Python Open Labs: April 23, 2018

Today was the last Python Open Lab of the semester – congrats to all of the students who have made it this far and picked up skills in a new programming language!

Over the course of the semester, we’ve been learning the basics of Python: how to initialize lists, create dictionaries, iterate through items, and define functions and classes.

The students wanted to see how programming could be applied to a specific problem and how it could be used to analyze existing information or data. I chose to design the last lesson around data visualization. We particularly focused on how to create visualizations using the seaborn library.

The seaborn library is a visualization library based off of matplotlib. It also has the capability to store datasets as dataframes, similar as to how pandas may store an external file. I have recently been exploring seaborn and already find it a very flexible and intuitive library. Borrowing concepts from a tutorial via DataCamp, we were able to create some very beautiful visualizations using only a few lines of code.

Check out some things we were able to make below!

a swarm plot displaying customer tip amounts

a facet grid displaying total bill amounts based on varying aspects of gender and dining time

a colored heat map displaying information related to airplane flights

Students really enjoyed using seaborn and some were even able to apply it to their own datasets. Lots of people were specifically fans of the swarm plots.

Yang Rui (left) and Elena Dubova (right) learning to master seaborn

If you’d like to follow the lesson for today’s class more closely, please click here for step-by-step instructions and enjoy coding things up in your favorite text editor.

Python has become a really popular programming language in the past years. I am glad to see more and more people taking the initiative to learn it and can’t wait to see the amazing challenges my students will take on in the future!

Navie Narula

Spring 2018 R Open Lab: Exploratory Data Analysis

Mar 28

This week ‘s topic is Exploratory Data Analysis in R. The goal of the lab is to give attendants some ideas about what they can learn when they first have a data set in their hands and the corresponding approaches. The lab started with introduction of data frame’s concept and how to create one in R. Then, we talked about different ways to import data into R. After that, we learned ways to explore features of a data frame using the data set “diamonds.csv”. With the information we learned, we started to manipulate the data frame into our desired form by reordering and subsetting. We ended this lab with 2 simple practices of what we learned so far.

Here is the link to the script for this open lab:

https://drive.google.com/file/d/12ejSGZspc5_rBjDfvbu61H3lGdmYjAyf/view?usp=sharing

Here is the data set we used for this lab:

https://drive.google.com/file/d/19crfzpYAS3T0ZXaVxkWQVgk8dFbboFdf/view?usp=sharing

Thank you all for showing up. If you have further questions regarding topics covered in the material, please feel free to drop by during next week’s lab or email me or leave a comment.

See you all next week!

Mid-Semester Reflection (Python Open Labs)

Stuart Walesh, an author and consultant, once said: “The computer is incredibly fast, accurate, and stupid. Man is unbelievably slow, inaccurate, and brilliant. The marriage of the two is a challenge and opportunity beyond imagination.”

Many of us use computers. Sometimes, the time we spend on them consume the majority of our day. Whether or not this is a good or bad thing can be debated in another blog post, but the fact is…technology is an overwhelming part of our diet.

Taking my first computer science class as an undergraduate made it apparent to me that learning about how code and algorithms work was a really important thing, especially if I wanted to solve problems on my own. I declared my major in computer science and focused on  learning more about how code could be used to analyze large amounts of text more efficiently. I have not regretted it since, and am beyond happy to see a good number of students show up to the Python Open Labs to learn more about how to write code to perhaps automate their own tasks.

The people who show up to our class are diverse in terms of major – coming from backgrounds ranging from education to international affairs to pure math/analytics. It’s been really nice to see people actively show up to our labs with a desire to learn how to code and truly curious about how to solve problems. It’s proven to me again and again that anyone can learn how to code, and it’s been wildly encouraging to see people who think they cannot do it actually do it!

This is my first semester helping to lead the Python Open Labs. I find that lessons introducing a new programming language or new programming concepts are best taught in a step-by-step manner. Jupyter Notebooks have allowed me to accomplish this very well, allowing for space to write comments in markdown and running code in cell blocks. The students in class love this medium as well, and at the end of the lesson, they can easily look back over the notebook and remember what we learned about.

I’ve really enjoyed helping out with the labs so far and answering so many questions from the students who show up. Anyone is welcome to stop by the Python Open Labs – even if you have never written a line of code before in your life. I look forward to learning more from my students as the semester goes on.

