CODEWITHSUNDEEP
pythonnumpymatplotlib
JBF
JBF

Reputation: 87

matplotlib histogram with frequency and counts

I have data (from a space delimited text file with two columns) which is already binned but only a width of 1. I want to increase this width to about 5. How can I do this using numpy/matplotlib in Python?

Using,

data = loadtxt('file.txt')
x = data[:, 0]
y = data[:, 1]
plt.bar(x,y)

creates too many bars and using,

plt.hist(data)

doesn't plot the histogram appropriately. I guess I don't understand how matplotlib's histogram plotting works.

See some of the data below.

264 1
265 1
266 4
267 2
268 2
269 2
270 2
271 2
272 5
273 3
274 2
275 6
276 7
277 3
278 7
279 5
280 9
281 4
282 8
283 11
284 9
285 15
286 19
287 11
288 12
289 10
290 13
291 18
292 20
293 14
294 15

Upvotes: 5

Views: 11918

Answers (2)

erik-e
erik-e

Reputation: 3891

I am not an expert at matplotlib but I find hist to be incredibly useful. The examples on the matplotlib site give a great overview of some of the features.

I don't know how to use your provided sample data without transforming it. I altered your example to dequantize those data before creating a histogram.

I calculated the bin size using this question's first answer.

import matplotlib.pyplot as plt
import numpy as np

data = np.loadtxt('file.txt')
dequantized = data[:,0].repeat(data[:,1].astype(int))

dequantized[0:7]
# Each row's first column is repeated the number of times found in the
# second column creating a single array.
# array([ 264.,  265.,  266.,  266.,  266.,  266.,  267.])

def bins(xmin, xmax, binwidth, padding):
    # Returns an array of integers which can be used to represent bins
    return np.arange(
        xmin - (xmin % binwidth) - padding,
        xmax + binwidth + padding,
        binwidth)

histbins = bins(min(dequantized), max(dequantized), 5, 5)
plt.figure(1)
plt.hist(dequantized, histbins)
plt.show()

This histogram displayed looks like this. Histogram with a binwidth of 5

I hope this example is useful.

Upvotes: 1

Akavall
Akavall

Reputation: 86326

What if you use numpy.reshape to transform your data before using plt.bar, for example:

In [83]: import numpy as np

In [84]: import matplotlib.pyplot as plt

In [85]: data = np.array([[1,2,3,4,5,6], [4,3,8,9,1,2]]).T

In [86]: data
Out[86]: 
array([[1, 4],
       [2, 3],
       [3, 8],
       [4, 9],
       [5, 1],
       [6, 2]])

In [87]: y = data[:,1].reshape(-1,2).sum(axis=1)

In [89]: y
Out[89]: array([ 7, 17,  3])


In [91]: x = data[:,0].reshape(-1,2).mean(axis=1)

In [92]: x
Out[92]: array([ 1.5,  3.5,  5.5])

In [96]: plt.bar(x, y)
Out[96]: <Container object of 3 artists>

In [97]: plt.show()

Upvotes: 1

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