def create_facet_grid(df, x=None, y=None, facet_row=None, facet_col=None, color_name=None, colormap=None, color_is_cat=False, facet_row_labels=None, facet_col_labels=None, height=None, width=None, trace_type='scatter', scales='fixed', dtick_x=None, dtick_y=None, show_boxes=True, ggplot2=False, binsize=1, **kwargs): """ Returns figure for facet grid. :param (pd.DataFrame) df: the dataframe of columns for the facet grid. :param (str) x: the name of the dataframe column for the x axis data. :param (str) y: the name of the dataframe column for the y axis data. :param (str) facet_row: the name of the dataframe column that is used to facet the grid into row panels. :param (str) facet_col: the name of the dataframe column that is used to facet the grid into column panels. :param (str) color_name: the name of your dataframe column that will function as the colormap variable. :param (str|list|dict) colormap: the param that determines how the color_name column colors the data. If the dataframe contains numeric data, then a dictionary of colors will group the data categorically while a Plotly Colorscale name or a custom colorscale will treat it numerically. To learn more about colors and types of colormap, run `help(plotly.colors)`. :param (bool) color_is_cat: determines whether a numerical column for the colormap will be treated as categorical (True) or sequential (False). Default = False. :param (str|dict) facet_row_labels: set to either 'name' or a dictionary of all the unique values in the faceting row mapped to some text to show up in the label annotations. If None, labeling works like usual. :param (str|dict) facet_col_labels: set to either 'name' or a dictionary of all the values in the faceting row mapped to some text to show up in the label annotations. If None, labeling works like usual. :param (int) height: the height of the facet grid figure. :param (int) width: the width of the facet grid figure. :param (str) trace_type: decides the type of plot to appear in the facet grid. The options are 'scatter', 'scattergl', 'histogram', 'bar', and 'box'. Default = 'scatter'. :param (str) scales: determines if axes have fixed ranges or not. Valid settings are 'fixed' (all axes fixed), 'free_x' (x axis free only), 'free_y' (y axis free only) or 'free' (both axes free). :param (float) dtick_x: determines the distance between each tick on the x-axis. Default is None which means dtick_x is set automatically. :param (float) dtick_y: determines the distance between each tick on the y-axis. Default is None which means dtick_y is set automatically. :param (bool) show_boxes: draws grey boxes behind the facet titles. :param (bool) ggplot2: draws the facet grid in the style of `ggplot2`. See http://ggplot2.tidyverse.org/reference/facet_grid.html for reference. Default = False :param (int) binsize: groups all data into bins of a given length. :param (dict) kwargs: a dictionary of scatterplot arguments. Examples 1: One Way Faceting ``` import plotly.plotly as py import plotly.figure_factory as ff import pandas as pd mpg = pd.read_table('https://raw.githubusercontent.com/plotly/datasets/master/mpg_2017.txt') fig = ff.create_facet_grid( mpg, x='displ', y='cty', facet_col='cyl', ) py.iplot(fig, filename='facet_grid_mpg_one_way_facet') ``` Example 2: Two Way Faceting ``` import plotly.plotly as py import plotly.figure_factory as ff import pandas as pd mpg = pd.read_table('https://raw.githubusercontent.com/plotly/datasets/master/mpg_2017.txt') fig = ff.create_facet_grid( mpg, x='displ', y='cty', facet_row='drv', facet_col='cyl', ) py.iplot(fig, filename='facet_grid_mpg_two_way_facet') ``` Example 3: Categorical Coloring ``` import plotly.plotly as py import plotly.figure_factory as ff import pandas as pd mpg = pd.read_table('https://raw.githubusercontent.com/plotly/datasets/master/mpg_2017.txt') fig = ff.create_facet_grid( mtcars, x='mpg', y='wt', facet_col='cyl', color_name='cyl', color_is_cat=True, ) py.iplot(fig, filename='facet_grid_mpg_default_colors') ``` Example 4: Sequential Coloring ``` import plotly.plotly as py import plotly.figure_factory as ff import pandas as pd tips = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/tips.csv') fig = ff.create_facet_grid( tips, x='total_bill', y='tip', facet_row='sex', facet_col='smoker', color_name='size', colormap='Viridis', ) py.iplot(fig, filename='facet_grid_tips_sequential_colors') ``` Example 5: Custom labels ``` import plotly.plotly as py import plotly.figure_factory as ff import pandas as pd mtcars = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/mtcars.csv') fig = ff.create_facet_grid( mtcars, x='wt', y='mpg', facet_col='cyl', facet_col_labels={4: "$\\alpha$", 6: '$\\beta$', 8: '$\sqrt[y]{x}$'}, ) py.iplot(fig, filename='facet_grid_mtcars_custom_labels') ``` Example 6: Other Trace Type ``` import plotly.plotly as py import plotly.figure_factory as ff import pandas as pd mtcars = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/mtcars.csv') fig = ff.create_facet_grid( mtcars, x='wt', facet_col='cyl', trace_type='histogram', ) py.iplot(fig, filename='facet_grid_mtcars_other_trace_type') ``` """ if not pd: raise exceptions.ImportError( "'pandas' must be installed for this figure_factory.") if not isinstance(df, pd.DataFrame): raise exceptions.PlotlyError("You must input a pandas DataFrame.") # make sure all columns are of homogenous datatype utils.validate_dataframe(df) if trace_type in ['scatter', 'scattergl']: if not x or not y: raise exceptions.PlotlyError( "You need to input 'x' and 'y' if you are you are using a " "trace_type of 'scatter' or 'scattergl'.") for key in [x, y, facet_row, facet_col, color_name]: if key is not None: try: df[key] except KeyError: raise exceptions.PlotlyError( "x, y, facet_row, facet_col and color_name must be keys " "in your dataframe.") # autoscale histogram bars if trace_type not in ['scatter', 'scattergl']: scales = 'free' # validate scales if scales not in ['fixed', 'free_x', 'free_y', 'free']: raise exceptions.PlotlyError( "'scales' must be set to 'fixed', 'free_x', 'free_y' and 'free'.") if trace_type not in VALID_TRACE_TYPES: raise exceptions.PlotlyError( "'trace_type' must be in {}".format(VALID_TRACE_TYPES)) if trace_type == 'histogram': SUBPLOT_SPACING = 0.06 else: SUBPLOT_SPACING = 0.015 # seperate kwargs for marker and else if 'marker' in kwargs: kwargs_marker = kwargs['marker'] else: kwargs_marker = {} marker_color = kwargs_marker.pop('color', None) kwargs.pop('marker', None) kwargs_trace = kwargs if 'size' not in kwargs_marker: if ggplot2: kwargs_marker['size'] = 5 else: kwargs_marker['size'] = 8 if 'opacity' not in kwargs_marker: if not ggplot2: kwargs_trace['opacity'] = 0.6 if 'line' not in kwargs_marker: if not ggplot2: kwargs_marker['line'] = {'color': 'darkgrey', 'width': 1} else: kwargs_marker['line'] = {} # default marker size if not ggplot2: if not marker_color: marker_color = 'rgb(31, 119, 180)' else: marker_color = 'rgb(0, 0, 0)' num_of_rows = 1 num_of_cols = 1 flipped_rows = False flipped_cols = False if facet_row: num_of_rows = len(df[facet_row].unique()) flipped_rows = _is_flipped(num_of_rows) if isinstance(facet_row_labels, dict): for key in df[facet_row].unique(): if key not in facet_row_labels.keys(): unique_keys = df[facet_row].unique().tolist() raise exceptions.PlotlyError( CUSTOM_LABEL_ERROR.format(unique_keys)) if facet_col: num_of_cols = len(df[facet_col].unique()) flipped_cols = _is_flipped(num_of_cols) if isinstance(facet_col_labels, dict): for key in df[facet_col].unique(): if key not in facet_col_labels.keys(): unique_keys = df[facet_col].unique().tolist() raise exceptions.PlotlyError( CUSTOM_LABEL_ERROR.format(unique_keys)) show_legend = False if color_name: if isinstance(df[color_name].iloc[0], str) or color_is_cat: show_legend = True if isinstance(colormap, dict): clrs.validate_colors_dict(colormap, 'rgb') for val in df[color_name].unique(): if val not in colormap.keys(): raise exceptions.PlotlyError( "If using 'colormap' as a dictionary, make sure " "all the values of the colormap column are in " "the keys of your dictionary.") else: # use default plotly colors for dictionary default_colors = clrs.DEFAULT_PLOTLY_COLORS colormap = {} j = 0 for val in df[color_name].unique(): if j >= len(default_colors): j = 0 colormap[val] = default_colors[j] j += 1 fig, annotations = _facet_grid_color_categorical( df, x, y, facet_row, facet_col, color_name, colormap, num_of_rows, num_of_cols, facet_row_labels, facet_col_labels, trace_type, flipped_rows, flipped_cols, show_boxes, SUBPLOT_SPACING, marker_color, kwargs_trace, kwargs_marker) elif isinstance(df[color_name].iloc[0], Number): if isinstance(colormap, dict): show_legend = True clrs.validate_colors_dict(colormap, 'rgb') for val in df[color_name].unique(): if val not in colormap.keys(): raise exceptions.PlotlyError( "If using 'colormap' as a dictionary, make sure " "all the values of the colormap column are in " "the keys of your dictionary.") fig, annotations = _facet_grid_color_categorical( df, x, y, facet_row, facet_col, color_name, colormap, num_of_rows, num_of_cols, facet_row_labels, facet_col_labels, trace_type, flipped_rows, flipped_cols, show_boxes, SUBPLOT_SPACING, marker_color, kwargs_trace, kwargs_marker) elif isinstance(colormap, list): colorscale_list = colormap clrs.validate_colorscale(colorscale_list) fig, annotations = _facet_grid_color_numerical( df, x, y, facet_row, facet_col, color_name, colorscale_list, num_of_rows, num_of_cols, facet_row_labels, facet_col_labels, trace_type, flipped_rows, flipped_cols, show_boxes, SUBPLOT_SPACING, marker_color, kwargs_trace, kwargs_marker) elif isinstance(colormap, str): if colormap in clrs.PLOTLY_SCALES.keys(): colorscale_list = clrs.PLOTLY_SCALES[colormap] else: raise exceptions.PlotlyError( "If 'colormap' is a string, it must be the name " "of a Plotly Colorscale. The available colorscale " "names are {}".format(clrs.PLOTLY_SCALES.keys())) fig, annotations = _facet_grid_color_numerical( df, x, y, facet_row, facet_col, color_name, colorscale_list, num_of_rows, num_of_cols, facet_row_labels, facet_col_labels, trace_type, flipped_rows, flipped_cols, show_boxes, SUBPLOT_SPACING, marker_color, kwargs_trace, kwargs_marker) else: colorscale_list = clrs.PLOTLY_SCALES['Reds'] fig, annotations = _facet_grid_color_numerical( df, x, y, facet_row, facet_col, color_name, colorscale_list, num_of_rows, num_of_cols, facet_row_labels, facet_col_labels, trace_type, flipped_rows, flipped_cols, show_boxes, SUBPLOT_SPACING, marker_color, kwargs_trace, kwargs_marker) else: fig, annotations = _facet_grid( df, x, y, facet_row, facet_col, num_of_rows, num_of_cols, facet_row_labels, facet_col_labels, trace_type, flipped_rows, flipped_cols, show_boxes, SUBPLOT_SPACING, marker_color, kwargs_trace, kwargs_marker) if not height: height = max(600, 100 * num_of_rows) if not width: width = max(600, 100 * num_of_cols) fig['layout'].update(height=height, width=width, title='', paper_bgcolor='rgb(251, 251, 251)') if ggplot2: fig['layout'].update(plot_bgcolor=PLOT_BGCOLOR, paper_bgcolor='rgb(255, 255, 255)', hovermode='closest') # axis titles x_title_annot = _axis_title_annotation(x, 'x') y_title_annot = _axis_title_annotation(y, 'y') # annotations annotations.append(x_title_annot) annotations.append(y_title_annot) # legend fig['layout']['showlegend'] = show_legend fig['layout']['legend']['bgcolor'] = LEGEND_COLOR fig['layout']['legend']['borderwidth'] = LEGEND_BORDER_WIDTH fig['layout']['legend']['x'] = 1.05 fig['layout']['legend']['y'] = 1 fig['layout']['legend']['yanchor'] = 'top' if show_legend: fig['layout']['showlegend'] = show_legend if ggplot2: if color_name: legend_annot = _legend_annotation(color_name) annotations.append(legend_annot) fig['layout']['margin']['r'] = 150 # assign annotations to figure fig['layout']['annotations'] = annotations # add shaded boxes behind axis titles if show_boxes and ggplot2: _add_shapes_to_fig(fig, ANNOT_RECT_COLOR, flipped_rows, flipped_cols) # all xaxis and yaxis labels axis_labels = {'x': [], 'y': []} for key in fig['layout']: if 'xaxis' in key: axis_labels['x'].append(key) elif 