Manage Chart Data Labels in Presentations with Python
Introduction
Data labels display information about chart series and individual data points, helping readers identify values and understand the chart. This article explains how to format values, display percentages, read label text, control labels beyond the axis maximum, adjust category axis label spacing, and position pie chart labels.
Set Data Precision in Chart Data Labels
Use number_format_of_values to format series values. This example creates a line chart with default data, displays its data table, and enables value labels for the first series. The format #,##0.00 displays a thousands separator and two decimal places without changing the underlying values.
import aspose.slides as slides
import aspose.slides.charts as charts
with slides.Presentation() as presentation:
slide = presentation.slides[0]
chart = slide.shapes.add_chart(charts.ChartType.LINE, 50, 50, 450, 300)
chart.has_data_table = True
series = chart.chart_data.series[0]
series.number_format_of_values = "#,##0.00"
series.labels.default_data_label_format.show_value = True
presentation.save("PrecisionOfDatalabels_out.pptx", slides.export.SaveFormat.PPTX)
Display Percentage as Labels
For a stacked column chart, calculate each value as a percentage of its category total and assign the text to text_frame_for_overriding. This example uses the default chart data and displays percentages with two decimal places in an 8-point font. Categories with a total of zero are skipped to avoid division by zero. Recalculate the custom label text if the chart data changes.
import aspose.slides as slides
import aspose.slides.charts as charts
with slides.Presentation() as presentation:
slide = presentation.slides[0]
chart = slide.shapes.add_chart(charts.ChartType.STACKED_COLUMN, 20, 20, 400, 400)
category_totals = [0.0] * len(chart.chart_data.categories)
for k in range(len(chart.chart_data.categories)):
for series in chart.chart_data.series:
point_value = float(series.data_points[k].value.data)
category_totals[k] += point_value
for series in chart.chart_data.series:
series.labels.default_data_label_format.show_legend_key = False
for j in range(len(series.data_points)):
label = series.data_points[j].label
if category_totals[j] == 0:
continue
point_value = float(series.data_points[j].value.data)
data_point_percent = point_value / category_totals[j] * 100
portion = slides.Portion()
portion.text = f"{data_point_percent:.2f} %"
portion.portion_format.font_height = 8
label.text_frame_for_overriding.text = ""
paragraph = label.text_frame_for_overriding.paragraphs[0]
paragraph.portions.add(portion)
label.data_label_format.show_value = True
label.data_label_format.show_series_name = False
label.data_label_format.show_percentage = False
label.data_label_format.show_legend_key = False
label.data_label_format.show_category_name = False
label.data_label_format.show_bubble_size = False
presentation.save("DisplayPercentageAsLabels_out.pptx", slides.export.SaveFormat.PPTX)
Set Percentage Sign with Chart Data Labels
When values are stored as fractions, use number_format to display percentages. Set is_number_format_linked_to_source to False to apply the label format independently of the source cells.
This example creates a 100% stacked column chart with red and blue series across four categories. Each pair of values adds up to 1. The label format 0.0% displays 0.30 as 30.0%, while the vertical axis uses two decimal places. Both series use white, 10-point label text.
