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Charts visualize data in PowerPoint presentations. python-pptx supports most PowerPoint chart types including column, bar, line, pie, XY (scatter), and bubble charts.

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Adding a chart

Create charts using chart data objects and the add_chart() method: 
from pptx import Presentation
from pptx.chart.data import CategoryChartData
from pptx.enum.chart import XL_CHART_TYPE
from pptx.util import Inches

prs = Presentation()
slide = prs.slides.add_slide(prs.slide_layouts[5])

# Define chart data
chart_data = CategoryChartData()
chart_data.categories = ['East', 'West', 'Midwest']
chart_data.add_series('Series 1', (19.2, 21.4, 16.7))

# Add chart to slide
x, y, cx, cy = Inches(2), Inches(2), Inches(6), Inches(4.5)
graphic_frame = slide.shapes.add_chart(
    XL_CHART_TYPE.COLUMN_CLUSTERED, x, y, cx, cy, chart_data
)

# Access the chart object
chart = graphic_frame.chart

prs.save('chart-01.pptx')
add_chart() returns a GraphicFrame shape containing the chart. Access the chart via the chart property.

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Category charts

Category charts use discrete categories for the X-axis (column, bar, line, pie, etc.).

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Single series chart

from pptx.chart.data import CategoryChartData
from pptx.enum.chart import XL_CHART_TYPE

chart_data = CategoryChartData()
chart_data.categories = ['Q1', 'Q2', 'Q3', 'Q4']
chart_data.add_series('Revenue', (125.3, 142.7, 156.2, 178.9))

graphic_frame = slide.shapes.add_chart(
    XL_CHART_TYPE.COLUMN_CLUSTERED, x, y, cx, cy, chart_data
)

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Multi-series chart

chart_data = CategoryChartData()
chart_data.categories = ['East', 'West', 'Midwest']
chart_data.add_series('Q1 Sales', (19.2, 21.4, 16.7))
chart_data.add_series('Q2 Sales', (22.3, 28.6, 15.2))
chart_data.add_series('Q3 Sales', (20.4, 26.3, 14.2))

graphic_frame = slide.shapes.add_chart(
    XL_CHART_TYPE.COLUMN_CLUSTERED, x, y, cx, cy, chart_data
)

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Chart types

from pptx.enum.chart import XL_CHART_TYPE

chart_data = CategoryChartData()
chart_data.categories = ['A', 'B', 'C']
chart_data.add_series('Series 1', (10, 20, 30))

graphic_frame = slide.shapes.add_chart(
    XL_CHART_TYPE.COLUMN_CLUSTERED,
    x, y, cx, cy, chart_data
)

​
XY and bubble charts

XY (scatter) charts use continuous values for both axes. Bubble charts add a third dimension via marker size.

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XY scatter chart

from pptx.chart.data import XyChartData
from pptx.enum.chart import XL_CHART_TYPE

chart_data = XyChartData()

series_1 = chart_data.add_series('Model 1')
series_1.add_data_point(0.7, 2.7)
series_1.add_data_point(1.8, 3.2)
series_1.add_data_point(2.6, 0.8)

series_2 = chart_data.add_series('Model 2')
series_2.add_data_point(1.3, 3.7)
series_2.add_data_point(2.7, 2.3)
series_2.add_data_point(1.6, 1.8)

chart = slide.shapes.add_chart(
    XL_CHART_TYPE.XY_SCATTER, x, y, cx, cy, chart_data
).chart

​
Bubble chart

from pptx.chart.data import BubbleChartData

chart_data = BubbleChartData()

series_1 = chart_data.add_series('Series 1')
series_1.add_data_point(0.7, 2.7, 10)  # x, y, bubble_size
series_1.add_data_point(1.8, 3.2, 4)
series_1.add_data_point(2.6, 0.8, 8)

chart = slide.shapes.add_chart(
    XL_CHART_TYPE.BUBBLE, x, y, cx, cy, chart_data
).chart

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Formatting charts

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Axes

Customize category and value axes: 
from pptx.enum.chart import XL_TICK_MARK
from pptx.util import Pt

chart = graphic_frame.chart

# Category axis
category_axis = chart.category_axis
category_axis.has_major_gridlines = True
category_axis.minor_tick_mark = XL_TICK_MARK.OUTSIDE
category_axis.tick_labels.font.italic = True
category_axis.tick_labels.font.size = Pt(24)

