使用来自另一个 pandas 列 matplotlib 的标签注释多行 plot 中的单行

Question

i have been looking around and i can find examples for annotating a single line chart by using iterrows for the dataframe. what i am struggling with is

a) selecting the single line in the plot instead of ax.lines (using ax.lines[#] ) is clearly not proper and

b) annotating the values for the line with values from a different column

the dataframe dfg is in a format such that (edited to provide a minimal, reproducible example ):

week    2016    2017    2018    2019    2020    2021    min     max     avg WoW Change
1       8188.0  9052.0  7658.0  7846.0  6730.0  6239.0  6730    9052    7893.7  
2       7779.0  8378.0  7950.0  7527.0  6552.0  6045.0  6552    8378    7588.0  -194.0
3       7609.0  7810.0  8041.0  8191.0  6432.0  5064.0  6432    8191    7529.4  -981.0
4       8256.0  8290.0  8430.0  7083.0  6660.0  6507.0  6660    8430    7687.0  1443.0
5       7124.0  9372.0  7892.0  7146.0  6615.0  5857.0  6615    9372    7733.7  -650.0
6       7919.0  8491.0  7888.0  6210.0  6978.0  5898.0  6210    8491    7455.3  41.0
7       7802.0  7286.0  7021.0  7522.0  6547.0  4599.0  6547    7802    7218.1  -1299.0
8       8292.0  7589.0  7282.0  5917.0  6217.0  6292.0  5917    8292    7072.3  1693.0
9       8048.0  8150.0  8003.0  7001.0  6238.0  5655.0  6238    8150    7404.0  -637.0
10      7693.0  7405.0  7585.0  6746.0  6412.0  5323.0  6412    7693    7135.1  -332.0
11      8384.0  8307.0  7077.0  6932.0  6539.0                  6539    8384    7451.7  
12      7748.0  8224.0  8148.0  6540.0  6117.0                  6117    8224    7302.6  
13      7254.0  7850.0  7898.0  6763.0  6047.0                  6047    7898    7108.1  
14      7940.0  7878.0  8650.0  6599.0  5874.0                  5874    8650    7352.1  
15      8187.0  7810.0  7930.0  5992.0  5680.0                  5680    8187    7066.6  
16      7550.0  8912.0  8469.0  7149.0  4937.0                  4937    8912    7266.6  
17      7660.0  8264.0  8549.0  7414.0  5302.0                  5302    8549    7291.4  
18      7655.0  7620.0  7323.0  6693.0  5712.0                  5712    7655    6910.0  
19      7677.0  8590.0  7601.0  7612.0  5391.0                  5391    8590    7264.6  
20      7315.0  8294.0  8159.0  6943.0  5197.0                  5197    8294    7057.0  
21      7839.0  7985.0  7631.0  6862.0  7200.0                  6862    7985    7480.6  
22      7705.0  8341.0  8346.0  7927.0  6179.0                  6179    8346    7574.7  
...     ...     ...     ...     ...     ...                     ...     ...     ...
51      8167.0  7993.0  7656.0  6809.0  5564.0                  5564    8167    7131.4  
52      7183.0  7966.0  7392.0  6352.0  5326.0                  5326    7966    6787.3  
53                                               5369.0         5369    5369    5369.0  

with the graph plotted by:

        fig, ax = plt.subplots(1, figsize=[14,4])

        ax.fill_between(dfg.index, dfg["min"], dfg["max"], label="5 Yr. Range", facecolor="oldlace")
        ax.plot(dfg.index, dfg[2020], label="2020", c="grey")
        ax.plot(dfg.index, dfg[2021], label="2021", c="coral") 
        ax.plot(dfg.index, dfg.avg, label="5 Yr. Avg.", c="goldenrod", ls=(0,(1,2)), lw=3).

I would like to label the dfg[2021] line with the values from dfg['WoW Change'] . Additionally, if anyone knows how to get the calculate the first value in the WoW column based on the last value from 2020 and the first value from 2021, that would be wonderful! It's currently just dfg['WoW Change'] = dfg[2021].diff()

Thanks!

Answer 1

Figured it out. Zipped the index and two columns up as a tuple. I ended up deciding I only wanted the last value to be shown but using below code:

    a = dfg.index.values
    b = dfg[2021]
    c = dfg['WoW Change']
    
    #zip 3 columns separately
    labels = list(zip(dfg.index.values,dfg[2021],dfg['WoW Change']))

    #remove tuples with index + 2 nan values
    labels_light = [i for i in labels if not any(isinstance(n,float) and math.isnan(n) for n in i)]

    #label last point using list accessors
    ax.annotate(str("w/w change: " + str("{:,}".format(int(labels_light[-1][2])))+link[1]),xy=(labels_light[-1][0],labels_light[-1][1]))

I'm sure this could have been done much better by someone who knows what they're doing, any feedback is appreciated.