Navie Narula

Spring 2018 R Open Lab: Character Strings

Feb 28

For this week, the topic we discussed is character strings in R. This lab’s content is a steppingstone for text  analysis.  We started with introducing the concept of characters, character strings, and character string vectors in R. Then, we talked about operations on strings such as getting substring and combining different character strings.  Finally, we learned about extracting and replacing certain patterns within a text-form data set.

Here is the link to the script for this open lab:

https://drive.google.com/file/d/12Etf6qQtpJPymIYrGRqLiaxt8LchNBUB/view?usp=sharing

Here is a reference for regular expressions in R:

https://www.rstudio.com/wp-content/uploads/2016/09/RegExCheatsheet.pdf

Thank you all for showing up. If you have further question regarding topics covered in the material, please feel free to drop by during next week’s lab or email me or leave a comment.

See you all next week!

Spring 2018 R Open Lab: More Fundamentals

Last week, we walked through the R starter kit which introduced most of the useful basic concepts in R such as vectors, matrices, and loops. This week, we continued to talk about more basics in R and demonstrated examples. The goal of this lab is to get attendants a better understanding of how R language works so that they can transform their specific real-life problem into R algorithms smoothly.

Here is the link to the script for this open lab:

https://drive.google.com/file/d/1SePSSVF980EJfCxv4eZ4AQlCP7vlTbe7/view?usp=sharing

The script also has comments and explanations. You can open it with R studio and run it step by step.

Thank you all for showing up. If you have further question regarding topics covered in the material, please feel free to drop by during next week’s lab or email me or leave a comment. See you all next week!

Highlights from the Data Collection: U.S. Election Data

The Libraries Numeric Data Catalog Holdings has some interesting data on the United States election results starting from 1912 to the most recent election year. Included are data on not only the presidential elections, but also gubernatorial, senatorial, congressional, and special senatorial elections.

The source of the data is Dave Leip’s Atlas of U.S. Presidential Elections, which you may already be familiar with. If you’re not, it’s a source that major news sources like The Atlantic and The Wall Street Journal, and The New York Times, have used for election reporting.

There are some fascinating things you can discover with the data sets. The following are some findings on the most recent presidential election in 2016.

Here is a visual representation of the total number of votes cast by state:

Total Votes by State – 2016 Presidential Election. Note: Alaska and Hawaii had 318,608 and 428,937 total votes cast, respectively.

 

And here, we can see the total number of votes cast by county across the country.  

Total Votes Cast by County – 2016 Presidential Election. Note: Alaska did not have any data on total votes cast by county.

 

Below, we have separated the Republican and Democrat votes and have displayed them by county as a percentage of the total votes per county.

2016 Presidential Election – Republican and Democrat Percentage of Total Votes, by County.

 

The data also has information about other presidential parties that received more than 5% of the total votes. We can see here, that in several counties around Salt Lake City, Utah, had around 20% or more of their total votes go to a candidate other than those supported by the Republican or Democrat parties.

2016 Presidential Election Votes to Other Party With Greater Than 5% of Total Votes, by County.

 

While total counts are useful and necessary information, we can do even more insightful things using this data set. One thing we can do with the data is see the differences in the percentage between the top candidates, also called “election competitiveness”, by state. For example, the Democratic candidate won California with a margin of over 20%, and in Pennsylvania the Republican candidate won by a slim margin of 0-5% of the votes.

Here is the election competitiveness by county across the 48 contiguous states:

Election Competitiveness, by State – 2016 Presidential Election.

 

and again, by county:

Election Competitiveness, by County. – 2016 Presidential Election. Note: All of Hawaii’s counties, Kauai, Honolulu, Maui, and Hawaii, had a Democratic win of a 20% plus margin. Alaska had no county data reported.

 

Since the data goes back all the way until 1912, let’s compare this information with a couple of the past recent presidential elections. 

Here’s the election competitiveness by state in the 2012 presidential elections compared to  2016:

Election Competitiveness in the 2012 vs. 2016 U.S. Presidential Elections, by State.

 

And 2008 compared to 2016:

Election Competitiveness in the 2008 vs. 2016 U.S. Presidential Elections, by State.

 

Just for fun, let’s take a look at the election competitiveness of the states from the election 20 years prior to the last one, in 1996.

