Python plotly.graph_objs 模块，Histogram() 实例源码

def create_data(self):
        bin_values, bin_edges = bin_datetimes(self.kamervraag_dates, range_years=7, bin_size_days=7)
        x, y_moving_avg = movingaverage_from_histogram(bin_values, bin_edges, window=8)

        moving_average_scatter = Scatter(
            x=x,
            y=y_moving_avg,
            mode='lines',
            name='lopende trend',
            line=dict(
                color=COLOR_WARNING,
                width=3,
            ),
        )

        hist_data = Histogram(
            x=self.kamervraag_dates,
            autobinx=False,
            xbins=dict(
                start=(timezone.now() - datetime.timedelta(days=7 * 365)).timestamp() * 1000,
                end=timezone.now().timestamp() * 1000,
                size=60 * 60 * 24 * 7 * 1000
            ),
            marker=dict(
                color=COLOR_INFO,
                line=dict(
                    color=COLOR_PRIMARY,
                    width=1,
                )
            ),
            name='vragen per week',
        )

        return [hist_data, moving_average_scatter]

def create_data(self):
        return [Histogram(
            x=self.kamervraag_durations,
            autobinx=False,
            xbins=dict(start=0, end=100, size=1),
            marker=dict(
                color=COLOR_INFO,
                line=dict(
                    color=COLOR_PRIMARY,
                    width=2,
                )
            ),
        )]

def replot(self, app_state):
        self.bins = int(app_state['bins'])

        hist_data = np.histogram(self.likes, bins = self.bins)
        binsize = hist_data[1][1] - hist_data[1][0]

        trace = go.Histogram(
            x=self.likes,
            autobinx=False,
            xbins=dict(
                start=hist_data[1][0],
                end=hist_data[1][-1]+1,
                size=binsize
           )
        )
        data = [trace]

        fig = {
            'data': data, 


            'layout': {
                'xaxis': {
                    'title': 'Number of likes',
                },
                'yaxis': {
                    'title': 'Count',
                },
                'bargroupgap' :0.05,
            }
        }

        messages = [
            {
                'id': 'hist',
                'task': 'newPlot',
                'data': fig['data'],
                'layout': fig['layout']
            }
        ]

        return messages

def summarize_plots(data, prediction_variable, legend=True):
    # !------- Check the parameters -------!
    typeErrors = []
    # ----- Check "data" parameter -----
    if not isinstance(data, pd.DataFrame):
        typeErrors.append("data parameter must be a Pandas Data Frame.")
    # ----- Check "prediction_variable" parameter -----
    if not isinstance(prediction_variable, str):
        typeErrors.append("prediction_variable must be a string.")
    if isinstance(prediction_variable, np.ndarray) and column.ndim >= 2:
        typeErrors.append("column parameter must not be a " + \
        "multil-dimensional numpy array.")
    # ----- Check "legend" parameter -----
    if not isinstance(legend, bool):
        typeErrors.append("legend parameter must be a boolean")
    if typeErrors:
        raise TypeError(typeErrors)

    plots = []
    box_plots = []
    histograms = []
    map_plots = [] # We will look at the data and see if there's some kind of
    # pattern for the
    bar_plots = []
    data_types = ('float64', 'int64', 'int32', int, float)
    plot_titles_numerical = []
    plot_titles_string = []

    for column in data:
        column_type = str(data[column].dtype)
        if column_type in data_types: # If there's a number in the column
            # Check if it's a binary variable
            if (len(data[column].unique()) == 2) and \
            ((1 in data[column].unique()) and (0 in data[column].unique())):
                # Make a bar plot
                pass
            else: # It must be numerical
                fig_box_w = tbs_plot.box_whisker_plot(data[column], column,
                plot=False)
                fig_histo = go.Histogram(x=data[column])
                plots.append(fig_box_w)
                plots.append(fig_histo)
                #box_plots.append(fig_box_w)
                #histograms.append(fig_histo)
                plot_titles_numerical.append(column)
                plot_titles_numerical.append(column)
    row = (len(plots)/2)
    tbs_plot.ml_plot_subplots(figs = plots, rows=int(row),
    cols=2, title="Eezzy Summary", subplot_titles=plot_titles_numerical,
    legend=False)