So do you know your bar plots? Here are some best practices to improve your game

Being one of the most common visualization types, a bar plot is technically very easy to create: we need to write just one short line of code. However, if we want to create a really informative, easily readable graph efficiently revealing the story behind the data, we have to keep in mind several important things, which we’re going to discuss in this article. Some of these suggestions are only specific to bar plots, the others apply to any kind of visualizations.

To practice our bar plots, we’ll use a very bar-related dataset from Kaggle — Alcohol Consumption around the World :crazy_face::champagne: The table is dated by 2010, so let’s travel a bit back in time.

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

df = pd.read_csv('drinks.csv')
print('Number of all the countries:', len(df), '\n')

# Removing the countries with 0 alcohol consumption
df = df[df['total_litres_of_pure_alcohol'] > 0]\
print(df.head(3), '\n')
print('Number of all the drinking countries:', len(df))


Number of all the countries: 193 

   country  beer_servings  spirit_servings  wine_servings  \
0  Albania             89              132             54   
1  Algeria             25                0             14   
2  Andorra            245              138            312   

0                           4.9  
1                           0.7  
2                          12.4   

Number of all the drinking countries: 180


As a general rule, we should maximize the data-ink ratio of the graph and, hence, exclude everything that doesn’t provide any additional information for our storytelling through the data.

Removing Redundant Features

To start with, we should avoid any features on the plot that could potentially distract the reader’s attention:

  • unnecessary spines and ticks,
  • the grid, if it’s redundant,
  • decimal numbers where possible, especially those with many decimal points,
  • putting the exact numbers (decimal or not) on top of each bar: if we really need them, we can supplement our graph with a corresponding table. Alternatively, we can use only these direct labels on top of the bars and remove the numeric axis, for not to duplicate the same information.

Labeling and Sizing

A seemingly obvious, but sometimes neglected or misused aspect of storytelling when creating bar plots is related to labeling and sizing:

  • sufficient width and height of the figure,
  • an easily readable font size of the graph title, axes labels, ticks, and annotations (if present),
  • the title as laconic as possible while still exhaustively descriptive, divided into no more than 2–3 rows (if long),
  • clear axes labels,
  • rotating tick labels (if necessary),
  • the units for the measured value (%, fractions, or whatever absolute values) included in the axis label or directly in the title,
  • if the values of the categorical axis are self-explanatory, we can omit this axis label.

Things to Always Avoid

The following features should be always avoided when creating bar plots:

  • 3D bar plots: they severely deform the reality creating an optical illusion and making it more difficult to identify the real height (length) of each bar. Moreover, the bars in the back can be completely covered by the bars in the front and hence just invisible to the reader.
  • Interactivity (except for very rare cases).
  • Decorations or color effects.

Let’s compare the 2 bar plots below, which are identical in terms of the data, but different in their style. Also, we’ll find out what countries consumed alcohol most of all in 2010:

top5_alcohol = df.sort_values('total_litres_of_pure_alcohol',

fig, ax = plt.subplots(figsize=(16,7))

ax = sns.barplot(x='country', y='total_litres_of_pure_alcohol', 
for p in ax.patches:
    ax.annotate(format(p.get_height(), '.1f'),
                (p.get_x() + p.get_width() / 2., p.get_height()),
                ha='center', va='center',
                xytext=(0, 7), textcoords='offset points')
plt.title('TOP5 countries by pure alcohol consumption')
plt.ylabel('Litres per person')

ax = sns.barplot(x='country', y='total_litres_of_pure_alcohol', 
plt.title('TOP5 countries by pure alcohol consumption', fontsize=30)
plt.xticks(fontsize=22, rotation=30)
plt.ylabel('Litres per person', fontsize=25)
ax.tick_params(bottom=False, left=True)
for _,s in ax.spines.items():

The second bar plot, even if still not ideal, is definitely much cleaner and better readable than the first one. We removed unnecessary spines, the ticks from the categorical axis, the grid, the bar values denotations, increased font size, rotated x-tick labels, omitted the categorical axis label.

And yes, we clearly see what countries drank more alcohol in 2010. Most probably, though, they were consuming different kinds of drinks. We’ll investigate this question soon.


We’ve already mentioned that using additional color effects, like background or font color, isn’t a good practice. There are a couple of other things to consider when selecting colors for a bar plot.

Highlighting Some Bars

When applying different colors doesn’t communicate anything about the data, it should be avoided. By default, each bar in a seaborn bar plot is colored differently, as we saw earlier. We can override it by introducing the color parameter and assigning the same color to all the bars.

