_plot_works(df.plot, table=df)

        ax = df.plot()
        assert len(ax.tables) == 0
        plotting.table(ax, df.T)
        assert len(ax.tables) == 1
Example #15
Source File: _doctools.py    From vnpy_crypto with MIT License 5 votes vote down vote up 
def _make_table(self, ax, df, title, height=None):
        if df is None:
            ax.set_visible(False)
            return

        import pandas.plotting as plotting

        idx_nlevels = df.index.nlevels
        col_nlevels = df.columns.nlevels
        # must be convert here to get index levels for colorization
        df = self._insert_index(df)
        tb = plotting.table(ax, df, loc=9)
        tb.set_fontsize(self.font_size)

        if height is None:
            height = 1.0 / (len(df) + 1)

        props = tb.properties()
        for (r, c), cell in compat.iteritems(props['celld']):
            if c == -1:
                cell.set_visible(False)
            elif r < col_nlevels and c < idx_nlevels:
                cell.set_visible(False)
            elif r < col_nlevels or c < idx_nlevels:
                cell.set_facecolor('#AAAAAA')
            cell.set_height(height)

        ax.set_title(title, size=self.font_size)
        ax.axis('off')
Example #16
Source File: _doctools.py    From vnpy_crypto with MIT License 5 votes vote down vote up 
def _conv(self, data):
        """Convert each input to appropriate for table outplot"""
        if isinstance(data, pd.Series):
            if data.name is None:
                data = data.to_frame(name='')
            else:
                data = data.to_frame()
        data = data.fillna('NaN')
        return data
Example #17
Source File: _doctools.py    From vnpy_crypto with MIT License 5 votes vote down vote up 
def _shape(self, df):
        """
        Calculate table chape considering index levels.
        """

        row, col = df.shape
        return row + df.columns.nlevels, col + df.index.nlevels
Example #18
Source File: test_frame.py    From vnpy_crypto with MIT License 5 votes vote down vote up 
def test_table(self):
        df = DataFrame(np.random.rand(10, 3),
                       index=list(string.ascii_letters[:10]))
        _check_plot_works(df.plot, table=True)
        _check_plot_works(df.plot, table=df)

        ax = df.plot()
        assert len(ax.tables) == 0
        plotting.table(ax, df.T)
        assert len(ax.tables) == 1
Example #19
Source File: _doctools.py    From recruit with Apache License 2.0 5 votes vote down vote up 
def _make_table(self, ax, df, title, height=None):
        if df is None:
            ax.set_visible(False)
            return

        import pandas.plotting as plotting

        idx_nlevels = df.index.nlevels
        col_nlevels = df.columns.nlevels
        # must be convert here to get index levels for colorization
        df = self._insert_index(df)
        tb = plotting.table(ax, df, loc=9)
        tb.set_fontsize(self.font_size)

        if height is None:
            height = 1.0 / (len(df) + 1)

        props = tb.properties()
        for (r, c), cell in compat.iteritems(props['celld']):
            if c == -1:
                cell.set_visible(False)
            elif r < col_nlevels and c < idx_nlevels:
                cell.set_visible(False)
            elif r < col_nlevels or c < idx_nlevels:
                cell.set_facecolor('#AAAAAA')
            cell.set_height(height)

        ax.set_title(title, size=self.font_size)
        ax.axis('off')
Example #20
Source File: _doctools.py    From recruit with Apache License 2.0 5 votes vote down vote up 
def _conv(self, data):
        """Convert each input to appropriate for table outplot"""
        if isinstance(data, pd.Series):
            if data.name is None:
                data = data.to_frame(name='')
            else:
                data = data.to_frame()
        data = data.fillna('NaN')
        return data
Example #21
Source File: _doctools.py    From recruit with Apache License 2.0 5 votes vote down vote up 
def _shape(self, df):
        """
        Calculate table chape considering index levels.
        """

        row, col = df.shape
        return row + df.columns.nlevels, col + df.index.nlevels