An **ogive **is a graph that shows how many data values lie above or below a certain value in a dataset. This tutorial explains how to create an ogive in Python.

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

Perform the following steps to create an ogive for a dataset in Python.

**Step 1: Create a dataset.**

First, we can create a simple dataset.

import numpy as np #create array of 1,000 random integers between 0 and 10 np.random.seed(1) data = np.random.randint(0, 10, 1000) #view first ten values data[:10] array([5, 8, 9, 5, 0, 0, 1, 7, 6, 9])

**Step 2: Create an ogive.**

Next, we can use the numpy.histogram function to automatically find the classes and the class frequencies. Then we can use matplotlib to actually create the ogive:

import numpy as np import matplotlib.pyplot as plt #obtain 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, 'ro-')

The ogive chart will look different based on the number of bins that we specify in the **numpy.histogram** function. For example, here’s what the chart would look like if we used 30 bins:

#obtain histogram values with 30 bins values, base = np.histogram(data, bins=10) #find the cumulative sums cumulative = np.cumsum(values) # plot the ogive plt.plot(base[:-1], cumulative, 'ro-')

The argument ‘**ro-‘ **specifies:

- Use the color red (r)
- Use circles at each class break (o)
- Use lines to connect the circles (-)

Feel free to change these options to change the aesthetics of the chart.