'yaxis' in key: axis_labels['y'].append(key) string_number_in_data = False for var in [v for v in [x, y] if v]: if isinstance(df[var].tolist()[0], str): for item in df[var]: try: int(item) string_number_in_data = True except ValueError: pass if string_number_in_data: for x_y in axis_labels.keys(): for axis_name in axis_labels[x_y]: fig['layout'][axis_name]['type'] = 'category' if scales == 'fixed': fixed_axes = ['x', 'y'] elif scales == 'free_x': fixed_axes = ['y'] elif scales == 'free_y': fixed_axes = ['x'] elif scales == 'free': fixed_axes = [] # fixed ranges for x_y in fixed_axes: min_ranges = [] max_ranges = [] for trace in fig['data']: if trace[x_y] is not None and len(trace[x_y]) > 0: min_ranges.append(min(trace[x_y])) max_ranges.append(max(trace[x_y])) while None in min_ranges: min_ranges.remove(None) while None in max_ranges: max_ranges.remove(None) min_range = min(min_ranges) max_range = max(max_ranges) range_are_numbers = (isinstance(min_range, Number) and isinstance(max_range, Number)) if range_are_numbers: min_range = math.floor(min_range) max_range = math.ceil(max_range) # extend widen frame by 5% on each side min_range -= 0.05 * (max_range - min_range) max_range += 0.05 * (max_range - min_range) if x_y == 'x': if dtick_x: dtick = dtick_x else: dtick = math.floor( (max_range - min_range) / MAX_TICKS_PER_AXIS) elif x_y == 'y': if dtick_y: dtick = dtick_y else: dtick = math.floor( (max_range - min_range) / MAX_TICKS_PER_AXIS) else: dtick = 1 for axis_title in axis_labels[x_y]: fig['layout'][axis_title]['dtick'] = dtick fig['layout'][axis_title]['ticklen'] = 0 fig['layout'][axis_title]['zeroline'] = False if ggplot2: fig['layout'][axis_title]['tickwidth'] = 1 fig['layout'][axis_title]['ticklen'] = 4 fig['layout'][axis_title]['gridwidth'] = GRID_WIDTH fig['layout'][axis_title]['gridcolor'] = GRID_COLOR fig['layout'][axis_title]['gridwidth'] = 2 fig['layout'][axis_title]['tickfont'] = { 'color': TICK_COLOR, 'size': 10 } # insert ranges into fig if x_y in fixed_axes: for key in fig['layout']: if '{}axis'.format(x_y) in key and range_are_numbers: fig['layout'][key]['range'] = [min_range, max_range] return fig
def create_gantt( df, colors=None, index_col=None, show_colorbar=False, reverse_colors=False, title="Gantt Chart", bar_width=0.2, showgrid_x=False, showgrid_y=False, height=600, width=None, tasks=None, task_names=None, data=None, group_tasks=False, show_hover_fill=True, ): """ Returns figure for a gantt chart :param (array|list) df: input data for gantt chart. Must be either a a dataframe or a list. If dataframe, the columns must include 'Task', 'Start' and 'Finish'. Other columns can be included and used for indexing. If a list, its elements must be dictionaries with the same required column headers: 'Task', 'Start' and 'Finish'. :param (str|list|dict|tuple) colors: either a plotly scale name, an rgb or hex color, a color tuple or a list of colors. An rgb color is of the form 'rgb(x, y, z)' where x, y, z belong to the interval [0, 255] and a color tuple is a tuple of the form (a, b, c) where a, b and c belong to [0, 1]. If colors is a list, it must contain the valid color types aforementioned as its members. If a dictionary, all values of the indexing column must be keys in colors. :param (str|float) index_col: the column header (if df is a data frame) that will function as the indexing column. If df is a list, index_col must be one of the keys in all the items of df. :param (bool) show_colorbar: determines if colorbar will be visible. Only applies if values in the index column are numeric. :param (bool) show_hover_fill: enables/disables the hovertext for the filled area of the chart. :param (bool) reverse_colors: reverses the order of selected colors :param (str) title: the title of the chart :param (float) bar_width: the width of the horizontal bars in the plot :param (bool) showgrid_x: show/hide the x-axis grid :param (bool) showgrid_y: show/hide the y-axis grid :param (float) height: the height of the chart :param (float) width: the width of the chart Example 1: Simple Gantt Chart >>> from plotly.figure_factory import create_gantt >>> # Make data for chart >>> df = [dict(Task="Job A", Start='2009-01-01', Finish='2009-02-30'), ... dict(Task="Job B", Start='2009-03-05', Finish='2009-04-15'), ... dict(Task="Job C", Start='2009-02-20', Finish='2009-05-30')] >>> # Create a figure >>> fig = create_gantt(df) >>> fig.show() Example 2: Index by Column with Numerical Entries >>> from plotly.figure_factory import create_gantt >>> # Make data for chart >>> df = [dict(Task="Job A", Start='2009-01-01', ... Finish='2009-02-30', Complete=10), ... dict(Task="Job B", Start='2009-03-05', ... Finish='2009-04-15', Complete=60), ... dict(Task="Job C", Start='2009-02-20', ... Finish='2009-05-30', Complete=95)] >>> # Create a figure with Plotly colorscale >>> fig = create_gantt(df, colors='Blues', index_col='Complete', ... show_colorbar=True, bar_width=0.5, ... showgrid_x=True, showgrid_y=True) >>> fig.show() Example 3: Index by Column with String Entries >>> from plotly.figure_factory import create_gantt >>> # Make data for chart >>> df = [dict(Task="Job A", Start='2009-01-01', ... Finish='2009-02-30', Resource='Apple'), ... dict(Task="Job B", Start='2009-03-05', ... Finish='2009-04-15', Resource='Grape'), ... dict(Task="Job C", Start='2009-02-20', ... Finish='2009-05-30', Resource='Banana')] >>> # Create a figure with Plotly colorscale >>> fig = create_gantt(df, colors=['rgb(200, 50, 25)', (1, 0, 1), '#6c4774'], ... index_col='Resource', reverse_colors=True, ... show_colorbar=True) >>> fig.show() Example 4: Use a dictionary for colors >>> from plotly.figure_factory import create_gantt >>> # Make data for chart >>> df = [dict(Task="Job A", Start='2009-01-01', ... Finish='2009-02-30', Resource='Apple'), ... dict(Task="Job B", Start='2009-03-05', ... Finish='2009-04-15', Resource='Grape'), ... dict(Task="Job C", Start='2009-02-20', ... Finish='2009-05-30', Resource='Banana')] >>> # Make a dictionary of colors >>> colors = {'Apple': 'rgb(255, 0, 0)', ... 'Grape': 'rgb(170, 14, 200)', ... 'Banana': (1, 1, 0.2)} >>> # Create a figure with Plotly colorscale >>> fig = create_gantt(df, colors=colors, index_col='Resource', ... show_colorbar=True) >>> fig.show() Example 5: Use a pandas dataframe >>> from plotly.figure_factory import create_gantt >>> import pandas as pd >>> # Make data as a dataframe >>> df = pd.DataFrame([['Run', '2010-01-01', '2011-02-02', 10], ... ['Fast', '2011-01-01', '2012-06-05', 55], ... ['Eat', '2012-01-05', '2013-07-05', 94]], ... columns=['Task', 'Start', 'Finish', 'Complete']) >>> # Create a figure with Plotly colorscale >>> fig = create_gantt(df, colors='Blues', index_col='Complete', ... show_colorbar=True, bar_width=0.5, ... showgrid_x=True, showgrid_y=True) >>> fig.show() """ # validate gantt input data chart = validate_gantt(df) if index_col: if index_col not in chart[0]: raise exceptions.PlotlyError( "In order to use an indexing column and assign colors to " "the values of the index, you must choose an actual " "column name in the dataframe or key if a list of " "dictionaries is being used.") # validate gantt index column index_list = [] for dictionary in chart: index_list.append(dictionary[index_col]) utils.validate_index(index_list) # Validate colors if isinstance(colors, dict): colors = clrs.validate_colors_dict(colors, "rgb") else: colors = clrs.validate_colors(colors, "rgb") if reverse_colors is True: colors.reverse() if not index_col: if isinstance(colors, dict): raise exceptions.PlotlyError( "Error. You have set colors to a dictionary but have not " "picked an index. An index is required if you are " "assigning colors to particular values in a dictioanry.") fig = gantt( chart, colors, title, bar_width, showgrid_x, showgrid_y, height, width, tasks=None, task_names=None, data=None, group_tasks=group_tasks, show_hover_fill=show_hover_fill, show_colorbar=show_colorbar, ) return fig else: if not isinstance(colors, dict): fig = gantt_colorscale( chart, colors, title, index_col, show_colorbar, bar_width, showgrid_x, showgrid_y, height, width, tasks=None, task_names=None, data=None, group_tasks=group_tasks, show_hover_fill=show_hover_fill, ) return fig else: fig = gantt_dict( chart, colors, title, index_col, show_colorbar, bar_width, showgrid_x, showgrid_y, height, width, tasks=None, task_names=None, data=None, group_tasks=group_tasks, show_hover_fill=show_hover_fill, ) return fig
def create_violin( data, data_header=None, group_header=None, colors=None, use_colorscale=False, group_stats=None, rugplot=True, sort=False, height=450, width=600, title="Violin and Rug Plot", ): """ **deprecated**, use instead the plotly.graph_objects trace :class:`plotly.graph_objects.Violin`. :param (list|array) data: accepts either a list of numerical values, a list of dictionaries all with identical keys and at least one column of numeric values, or a pandas dataframe with at least one column of numbers. :param (str) data_header: the header of the data column to be used from an inputted pandas dataframe. Not applicable if 'data' is a list of numeric values. :param (str) group_header: applicable if grouping data by a variable. 'group_header' must be set to the name of the grouping variable. :param (str|tuple|list|dict) colors: either a plotly scale name, an rgb or hex color, a color tuple, a list of colors or a dictionary. An rgb color is of the form 'rgb(x, y, z)' where x, y and z belong to the interval [0, 255] and a color tuple is a tuple of the form (a, b, c) where a, b and c belong to [0, 1]. If colors is a list, it must contain valid color types as its members. :param (bool) use_colorscale: only applicable if grouping by another variable. Will implement a colorscale based on the first 2 colors of param colors. This means colors must be a list with at least 2 colors in it (Plotly colorscales are accepted since they map to a list of two rgb colors). Default = False :param (dict) group_stats: a dictioanry where each key is a unique value from the group_header column in data. Each value must be a number and will be used to color the violin plots if a colorscale is being used. :param (bool) rugplot: determines if a rugplot is draw on violin plot. Default = True :param (bool) sort: determines if violins are sorted alphabetically (True) or by input order (False). Default = False :param (float) height: the height of the violin plot. :param (float) width: the width of the violin plot. :param (str) title: the title of the violin plot. Example 1: Single Violin Plot >>> from plotly.figure_factory import create_violin >>> import plotly.graph_objs as graph_objects >>> import numpy as np >>> from scipy import stats >>> # create list of random values >>> data_list = np.random.randn(100) >>> # create violin fig >>> fig = create_violin(data_list, colors='#604d9e') >>> # plot >>> fig.show() Example 2: Multiple Violin Plots with Qualitative Coloring >>> from plotly.figure_factory import create_violin >>> import plotly.graph_objs as graph_objects >>> import numpy as np >>> import pandas as pd >>> from scipy import stats >>> # create dataframe >>> np.random.seed(619517) >>> Nr=250 >>> y = np.random.randn(Nr) >>> gr = np.random.choice(list("ABCDE"), Nr) >>> norm_params=[(0, 1.2), (0.7, 1), (-0.5, 1.4), (0.3, 1), (0.8, 0.9)] >>> for i, letter in enumerate("ABCDE"): ... y[gr == letter] *=norm_params[i][1]+ norm_params[i][0] >>> df = pd.DataFrame(dict(Score=y, Group=gr)) >>> # create violin fig >>> fig = create_violin(df, data_header='Score', group_header='Group', ... sort=True, height=600, width=1000) >>> # plot >>> fig.show() Example 3: Violin Plots with Colorscale >>> from plotly.figure_factory import create_violin >>> import plotly.graph_objs as graph_objects >>> import numpy as np >>> import pandas as pd >>> from scipy import stats >>> # create dataframe >>> np.random.seed(619517) >>> Nr=250 >>> y = np.random.randn(Nr) >>> gr = np.random.choice(list("ABCDE"), Nr) >>> norm_params=[(0, 1.2), (0.7, 1), (-0.5, 1.4), (0.3, 1), (0.8, 0.9)] >>> for i, letter in enumerate("ABCDE"): ... y[gr == letter] *=norm_params[i][1]+ norm_params[i][0] >>> df = pd.DataFrame(dict(Score=y, Group=gr)) >>> # define header params >>> data_header = 'Score' >>> group_header = 'Group' >>> # make groupby object with pandas >>> group_stats = {} >>> groupby_data = df.groupby([group_header]) >>> for group in "ABCDE": ... data_from_group = groupby_data.get_group(group)[data_header] ... # take a stat of the grouped data ... stat = np.median(data_from_group) ... # add to dictionary ... group_stats[group] = stat >>> # create violin fig >>> fig = create_violin(df, data_header='Score', group_header='Group', ... height=600, width=1000, use_colorscale=True, ... group_stats=group_stats) >>> # plot >>> fig.show() """ # Validate colors if isinstance(colors, dict): valid_colors = clrs.validate_colors_dict(colors, "rgb") else: valid_colors = clrs.validate_colors(colors, "rgb") # validate data and choose plot type if group_header is None: if isinstance(data, list): if len(data) <= 0: raise exceptions.PlotlyError("If data is a list, it must be " "nonempty and contain either " "numbers or dictionaries.") if not all(isinstance(element, Number) for element in data): raise exceptions.PlotlyError("If data is a list, it must " "contain only numbers.") if pd and isinstance(data, pd.core.frame.DataFrame): if data_header is None: raise exceptions.PlotlyError("data_header must be the " "column name with the " "desired numeric data for " "the violin plot.") data = data[data_header].values.tolist() # call the plotting functions plot_data, plot_xrange = violinplot(data, fillcolor=valid_colors[0], rugplot=rugplot) layout = graph_objs.Layout( title=title, autosize=False, font=graph_objs.layout.Font(size=11), height=height, showlegend=False, width=width, xaxis=make_XAxis("", plot_xrange), yaxis=make_YAxis(""), hovermode="closest", ) layout["yaxis"].update( dict(showline=False, showticklabels=False, ticks="")) fig = graph_objs.Figure(data=plot_data, layout=layout) return fig else: if not isinstance(data, pd.core.frame.DataFrame): raise exceptions.PlotlyError("Error. You must use a pandas " "DataFrame if you are using a " "group header.") if data_header is None: raise exceptions.PlotlyError("data_header must be the column " "name with the desired numeric " "data for the violin plot.") if use_colorscale is False: if isinstance(valid_colors, dict): # validate colors dict choice below fig = violin_dict( data, data_header, group_header, valid_colors, use_colorscale, group_stats, rugplot, sort, height, width, title, ) return fig else: fig = violin_no_colorscale( data, data_header, group_header, valid_colors, use_colorscale, group_stats, rugplot, sort, height, width, title, ) return fig else: if isinstance(valid_colors, dict): raise exceptions.PlotlyError("The colors param cannot be " "a dictionary if you are " "using a colorscale.") if len(valid_colors) < 2: raise exceptions.PlotlyError("colors must be a list with " "at least 2 colors. A " "Plotly scale is allowed.") if not isinstance(group_stats, dict): raise exceptions.PlotlyError("Your group_stats param " "must be a dictionary.") fig = violin_colorscale( data, data_header, group_header, valid_colors, use_colorscale, group_stats, rugplot, sort, height, width, title, ) return fig
def create_gantt(df, colors=None, index_col=None, show_colorbar=False, reverse_colors=False, title='Gantt Chart', bar_width=0.2, showgrid_x=False, showgrid_y=False, height=600, width=900, tasks=None, task_names=None, data=None, group_tasks=False): """ Returns figure for a gantt chart :param (array|list) df: input data for gantt chart. Must be either a a dataframe or a list. If dataframe, the columns must include 'Task', 'Start' and 'Finish'. Other columns can be included and used for indexing. If a list, its elements must be dictionaries with the same required column headers: 'Task', 'Start' and 'Finish'. :param (str|list|dict|tuple) colors: either a plotly scale name, an rgb or hex color, a color tuple or a list of colors. An rgb color is of the form 'rgb(x, y, z)' where x, y, z belong to the interval [0, 255] and a color tuple is a tuple of the form (a, b, c) where a, b and c belong to [0, 1]. If colors is a list, it must contain the valid color types aforementioned as its members. If a dictionary, all values of the indexing column must be keys in colors. :param (str|float) index_col: the column header (if df is a data frame) that will function as the indexing column. If df is a list, index_col must be one of the keys in all the items of df. :param (bool) show_colorbar: determines if colorbar will be visible. Only applies if values in the index column are numeric. :param (bool) reverse_colors: reverses the order of selected colors :param (str) title: the title of the chart :param (float) bar_width: the width of the horizontal bars in the plot :param (bool) showgrid_x: show/hide the x-axis grid :param (bool) showgrid_y: show/hide the y-axis grid :param (float) height: the height of the chart :param (float) width: the width of the chart Example 1: Simple Gantt Chart ``` import plotly.plotly as py from plotly.figure_factory import create_gantt # Make data for chart df = [dict(Task="Job A", Start='2009-01-01', Finish='2009-02-30'), dict(Task="Job B", Start='2009-03-05', Finish='2009-04-15'), dict(Task="Job C", Start='2009-02-20', Finish='2009-05-30')] # Create a figure fig = create_gantt(df) # Plot the data py.iplot(fig, filename='Simple Gantt Chart', world_readable=True) ``` Example 2: Index by Column with Numerical Entries ``` import plotly.plotly as py from plotly.figure_factory import create_gantt # Make data for chart df = [dict(Task="Job A", Start='2009-01-01', Finish='2009-02-30', Complete=10), dict(Task="Job B", Start='2009-03-05', Finish='2009-04-15', Complete=60), dict(Task="Job C", Start='2009-02-20', Finish='2009-05-30', Complete=95)] # Create a figure with Plotly colorscale fig = create_gantt(df, colors='Blues', index_col='Complete', show_colorbar=True, bar_width=0.5, showgrid_x=True, showgrid_y=True) # Plot the data py.iplot(fig, filename='Numerical Entries', world_readable=True) ``` Example 3: Index by Column with String Entries ``` import plotly.plotly as py from plotly.figure_factory import create_gantt # Make data for chart df = [dict(Task="Job A", Start='2009-01-01', Finish='2009-02-30', Resource='Apple'), dict(Task="Job B", Start='2009-03-05', Finish='2009-04-15', Resource='Grape'), dict(Task="Job C", Start='2009-02-20', Finish='2009-05-30', Resource='Banana')] # Create a figure with Plotly colorscale fig = create_gantt(df, colors=['rgb(200, 50, 25)', (1, 0, 1), '#6c4774'], index_col='Resource', reverse_colors=True, show_colorbar=True) # Plot the data py.iplot(fig, filename='String Entries', world_readable=True) ``` Example 4: Use a dictionary for colors ``` import plotly.plotly as py from plotly.figure_factory import create_gantt # Make data for chart df = [dict(Task="Job A", Start='2009-01-01', Finish='2009-02-30', Resource='Apple'), dict(Task="Job B", Start='2009-03-05', Finish='2009-04-15', Resource='Grape'), dict(Task="Job C", Start='2009-02-20', Finish='2009-05-30', Resource='Banana')] # Make a dictionary of colors colors = {'Apple': 'rgb(255, 0, 0)', 'Grape': 'rgb(170, 14, 200)', 'Banana': (1, 1, 0.2)} # Create a figure with Plotly colorscale fig = create_gantt(df, colors=colors, index_col='Resource', show_colorbar=True) # Plot the data py.iplot(fig, filename='dictioanry colors', world_readable=True) ``` Example 5: Use a pandas dataframe ``` import plotly.plotly as py from plotly.figure_factory import create_gantt import pandas as pd # Make data as a dataframe df = pd.DataFrame([['Run', '2010-01-01', '2011-02-02', 10], ['Fast', '2011-01-01', '2012-06-05', 55], ['Eat', '2012-01-05', '2013-07-05', 94]], columns=['Task', 'Start', 'Finish', 'Complete']) # Create a figure with Plotly colorscale fig = create_gantt(df, colors='Blues', index_col='Complete', show_colorbar=True, bar_width=0.5, showgrid_x=True, showgrid_y=True) # Plot the data py.iplot(fig, filename='data with dataframe', world_readable=True) ``` """ # validate gantt input data chart = validate_gantt(df) if index_col: if index_col not in chart[0]: raise exceptions.PlotlyError( "In order to use an indexing column and assign colors to " "the values of the index, you must choose an actual " "column name in the dataframe or key if a list of " "dictionaries is being used.") # validate gantt index column index_list = [] for dictionary in chart: index_list.append(dictionary[index_col]) utils.validate_index(index_list) # Validate colors if isinstance(colors, dict): colors = clrs.validate_colors_dict(colors, 'rgb') else: colors = clrs.validate_colors(colors, 'rgb') if reverse_colors is True: colors.reverse() if not index_col: if isinstance(colors, dict): raise exceptions.PlotlyError( "Error. You have set colors to a dictionary but have not " "picked an index. An index is required if you are " "assigning colors to particular values in a dictioanry." ) fig = gantt( chart, colors, title, bar_width, showgrid_x, showgrid_y, height, width, tasks=None, task_names=None, data=None, group_tasks=group_tasks ) return fig else: if not isinstance(colors, dict): fig = gantt_colorscale( chart, colors, title, index_col, show_colorbar, bar_width, showgrid_x, showgrid_y, height, width, tasks=None, task_names=None, data=None, group_tasks=group_tasks ) return fig else: fig = gantt_dict( chart, colors, title, index_col, show_colorbar, bar_width, showgrid_x, showgrid_y, height, width, tasks=None, task_names=None, data=None, group_tasks=group_tasks ) return fig
def create_scatterplotmatrix(df, index=None, endpts=None, diag='scatter', height=500, width=500, size=6, title='Scatterplot Matrix', colormap=None, colormap_type='cat', dataframe=None, headers=None, index_vals=None, **kwargs): """ Returns data for a scatterplot matrix. :param (array) df: array of the data with column headers :param (str) index: name of the index column in data array :param (list|tuple) endpts: takes an increasing sequece of numbers that defines intervals on the real line. They are used to group the entries in an index of numbers into their corresponding interval and therefore can be treated as categorical data :param (str) diag: sets the chart type for the main diagonal plots. The options are 'scatter', 'histogram' and 'box'. :param (int|float) height: sets the height of the chart :param (int|float) width: sets the width of the chart :param (float) size: sets the marker size (in px) :param (str) title: the title label of the scatterplot matrix :param (str|tuple|list|dict) colormap: either a plotly scale name, an rgb or hex color, a color tuple, a list of colors or a dictionary. An rgb color is of the form 'rgb(x, y, z)' where x, y and z belong to the interval [0, 255] and a color tuple is a tuple of the form (a, b, c) where a, b and c belong to [0, 1]. If colormap is a list, it must contain valid color types as its members. If colormap is a dictionary, all the string entries in the index column must be a key in colormap. In this case, the colormap_type is forced to 'cat' or categorical :param (str) colormap_type: determines how colormap is interpreted. Valid choices are 'seq' (sequential) and 'cat' (categorical). If 'seq' is selected, only the first two colors in colormap will be considered (when colormap is a list) and the index values will be linearly interpolated between those two colors. This option is forced if all index values are numeric. If 'cat' is selected, a color from colormap will be assigned to each category from index, including the intervals if endpts is being used :param (dict) **kwargs: a dictionary of scatterplot arguments The only forbidden parameters are 'size', 'color' and 'colorscale' in 'marker' Example 1: Vanilla Scatterplot Matrix ``` import plotly.plotly as py from plotly.graph_objs import graph_objs from plotly.figure_factory import create_scatterplotmatrix import numpy as np import pandas as pd # Create dataframe df = pd.DataFrame(np.random.randn(10, 2), columns=['Column 1', 'Column 2']) # Create scatterplot matrix fig = create_scatterplotmatrix(df) # Plot