import aspose.slides as slides
import aspose.slides.charts as charts
import aspose.pydrawing as drawing
with slides.Presentation() as presentation:
slide = presentation.slides[0]
chart = slide.shapes.add_chart(charts.ChartType.PERCENTS_STACKED_COLUMN, 20, 20, 500, 400)
chart.axes.vertical_axis.is_number_format_linked_to_source = False
chart.axes.vertical_axis.number_format = "0.00%"
chart.chart_data.series.clear()
chart.chart_data.categories.clear()
workbook = chart.chart_data.chart_data_workbook
worksheet_index = 0
for i in range(4):
category_cell = workbook.get_cell(worksheet_index, i + 1, 0, f"Category {i + 1}")
chart.chart_data.categories.add(category_cell)
series_names = ["Reds", "Blues"]
series_colors = [drawing.Color.red, drawing.Color.blue]
values = [[0.30, 0.50, 0.80, 0.65], [0.70, 0.50, 0.20, 0.35]]
for i in range(len(series_names)):
series_cell = workbook.get_cell(worksheet_index, 0, i + 1, series_names[i])
series = chart.chart_data.series.add(series_cell, chart.type)
for j in range(4):
value_cell = workbook.get_cell(worksheet_index, j + 1, i + 1, values[i][j])
series.data_points.add_data_point_for_bar_series(value_cell)
series.format.fill.fill_type = slides.FillType.SOLID
series.format.fill.solid_fill_color.color = series_colors[i]
label_format = series.labels.default_data_label_format
label_format.show_value = True
label_format.is_number_format_linked_to_source = False
label_format.number_format = "0.0%"
label_format.text_format.portion_format.font_height = 10
label_format.text_format.portion_format.fill_format.fill_type = slides.FillType.SOLID
label_format.text_format.portion_format.fill_format.solid_fill_color.color = drawing.Color.white
presentation.save("SetDataLabelsPercentageSign_out.pptx", slides.export.SaveFormat.PPTX)
Read the Actual Text of Data Labels
Use get_actual_label_text to retrieve the text produced by a data label’s settings. This is useful when extracting labels for reports, searching presentation content, or validating generated charts. In the example below, the default data label format combines each category name, series name, and value. One point formats its value as a percentage, and another uses custom text from text_frame_for_overriding.
import aspose.slides as slides
import aspose.slides.charts as charts
with slides.Presentation() as presentation:
slide = presentation.slides[0]
chart = slide.shapes.add_chart(charts.ChartType.CLUSTERED_COLUMN, 20, 20, 500, 300)
chart.chart_data.series.clear()
chart.chart_data.categories.clear()
workbook = chart.chart_data.chart_data_workbook
for i, category_name in enumerate(["Q1", "Q2"]):
category_cell = workbook.get_cell(0, i + 1, 0, category_name)
chart.chart_data.categories.add(category_cell)
north_cell = workbook.get_cell(0, 0, 1, "North")
north = chart.chart_data.series.add(north_cell, chart.type)
for i, value in enumerate([0.25, 0.75]):
value_cell = workbook.get_cell(0, i + 1, 1, value)
north.data_points.add_data_point_for_bar_series(value_cell)
south_cell = workbook.get_cell(0, 0, 2, "South")
south = chart.chart_data.series.add(south_cell, chart.type)
for i, value in enumerate([0.40, 0.60]):
value_cell = workbook.get_cell(0, i + 1, 2, value)
south.data_points.add_data_point_for_bar_series(value_cell)
for series in chart.chart_data.series:
label_format = series.labels.default_data_label_format
label_format.show_category_name = True
label_format.show_series_name = True
label_format.show_value = True
north.labels[1].data_label_format.is_number_format_linked_to_source = False
north.labels[1].data_label_format.number_format = "0%"
south.labels[0].text_frame_for_overriding.text = "Reviewed"
for series in chart.chart_data.series:
for point in series.data_points:
label = point.label
if not label.is_visible:
continue
label_text = label.get_actual_label_text()
print(f"Value: {point.value.data}; label: {label_text}")
The number stored in a data point remains 0.75, even when its label shows 75% along with the category and series names. Custom text replaces the generated label text. get_actual_label_text returns the resulting label string in either case. Check is_visible separately, as shown above, when you want to extract only visible labels.
Control Data Labels Beyond the Axis Maximum
When you limit an axis range manually, some data points may exceed its maximum. Use show_data_labels_over_maximum to control whether their data labels are shown. This setting changes label visibility; it does not change the axis range or the underlying data values.
The example below creates a 2D clustered column chart with values of 60 and 120. It sets is_automatic_max_value to False and max_value to 100 on the vertical axis. The first slide allows labels beyond the maximum; a copy of that slide disables them. Both slides are saved in DataLabelsOverMaximum.pptx.
