How to Add Value Labels on a Bar Chart

Matplotlib - Adding value labels to bar graph

Use ax.bar_label:

ax = df.plot(x='Pillar', y='%', kind='bar', legend=False, rot=0)
ax.bar_label(ax.containers[0], label_type='edge')
plt.tight_layout()

Adding data labels to a horizontal bar chart in matplotlib

Adding xlabel and ylabel should solve,

plt.xlabel("Cost")
plt.ylabel("Category")

You might also want to create the dataframe:

import pandas as pd
df = {}
df["Category"] = Category
df["Cost"] = Cost
df = pd.DataFrame.from_dict(df)

For adding the data value of each of the bar you can modify your code as follows:

# First make a subplot, so that axes is available containing the function bar_label.
fig, ax = plt.subplots()
g=ax.barh(df['Category'], df['Cost'])
ax.set_xlabel("Cost")
ax.set_ylabel("Category")
ax.bar_label(g, label_type="center") # This provides the labelling, this only available at higher version. You can do pip install -U matplotlib
plt.show()

Reference:

1. Axis Label
2. matplotlib 3.4.2 and above has this

Output:

Add the label for the value to display above the bars

Example using patches and annotate:

# Import the libraries
import pandas as pd
from matplotlib import pyplot as plt

# Create the DataFrame
df = pd.DataFrame(
    {
        "city_code": [1200013, 1200104, 1200138, 1200179, 1200203],
        "index": ["good", "bad", "good", "good", "bad"],
    }
)

# Plot the graph
ax = df["index"].value_counts().plot(kind="bar", color="darkcyan", figsize=[15, 10])
plt.xticks(rotation=0, horizontalalignment="center", fontsize=14)
plt.ylabel("cities", fontsize=16)
for p in ax.patches:
    ax.annotate(
        str(p.get_height()), xy=(p.get_x() + 0.25, p.get_height() + 0.1), fontsize=20
    )
plt.savefig("test.png")

Result:

Matplotlib - Add value label on bar chart from column in dataframe

Ah I think I see it now. The proper usage is:

plt.text(x, y, s)

Where your x is index, y is the corrosponding 'Overdue' value (plus an offset to make sure it displays above the bar) and then your s is already in order.

So that would result something like:

for index, value in enumerate(z):
    plt.text(index, y[index] + offset, str(value))

How to display the value of the bar on each bar with pyplot.barh()

Update: there's a built in method for this now! Scroll down a couple answers to "New in matplotlib 3.4.0".

If you can't upgrade that far, it doesn't take much code. Add:

for i, v in enumerate(y):
    ax.text(v + 3, i + .25, str(v), color='blue', fontweight='bold')

result:

The y-values v are both the x-location and the string values for ax.text, and conveniently the barplot has a metric of 1 for each bar, so the enumeration i is the y-location.

Adding value labels on a bar chart using matplotlib

I think you've mixed up your labels. Try this:

ax = graph_by_duration.plot(kind='bar', width=0.5)
[label.set_rotation(25) for label in ax.get_xticklabels()]

labels = [int(round(graph_by_duration.loc[i, y]))
          for y in graph_by_duration.columns.tolist()
          for i in graph_by_duration.index]

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', rotation=15)

plt.show()

Pandas bar how to label desired values

The first bars are stored in ax.containers[0], the second in ax.containers[1]. You can call ax.bar_label(...) using these containers together with a list of the corresponding labels.

By the way, you are missing x= in the second bar plot.

from matplotlib import pyplot as plt
import pandas as pd

d = {'X': [1, 2, 3, 4], 'A': [50, 40, 20, 60], '% of Total in A': [29.4, 23.5, 11.8, 35.3], 'B': [25, 10, 5, 15], '% in A': [50, 25, 25, 25]}
df = pd.DataFrame(d)
ax = df.plot(x='X', y="A", kind="bar")
df.plot(x='X', y="B", kind="bar", color='C2', ax=ax)

ax.bar_label(ax.containers[0], labels=df['% of Total in A'])
ax.bar_label(ax.containers[1], labels=df['% in A'], color='white')
plt.show()

To further accentuate that B is a part of A, you could give them the same color, and hatch B. For example:

ax = df.plot(x='X', y="A", kind="bar", color='dodgerblue')
df.plot(x='X', y="B", kind="bar", facecolor='dodgerblue', hatch='xx', rot=0, ax=ax)

ax.bar_label(ax.containers[0], labels=[f'{p} %' for p in df['% of Total in A']])
ax.bar_label(ax.containers[1], labels=[f'{p} %' for p in df['% in A']], color='white')
for spine in ['top', 'right']:
    ax.spines[spine].set_visible(False)

Add label values to bar chart and line chart in matplotlib

Here is a modified function that will achieve the required task. The trick is to extract the x and y values based on the type of the chart you have. For a line chart, you can use ax.lines[0] and then get_xdata and get_ydata

def add_value_labels(ax, typ, spacing=5):
    space = spacing
    va = 'bottom'

    if typ == 'bar':
        for i in ax.patches:
            y_value = i.get_height()
            x_value = i.get_x() + i.get_width() / 2

            label = "{:.0f}".format(y_value)
            ax.annotate(label,(x_value, y_value), xytext=(0, space), 
                    textcoords="offset points", ha='center', va=va)     
    if typ == 'line':
        line = ax.lines[0]
        for x_value, y_value in zip(line.get_xdata(), line.get_ydata()):
            label = "{:.2f}".format(y_value)
            ax.annotate(label,(x_value, y_value), xytext=(0, space), 
                textcoords="offset points", ha='center', va=va)   

add_value_labels(ax, typ='bar')
add_value_labels(ax2, typ='line')

---

Related Topics