Election Competitiveness in the 1996 vs. 2016 U.S. Presidential Elections, by State.

 

As you can see, this data set is very relevant and has plenty of intriguing information for those who are looking to do compelling analysis on U.S. elections.

The data includes detailed information such as the candidate names and party ballot listing per state, a national summary which summarizes vote totals by state for each candidate, and even data for New England towns (ME, MA, CT, RI, VT, NH) for those focused on election data of a particular town in the Northeast. You can read more about the what is included in this study, at it’s README file here.

Python Open Lab Nov 28: Blog Style!

Hello all,

Due to some complex scheduling issues, I am posting here the material we would have covered in lab tomorrow. Please feel free to contact me for any questions (data@library.columbia.edu). Enjoy!

Python Objects and Classes (cont’d!)

Self: 

What is the self variable in Python?

The self variable represents the instance of the object itself. Unlike most object-oriented languages that pass the instance of an object as a hidden parameter to the methods defined on an object; Python does not. It must be explicitly declared. All methods in python, including some special methods like initializer, have self.

In other words, the self  variable refers to the object which invokes the method. When you create new object the self parameter in the __init__  method is automatically set to reference the object you have just created.

 

More theory…do’s and don’ts of Self:

You use self when:

  1. Defining an instance method. It is passed automatically as the first parameter when you call a method on an instance, and it is the instance on which the method was called.
  2. Referencing a class or instance attribute from inside an instance method. Use it you want to call a method or access a name (variable) on the instance the method was called on, from inside that method.

You don’t use self when:

  1. You call an instance method normally. For example if you input [instance = MyClass()], you call [MyClass.my_method] as [instance.my_method(some_var)] not as [instance.my_method(self, some_var)].
  2. You reference a class attribute from outside an instance method but inside the class definition.

Let’s try an example!

First, we must create an object from class:

Input:

1       p1 – Person(‘anna’)   # here we have created a new person object called p1
2       print(p1.whoami())
3       print(p1.name)

 

1      You are anna
2      anna
As discussed in lab, it is bad practice to give access to your data fields outside of the class itself. Let’s see how we can hide data fields: To hide data fields, first you have to define private data fields. In Python, this can be done by using two leading underscores (__). Moreover, a private method can also be defined using two leading underscores.

Here’s an example I created on Jupyter notebook. If you’d like, I can email you the notebook as lines are colour formatted and the spaces match up to the correct inputs (as you know, your code will be affected by the spacing).

 

Expected Output:

 

 

Now, I’d like to show you if it’s possible to access  __balance  data field outside of the class.

Input:

 

As you can see, now __balance  is not accessible outside the class.

What questions do you have at this point? Does this match up with the aforementioned theoretical concept of self/hidden self data? Would you like more practice?

Fall 2017 Python Open Lab Week 3

October 10, 2017

Week 3’s lab was intense! We started with list methods, where we left off last week and went through the following:

          list.append(x): Add an item to the end of the list.

          list.extend(L): Extend the list by appending all the items in the given list.

          list.insert(i, x): Insert an item at a given position.

          list.remove(x): Remove the first item from the list whose value is x  (it will come up as an              error if there is no such item).

          list.pop([i]): Remove the item at the given position in the list, and return it. If no index is                specified, a.pop() removes and returns the last item in the list.

          list.index(x): Return the index in the list of the first item whose value is x (it will come up                as an error if there is no such item).

list.count(x): Return the number of times x appears in the list.

list.sort(cmp=None, key=None, reverse=False): Sort the items of the list in place.

list.reverse(): Reverse the elements of the list, in place.

 

Here is an example that uses most of the list methods 

>>> a = [66.25, 333, 333, 1, 1234.5]
>>> print(a.count(333), a.count(66.25), a.count(‘x’))
2 1 0
>>> a.insert(2, 1)
>>> a.append(333)
>>> a
[66.25, 333, -1, 333, 1, 1234.5, 333]
>>> a.index(333)
1
>>> a.remove(333)
>>> a
[66.25, -1, 333, 1, 1234.5, 333]
>>> a.reverse()
>>> a
[333, 1234.5, 1, 333, -1, 66.25]
>>> a.sort()
>>> a
[-1, 1, 66.25, 333, 333, 1234.5]
>>> a.pop()
1234.5
>>> a
[-1, 1, 66.25, 333, 333]

 

We then introduced the Python Dictionary:

Python Dictionary

In the Python dictionary, each key is separated from its value by a colon (:), the items are separated by commas, and the whole thing is enclosed in curly braces.

