Question

I have some data (TEMP_2) and I wanted to obtain a distribution for that data. I know how to do the histogram using:

import numpy as np
from pylab import *

plt.figure(1)
data1 = loadtxt("TEMP_2")

a= data1[:,1]
plt.hist(a,100, normed=True,)
show ()

But, I wanted to have a distribution. Can anybody please help me with this..

data file:

1000 299.23
2000 310.56
3000 308.21
4000 305.86
5000 305.21
6000 301.35
7000 295.37
8000 307.80
9000 295.61
:      :
:      :
200000 307.18

Answer 1

In order to plot a normal distribution that fits your data you need to do the following:

First you need to calculate, which normal distribution best fits your data. In scipy there is norm.fit. Next you just need to plot a normal distribution with the given properties (mean, stdev).

Full script:

# Load data
import numpy as np
from pylab import *
data1 = loadtxt("TEMP_2")
a = data1[:,1]

# Fit data into normal distribution
from scipy.stats import norm
mean, stdev = norm.fit(a)

# Plot normal distribution
import matplotlib.mlab as mlab
x = np.linspace(min(a), max(a), 100)
plot(x, mlab.normpdf(x, mean, stdev))
show()

Result:

If you want to plot the “bins” too, then just add before plot.show():

plt.hist(a, len(data1), normed=True,)