# How do you norm a histogram in python?

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:

What am I doing wrong?

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.

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]`

``` (adsbygoogle = window.adsbygoogle || []).push({}); ```

`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:

1. HowTo
2. Python Matplotlib Howto's
3. Create a Normalized Histogram Using Python Matplotlib

Created: December-10, 2021

A histogram is a frequency distribution that depicts the frequencies of different elements in a dataset. This graph is generally used to study frequencies and determine how the values are distributed in a dataset.

Normalization of histogram refers to mapping the frequencies of a dataset between the range `[0, 1]` both inclusive. In this article, we will learn how to create a normalized histogram in Python.

## Create a Normalized Histogram Using the Matplotlib Library in Python

The `Matplotlib` module is a comprehensive Python module for creating static and interactive plots. It is a very robust and straightforward package that is widely used in data science for visualization purposes. `Matplotlib` can be used to create a normalized histogram. This module has a `hist()` function. that is used for creating histograms. Following is the function definition of the `hist()` method.

``````matplotlib.pyplot.hist(x, bins=None, range=None, density=False, weights=None, cumulative=False, bottom=None, histtype='bar', align='mid', orientation='vertical', rwidth=None, log=False, color=None, label=None, stacked=False, *, data=None, **kwargs)
``````

Following is a brief explanation of the arguments we will use to generate a normalized histogram.

• `x`: A list, a tuple, or a NumPy array of input values.
• `density`: A boolean flag for plotting normalized values. By default, it is `False`.
• `color`: The colour of the bars in the histogram.
• `label`: A label for the plotted values.

Refer to the following Python code to create a normalized histogram.

``````import matplotlib.pyplot as plt

x = [1, 9, 5, 7, 1, 1, 2, 4, 9, 9, 9, 3, 4, 5, 5, 5, 6, 5, 5, 7]
plt.hist(x, density = True, color = "green", label = "Numbers")
plt.legend()
plt.show()
``````

Output:

### How do you create a normalized histogram?

Steps:.
Read the image..
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.

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

Use scipy..
data = np. random. normal(0, 1, 1000) ... .
_, bins, _ = plt. hist(data, 20, density=1, alpha=0.5) create histogram from `data`.
mu, sigma = scipy. stats. norm. ... .
best_fit_line = scipy. stats. norm. ... .
plt. plot(bins, best_fit_line).

### How do you normalize data in Python?

Using MinMaxScaler() to Normalize Data in Python This is a more popular choice for normalizing datasets. You can see that the values in the output are between (0 and 1). MinMaxScaler also gives you the option to select feature range. By default, the range is set to (0,1).