Charts & graphs¶

Charts¶

Using Matplotlib¶

Before running this example, please install the required dependencies using the command below:

pip install fpdf2 matplotlib

Example taken from Matplotlib artist tutorial:

from fpdf import FPDF
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure
import numpy as np
from PIL import Image

fig = Figure(figsize=(6, 4), dpi=300)
fig.subplots_adjust(top=0.8)
ax1 = fig.add_subplot(211)
ax1.set_ylabel("volts")
ax1.set_title("a sine wave")

t = np.arange(0.0, 1.0, 0.01)
s = np.sin(2 * np.pi * t)
(line,) = ax1.plot(t, s, color="blue", lw=2)

# Fixing random state for reproducibility
np.random.seed(19680801)

ax2 = fig.add_axes([0.15, 0.1, 0.7, 0.3])
n, bins, patches = ax2.hist(
    np.random.randn(1000), 50, facecolor="yellow", edgecolor="yellow"
)
ax2.set_xlabel("time (s)")

# Converting Figure to an image:
canvas = FigureCanvas(fig)
canvas.draw()
img = Image.fromarray(np.asarray(canvas.buffer_rgba()))

pdf = FPDF()
pdf.add_page()
pdf.image(img, w=pdf.epw)  # Make the image full width
pdf.output("matplotlib.pdf")

Result:

You can also embed a figure as SVG:

from fpdf import FPDF
import matplotlib.pyplot as plt
import numpy as np

plt.figure(figsize=[2, 2])
x = np.arange(0, 10, 0.00001)
y = x*np.sin(2* np.pi * x)
plt.plot(y)
plt.savefig("figure.svg", format="svg")

pdf = FPDF()
pdf.add_page()
pdf.image("figure.svg")
pdf.output("doc-with-figure.pdf")

Using Pandas¶

The dependencies required for the following examples can be installed using this command:

pip install fpdf2 matplotlib pandas

Create a plot using pandas.DataFrame.plot:

from io import BytesIO
from fpdf import FPDF
import pandas as pd
import matplotlib.pyplot as plt
import io

DATA = {
    "Unemployment_Rate": [6.1, 5.8, 5.7, 5.7, 5.8, 5.6, 5.5, 5.3, 5.2, 5.2],
    "Stock_Index_Price": [1500, 1520, 1525, 1523, 1515, 1540, 1545, 1560, 1555, 1565],
}
COLUMNS = tuple(DATA.keys())

plt.figure()  # Create a new figure object
df = pd.DataFrame(DATA, columns=COLUMNS)
df.plot(x=COLUMNS[0], y=COLUMNS[1], kind="scatter")

# Converting Figure to an image:
img_buf = BytesIO()  # Create image object
plt.savefig(img_buf, dpi=200)  # Save the image

pdf = FPDF()
pdf.add_page()
pdf.image(img_buf, w=pdf.epw)  # Make the image full width
pdf.output("matplotlib_pandas.pdf")
img_buf.close()

Result:

Create a table with pandas DataFrame:

from fpdf import FPDF
import pandas as pd

DF = pd.DataFrame(
    {
        "First name": ["Jules", "Mary", "Carlson", "Lucas"],
        "Last name": ["Smith", "Ramos", "Banks", "Cimon"],
        "Age": [34, 45, 19, 31],
        "City": ["San Juan", "Orlando", "Los Angeles", "Saint-Mahturin-sur-Loire"],
    }
    # Convert all data inside dataframe into string type:
).applymap(str)

COLUMNS = [list(DF)]  # Get list of dataframe columns
ROWS = DF.values.tolist()  # Get list of dataframe rows
DATA = COLUMNS + ROWS  # Combine columns and rows in one list

pdf = FPDF()
pdf.add_page()
pdf.set_font("Times", size=10)
with pdf.table(
    borders_layout="MINIMAL",
    cell_fill_color=200,  # grey
    cell_fill_mode="ROWS",
    line_height=pdf.font_size * 2.5,
    text_align="CENTER",
    width=160,
) as table:
    for data_row in DATA:
        row = table.row()
        for datum in data_row:
            row.cell(datum)
pdf.output("table_from_pandas.pdf")

Result:

Using Plotly¶

Before running this example, please install the required dependencies using the command below:

pip install fpdf2 plotly kaleido numpy

kaleido is a cross-platform library for generating static images that is used by plotly.