py.iplot(fig, filename='Vanilla Scatterplot Matrix') ``` Example 2: Indexing a Column ``` import plotly.plotly as py from plotly.graph_objs import graph_objs from plotly.figure_factory import create_scatterplotmatrix import numpy as np import pandas as pd # Create dataframe with index df = pd.DataFrame(np.random.randn(10, 2), columns=['A', 'B']) # Add another column of strings to the dataframe df['Fruit'] = pd.Series(['apple', 'apple', 'grape', 'apple', 'apple', 'grape', 'pear', 'pear', 'apple', 'pear']) # Create scatterplot matrix fig = create_scatterplotmatrix(df, index='Fruit', size=10) # Plot py.iplot(fig, filename = 'Scatterplot Matrix with Index') ``` Example 3: Styling the Diagonal Subplots ``` import plotly.plotly as py from plotly.graph_objs import graph_objs from plotly.figure_factory import create_scatterplotmatrix import numpy as np import pandas as pd # Create dataframe with index df = pd.DataFrame(np.random.randn(10, 4), columns=['A', 'B', 'C', 'D']) # Add another column of strings to the dataframe df['Fruit'] = pd.Series(['apple', 'apple', 'grape', 'apple', 'apple', 'grape', 'pear', 'pear', 'apple', 'pear']) # Create scatterplot matrix fig = create_scatterplotmatrix(df, diag='box', index='Fruit', height=1000, width=1000) # Plot py.iplot(fig, filename = 'Scatterplot Matrix - Diagonal Styling') ``` Example 4: Use a Theme to Style the Subplots ``` import plotly.plotly as py from plotly.graph_objs import graph_objs from plotly.figure_factory import create_scatterplotmatrix import numpy as np import pandas as pd # Create dataframe with random data df = pd.DataFrame(np.random.randn(100, 3), columns=['A', 'B', 'C']) # Create scatterplot matrix using a built-in # Plotly palette scale and indexing column 'A' fig = create_scatterplotmatrix(df, diag='histogram', index='A', colormap='Blues', height=800, width=800) # Plot py.iplot(fig, filename = 'Scatterplot Matrix - Colormap Theme') ``` Example 5: Example 4 with Interval Factoring ``` import plotly.plotly as py from plotly.graph_objs import graph_objs from plotly.figure_factory import create_scatterplotmatrix import numpy as np import pandas as pd # Create dataframe with random data df = pd.DataFrame(np.random.randn(100, 3), columns=['A', 'B', 'C']) # Create scatterplot matrix using a list of 2 rgb tuples # and endpoints at -1, 0 and 1 fig = create_scatterplotmatrix(df, diag='histogram', index='A', colormap=['rgb(140, 255, 50)', 'rgb(170, 60, 115)', '#6c4774', (0.5, 0.1, 0.8)], endpts=[-1, 0, 1], height=800, width=800) # Plot py.iplot(fig, filename = 'Scatterplot Matrix - Intervals') ``` Example 6: Using the colormap as a Dictionary ``` import plotly.plotly as py from plotly.graph_objs import graph_objs from plotly.figure_factory import create_scatterplotmatrix import numpy as np import pandas as pd import random # Create dataframe with random data df = pd.DataFrame(np.random.randn(100, 3), columns=['Column A', 'Column B', 'Column C']) # Add new color column to dataframe new_column = [] strange_colors = ['turquoise', 'limegreen', 'goldenrod'] for j in range(100): new_column.append(random.choice(strange_colors)) df['Colors'] = pd.Series(new_column, index=df.index) # Create scatterplot matrix using a dictionary of hex color values # which correspond to actual color names in 'Colors' column fig = create_scatterplotmatrix( df, diag='box', index='Colors', colormap= dict( turquoise = '#00F5FF', limegreen = '#32CD32', goldenrod = '#DAA520' ), colormap_type='cat', height=800, width=800 ) # Plot py.iplot(fig, filename = 'Scatterplot Matrix - colormap dictionary ') ``` """ # TODO: protected until #282 if dataframe is None: dataframe = [] if headers is None: headers = [] if index_vals is None: index_vals = [] validate_scatterplotmatrix(df, index, diag, colormap_type, **kwargs) # Validate colormap if isinstance(colormap, dict): colormap = clrs.validate_colors_dict(colormap, 'rgb') elif isinstance(colormap, six.string_types) and 'rgb' not in colormap and '#' not in colormap: if colormap not in clrs.PLOTLY_SCALES.keys(): raise exceptions.PlotlyError( "If 'colormap' is a string, it must be the name " "of a Plotly Colorscale. The available colorscale " "names are {}".format(clrs.PLOTLY_SCALES.keys()) ) else: # TODO change below to allow the correct Plotly colorscale colormap = clrs.colorscale_to_colors(clrs.PLOTLY_SCALES[colormap]) # keep only first and last item - fix later colormap = [colormap[0]] + [colormap[-1]] colormap = clrs.validate_colors(colormap, 'rgb') else: colormap = clrs.validate_colors(colormap, 'rgb') if not index: for name in df: headers.append(name) for name in headers: dataframe.append(df[name].values.tolist()) # Check for same data-type in df columns utils.validate_dataframe(dataframe) figure = scatterplot(dataframe, headers, diag, size, height, width, title, **kwargs) return figure else: # Validate index selection if index not in df: raise exceptions.PlotlyError("Make sure you set the index " "input variable to one of the " "column names of your " "dataframe.") index_vals = df[index].values.tolist() for name in df: if name != index: headers.append(name) for name in headers: dataframe.append(df[name].values.tolist()) # check for same data-type in each df column utils.validate_dataframe(dataframe) utils.validate_index(index_vals) # check if all colormap keys are in the index # if colormap is a dictionary if isinstance(colormap, dict): for key in colormap: if not all(index in colormap for index in index_vals): raise exceptions.PlotlyError("If colormap is a " "dictionary, all the " "names in the index " "must be keys.") figure = scatterplot_dict( dataframe, headers, diag, size, height, width, title, index, index_vals, endpts, colormap, colormap_type, **kwargs ) return figure else: figure = scatterplot_theme( dataframe, headers, diag, size, height, width, title, index, index_vals, endpts, colormap, colormap_type, **kwargs ) return figure
def create_facet_grid(df, x=None, y=None, facet_row=None, facet_col=None, color_name=None, colormap=None, color_is_cat=False, facet_row_labels=None, facet_col_labels=None, height=None, width=None, trace_type="scatter", scales="fixed", dtick_x=None, dtick_y=None, show_boxes=True, ggplot2=False, binsize=1, **kwargs): """ Returns figure for facet grid; **this function is deprecated**, since plotly.express functions should be used instead, for example >>> import plotly.express as px >>> tips = px.data.tips() >>> fig = px.scatter(tips, ... x='total_bill', ... y='tip', ... facet_row='sex', ... facet_col='smoker', ... color='size') :param (pd.DataFrame) df: the dataframe of columns for the facet grid. :param (str) x: the name of the dataframe column for the x axis data. :param (str) y: the name of the dataframe column for the y axis data. :param (str) facet_row: the name of the dataframe column that is used to facet the grid into row panels. :param (str) facet_col: the name of the dataframe column that is used to facet the grid into column panels. :param (str) color_name: the name of your dataframe column that will function as the colormap variable. :param (str|list|dict) colormap: the param that determines how the color_name column colors the data. If the dataframe contains numeric data, then a dictionary of colors will group the data categorically while a Plotly Colorscale name or a custom colorscale will treat it numerically. To learn more about colors and types of colormap, run `help(plotly.colors)`. :param (bool) color_is_cat: determines whether a numerical column for the colormap will be treated as categorical (True) or sequential (False). Default = False. :param (str|dict) facet_row_labels: set to either 'name' or a dictionary of all the unique values in the faceting row mapped to some text to show up in the label annotations. If None, labeling works like usual. :param (str|dict) facet_col_labels: set to either 'name' or a dictionary of all the values in the faceting row mapped to some text to show up in the label annotations. If None, labeling works like usual. :param (int) height: the height of the facet grid figure. :param (int) width: the width of the facet grid figure. :param (str) trace_type: decides the type of plot to appear in the facet grid. The options are 'scatter', 'scattergl', 'histogram', 'bar', and 'box'. Default = 'scatter'. :param (str) scales: determines if axes have fixed ranges or not. Valid settings are 'fixed' (all axes fixed), 'free_x' (x axis free only), 'free_y' (y axis free only) or 'free' (both axes free). :param (float) dtick_x: determines the distance between each tick on the x-axis. Default is None which means dtick_x is set automatically. :param (float) dtick_y: determines the distance between each tick on the y-axis. Default is None which means dtick_y is set automatically. :param (bool) show_boxes: draws grey boxes behind the facet titles. :param (bool) ggplot2: draws the facet grid in the style of `ggplot2`. See http://ggplot2.tidyverse.org/reference/facet_grid.html for reference. Default = False :param (int) binsize: groups all data into bins of a given length. :param (dict) kwargs: a dictionary of scatterplot arguments. Examples 1: One Way Faceting >>> import plotly.figure_factory as ff >>> import pandas as pd >>> mpg = pd.read_table('https://raw.githubusercontent.com/plotly/datasets/master/mpg_2017.txt') >>> fig = ff.create_facet_grid( ... mpg, ... x='displ', ... y='cty', ... facet_col='cyl', ... ) >>> fig.show() Example 2: Two Way Faceting >>> import plotly.figure_factory as ff >>> import pandas as pd >>> mpg = pd.read_table('https://raw.githubusercontent.com/plotly/datasets/master/mpg_2017.txt') >>> fig = ff.create_facet_grid( ... mpg, ... x='displ', ... y='cty', ... facet_row='drv', ... facet_col='cyl', ... ) >>> fig.show() Example 3: Categorical Coloring >>> import plotly.figure_factory as ff >>> import pandas as pd >>> mtcars = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/mtcars.csv') >>> mtcars.cyl = mtcars.cyl.astype(str) >>> fig = ff.create_facet_grid( ... mtcars, ... x='mpg', ... y='wt', ... facet_col='cyl', ... color_name='cyl', ... color_is_cat=True, ... ) >>> fig.show() """ if not pd: raise ImportError( "'pandas' must be installed for this figure_factory.") if not isinstance(df, pd.DataFrame): raise exceptions.PlotlyError("You must input a pandas DataFrame.") # make sure all columns are of homogenous datatype utils.validate_dataframe(df) if trace_type in ["scatter", "scattergl"]: if not x or not y: raise exceptions.PlotlyError( "You need to input 'x' and 'y' if you are you are using a " "trace_type of 'scatter' or 'scattergl'.") for key in [x, y, facet_row, facet_col, color_name]: if key is not None: try: df[key] except KeyError: raise exceptions.PlotlyError( "x, y, facet_row, facet_col and color_name must be keys " "in your dataframe.") # autoscale histogram bars if trace_type not in ["scatter", "scattergl"]: scales = "free" # validate scales if scales not in ["fixed", "free_x", "free_y", "free"]: raise exceptions.PlotlyError( "'scales' must be set to 'fixed', 'free_x', 'free_y' and 'free'.") if trace_type not in VALID_TRACE_TYPES: raise exceptions.PlotlyError( "'trace_type' must be in {}".format(VALID_TRACE_TYPES)) if trace_type == "histogram": SUBPLOT_SPACING = 0.06 else: SUBPLOT_SPACING = 0.015 # seperate kwargs for marker and else if "marker" in kwargs: kwargs_marker = kwargs["marker"] else: kwargs_marker = {} marker_color = kwargs_marker.pop("color", None) kwargs.pop("marker", None) kwargs_trace = kwargs if "size" not in kwargs_marker: if ggplot2: kwargs_marker["size"] = 5 else: kwargs_marker["size"] = 8 if "opacity" not in kwargs_marker: if not ggplot2: kwargs_trace["opacity"] = 0.6 if "line" not in kwargs_marker: if not ggplot2: kwargs_marker["line"] = {"color": "darkgrey", "width": 1} else: kwargs_marker["line"] = {} # default marker size if not ggplot2: if not marker_color: marker_color = "rgb(31, 119, 180)" else: marker_color = "rgb(0, 0, 0)" num_of_rows = 1 num_of_cols = 1 flipped_rows = False flipped_cols = False if facet_row: num_of_rows = len(df[facet_row].unique()) flipped_rows = _is_flipped(num_of_rows) if isinstance(facet_row_labels, dict): for