def plot_group_contribution()

in jobs/desktop-mobile-mau-2020/desktop_mau/desktop_mau_dau.py [0:0]

• 71 lines of code
• 5 McCabe index (conditional complexity)

def plot_group_contribution(full_data, country_list, cat, metric):
    full_data = full_data[["date", "country", metric]]
    if metric == "MAU":
        dat = pd.merge(
            full_data[full_data["country"].isin(country_list)],
            full_data[full_data["country"] == "Global"],
            on=["date"],
        ).sort_values(by=["country_x", "date"])
        dat["pcnt"] = dat["MAU_x"] / dat["MAU_y"]

    if metric == "DAU":
        dat = pd.merge(
            full_data[full_data["country"].isin(country_list)],
            full_data[full_data["country"] == "Global"],
            on=["date"],
        ).sort_values(by=["country_x", "date"])
        dat["DAU_global_7dMA"] = dat["DAU_y"].rolling(window=7).mean()
        dat["DAU_7dMA"] = dat["DAU_x"].rolling(window=7).mean()
        dat["pcnt"] = dat["DAU_7dMA"] / dat["DAU_global_7dMA"]

    plt.style.use("seaborn-white")
    fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(20, 5), sharex="col")
    dat = dat[dat["date"] >= pd.to_datetime("20190101")]
    # individual
    for country in country_list:
        axs[0].plot(
            dat[dat["country_x"] == country]["date"],
            dat[dat["country_x"] == country]["pcnt"],
            label=str(country),
            linestyle="solid",
        )
    axs[0].set(ylim=(0, 0.2))
    axs[0].xaxis.set_major_formatter(mdates.DateFormatter("%Y-%b"))
    axs[0].yaxis.set_major_formatter(
        matplotlib.ticker.FuncFormatter(lambda x, pos: "{:,.0f}".format(x * 100) + "%")
    )
    axs[0].legend(
        country_list,
        bbox_to_anchor=(0.9, 0.8, 0.2, 0.2),
        loc="upper left",
        ncol=1,
        mode="expand",
        fontsize=10,
    )

    # stacked
    dat2 = dat[dat["country_x"].isin(country_list)][
        ["date", "country_x", "pcnt"]
    ].pivot(index="date", columns="country_x", values="pcnt")
    dat2 = pd.DataFrame(data=dat2)
    y = np.vstack([dat2[x] for x in country_list])
    axs[1].stackplot(dat2.index, y, labels=country_list, alpha=0.5)
    # axs[1].set(ylim=(0, 0.4))
    axs[1].xaxis.set_major_formatter(mdates.DateFormatter("%Y-%b"))
    axs[1].yaxis.set_major_formatter(
        matplotlib.ticker.FuncFormatter(lambda x, pos: "{:,.0f}".format(x * 100) + "%")
    )
    axs[1].legend(
        country_list,
        bbox_to_anchor=(0.9, 0.8, 0.2, 0.2),
        loc="upper left",
        ncol=1,
        mode="expand",
        fontsize=10,
    )
    axs[1].grid(which="major", linestyle="-", linewidth="0.5", color="lightgray")

    axs[0].set_title(
        "Contribution of {} from Tier1 as of {} (non-stacked/stacked)".format(
            metric, dat["date"].max().strftime("%Y-%m-%d")
        ),
        loc="left",
        fontdict={"fontsize": 18, "color": "black"},
    )

    filename = f"desktop_{metric}_{cat}_contribution.jpeg"
    plt.savefig(IMG_DIR / filename)
    plt.close(fig)
    return filename