python - 如何在美国散点图上使用离散色标

我在使用plotly（在python中）时尝试使用离散色标。我需要一个离散的色阶，因为我为特定城市绘制的某些值与所有其他城市相比太大了，因此离散的色阶将帮助我轻松地可视化所有值。这是一个示例，可以更好地说明我的情况：
我有一个数据集，其中包含按城市（在美国）某些事件的详细信息。
该事件在纽约市发生了50000次，而在美国其他城市中，同一事件发生了不到1000次。当我使用连续的色标时，所有其他城市值都下降到低端，而NYC是唯一利用色标顶部的颜色的值。

谢谢您的帮助！
最好的祝福，
里沙卜

最佳答案

对于几个商店的10个不同的群集ID，这就是我生成10个离散的自定义色标的方法：

import matplotlib

def matplotlib_to_plotly(cmap, pl_entries):
# Converts matplotlib colormap to plotly colormap. It also shuffles the color map

    h = 1.0/(pl_entries)
    pl_colorscale = []
    c_order = h * np.arange(pl_entries+1)
    c_order_shuffled = c_order.copy()
    # Shuffles the colormap
    np.random.shuffle(c_order_shuffled)
    for i in range(pl_entries):
        C = map(np.uint8, np.array(cmap(c_order_shuffled[i])[:3])*255)
        pl_colorscale.append([c_order[i], 'rgb'+str((C[0], C[1], C[2]))])
        # To have clear boundaries between colors in the colorbar
        if i < (pl_entries):
             pl_colorscale.append([c_order[i+1], 'rgb'+str((C[0], C[1], C[2]))])
    return pl_colorscale

# Sets the colormap of your choice
cmap = matplotlib.cm.get_cmap('jet')
# Passes the number of distinct colors you need to generate. In this case we have 10 cluster ids in stores_info_df
custom_colorscale = matplotlib_to_plotly(cmap, stores_info_df['CLUSTER_ID'].max())
custom_colorscale

然后，我在plot函数中使用了上述色标：

def visualize_geo_store_clusters(stores_info_df, fig_name='store_similarity_US_map', cluster_id = 'CLUSTER_ID'):

    max_cluster_id = stores_info_df[cluster_id].max()

    data = [ dict(
        type = 'scattergeo',
        locationmode = 'USA-states',
        lon = stores_info_df['LONGTITUDE'],
        lat = stores_info_df['LATITUDE'],
        text = stores_info_df['TEXT'],
        mode = 'markers',
        marker = dict(
            colorscale= custom_colorscale,
            cmin = stores_info_df[cluster_id].min(),
            color = stores_info_df[cluster_id],
            cmax = max_cluster_id,
            colorbar = dict(
                title = 'Cluster ID',
                titleside = 'top',
                tickmode = 'array',
                tickvals =  np.arange(1, max_cluster_id+1),
                ticktext = np.arange(1, max_cluster_id+1),
                #ticks = 'outside',
            )

        ))]

     layout = dict(
        title = 'Similarity between Stores',
        geo = dict(
            scope='usa',
            projection=dict( type='albers usa' ),
            showland = True,
            landcolor = "rgb(250, 250, 250)",
            subunitcolor = "rgb(217, 217, 217)",
            countrycolor = "rgb(217, 217, 217)",
            countrywidth = 0.5,
            subunitwidth = 0.5
        ),
    )

fig = dict(data=data, layout=layout)
plotly.offline.iplot(fig, validate=False)

它生成以下图。

关于python - 如何在美国散点图上使用离散色标，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/50037213/