key in df[facet_row].unique(): if key not in facet_row_labels.keys(): unique_keys = df[facet_row].unique().tolist() raise exceptions.PlotlyError( CUSTOM_LABEL_ERROR.format(unique_keys)) if facet_col: num_of_cols = len(df[facet_col].unique()) flipped_cols = _is_flipped(num_of_cols) if isinstance(facet_col_labels, dict): for key in df[facet_col].unique(): if key not in facet_col_labels.keys(): unique_keys = df[facet_col].unique().tolist() raise exceptions.PlotlyError( CUSTOM_LABEL_ERROR.format(unique_keys)) show_legend = False if color_name: if isinstance(df[color_name].iloc[0], str) or color_is_cat: show_legend = True if isinstance(colormap, dict): clrs.validate_colors_dict(colormap, "rgb") for val in df[color_name].unique(): if val not in colormap.keys(): raise exceptions.PlotlyError( "If using 'colormap' as a dictionary, make sure " "all the values of the colormap column are in " "the keys of your dictionary.") else: # use default plotly colors for dictionary default_colors = clrs.DEFAULT_PLOTLY_COLORS colormap = {} j = 0 for val in df[color_name].unique(): if j >= len(default_colors): j = 0 colormap[val] = default_colors[j] j += 1 fig, annotations = _facet_grid_color_categorical( df, x, y, facet_row, facet_col, color_name, colormap, num_of_rows, num_of_cols, facet_row_labels, facet_col_labels, trace_type, flipped_rows, flipped_cols, show_boxes, SUBPLOT_SPACING, marker_color, kwargs_trace, kwargs_marker, ) elif isinstance(df[color_name].iloc[0], Number): if isinstance(colormap, dict): show_legend = True clrs.validate_colors_dict(colormap, "rgb") for val in df[color_name].unique(): if val not in colormap.keys(): raise exceptions.PlotlyError( "If using 'colormap' as a dictionary, make sure " "all the values of the colormap column are in " "the keys of your dictionary.") fig, annotations = _facet_grid_color_categorical( df, x, y, facet_row, facet_col, color_name, colormap, num_of_rows, num_of_cols, facet_row_labels, facet_col_labels, trace_type, flipped_rows, flipped_cols, show_boxes, SUBPLOT_SPACING, marker_color, kwargs_trace, kwargs_marker, ) elif isinstance(colormap, list): colorscale_list = colormap clrs.validate_colorscale(colorscale_list) fig, annotations = _facet_grid_color_numerical( df, x, y, facet_row, facet_col, color_name, colorscale_list, num_of_rows, num_of_cols, facet_row_labels, facet_col_labels, trace_type, flipped_rows, flipped_cols, show_boxes, SUBPLOT_SPACING, marker_color, kwargs_trace, kwargs_marker, ) elif isinstance(colormap, str): if colormap in clrs.PLOTLY_SCALES.keys(): colorscale_list = clrs.PLOTLY_SCALES[colormap] else: raise exceptions.PlotlyError( "If 'colormap' is a string, it must be the name " "of a Plotly Colorscale. The available colorscale " "names are {}".format(clrs.PLOTLY_SCALES.keys())) fig, annotations = _facet_grid_color_numerical( df, x, y, facet_row, facet_col, color_name, colorscale_list, num_of_rows, num_of_cols, facet_row_labels, facet_col_labels, trace_type, flipped_rows, flipped_cols, show_boxes, SUBPLOT_SPACING, marker_color, kwargs_trace, kwargs_marker, ) else: colorscale_list = clrs.PLOTLY_SCALES["Reds"] fig, annotations = _facet_grid_color_numerical( df, x, y, facet_row, facet_col, color_name, colorscale_list, num_of_rows, num_of_cols, facet_row_labels, facet_col_labels, trace_type, flipped_rows, flipped_cols, show_boxes, SUBPLOT_SPACING, marker_color, kwargs_trace, kwargs_marker, ) else: fig, annotations = _facet_grid( df, x, y, facet_row, facet_col, num_of_rows, num_of_cols, facet_row_labels, facet_col_labels, trace_type, flipped_rows, flipped_cols, show_boxes, SUBPLOT_SPACING, marker_color, kwargs_trace, kwargs_marker, ) if not height: height = max(600, 100 * num_of_rows) if not width: width = max(600, 100 * num_of_cols) fig["layout"].update(height=height, width=width, title="", paper_bgcolor="rgb(251, 251, 251)") if ggplot2: fig["layout"].update( plot_bgcolor=PLOT_BGCOLOR, paper_bgcolor="rgb(255, 255, 255)", hovermode="closest", ) # axis titles x_title_annot = _axis_title_annotation(x, "x") y_title_annot = _axis_title_annotation(y, "y") # annotations annotations.append(x_title_annot) annotations.append(y_title_annot) # legend fig["layout"]["showlegend"] = show_legend fig["layout"]["legend"]["bgcolor"] = LEGEND_COLOR fig["layout"]["legend"]["borderwidth"] = LEGEND_BORDER_WIDTH fig["layout"]["legend"]["x"] = 1.05 fig["layout"]["legend"]["y"] = 1 fig["layout"]["legend"]["yanchor"] = "top" if show_legend: fig["layout"]["showlegend"] = show_legend if ggplot2: if color_name: legend_annot = _legend_annotation(color_name) annotations.append(legend_annot) fig["layout"]["margin"]["r"] = 150 # assign annotations to figure fig["layout"]["annotations"] = annotations # add shaded boxes behind axis titles if show_boxes and ggplot2: _add_shapes_to_fig(fig, ANNOT_RECT_COLOR, flipped_rows, flipped_cols) # all xaxis and yaxis labels axis_labels = {"x": [], "y": []} for key in fig["layout"]: if "xaxis" in key: axis_labels["x"].append(key) elif "yaxis" in key: axis_labels["y"].append(key) string_number_in_data = False for var in [v for v in [x, y] if v]: if isinstance(df[var].tolist()[0], str): for item in df[var]: try: int(item) string_number_in_data = True except ValueError: pass if string_number_in_data: for x_y in axis_labels.keys(): for axis_name in axis_labels[x_y]: fig["layout"][axis_name]["type"] = "category" if scales == "fixed": fixed_axes = ["x", "y"] elif scales == "free_x": fixed_axes = ["y"] elif scales == "free_y": fixed_axes = ["x"] elif scales == "free": fixed_axes = [] # fixed ranges for x_y in fixed_axes: min_ranges = [] max_ranges = [] for trace in fig["data"]: if trace[x_y] is not None and len(trace[x_y]) > 0: min_ranges.append(min(trace[x_y])) max_ranges.append(max(trace[x_y])) while None in min_ranges: min_ranges.remove(None) while None in max_ranges: max_ranges.remove(None) min_range = min(min_ranges) max_range = max(max_ranges) range_are_numbers = isinstance(min_range, Number) and isinstance( max_range, Number) if range_are_numbers: min_range = math.floor(min_range) max_range = math.ceil(max_range) # extend widen frame by 5% on each side min_range -= 0.05 * (max_range - min_range) max_range += 0.05 * (max_range - min_range) if x_y == "x": if dtick_x: dtick = dtick_x else: dtick = math.floor( (max_range - min_range) / MAX_TICKS_PER_AXIS) elif x_y == "y": if dtick_y: dtick = dtick_y else: dtick = math.floor( (max_range - min_range) / MAX_TICKS_PER_AXIS) else: dtick = 1 for axis_title in axis_labels[x_y]: fig["layout"][axis_title]["dtick"] = dtick fig["layout"][axis_title]["ticklen"] = 0 fig["layout"][axis_title]["zeroline"] = False if ggplot2: fig["layout"][axis_title]["tickwidth"] = 1 fig["layout"][axis_title]["ticklen"] = 4 fig["layout"][axis_title]["gridwidth"] = GRID_WIDTH fig["layout"][axis_title]["gridcolor"] = GRID_COLOR fig["layout"][axis_title]["gridwidth"] = 2 fig["layout"][axis_title]["tickfont"] = { "color": TICK_COLOR, "size": 10, } # insert ranges into fig if x_y in fixed_axes: for key in fig["layout"]: if "{}axis".format(x_y) in key and range_are_numbers: fig["layout"][key]["range"] = [min_range, max_range] return fig
def create_facet_grid(df, x=None, y=None, facet_row=None, facet_col=None, color_name=None, colormap=None, color_is_cat=False, facet_row_labels=None, facet_col_labels=None, height=None, width=None, trace_type='scatter', scales='fixed', dtick_x=None, dtick_y=None, show_boxes=True, ggplot2=False, binsize=1, **kwargs): """ Returns figure for facet grid. :param (pd.DataFrame) df: the dataframe of columns for the facet grid. :param (str) x: the name of the dataframe column for the x axis data. :param (str) y: the name of the dataframe column for the y axis data. :param (str) facet_row: the name of the dataframe column that is used to facet the grid into row panels. :param (str) facet_col: the name of the dataframe column that is used to facet the grid into column panels. :param (str) color_name: the name of your dataframe column that will function as the colormap variable. :param (str|list|dict) colormap: the param that determines how the color_name column colors the data. If the dataframe contains numeric data, then a dictionary of colors will group the data categorically while a Plotly Colorscale name or a custom colorscale will treat it numerically. To learn more about colors and types of colormap, run `help(plotly.colors)`. :param (bool) color_is_cat: determines whether a numerical column for the colormap will be treated as categorical (True) or sequential (False). Default = False. :param (str|dict) facet_row_labels: set to either 'name' or a dictionary of all the unique values in the faceting row mapped to some text to show up in the label annotations. If None, labeling works like usual. :param (str|dict) facet_col_labels: set to either 'name' or a dictionary of all the values in the faceting row mapped to some text to show up in the label annotations. If None, labeling works like usual. :param (int) height: the height of the facet grid figure. :param (int) width: the width of the facet grid figure. :param (str) trace_type: decides the type of plot to appear in the facet grid. The options are 'scatter', 'scattergl', 'histogram', 'bar', and 'box'. Default = 'scatter'. :param (str) scales: determines if axes have fixed ranges or not. Valid settings are 'fixed' (all axes fixed), 'free_x' (x axis free only), 'free_y' (y axis free only) or 'free' (both axes free). :param (float) dtick_x: determines the distance between each tick on the x-axis. Default is None which means dtick_x is set automatically. :param (float) dtick_y: determines the distance between each tick on the y-axis. Default is None which means dtick_y is set automatically. :param (bool) show_boxes: draws grey boxes behind the facet titles. :param (bool) ggplot2: draws the facet grid in the style of `ggplot2`. See http://ggplot2.tidyverse.org/reference/facet_grid.html for reference. Default = False :param (int) binsize: groups all data into bins of a given length. :param (dict) kwargs: a dictionary of scatterplot arguments. Examples 1: One Way Faceting ``` import plotly.plotly as py import plotly.figure_factory as ff import pandas as pd mpg = pd.read_table('https://raw.githubusercontent.com/plotly/datasets/master/mpg_2017.txt') fig = ff.create_facet_grid( mpg, x='displ', y='cty', facet_col='cyl', ) py.iplot(fig, filename='facet_grid_mpg_one_way_facet') ``` Example 2: Two Way Faceting ``` import plotly.plotly as py import plotly.figure_factory as ff import pandas as pd mpg = pd.read_table('https://raw.githubusercontent.com/plotly/datasets/master/mpg_2017.txt') fig = ff.create_facet_grid( mpg, x='displ', y='cty', facet_row='drv', facet_col='cyl', ) py.iplot(fig, filename='facet_grid_mpg_two_way_facet') ``` Example 3: Categorical Coloring ``` import plotly.plotly as py import plotly.figure_factory as ff import pandas as pd mpg = pd.read_table('https://raw.githubusercontent.com/plotly/datasets/master/mpg_2017.txt') fig = ff.create_facet_grid( mtcars, x='mpg', y='wt', facet_col='cyl', color_name='cyl', color_is_cat=True, ) py.iplot(fig, filename='facet_grid_mpg_default_colors') ``` Example 4: Sequential Coloring ``` import plotly.plotly as py import plotly.figure_factory as ff import pandas as pd tips = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/tips.csv') fig = ff.create_facet_grid( tips, x='total_bill', y='tip', facet_row='sex', facet_col='smoker', color_name='size', colormap='Viridis', ) py.iplot(fig, filename='facet_grid_tips_sequential_colors') ``` Example 5: Custom labels ``` import plotly.plotly as py import plotly.figure_factory as ff import pandas as pd mtcars = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/mtcars.csv') fig = ff.create_facet_grid( mtcars, x='wt', y='mpg', facet_col='cyl', facet_col_labels={4: "$\\alpha$", 6: '$\\beta$', 8: '$\sqrt[y]{x}$'}, ) py.iplot(fig, filename='facet_grid_mtcars_custom_labels') ``` Example 6: Other Trace Type ``` import plotly.plotly as py import plotly.figure_factory as ff import pandas as pd mtcars = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/mtcars.csv') fig = ff.create_facet_grid( mtcars, x='wt', facet_col='cyl', trace_type='histogram', ) py.iplot(fig, filename='facet_grid_mtcars_other_trace_type') ``` """ if not pd: raise ImportError( "'pandas' must be installed for this figure_factory." ) if not isinstance(df, pd.DataFrame): raise exceptions.PlotlyError( "You must input a pandas DataFrame." ) # make sure all columns are of homogenous datatype utils.validate_dataframe(df) if trace_type in ['scatter', 'scattergl']: if not x or not y: raise exceptions.PlotlyError( "You need to input 'x' and 'y' if you are you are using a " "trace_type of 'scatter' or 'scattergl'." ) for key in [x, y, facet_row, facet_col, color_name]: if key is not None: try: df[key] except KeyError: raise exceptions.PlotlyError( "x, y, facet_row, facet_col and color_name must be keys " "in your dataframe." ) # autoscale histogram bars if trace_type not in ['scatter', 'scattergl']: scales = 'free' # validate scales if scales not in ['fixed', 'free_x', 'free_y', 'free']: raise exceptions.PlotlyError( "'scales' must be set to 'fixed', 'free_x', 'free_y' and 'free'." ) if trace_type not in VALID_TRACE_TYPES: raise exceptions.PlotlyError( "'trace_type' must be in {}".format(VALID_TRACE_TYPES) ) if trace_type == 'histogram': SUBPLOT_SPACING = 0.06 else: SUBPLOT_SPACING = 0.015 # seperate kwargs for marker and else if 'marker' in kwargs: kwargs_marker = kwargs['marker'] else: kwargs_marker = {} marker_color = kwargs_marker.pop('color', None) kwargs.pop('marker', None) kwargs_trace = kwargs if 'size' not in kwargs_marker: if ggplot2: kwargs_marker['size'] = 5 else: kwargs_marker['size'] = 8 if 'opacity' not in kwargs_marker: if not ggplot2: kwargs_trace['opacity'] = 0.6 if 'line' not in kwargs_marker: if not ggplot2: kwargs_marker['line'] = {'color': 'darkgrey', 'width': 1} else: kwargs_marker['line'] = {} # default marker size if not ggplot2: if not marker_color: marker_color = 'rgb(31, 119, 180)' else: marker_color = 'rgb(0, 0, 0)' num_of_rows = 1 num_of_cols = 1 flipped_rows = False flipped_cols = False if facet_row: num_of_rows = len(df[facet_row].unique()) flipped_rows = _is_flipped(num_of_rows) if isinstance(facet_row_labels, dict): for key in df[facet_row].unique(): if key not in facet_row_labels.keys(): unique_keys = df[facet_row].unique().tolist() raise exceptions.PlotlyError( CUSTOM_LABEL_ERROR.format(unique_keys) ) if facet_col: num_of_cols = len(df[facet_col].unique()) flipped_cols = _is_flipped(num_of_cols) if isinstance(facet_col_labels, dict): for key in df[facet_col].unique(): if key not in facet_col_labels.keys(): unique_keys = df[facet_col].unique().tolist() raise exceptions.PlotlyError( CUSTOM_LABEL_ERROR.format(unique_keys) ) show_legend = False if color_name: if isinstance(df[color_name].iloc[0], str) or color_is_cat: show_legend = True if isinstance(colormap, dict): clrs.validate_colors_dict(colormap, 'rgb') for val in df[color_name].unique(): if val not in colormap.keys(): raise exceptions.PlotlyError( "If using 'colormap' as a dictionary, make sure " "all the values of the colormap column are in " "the keys of your dictionary." ) else: # use default plotly colors for dictionary default_colors = clrs.DEFAULT_PLOTLY_COLORS colormap = {} j = 0 for val in df[color_name].unique(): if j >= len(default_colors): j = 0 colormap[val] = default_colors[j] j += 1 fig, annotations = _facet_grid_color_categorical( df, x, y, facet_row, facet_col, color_name, colormap, num_of_rows, num_of_cols, facet_row_labels, facet_col_labels, trace_type, flipped_rows, flipped_cols, show_boxes, SUBPLOT_SPACING, marker_color, kwargs_trace, kwargs_marker ) elif isinstance(df[color_name].iloc[0], Number): if isinstance(colormap, dict): show_legend = True clrs.validate_colors_dict(colormap, 'rgb') for val in df[color_name].unique(): if val not in colormap.keys(): raise exceptions.PlotlyError( "If using 'colormap' as a dictionary, make sure " "all the values of the colormap column are in " "the keys of your dictionary." ) fig, annotations = _facet_grid_color_categorical( df, x, y, facet_row, facet_col, color_name, colormap, num_of_rows, num_of_cols, facet_row_labels, facet_col_labels, trace_type, flipped_rows, flipped_cols, show_boxes, SUBPLOT_SPACING, marker_color, kwargs_trace, kwargs_marker ) elif isinstance(colormap, list): colorscale_list = colormap clrs.validate_colorscale(colorscale_list) fig, annotations = _facet_grid_color_numerical( df, x, y, facet_row, facet_col, color_name, colorscale_list, num_of_rows, num_of_cols, facet_row_labels, facet_col_labels, trace_type, flipped_rows, flipped_cols, show_boxes, SUBPLOT_SPACING, marker_color, kwargs_trace, kwargs_marker ) elif isinstance(colormap, str): if colormap in clrs.PLOTLY_SCALES.keys(): colorscale_list = clrs.PLOTLY_SCALES[colormap] else: raise exceptions.PlotlyError( "If 'colormap' is a string, it must be the name " "of a Plotly Colorscale. The available colorscale " "names are {}".format(clrs.PLOTLY_SCALES.keys()) ) fig, annotations = _facet_grid_color_numerical( df, x, y, facet_row, facet_col, color_name, colorscale_list, num_of_rows, num_of_cols, facet_row_labels, facet_col_labels, trace_type, flipped_rows, flipped_cols, show_boxes, SUBPLOT_SPACING, marker_color, kwargs_trace, kwargs_marker ) else: colorscale_list = clrs.PLOTLY_SCALES['Reds'] fig, annotations = _facet_grid_color_numerical( df, x, y, facet_row, facet_col, color_name, colorscale_list, num_of_rows, num_of_cols, facet_row_labels, facet_col_labels, trace_type, flipped_rows, flipped_cols, show_boxes, SUBPLOT_SPACING, marker_color, kwargs_trace, kwargs_marker ) else: fig, annotations = _facet_grid( df, x, y, facet_row, facet_col, num_of_rows, num_of_cols, facet_row_labels, facet_col_labels, trace_type, flipped_rows, flipped_cols, show_boxes, SUBPLOT_SPACING, marker_color, kwargs_trace, kwargs_marker ) if not height: height = max(600, 100 * num_of_rows) if not width: width = max(600, 100 * num_of_cols) fig['layout'].update(height=height, width=width, title='', paper_bgcolor='rgb(251, 251, 251)') if ggplot2: fig['layout'].update(plot_bgcolor=PLOT_BGCOLOR, paper_bgcolor='rgb(255, 255, 255)', hovermode='closest') # axis titles x_title_annot = _axis_title_annotation(x, 'x') y_title_annot = _axis_title_annotation(y, 'y') # annotations annotations.append(x_title_annot) annotations.append(y_title_annot) # legend fig['layout']['showlegend'] = show_legend fig['layout']['legend']['bgcolor'] = LEGEND_COLOR fig['layout']['legend']['borderwidth'] = LEGEND_BORDER_WIDTH fig['layout']['legend']['x'] = 1.05 fig['layout']['legend']['y'] = 1 fig['layout']['legend']['yanchor'] = 'top' if show_legend: fig['layout']['showlegend'] = show_legend if ggplot2: if color_name: legend_annot = _legend_annotation(color_name) annotations.append(legend_annot) fig['layout']['margin']['r'] = 150 # assign annotations to figure fig['layout']['annotations'] = annotations # add shaded boxes behind axis titles if show_boxes and ggplot2: _add_shapes_to_fig(fig, ANNOT_RECT_COLOR, flipped_rows, flipped_cols) # all xaxis and yaxis labels axis_labels = {'x': [], 'y': []} for key in fig['layout']: if 'xaxis' in key: axis_labels['x'].append(key) elif 'yaxis' in key: axis_labels['y'].append(key) string_number_in_data = False for var in [v for v in [x, y] if v]: if isinstance(df[var].tolist()[0], str): for item in df[var]: try: int(item) string_number_in_data = True except ValueError: pass if string_number_in_data: for x_y in axis_labels.keys(): for axis_name in axis_labels[x_y]: fig['layout'][axis_name]['type'] = 'category' if scales == 'fixed': fixed_axes = ['x', 'y'] elif scales == 'free_x': fixed_axes = ['y'] elif scales == 'free_y': fixed_axes = ['x'] elif scales == 'free': fixed_axes = [] # fixed ranges for x_y in fixed_axes: min_ranges = [] max_ranges = [] for trace in fig['data']: if trace[x_y] is not None and len(trace[x_y]) > 0: min_ranges.append(min(trace[x_y])) max_ranges.append(max(trace[x_y])) while None in min_ranges: min_ranges.remove(None) while None in max_ranges: max_ranges.remove(None) min_range = min(min_ranges) max_range = max(max_ranges) range_are_numbers = (isinstance(min_range, Number) and isinstance(max_range, Number)) if range_are_numbers: min_range = math.floor(min_range) max_range = math.ceil(max_range) # extend widen frame by 5% on each side min_range -= 0.05 * (max_range - min_range) max_range += 0.05 * (max_range - min_range) if x_y == 'x': if dtick_x: dtick = dtick_x else: dtick = math.floor( (max_range - min_range) / MAX_TICKS_PER_AXIS ) elif x_y == 'y': if dtick_y: dtick = dtick_y else: dtick = math.floor( (max_range - min_range) / MAX_TICKS_PER_AXIS ) else: dtick = 1 for axis_title in axis_labels[x_y]: fig['layout'][axis_title]['dtick'] = dtick fig['layout'][axis_title]['ticklen'] = 0 fig['layout'][axis_title]['zeroline'] = False if ggplot2: fig['layout'][axis_title]['tickwidth'] = 1 fig['layout'][axis_title]['ticklen'] = 4 fig['layout'][axis_title]['gridwidth'] = GRID_WIDTH fig['layout'][axis_title]['gridcolor'] = GRID_COLOR fig['layout'][axis_title]['gridwidth'] = 2 fig['layout'][axis_title]['tickfont'] = { 'color': TICK_COLOR, 'size': 10 } # insert ranges into fig if x_y in fixed_axes: for key in fig['layout']: if '{}axis'.format(x_y) in key and range_are_numbers: fig['layout'][key]['range'] = [min_range, max_range] return fig
def create_violin(data, data_header=None, group_header=None, colors=None, use_colorscale=False, group_stats=None, rugplot=True, sort=False, height=450, width=600, title='Violin and Rug Plot'): """ Returns figure for a violin plot :param (list|array) data: accepts either a list of numerical values, a list of dictionaries all with identical keys and at least one column of numeric values, or a pandas dataframe with at least one column of numbers. :param (str) data_header: the header of the data column to be used from an inputted pandas dataframe. Not applicable if 'data' is a list of numeric values. :param (str) group_header: applicable if grouping data by a variable. 'group_header' must be set to the name of the grouping variable. :param (str|tuple|list|dict) colors: either a plotly scale name, an rgb or hex color, a color tuple, a list of colors or a dictionary. An rgb color is of the form 'rgb(x, y, z)' where x, y and z belong to the interval [0, 255] and a color tuple is a tuple of the form (a, b, c) where a, b and c belong to [0, 1]. If colors is a list, it must contain valid color types as its members. :param (bool) use_colorscale: only applicable if grouping by another variable. Will implement a colorscale based on the first 2 colors of param colors. This means colors must be a list with at least 2 colors in it (Plotly colorscales are accepted since they map to a list of two rgb colors). Default = False :param (dict) group_stats: a dictioanry where each key is a unique value from the group_header column in data. Each value must be a number and will be used to color the violin plots if a colorscale is being used. :param (bool) rugplot: determines if a rugplot is draw on violin plot. Default = True :param (bool) sort: determines if violins are sorted alphabetically (True) or by input order (False). Default = False :param (float) height: the height of the violin plot. :param (float) width: the width of the violin plot. :param (str) title: the title of the violin plot. Example 1: Single Violin Plot ``` import plotly.plotly as py from plotly.figure_factory import create_violin from plotly.graph_objs import graph_objs import numpy as np from scipy import stats # create list of random values data_list = np.random.randn(100) data_list.tolist() # create violin fig fig = create_violin(data_list, colors='#604d9e') # plot py.iplot(fig, filename='Violin Plot') ``` Example 2: Multiple Violin Plots with Qualitative Coloring ``` import plotly.plotly as py from plotly.figure_factory import create_violin from plotly.graph_objs import graph_objs import numpy as np import pandas as pd from scipy import stats # create dataframe np.random.seed(619517) Nr=250 y = np.random.randn(Nr) gr = np.random.choice(list("ABCDE"), Nr) norm_params=[(0, 1.2), (0.7, 1), (-0.5, 1.4), (0.3, 1), (0.8, 0.9)] for i, letter in enumerate("ABCDE"): y[gr == letter] *=norm_params[i][1]+ norm_params[i][0] df = pd.DataFrame(dict(Score=y, Group=gr)) # create violin fig fig = create_violin(df, data_header='Score', group_header='Group', sort=True, height=600, width=1000) # plot py.iplot(fig, filename='Violin Plot with Coloring') ``` Example 3: Violin Plots with Colorscale ``` import plotly.plotly as py from plotly.figure_factory import create_violin from plotly.graph_objs import graph_objs import numpy as np import pandas as pd from scipy import stats # create dataframe np.random.seed(619517) Nr=250 y = np.random.randn(Nr) gr = np.random.choice(list("ABCDE"), Nr) norm_params=[(0, 1.2), (0.7, 1), (-0.5, 1.4), (0.3, 1), (0.8, 0.9)] for i, letter in enumerate("ABCDE"): y[gr == letter] *=norm_params[i][1]+ norm_params[i][0] df = pd.DataFrame(dict(Score=y, Group=gr)) # define header params data_header = 'Score' group_header = 'Group' # make groupby object with pandas group_stats = {} groupby_data = df.groupby([group_header]) for group in "ABCDE": data_from_group = groupby_data.get_group(group)[data_header] # take a stat of the grouped data stat = np.median(data_from_group) # add to dictionary group_stats[group] = stat # create violin fig fig = create_violin(df, data_header='Score', group_header='Group', height=600, width=1000, use_colorscale=True, group_stats=group_stats) # plot py.iplot(fig, filename='Violin Plot with Colorscale') ``` """ # Validate colors if isinstance(colors, dict): valid_colors = clrs.validate_colors_dict(colors, 'rgb') else: valid_colors = clrs.validate_colors(colors, 'rgb') # validate data and choose plot type if group_header is None: if isinstance(data, list): if len(data) <= 0: raise exceptions.PlotlyError("If data is a list, it must be " "nonempty and contain either " "numbers or dictionaries.") if not all(isinstance(element, Number) for element in data): raise exceptions.PlotlyError("If data is a list, it must " "contain only numbers.") if pd and isinstance(data, pd.core.frame.DataFrame): if data_header is None: raise exceptions.PlotlyError("data_header must be the " "column name with the " "desired numeric data for " "the violin plot.") data = data[data_header].values.tolist() # call the plotting functions plot_data, plot_xrange = violinplot(data, fillcolor=valid_colors[0], rugplot=rugplot) layout = graph_objs.Layout( title=title, autosize=False, font=graph_objs.layout.Font(size=11), height=height, showlegend=False, width=width, xaxis=make_XAxis('', plot_xrange), yaxis=make_YAxis(''), hovermode='closest' ) layout['yaxis'].update(dict(showline=False, showticklabels=False, ticks='')) fig = graph_objs.Figure(data=plot_data, layout=layout) return fig else: if not isinstance(data, pd.core.frame.DataFrame): raise exceptions.PlotlyError("Error. You must use a pandas " "DataFrame if you are using a " "group header.") if data_header is None: raise exceptions.PlotlyError("data_header must be the column " "name with the desired numeric " "data for the violin plot.") if use_colorscale is False: if isinstance(valid_colors, dict): # validate colors dict choice below fig = violin_dict( data, data_header, group_header, valid_colors, use_colorscale, group_stats, rugplot, sort, height, width, title ) return fig else: fig = violin_no_colorscale( data, data_header, group_header, valid_colors, use_colorscale, group_stats, rugplot, sort, height, width, title ) return fig else: if isinstance(valid_colors, dict): raise exceptions.PlotlyError("The colors param cannot be " "a dictionary if you are " "using a colorscale.") if len(valid_colors) < 2: raise exceptions.PlotlyError("colors must be a list with " "at least 2 colors. A " "Plotly scale is allowed.") if not isinstance(group_stats, dict): raise exceptions.PlotlyError("Your group_stats param " "must be a dictionary.") fig = violin_colorscale( data, data_header, group_header, valid_colors, use_colorscale, group_stats, rugplot, sort, height, width, title ) return fig
def create_project_gantt( df, colors, title="Gantt Chart", height=None, width=None, bar_width=0.2, showgrid_x=False, showgrid_y=False, task_length=27, index_col='Resource', showlegend=True ): # validate gantt input data chart = validate_gantt(df) if index_col: if index_col not in chart[0]: raise exceptions.PlotlyError( "In order to use an indexing column and assign colors to " "the values of the index, you must choose an actual " "column name in the dataframe or key if a list of " "dictionaries is being used." ) # validate gantt index column index_list = [] for dictionary in chart: index_list.append(dictionary[index_col]) utils.validate_index(index_list) # Validate colors colors = clrs.validate_colors_dict(colors, "rgb") hoverinfo = "text" scatter_data_template = { "x": [], "y": [], "mode": "none", "fill": "toself", "taskname": "", "percent": 0.0, "hoverinfo": hoverinfo, "legendgroup": "", } marker_data_template = { "x": [], "y": [], "mode": "markers", "text": [], "marker": dict(color="", size=1, opacity=0), "name": "", "showlegend": False, } # create a scatter trace for every task scatter_data_dict = OrderedDict() # create scatter traces for the start- and endpoints marker_data_dict = OrderedDict() tasks = [] task_names = [] index_vals = [] # Generate list of tasks for index in range(len(chart)): task = dict( x0=chart[index]["Start"], x1=chart[index]["Finish"], resource=chart[index]["Resource"], percent = chart[index]["Percent"] ) if len(chart[index]["Task"]) > task_length: str_break = task_length for i in range(task_length, 0, -1): if chart[index]["Task"][i] == ' ': str_break = i break name = chart[index]["Task"][0:str_break] name += '...' else: name = chart[index]["Task"] task["name"] = name if chart[index]['Description']: task["description"] = chart[index]["Description"] else: task["description"] = chart[index]["Task"] tasks.append(task) # Make sure the resource column has an associated color if task['resource'] not in colors: raise exceptions.PlotlyError( "If you are using colors as a dictionary, all of its " "keys must be all the values in the index column." ) # create the list of task names for index in range(len(tasks)): tn = tasks[index]["name"] if tn not in task_names: task_names.append(tn) # Shorten task names if needed? for index in range(len(tasks)): # del tasks[index]["name"] # Separate task bars by index tasks[index]["y0"] = index - bar_width tasks[index]["y1"] = index + bar_width # Get the fill color from the color dictionary # tasks[index]["fillcolor"] = colors[chart[index][index_col]] # color_id = tasks[index]["fillcolor"] scatter_data_dict[index] = copy.deepcopy(scatter_data_template) color = colors[chart[index][index_col]] scatter_data_dict[index]["legendgroup"] = color scatter_data_dict[index]["fillcolor"] = color scatter_data_dict[index]["text"] = tasks[index]['description'] scatter_data_dict[index]["percent"] = tasks[index]['percent'] scatter_data_dict[index]["taskname"] = tasks[index]["name"] # Only used for processing at the end # if this is the first instance of the group name appearing, make sure to put it in the legend group = tasks[index]['resource'] if group not in index_vals and showlegend: scatter_data_dict[index]["name"] = group scatter_data_dict[index]["showlegend"] = True index_vals.append(group) else: scatter_data_dict[index]["name"] = None scatter_data_dict[index]["showlegend"] = False xs, ys = _get_corner_points( tasks[index]["x0"], tasks[index]["y0"], tasks[index]["x1"], tasks[index]["y1"], ) scatter_data_dict[index]["x"] += xs scatter_data_dict[index]["y"] += ys # append dummy markers for showing start and end of interval marker_data_dict[index] = copy.deepcopy(marker_data_template) marker_data_dict[index]["marker"]["color"] = color marker_data_dict[index]["legendgroup"] = color marker_data_dict[index]["x"].append(tasks[index]["x0"]) marker_data_dict[index]["x"].append(tasks[index]["x1"]) marker_data_dict[index]["y"].append(index) marker_data_dict[index]["y"].append(index) marker_data_dict[index]["text"].append(tasks[index]["description"]) marker_data_dict[index]["text"].append(tasks[index]["description"]) layout = dict( title=title, showlegend=True, height=height, width=width, shapes=[], hovermode="closest", yaxis=dict( showgrid=showgrid_y, ticktext=task_names, tickvals=list(range(len(task_names))), range=[-1, len(task_names) + 1], autorange=False, zeroline=False, ), xaxis=dict( showgrid=showgrid_x, zeroline=False, rangeselector=dict( buttons=list( [ dict(count=7, label="1w", step="day", stepmode="backward"), dict(count=1, label="1m", step="month", stepmode="backward"), dict(count=6, label="6m", step="month", stepmode="backward"), dict(count=1, label="YTD", step="year", stepmode="todate"), dict(count=1, label="1y", step="year", stepmode="backward"), dict(step="all"), ] ) ), type="date", ), ) data = [scatter_data_dict[k] for k in scatter_data_dict] data += [marker_data_dict[k] for k in marker_data_dict] percent_data = [] rgb_pattern = re.compile('rgb\((?P<r>[0-9]+), (?P<g>[0-9]+), (?P<b>[0-9]+)\)') for item in data: if 'taskname' in item: entry = copy.deepcopy(item) match = re.match(rgb_pattern, entry['fillcolor']) if match: colors = [int(match.groupdict()['r']), int(match.groupdict()['g']), int(match.groupdict()['b'])] new_colors = [] for i in range(0, len(colors)): new_colors.append(int(max(0, floor((float(colors[i]) - (float(colors[i]) * 0.35)))))) entry['fillcolor'] = 'rgb({r}, {g}, {b})'.format(r=new_colors[0], g=new_colors[1], b=new_colors[2]) else: entry['fillcolor'] = '#000000' entry['name'] = '' entry['showlegend'] = False start = datetime.strptime(entry['x'][0], '%Y-%m-%d') end = datetime.strptime(entry['x'][1], '%Y-%m-%d') delta = (end - start).total_seconds() delta = int(delta * item['percent']) days = delta // 86400 # seconds per day hours = (delta - (days * 86400)) // 3600 # seconds per hour end = start + timedelta(days=days, hours=hours) end_date = str(adjust_end_date(end)) entry['x'][1] = end_date entry['x'][2] = end_date percent_data.append(entry) data += percent_data fig = go.Figure(data=data, layout=layout) return fig
def create_facet_grid(df, x=None, y=None, facet_row=None, facet_col=None, color_name=None, colormap=None, color_is_cat=False, facet_row_labels=None, facet_col_labels=None, height=None, width=None, trace_type='scatter', hide_x_labels=False, hide_y_labels=False, scales='fixed', dtick_x=None, dtick_y=None, text_name=None, show_boxes=True, ggplot2=False, binsize=1, jitter=0, **kwargs): """ Returns figure for facet grid. :param (pd.DataFrame) df: the dataframe of columns for the facet grid. :param (str) x: the name of the dataframe column for the x axis data. :param (str) y: the name of the dataframe column for the y axis data. :param (str) facet_row: the name of the dataframe column that is used to facet the grid into row panels. :param (str) facet_col: the name of the dataframe column that is used to facet the grid into column panels. :param (str) color_name: the name of your dataframe column that will function as the colormap variable. :param (str|list|dict) colormap: the param that determines how the color_name column colors the data. If the dataframe contains numeric data, then a dictionary of colors will group the data categorically while a Plotly Colorscale name or a custom colorscale will treat it numerically. To learn more about colors and types of colormap, run `help(plotly.colors)`. :param (bool) color_is_cat: determines whether a numerical column for the colormap will be treated as categorical (True) or sequential (False). Default = False. :param (str|dict) facet_row_labels: set to either 'name' or a dictionary of all the unique values in the faceting row mapped to some text to show up in the label annotations. If None, labeling works like usual. :param (str|dict) facet_col_labels: set to either 'name' or a dictionary of all the values in the faceting row mapped to some text to show up in the label annotations. If None, labeling works like usual. :param (int) height: the height of the facet grid figure. :param (int) width: the width of the facet grid figure. :param (str) trace_type: decides the