Python: Frequency of occurrences

Question

I have list of integers and want to get frequency of each integer. This was discussed here

The problem is that approach I'm using gives me frequency of floating numbers when my data set consist of integers only. Why that happens and how I can get frequency of integers from my data?

I'm using pyplot.histogram to plot a histogram with frequency of occurrences

import numpy as np
import matplotlib.pyplot as plt
from numpy import *
data = loadtxt('data.txt',dtype=int,usecols=(4,)) #loading 5th column of csv file into array named data. 
plt.hist(data) #plotting the column as histogram 

I'm getting the histogram, but I've noticed that if I "print" hist(data)

hist=np.histogram(data)
print hist(data)

I get this:

(array([ 2323, 16338,  1587,   212,    26,    14,     3,     2,     2,     2]), 
array([  1. ,   2.8,   4.6,   6.4,   8.2,  10. ,  11.8,  13.6,  15.4,
    17.2,  19. ]))

Where the second array represent values and first array represent number of occurrences.

In my data set all values are integers, how that happens that second array have floating numbers and how should I get frequency of integers?

UPDATE:

This solves the problem, thank you Lev for the reply.

plt.hist(data, bins=np.arange(data.min(), data.max()+1))

To avoid creating a new question how I can plot columns "in the middle" for each integer? Say, I want column for integer 3 take space between 2.5 and 3.5 not between 3 and 4.

Answer 1

If you don't specify what bins to use, np.histogram and pyplot.hist will use a default setting, which is to use 10 equal bins. The left border of the 1st bin is the smallest value and the right border of the last bin is the largest.

This is why the bin borders are floating point numbers. You can use the bins keyword arguments to enforce another choice of bins, e.g.:

plt.hist(data, bins=np.arange(data.min(), data.max()+1))

Edit: the easiest way to shift all bins to the left is probably just to subtract 0.5 from all bin borders:

plt.hist(data, bins=np.arange(data.min(), data.max()+1)-0.5)

Another way to achieve the same effect (not equivalent if non-integers are present):

plt.hist(data, bins=np.arange(data.min(), data.max()+1), align='left')

Answer 2

(Late to the party, just thought I would add a seaborn implementation)

Seaborn Implementation of the above question:

seaborn.__version__ = 0.9.0 at time of writing.

Load the libraries and setup mock data.

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

data = np.array([3]*10 + [5]*20 + [7]*5 + [9]*27 + [11]*2)

Plot the data using seaborn.distplot:

Using specified bins, calculated as per the above question.

sns.distplot(data,bins=np.arange(data.min(), data.max()+1),kde=False,hist_kws={"align" : "left"})
plt.show()

Trying numpy built-in binning methods

I used the doane binning method below, which produced integer bins, migth be worth trying out the standard binning methods from numpy.histogram_bin_edges as this is how matplotlib.hist() bins the data.

sns.distplot(data,bins="doane",kde=False,hist_kws={"align" : "left"})
plt.show()

Produces the below Histogram: