Seaborn countplot percentage

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mwaskom / seaborn Public

That's certainly one way to do it. But it is by no means the only way to do it. What if someone wants to have both and but normalize so all bars add up to 1? Or what if they want to use facets? The challenge, which might not always appreciated by a userswho is focused on their particular use-case, is coming up with a suitably general API.

That said, I think people are somewhat forgetting that, while it can be convenient to be able to pass a full dataset to a plotting function and get a figure in one step, pandas is quite useful. It's really not very difficult to generate the plot you want, exactly the way you want it, with just one more step external to seaborn:


You can even do this in one method chain, saving a temporary variable name, if that's your preferred style:

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How to show percentage (text) next to the horizontal bars in matplotlib? [closed]

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I am using seaborn's countplot to show count distribution of 2 categorical data. Fine it works but I want the percentages to show on top of the bars for each of the plot. Please how do I do it?

enter image description here

I have made edits based on the comments made but I can't get the percentages to the right of horizontal bars. This is what I have done.

enter image description here


How to add percentages on top of bars in Seaborn using Matplotlib?

To add percentages on top of bars in Seaborn, we can take the following steps −

  • Create the lists, x, y and percentages to plot using Seaborn.

  • Using barplot, show point estimates and confidence intervals with bars. Store the returned axis.

  • Find patches from the returned axis (In step 2).

  • Iterate the patches (returned in step 3).

  • Find and from the patches to place the percentage value at the top of the bars.

  • To display the figure, use show() method.


import matplotlib.pyplot as plt import seaborn as sns plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True x = ['A', 'B', 'C', 'D', 'E'] y = [1, 3, 2, 0, 4] percentage = [10, 30, 20, 0, 40] ax = sns.barplot(x=x, y=y, palette='PuBuGn_r') patches = ax.patches for i in range(len(patches)):    x = patches[i].get_x() + patches[i].get_width()/2    y = patches[i].get_height()+.05    ax.annotate('{:.1f}%'.format(percentage[i]), (x, y), ha='center')



Countplot percentage seaborn

I have a Pandas DataFrame with a column called "AXLES", which can take an integer value between 3-12. I am trying to use Seaborn's countplot() option to achieve the following plot:

  1. left y axis shows the frequencies of these values occurring in the data. The axis extends are [0%-100%], tick marks at every 10%.
  2. right y axis shows the actual counts, values correspond to tick marks determined by the left y axis (marked at every 10%.)
  3. x axis shows the categories for the bar plots [3, 4, 5, 6, 7, 8, 9, 10, 11, 12].
  4. Annotation on top of the bars show the actual percentage of that category.

The following code gives me the plot below, with actual counts, but I could not find a way to convert them into frequencies. I can get the frequencies using but I am not sure about how to plug this information into Seaborn's .

I also found a workaround for the annotations, but I am not sure if that is the best implementation.

Any help would be appreciated!


enter image description here


I got closer to what I need with the following code, using Pandas' bar plot, ditching Seaborn. Feels like I'm using so many workarounds, and there has to be an easier way to do it. The issues with this approach:

  • There is no keyword in Pandas' bar plot function as Seaborn's countplot() has, so I cannot plot all categories from 3-12 as I did in the countplot(). I need to have them shown even if there is no data in that category.
  • The secondary y-axis messes up the bars and the annotation for some reason (see the white gridlines drawn over the text and bars).

enter image description here

count plot in seaborn english

Seaborn countplot with normalized y axis per group

With newer versions of seaborn you can do following:


enter image description here


If you also want percentages, you can do following:

enter image description here

I might be confused. The difference between your output and the output of

is, it seems to me, only the order of the columns.

enter image description here

And you seem to care about that, since you pass . But then, in your code the order is determined uniquely by chance (and the order in which the dictionary is iterated even changes from run to run with Python 3.5).

You can use the library Dexplot to do counting as well as normalizing over any variable to get relative frequencies.

Pass the function the name of the variable you would like to count and it will automatically produce a bar plot of the counts of all unique values. Use to subdivide the counts by another variable. Notice that Dexplot automatically wraps the x-tick labels.

enter image description here

Use the parameter to normalize the counts over any variable (or combination of variables with a list). You can also use to normalize over the grand total of counts.

dxp.count('occupation', data=df, split='income', normalize='income')

enter image description here


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