CODEWITHSUNDEEP
pythonmatplotlibhistogramloglog
Alexandra Bradan
Alexandra Bradan

Reputation: 185

Both logarithmic scale hist axes

I need to plot a loglog histogram (bot x and y in log10 scale) using Matplotlib, but the following code isn't showing my desired output:

import matplotlib.pyplot as plt
import numpy as np
fig, ax1 = plt.subplots()
# suppose to have an array x
ax1.hist(x, ec='white', color="red")
plt.xscale("log")
plt.yscale("log")
plt.show()

My desired output is a histogram where x=np.log10(x) and equivalently y=np.log10(y), where each element in y is the height of each bin. I even tried to use a bar plot, but I couldn't solve the problem of the overlapping bins: enter image description here

   import matplotlib.pyplot as plt
    import numpy as np
    frequency_dict = Counter(x)
    new_x = list(frequency_dict.keys())
    y = list(frequency_dict.values())
    ax1.bar(np.log10(new_x), np.log10(y), ec='white', color="red")
    plt.show()

Upvotes: 1

Views: 1525

Answers (2)

JohanC
JohanC

Reputation: 80554

You can create bins that are distributed evenly in logspace. These bin boundaries can be calculated with np.logspace(min, max, num_bins).

from matplotlib import pyplot as plt
import numpy as np

x = np.abs(np.random.normal(500, 200, 1000))
assert x.min() > 0, "all values need to be positive for log scale"

plt.hist(x, bins=np.logspace(np.log10(x.min()), np.log10(x.max()), 15), ec='white', color="red")
plt.loglog()

plt.show()

At the left the histogram with linear axes and at the right with log-log axes. Both histograms using the log-style bins.

demo plot

Upvotes: 1

plasmon360
plasmon360

Reputation: 4199

You can use "log = True" in the hist function to set the y-axis in log scale. You can manually log the x data before creating histogram. So this is an example that shows how to do that. I hope this is what you are looking for.

import matplotlib.pyplot as plt
import numpy as np

# Simulate a normally distributed dataset
x = np.random.normal(loc = 500, scale = 200, size = 100)

fig, ax = plt.subplots(ncols = 2, figsize=(12,3))
ax[0].hist(x, ec='white', color="red", log = False)
ax[0].set_xlabel('x')
ax[0].set_ylabel('Freq counts')

ax[1].hist(np.log10(x), ec='white', color="red", log = True)
ax[1].set_xlabel('log10(x)')
ax[1].set_ylabel('Freq counts in log scale')

plt.show()

which results in

enter image description here

Upvotes: 0

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