How to add value labels on a bar chart

Firstly freq_series.plot returns an axis not a figure so to make my answer a little more clear I've changed your given code to refer to it as ax rather than fig to be more consistent with other code examples.

You can get the list of the bars produced in the plot from the ax.patches member. Then you can use the technique demonstrated in this matplotlib gallery example to add the labels using the ax.text method.

import pandas as pd
import matplotlib.pyplot as plt

# Bring some raw data.
frequencies = [6, 16, 75, 160, 244, 260, 145, 73, 16, 4, 1]
# In my original code I create a series and run on that,
# so for consistency I create a series from the list.
freq_series = pd.Series(frequencies)

x_labels = [
    108300.0,
    110540.0,
    112780.0,
    115020.0,
    117260.0,
    119500.0,
    121740.0,
    123980.0,
    126220.0,
    128460.0,
    130700.0,
]

# Plot the figure.
plt.figure(figsize=(12, 8))
ax = freq_series.plot(kind="bar")
ax.set_title("Amount Frequency")
ax.set_xlabel("Amount ($)")
ax.set_ylabel("Frequency")
ax.set_xticklabels(x_labels)

rects = ax.patches

# Make some labels.
labels = [f"label{i}" for i in range(len(rects))]

for rect, label in zip(rects, labels):
    height = rect.get_height()
    ax.text(
        rect.get_x() + rect.get_width() / 2, height + 5, label, ha="center", va="bottom"
    )

plt.show()

This produces a labeled plot that looks like:

Based on a feature mentioned in this answer to another question I have found a very generally applicable solution for placing labels on a bar chart.

Other solutions unfortunately do not work in many cases, because the spacing between label and bar is either given in absolute units of the bars or is scaled by the height of the bar. The former only works for a narrow range of values and the latter gives inconsistent spacing within one plot. Neither works well with logarithmic axes.

The solution I propose works independent of scale (i.e. for small and large numbers) and even correctly places labels for negative values and with logarithmic scales because it uses the visual unit points for offsets.

I have added a negative number to showcase the correct placement of labels in such a case.

The value of the height of each bar is used as a label for it. Other labels can easily be used with Simon's for rect, label in zip(rects, labels) snippet.

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

# Bring some raw data.
frequencies = [6, -16, 75, 160, 244, 260, 145, 73, 16, 4, 1]

# In my original code I create a series and run on that,
# so for consistency I create a series from the list.
freq_series = pd.Series.from_array(frequencies)

x_labels = [108300.0, 110540.0, 112780.0, 115020.0, 117260.0, 119500.0,
            121740.0, 123980.0, 126220.0, 128460.0, 130700.0]

# Plot the figure.
plt.figure(figsize=(12, 8))
ax = freq_series.plot(kind='bar')
ax.set_title('Amount Frequency')
ax.set_xlabel('Amount ($)')
ax.set_ylabel('Frequency')
ax.set_xticklabels(x_labels)


def add_value_labels(ax, spacing=5):
    """Add labels to the end of each bar in a bar chart.

    Arguments:
        ax (matplotlib.axes.Axes): The matplotlib object containing the axes
            of the plot to annotate.
        spacing (int): The distance between the labels and the bars.
    """

    # For each bar: Place a label
    for rect in ax.patches:
        # Get X and Y placement of label from rect.
        y_value = rect.get_height()
        x_value = rect.get_x() + rect.get_width() / 2

        # Number of points between bar and label. Change to your liking.
        space = spacing
        # Vertical alignment for positive values
        va = 'bottom'

        # If value of bar is negative: Place label below bar
        if y_value < 0:
            # Invert space to place label below
            space *= -1
            # Vertically align label at top
            va = 'top'

        # Use Y value as label and format number with one decimal place
        label = "{:.1f}".format(y_value)

        # Create annotation
        ax.annotate(
            label,                      # Use `label` as label
            (x_value, y_value),         # Place label at end of the bar
            xytext=(0, space),          # Vertically shift label by `space`
            textcoords="offset points", # Interpret `xytext` as offset in points
            ha='center',                # Horizontally center label
            va=va)                      # Vertically align label differently for
                                        # positive and negative values.


# Call the function above. All the magic happens there.
add_value_labels(ax)

plt.savefig("image.png")

Edit: I have extracted the relevant functionality in a function, as suggested by barnhillec.

This produces the following output:

And with logarithmic scale (and some adjustment to the input data to showcase logarithmic scaling), this is the result:

Building off the above (great!) answer, we can also make a horizontal bar plot with just a few adjustments:

# Bring some raw data.
frequencies = [6, -16, 75, 160, 244, 260, 145, 73, 16, 4, 1]

freq_series = pd.Series(frequencies)

y_labels = [108300.0, 110540.0, 112780.0, 115020.0, 117260.0, 119500.0, 
            121740.0, 123980.0, 126220.0, 128460.0, 130700.0]

# Plot the figure.
plt.figure(figsize=(12, 8))
ax = freq_series.plot(kind='barh')
ax.set_title('Amount Frequency')
ax.set_xlabel('Frequency')
ax.set_ylabel('Amount ($)')
ax.set_yticklabels(y_labels)
ax.set_xlim(-40, 300) # expand xlim to make labels easier to read

rects = ax.patches

# For each bar: Place a label
for rect in rects:
    # Get X and Y placement of label from rect.
    x_value = rect.get_width()
    y_value = rect.get_y() + rect.get_height() / 2

    # Number of points between bar and label. Change to your liking.
    space = 5
    # Vertical alignment for positive values
    ha = 'left'

    # If value of bar is negative: Place label left of bar
    if x_value < 0:
        # Invert space to place label to the left
        space *= -1
        # Horizontally align label at right
        ha = 'right'

