py_af_colours

This page contains the documentation for the Python package containing the UK Government Analysis Function’s colour palettes. The py_af_colours package aims to maintain the accessibility of graph outputs generated in Python across the Analysis Function by simplifying the process by which users can access the standardised colour palettes.

py_af_colours sits alongside an R equivalent, af_colours.

Requirements

To run af_colours in Python, your system requires the following installations:

1. Python 3.
2. the PyYAML package.
3. the py_af_colours package.

Installing py_af_colours

py_af_colours is now on PyPI. To install the package:

1. Open the anaconda powershell;
2. Run:

pip install py-af-colours

How to obtain colours

In your python environment, import the af_colours function:

from py_af_colours import af_colours

The af_colours function takes four arguments: palette, colour_format, number_of_colours, and config_path.

• palette is a required parameter, chosen by the user based on the Analysis Function colour guidance. It takes one of four possible string values corresponding to the options:

  • “duo”
    
  • “focus”
    
  • “sequential”
    
  • “categorical”

• colour_format is an optional parameter. It takes one of two possible string values:

  • “hex”; meaning hexadecimal colour code, for example #12436D. “hex” is the default value of colour_format if none is specified.
  • “rgb”; meaning red green blue colour code, for example (18, 67, 109).

• number_of_colours is an optional parameter for the categorical palette. It takes an integer value up to 6.

• config_path is an optional parameter that links to where the palette values are stored. It is not recommended for the user to specify a value for this parameter (see Basic examples) This should not be changed unless the user moves the file.

af_colours returns hex codes as a list of strings, and rgb codes as a list of tuples.

Basic examples

For example, to return the duo colour palette hex codes:

af_colours("duo")

['#12436D', '#F46A25']

To return a five colour categorical palette as rgb codes:

af_colours("categorical", "rgb", 5)

UserWarning: It is best practice to limit graphs to four categories where possible to avoid graphs becoming cluttered.
  af_colours("categorical", "rgb", 5)

[(18, 67, 109), (40, 161, 151), (128, 22, 80), (244, 106, 37), (61, 61, 61)]

See also that a warning is given for colours lists greater than four to let the user know that for accessibility best practice, it is prefered to use a limit of four categories.

Application

Categorical

This palette is for data which can be divided into groups or categories by using names or labels.

Colour name	Hex code	RGB code	Colour fill
Dark blue	#12436D	(18, 67, 109)
Turquoise	#28A197	(40, 161, 151)
Dark pink	#801650	(128, 22, 80)
Orange	#F46A25	(244, 106, 37)
Dark grey	#3D3D3D	(61, 61, 61)
Light purple	#A285D1	(162, 133, 209)

Figure 1: Bar chart using the Categorical colour palette

This clustered bar chart uses data based on the world population dataset and shows data for four countries over five years. The legend is presented in the same order as the bars in the clusters. Data is available with the py_af_colours package.

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from py_af_colours import af_colours

world_population = pd.read_csv("data\world_population_in_billions.csv")
top_four = world_population[0:4]
top_four_data = top_four.set_index("Country/Territory")
years_of_interest  = top_four_data.columns.tolist()[-4:]

categorical = sns.color_palette(af_colours("categorical", "hex", 4))

population_over_time = pd.melt(top_four_data.reset_index(),
                                   id_vars = "Country/Territory",
                                   value_vars = years_of_interest,
                                   var_name = "Year",
                                   value_name = "Population")

fig, ax = plt.subplots()

sns.barplot(x = "Year", y = "Population",
            data = population_over_time,
            zorder = 2, hue = "Country/Territory",
            edgecolor = "white",
            palette = categorical)

plt.box(False)

ax.set_ylabel("Population (Billion)", rotation = 0)
ax.yaxis.set_label_coords(-0.15, 1.05)
plt.grid(visible = True, which = "both", axis = "y", color = "#D6D6D6")
ax.tick_params(color = "#D6D6D6")
ax.set_ylim(bottom = -0.01, top = 1.6)
plt.legend(frameon = False, loc = "upper left", ncol = 4,
            handlelength = 0.7, bbox_to_anchor = (0, 0.62, 0.5, 0.5))

plt.show()

