Annotate bars with values on Pandas bar plots

Question

I was looking for a way to annotate my bars in a Pandas bar plot with the rounded numerical values from my DataFrame.

>>> df=pd.DataFrame({'A':np.random.rand(2),'B':np.random.rand(2)},index=['value1','value2'] )         
>>> df
                 A         B
  value1  0.440922  0.911800
  value2  0.588242  0.797366

I would like to get something like this:

bar plot annotation example

I tried with this code sample, but the annotations are all centered on the x ticks:

>>> ax = df.plot(kind='bar') 
>>> for idx, label in enumerate(list(df.index)): 
        for acc in df.columns:
            value = np.round(df.ix[idx][acc],decimals=2)
            ax.annotate(value,
                        (idx, value),
                         xytext=(0, 15), 
                         textcoords='offset points')

Tom nailed it, but I've got a more complex solution here: http://stackoverflow.com/questions/19917587/matplotlib-advanced-bar-plot/19919397#19919397 — Paul H, Aug 22 '14 at 14:21

score 199 · Accepted Answer · edited Jan 02 '20 at 12:52

199

You get it directly from the axes' patches:

for p in ax.patches:
    ax.annotate(str(p.get_height()), (p.get_x() * 1.005, p.get_height() * 1.005))

You'll want to tweak the string formatting and the offsets to get things centered, maybe use the width from p.get_width(), but that should get you started. It may not work with stacked bar plots unless you track the offsets somewhere.

edited Jan 02 '20 at 12:52

Jaroslav Bezděk

4,527
4
23
38

answered Aug 22 '14 at 14:19

TomAugspurger

26,229
8
81
69

40

Thanks @TomAugsPurger, the following worked: `for p in ax.patches: ax.annotate(np.round(p.get_height(),decimals=2), (p.get_x()+p.get_width()/2., p.get_height()), ha='center', va='center', xytext=(0, 10), textcoords='offset points')` – leroygr Aug 22 '14 at 14:34
2

Another question: how would you handle bars with negative values? With the code above, the labels are always with positive coordinates, even if the bar value is negative. – leroygr Aug 22 '14 at 15:05
Hmm, you'd think there would be an easy way. I bet there is, but you could use np.sign(df) (which will be 1 or -1) and multiply that by the label location. Seems too complicated though. – TomAugspurger Aug 22 '14 at 15:22
1

Is there a similar solution for horizontal bars: kind=barh? – Nickpick Oct 05 '15 at 15:42
6

I found this to work for barh: `ax.annotate(str(p.get_width()), (p.get_x() + p.get_width(), p.get_y()), xytext=(5, 10), textcoords='offset points')` – Kamil Sindi Jan 04 '16 at 20:00
2

Thanks @capitalistpug. I found that adding horizontalalignment made yours better still. `ax.annotate(str(int(p.get_width())), (p.get_x() + p.get_width(), p.get_y()), xytext=(-2, 4), textcoords='offset points', horizontalalignment='right')` +1 – DaveL17 Dec 14 '16 at 22:02
And related to line graphs? – Lucas Brito Mar 02 '22 at 19:57

tdy · Answer 2 · 2021-06-24T05:19:27.510

As of matplotlib 3.4.0:

A new Axes.bar_label helper method has been added for auto-labeling bar charts.

For single-group bar charts, supply ax.containers[0]:

df = pd.DataFrame({'A': np.random.rand(2)}, index=['value1', 'value2'])
ax = df.plot.barh()

ax.bar_label(ax.containers[0])

For multi-group bar charts, iterate ax.containers:

df = pd.DataFrame({'A': np.random.rand(2), 'B': np.random.rand(2)}, index=['value1', 'value2'])
ax = df.plot.bar()

for container in ax.containers:
    ax.bar_label(container)

See matplotlib's bar label demos for comprehensive examples using the optional styling params:

Axes.bar_label(self, container, labels=None, *, fmt='%g', label_type='edge', padding=0, **kwargs)

Great answer and works very fine with pandas – meduz Oct 11 '21 at 09:12 — meduz, Oct 11 '21 at 09:12

score 38 · Answer 3 · edited Aug 27 '19 at 13:15

Solution which also handles the negative values with sample float formatting.

Still needs tweaking offsets.

df=pd.DataFrame({'A':np.random.rand(2)-1,'B':np.random.rand(2)},index=['val1','val2'] )
ax = df.plot(kind='bar', color=['r','b']) 
x_offset = -0.03
y_offset = 0.02
for p in ax.patches:
    b = p.get_bbox()
    val = "{:+.2f}".format(b.y1 + b.y0)        
    ax.annotate(val, ((b.x0 + b.x1)/2 + x_offset, b.y1 + y_offset))

score 1 · Answer 4 · answered Aug 16 '20 at 16:32

The ax gives us the size of the box.

x_position=##define a value
y_position=##define a value
for patch in ax.patches:
    b= patch.get_bbox()
    y_value=b.y1-b.y0
    ax.annotate(y_value, "x_position" , "y_position"))
plt.show()

for more clarity::
Bbox(x0=3.75, y0=0.0, x1=4.25, y1=868.0)
Bbox(x0=4.75, y0=0.0, x1=5.25, y1=868.0)
Bbox(x0=5.75, y0=0.0, x1=6.25, y1=1092.0)
Bbox(x0=6.75, y0=0.0, x1=7.25, y1=756.0)
Bbox(x0=7.75, y0=0.0, x1=8.25, y1=756.0)
Bbox(x0=8.75, y0=0.0, x1=9.25, y1=588.0)
Bbox(x0=3.75, y0=868.0, x1=4.25, y1=3724.0)
Bbox(x0=4.75, y0=868.0, x1=5.25, y1=3528.0)
Bbox(x0=5.75, y0=1092.0, x1=6.25, y1=3948.0)
Bbox(x0=6.75, y0=756.0, x1=7.25, y1=2884.0)
Bbox(x0=7.75, y0=756.0, x1=8.25, y1=3024.0)
Bbox(x0=0.75, y0=4004.0, x1=1.25, y1=4396.0)
Bbox(x0=1.75, y0=3668.0, x1=2.25, y1=4060.0)
Bbox(x0=2.75, y0=3864.0, x1=3.25, y1=4060.0)

this is the output of patch.get_bbox() in my program.
we can extract the bounding box details from here and manipulate for our requirement

Annotate bars with values on Pandas bar plots

4 Answers4

As of matplotlib 3.4.0:

Linked

Related