Python: Tuples and Sets


Tuples are a grouping of data in Python similar to a list, except tuples are immutable, meaning you cannot add or delete items from the tuple.

Tuples are created using  () instead of []. A tuple with a single value still requires a comma though. x = (1,)


Notice attempting to delete an element from the tuple results in an error.


Sets are another method of hold data collections, but they have some interesting factors other methods do not. Sets are very useful as they only return unique elements for the data they store.



Looking at the example below, notice how the second set has 4 elements, but the output is only 3. This is due to the fact that sets only return unique values.

You create a set using set() like above, or you can just use {}


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Python: Working with Lists

Making a list in Python is simple: x = [1,2,3,4]

Now let us see how we can work with them.


First thing to understand is that a list is indexed. Each item in the list is given a number that tells you its position in the list.

It is important to note that Python is a 0 index language. This means indexes begin at 0 not 1. So the first item in the list, is found by calling: x[0]


Note that x[4] returns an error. Since there are 4 items in the list, the indexes go 0,1,2,3. Index 4 is out of range.

Another interesting point to understand about indexes is that you can use a negative index. x[-2]  returns Duck

Index Range

Use a “:” to return a range of items from a list: x[start:end]. If you leave out the start or end number, the index starts or ends at the start or end of the list


Update Values in a List

If you want to change a value in a list, just assign it a new value like you would with a regular variable.



If you want to add an item to then end of a list, you can use the Append() function



To delete a item from a list, use the Del command.



Remove works like Del, except instead of using index values, Remove() uses the values stored in the list.


Pop() and Insert()

Pop() simply returns the last item from a list

Insert() lets you add a item to a list, but it also lets you choose what position in the list to add it.



Len() returns a count of the number of items in a list.


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Python: Intro to Graphs

Visualizations are big part of analytics. You will need to produce visually engaging graphics for presentations, reports, and dashboards. You will also make graphs for your own use in data discovery and analysis. As bonus, unlike data cleaning, data viz can be pretty fun.

Matplotlab and Pyplot

Matplotlab is a module you can import into Python that will help you to build some basic graphs and charts. Pyplot is part of Matplotlab and the part we will be using in the following example.

**If you are using the Anaconda Python distribution, Matplotlab is already installed. If not, you may need to download it from another source.

Line Graph


  • %matplotlib inline – this code allows you to view your graphs inside jupyter notebooks
  • from matplotlib import pyplot as plt – here we import pyplot from matplotlib into our program (note, we only want pyplot not all the functions in matplotlib).Adding “as plt” gives us a shorter alias to work with
  • age and height lines – fill our lists with age and height information for an individual
  • plt.plot(age, height, color = ‘blue’) – here we tell Python to plot age against height and to color our line blue
  • – prints out our graph


Bar Chart

For this example, we will make a bar charting showing ages of 4 people.


  • You should understand the first few lines from the first example
  • count_names = [i for i,_ in enumerate(name)]  – since the name list is a list of strings, we cannot really graph that onto a chart. We need a way to convert these strings into numbers.

Wait? What does for i,_ mean? Let’s jump to the next code sample


While you don’t see it when making the list, a Python list is technically a list of tuples (index number, element). So if instead of i,_ we asked for both elements in the tuple, (i,j) we would get the following.


So by iterating by for i,_ we only return the first element in the tuple (the index)

** notice we are using a list comprehension. If you are unfamiliar with list comprehensions, check out my earlier post: Python: List Comprehension

Let’s clean up our bar chart a little now.

  • plt.ylabel(‘Age’) – label the y-axis Age
  • plt.title(‘Age of People’) – give the graph a title
  • plt.xticks([i+0.5 for i,_ in enumerate(name)], name) – this label function is using a list comprehension to first chose the position on the X-axis, and name provides the person’s name for the label.


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