type of plot to appear in the facet grid. The options are 'scatter', 'scattergl', 'histogram', 'bar', and 'box'. Default = 'scatter'. :param (str) scales: determines if axes have fixed ranges or not. Valid settings are 'fixed' (all axes fixed), 'free_x' (x axis free only), 'free_y' (y axis free only) or 'free' (both axes free). :param (float) dtick_x: determines the distance between each tick on the x-axis. Default is None which means dtick_x is set automatically. :param (float) dtick_y: determines the distance between each tick on the y-axis. Default is None which means dtick_y is set automatically. :param (bool) show_boxes: draws grey boxes behind the facet titles. :param (bool) ggplot2: draws the facet grid in the style of `ggplot2`. See http://ggplot2.tidyverse.org/reference/facet_grid.html for reference. Default = False :param (int) binsize: groups all data into bins of a given length. :param (int) jitter: Amount to offset an individual categorical x-axis datapoint. The higher the number, the more extreme the jitter Default: 0 (no jitter) :param (dict) kwargs: a dictionary of scatterplot arguments. Examples 1: One Way Faceting ``` import plotly.plotly as py import plotly.figure_factory as ff import pandas as pd mpg = pd.read_table('https://raw.githubusercontent.com/plotly/datasets/master/mpg_2017.txt') fig = ff.create_facet_grid( mpg, x='displ', y='cty', facet_col='cyl', ) py.iplot(fig, filename='facet_grid_mpg_one_way_facet') ``` Example 2: Two Way Faceting ``` import plotly.plotly as py import plotly.figure_factory as ff import pandas as pd mpg = pd.read_table('https://raw.githubusercontent.com/plotly/datasets/master/mpg_2017.txt') fig = ff.create_facet_grid( mpg, x='displ', y='cty', facet_row='drv', facet_col='cyl', ) py.iplot(fig, filename='facet_grid_mpg_two_way_facet') ``` Example 3: Categorical Coloring ``` import plotly.plotly as py import plotly.figure_factory as ff import pandas as pd mpg = pd.read_table('https://raw.githubusercontent.com/plotly/datasets/master/mpg_2017.txt') fig = ff.create_facet_grid( mtcars, x='mpg', y='wt', facet_col='cyl', color_name='cyl', color_is_cat=True, ) py.iplot(fig, filename='facet_grid_mpg_default_colors') ``` Example 4: Sequential Coloring ``` import plotly.plotly as py import plotly.figure_factory as ff import pandas as pd tips = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/tips.csv') fig = ff.create_facet_grid( tips, x='total_bill', y='tip', facet_row='sex', facet_col='smoker', color_name='size', colormap='Viridis', ) py.iplot(fig, filename='facet_grid_tips_sequential_colors') ``` Example 5: Custom labels ``` import plotly.plotly as py import plotly.figure_factory as ff import pandas as pd mtcars = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/mtcars.csv') fig = ff.create_facet_grid( mtcars, x='wt', y='mpg', facet_col='cyl', facet_col_labels={4: "$\\alpha$", 6: '$\\beta$', 8: '$\sqrt[y]{x}$'}, ) py.iplot(fig, filename='facet_grid_mtcars_custom_labels') ``` Example 6: Other Trace Type ``` import plotly.plotly as py import plotly.figure_factory as ff import pandas as pd mtcars = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/mtcars.csv') fig = ff.create_facet_grid( mtcars, x='wt', facet_col='cyl', trace_type='histogram', ) py.iplot(fig, filename='facet_grid_mtcars_other_trace_type') ``` """ if not pd: raise exceptions.ImportError( "'pandas' must be installed for this figure_factory.") if not isinstance(df, pd.DataFrame): raise exceptions.PlotlyError("You must input a pandas DataFrame.") # make sure all columns are of homogenous datatype utils.validate_dataframe(df) # the tsne_dynamic trace type is an alias for scatter if trace_type == 'tsne_dynamic': trace_type = 'scatter' if PLOT_LOGGING: print("DEBUG: trace_type is: {0}".format(trace_type), file=sys.stderr) if trace_type in ['scatter', 'scattergl']: if not x or not y: raise exceptions.PlotlyError( "You need to input 'x' and 'y' if you are you are using a " "trace_type of 'scatter' or 'scattergl'.") for key in [x, y, facet_row, facet_col, color_name]: if key is not None: try: df[key] except KeyError: raise exceptions.PlotlyError( "x, y, facet_row, facet_col and color_name must be keys " "in your dataframe.") if trace_type not in VALID_TRACE_TYPES: raise exceptions.PlotlyError( "'trace_type' must be in {}".format(VALID_TRACE_TYPES)) if trace_type == 'histogram' or trace_type == 'line': SUBPLOT_SPACING = 0.06 else: SUBPLOT_SPACING = 0.015 # seperate kwargs for marker and else if 'marker' in kwargs: kwargs_marker = kwargs['marker'] else: kwargs_marker = {} marker_color = kwargs_marker.pop('color', None) kwargs.pop('marker', None) kwargs_trace = kwargs if 'size' not in kwargs_marker: kwargs_marker['size'] = 3 # Bar plots do not accept size markers if trace_type == 'bar': kwargs_marker.pop('size', None) if 'opacity' not in kwargs_marker: kwargs_trace['opacity'] = 0.6 # if 'line' not in kwargs_marker: # kwargs_marker['line'] = {'color': 'darkgrey', 'width': 1} # default marker size if not ggplot2: if not marker_color: marker_color = '#401362' else: marker_color = 'rgb(0, 0, 0)' num_of_rows = 1 num_of_cols = 1 flipped_rows = False flipped_cols = False if facet_row: num_of_rows = len(df[facet_row].unique()) flipped_rows = _is_flipped(num_of_rows) if isinstance(facet_row_labels, dict): for key in df[facet_row].unique(): if key not in facet_row_labels.keys(): unique_keys = df[facet_row].unique().tolist() raise exceptions.PlotlyError( CUSTOM_LABEL_ERROR.format(unique_keys)) if facet_col: num_of_cols = len(df[facet_col].unique()) flipped_cols = _is_flipped(num_of_cols) if isinstance(facet_col_labels, dict): for key in df[facet_col].unique(): if key not in facet_col_labels.keys(): unique_keys = df[facet_col].unique().tolist() raise exceptions.PlotlyError( CUSTOM_LABEL_ERROR.format(unique_keys)) # Set up some args to pass to _gear_facet_grid function show_legend = False colormapping = None color_type = None # None, 'categorical', or 'numerical' # If there is a color label, use either the categorial or numerical facet grid if color_name: if isinstance(colormap, dict): show_legend = True color_type = "categorical" clrs.validate_colors_dict(colormap, 'rgb') for val in df[color_name].unique(): if val not in colormap.keys(): raise exceptions.PlotlyError( "If using 'colormap' as a dictionary, make sure " "all the values of the colormap column are in " "the keys of your dictionary.") colormapping = colormap if PLOT_LOGGING: print("DEBUG: Color type is 'categorical' with colormap dict", file=sys.stderr) elif isinstance(colormap, list): color_type = "numerical" colormapping = colormap clrs.validate_colorscale(colormapping) if PLOT_LOGGING: print("DEBUG: Color type is 'numerical' from colormap list", file=sys.stderr) elif isinstance(colormap, str): color_type = "numerical" if colormap in clrs.PLOTLY_SCALES.keys(): colormapping = clrs.PLOTLY_SCALES[colormap] else: raise exceptions.PlotlyError( "If 'colormap' is a string, it must be the name " "of a Plotly Colorscale. The available colorscale " "names are {}".format(clrs.PLOTLY_SCALES.keys())) if PLOT_LOGGING: print("DEBUG: Color type is 'numerical' from colormap string", file=sys.stderr) else: if isinstance(df[color_name].iloc[0], str) or color_is_cat: color_type = "categorical" # use default plotly colors for dictionary default_colors = clrs.DEFAULT_PLOTLY_COLORS colormap = {} j = 0 for val in df[color_name].unique(): if j >= len(default_colors): j = 0 colormap[val] = default_colors[j] j += 1 colormapping = colormap else: color_type = "numerical" colormapping = [[0, 'rgb(218, 183, 193)'], [0.35, 'rgb(194, 137, 166)'], [0.5, 'rgb(169, 98, 151)'], [0.6, 'rgb(145, 66, 143)'], [0.7, 'rgb(105, 39, 122)'], [1, 'rgb(63, 19, 98)']] if PLOT_LOGGING: print("DEBUG: Color type is 'numerical' with no colormap", file=sys.stderr) else: if PLOT_LOGGING: print("DEBUG: Color type is 'None'", file=sys.stderr) fig, annotations = _gear_facet_grid( df, x, y, facet_row, facet_col, color_name, colormapping, color_type, num_of_rows, num_of_cols, facet_row_labels, facet_col_labels, trace_type, flipped_rows, flipped_cols, SUBPLOT_SPACING, marker_color, text_name, jitter, kwargs_trace, kwargs_marker) ### General layout adjustments fig['layout'].update(title='', paper_bgcolor=PAPER_BGCOLOR) fig['layout']['hovermode'] = "closest" # Default "plotly" theme produces gray plot backgrounds fig['layout']['template'] = "none" # axis titles x_title_annot = _axis_title_annotation('', 'x') y_title_annot = _axis_title_annotation('', 'y') # annotations annotations.append(x_title_annot) annotations.append(y_title_annot) # all xaxis and yaxis labels axis_labels = {'x': [], 'y': []} for key in fig['layout']: if 'xaxis' in key: axis_labels['x'].append(key) elif 'yaxis' in key: axis_labels['y'].append(key) string_number_in_data = False for var in [v for v in [x, y] if v]: if isinstance(df[var].tolist()[0], str): for item in df[var]: try: int(item) string_number_in_data = True except ValueError: pass # Iterated through 'x' or 'y' axis for x_y in axis_labels.keys(): # Iterate through all faceted axes for axis_name in axis_labels[x_y]: # Common to both x and y if string_number_in_data: fig['layout'][axis_name]['type'] = 'category' fig['layout'][axis_name]['showgrid'] = False fig['layout'][axis_name]['automargin'] = True fig['layout'][axis_name]['zeroline'] = False # Specific axis only if x_y == 'x': if hide_x_labels: #TODO: test with 'visible' attribute instead of 'showticklabels' fig['layout'][axis_name]['showticklabels'] = False # Uniformity of tick angles if facet groupings are present if facet_col: fig['layout'][axis_name]['tickangle'] = 270 elif x_y == 'y': fig['layout'][axis_name]['hoverformat'] = '.2f' if hide_y_labels: fig['layout'][axis_name]['showticklabels'] = False fig['layout']['autosize'] = True # legend fig['layout']['showlegend'] = show_legend fig['layout']['legend']['bgcolor'] = LEGEND_COLOR fig['layout']['legend']['borderwidth'] = LEGEND_BORDER_WIDTH fig['layout']['legend']['x'] = 1.05 fig['layout']['legend']['y'] = 1 fig['layout']['legend']['yanchor'] = 'top' # Colorbar adjustments if color_type == "numerical": fig['layout']['coloraxis'] = { "colorscale": colormapping, # Defines the range of colors for a numerical color group "colorbar": { 'x': 1.15 }, "showscale": True, } # Violin plot settings if trace_type == 'violin': if color_name: fig['layout']['violinmode'] = 'group' else: fig['layout']['violinmode'] = 'overlay' # assign annotations to figure fig['layout']['annotations'] = annotations # autoscale histogram bars if trace_type not in ['scatter', 'line', 'scattergl']: scales = 'free' # validate scales if scales not in ['fixed', 'free_x', 'free_y', 'free']: raise exceptions.PlotlyError( "'scales' must be set to 'fixed', 'free_x', 'free_y' and 'free'.") fixed_axes = None if scales == 'fixed': fixed_axes = ['x', 'y'] elif scales == 'free_x': fixed_axes = ['y'] elif scales == 'free_y': fixed_axes = ['x'] elif scales == 'free': fixed_axes = [] else: raise ( "Invalid scale type provided. Must be 'fixed', 'free_x', 'free_y', or 'free'" ) # SAdkins - Removed checks for None and length and sparse matrix check # since recent edits should have all traces populated with data if len(fig['data']): # fixed ranges for x_y in fixed_axes: min_range = min(chain(*(trace[x_y] for trace in fig['data']))) max_range = max(chain(*(trace[x_y] for trace in fig['data']))) range_are_numbers = (isinstance(min_range, Number) and isinstance(max_range, Number)) if PLOT_LOGGING: print("DEBUG: On axis:{0} min_range:{1} max_range:{2}".format( x_y, min_range, max_range), file=sys.stderr) user_dtick = None if x_y == 'x': user_dtick = dtick_x elif x_y == 'y': user_dtick = dtick_y dtick, min_range, max_range = _calculate_dtick( min_range, max_range, range_are_numbers, user_dtick) # For the given axis dimension set tick attributes for axis_title in axis_labels[x_y]: fig['layout'][axis_title]['dtick'] = dtick fig['layout'][axis_title]['ticklen'] = 0 if range_are_numbers: fig['layout'][axis_title]['range'] = [min_range, max_range] else: if PLOT_LOGGING: print("DEBUG: No trace data for current plot") return fig