However, we still can emphasize some bars in particular and display the other ones in grey color. For example, of our TOP5 countries above, let’s highlight the leader in drinking exactly spirit. Besides color emphasizing, we’ll add also a corresponding annotation:

spirit_top = top5_alcohol['spirit_servings']
colors = ['grey' if (s < max(spirit_top)) else 'red' for s in spirit_top]
fig, ax = plt.subplots(figsize=(10,5))
ax=sns.barplot(x='country', y='total_litres_of_pure_alcohol', 
               data=top5_alcohol, palette=colors)
plt.title('TOP5 countries by pure alcohol consumption', fontsize=25)
plt.ylabel('Litres per person', fontsize=20)
ax.text(x=2.5, y=12.3, s='the highest \nspirit servings', 
        color='red', size=17, weight='bold')
ax.tick_params(bottom=False, left=True)


A small island Caribbean country Grenada is in 4th place by pure alcohol consumption, and among the TOP5 countries, it’s the one with the highest number of strong spirit servings.

Using Colorblind Palette

For our bar plots to reach a wider audience, we should consider using colorblind-friendly colors. There are various online tools (e.g. Stark or Colblindor) for testing how an image looks for different types of color blindness. However, the most common form of it involves differentiating between red and green, so a good approach would be to avoid palettes with both of them. Another way is to use the Color Blind 10 palette of Tableau. The drawback is that it offers quite a limited choice of colors.

Avoiding Counterintuitive Colors

Some colors have strong associations with certain categories of phenomena or qualities for the majority of people. For example, fuchsia is widely considered to be a feminine color, traffic light palette is commonly used to distinguish between danger, neutral, and safe zones, the red-blue palette is related to the temperature, etc. Even if you are a convinced nonconformist, who is always against any stereotypes, you’d better follow these unwritten conventions when creating a grouped bar plot, as not to mislead the reader.

If there are no particular conventions for our groups in question, a good practice is to try to come up (if possible) with some contextual, but still easy-to-understand decisions. Say, we’re going to create a grouped bar plot of the worldwide population of koalas and foxes in the last 10 years. In this case, we can think of using orange color for foxes and grey for koala, and not vice versa.

Let’s return to our TOP5 countries by pure alcohol consumption and check the proportions of drinking beer and wine in each of them. Of course, some types of beer have dark red color (e.g. the cherry’s one) and some wines — golden color (white or plum wine). Despite that, the most intuitively comprehensible color associations for these drink types are dark red for wine and golden for beer:

fig, ax = plt.subplots(figsize=(10,5))
x = np.arange(len(top5_alcohol))
width = 0.4, top5_alcohol['wine_servings'],
        width, color='tab:red', label='wine'), top5_alcohol['beer_servings'],
        width, color='gold', label='beer')
plt.title('TOP5 countries by pure alcohol consumption', fontsize=25)
plt.xticks(top5_alcohol.index, top5_alcohol['country'], fontsize=17)
plt.ylabel('Servings per person', fontsize=20)
ax.tick_params(bottom=False, left=True)
plt.legend(frameon=False, fontsize=15)


Now we can easily capture that in France people drink much more wine than beer, while in Lithuania and Grenada — vice versa. In Andorra, both drinks are rather popular, with wine slightly dominating.


Vertical vs. Horizontal

Even though a vertical bar plot is usually a default one, sometimes a horizontal version is preferred:

  • for plotting nominal variables,
  • when x-tick labels are too long, and rotating them would help to avoid overlapping, but decrease readability,
  • when we have a large number of categories (bars).

In the last case, horizontal bar plots are especially advantageous for viewing the graph from a narrow screen of a mobile phone.

A vertical bar plot, instead, is more suitable for plotting ordinal variables or time series. For example, we can use it to plot the overall biomass on Earth by geological period, or the number of UFO sightings by month, etc.

Since the country column represents a nominal variable, and the names of some countries are rather long, let’s select many categories (the TOP20 countries by beer consumption per person) and see the horizontal bar plot in action:

top20_beer = df.sort_values('beer_servings', ascending=False)[:20]

fig, ax = plt.subplots(figsize=(40,18))

# Creating a case-specific function to avoid code repetition
def plot_hor_vs_vert(subplot, x, y, xlabel, ylabel, rotation, 
                     tick_bottom, tick_left):
    sns.barplot(x, y, data=top20_beer, color='slateblue')
    plt.title('TOP20 countries \nby beer consumption', fontsize=85)
    plt.xlabel(xlabel, fontsize=60)
    plt.xticks(fontsize=45, rotation=rotation)
    plt.ylabel(ylabel, fontsize=60)
    sns.despine(bottom=False, left=True)
    ax.tick_params(bottom=tick_bottom, left=tick_left)
    return None

plot_hor_vs_vert(1, x='country', y='beer_servings',
                 xlabel=None, ylabel='Servings per person',
                 rotation=90, tick_bottom=False, tick_left=True)
plot_hor_vs_vert(2, x='beer_servings', y='country',
                 xlabel='Servings per person', ylabel=None,
                 rotation=None, tick_bottom=True, tick_left=False)    

Having all the words flipped horizontally (including the label of the measured value axis) makes the second graph significantly more readable.

