Python pandas.core.series.Series.hist() Examples
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Example #1
Source File: _core.py From coffeegrindsize with MIT License | 6 votes |
def hist(self, bins=10, **kwds): """ Histogram. Parameters ---------- bins : integer, default 10 Number of histogram bins to be used `**kwds` : optional Additional keyword arguments are documented in :meth:`pandas.Series.plot`. Returns ------- axes : :class:`matplotlib.axes.Axes` or numpy.ndarray of them """ return self(kind='hist', bins=bins, **kwds)
Example #2
Source File: _core.py From recruit with Apache License 2.0 | 6 votes |
def hist(self, bins=10, **kwds): """ Histogram. Parameters ---------- bins : integer, default 10 Number of histogram bins to be used `**kwds` : optional Additional keyword arguments are documented in :meth:`pandas.Series.plot`. Returns ------- axes : :class:`matplotlib.axes.Axes` or numpy.ndarray of them """ return self(kind='hist', bins=bins, **kwds)
Example #3
Source File: _core.py From twitter-stock-recommendation with MIT License | 6 votes |
def hist(self, bins=10, **kwds): """ Histogram Parameters ---------- bins: integer, default 10 Number of histogram bins to be used `**kwds` : optional Additional keyword arguments are documented in :meth:`pandas.Series.plot`. Returns ------- axes : :class:`matplotlib.axes.Axes` or numpy.ndarray of them """ return self(kind='hist', bins=bins, **kwds)
Example #4
Source File: _core.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
def hist(self, bins=10, **kwds): """ Histogram. Parameters ---------- bins : integer, default 10 Number of histogram bins to be used `**kwds` : optional Additional keyword arguments are documented in :meth:`pandas.Series.plot`. Returns ------- axes : :class:`matplotlib.axes.Axes` or numpy.ndarray of them """ return self(kind='hist', bins=bins, **kwds)
Example #5
Source File: _core.py From vnpy_crypto with MIT License | 6 votes |
def hist(self, bins=10, **kwds): """ Histogram Parameters ---------- bins: integer, default 10 Number of histogram bins to be used `**kwds` : optional Additional keyword arguments are documented in :meth:`pandas.Series.plot`. Returns ------- axes : :class:`matplotlib.axes.Axes` or numpy.ndarray of them """ return self(kind='hist', bins=bins, **kwds)
Example #6
Source File: _core.py From twitter-stock-recommendation with MIT License | 5 votes |
def _plot(cls, ax, y, style=None, bins=None, bottom=0, column_num=0, stacking_id=None, **kwds): if column_num == 0: cls._initialize_stacker(ax, stacking_id, len(bins) - 1) y = y[~isna(y)] base = np.zeros(len(bins) - 1) bottom = bottom + \ cls._get_stacked_values(ax, stacking_id, base, kwds['label']) # ignore style n, bins, patches = ax.hist(y, bins=bins, bottom=bottom, **kwds) cls._update_stacker(ax, stacking_id, n) return patches
Example #7
Source File: _core.py From coffeegrindsize with MIT License | 5 votes |
def _plot(cls, ax, y, style=None, bins=None, bottom=0, column_num=0, stacking_id=None, **kwds): if column_num == 0: cls._initialize_stacker(ax, stacking_id, len(bins) - 1) y = y[~isna(y)] base = np.zeros(len(bins) - 1) bottom = bottom + \ cls._get_stacked_values(ax, stacking_id, base, kwds['label']) # ignore style n, bins, patches = ax.hist(y, bins=bins, bottom=bottom, **kwds) cls._update_stacker(ax, stacking_id, n) return patches
Example #8
Source File: _core.py From coffeegrindsize with MIT License | 5 votes |
def _args_adjust(self): if is_integer(self.bins): # create common bin edge values = (self.data._convert(datetime=True)._get_numeric_data()) values = np.ravel(values) values = values[~isna(values)] hist, self.bins = np.histogram( values, bins=self.bins, range=self.kwds.get('range', None), weights=self.kwds.get('weights', None)) if is_list_like(self.bottom): self.bottom = np.array(self.bottom)
Example #9
Source File: _core.py From twitter-stock-recommendation with MIT License | 5 votes |
def _args_adjust(self): if is_integer(self.bins): # create common bin edge values = (self.data._convert(datetime=True)._get_numeric_data()) values = np.ravel(values) values = values[~isna(values)] hist, self.bins = np.histogram( values, bins=self.bins, range=self.kwds.get('range', None), weights=self.kwds.get('weights', None)) if is_list_like(self.bottom): self.bottom = np.array(self.bottom)
Example #10
Source File: _core.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
def _plot(cls, ax, y, style=None, bins=None, bottom=0, column_num=0, stacking_id=None, **kwds): if column_num == 0: cls._initialize_stacker(ax, stacking_id, len(bins) - 1) y = y[~isna(y)] base = np.zeros(len(bins) - 1) bottom = bottom + \ cls._get_stacked_values(ax, stacking_id, base, kwds['label']) # ignore style n, bins, patches = ax.hist(y, bins=bins, bottom=bottom, **kwds) cls._update_stacker(ax, stacking_id, n) return patches
Example #11
Source File: _core.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
def _args_adjust(self): if is_integer(self.bins): # create common bin edge values = (self.data._convert(datetime=True)._get_numeric_data()) values = np.ravel(values) values = values[~isna(values)] hist, self.bins = np.histogram( values, bins=self.bins, range=self.kwds.get('range', None), weights=self.kwds.get('weights', None)) if is_list_like(self.bottom): self.bottom = np.array(self.bottom)
Example #12
Source File: _core.py From vnpy_crypto with MIT License | 5 votes |
def _plot(cls, ax, y, style=None, bins=None, bottom=0, column_num=0, stacking_id=None, **kwds): if column_num == 0: cls._initialize_stacker(ax, stacking_id, len(bins) - 1) y = y[~isna(y)] base = np.zeros(len(bins) - 1) bottom = bottom + \ cls._get_stacked_values(ax, stacking_id, base, kwds['label']) # ignore style n, bins, patches = ax.hist(y, bins=bins, bottom=bottom, **kwds) cls._update_stacker(ax, stacking_id, n) return patches
Example #13
Source File: _core.py From vnpy_crypto with MIT License | 5 votes |
def _args_adjust(self): if is_integer(self.bins): # create common bin edge values = (self.data._convert(datetime=True)._get_numeric_data()) values = np.ravel(values) values = values[~isna(values)] hist, self.bins = np.histogram( values, bins=self.bins, range=self.kwds.get('range', None), weights=self.kwds.get('weights', None)) if is_list_like(self.bottom): self.bottom = np.array(self.bottom)
Example #14
Source File: _core.py From recruit with Apache License 2.0 | 5 votes |
def _args_adjust(self): if is_integer(self.bins): # create common bin edge values = (self.data._convert(datetime=True)._get_numeric_data()) values = np.ravel(values) values = values[~isna(values)] hist, self.bins = np.histogram( values, bins=self.bins, range=self.kwds.get('range', None), weights=self.kwds.get('weights', None)) if is_list_like(self.bottom): self.bottom = np.array(self.bottom)
Example #15
Source File: _core.py From recruit with Apache License 2.0 | 5 votes |
def _plot(cls, ax, y, style=None, bins=None, bottom=0, column_num=0, stacking_id=None, **kwds): if column_num == 0: cls._initialize_stacker(ax, stacking_id, len(bins) - 1) y = y[~isna(y)] base = np.zeros(len(bins) - 1) bottom = bottom + \ cls._get_stacked_values(ax, stacking_id, base, kwds['label']) # ignore style n, bins, patches = ax.hist(y, bins=bins, bottom=bottom, **kwds) cls._update_stacker(ax, stacking_id, n) return patches
Example #16
Source File: _core.py From twitter-stock-recommendation with MIT License | 4 votes |
def hist(self, by=None, bins=10, **kwds): """ Draw one histogram of the DataFrame's columns. A histogram is a representation of the distribution of data. This function groups the values of all given Series in the DataFrame into bins and draws all bins in one :class:`matplotlib.axes.Axes`. This is useful when the DataFrame's Series are in a similar scale. Parameters ---------- by : str or sequence, optional Column in the DataFrame to group by. bins : int, default 10 Number of histogram bins to be used. **kwds Additional keyword arguments are documented in :meth:`pandas.DataFrame.plot`. Returns ------- axes : matplotlib.AxesSubplot histogram. See Also -------- DataFrame.hist : Draw histograms per DataFrame's Series. Series.hist : Draw a histogram with Series' data. Examples -------- When we draw a dice 6000 times, we expect to get each value around 1000 times. But when we draw two dices and sum the result, the distribution is going to be quite different. A histogram illustrates those distributions. .. plot:: :context: close-figs >>> df = pd.DataFrame( ... np.random.randint(1, 7, 6000), ... columns = ['one']) >>> df['two'] = df['one'] + np.random.randint(1, 7, 6000) >>> ax = df.plot.hist(bins=12, alpha=0.5) """ return self(kind='hist', by=by, bins=bins, **kwds)
Example #17
Source File: _core.py From twitter-stock-recommendation with MIT License | 4 votes |
def grouped_hist(data, column=None, by=None, ax=None, bins=50, figsize=None, layout=None, sharex=False, sharey=False, rot=90, grid=True, xlabelsize=None, xrot=None, ylabelsize=None, yrot=None, **kwargs): """ Grouped histogram Parameters ---------- data: Series/DataFrame column: object, optional by: object, optional ax: axes, optional bins: int, default 50 figsize: tuple, optional layout: optional sharex: boolean, default False sharey: boolean, default False rot: int, default 90 grid: bool, default True kwargs: dict, keyword arguments passed to matplotlib.Axes.hist Returns ------- axes: collection of Matplotlib Axes """ _raise_if_no_mpl() _converter._WARN = False def plot_group(group, ax): ax.hist(group.dropna().values, bins=bins, **kwargs) xrot = xrot or rot fig, axes = _grouped_plot(plot_group, data, column=column, by=by, sharex=sharex, sharey=sharey, ax=ax, figsize=figsize, layout=layout, rot=rot) _set_ticks_props(axes, xlabelsize=xlabelsize, xrot=xrot, ylabelsize=ylabelsize, yrot=yrot) fig.subplots_adjust(bottom=0.15, top=0.9, left=0.1, right=0.9, hspace=0.5, wspace=0.3) return axes
Example #18
Source File: _core.py From coffeegrindsize with MIT License | 4 votes |
def hist(self, by=None, bins=10, **kwds): """ Draw one histogram of the DataFrame's columns. A histogram is a representation of the distribution of data. This function groups the values of all given Series in the DataFrame into bins and draws all bins in one :class:`matplotlib.axes.Axes`. This is useful when the DataFrame's Series are in a similar scale. Parameters ---------- by : str or sequence, optional Column in the DataFrame to group by. bins : int, default 10 Number of histogram bins to be used. **kwds Additional keyword arguments are documented in :meth:`pandas.DataFrame.plot`. Returns ------- axes : matplotlib.AxesSubplot histogram. See Also -------- DataFrame.hist : Draw histograms per DataFrame's Series. Series.hist : Draw a histogram with Series' data. Examples -------- When we draw a dice 6000 times, we expect to get each value around 1000 times. But when we draw two dices and sum the result, the distribution is going to be quite different. A histogram illustrates those distributions. .. plot:: :context: close-figs >>> df = pd.DataFrame( ... np.random.randint(1, 7, 6000), ... columns = ['one']) >>> df['two'] = df['one'] + np.random.randint(1, 7, 6000) >>> ax = df.plot.hist(bins=12, alpha=0.5) """ return self(kind='hist', by=by, bins=bins, **kwds)
Example #19
Source File: _core.py From coffeegrindsize with MIT License | 4 votes |
def grouped_hist(data, column=None, by=None, ax=None, bins=50, figsize=None, layout=None, sharex=False, sharey=False, rot=90, grid=True, xlabelsize=None, xrot=None, ylabelsize=None, yrot=None, **kwargs): """ Grouped histogram Parameters ---------- data : Series/DataFrame column : object, optional by : object, optional ax : axes, optional bins : int, default 50 figsize : tuple, optional layout : optional sharex : boolean, default False sharey : boolean, default False rot : int, default 90 grid : bool, default True kwargs : dict, keyword arguments passed to matplotlib.Axes.hist Returns ------- axes : collection of Matplotlib Axes """ _raise_if_no_mpl() _converter._WARN = False def plot_group(group, ax): ax.hist(group.dropna().values, bins=bins, **kwargs) xrot = xrot or rot fig, axes = _grouped_plot(plot_group, data, column=column, by=by, sharex=sharex, sharey=sharey, ax=ax, figsize=figsize, layout=layout, rot=rot) _set_ticks_props(axes, xlabelsize=xlabelsize, xrot=xrot, ylabelsize=ylabelsize, yrot=yrot) fig.subplots_adjust(bottom=0.15, top=0.9, left=0.1, right=0.9, hspace=0.5, wspace=0.3) return axes
