We must open a Python section of our Notebook to use Python coding. So, start your Notebook, and then in the upper-right menu, select Python 3:

This will open a Python window to work in:

As mentioned in the previous chapter, the new window shows an empty cell so that you can enter Python code.

Let's give the new work area a name, Learning Jupyter 5, Chapter 2. Autosave should be on (as you can see next to the title). With an accurate name, we can find this section again easily from the Notebook home page. If you select your browser's Home tab and refresh it, you will see this new window name being displayed:

Note that it has an Notebook icon versus a folder icon. The extension that's automatically assigned is .ipynb (Python Notebook). And, since the item is in a browser in a Jupyter environment, it is marked as running. There is...

Now that we have seen how Python works in Jupyter, including the underlying encoding, how does Python access a large dataset of work in Jupyter?

I started another view for pandas, using Python data access as the name. From here, we will read in a large dataset and compute some standard statistics on the data. We are interested in seeing how we use pandas in Jupyter, how well the script performs, and what information is stored in the metadata (especially if it is a larger dataset).

Our script accesses the iris dataset that is built-in to one of the Python packages. All we are looking to do is read in a slightly large number of items and calculate some basic operations on the dataset. We are really interested to see how much of the data is cached in the .pynb file.

The Python code is as follows: 

# import the datasets package 
from sklearn import...

One of the most widely used features of Python is pandas. The pandas are built-in libraries of data analysis packages that can be used freely. In this example, we will develop a Python script that uses pandas to see if there is any affect of using them in Jupyter.

I am using the Titanic dataset from https://www.kaggle.com/c/titanic/data. I am sure that the same data is available from a variety of sources.

Here is our Python script that we want to run in Jupyter:

from pandas import * 
training_set = read_csv('train.csv') 
training_set.head() 
male = training_set[training_set.Sex == 'male'] 
female = training_set[training_set.Sex =='female'] 
womens_survival_rate = float(sum(female.Survived))/len(female) 
mens_survival_rate = float(sum(male...

How do Python graphics work in Jupyter?

I started another view for this named Python graphics so as to distinguish the work.

If we were to build a sample dataset of baby names and the number of births in a year of that name, we could then plot the data.

The Python coding is simple:

import pandas 
import matplotlib 
 
%matplotlib inline 
 
# define our two columns of data 
baby_name = ['Alice','Charles','Diane','Edward'] 
number_births = [96, 155, 66, 272] 
 
# create a dataset from the to sets 
dataset = list(zip(baby_name,number_births)) 
dataset 
 
# create a Python dataframe from the dataset 
df = pandas.DataFrame(data = dataset, columns=['Name', 'Number']) 
df 
 
# plot the data 
df['Number'].plot() 

The steps for the script are as follows:

1. Import the graphics library (and...

For many analyses, we are interested in calculating repeatable results. However, much of this analysis relies on some random numbers being used. In Python, you can set seed for the random number generator to achieve repeatable results with the random.seed() function.

In this example, we simulate rolling a pair of dice and look at the outcome. We would expect the average total of the two dice to be six, which is the half-way point between the faces.

The script we are using is as follows:

# using pylab statistics and histogram
import pylab
import random

# set random seed so we can reproduce results
random.seed(113)
samples = 1000

# declare our dataset store
dice = []

# generate and save the samples
for i in range(samples):
    total = random.randint(1,6) + random.randint(1,6)
    dice.append(total)

# compute some statistics on the dice throws
print("Throw...

In this chapter, we walked through a simple Notebook and the underlying structure. Then, we saw an example of using pandas and looked at a graphics example. Finally, we looked at an example using random numbers in a Python script.

In the next chapter, we will learn about R scripting in a Jupyter Notebook.