An empty dictionary without any items is written with just two curly braces, like this: {}. Keys within a dictionary must be a data type such as strings, numbers, or tuples.

Example:

dict = {‘Name’: ‘Michael’, ‘Age’: 7, ‘Class’: ‘First’}

print “dict[‘Name’]: “, dict[‘Name’]
print “dict[‘Age’]: “, dict[‘Age’]

When we run this code, it produces the following result:

dict[‘Name’]:  Michael
dict[‘Age’]:  7

 

And finally, we very briefly touched upon string methods and boolean methods – by no means did we cover all the material we intended (as you see below) – but will pick up in Week 4 with string methods first!

 

String Methods

The string data type has multiple methods. Here are all of the methods of list objects:

str.upper()  – Making Strings uppercase

str.lower() – Making Strings lowercase

str.join() method will concatenate two strings, but in a way that passes one string through another.

str.split() method returns a list of strings that are separated by whitespace if no other parameter is given.

str.replace() method can take an original string and return an updated string with some replacement.

Boolean methods:

Method True if
str.isalnum() If string consists of only alpha-numeric values
str.isalpha() If string consists of only alphabets
str.islower() If string consists of only lower-case  values
str.isnumeric() String consists of only numeric characters
str.isspace() String consists of only whitespace characters
str.istitle() String is in title case
str.isupper() String’s alphabetic characters are all upper case

Example:

>>> string = “Hello”

>>> string.upper()

‘HELLO’

>>> string.lower()

‘hello’

>>> string = “Hello,world”

>>> string.split(“,”)

[‘Hello’, ‘world’]

>>> string[::-1]

‘dlrow,olleH’

>>> len(string)

11

>>> string1 = “Hello”

>>> string2 = “World”

>>> string1+string2

‘HelloWorld’

Hope this material is helpful and inspiring. Please don’t hesitate to contact us for any clarifications. And as always, if you have any requests for material to cover in our labs, please email or contact us!

 

Fall 2017 Python Open Lab Week 2

Week 2: October 3rd

This week we started with a brief review of the basics from week 1 and the starter kit. We continued on with Data Structures and worked through Lists, Tuples and Dictionaries. These concepts were easy to approach and we went over many practice examples on the way. Towards the end of the session, we introduced the different types of lists that one can use in Python as well as list methods. List methods, however, are quite complex and we only got through .append and .extend in great detail.

Next week we will continue with list methods!

Please comment or email us for any questions. Below is the worksheet we used for this week:

 

Python Open Lab Week II

Outline and reference:

  1. Summary from Open Lab I – Starter Kit
  2. Data Structures:

Lists:

Lists are what they seem – a list of values. Each one of them is numbered, starting from zero – the first one is numbered zero, the second 1, the third 2, etc. You can remove values from the list, and add new values to the end. Example: Your many cats’ names.

Tuples:

Tuples are just like lists, but you can’t change their values. The values that you give it first up, are the values that you are stuck with for the rest of the program. Again, each value is numbered starting from zero, for easy reference. Example: the names of the months of the year.

Dictionaries:

Dictionaries are similar to what their name suggests – a dictionary. In a dictionary, you have an ‘index’ of words, and for each of them a definition. In python, the word is called a ‘key’, and the definition a ‘value’. The values in a dictionary aren’t numbered – tare similar to what their name suggests – a dictionary. In a dictionary, you have an ‘index’ of words, and for each of them a definition. In python, the word is called a ‘key’, and the definition a ‘value’. The values in a dictionary aren’t numbered – they aren’t in any specific order, either – the key does the same thing. You can add, remove, and modify the values in dictionaries. Example: telephone book.

Lists:

  • Indexing
  • Slicing
  • List operations (concat, etc.)
  • Multiplying
  • “In” operator

*Question: Given a year, month, day, print as follows:

21st July, 1991

Methods:

  • Append
  • Extend
  • Count
  • Index
  • Insert
  • Pop
  • Remove
  • Reverse
  • Sort/Sorted