Enable value labels with show_value. The chart-level setting does not enable value display by itself or override an individual label’s disabled value display. This example enables values for the entire series and uses position to place labels at the outside end of each column.
import aspose.slides as slides
import aspose.slides.charts as charts
with slides.Presentation() as presentation:
slide = presentation.slides[0]
chart = slide.shapes.add_chart(charts.ChartType.CLUSTERED_COLUMN, 50, 50, 600, 400)
chart.has_legend = False
chart.chart_data.series.clear()
chart.chart_data.categories.clear()
workbook = chart.chart_data.chart_data_workbook
first_category = workbook.get_cell(0, 1, 0, "Within range")
second_category = workbook.get_cell(0, 2, 0, "Above maximum")
chart.chart_data.categories.add(first_category)
chart.chart_data.categories.add(second_category)
series_name = workbook.get_cell(0, 0, 1, "Values")
series = chart.chart_data.series.add(series_name, chart.type)
first_value = workbook.get_cell(0, 1, 1, 60)
second_value = workbook.get_cell(0, 2, 1, 120)
series.data_points.add_data_point_for_bar_series(first_value)
series.data_points.add_data_point_for_bar_series(second_value)
series.labels.default_data_label_format.show_value = True
series.labels.default_data_label_format.position = charts.LegendDataLabelPosition.OUTSIDE_END
chart.axes.vertical_axis.is_automatic_max_value = False
chart.axes.vertical_axis.max_value = 100
chart.show_data_labels_over_maximum = True
second_slide = presentation.slides.add_clone(slide)
second_chart = second_slide.shapes[0]
second_chart.show_data_labels_over_maximum = False
presentation.save("DataLabelsOverMaximum.pptx", slides.export.SaveFormat.PPTX)
The following images show the saved slides rendered by Microsoft PowerPoint. With True, the label 120 is visible at the upper boundary; with False, it is hidden. The label 60 remains visible, the axis maximum stays at 100, and the second data point remains 120 in both cases.
| show_data_labels_over_maximum = True | show_data_labels_over_maximum = False |
|---|---|
 |
 |
Chart Type
This example uses a 2D column chart with a value axis. Charts without a value axis, such as pie and doughnut charts, do not have an axis maximum to limit in this way.Set Label Distance from an Axis
Use label_offset to control the distance between category axis labels and the axis. The value is a percentage of the maximum font size of the axis labels. This example creates a clustered column chart and sets the horizontal axis label offset to 500. This setting affects category axis labels rather than labels attached to individual data points.
import aspose.slides as slides
import aspose.slides.charts as charts
with slides.Presentation() as presentation:
slide = presentation.slides[0]
chart = slide.shapes.add_chart(charts.ChartType.CLUSTERED_COLUMN, 20, 20, 500, 300)
chart.axes.horizontal_axis.label_offset = 500
presentation.save("SetCategoryAxisLabelDistance_out.pptx", slides.export.SaveFormat.PPTX)
Adjust Label Location
On a pie chart, adjust data label positions to improve spacing and make room for leader lines.
This example displays the value of the first data point, places its label outside the slice, and adjusts its x and y offsets. These offsets are relative to the chart width and height, respectively.
import aspose.slides as slides
import aspose.slides.charts as charts
with slides.Presentation() as presentation:
slide = presentation.slides[0]
chart = slide.shapes.add_chart(charts.ChartType.PIE, 50, 50, 200, 200)
series = chart.chart_data.series
label = series[0].labels[0]
label.data_label_format.show_value = True
label.data_label_format.position = charts.LegendDataLabelPosition.OUTSIDE_END
label.x = 0.71
label.y = 0.04
presentation.save("presentation.pptx", slides.export.SaveFormat.PPTX)
FAQ
How can I prevent data labels from overlapping on dense charts?
Combine automatic label placement, leader lines, and reduced font size; if necessary, hide some fields (for example, the category) or show labels only for extreme values or key points.
How can I disable labels only for zero, negative, or empty values?
Filter data points before enabling labels and turn off display for values of 0, negative values, or missing values according to a defined rule.
How can I ensure a consistent label style when exporting to PDF/images?
Explicitly set the font family and size and verify that the font is available in the rendering environment to avoid fallback.