Example #20
Source File: _core.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 4 votes |
def hist(self, by=None, bins=10, **kwds): """ Draw one histogram of the DataFrame's columns. A histogram is a representation of the distribution of data. This function groups the values of all given Series in the DataFrame into bins and draws all bins in one :class:`matplotlib.axes.Axes`. This is useful when the DataFrame's Series are in a similar scale. Parameters ---------- by : str or sequence, optional Column in the DataFrame to group by. bins : int, default 10 Number of histogram bins to be used. **kwds Additional keyword arguments are documented in :meth:`pandas.DataFrame.plot`. Returns ------- axes : matplotlib.AxesSubplot histogram. See Also -------- DataFrame.hist : Draw histograms per DataFrame's Series. Series.hist : Draw a histogram with Series' data. Examples -------- When we draw a dice 6000 times, we expect to get each value around 1000 times. But when we draw two dices and sum the result, the distribution is going to be quite different. A histogram illustrates those distributions. .. plot:: :context: close-figs >>> df = pd.DataFrame( ... np.random.randint(1, 7, 6000), ... columns = ['one']) >>> df['two'] = df['one'] + np.random.randint(1, 7, 6000) >>> ax = df.plot.hist(bins=12, alpha=0.5) """ return self(kind='hist', by=by, bins=bins, **kwds)
Example #21
Source File: _core.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 4 votes |
def grouped_hist(data, column=None, by=None, ax=None, bins=50, figsize=None, layout=None, sharex=False, sharey=False, rot=90, grid=True, xlabelsize=None, xrot=None, ylabelsize=None, yrot=None, **kwargs): """ Grouped histogram Parameters ---------- data : Series/DataFrame column : object, optional by : object, optional ax : axes, optional bins : int, default 50 figsize : tuple, optional layout : optional sharex : boolean, default False sharey : boolean, default False rot : int, default 90 grid : bool, default True kwargs : dict, keyword arguments passed to matplotlib.Axes.hist Returns ------- axes : collection of Matplotlib Axes """ _raise_if_no_mpl() _converter._WARN = False def plot_group(group, ax): ax.hist(group.dropna().values, bins=bins, **kwargs) xrot = xrot or rot fig, axes = _grouped_plot(plot_group, data, column=column, by=by, sharex=sharex, sharey=sharey, ax=ax, figsize=figsize, layout=layout, rot=rot) _set_ticks_props(axes, xlabelsize=xlabelsize, xrot=xrot, ylabelsize=ylabelsize, yrot=yrot) fig.subplots_adjust(bottom=0.15, top=0.9, left=0.1, right=0.9, hspace=0.5, wspace=0.3) return axes
Example #22
Source File: _core.py From vnpy_crypto with MIT License | 4 votes |
def hist(self, by=None, bins=10, **kwds): """ Draw one histogram of the DataFrame's columns. A histogram is a representation of the distribution of data. This function groups the values of all given Series in the DataFrame into bins and draws all bins in one :class:`matplotlib.axes.Axes`. This is useful when the DataFrame's Series are in a similar scale. Parameters ---------- by : str or sequence, optional Column in the DataFrame to group by. bins : int, default 10 Number of histogram bins to be used. **kwds Additional keyword arguments are documented in :meth:`pandas.DataFrame.plot`. Returns ------- axes : matplotlib.AxesSubplot histogram. See Also -------- DataFrame.hist : Draw histograms per DataFrame's Series. Series.hist : Draw a histogram with Series' data. Examples -------- When we draw a dice 6000 times, we expect to get each value around 1000 times. But when we draw two dices and sum the result, the distribution is going to be quite different. A histogram illustrates those distributions. .. plot:: :context: close-figs >>> df = pd.DataFrame( ... np.random.randint(1, 7, 6000), ... columns = ['one']) >>> df['two'] = df['one'] + np.random.randint(1, 7, 6000) >>> ax = df.plot.hist(bins=12, alpha=0.5) """ return self(kind='hist', by=by, bins=bins, **kwds)