Example taken from Plotly static image export tutorial:

import io
import plotly.graph_objects as go
import numpy as np
from fpdf import FPDF

np.random.seed(1)

N = 100
x = np.random.rand(N)
y = np.random.rand(N)
colors = np.random.rand(N)
sz = np.random.rand(N) * 30

fig = go.Figure()
fig.add_trace(
    go.Scatter(
        x=x,
        y=y,
        mode="markers",
        marker=go.scatter.Marker(
            size=sz, color=colors, opacity=0.6, colorscale="Viridis"
        ),
    )
)

# Convert the figure to png using kaleido
image_data = fig.to_image(format="png", engine="kaleido")

# Create an io.BytesIO object which can be used by FPDF2
image = io.BytesIO(image_data)
pdf = FPDF()
pdf.add_page()
pdf.image(image, w=pdf.epw)  # Width of the image is equal to the width of the page
pdf.output("plotly_demo.pdf")

Result:

You can also embed a figure as SVG but this is not recommended because the text data such as the x and y axis bars might not show as illustrated in the result image because plotly places this data in a svg text tag which is currently not supported by FPDF2.

Before running this example, please install the required dependencies:

pip install fpdf2 plotly kaleido pandas

from fpdf import FPDF
import plotly.express as px

fig = px.bar(x=["a", "b", "c"], y=[1, 3, 2])
fig.write_image("figure.svg")

pdf = FPDF()
pdf.add_page()
pdf.image("figure.svg", w=pdf.epw)
pdf.output("plotly.pdf")

Result:

Using Pygal¶

Pygal is a Python graph plotting library. You can install it using: pip install pygal

fpdf2 can embed graphs and charts generated using Pygal library. However, they cannot be embedded as SVG directly, because Pygal inserts <style> & <script> tags in the images it produces (cf. pygal/svg.py), which is currently not supported by fpdf2. The full list of supported & unsupported SVG features can be found there: SVG page.

You can find documentation on how to convert vector images (SVG) to raster images (PNG, JPG), with a practical example of embedding PyGal charts, there: SVG page.

Mathematical formulas¶

fpdf2 can only insert mathematical formula in the form of images. The following sections will explaing how to generate and embed such images.

Using Google Charts API¶

Official documentation: Google Charts Infographics - Mathematical Formulas.

Example:

from io import BytesIO
from urllib.parse import quote
from urllib.request import urlopen
from fpdf import FPDF

formula = "x^n + y^n = a/b"
height = 170
url = f"https://chart.googleapis.com/chart?cht=tx&chs={height}&chl={quote(formula)}"
with urlopen(url) as img_file:  # nosec B310
    img = BytesIO(img_file.read())

pdf = FPDF()
pdf.add_page()
pdf.image(img, w=30)
pdf.output("equation_google_charts.pdf")

Result:

Using LaTeX & Matplotlib¶

Matplotlib can render LaTeX: Text rendering With LaTeX.

Example:

from io import BytesIO
from fpdf import FPDF
from matplotlib.figure import Figure

fig = Figure(figsize=(6, 2))
gca = fig.gca()
gca.text(0, 0.5, r"$x^n + y^n = \frac{a}{b}$", fontsize=60)
gca.axis("off")

# Converting Figure to a SVG image:
img = BytesIO()
fig.savefig(img, format="svg")

pdf = FPDF()
pdf.add_page()
pdf.image(img, w=100)
pdf.output("equation_matplotlib.pdf")

Result:

If you have trouble with the SVG export, you can also render the matplotlib figure as pixels:

from fpdf import FPDF
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure
import numpy as np
from PIL import Image

fig = Figure(figsize=(6, 2), dpi=300)
gca = fig.gca()
gca.text(0, 0.5, r"$x^n + y^n = \frac{a}{b}$", fontsize=60)
gca.axis("off")

canvas = FigureCanvas(fig)
canvas.draw()
img = Image.fromarray(np.asarray(canvas.buffer_rgba()))

pdf = FPDF()
pdf.add_page()
pdf.image(img, w=100)
pdf.output("equation_matplotlib_raster.pdf")