# Value axis
value_axis = chart.value_axis
value_axis.maximum_scale = 50.0
value_axis.minimum_scale = 0.0
value_axis.minor_tick_mark = XL_TICK_MARK.OUTSIDE
value_axis.has_minor_gridlines = True

# Format tick labels
tick_labels = value_axis.tick_labels
tick_labels.number_format = '0"%"'  # Format as percentage
tick_labels.font.bold = True
tick_labels.font.size = Pt(14)

​
Data labels

Add and format data labels: 
from pptx.dml.color import RGBColor
from pptx.enum.chart import XL_LABEL_POSITION
from pptx.util import Pt

plot = chart.plots[0]
plot.has_data_labels = True
data_labels = plot.data_labels

data_labels.font.size = Pt(13)
data_labels.font.color.rgb = RGBColor(0x0A, 0x42, 0x80)
data_labels.position = XL_LABEL_POSITION.INSIDE_END
data_labels.number_format = '0.0'  # One decimal place

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Legend

Add and position a legend: 
from pptx.enum.chart import XL_LEGEND_POSITION

chart.has_legend = True
chart.legend.position = XL_LEGEND_POSITION.RIGHT
chart.legend.include_in_layout = False
Legend position options:
  • XL_LEGEND_POSITION.BOTTOM
  • XL_LEGEND_POSITION.LEFT
  • XL_LEGEND_POSITION.RIGHT
  • XL_LEGEND_POSITION.TOP
  • XL_LEGEND_POSITION.CORNER

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Complete examples

from pptx import Presentation
from pptx.chart.data import CategoryChartData
from pptx.enum.chart import XL_CHART_TYPE, XL_LEGEND_POSITION
from pptx.util import Inches

prs = Presentation()
slide = prs.slides.add_slide(prs.slide_layouts[5])

# Create chart data
chart_data = CategoryChartData()
chart_data.categories = ['Q1', 'Q2', 'Q3', 'Q4']
chart_data.add_series('Revenue', (125.3, 142.7, 156.2, 178.9))
chart_data.add_series('Costs', (95.2, 105.8, 112.3, 125.6))
chart_data.add_series('Profit', (30.1, 36.9, 43.9, 53.3))

# Add chart
x, y, cx, cy = Inches(0.5), Inches(1), Inches(9), Inches(5)
graphic_frame = slide.shapes.add_chart(
    XL_CHART_TYPE.COLUMN_CLUSTERED, x, y, cx, cy, chart_data
)

chart = graphic_frame.chart

# Add legend
chart.has_legend = True
chart.legend.position = XL_LEGEND_POSITION.BOTTOM
chart.legend.include_in_layout = False

# Format value axis
value_axis = chart.value_axis
value_axis.maximum_scale = 200
value_axis.has_major_gridlines = True

prs.save('revenue-chart.pptx')

​
Understanding plots

A plot (called ChartGroup in the Microsoft API) is a sub-chart containing one or more series. Most charts have a single plot, but combination charts can have multiple plots. 
# Access the first plot
plot = chart.plots[0]

# Set data labels on the plot
plot.has_data_labels = True

# Access series through the plot
for series in plot.series:
    print(series.name)

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Working with series

Access and customize individual series: 
# Access series by index
series = chart.series[0]

# Series properties
print(series.name)
print(len(series.values))

# Format series (line charts)
series.smooth = True  # Smooth the line

# Access data points
for point in series.points:
    # Customize individual points
    pass

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Chart colors

By default, charts use theme colors (Accent 1 through Accent 6). To customize colors:
  1. Use theme colors: Modify the theme colors in your template presentation
  2. Set data point colors: Customize individual data points programmatically (support varies by chart type)
Color customization support varies by chart type. The most reliable approach is to use theme colors in your template.

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Best practices

  • Use CategoryChartData for column, bar, line, and pie charts
  • Use XyChartData for scatter charts
  • Use BubbleChartData for bubble charts
  • Set include_in_layout = False for legends to prevent chart resizing
  • Format tick labels with appropriate number formats ('0%', '$#,##0', etc.)
  • Enable gridlines for easier data reading
  • Keep legends concise and position them appropriately
  • Use consistent chart dimensions across slides for professional appearance

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