Create or Update PowerPoint Presentation Charts in Python
Overview
This article provides a comprehensive guide on how to create and customize charts using Aspose.Slides for Python via .NET. You will learn how to programmatically add a chart to a slide, populate it with data, and apply various formatting options to match your specific design requirements. Throughout the article, detailed code examples illustrate each step, from initializing the presentation and chart object to configuring series, axes, and legends. By following this guide, you’ll gain a solid understanding of how to integrate dynamic chart generation into your Python applications, streamlining the process of creating data-driven presentations.
Create a Chart
Charts help people quickly visualize data and gain insights that may not be immediately obvious from a table or spreadsheet.
Why Create Charts?
Using charts, you can:
- aggregate, condense, or summarize large amounts of data on a single slide in a presentation;
- expose patterns and trends in data;
- deduce the direction and momentum of data over time or with respect to a specific unit of measurement;
- spot outliers, aberrations, deviations, errors, and nonsensical data;
- communicate or present complex data.
In PowerPoint, you can create charts through the Insert function, which provides templates for designing many types of charts. Using Aspose.Slides, you can create both regular charts (based on popular chart types) and custom charts.
Note
Use the ChartType enumeration under the aspose.slides.charts module. The values in this enumeration correspond to different chart types.Create Clustered Column Charts
This example uses add_chart to create a clustered column chart that compares two series across three categories. The series use red and green fills, and individual labels demonstrate category names, series names, and values. Worksheet index 0 refers to the first sheet in the embedded chart workbook.
import aspose.pydrawing as draw
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_title.add_text_frame_for_overriding("Sample Title")
chart.chart_title.text_frame_for_overriding.text_frame_format.center_text = slides.NullableBool.TRUE
chart.chart_title.height = 20
chart.has_title = True
workbook = chart.chart_data.chart_data_workbook
worksheet_index = 0
chart.chart_data.series.clear()
chart.chart_data.categories.clear()
series_name_cell = workbook.get_cell(worksheet_index, 0, 1, "Series 1")
chart.chart_data.series.add(series_name_cell, chart.type)
series_name_cell = workbook.get_cell(worksheet_index, 0, 2, "Series 2")
chart.chart_data.series.add(series_name_cell, chart.type)
category_cell = workbook.get_cell(worksheet_index, 1, 0, "Category 1")
chart.chart_data.categories.add(category_cell)
category_cell = workbook.get_cell(worksheet_index, 2, 0, "Category 2")
chart.chart_data.categories.add(category_cell)
category_cell = workbook.get_cell(worksheet_index, 3, 0, "Category 3")
chart.chart_data.categories.add(category_cell)
series = chart.chart_data.series[0]
value_cell = workbook.get_cell(worksheet_index, 1, 1, 20)
series.data_points.add_data_point_for_bar_series(value_cell)
value_cell = workbook.get_cell(worksheet_index, 2, 1, 50)
series.data_points.add_data_point_for_bar_series(value_cell)
value_cell = workbook.get_cell(worksheet_index, 3, 1, 30)
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 = draw.Color.red
series = chart.chart_data.series[1]
value_cell = workbook.get_cell(worksheet_index, 1, 2, 30)
series.data_points.add_data_point_for_bar_series(value_cell)
value_cell = workbook.get_cell(worksheet_index, 2, 2, 10)
series.data_points.add_data_point_for_bar_series(value_cell)
value_cell = workbook.get_cell(worksheet_index, 3, 2, 60)
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 = draw.Color.green
label = series.data_points[0].label
label.data_label_format.show_category_name = True
label = series.data_points[1].label
label.data_label_format.show_series_name = True
label = series.data_points[2].label
label.data_label_format.show_value = True
label.data_label_format.show_series_name = True
label.data_label_format.separator = "/"
presentation.save("AsposeChart.pptx", slides.export.SaveFormat.PPTX)
The result:
Create Scatter Charts
Scatter charts (also known as scatter plots or x-y graphs) are often used to check for patterns or demonstrate correlations between two variables.
Use a scatter chart when:
- You have paired numerical data.
- You have two variables that pair well together.
- You want to determine whether the two variables are related.
- You have an independent variable that has multiple values for a dependent variable.
This example plots two sets of x-y values. The first series uses straight lines with star markers, while the second uses smooth lines with circle markers; both use a size of 10. The symbol property controls the marker shape.
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.SCATTER_WITH_SMOOTH_LINES, 20, 20, 500, 300)
workbook = chart.chart_data.chart_data_workbook
worksheet_index = 0
chart.chart_data.series.clear()
series_name_cell = workbook.get_cell(worksheet_index, 1, 1, "Series 1")
chart.chart_data.series.add(series_name_cell, chart.type)
series_name_cell = workbook.get_cell(worksheet_index, 1, 3, "Series 2")
chart.chart_data.series.add(series_name_cell, chart.type)
series = chart.chart_data.series[0]
x_value_cell = workbook.get_cell(worksheet_index, 2, 1, 1)
y_value_cell = workbook.get_cell(worksheet_index, 2, 2, 3)
series.data_points.add_data_point_for_scatter_series(x_value_cell, y_value_cell)
x_value_cell = workbook.get_cell(worksheet_index, 3, 1, 2)
y_value_cell = workbook.get_cell(worksheet_index, 3, 2, 10)
series.data_points.add_data_point_for_scatter_series(x_value_cell, y_value_cell)
series.type = charts.ChartType.SCATTER_WITH_STRAIGHT_LINES_AND_MARKERS
series.marker.size = 10
series.marker.symbol = charts.MarkerStyleType.STAR
series = chart.chart_data.series[1]
x_value_cell = workbook.get_cell(worksheet_index, 2, 3, 5)
y_value_cell = workbook.get_cell(worksheet_index, 2, 4, 2)
series.data_points.add_data_point_for_scatter_series(x_value_cell, y_value_cell)
x_value_cell = workbook.get_cell(worksheet_index, 3, 3, 3)
y_value_cell = workbook.get_cell(worksheet_index, 3, 4, 1)
series.data_points.add_data_point_for_scatter_series(x_value_cell, y_value_cell)
x_value_cell = workbook.get_cell(worksheet_index, 4, 3, 2)
y_value_cell = workbook.get_cell(worksheet_index, 4, 4, 2)
series.data_points.add_data_point_for_scatter_series(x_value_cell, y_value_cell)
x_value_cell = workbook.get_cell(worksheet_index, 5, 3, 5)
y_value_cell = workbook.get_cell(worksheet_index, 5, 4, 1)
series.data_points.add_data_point_for_scatter_series(x_value_cell, y_value_cell)
series.marker.size = 10
series.marker.symbol = charts.MarkerStyleType.CIRCLE
presentation.save("AsposeChart.pptx", slides.export.SaveFormat.PPTX)
The result:
Create Pie Charts
Pie charts are best used to show the part-to-whole relationship in data, especially when the data contains categorical labels with numeric values. However, if your data contains many parts or labels, you might want to consider using a bar chart instead.
