Python 添加Bokeh滑块以按年份可视化GIS数据

我试图可视化从GIS导入的数据。大约有70个点，每个点都有地名和每年的数据和值

     name       year    mean    geometry
0    Place 1    2008    105816    POINT (253662.995 663270.013)
1    Place 1    2009    94381    POINT (253662.995 663270.013)
2    Place 1    2010    101280    POINT (253662.995 663270.013)
3    Place 1    2011    86664    POINT (253662.995 663270.013)
4    Place 1    2012    83828    POINT (253662.995 663270.013)
5    Place 1    2013    91433    POINT (253662.995 663270.013)
6    Place 1    2014    90971    POINT (253662.995 663270.013)
7    Place 1    2015    140151    POINT (253662.995 663270.013)
8    Place 1    2016    104499    POINT (253662.995 663270.013)
9    Place 1    2017    110172    POINT (253662.995 663270.013)
10    Place 1    2018    111700    POINT (253662.995 663270.013)
11    Place 2    2008    99176    POINT (262062.995 669070.013)
12    Place 2    2009    101865    POINT (262062.995 669070.013)
13    Place 2    2010    80560    POINT (262062.995 669070.013)
14    Place 2    2011    61915    POINT (262062.995 669070.013)
15    Place 2    2012    74723    POINT (262062.995 669070.013)
16    Place 2    2013    71550    POINT (262062.995 669070.013)
17    Place 2    2014    239955    POINT (262062.995 669070.013)
18    Place 2    2015    93824    POINT (262062.995 669070.013)
19    Place 2    2016    71751    POINT (262062.995 669070.013)
20    Place 2    2017    86586    POINT (262062.995 669070.013)
21    Place 2    2018    74684    POINT (262062.995 669070.013)
22    Place 3    2008    180296    POINT (251662.995 663270.013)
23    Place 3    2009    165689    POINT (251662.995 663270.013)
24    Place 3    2010    175376    POINT (251662.995 663270.013)

我想用一个滑块来可视化数据，以便只显示具有所选年份值的点

这就是我正在做的

def getPointCoords(row, geom, coord_type):
    """Calculates coordinates ('x' or 'y') of a Point geometry"""
    if coord_type == 'x':
        return row[geom].x
    elif coord_type == 'y':
        return row[geom].y

# Calculate x and y coordinates of the points

stations['x'] = stations.apply(getPointCoords, geom='geometry', coord_type='x', axis=1)
stations['y'] = stations.apply(getPointCoords, geom='geometry', coord_type='y', axis=1)

# Make a copy, drop the geometry column and create ColumnDataSource
st_df = stations.drop('geometry', axis=1).copy()
stsource = ColumnDataSource(st_df)

#colour based on mean value
colormap = LinearColorMapper(palette='Magma256', low=min(stsource.data['mean']),high=max(stsource.data['mean']))

p= figure(plot_height=400, plot_width=400)
p.circle(x="x", y="y", source=stsource,color = {'field': 'mean', 'transform': colormap})

# Define the callback function: update_plot
def update_plot(attr, old, new):
    # Set the year name to slider.value and new_data to source.data
    year = slider.value
    new_data = {
        'x'       : stsource.data['year'].x,
        'y'       :stsource.data['year'].y,
        'mean' : stsource.data['year'].mean,

    }
    stsource.data = new_data


# Make a slider object: slider
slider = Slider(title = 'slider', start = 2008, end = 2020, step = 1, value = 2012)

# Attach the callback to the 'value' property of slider
slider.on_change('value', update_plot)

# Make a row layout of widgetbox(slider) and plot and add it to the current document
layout = row(widgetbox(slider), p)
curdoc().add_root(layout)

这就是我得到的结果

我看到了所有的要点（很好！），但我也看到了所有的价值观 这部分似乎不起作用，但我不明白为什么

# Define the callback function: update_plot
def update_plot(attr, old, new):
    # Set the year name to slider.value and new_data to source.data
    year = slider.value
    new_data = {
        'x'       : stsource.data['year'].x,
        'y'       :stsource.data['year'].y,
        'mean' : stsource.data['year'].mean,

    }
    stsource.data = new_data

请帮忙！

---

谢谢

这最终对我起了作用

# Define the callback function: update_plot
def update_plot(attr, old, new):
    # Set the year name to slider.value and new_data to source.data
    year = slider.value
    stsource.data  = st_df[st_df.year == year]

我意识到，由于我的数据的性质，我不需要单独更新x，y，因为它们总是相同的，但只有一个“平均”值。 因此，对我来说，最简单的方法似乎是根据年份值更新源数据