How to make a histogram from a list of data

Question

do you have any idea how to make 200 evenly spaced out bins, and have your program store the data in the appropriate bins?

You can, for example, use NumPy's arange for a fixed bin size (or Python's standard range object), and NumPy's linspace for evenly spaced bins. Here are 2 simple examples from my matplotlib gallery

Fixed bin size

import numpy as np
import random
from matplotlib import pyplot as plt

data = np.random.normal(0, 20, 1000) 

# fixed bin size
bins = np.arange(-100, 100, 5) # fixed bin size

plt.xlim([min(data)-5, max(data)+5])

plt.hist(data, bins=bins, alpha=0.5)
plt.title('Random Gaussian data (fixed bin size)')
plt.xlabel('variable X (bin size = 5)')
plt.ylabel('count')

plt.show()

Fixed number of bins

import numpy as np
import math
from matplotlib import pyplot as plt

data = np.random.normal(0, 20, 1000) 

bins = np.linspace(math.ceil(min(data)), 
                   math.floor(max(data)),
                   20) # fixed number of bins

plt.xlim([min(data)-5, max(data)+5])

plt.hist(data, bins=bins, alpha=0.5)
plt.title('Random Gaussian data (fixed number of bins)')
plt.xlabel('variable X (20 evenly spaced bins)')
plt.ylabel('count')

plt.show()

Automatic binning

how to make 200 evenly spaced out bins, and have your program store the data in the appropriate bins?

The accepted answer manually creates 200 bins with numpy.arange and numpy.linspace, but there are functions for automatic binning:

• numpy.histogram

  Returns edges that work directly with pyplot.stairs (new in matplotlib 3.4.0):

  values, edges = np.histogram(data, bins=200)
  plt.stairs(values, edges, fill=True)
  

• pandas.cut

  Returns bins that work directly with pyplot.hist:

  _, bins = pd.cut(data, bins=200, retbins=True)
  plt.hist(data, bins)
  

  

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If you don't need to store the bins, then skip the binning step and just plot the histogram with bins as an integer:

1. pyplot.hist

   plt.hist(data, bins=200)
   

2. seaborn.histplot

   sns.histplot(data, bins=200)
   

3. pandas.DataFrame[.plot].hist or pandas.Series[.plot].hist

   pd.Series(data).plot.hist(bins=200)