    # Use X value as label and format number with one decimal place
    label = "{:.1f}".format(x_value)

    # Create annotation
    plt.annotate(
        label,                      # Use `label` as label
        (x_value, y_value),         # Place label at end of the bar
        xytext=(space, 0),          # Horizontally shift label by `space`
        textcoords="offset points", # Interpret `xytext` as offset in points
        va='center',                # Vertically center label
        ha=ha)                      # Horizontally align label differently for
                                    # positive and negative values.

plt.savefig("image.png")

If you want to just label the data points above the bar, you could use plt.annotate()

My code:

import numpy as np
import matplotlib.pyplot as plt

n = [1,2,3,4,5,]
s = [i**2 for i in n]
line = plt.bar(n,s)
plt.xlabel('Number')
plt.ylabel("Square")

for i in range(len(s)):
    plt.annotate(str(s[i]), xy=(n[i],s[i]), ha='center', va='bottom')

plt.show()

By specifying a horizontal and vertical alignment of 'center' and 'bottom' respectively one can get centered annotations.

As of matplotlib v3.4.0

• Use matplotlib.pyplot.bar_label
  • The default label position, set with the parameter label_type, is 'edge'. To center the labels in the middle of the bar, use 'center'
  • Additional kwargs are passed to Axes.annotate, which accepts Text kwargs.
    • Properties like color, rotation, fontsize, etc., can be used.
• See the matplotlib: Bar Label Demo page for additional formatting options.
• Tested in python 3.10, pandas 1.4.2, matplotlib 3.5.1, seaborn 0.11.2
• ax.containers is a list of BarContainer artists
  • With a single level bar plot, it's a list of len 1, hence [0] is used.
  • For grouped and stacked bar plots there will be more objects in the list

• Simple label formatting can be done with the fmt parameter, as shown in the Demo examples and at How to annotate a seaborn barplot with the aggregated value.
• More sophisticated label formatting should use the label parameter, as shown in the Demo examples and the following

import pandas as pd

# dataframe using frequencies and x_labels from the OP
df = pd.DataFrame({'Frequency': frequencies}, index=x_labels)

# display(df)
          Frequency
108300.0          6
110540.0         16
112780.0         75
115020.0        160
117260.0        244

# plot
ax = df.plot(kind='bar', figsize=(12, 8), title='Amount Frequency',
             xlabel='Amount ($)', ylabel='Frequency', legend=False)

# annotate
ax.bar_label(ax.containers[0], label_type='edge')

# pad the spacing between the number and the edge of the figure
ax.margins(y=0.1)

• Specify additional kwargs for additional customization
  • Accepts parameters from matplotlib.axes.Axes.text

ax.bar_label(ax.containers[0], label_type='edge', color='red', rotation=90, fontsize=7, padding=3)

Seaborn axes-level plot

• As can be seen, the is exactly the same as with ax.bar(...), plt.bar(...), and df.plot(kind='bar',...)

import seaborn as sns

# plot data
fig, ax = plt.subplots(figsize=(12, 8))
sns.barplot(x=x_labels, y=frequencies, ax=ax)

# annotate
ax.bar_label(ax.containers[0], label_type='edge')

# pad the spacing between the number and the edge of the figure
ax.margins(y=0.1)

Seaborn figure-level plot

• seaborn.catplot accepts a dataframe for data.
• Since .catplot is a FacetGrid (subplots), the only difference is to iterate through each axes of the figure to use .bar_labels.

import pandas as pd
import seaborn as sns

# load the data into a dataframe
df = pd.DataFrame({'Frequency': frequencies, 'amount': x_labels})

# plot
g = sns.catplot(kind='bar', data=df, x='amount', y='Frequency', height=6, aspect=1.5)

# iterate through the axes
for ax in g.axes.flat:

    # annotate
    ax.bar_label(ax.containers[0], label_type='edge')

    # pad the spacing between the number and the edge of the figure; should be in the loop, otherwise only the last subplot would be adjusted
    ax.margins(y=0.1)

matplotlib.axes.Axes.bar

• It will be similar if just using matplotlib.pyplot.bar

import matplotlib.pyplot as plt

# create the xticks beginning a index 0
xticks = range(len(frequencies))

# plot
fig, ax = plt.subplots(figsize=(12, 8))
ax.bar(x=xticks, height=frequencies)

# label the xticks
ax.set_xticks(xticks, x_labels)

# annotate
ax.bar_label(ax.containers[0], label_type='edge')

# pad the spacing between the number and the edge of the figure
ax.margins(y=0.1)

Other examples using bar_label

I needed the bar labels too, note that my y-axis is having a zoomed view using limits on y axis. The default calculations for putting the labels on top of the bar still works using height (use_global_coordinate=False in the example). But I wanted to show that the labels can be put in the bottom of the graph too in zoomed view using global coordinates in matplotlib 3.0.2. Hope it help someone.

def autolabel(rects,data):
"""
Attach a text label above each bar displaying its height
"""
c = 0
initial = 0.091
offset = 0.205
use_global_coordinate = True

if use_global_coordinate:
    for i in data:        
        ax.text(initial+offset*c, 0.05, str(i), horizontalalignment='center',
                verticalalignment='center', transform=ax.transAxes,fontsize=8)
        c=c+1
else:
    for rect,i in zip(rects,data):
        height = rect.get_height()
        ax.text(rect.get_x() + rect.get_width()/2., height,str(i),ha='center', va='bottom')

If you only want to add Datapoints above the bars, you could easily do it with:

 for i in range(len(frequencies)): # your number of bars
    plt.text(x = x_values[i]-0.25, #takes your x values as horizontal positioning argument 
    y = y_values[i]+1, #takes your y values as vertical positioning argument 
    s = data_labels[i], # the labels you want to add to the data
    size = 9) # font size of datalabels