How can I remove the negative sign from y tick labels in matplotlib.pyplot figure?

Question

I am using the matplotlib.pylot module to generate thousands of figures that all deal with a value called "Total Vertical Depth(TVD)". The data that these values come from are all negative numbers but the industry standard is to display them as positive (I.E. distance from zero / absolute value). My y axis is used to display the numbers and of course uses the actual value (negative) to label the axis ticks. I do not want to change the values, but am wondering how to access the text elements and just remove the negative symbols from each value(shown in red circles on the image).

Several iterations of code after diving into the matplotlib documentation has gotten me to the following code, but I am still getting an error.

locs, labels = plt.yticks()

newLabels = []

for lbl in labels:
    newLabels.append((lbl[0], lbl[1], str(float(str(lbl[2])) * -1)))

plt.yticks(locs, newLabels)

It appears that some of the strings in the "labels" list are empty and therefore the cast isn't working correctly, but I don't understand how it has any empty values if the yticks() method is retrieving the current tick configuration.

Answer 1

Edit:

based on the Amit's great answer, here's the solution if you want to edit the data instead of the tick formatter:

import matplotlib.pyplot as plt
import numpy as np

y = np.linspace(-3000,-1000,2001)
fig, ax = plt.subplots()
ax.plot(-y)  # invert y-values of the data
ax.invert_yaxis()  # invert the axis so that larger values are displayed at the bottom
plt.show()

Answer 2

@SiHA points out that if we change the data then the order of labels on the y-axis will be reversed. So we can use a ticker formatter to just change the labels without changing the data as shown in the example below:

import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import numpy as np

@ticker.FuncFormatter
def major_formatter(x, pos):
    label = str(-x) if x < 0 else str(x)
    return label

y = np.linspace(-3000,-1000,2001)
fig, ax = plt.subplots()
ax.plot(y)
ax.yaxis.set_major_formatter(major_formatter)
plt.show()

This gives me the following plot, notice the order of y-axis labels.