This list is opened by Namibia, followed by the Czech Republic. We don’t see anymore the countries with the highest alcohol consumption except for Lithuania, which has dropped to 5th place. It seems that their high positions in the previous rating were explained by drinking spirit and wine rather than beer.


If we extract all the countries where people drink wine more than average and then visualize this data as a bar plot, the resulting bars will be ordered by the underlying categories (countries) in alphabetical order. Most probably, though, in this case, we’re more interested in seeing this data ordered by the number of wine servings per person. Let’s compare both approaches:

wine_more_than_mean = (df[df['wine_servings'] > df['wine_servings']\
sort_wine_more_than_mean = wine_more_than_mean\

fig, ax = plt.subplots(figsize=(30,30))

# Creating a case-specific function to avoid code repetition
def plot_hor_bar(subplot, data):
    ax = sns.barplot(y='country', x='wine_servings', data=data,
    plt.title('Countries drinking wine \nmore than average',
    plt.xlabel('Servings per person', fontsize=50)
    ax.tick_params(bottom=True, left=False)
    return None

plot_hor_bar(1, wine_more_than_mean)
plot_hor_bar(2, sort_wine_more_than_mean)

In the first plot, we can somehow distinguish the first and the last 3 countries by wine servings per person (referring only to those where people drink wine more than average), then the things become excessively complicated. In the second plot, we can easily trace the whole country rating. For obtaining a more realistic picture, we should take into account the population of each country (certainly, it’s not exactly correct to compare Russian Federation with the Cook Islands and St. Lucia) and, probably, exclude abstainers. However, the point here is that we should always consider ordering the data before plotting it if we want to get the maximum information from our visualization. It doesn’t obligatory have to be an ordering by values: instead, we can decide to rank the data by categories themselves (if they are ordinal, like age ranges), or there could be whatever other logic behind it, if necessary.

Starting at 0

While other types of plots don’t have to, bar plots do always have to start at zero. The reason behind it is that a bar plot is supposed to show the magnitude of each data point and the proportions between all the data points, instead of just a change of a variable, as it happens in line plots. If we truncate the y-axis (or the x-axis, in case of a horizontal bar plot) starting it at a value other than 0, we cut also the length of each bar, so our graph doesn’t display correctly anymore neither individual values for each category nor the ratios between them:

usa = df[df['country']=='USA'].transpose()[1:4].reset_index()
usa.columns = ['drinks', 'servings']

fig = plt.figure(figsize=(16,6))

# Creating a case-specific function to avoid code repetition
def plot_vert_bar(subplot, y_min):
    ax = sns.barplot(x='drinks', y='servings', 
                     data=usa, color='slateblue')
    plt.title('Drink consumption in the USA', fontsize=30)
    plt.xticks(usa.index, ['Beer', 'Spirit', 'Wine'], fontsize=25)
    plt.ylabel('Servings per person', fontsize=25)
    plt.ylim(y_min, None)
    ax.tick_params(bottom=False, left=True)
    return None
plot_vert_bar(1, y_min=80)
plot_vert_bar(2, y_min=None)

The plot on the left gives us a misleading impression that the consumption of wine in the USA is around 15 times lower than that of spirit, which, in turn, is less than half of that of beer. On the right plot, we see completely different proportions, which are the correct ones.

Grouping and Stacking

Visually Evident Grouping

Creating a grouped bar plot, it’s important to mind the distances between the bars, which are considered to be grouped properly when the gaps between bars inside each group are smaller (up to 0) than those between the bars of adjacent groups.

Back to the TOP5 countries by pure alcohol consumption, let’s now check the proportions of drinking spirit and wine in each of them:

top5_alcohol_rev = top5_alcohol\

fig, ax = plt.subplots(figsize=(20,9))

# Creating a case-specific function to avoid code repetition
def plot_grouped_bar(subplot, width, gap):
    x = np.arange(len(top5_alcohol_rev['wine_servings']))
    plt.barh(x, top5_alcohol_rev['wine_servings'], 
             width, color='tab:red', label='wine')
    plt.barh(x+width+gap, top5_alcohol_rev['spirit_servings'], 
             width, color='aqua', label='spirit')
    plt.yticks(x+width/2, top5_alcohol_rev['country'], fontsize=28)
    plt.title('TOP5 countries \nby pure alcohol consumption',
    plt.xlabel('Servings per person', fontsize=30)
    plt.tick_params(bottom=True, left=False)
    plt.legend(loc='right', frameon=False, fontsize=23)
    return None

plot_grouped_bar(1, width=0.4, gap=0.1)
plot_grouped_bar(2, width=0.3, gap=0)