This example creates a pie chart with three quarterly values. Each slice has a custom fill and border, and the labels display different combinations of values, percentages, and series information. The first_slice_angle property sets the first slice to start at 180 degrees.
import aspose.pydrawing as draw
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, 20, 20, 500, 300)
chart.chart_title.add_text_frame_for_overriding("Sample Title")
chart.chart_title.text_frame_for_overriding.text_frame_format.center_text = slides.NullableBool.TRUE
chart.chart_title.height = 20
chart.has_title = True
chart.chart_data.series[0].labels.default_data_label_format.show_value = True
workbook = chart.chart_data.chart_data_workbook
worksheet_index = 0
chart.chart_data.series.clear()
chart.chart_data.categories.clear()
category_cell = workbook.get_cell(0, 1, 0, "1st Qtr")
chart.chart_data.categories.add(category_cell)
category_cell = workbook.get_cell(0, 2, 0, "2nd Qtr")
chart.chart_data.categories.add(category_cell)
category_cell = workbook.get_cell(0, 3, 0, "3rd Qtr")
chart.chart_data.categories.add(category_cell)
series_name_cell = workbook.get_cell(0, 0, 1, "Series 1")
series = chart.chart_data.series.add(series_name_cell, chart.type)
value_cell = workbook.get_cell(worksheet_index, 1, 1, 20)
series.data_points.add_data_point_for_pie_series(value_cell)
value_cell = workbook.get_cell(worksheet_index, 2, 1, 50)
series.data_points.add_data_point_for_pie_series(value_cell)
value_cell = workbook.get_cell(worksheet_index, 3, 1, 30)
series.data_points.add_data_point_for_pie_series(value_cell)
chart.chart_data.series_groups[0].is_color_varied = True
first_slice = series.data_points[0]
first_slice.format.fill.fill_type = slides.FillType.SOLID
first_slice.format.fill.solid_fill_color.color = draw.Color.cyan
first_slice.format.line.fill_format.fill_type = slides.FillType.SOLID
first_slice.format.line.fill_format.solid_fill_color.color = draw.Color.gray
first_slice.format.line.width = 3.0
first_slice.format.line.style = slides.LineStyle.THIN_THICK
first_slice.format.line.dash_style = slides.LineDashStyle.LARGE_DASH
second_slice = series.data_points[1]
second_slice.format.fill.fill_type = slides.FillType.SOLID
second_slice.format.fill.solid_fill_color.color = draw.Color.brown
second_slice.format.line.fill_format.fill_type = slides.FillType.SOLID
second_slice.format.line.fill_format.solid_fill_color.color = draw.Color.blue
second_slice.format.line.width = 3.0
second_slice.format.line.style = slides.LineStyle.SINGLE
second_slice.format.line.dash_style = slides.LineDashStyle.LARGE_DASH_DOT
third_slice = series.data_points[2]
third_slice.format.fill.fill_type = slides.FillType.SOLID
third_slice.format.fill.solid_fill_color.color = draw.Color.coral
third_slice.format.line.fill_format.fill_type = slides.FillType.SOLID
third_slice.format.line.fill_format.solid_fill_color.color = draw.Color.red
third_slice.format.line.width = 2.0
third_slice.format.line.style = slides.LineStyle.THIN_THIN
third_slice.format.line.dash_style = slides.LineDashStyle.LARGE_DASH_DOT_DOT
first_label = series.data_points[0].label
first_label.data_label_format.show_value = True
second_label = series.data_points[1].label
second_label.data_label_format.show_value = True
second_label.data_label_format.show_legend_key = True
second_label.data_label_format.show_percentage = True
third_label = series.data_points[2].label
third_label.data_label_format.show_series_name = True
third_label.data_label_format.show_percentage = True
series.labels.default_data_label_format.show_leader_lines = True
chart.chart_data.series_groups[0].first_slice_angle = 180
presentation.save("PieChart.pptx", slides.export.SaveFormat.PPTX)
The result:
To improve the readability of labels outside the pie, see Format Leader Lines for an example that positions labels and customizes the lines' color, width, transparency, and dash style.
Create Line Charts
Line charts (also known as line graphs) are best used in situations where you want to demonstrate changes in value over time. Using a line chart, you can compare a large amount of data at once, track changes and trends over time, highlight anomalies in data series, and more.
This example uses add_chart to create a line chart with the default series and categories, then saves the presentation as PPTX.
import aspose.slides as slides
import aspose.slides.charts as charts
with slides.Presentation() as presentation:
slide = presentation.slides[0]
line_chart = slide.shapes.add_chart(charts.ChartType.LINE, 20, 20, 500, 300)
presentation.save("lineChart.pptx", slides.export.SaveFormat.PPTX)
By default, points on a line chart are joined by straight continuous lines. If you want the points to be joined by dashes instead, set dash_style to a value from LineDashStyle:
import aspose.slides as slides
import aspose.slides.charts as charts
with slides.Presentation() as presentation:
slide = presentation.slides[0]
line_chart = slide.shapes.add_chart(charts.ChartType.LINE, 20, 20, 500, 300)
for series in line_chart.chart_data.series:
series.format.line.dash_style = slides.LineDashStyle.DASH
The result:
Create Tree Map Charts
Tree map charts are best used for sales data when you want to show the relative size of data categories and quickly draw attention to items that are large contributors within each category.
This example creates a tree map with eight leaves grouped into four stems and two branches. The set_grouping_item method assigns stems at grouping level 1 and branches at level 2. Values determine the relative rectangle sizes, while category labels and overlapping parent labels identify the hierarchy.
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.TREEMAP, 20, 20, 500, 300)
chart.chart_data.categories.clear()
chart.chart_data.series.clear()
workbook = chart.chart_data.chart_data_workbook
workbook.clear(0)
# Branch 1
category_cell = workbook.get_cell(0, "C1", "Leaf1")
leaf = chart.chart_data.categories.add(category_cell)
leaf.grouping_levels.set_grouping_item(1, "Stem1")
leaf.grouping_levels.set_grouping_item(2, "Branch1")
category_cell = workbook.get_cell(0, "C2", "Leaf2")
chart.chart_data.categories.add(category_cell)
category_cell = workbook.get_cell(0, "C3", "Leaf3")
leaf = chart.chart_data.categories.add(category_cell)
leaf.grouping_levels.set_grouping_item(1, "Stem2")
category_cell = workbook.get_cell(0, "C4", "Leaf4")
chart.chart_data.categories.add(category_cell)
# Branch 2
category_cell = workbook.get_cell(0, "C5", "Leaf5")
leaf = chart.chart_data.categories.add(category_cell)
leaf.grouping_levels.set_grouping_item(1, "Stem3")
leaf.grouping_levels.set_grouping_item(2, "Branch2")
category_cell = workbook.get_cell(0, "C6", "Leaf6")
chart.chart_data.categories.add(category_cell)
category_cell = workbook.get_cell(0, "C7", "Leaf7")
leaf = chart.chart_data.categories.add(category_cell)
leaf.grouping_levels.set_grouping_item(1, "Stem4")
category_cell = workbook.get_cell(0, "C8", "Leaf8")
chart.chart_data.categories.add(category_cell)
series = chart.chart_data.series.add(charts.ChartType.TREEMAP)
series.labels.default_data_label_format.show_category_name = True
value_cell = workbook.get_cell(0, "D1", 4)
series.data_points.add_data_point_for_treemap_series(value_cell)
value_cell = workbook.get_cell(0, "D2", 5)
series.data_points.add_data_point_for_treemap_series(value_cell)
value_cell = workbook.get_cell(0, "D3", 3)
series.data_points.add_data_point_for_treemap_series(value_cell)
value_cell = workbook.get_cell(0, "D4", 6)
series.data_points.add_data_point_for_treemap_series(value_cell)
value_cell = workbook.get_cell(0, "D5", 9)
series.data_points.add_data_point_for_treemap_series(value_cell)
value_cell = workbook.get_cell(0, "D6", 9)
series.data_points.add_data_point_for_treemap_series(value_cell)
value_cell = workbook.get_cell(0, "D7", 4)
series.data_points.add_data_point_for_treemap_series(value_cell)
value_cell = workbook.get_cell(0, "D8", 3)
series.data_points.add_data_point_for_treemap_series(value_cell)
series.parent_label_layout = charts.ParentLabelLayoutType.OVERLAPPING
presentation.save("Treemap.pptx", slides.export.SaveFormat.PPTX)
The result:
Create Waterfall Charts
A waterfall chart shows how positive and negative changes contribute to a final value. For example, a profit breakdown can start with revenue, subtract costs, and add other income. Change columns float between the previous and new running values, while total columns extend from zero to the accumulated value.
The following example uses amounts in thousands of US dollars:
| Category | Workbook value | Column type |
|---|---|---|
| Revenue | 1000 | Increase |
| Cost of sales | -400 | Decrease |
| Gross profit | 600 | Intermediate total |
| Operating expenses | -200 | Decrease |
| Other income | 50 | Increase |
| Net profit | 450 | Final total |
To mark a point as an intermediate or final total, set its set_as_total property to True. Supply the actual accumulated amount in that point’s workbook cell: 600 for gross profit (1000 - 400) and 450 for net profit (600 - 200 + 50). The property marks the column as a total; it does not calculate or populate the cell value. Do not use zero as a placeholder or supply another change for a total point. A total column displays the accumulated value without adding it again to the running value.
This complete C# example creates a chart using the Waterfall member of ChartType, clears the default data, and fills the embedded workbook. Worksheet index 0 selects the first worksheet; column A contains category names and column B contains values. Each value cell is added with add_data_point_for_waterfall_series. The zero-based point indices 2 and 5 identify the intermediate and final totals. Enabling show_value displays the amounts on the columns. No input presentation or workbook is required.
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.WATERFALL, 20, 20, 680, 200)
chart.has_title = True
chart.chart_title.add_text_frame_for_overriding("Profit breakdown (USD thousands)")
chart.chart_title.overlay = False
chart.has_legend = False
chart.chart_data.series.clear()
chart.chart_data.categories.clear()
workbook = chart.chart_data.chart_data_workbook
worksheet_index = 0
workbook.clear(worksheet_index)
series_name_cell = workbook.get_cell(worksheet_index, 0, 1, "Profit")
series = chart.chart_data.series.add(series_name_cell, charts.ChartType.WATERFALL)
categories = ["Revenue", "Cost of sales", "Gross profit", "Operating expenses", "Other income", "Net profit"]
values = [1000, -400, 600, -200, 50, 450]
for i in range(len(categories)):
category_cell = workbook.get_cell(worksheet_index, i + 1, 0, categories[i])
chart.chart_data.categories.add(category_cell)
value_cell = workbook.get_cell(worksheet_index, i + 1, 1, values[i])
series.data_points.add_data_point_for_waterfall_series(value_cell)
# The third and sixth points represent totals, not additional changes.
series.data_points[2].set_as_total = True
series.data_points[5].set_as_total = True
series.show_connector_lines = True
series.labels.default_data_label_format.show_value = True
presentation.save("WaterfallChart.pptx", slides.export.SaveFormat.PPTX)
Set the series-level show_connector_lines property to True to show lines linking consecutive columns and make the running value easier to follow. To hide these lines, replace the assignment in the example with series.show_connector_lines = False before saving. This changes only the line visibility, not the data or totals.
The saved presentation shows gross profit of 600 and net profit of 450, with both totals drawn from zero:
Create Stock Charts
Stock charts are used to display financial data such as open, high, low, and close prices, helping analyze market trends and volatility. They offer essential insights into stock performance, aiding investors and analysts in making informed decisions.
This example creates an open-high-low-close chart with four series in that order and three categories. The has_up_down_bars property enables up/down bars, and hi_low_lines_format controls the high-low lines that show the price ranges, while the individual series lines and minor horizontal gridlines are hidden.
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.OPEN_HIGH_LOW_CLOSE, 20, 20, 500, 300, False)
workbook = chart.chart_data.chart_data_workbook
category_cell = workbook.get_cell(0, 1, 0, "A")
chart.chart_data.categories.add(category_cell)
category_cell = workbook.get_cell(0, 2, 0, "B")
chart.chart_data.categories.add(category_cell)
category_cell = workbook.get_cell(0, 3, 0, "C")
chart.chart_data.categories.add(category_cell)
series_name_cell = workbook.get_cell(0, 0, 1, "Open")
chart.chart_data.series.add(series_name_cell, charts.ChartType.OPEN_HIGH_LOW_CLOSE)
series_name_cell = workbook.get_cell(0, 0, 2, "High")
chart.chart_data.series.add(series_name_cell, charts.ChartType.OPEN_HIGH_LOW_CLOSE)
series_name_cell = workbook.get_cell(0, 0, 3, "Low")
chart.chart_data.series.add(series_name_cell, charts.ChartType.OPEN_HIGH_LOW_CLOSE)
series_name_cell = workbook.get_cell(0, 0, 4, "Close")
chart.chart_data.series.add(series_name_cell, charts.ChartType.OPEN_HIGH_LOW_CLOSE)
series = chart.chart_data.series[0]
value_cell = workbook.get_cell(0, 1, 1, 72)
series.data_points.add_data_point_for_stock_series(value_cell)
value_cell = workbook.get_cell(0, 2, 1, 25)
series.data_points.add_data_point_for_stock_series(value_cell)
value_cell = workbook.get_cell(0, 3, 1, 38)
series.data_points.add_data_point_for_stock_series(value_cell)
series = chart.chart_data.series[1]
value_cell = workbook.get_cell(0, 1, 2, 172)
series.data_points.add_data_point_for_stock_series(value_cell)
value_cell = workbook.get_cell(0, 2, 2, 57)
series.data_points.add_data_point_for_stock_series(value_cell)
value_cell = workbook.get_cell(0, 3, 2, 57)
series.data_points.add_data_point_for_stock_series(value_cell)
series = chart.chart_data.series[2]
value_cell = workbook.get_cell(0, 1, 3, 12)
series.data_points.add_data_point_for_stock_series(value_cell)
value_cell = workbook.get_cell(0, 2, 3, 12)
series.data_points.add_data_point_for_stock_series(value_cell)
value_cell = workbook.get_cell(0, 3, 3, 13)
series.data_points.add_data_point_for_stock_series(value_cell)
series = chart.chart_data.series[3]
value_cell = workbook.get_cell(0, 1, 4, 25)
series.data_points.add_data_point_for_stock_series(value_cell)
value_cell = workbook.get_cell(0, 2, 4, 38)
series.data_points.add_data_point_for_stock_series(value_cell)
value_cell = workbook.get_cell(0, 3, 4, 50)
series.data_points.add_data_point_for_stock_series(value_cell)
chart.chart_data.series_groups[0].up_down_bars.has_up_down_bars = True
chart.chart_data.series_groups[0].hi_low_lines_format.line.fill_format.fill_type = slides.FillType.SOLID
for stock_series in chart.chart_data.series:
stock_series.format.line.fill_format.fill_type = slides.FillType.NO_FILL
chart.axes.vertical_axis.minor_grid_lines_format.line.fill_format.fill_type = slides.FillType.NO_FILL
presentation.save("Stock-chart.pptx", slides.export.SaveFormat.PPTX)
The result:
Create Box and Whisker Charts
Box and whisker charts are used to display the distribution of data by summarizing key statistical measures, such as the median, quartiles, and potential outliers. They are particularly useful in exploratory data analysis and statistical studies to quickly understand data variability and identify any anomalies.
This example creates a box and whisker chart from six values. The quartile_method property selects the exclusive quartile calculation. The example also enables mean lines, mean markers, inner points, and outlier 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.BOX_AND_WHISKER, 20, 20, 500, 300)
chart.chart_data.categories.clear()
chart.chart_data.series.clear()
workbook = chart.chart_data.chart_data_workbook
workbook.clear(0)
category_cell = workbook.get_cell(0, "A1", "Category 1")
chart.chart_data.categories.add(category_cell)
category_cell = workbook.get_cell(0, "A2", "Category 2")
chart.chart_data.categories.add(category_cell)
category_cell = workbook.get_cell(0, "A3", "Category 3")
chart.chart_data.categories.add(category_cell)
category_cell = workbook.get_cell(0, "A4", "Category 4")
chart.chart_data.categories.add(category_cell)
category_cell = workbook.get_cell(0, "A5", "Category 5")
chart.chart_data.categories.add(category_cell)
category_cell = workbook.get_cell(0, "A6", "Category 6")
chart.chart_data.categories.add(category_cell)
series = chart.chart_data.series.add(charts.ChartType.BOX_AND_WHISKER)
series.quartile_method = charts.QuartileMethodType.EXCLUSIVE
series.show_mean_line = True
series.show_mean_markers = True
series.show_inner_points = True
series.show_outlier_points = True
value_cell = workbook.get_cell(0, "B1", 15)
series.data_points.add_data_point_for_box_and_whisker_series(value_cell)
value_cell = workbook.get_cell(0, "B2", 41)
series.data_points.add_data_point_for_box_and_whisker_series(value_cell)
value_cell = workbook.get_cell(0, "B3", 16)
series.data_points.add_data_point_for_box_and_whisker_series(value_cell)
value_cell = workbook.get_cell(0, "B4", 10)
series.data_points.add_data_point_for_box_and_whisker_series(value_cell)
value_cell = workbook.get_cell(0, "B5", 23)
series.data_points.add_data_point_for_box_and_whisker_series(value_cell)
value_cell = workbook.get_cell(0, "B6", 16)
series.data_points.add_data_point_for_box_and_whisker_series(value_cell)
presentation.save("BoxAndWhisker.pptx", slides.export.SaveFormat.PPTX)
Create Funnel Charts
Funnel charts are used to visualize processes that involve sequential stages, where the volume of data decreases as it progresses from one step to the next. They are especially helpful for analyzing conversion rates, identifying bottlenecks, and tracking the efficiency of sales or marketing processes.
This example uses add_chart to add a funnel chart to the first slide of an existing presentation. It replaces the default data with six categories and values ranging from 50 to 500, then saves the updated presentation. The input presentation must contain at least one slide.
import aspose.slides as slides
import aspose.slides.charts as charts
with slides.Presentation("test.pptx") as presentation:
slide = presentation.slides[0]
chart = slide.shapes.add_chart(charts.ChartType.FUNNEL, 50, 50, 500, 400)
chart.chart_data.categories.clear()
chart.chart_data.series.clear()
workbook = chart.chart_data.chart_data_workbook
workbook.clear(0)
category_cell = workbook.get_cell(0, "A1", "Category 1")
chart.chart_data.categories.add(category_cell)
category_cell = workbook.get_cell(0, "A2", "Category 2")
chart.chart_data.categories.add(category_cell)
category_cell = workbook.get_cell(0, "A3", "Category 3")
chart.chart_data.categories.add(category_cell)
category_cell = workbook.get_cell(0, "A4", "Category 4")
chart.chart_data.categories.add(category_cell)
category_cell = workbook.get_cell(0, "A5", "Category 5")
chart.chart_data.categories.add(category_cell)
category_cell = workbook.get_cell(0, "A6", "Category 6")
chart.chart_data.categories.add(category_cell)
series = chart.chart_data.series.add(charts.ChartType.FUNNEL)
value_cell = workbook.get_cell(0, "B1", 50)
series.data_points.add_data_point_for_funnel_series(value_cell)
value_cell = workbook.get_cell(0, "B2", 100)
series.data_points.add_data_point_for_funnel_series(value_cell)
value_cell = workbook.get_cell(0, "B3", 200)
series.data_points.add_data_point_for_funnel_series(value_cell)
value_cell = workbook.get_cell(0, "B4", 300)
series.data_points.add_data_point_for_funnel_series(value_cell)
value_cell = workbook.get_cell(0, "B5", 400)
series.data_points.add_data_point_for_funnel_series(value_cell)
value_cell = workbook.get_cell(0, "B6", 500)
series.data_points.add_data_point_for_funnel_series(value_cell)
presentation.save("Funnel.pptx", slides.export.SaveFormat.PPTX)
The result:
Create Sunburst Charts
Sunburst charts are used to visualize hierarchical data, displaying levels as concentric rings. They help illustrate part-to-whole relationships and are ideal for representing nested categories and subcategories in a clear, compact format.
This example creates a sunburst chart with eight leaves grouped into four stems and two branches. The set_grouping_item method assigns stems at grouping level 1 and branches at level 2. Category labels identify the segments, and the values determine their relative sizes.
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.SUNBURST, 20, 20, 500, 300)
chart.chart_data.categories.clear()
chart.chart_data.series.clear()
workbook = chart.chart_data.chart_data_workbook
workbook.clear(0)
# Branch 1
category_cell = workbook.get_cell(0, "C1", "Leaf1")
leaf = chart.chart_data.categories.add(category_cell)
leaf.grouping_levels.set_grouping_item(1, "Stem1")
leaf.grouping_levels.set_grouping_item(2, "Branch1")
category_cell = workbook.get_cell(0, "C2", "Leaf2")
chart.chart_data.categories.add(category_cell)
category_cell = workbook.get_cell(0, "C3", "Leaf3")
leaf = chart.chart_data.categories.add(category_cell)
leaf.grouping_levels.set_grouping_item(1, "Stem2")
category_cell = workbook.get_cell(0, "C4", "Leaf4")
chart.chart_data.categories.add(category_cell)
# Branch 2
category_cell = workbook.get_cell(0, "C5", "Leaf5")
leaf = chart.chart_data.categories.add(category_cell)
leaf.grouping_levels.set_grouping_item(1, "Stem3")
leaf.grouping_levels.set_grouping_item(2, "Branch2")
category_cell = workbook.get_cell(0, "C6", "Leaf6")
chart.chart_data.categories.add(category_cell)
category_cell = workbook.get_cell(0, "C7", "Leaf7")
leaf = chart.chart_data.categories.add(category_cell)
leaf.grouping_levels.set_grouping_item(1, "Stem4")
category_cell = workbook.get_cell(0, "C8", "Leaf8")
chart.chart_data.categories.add(category_cell)
series = chart.chart_data.series.add(charts.ChartType.SUNBURST)
series.labels.default_data_label_format.show_category_name = True
value_cell = workbook.get_cell(0, "D1", 4)
series.data_points.add_data_point_for_sunburst_series(value_cell)
value_cell = workbook.get_cell(0, "D2", 5)
series.data_points.add_data_point_for_sunburst_series(value_cell)
value_cell = workbook.get_cell(0, "D3", 3)
series.data_points.add_data_point_for_sunburst_series(value_cell)
value_cell = workbook.get_cell(0, "D4", 6)
series.data_points.add_data_point_for_sunburst_series(value_cell)
value_cell = workbook.get_cell(0, "D5", 9)
series.data_points.add_data_point_for_sunburst_series(value_cell)
value_cell = workbook.get_cell(0, "D6", 9)
series.data_points.add_data_point_for_sunburst_series(value_cell)
value_cell = workbook.get_cell(0, "D7", 4)
series.data_points.add_data_point_for_sunburst_series(value_cell)
value_cell = workbook.get_cell(0, "D8", 3)
series.data_points.add_data_point_for_sunburst_series(value_cell)
presentation.save("Sunburst.pptx", slides.export.SaveFormat.PPTX)
The result:
Create Histogram Charts
Histogram charts show how numerical values are distributed across intervals (bins). For monthly reports on order processing times, use the same bin width and thresholds to keep intervals consistent and make distributions easier to compare.
Set the horizontal axis aggregation_type to a mode from AxisAggregationType. bin_width applies only to BY_BIN_WIDTH; number_of_bins applies only to BY_NUMBER_OF_BINS. Use AUTOMATIC to let the chart choose the bins.
Use a Fixed Bin Width
This example groups processing times into 10-minute intervals, with separate bins for times at or below 10 minutes and above 40 minutes. Enable these bins with is_underflow_bin and is_overflow_bin. When supplying explicit underflow_bin and overflow_bin thresholds, set is_automatic_underflow_bin and is_automatic_overflow_bin to False.
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.HISTOGRAM, 20, 20, 680, 200)
chart.has_legend = False
chart.has_title = False
chart.chart_data.categories.clear()
chart.chart_data.series.clear()
workbook = chart.chart_data.chart_data_workbook
workbook.clear(0)
series = chart.chart_data.series.add(charts.ChartType.HISTOGRAM)
values = [5, 10, 10, 11, 15, 20, 20, 21, 25, 30, 30, 31, 35, 40, 40, 41, 50]
for i, value in enumerate(values):
value_cell = workbook.get_cell(0, i, 0, value)
series.data_points.add_data_point_for_histogram_series(value_cell)
axis = chart.axes.horizontal_axis
axis.aggregation_type = charts.AxisAggregationType.BY_BIN_WIDTH
axis.bin_width = 10
axis.is_underflow_bin = True
axis.is_automatic_underflow_bin = False
axis.underflow_bin = 10
axis.is_overflow_bin = True
axis.is_automatic_overflow_bin = False
axis.overflow_bin = 40
presentation.save("Histogram-bin-width.pptx", slides.export.SaveFormat.PPTX)
The presentation displayed in PowerPoint shows bin counts of 3, 4, 4, 4, and 2. The underflow bin includes 5, 10, 10, so its threshold is inclusive. The overflow bin includes only 41, 50, so its threshold is exclusive; both values equal to 40 remain in the preceding interval. The interior bins contain values greater than their lower boundary and less than or equal to their upper boundary.
