---
date: "2022-12-16"
author: Moritz Boos
tags:
  - snippets
  - python
  - til
  - seaborn
---

# TIL: Making a seaborn count plot with hue and labels

It's surprisingly hard to label bars in a seaborn [countplot](https://seaborn.pydata.org/generated/seaborn.countplot.html), especially if you use more than one column (e.g. when using `hue`). The function below does the labeling even when using two columns for indexing.


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


def make_labelled_count_plot(
    data: pd.DataFrame, x_col: str, hue_col: str, **kwargs
) -> plt.Figure:
    """Creates a count plot from `data` and labels multi-indexed bars."""
    vals = data.groupby(x_col)[hue_col].value_counts()
    hue_vals = vals.index.unique(level=hue_col).values
    x_order = vals.index.unique(level=x_col).values

    fig, ax = plt.subplots(1, 1, **kwargs)
    sns.countplot(
        data=data, hue=hue_col, x=x_col, hue_order=hue_vals, order=x_order, ax=ax
    )

    for container, hue_val in zip(ax.containers, hue_vals):
        # take intersection because values could be missing
        safe_ordered = np.intersect1d(x_order, vals[:, hue_val].index)
        _ = ax.bar_label(container=container, labels=vals[:, hue_val][safe_ordered])
    return fig


data = pd.DataFrame(
    {
        "col_A": np.random.choice(["A", "B", "C"], 100),
        "col_B": np.random.choice(["H", "I", "J", "K"], 100),
    }
)
data.head()
```


```python
fig = make_labelled_count_plot(data, x_col="col_A", hue_col="col_B")
```


![png](/images/TIL-2022-12-16_output_2_0.png)