How to get legend with correct line to represent specific label (for specific dataframe) while plotting pandas dataframes using matplotlib in Python?

hbstha123 0
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I have three pandas dataframes: df_demand, df_wind, and df_net_load. They have the same index and columns. For example, df_demand is as shown below:

0   15  30  45  60  75
Time of the year (hour)                     
Average >0  0   58.080597   113.445783  167.798799  223.263636  274.433735
Average <0  0   -48.649215  -93.236364  -138.090909 -180.785530 -225.054688

These are represented in dictionary forms as shown below for convenience:

df_demand.to_dict() is:

{0: {'Average >0': 0, 'Average <0': 0},
 15: {'Average >0': 58.080597014925374, 'Average <0': -48.64921465968586},
 30: {'Average >0': 113.44578313253012, 'Average <0': -93.23636363636363},
 45: {'Average >0': 167.7987987987988, 'Average <0': -138.0909090909091},
 60: {'Average >0': 223.26363636363635, 'Average <0': -180.78552971576227},
 75: {'Average >0': 274.43373493975906, 'Average <0': -225.0546875}}

df_wind.to_dict() is:

{0: {'Average >0': 0, 'Average <0': 0},
 15: {'Average >0': 31.842261904761905, 'Average <0': -33.68783068783069},
 30: {'Average >0': 47.05278592375367, 'Average <0': -53.523936170212764},
 45: {'Average >0': 61.620588235294115, 'Average <0': -71.53439153439153},
 60: {'Average >0': 73.28323699421965, 'Average <0': -90.23783783783784},
 75: {'Average >0': 88.0632530120482, 'Average <0': -102.3733681462141}}

And df_net_load.to_dict() is:

{0: {'Average >0': 0, 'Average <0': 0},
 15: {'Average >0': 31.842261904761905, 'Average <0': -33.68783068783069},
 30: {'Average >0': 47.05278592375367, 'Average <0': -53.523936170212764},
 45: {'Average >0': 61.620588235294115, 'Average <0': -71.53439153439153},
 60: {'Average >0': 73.28323699421965, 'Average <0': -90.23783783783784},
 75: {'Average >0': 88.0632530120482, 'Average <0': -102.3733681462141}}

I want to plot these dataframes. I used:

fig, ax = plt.subplots()

df_demand.T.plot(ax = ax, color = ["blue", "blue"])
df_wind.T.plot(ax = ax, color = ["green", "green"])
df_net_load.T.plot(ax = ax, color = ["red", "red"])

plt.show()

I get the following plot:
enter image description here

In the legend, I want to get only one line each for blue, green and red and label them as Demand, Wind and Net load respectively.

I used the following code:

lines, labels = ax.get_legend_handles_labels()
plt.legend(lines[::2], labels[::2])

I get the correct lines, but I don't get the required labels: enter image description here

I tried to change the labels manually:

lines= ax.get_legend_handles_labels()[0]
plt.legend(lines[::2], labels = ["Demand", "Wind", "Net load"])

But then I get the incorrect positioning of lines and labels in the plot: enter image description here

What is the correct way to plot the correct labels to correct lines in the legends?

legend matplotlib dataframe pandas python
1 Answers

Here is one way to do it:

If you use the ax object to plot it, it returns all the lines that have been plotted, so we always just grab the first one (e.g l1,_, the underscore is the 2nd line we don't need). Specify the label in each plot and get the legend at the end.

df1 = df_demand.T
df2 = df_wind.T
df3 = df1.copy()*1.1 # df_net_load data is wrong in your question. It is just the same data as wind

fig, ax = plt.subplots()
l1,_ = ax.plot(df1, 'b', label='demand')
l2,_ = ax.plot(df2, 'g', label='wind')
l3,_ = ax.plot(df3, 'r', label='net_load')
ax.legend(handles=[l1,l2,l3])

enter image description here

Rabinzel0
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