How to Format Large Tick Values¶

Date published: 2018-09-20

Category: Data Visualizations

Subcategory: Best Practices

Tags: bar chart

In this tutorial, I'll illustrate an example of how it's difficult to read very large tick values (typically in the thousands or greater) on data visualizations and how you can re-format those tick values to be easier to understand for readers.

Import Modules¶

In [1]:

import matplotlib.pyplot as plt
import matplotlib.ticker as tick
import pandas as pd
import seaborn as sns
import numpy as np
% matplotlib inline

import matplotlib.pyplot as plt import matplotlib.ticker as tick import pandas as pd import seaborn as sns import numpy as np % matplotlib inline

Visualization Setup Code¶

In [2]:

sns.set(font_scale=1.4)

def reformat_large_tick_values(tick_val, pos):
    """
    Turns large tick values (in the billions, millions and thousands) such as 4500 into 4.5K and also appropriately turns 4000 into 4K (no zero after the decimal).
    """
    if tick_val >= 1000000000:
        val = round(tick_val/1000000000, 1)
        new_tick_format = '{:}B'.format(val)
    elif tick_val >= 1000000:
        val = round(tick_val/1000000, 1)
        new_tick_format = '{:}M'.format(val)
    elif tick_val >= 1000:
        val = round(tick_val/1000, 1)
        new_tick_format = '{:}K'.format(val)
    elif tick_val < 1000:
        new_tick_format = round(tick_val, 1)
    else:
        new_tick_format = tick_val

    # make new_tick_format into a string value
    new_tick_format = str(new_tick_format)
    
    # code below will keep 4.5M as is but change values such as 4.0M to 4M since that zero after the decimal isn't needed
    index_of_decimal = new_tick_format.find(".")
    
    if index_of_decimal != -1:
        value_after_decimal = new_tick_format[index_of_decimal+1]
        if value_after_decimal == "0":
            # remove the 0 after the decimal point since it's not needed
            new_tick_format = new_tick_format[0:index_of_decimal] + new_tick_format[index_of_decimal+2:]
            
    return new_tick_format

sns.set(font_scale=1.4) def reformat_large_tick_values(tick_val, pos): """ Turns large tick values (in the billions, millions and thousands) such as 4500 into 4.5K and also appropriately turns 4000 into 4K (no zero after the decimal). """ if tick_val >= 1000000000: val = round(tick_val/1000000000, 1) new_tick_format = '{:}B'.format(val) elif tick_val >= 1000000: val = round(tick_val/1000000, 1) new_tick_format = '{:}M'.format(val) elif tick_val >= 1000: val = round(tick_val/1000, 1) new_tick_format = '{:}K'.format(val) elif tick_val < 1000: new_tick_format = round(tick_val, 1) else: new_tick_format = tick_val # make new_tick_format into a string value new_tick_format = str(new_tick_format) # code below will keep 4.5M as is but change values such as 4.0M to 4M since that zero after the decimal isn't needed index_of_decimal = new_tick_format.find(".") if index_of_decimal != -1: value_after_decimal = new_tick_format[index_of_decimal+1] if value_after_decimal == "0": # remove the 0 after the decimal point since it's not needed new_tick_format = new_tick_format[0:index_of_decimal] + new_tick_format[index_of_decimal+2:] return new_tick_format

Example: Visualizing High Net Worth University of Michigan Alumni¶

Below is a small dataset of eight high net worth individuals that graduated from the University of Michigan - Ann Arbor.

In [3]:

data ={'net_worth_us_dollars': [800000000, 180000000, 3500000000, 1800000000, 2500000000, 1300000000, 2500000000, 185000000], 
       'name': ['Tony Fadell', 'Tom Brady', 'William Davidson', 'Charlie Munger', 'Steve Blank', 'Niklas Zennström', 'Eric Paul Lefkofsky', 'Derek Jeter']}
df = pd.DataFrame(data)

data ={'net_worth_us_dollars': [800000000, 180000000, 3500000000, 1800000000, 2500000000, 1300000000, 2500000000, 185000000], 'name': ['Tony Fadell', 'Tom Brady', 'William Davidson', 'Charlie Munger', 'Steve Blank', 'Niklas Zennström', 'Eric Paul Lefkofsky', 'Derek Jeter']} df = pd.DataFrame(data)

In [4]:

df.sort_values(by='net_worth_us_dollars', ascending=False)

df.sort_values(by='net_worth_us_dollars', ascending=False)

Out[4]:

	name	net_worth_us_dollars
2	William Davidson	3500000000
4	Steve Blank	2500000000
6	Eric Paul Lefkofsky	2500000000
3	Charlie Munger	1800000000
5	Niklas Zennström	1300000000
0	Tony Fadell	800000000
7	Derek Jeter	185000000
1	Tom Brady	180000000

Bar Chart with Scientific Notation on X-Ticks¶

This visualization below is the simplest plot of the data. However, it's confusing. It's difficult to immediately translate the scientific notation values into a colloquial format.

If I were to ask you the net worth of Steve Blank, it would take you a bit to convert 2.5e9 to $2.5B.

In [5]:

df.set_index('name')['net_worth_us_dollars'].sort_values().plot(kind='barh', figsize=(10, 8))
plt.xlabel("Net Worth [$]", labelpad=16)
plt.ylabel("Name", labelpad=16)
plt.title("Net Worth of a Sample of University of Michigan Alumni", y=1.02, fontsize=22);

df.set_index('name')['net_worth_us_dollars'].sort_values().plot(kind='barh', figsize=(10, 8)) plt.xlabel("Net Worth [$]", labelpad=16) plt.ylabel("Name", labelpad=16) plt.title("Net Worth of a Sample of University of Michigan Alumni", y=1.02, fontsize=22);

Another similar issue that may arise with tick values - similar to the one above is if the full numerical values were listed out. For example, William Davidson's horizontal bar would align with the x-tick value of 3500000000. It would take someone a few seconds to convert that value into a more understandable format such as 3,500,000,000 or 3.5B.

Bar Chart with Easily Interpretable Dollar Amounts on X-Ticks¶

This plot below is much better!

We can easily identify an approximation for the net worth of those eight individuals that would easily make sense to a mass audience. For example, William Davidson has a net worth of $3.5B.

In [6]:

df.set_index('name')['net_worth_us_dollars'].sort_values().plot(kind='barh', figsize=(10, 8))
plt.xlabel("Net Worth [$]", labelpad=16)
plt.ylabel("Name", labelpad=16)
plt.title("Net Worth of a Sample of University of Michigan Alumni", y=1.02, fontsize=22)
ax = plt.gca()
ax.xaxis.set_major_formatter(tick.FuncFormatter(reformat_large_tick_values));

df.set_index('name')['net_worth_us_dollars'].sort_values().plot(kind='barh', figsize=(10, 8)) plt.xlabel("Net Worth [$]", labelpad=16) plt.ylabel("Name", labelpad=16) plt.title("Net Worth of a Sample of University of Michigan Alumni", y=1.02, fontsize=22) ax = plt.gca() ax.xaxis.set_major_formatter(tick.FuncFormatter(reformat_large_tick_values));