基于 Jupyter Notebook 和Plotly的交互式COVID-19实时追踪可视化系统(下)

标签：lse   情况下   lambda   tps   isp   count   散点图   ali   org   

基于 Jupyter Notebook 和Plotly的交互式COVID-19实时追踪可视化系统

3.“散点图”图表

renamed_columns_map = {
    "Country/Region": "country",
    "Province/State": "location",
    "Lat": "latitude",
    "Long": "longitude"
}

confirmed_cases_df = (
    pd.read_csv(confirmed_cases_url)
    .rename(columns=renamed_columns_map)
    .rename(columns=reformat_dates)
    .fillna(method="bfill", axis=1)
)
deaths_df = (
    pd.read_csv(deaths_url)
    .rename(columns=renamed_columns_map)
    .rename(columns=reformat_dates)
    .fillna(method="bfill", axis=1)
)

display(confirmed_cases_df.head())
display(deaths_df.head())

fig = go.Figure()

geo_data_cols = ["country", "location", "latitude", "longitude"]
geo_data_df = confirmed_cases_df[geo_data_cols]
dates_list = (
    confirmed_cases_df.filter(regex=r"(\d{2}/\d{2}/\d{4})", axis=1)
    .columns
    .to_list()
)

＃创建日期的映射- >数据帧，其中每个DF持有的情况下，每个国家的死亡日常计数
cases_by_date = {}
for date in dates_list:
    ＃得到的所有情况pd.Series当天
    confirmed_cases_day_df = (
        confirmed_cases_df.filter(like=date, axis=1)
        .rename(columns=lambda col: "confirmed_cases")
        .astype("uint32")
    )
    
    ＃得到所有死亡人数的pd.Series当天
    deaths_day_df = (
        deaths_df.filter(like=date, axis=1)
        .rename(columns=lambda col: "deaths")
        .astype("uint32")
    )
    
    cases_df = confirmed_cases_day_df.join(deaths_day_df)  ＃合并案例和死亡dfs 
    cases_df = geo_data_df.join(cases_df)  ＃添加地理数据
    cases_df = cases_df[cases_df["confirmed_cases"] > 0]  ＃删除没有案例的任何行
    
    cases_by_date[date] = cases_df
    
＃每个数据帧看起来是这样的：
cases_by_date[dates_list[-1]].head()

＃生成每一天的数据
fig.data = []
for date, df in cases_by_date.items():
    df["confirmed_cases_norm"] = np.log1p(df["confirmed_cases"])
    df["text"] = (
        date
        + "<br>"
        + df["country"]
        + "<br>"
        + df["location"]
        + "<br>Confirmed cases: "
        + df["confirmed_cases"].astype(str)
        + "<br>Deaths: "
        + df["deaths"].astype(str)
    )
    fig.add_trace(
        go.Scattergeo(
            name="",
            lat=df["latitude"],
            lon=df["longitude"],
            visible=False,
            hovertemplate=df["text"],
            showlegend=False,
            marker={
                "size": df["confirmed_cases_norm"] * 100,
                "color": "red",
                "opacity": 0.75,
                "sizemode": "area",
            },
        )
    )

＃整理出注释和滑块步骤
annotation_text_template = "<b>Worldwide Totals</b>"                            "<br>{date}<br><br>"                            "Confirmed cases: {confirmed_cases:,d}<br>"                            "Deaths: {deaths:,d}<br>"                            "Mortality rate: {mortality_rate:.1%}"
annotation_dict = {
    "x": 0.03,
    "y": 0.35,
    "width": 175,
    "height": 110,
    "showarrow": False,
    "text": "",
    "valign": "middle",
    "visible": False,
    "bordercolor": "black",
}

steps = []
for i, data in enumerate(fig.data):
    step = {
        "method": "update",
        "args": [
            {"visible": [False] * len(fig.data)},
            {"annotations": [dict(annotation_dict) for _ in range(len(fig.data))]},
        ],
        "label": dates_list[i],
    }

    ＃将第i个跟踪和注释框切换到可见
    step["args"][0]["visible"][i] = True
    step["args"][1]["annotations"][i]["visible"] = True

    df = cases_by_date[dates_list[i]]
    confirmed_cases = df["confirmed_cases"].sum()
    deaths = df["deaths"].sum()
    mortality_rate = deaths / confirmed_cases
    step["args"][1]["annotations"][i]["text"] = annotation_text_template.format(
        date=dates_list[i],
        confirmed_cases=confirmed_cases,
        deaths=deaths,
        mortality_rate=mortality_rate,
    )

    steps.append(step)

sliders = [
    {
        "active": 0,
        "currentvalue": {"prefix": "Date: "},
        "steps": steps,
        "len": 0.9,
        "x": 0.05,
    }
]

first_annotation_dict = {**annotation_dict}
first_annotation_dict.update(
    {
        "visible": True,
        "text": annotation_text_template.format(
            date="10/01/2020", confirmed_cases=44, deaths=1, mortality_rate=0.0227
        ),
    }
)
fig.layout.title = {"text": "Covid-19 Global Case Tracker", "x": 0.5}
fig.update_layout(
    height=650,
    margin={"t": 50, "b": 20, "l": 20, "r": 20},
    annotations=[go.layout.Annotation(**first_annotation_dict)],
    sliders=sliders,
)
fig.data[0].visible = True  ＃设置第一个数据点可见

fig

4.参考：

• [Michael]（https://mfreeborn.github.io/）

• [Jupyter Notebook]（https://jupyter.org/）

• [Plotly]（https://pypi.org/project/plotly/）

基于 Jupyter Notebook 和Plotly的交互式COVID-19实时追踪可视化系统(下)

标签：lse   情况下   lambda   tps   isp   count   散点图   ali   org   

原文地址：https://www.cnblogs.com/codehao/p/13170085.html