Plotting a cumulative distribution function

Another interesting plot that we can create is one showing cumulative distribution. This plot shows the probability of finding a number in a bin or any lower bin. We do this by adding a single argument to the hist() function.

How to do it…

  1. To create a cumulative distribution plot for a single column in a Pandas DataFrame, begin by importing all the required libraries. To show the matplotlib plots in IPython Notebook, we will use an IPython magic function which starts with %:
    %matplotlib inline
import pandas as pd
import numpy as np
from pymongo import MongoClient
import matplotlib as mpl
import matplotlib.pyplot as plt
  2. Next, connect to MongoDB and run a query specifying the five fields to be retrieved from the MongoDB data:
    client = MongoClient('localhost', 27017)
db = client.pythonbicookbook
collection = db.accidents
fields = {'Date':1,
          'Police_Force':1,
          'Accident_Severity':1,
          'Number_of_Vehicles':1,
          'Number_of_Casualties':1}
data = collection.find({}, fields)
  3. Next, create a DataFrame from the results of the query:
    accidents = pd.DataFrame(list(data))
  4. Finally, create the cumulative distribution function and render it inline:
    plt.hist(casualty_count['Number_of_Casualties'],
         bins=20,
         normed=True,
         cumulative=True)
plt.title('Cumulative Distribution)
plt.xlabel('Value')
plt.ylabel('Frequency')
plt.show()

How it works…

This recipe works exactly like the previous recipe with the exception of the way we create the histogram:

plt.hist(casualty_count['Number_of_Casualties'],
         bins=20,
         normed=True,
         cumulative=True)

With the addition of cumulative=True, we turn the histogram into a cumulative distribution plot, and see the following plot as a result:

How it works…
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