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Asked: 2026-06-15T19:42:27+00:00

I have made a script which produces a graph from data in a nested

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I have made a script which produces a graph from data in a nested list (temperature and changes).

#!/usr/bin/python
import matplotlib.pyplot as plt

temperature = [['65', '65.5', '66', '66.5', '67', '67.5', '68', '68.5', '69', '69.5', '70', '70.5', '71', '71.5', '72', '72.5', '73', '73.5', '74', '74.5', '75', '75.5', '76', '76.5', '77', '77.5', '78', '78.5', '79', '79.5', '80', '80.5', '81', '81.5', '82', '82.5', '83', '83.5', '84', '84.5', '85', '85.5', '86', '86.5', '87', '87.5', '88', '88.5', '89', '89.5', '90', '90.5', '91', '91.5', '92', '92.5', '93', '93.5', '94', '94.5', '95'], ['65', '65.5', '66', '66.5', '67', '67.5', '68', '68.5', '69', '69.5', '70', '70.5', '71', '71.5', '72', '72.5', '73', '73.5', '74', '74.5', '75', '75.5', '76', '76.5', '77', '77.5', '78', '78.5', '79', '79.5', '80', '80.5', '81', '81.5', '82', '82.5', '83', '83.5', '84', '84.5', '85', '85.5', '86', '86.5', '87', '87.5', '88', '88.5', '89', '89.5', '90', '90.5', '91', '91.5', '92', '92.5', '93', '93.5', '94', '94.5', '95'], ['65', '65.5', '66', '66.5', '67', '67.5', '68', '68.5', '69', '69.5', '70', '70.5', '71', '71.5', '72', '72.5', '73', '73.5', '74', '74.5', '75', '75.5', '76', '76.5', '77', '77.5', '78', '78.5', '79', '79.5', '80', '80.5', '81', '81.5', '82', '82.5', '83', '83.5', '84', '84.5', '85', '85.5', '86', '86.5', '87', '87.5', '88', '88.5', '89', '89.5', '90', '90.5', '91', '91.5', '92', '92.5', '93', '93.5', '94', '94.5', '95']]
changes = [['94.566', '94.210', '93.836', '93.443', '93.030', '92.597', '92.145', '91.673', '91.181', '90.669', '90.137', '89.585', '89.011', '88.409', '87.760', '87.019', '86.063', '84.577', '81.806', '76.130', '65.071', '47.659', '28.454', '14.158', '6.305', '2.678', '1.128', '0.480', '0.210', '0.095', '0.045', '0.022', '0.012', '0.006', '0.004', '0.002', '0.002', '0.001', '0.001', '0.001', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000'], ['94.566', '94.210', '93.836', '93.443', '93.030', '92.597', '92.145', '91.673', '91.181', '90.669', '90.138', '89.588', '89.016', '88.420', '87.788', '87.088', '86.239', '85.028', '82.929', '78.744', '70.282', '55.446', '36.209', '19.361', '8.976', '3.874', '1.634', '0.691', '0.298', '0.132', '0.060', '0.029', '0.015', '0.008', '0.004', '0.003', '0.002', '0.001', '0.001', '0.001', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000'], ['94.566', '94.210', '93.836', '93.443', '93.030', '92.597', '92.145', '91.673', '91.181', '90.669', '90.138', '89.588', '89.016', '88.421', '87.790', '87.093', '86.255', '85.072', '83.059', '79.131', '71.434', '58.441', '41.784', '25.977', '14.170', '6.919', '3.146', '1.386', '0.608', '0.270', '0.122', '0.057', '0.028', '0.014', '0.007', '0.004', '0.003', '0.001', '0.001', '0.001', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000']]

products=[]
for t,c in zip(temperature,changes):
    products.append(t)
    products.append(c)

plt.plot(*products)
plt.xlabel('temperature')
plt.ylabel('changes')
plt.show()

And the graph looks like:

https://i.stack.imgur.com/k2pBh.png

Just wondering how to go about making a differentiated version of the graph as well (E.G. rate of change over temperature)?

The examples for plotting derivatives I have tried working with all deal with two or three numbers, and nothing this big and nested, hence I had a hard time trying to apply them to my dataset. (The data generated and fed into this script is always in the form of a nested list).