Specify the Number of Bins
For a fixed level of detail instead of fixed intervals, request five bins for the same data. This example leaves underflow and overflow bins disabled. The chart determines the width from the data range, so boundaries can change between reports.
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.HISTOGRAM, 20, 20, 680, 200)
chart.has_legend = False
chart.has_title = False
chart.chart_data.categories.clear()
chart.chart_data.series.clear()
workbook = chart.chart_data.chart_data_workbook
workbook.clear(0)
series = chart.chart_data.series.add(charts.ChartType.HISTOGRAM)
values = [5, 10, 10, 11, 15, 20, 20, 21, 25, 30, 30, 31, 35, 40, 40, 41, 50]
for i, value in enumerate(values):
value_cell = workbook.get_cell(0, i, 0, value)
series.data_points.add_data_point_for_histogram_series(value_cell)
axis = chart.axes.horizontal_axis
axis.aggregation_type = charts.AxisAggregationType.BY_NUMBER_OF_BINS
axis.number_of_bins = 5
presentation.save("Histogram-bin-count.pptx", slides.export.SaveFormat.PPTX)
The five bins have counts of 4, 4, 4, 4, and 1. Compare this distribution with the fixed-width result above: the input values are identical, but the boundaries differ.
Create Radar Charts
Radar charts are used to display multivariate data in a two-dimensional format, allowing for easy comparison of several variables simultaneously. They are particularly useful for identifying patterns, strengths, and weaknesses across multiple performance metrics or attributes.
This example uses add_chart to create a radar chart with the default series and categories, then saves the presentation as PPTX.
import aspose.slides as slides
import aspose.slides.charts as charts
with slides.Presentation() as presentation:
slide = presentation.slides[0]
slide.shapes.add_chart(charts.ChartType.RADAR, 20, 20, 500, 300)
presentation.save("Radar-chart.pptx", slides.export.SaveFormat.PPTX)
The result:
Create Multi-Category Charts
Multi-category charts are used to display data that involves more than one categorical grouping, allowing you to compare values across multiple dimensions simultaneously. They are particularly helpful when you need to analyze trends and relationships within complex, multi-layered datasets.
This example creates a clustered column chart with eight categories arranged in four pairs. The set_grouping_item method gives each pair a shared label at grouping level 1, producing a category axis with both individual and group labels.
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
workbook.clear(0)
worksheet_index = 0
category_cell = workbook.get_cell(0, "c2", "A")
category = chart.chart_data.categories.add(category_cell)
category.grouping_levels.set_grouping_item(1, "Group1")
category_cell = workbook.get_cell(0, "c3", "B")
category = chart.chart_data.categories.add(category_cell)
category_cell = workbook.get_cell(0, "c4", "C")
category = chart.chart_data.categories.add(category_cell)
category.grouping_levels.set_grouping_item(1, "Group2")
category_cell = workbook.get_cell(0, "c5", "D")
category = chart.chart_data.categories.add(category_cell)
category_cell = workbook.get_cell(0, "c6", "E")
category = chart.chart_data.categories.add(category_cell)
category.grouping_levels.set_grouping_item(1, "Group3")
category_cell = workbook.get_cell(0, "c7", "F")
category = chart.chart_data.categories.add(category_cell)
category_cell = workbook.get_cell(0, "c8", "G")
category = chart.chart_data.categories.add(category_cell)
category.grouping_levels.set_grouping_item(1, "Group4")
category_cell = workbook.get_cell(0, "c9", "H")
category = chart.chart_data.categories.add(category_cell)
series_name_cell = workbook.get_cell(0, "D1", "Series 1")
series = chart.chart_data.series.add(series_name_cell, charts.ChartType.CLUSTERED_COLUMN)
value_cell = workbook.get_cell(worksheet_index, "D2", 10)
series.data_points.add_data_point_for_bar_series(value_cell)
value_cell = workbook.get_cell(worksheet_index, "D3", 20)
series.data_points.add_data_point_for_bar_series(value_cell)
value_cell = workbook.get_cell(worksheet_index, "D4", 30)
series.data_points.add_data_point_for_bar_series(value_cell)
value_cell = workbook.get_cell(worksheet_index, "D5", 40)
series.data_points.add_data_point_for_bar_series(value_cell)
value_cell = workbook.get_cell(worksheet_index, "D6", 50)
series.data_points.add_data_point_for_bar_series(value_cell)
value_cell = workbook.get_cell(worksheet_index, "D7", 60)
series.data_points.add_data_point_for_bar_series(value_cell)
value_cell = workbook.get_cell(worksheet_index, "D8", 70)
series.data_points.add_data_point_for_bar_series(value_cell)
value_cell = workbook.get_cell(worksheet_index, "D9", 80)
series.data_points.add_data_point_for_bar_series(value_cell)
presentation.save("AsposeChart.pptx", slides.export.SaveFormat.PPTX)
The result:
Create Map Charts
Map charts are used to visualize geographical data by mapping information to specific locations such as countries, states, or cities. They are particularly useful for analyzing regional trends, demographic data, and spatial distributions in a clear, visually engaging manner.
This example uses add_chart to create a map chart with the default geographical data and saves it for viewing in PowerPoint.
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.MAP, 20, 20, 500, 300)
presentation.save("mapChart.pptx", slides.export.SaveFormat.PPTX)
The result:
Note
The picture above shows the saved presentation opened in PowerPoint. Aspose.Slides writes the map chart and its data correctly, but it does not draw map charts itself: when a slide holding one is rendered to an image or converted to PDF or SVG, the chart area comes out blank. Other shapes on the same slide are unaffected.Create Combination Charts
A combination chart (or combo chart) combines two or more chart types in a single graph. This chart lets you highlight, compare, or examine differences between two or more data sets, helping you identify relationships between them.