Figure 2: Pie chart using the Categorical colour palette

This a pie chart showing the proportional populations of the four most populous countries, China, India, the United States, and Indonesia. This data is from the world population dataset, available with the py_af_colours package.

import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
from py_af_colours import af_colours

world_population = pd.read_csv("data\world_population_in_billions.csv")
top_four = world_population[0:4]
top_four_data =top_four.set_index("Country/Territory")
most_recent = top_four_data.iloc[:,-1:]
data = most_recent.reset_index()
countries = top_four["Country/Territory"].values

colours_list = af_colours("categorical", "hex", 4)
categorical = sns.color_palette(colours_list)

percentage_labels = []
for n in np.arange(most_recent.shape[0]):
    percentage = 100 * ((most_recent.iloc[n].values.item())
                    /(most_recent.values.sum().item()))
    value = round(percentage, 1)
    country = str(data["Country/Territory"].iloc[n])
    percentage_labels.append((country + " " + str(value) + "%"))

ax = most_recent.plot.pie(x = "Country/Territory", y = "2022",
                          labels = percentage_labels,
                          legend = False,
                          colors = categorical,
                          pctdistance = 1,
                          counterclock = False,
                          startangle = 90,
                          wedgeprops = {"edgecolor": "white",
                                        "linewidth": 1.5,
                                        "antialiased": True})
ax.set_ylabel(None)
ax.set_title("Population of Four Most Populous Countries in 2022")

plt.show()

Duo

This palette is for categorical data when there are two categories.

Colour name	Hex code	RGB code	Colour fill
Dark blue	#12436D	(18, 67, 109)
Orange	#F46A25	(244, 106, 37)

Figure 3: Line chart using the Duo colour palette

This line chart has two lines, one dark blue and one orange. This line chart uses data based on the world population dataset. Data is available with the py_af_colours package.

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from py_af_colours import af_colours

world_population = pd.read_csv("data\world_population_in_billions.csv")
top_two_data = world_population.set_index("Country/Territory")[0:2]

duo = sns.color_palette(af_colours("duo"))

fig, ax = plt.subplots()

sns.lineplot(data = (top_two_data.transpose()), 
                     palette = duo,
                     dashes = False,
                     legend = False)

ax.set_xlabel("Year")
ax.set_ylabel("Population (Billion)", rotation = 0)
ax.yaxis.set_label_coords(-0.15, 1.05)

ax.grid(visible = True, which = "both", axis = "y", color = "#BEBEBE")
ax.set_axisbelow(True)
ax.set_frame_on(False)
ax.tick_params(color = "#D6D6D6")

x_label_coord = len(top_two_data.columns) - 1
y_label_coord1 = top_two_data.values[0][-1] + 0.03
y_label_coord2 = top_two_data.values[1][-1] - 0.05

ax.annotate(" China", xy = (x_label_coord, y_label_coord1))
ax.annotate(" India", xy = (x_label_coord, y_label_coord2))

ax.set_xlim(left = 0, right = len(top_two_data.columns) - 1)
ax.set_ylim(bottom = -0.01, top = 1.6)

plt.tight_layout()
plt.show()

Sequential

This palette is for data where the order of the data is meaningful, such as for age groups.

Colour name	Hex code	RGB code	Colour fill
Dark blue	#12436D	(18, 67, 109)
Mid blue	#2073BC	(32, 115, 188)
Light blue	#6BACE6	(107, 172, 230)

Figure 4: Bar chart using the Sequential colour palette

This clustered bar chart uses data based on the population by age dataset and shows data for five countries. The legend is presented in the same order as the bars in the clusters. Data is available with the py_af_colours package.

Distinct from previous examples, this section features how to use the rgb list returned by af_colours. It is generally easier to use hex codes, but this example is included for completeness. The end result is the same.

The spacing in this example is to account for the bar borders, which are required for accessibility purposes.

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from py_af_colours import af_colours

world_population_by_age = pd.read_csv("data\population_by_age.csv")
top_five_age = world_population_by_age[0:5]
top_five_age_data = top_five_age.set_index(top_five_age["Country"])
countries = top_five_age["Country"].values

fig, ax = plt.subplots()

x_spacing = np.arange(top_five_age.shape[0])
colours = af_colours("sequential", "rgb")
sequential = [tuple(t / 255 for t in x) for x in colours]

plt.bar(x_spacing - 0.25, top_five_age["Under 25"].values,
        width = 0.19, zorder = 2, edgecolor = sequential[0],
        label = "0 to 25", color = sequential[0])
plt.bar(x_spacing, top_five_age["25-64 years"].values,
        width = 0.19, zorder = 2, edgecolor = sequential[0],
        label = "25 to 64", color=sequential[1])
plt.bar(x_spacing + 0.25, top_five_age["65+"].values,
        width = 0.19, zorder = 2, edgecolor = sequential[0],
        label = "65+", color = sequential[2])

plt.box(False)
plt.grid(visible = True, which = "both", axis = "y", color = "#D6D6D6") 
plt.xticks(x_spacing, top_five_age["Country"].values)
plt.tick_params(color = "white")
ax.set_xlabel("Country")
ax.set_ylabel("Population (Billion)", rotation = 0)
ax.yaxis.set_label_coords(-0.15, 1.05)
ax.set_ylim(top = 0.9)

plt.legend(bbox_to_anchor = (0.025, 0.62, 0.5, 0.5), ncol = 4,
            handlelength = 0.7, frameon = False)
plt.show()

Focus

This palette should be used when you want to highlight specific elements to help users understand the information.

Colour name	Hex code	RGB code	Colour fill
Dark blue	#12436D	(18, 67, 109)
Grey	#BFBFBF	(191, 191, 191)

Figure 5: Line chart using the Focus colour palette

This line chart uses data based on the world population dataset. All lines are light grey, except the line showing the time series for India. Data is available with the py_af_colours package.

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from py_af_colours import af_colours

world_population = pd.read_csv("data\world_population_in_billions.csv")
top_five = world_population[0:5]
top_five_data = top_five.set_index("Country/Territory")

focus_country = "India"
focus_colours = af_colours("focus")
focus_index = top_five.index[top_five["Country/Territory"] 
                                  == focus_country].tolist()
focus_palette = int(top_five_data.shape[0]) * [focus_colours[1]]
focus_palette[focus_index[0]] = focus_colours[0]
focus = sns.color_palette(focus_palette)

fig, ax = plt.subplots()

sns.lineplot(data = top_five_data.transpose(), 
                    palette = focus,
                    dashes = False,
                    legend = False)

ax.set_xlabel("Year")
ax.set_ylabel("Population (Billion)", rotation = 0)
ax.yaxis.set_label_coords(-0.15, 1.05)

ax.grid(visible = True, which = "both", axis = "y", color = "#D6D6D6")
ax.set_axisbelow(True)
ax.set_frame_on(False)
ax.tick_params(color = "#D6D6D6")

x_coord = len(top_five.columns) - 2
y_coord = []
for n in np.arange(top_five_data.shape[0]):
    y = top_five_data.values[n][-1]
    y_coord.append(y)

ax.annotate(" China", xy = (x_coord, y_coord[0]))
ax.annotate(" India", xy = (x_coord, y_coord[1] - 0.04))
ax.annotate(" United States", xy = (x_coord, y_coord[2] + 0.01))
ax.annotate(" Indonesia", xy = (x_coord, y_coord[3]))
ax.annotate(" Pakistan", xy = (x_coord, y_coord[4] - 0.02))

ax.set_xlim(left = 0, right = (len(top_five.columns) - 2))
ax.set_ylim(bottom = -0.01, top = 1.6)

plt.tight_layout()
plt.show()

Maintenance

The Analysis Function colour palettes are stored inside the config file as hex codes, so that in the event of future updates to the colours, package maintenance can be contained to this file.

This guidance page will undergo continual updates as changes to the package are rolled out.