Bin data using custom intervals with pandas (floats)

bin_floats.py

import pandas as pd
import numpy as np
import plotly.express as px
from typing import List, Union


def bin_work_hours(work_hours_series: pd.Series) -> pd.Series:
    work_hours_labels = [f"[{i}, {i+8})" for i in range(8, 73, 8)]

    work_hours_bins = pd.IntervalIndex.from_tuples(
        [(i, i+8) for i in range(8, 73, 8)],
        closed="left"
    )

    work_hours_binned = pd.cut(
        work_hours_series,
        work_hours_bins,
        labels=work_hours_labels,
        precision=2,
        include_lowest=True
    )

    work_hours_binned.sort_values(ascending=True, inplace=True)
    # Change the values from categorical to string to be able to plot them
    work_hours_binned = work_hours_binned.astype("str")

    return work_hours_binned


def plot_histogram(
    data_series: pd.Series,
    nbins: int,
    title: str,
    axes_titles: List[Union[str, None]]
) -> None:
    fig = px.histogram(
        x=data_series,
        nbins=nbins,
        title=title
    )

    fig.update_layout(
        xaxis_title=axes_titles[0],
        yaxis_title=axes_titles[1]
    )

    fig.update_layout(
        uniformtext_minsize=14,
        uniformtext_mode="hide",
        bargap=0,
        title_x=0.5
    )

    fig.show()