This example combines two clustered column series with a line series across four categories. The columns use the primary vertical axis, while plot_on_second_axis places the line on a secondary vertical axis on the right. The helper functions configure the title, legend, axis labels, and gridlines.
import aspose.pydrawing as draw
import aspose.slides as slides
import aspose.slides.charts as charts
def create_combo_chart():
with slides.Presentation() as presentation:
chart = create_chart_with_first_series(presentation.slides[0])
add_second_series_to_chart(chart)
add_third_series_to_chart(chart)
set_primary_axes_format(chart)
set_secondary_axes_format(chart)
presentation.save("combo-chart.pptx", slides.export.SaveFormat.PPTX)
def create_chart_with_first_series(slide):
chart = slide.shapes.add_chart(charts.ChartType.CLUSTERED_COLUMN, 50, 50, 600, 400)
chart.has_title = True
chart.chart_title.add_text_frame_for_overriding("Chart Title")
chart.chart_title.overlay = False
portion_format = chart.chart_title.text_frame_for_overriding.paragraphs[0].paragraph_format.default_portion_format
portion_format.font_bold = slides.NullableBool.FALSE
portion_format.font_height = 18
chart.legend.position = charts.LegendPositionType.BOTTOM
chart.legend.text_format.portion_format.font_height = 12
chart.chart_data.series.clear()
chart.chart_data.categories.clear()
workbook = chart.chart_data.chart_data_workbook
worksheet_index = 0
category_cell = workbook.get_cell(worksheet_index, 1, 0, "Category 1")
chart.chart_data.categories.add(category_cell)
category_cell = workbook.get_cell(worksheet_index, 2, 0, "Category 2")
chart.chart_data.categories.add(category_cell)
category_cell = workbook.get_cell(worksheet_index, 3, 0, "Category 3")
chart.chart_data.categories.add(category_cell)
category_cell = workbook.get_cell(worksheet_index, 4, 0, "Category 4")
chart.chart_data.categories.add(category_cell)
series_name_cell = workbook.get_cell(worksheet_index, 0, 1, "Series 1")
series = chart.chart_data.series.add(series_name_cell, chart.type)
series.parent_series_group.overlap = -25
series.parent_series_group.gap_width = 220
value_cell = workbook.get_cell(worksheet_index, 1, 1, 4.3)
series.data_points.add_data_point_for_bar_series(value_cell)
value_cell = workbook.get_cell(worksheet_index, 2, 1, 2.5)
series.data_points.add_data_point_for_bar_series(value_cell)
value_cell = workbook.get_cell(worksheet_index, 3, 1, 3.5)
series.data_points.add_data_point_for_bar_series(value_cell)
value_cell = workbook.get_cell(worksheet_index, 4, 1, 4.5)
series.data_points.add_data_point_for_bar_series(value_cell)
return chart
def add_second_series_to_chart(chart):
workbook = chart.chart_data.chart_data_workbook
worksheet_index = 0
series_name_cell = workbook.get_cell(worksheet_index, 0, 2, "Series 2")
series = chart.chart_data.series.add(series_name_cell, charts.ChartType.CLUSTERED_COLUMN)
series.parent_series_group.overlap = -25
series.parent_series_group.gap_width = 220
value_cell = workbook.get_cell(worksheet_index, 1, 2, 2.4)
series.data_points.add_data_point_for_bar_series(value_cell)
value_cell = workbook.get_cell(worksheet_index, 2, 2, 4.4)
series.data_points.add_data_point_for_bar_series(value_cell)
value_cell = workbook.get_cell(worksheet_index, 3, 2, 1.8)
series.data_points.add_data_point_for_bar_series(value_cell)
value_cell = workbook.get_cell(worksheet_index, 4, 2, 2.8)
series.data_points.add_data_point_for_bar_series(value_cell)
def add_third_series_to_chart(chart):
workbook = chart.chart_data.chart_data_workbook
worksheet_index = 0
series_name_cell = workbook.get_cell(worksheet_index, 0, 3, "Series 3")
series = chart.chart_data.series.add(series_name_cell, charts.ChartType.LINE)
value_cell = workbook.get_cell(worksheet_index, 1, 3, 2.0)
series.data_points.add_data_point_for_line_series(value_cell)
value_cell = workbook.get_cell(worksheet_index, 2, 3, 2.0)
series.data_points.add_data_point_for_line_series(value_cell)
value_cell = workbook.get_cell(worksheet_index, 3, 3, 3.0)
series.data_points.add_data_point_for_line_series(value_cell)
value_cell = workbook.get_cell(worksheet_index, 4, 3, 5.0)
series.data_points.add_data_point_for_line_series(value_cell)
series.plot_on_second_axis = True
def set_primary_axes_format(chart):
horizontal_axis = chart.axes.horizontal_axis
horizontal_axis.text_format.portion_format.font_height = 12
horizontal_axis.format.line.fill_format.fill_type = slides.FillType.NO_FILL
set_axis_title(horizontal_axis, "X Axis")
vertical_axis = chart.axes.vertical_axis
vertical_axis.text_format.portion_format.font_height = 12
vertical_axis.format.line.fill_format.fill_type = slides.FillType.NO_FILL
set_axis_title(vertical_axis, "Y Axis 1")
major_grid_lines_format = vertical_axis.major_grid_lines_format.line.fill_format
major_grid_lines_format.fill_type = slides.FillType.SOLID
major_grid_lines_format.solid_fill_color.color = draw.Color.from_argb(217, 217, 217)
def set_secondary_axes_format(chart):
secondary_horizontal_axis = chart.axes.secondary_horizontal_axis
secondary_horizontal_axis.position = charts.AxisPositionType.BOTTOM
secondary_horizontal_axis.cross_type = charts.CrossesType.MAXIMUM
secondary_horizontal_axis.is_visible = False
secondary_horizontal_axis.major_grid_lines_format.line.fill_format.fill_type = slides.FillType.NO_FILL
secondary_horizontal_axis.minor_grid_lines_format.line.fill_format.fill_type = slides.FillType.NO_FILL
secondary_vertical_axis = chart.axes.secondary_vertical_axis
secondary_vertical_axis.position = charts.AxisPositionType.RIGHT
secondary_vertical_axis.text_format.portion_format.font_height = 12
secondary_vertical_axis.format.line.fill_format.fill_type = slides.FillType.NO_FILL
secondary_vertical_axis.major_grid_lines_format.line.fill_format.fill_type = slides.FillType.NO_FILL
secondary_vertical_axis.minor_grid_lines_format.line.fill_format.fill_type = slides.FillType.NO_FILL
set_axis_title(secondary_vertical_axis, "Y Axis 2")
def set_axis_title(axis, axis_title):
axis.has_title = True
axis.title.overlay = False
title_portion_format = axis.title.add_text_frame_for_overriding(axis_title).paragraphs[0].paragraph_format.default_portion_format
title_portion_format.font_bold = slides.NullableBool.FALSE
title_portion_format.font_height = 12
create_combo_chart()
Update Charts
Aspose.Slides for Python via .NET enables you to update PowerPoint charts by modifying chart data, formatting, and styling. This functionality simplifies the process of keeping presentations up-to-date with dynamic content and ensures that charts accurately reflect current data and visual standards.
This example updates the chart named “My chart” on the first slide of an existing presentation. It requires two column series with three data points each, with category labels in column A and series names in the first row of the first worksheet. The example uses get_cell to change workbook cells, updates series values, adds a third series, and changes the chart to clustered cylinders through type.
import aspose.slides as slides
import aspose.slides.charts as charts
chart_name = "My chart"
with slides.Presentation("ExistingChart.pptx") as presentation:
slide = presentation.slides[0]
for shape in slide.shapes:
if isinstance(shape, charts.Chart) and shape.name == chart_name:
chart = shape
worksheet_index = 0
workbook = chart.chart_data.chart_data_workbook
workbook.get_cell(worksheet_index, 1, 0, "Modified Category 1")
workbook.get_cell(worksheet_index, 2, 0, "Modified Category 2")
series = chart.chart_data.series[0]
workbook.get_cell(worksheet_index, 0, 1, "New_Series 1")
series.data_points[0].value.data = 90
series.data_points[1].value.data = 123
series.data_points[2].value.data = 44
series = chart.chart_data.series[1]
workbook.get_cell(worksheet_index, 0, 2, "New_Series 2")
series.data_points[0].value.data = 23
series.data_points[1].value.data = 67
series.data_points[2].value.data = 99
series_name_cell = workbook.get_cell(worksheet_index, 0, 3, "Series 3")
series = chart.chart_data.series.add(series_name_cell, chart.type)
value_cell = workbook.get_cell(worksheet_index, 1, 3, 20)
series.data_points.add_data_point_for_bar_series(value_cell)
value_cell = workbook.get_cell(worksheet_index, 2, 3, 50)
series.data_points.add_data_point_for_bar_series(value_cell)
value_cell = workbook.get_cell(worksheet_index, 3, 3, 30)
series.data_points.add_data_point_for_bar_series(value_cell)
chart.type = charts.ChartType.CLUSTERED_CYLINDER
presentation.save("AsposeChartModified.pptx", slides.export.SaveFormat.PPTX)
Set Data Range for a Chart
To inspect the range already used by an existing chart, see Retrieve a Chart’s Data Range.
Aspose.Slides for Python via .NET provides the flexibility to define a specific data range from a worksheet as the source for your chart’s data. This means you can directly map a portion of your worksheet to the chart, allowing you to control which cells contribute to the chart’s series and categories. As a result, you can easily update and synchronize your charts with the latest data changes in your worksheet, ensuring that your PowerPoint presentations reflect current and accurate information.
This example uses set_range to set the data source of the chart named “My chart” on the first slide of an existing presentation to Sheet1!A1:B4. The chart workbook must contain that worksheet and a populated range with a series heading in the first row, category labels in column A, and three values in column B.
import aspose.slides as slides
import aspose.slides.charts as charts
chart_name = "My chart"
with slides.Presentation("ExistingChart.pptx") as presentation:
slide = presentation.slides[0]
for shape in slide.shapes:
if isinstance(shape, charts.Chart) and shape.name == chart_name:
chart = shape
chart.chart_data.set_range("Sheet1!A1:B4")
presentation.save("SetDataRange.pptx", slides.export.SaveFormat.PPTX)
Use Default Markers in Charts
When you use default markers in charts, each chart series gets a different default marker symbol automatically.
This example uses add_chart to create a chart with two line series and a legend. The marker settings retain the automatically assigned marker symbols. The first series includes a negative value and a missing value, represented by None, while the second has a value for every category.
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_WITH_MARKERS, 10, 10, 400, 400)
chart.chart_data.series.clear()
chart.chart_data.categories.clear()
workbook = chart.chart_data.chart_data_workbook
series_name_cell = workbook.get_cell(0, 0, 1, "Series 1")
series = chart.chart_data.series.add(series_name_cell, chart.type)
category_cell = workbook.get_cell(0, 1, 0, "C1")
chart.chart_data.categories.add(category_cell)
value_cell = workbook.get_cell(0, 1, 1, 24)
series.data_points.add_data_point_for_line_series(value_cell)
category_cell = workbook.get_cell(0, 2, 0, "C2")
chart.chart_data.categories.add(category_cell)
value_cell = workbook.get_cell(0, 2, 1, 23)
series.data_points.add_data_point_for_line_series(value_cell)
category_cell = workbook.get_cell(0, 3, 0, "C3")
chart.chart_data.categories.add(category_cell)
value_cell = workbook.get_cell(0, 3, 1, -10)
series.data_points.add_data_point_for_line_series(value_cell)
category_cell = workbook.get_cell(0, 4, 0, "C4")
chart.chart_data.categories.add(category_cell)
value_cell = workbook.get_cell(0, 4, 1, None)
series.data_points.add_data_point_for_line_series(value_cell)
series_name_cell = workbook.get_cell(0, 0, 2, "Series 2")
second_series = chart.chart_data.series.add(series_name_cell, chart.type)
value_cell = workbook.get_cell(0, 1, 2, 30)
second_series.data_points.add_data_point_for_line_series(value_cell)
value_cell = workbook.get_cell(0, 2, 2, 10)
second_series.data_points.add_data_point_for_line_series(value_cell)
value_cell = workbook.get_cell(0, 3, 2, 60)
second_series.data_points.add_data_point_for_line_series(value_cell)
value_cell = workbook.get_cell(0, 4, 2, 40)
second_series.data_points.add_data_point_for_line_series(value_cell)
chart.has_legend = True
chart.legend.overlay = False
presentation.save("DefaultMarkersInChart.pptx", slides.export.SaveFormat.PPTX)
FAQ
What chart types are supported by Aspose.Slides for Python via .NET?
Aspose.Slides for Python via .NET supports a wide range of chart types, including bar, line, pie, area, scatter, histogram, radar, and many more. This flexibility allows you to choose the most appropriate chart type for your data visualization needs.
How do I add a new chart to a slide?
To add a chart, you first create an instance of the Presentation class, retrieve the desired slide using its index, and then call the method to add a chart, specifying the chart type and initial data. This process integrates the chart directly into your presentation.
How can I update the data displayed in a chart?
You can update a chart’s data by accessing its data workbook (ChartDataWorkbook), clearing any default series and categories, and then adding your custom data. This allows you to programmatically refresh the chart to reflect the latest data.
Is it possible to customize the appearance of the chart?
Yes, Aspose.Slides for Python via .NET provides extensive customization options. You can modify colors, fonts, labels, legends, and other formatting elements to tailor the chart’s appearance to your specific design requirements.
Related feature: Create editable charts.