**Ogive graphs are used to estimate how many numbers lie below or above a particular variable or value in data**. To construct an Ogive, the cumulative frequency of the variables is calculated using a frequency table.

**Ogive graphs** are also known as

.**cumulative frequency graph**

In this tutorial, we will discuss about **how to create an Ogive Graph in python** and ogive graph examples.

## pip install numpy

If you don’t have `numpy `

package installed on your system, installed it using the below commands on the window system

pip install numpy

**Example 1: How to Create an Ogive in Python**

Let’s discuss with example to create an ogive graph in python

### Step 1 – Create Dataset

Let’s generate an random array of 1000 values between 0 and 10.

#import modules import numpy as np import matplotlib.pyplot as plt # Using seed function to generate the same random number every time with the given seed value np.random.seed(0) #create array of 1,000 random integers between 0 and 10 data = np.random.randint(0, 10, 1000) #Print the first ten elements of array print('The firts ten elements are as follows:',data[:10]) #Find histogram values with 10 bins values, base = np.histogram(data, bins=10) #find the cumulative sums cumulative = np.cumsum(values) # plot the ogive plt.plot(base[:-1], cumulative, 'go-')

In the above code, we import `numpy`

package to use `random.randint() `

function to generate a random array.

### Step2 – Create an Ogive graph

`histogram()`

function is used to calculate the classes, class frequencies and cumsum() function is used to calculate the cumulative sums for the calculated class frequencies.

`matplotlib.pyplot`

package is used to plot the ogive to visualize data for generated data values.

using data[0:10], it prints first 10 rows of data values.

To visualize distribution data values, we use `plot() `

function to display ogive of the samples data values.

The arguments used in plot function ‘go-‘ defines:

g : Use the green(g) color for the plot

o : Use the circles at each class break.

– : Use lines to connect the circles.

You can easily change these options according to the requirements.

Output of above program:

The firts ten elements are as follows: [5 0 3 3 7 9 3 5 2 4]

**Example 2: How to Create an Ogive Graph in Python**

In this example, we will create an `ogive graph for random generated array`

using below python code.

### Step1 – Create Dataset

In the above python code, generate a random array using the `numpy `

library.

### Step2 – Create an Ogive Graph

#import modules import numpy as np import matplotlib.pyplot as plt # Using seed function to generate the same random number every time with the given seed value np.random.seed(0) #create array of 100 random integers between 90 and 100 data = np.random.randint(90, 100, 100) #Print the first ten elements of array print('The firts ten elements are as follows:',data[:10]) #Find histogram values with 30 bins values, base = np.histogram(data, bins=30) #find the cumulative sums cumulative = np.cumsum(values) # plot the ogive plt.plot(base[:-1], cumulative, 'b--')

The output of the above python code is below, we have used print(data[0:10]) to print the first 10 rows of distribution data.

The firts ten elements are as follows: [95 90 93 93 97 99 93 95 92 94]

### Step3 – Visualize Graph

To visualize distribution data values, we use `plot() `

function to display ogive of the samples data values. Here we find the classes and class frequencies with respect to 30 bins.

In the above graph we use the “b–” arguments in the plot() function which defines:

b : Use the blue(b) color for the plot

– : Use the lines at each class break.

– : Use lines to connect the class break lines.

## Conclusion

I hope you may have liked the above article about **how to create an ogive graph in python** with step by step guide and with illustrative ogive graph examples.