Example #23
Source File: _core.py From vnpy_crypto with MIT License | 4 votes |
def grouped_hist(data, column=None, by=None, ax=None, bins=50, figsize=None, layout=None, sharex=False, sharey=False, rot=90, grid=True, xlabelsize=None, xrot=None, ylabelsize=None, yrot=None, **kwargs): """ Grouped histogram Parameters ---------- data: Series/DataFrame column: object, optional by: object, optional ax: axes, optional bins: int, default 50 figsize: tuple, optional layout: optional sharex: boolean, default False sharey: boolean, default False rot: int, default 90 grid: bool, default True kwargs: dict, keyword arguments passed to matplotlib.Axes.hist Returns ------- axes: collection of Matplotlib Axes """ _raise_if_no_mpl() _converter._WARN = False def plot_group(group, ax): ax.hist(group.dropna().values, bins=bins, **kwargs) xrot = xrot or rot fig, axes = _grouped_plot(plot_group, data, column=column, by=by, sharex=sharex, sharey=sharey, ax=ax, figsize=figsize, layout=layout, rot=rot) _set_ticks_props(axes, xlabelsize=xlabelsize, xrot=xrot, ylabelsize=ylabelsize, yrot=yrot) fig.subplots_adjust(bottom=0.15, top=0.9, left=0.1, right=0.9, hspace=0.5, wspace=0.3) return axes
Example #24
Source File: _core.py From recruit with Apache License 2.0 | 4 votes |
def hist(self, by=None, bins=10, **kwds): """ Draw one histogram of the DataFrame's columns. A histogram is a representation of the distribution of data. This function groups the values of all given Series in the DataFrame into bins and draws all bins in one :class:`matplotlib.axes.Axes`. This is useful when the DataFrame's Series are in a similar scale. Parameters ---------- by : str or sequence, optional Column in the DataFrame to group by. bins : int, default 10 Number of histogram bins to be used. **kwds Additional keyword arguments are documented in :meth:`pandas.DataFrame.plot`. Returns ------- axes : matplotlib.AxesSubplot histogram. See Also -------- DataFrame.hist : Draw histograms per DataFrame's Series. Series.hist : Draw a histogram with Series' data. Examples -------- When we draw a dice 6000 times, we expect to get each value around 1000 times. But when we draw two dices and sum the result, the distribution is going to be quite different. A histogram illustrates those distributions. .. plot:: :context: close-figs >>> df = pd.DataFrame( ... np.random.randint(1, 7, 6000), ... columns = ['one']) >>> df['two'] = df['one'] + np.random.randint(1, 7, 6000) >>> ax = df.plot.hist(bins=12, alpha=0.5) """ return self(kind='hist', by=by, bins=bins, **kwds)
Example #25
Source File: _core.py From recruit with Apache License 2.0 | 4 votes |
def grouped_hist(data, column=None, by=None, ax=None, bins=50, figsize=None, layout=None, sharex=False, sharey=False, rot=90, grid=True, xlabelsize=None, xrot=None, ylabelsize=None, yrot=None, **kwargs): """ Grouped histogram Parameters ---------- data : Series/DataFrame column : object, optional by : object, optional ax : axes, optional bins : int, default 50 figsize : tuple, optional layout : optional sharex : boolean, default False sharey : boolean, default False rot : int, default 90 grid : bool, default True kwargs : dict, keyword arguments passed to matplotlib.Axes.hist Returns ------- axes : collection of Matplotlib Axes """ _raise_if_no_mpl() _converter._WARN = False def plot_group(group, ax): ax.hist(group.dropna().values, bins=bins, **kwargs) xrot = xrot or rot fig, axes = _grouped_plot(plot_group, data, column=column, by=by, sharex=sharex, sharey=sharey, ax=ax, figsize=figsize, layout=layout, rot=rot) _set_ticks_props(axes, xlabelsize=xlabelsize, xrot=xrot, ylabelsize=ylabelsize, yrot=yrot) fig.subplots_adjust(bottom=0.15, top=0.9, left=0.1, right=0.9, hspace=0.5, wspace=0.3) return axes