# Hướng dẫn normalize histogram python

To normalize a histogram in Python, we can use hist() method. In normalized bar, the area underneath the plot should be 1.

Nội dung chính

• How do you create a normalized histogram in Python?
• How do you create a normalized histogram?
• How do I normalize a histogram in MatPlotLib?
• How do you fit a normal distribution to a histogram in Python?

Contents

## Steps

• Make a list of numbers.

• Plot a histogram with density=True.

• To display the figure, use show() method.

## Example

```import matplotlib.pyplot as plt

plt.rcParams["figure.figsize"] = [7.00, 3.50]
plt.rcParams["figure.autolayout"] = True

k = [5, 5, 5, 5]
x, bins, p = plt.hist(k, density=True)

plt.show()```

## Output

Updated on 08-May-2021 08:55:40

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We can normalize a histogram in Matplotlib using the `density` keyword argument and setting it to `True`. By normalizing a histogram, the sum of the bar area equals 1.

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Consider the below histogram where we normalize the data:

`nums1 = [1,1,2,3,3,3,3,3,4,5,6,6,6,7,8,8,9,10,12,12,12,12,14,18]`

`nums2= [10,12,13,13,14,14,15,15,15,16,17,18,20,22,23]`

`fig,ax = plt.subplots() # Instantiate figure and axes object`

`ax.hist(nums1, label="nums1", histtype="step", density=True) # Plot histogram of nums1`

`ax.hist(nums2, label="nums2", histtype="step", density=True) # Plot histogram of nums2`

`plt.legend()`

`plt.show()`

``` `````` ```

Normalized histogram:

This is a follow-up question to this answer. I’m trying to plot normed histogram, but instead of getting 1 as maximum value on y axis, I’m getting different numbers.

For array k=(1,4,3,1)

`````` import numpy as np

def plotGraph():

import matplotlib.pyplot as plt

k=(1,4,3,1)

plt.hist(k, normed=1)

from numpy import *
plt.xticks( arange(10) ) # 10 ticks on x axis

plt.show()

plotGraph()
``````

I get this histogram, that doesn’t look like normed.

For a different array k=(3,3,3,3)

`````` import numpy as np

def plotGraph():

import matplotlib.pyplot as plt

k=(3,3,3,3)

plt.hist(k, normed=1)

from numpy import *
plt.xticks( arange(10) ) # 10 ticks on x axis

plt.show()

plotGraph()
``````

I get this histogram with max y-value is 10.

For different k I get different max value of y even though normed=1
or normed=True.

Why the normalization (if it works) changes based on the data and how can I make maximum value of y equals to 1?

UPDATE:

I am trying to implement Carsten König answer from plotting histograms whose bar heights sum to 1 in matplotlib and getting very weird result:

``````import numpy as np

def plotGraph():

import matplotlib.pyplot as plt

k=(1,4,3,1)

weights = np.ones_like(k)/len(k)
plt.hist(k, weights=weights)

from numpy import *
plt.xticks( arange(10) ) # 10 ticks on x axis

plt.show()

plotGraph()
``````

Result:

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What am I doing wrong?

### How do you create a normalized histogram in Python?

To normalize a histogram in Python, we can use hist() method. In normalized bar, the area
underneath the plot should be 1.
.

Make a list of numbers..

Plot a histogram with density=True..

To display the figure, use show() method..

### How do you create a normalized histogram?

Steps:.

Convert color image into grayscale..

Display histogram..

Observe maximum and minimum intensities from the histogram..

Change image type from uint8 to
double..

Apply a formula for histogram normalization..

Convert back into unit format..

Display image and modified histogram..

### How do I normalize a histogram in MatPlotLib?

We can normalize a histogram in Matplotlib using the density keyword argument and setting it to True . By normalizing a histogram, the sum of the bar area equals 1. Hit / to insta-search docs and recipes!

### How do you fit a normal distribution to a histogram in Python?

How to fit a distribution to a histogram in Python.

data = np. random. normal(0, 1, 1000) generate random normal dataset..

_, bins, _ = plt. hist(data, 20, density=1, alpha=0.5) create histogram from `data`.

mu, sigma = scipy. stats. norm. fit(data).

best_fit_line = scipy. stats. norm. … .

plt. plot(bins,
best_fit_line).

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