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1 Answer

  1. Editorial Team

    You can easily approximate derivatives by calculating finite differences. While you can do this with nested lists, you are much better off using numpy arrays. See below:

    import matplotlib.pyplot as plt
import numpy as np

temperature = [['65', '65.5', '66', '66.5', '67', '67.5', '68', '68.5', '69', '69.5', '70', '70.5', '71', '71.5', '72', '72.5', '73', '73.5', '74', '74.5', '75', '75.5', '76', '76.5', '77', '77.5', '78', '78.5', '79', '79.5', '80', '80.5', '81', '81.5', '82', '82.5', '83', '83.5', '84', '84.5', '85', '85.5', '86', '86.5', '87', '87.5', '88', '88.5', '89', '89.5', '90', '90.5', '91', '91.5', '92', '92.5', '93', '93.5', '94', '94.5', '95'], ['65', '65.5', '66', '66.5', '67', '67.5', '68', '68.5', '69', '69.5', '70', '70.5', '71', '71.5', '72', '72.5', '73', '73.5', '74', '74.5', '75', '75.5', '76', '76.5', '77', '77.5', '78', '78.5', '79', '79.5', '80', '80.5', '81', '81.5', '82', '82.5', '83', '83.5', '84', '84.5', '85', '85.5', '86', '86.5', '87', '87.5', '88', '88.5', '89', '89.5', '90', '90.5', '91', '91.5', '92', '92.5', '93', '93.5', '94', '94.5', '95'], ['65', '65.5', '66', '66.5', '67', '67.5', '68', '68.5', '69', '69.5', '70', '70.5', '71', '71.5', '72', '72.5', '73', '73.5', '74', '74.5', '75', '75.5', '76', '76.5', '77', '77.5', '78', '78.5', '79', '79.5', '80', '80.5', '81', '81.5', '82', '82.5', '83', '83.5', '84', '84.5', '85', '85.5', '86', '86.5', '87', '87.5', '88', '88.5', '89', '89.5', '90', '90.5', '91', '91.5', '92', '92.5', '93', '93.5', '94', '94.5', '95']]
changes = [['94.566', '94.210', '93.836', '93.443', '93.030', '92.597', '92.145', '91.673', '91.181', '90.669', '90.137', '89.585', '89.011', '88.409', '87.760', '87.019', '86.063', '84.577', '81.806', '76.130', '65.071', '47.659', '28.454', '14.158', '6.305', '2.678', '1.128', '0.480', '0.210', '0.095', '0.045', '0.022', '0.012', '0.006', '0.004', '0.002', '0.002', '0.001', '0.001', '0.001', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000'], ['94.566', '94.210', '93.836', '93.443', '93.030', '92.597', '92.145', '91.673', '91.181', '90.669', '90.138', '89.588', '89.016', '88.420', '87.788', '87.088', '86.239', '85.028', '82.929', '78.744', '70.282', '55.446', '36.209', '19.361', '8.976', '3.874', '1.634', '0.691', '0.298', '0.132', '0.060', '0.029', '0.015', '0.008', '0.004', '0.003', '0.002', '0.001', '0.001', '0.001', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000'], ['94.566', '94.210', '93.836', '93.443', '93.030', '92.597', '92.145', '91.673', '91.181', '90.669', '90.138', '89.588', '89.016', '88.421', '87.790', '87.093', '86.255', '85.072', '83.059', '79.131', '71.434', '58.441', '41.784', '25.977', '14.170', '6.919', '3.146', '1.386', '0.608', '0.270', '0.122', '0.057', '0.028', '0.014', '0.007', '0.004', '0.003', '0.001', '0.001', '0.001', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000', '0.000']]

temperature = np.array(temperature, dtype=np.float32).transpose()
changes = np.array(changes, dtype=np.float32).transpose()

plt.figure()
plt.plot(temperature, changes)
plt.xlabel('temperature')
plt.ylabel('changes')
plt.show()

delta_t = temperature[1:]-temperature[:-1]
t_av = 0.5*(temperature[1:]+temperature[:-1])
dc_dt = (changes[1:]-changes[:-1])

plt.figure()
plt.plot(t_av, dc_dt)
plt.xlabel('temperature')
plt.ylabel('dc/dt')
plt.show()
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