From the graph on the left, it’s difficult to immediately distinguish the boundaries between adjacent groups, since the distances between the bars inside each group and between the groups are equal. The graph on the right, instead, clearly displays to which country each bar is related. We see now that people in Grenada, Belarus, and Lithuania prefer much more spirit than wine, while in France and Andorra — just the opposite.

Stacked vs. Grouped

Choosing between a stacked and a grouped bar plots, we should consider the main message of our visualization:

  • If we’re mostly interested in the overall values across several categories, and, as a secondary goal, we’d like to roughly estimate which of the components contributes most of all in the biggest or smallest total values, the best choice would be a stacked bar plot. However, the issue here is that it can be rather difficult to figure out the trends of its individual elements apart from the first one (i.e. the lowermost in a vertically stacked bar plot or the leftmost in a horizontal). It especially counts in a situation when we have a lot of bars, and sometimes, we can even get a deceiving impression and come to a wrong conclusion.
  • If we want to trace the trends of each individual component across the categories, we’d better use a grouped bar plot. Evidently, in this case, we can say nothing about the total values by category.

Let’s apply stacked and grouped bar plots to the Baltic countries, to find out their drinking preferences:

baltics = df[(df['country']=='Latvia')|(df['country']=='Lithuania')\
baltics.columns = ['country', 'beer', 'spirit', 'wine']
baltics.reset_index(drop=True, inplace=True)

labels = baltics['country'].tolist()
beer = np.array(baltics['beer'])
spirit = np.array(baltics['spirit'])
wine = np.array(baltics['wine'])

fig, ax = plt.subplots(figsize=(16,7))

# Creating a case-specific function to avoid code repetition
def plot_stacked_grouped(subplot, shift, width, bot1, bot2):
    x = np.arange(len(baltics))
    plt.subplot(1,2,subplot), beer, width, 
            label='beer', color='gold'), spirit, width, bottom=bot1, 
            label='spirit', color='aqua'), wine, width, bottom=bot2, 
            label='wine', color='tab:red')
    plt.title('Drink consumption \nin Baltic countries',
    plt.xticks(baltics.index, labels, fontsize=25)
    plt.ylabel('Servings per person', fontsize=27)
    plt.tick_params(bottom=False, left=True)
    plt.legend(frameon=False, fontsize=17)
    return None

plot_stacked_grouped(1, shift=0, width=0.35, 
                     bot1=beer, bot2=beer+spirit)
plot_stacked_grouped(2, shift=0.2, width=0.2, 
                     bot1=0, bot2=0)

  • From the stacked plot above we see that of all the 3 Baltic countries, Lithuania shows the highest level of alcohol consumption, while Estonia — the lowest. The main contribution in both cases comes from beer. About the consumption of spirit and wine in these countries, we can say nothing precise from this plot. Indeed, the amounts seem equal.

  • The grouped plot clearly shows that Lithuania leads also in drinking spirit, while Estonia again shows the lowest level. The difference for this type of drink is not so evident, though, as it was for the beer. As for the wine, the difference is even less noticeable, but it seems that in Latvia the wine consumption is the highest, while in Lithuania — the lowest. From this plot, however, it’s already more difficult to guess the overall alcohol consumption in these countries. We’d have to do some mental arithmetics for it, and in the case of more than 3 bar groups, this task would become impracticable.


As we saw, bar plots are not as banal as they could seem. Before creating a meaningful visualization and obtaining the correct insights from it, we have to consider many details, including our goal, our target audience, what can be the most important takeaway from our graph, how to emphasize it while displaying also additional helpful information, and how to exclude the features that are completely useless for our storytelling.

Thanks for reading, and za zdorovie! :wink::clinking_glasses:


Brilliant article. Love the humorous play on words with the “bar related dataset” :grinning:. Excellent explanations on bar plots uses, do’s and don’ts.
Only thing I can suggest is to make the intro a bit more conversational and engaging. You did this really well later on in the article.
Great article for when I get back to visualisations. Saving right now!


Thank you very much @Achi! :heart_eyes: I accumulateed all these ideas based the guided projects that I reviewed, thinking that it may be useful :nerd_face: And about the short introduction, I was just already in a hurry to start drinking! :smiley: :joy:

By the way, today I published the 2nd part of this topic. Always about the same drinks, of course :yum: