def METHOD_NAME(timeseries_df):
    df = timeseries_df
    df["machine_info_cpu_brand_raw"] = df["machine_info_cpu_brand_raw"].str.replace(
        "Intel(R)", "", regex=False
    )
    df["machine_info_cpu_brand_raw"] = df["machine_info_cpu_brand_raw"].str.replace(
        "Xeon(R)", "", regex=False
    )
    df["machine_info_cpu_brand_raw"] = df["machine_info_cpu_brand_raw"].str.replace(
        "Core(TM)", "", regex=False
    )
    df["machine_info_cpu_brand_raw"] = df["machine_info_cpu_brand_raw"].str.replace(
        "Platinum", "", regex=False
    )
    df["machine_info_cpu_brand_raw"] = df["machine_info_cpu_brand_raw"].str.strip()
    c = (
        alt.Chart(df)
        .mark_line(point=True)
        .encode(
            x=alt.X("commit_hash:N", sort=["datetime"]),
            y=alt.Y("stats_min", axis=alt.Axis(title=None)),
            row=alt.Row(
                "name_of_test",
                title=None,
                header=alt.Header(labelAngle=0, labelAlign="left"),
            ),
            color="machine_info_cpu_brand_raw",
            tooltip=[
                "name_of_test",
                "stats_min",
                "commit_info_branch",
                "date",
                "time",
                "machine_info_cpu_brand_raw",
            ],
        )
        .properties(
            height=100,
            title={
                "text": "Runtime of benchmarks by commit",
                "subtitle": "Hover over lines for tooltip",
            },
        )
        .configure_title(align="center", anchor="middle")
        .resolve_scale(y="independent")
    )
    return c