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Intro to Programming in Python

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Disclaimer!

  • This intro programming tutorial in Python is NOT nearly as comprehensive as the intro programming tutorial in R.
  • If you have never programmed in any language before, this tutorial may skip key pieces of information.
  • Instead, the goal of this tutorial is to provide some key pieces of ’translation’ information for people with some programming experience who would like to use Python.

Goals of this Lesson

Students will:

  • Understand and be comfortable working with the basic data types in Python
  • Be able to use arithmetic, relational operators, and lists in Python
  • For those coming from R, be aware of differences across languages, especially in indexing

The files for all tutorials can be downloaded from the Columbia Psychology Scientific Computing GitHub page using these instructions. This particular file is located here: /content/tutorials/python/1-r2python-translation/2-intro-programming.ipynb.

For a video recording of this tutorial from the Fall 2020 workshop, please visit the Workshop Recording: Session 3 page.

Variables

  • Variables in Python work much in the same way they do in R.
  • In Python, a single equals sign = is used for saving variables.
myVar = 2
myVar

2

Just as in R, you can reassign a variable however you want.

myVar = myVar *10
myVar

20

Data Types

Variables usually fall into 4 main data types in Python:

  • integers
  • floating point numbers
  • strings
  • boolean variables

You can use the function type() to find out what kind of data type something is.

var1 = 2
type(var1)

int

var2 = 2.0
type(var2)

float

var3 = 'I like to boogie'
type(var3)

str

var4 = True
type(var4)

bool

Arithmetic

The basic arithmetic operators work the same way as in R, except for exponents!

  • + addition
  • - subtraction
  • * multiplication
  • / division
  • ** exponent
  • () parentheses
3*5**2+1

76

Just as in R, boolean variables are treated as numbers for math, such that True = 1 and False = 0.

True + 1

2

False + 1

1

Just like in R, you can’t do arithmetic on strings.

'hi' + 1

---------------------------------------------------------------------------

TypeError                                 Traceback (most recent call last)

<ipython-input-10-d13b8651de05> in <module>
----> 1 'hi' + 1


TypeError: can only concatenate str (not "int") to str

With strings, it is often also useful to see how long they are.

stringVar = 'myString'
len(stringVar)

8

Printing Outputs

By default, Jupyter notebooks only print the last command in a chunk as output. However, we can get around this by using the print() function.

var3
var4

True

print(var3)
print(var4)

I like to boogie
True

Lists

Lists in Python are a rough equivalent of vectors in R. They are ordered sequences of data, and are defined by square brackets [ ].

If our basic datatypes are ‘1-dimensional’, then lists containing numeric/boolean/string data are usually thought of as ‘2-dimensional’.

numList = [5,6,7,8]
numList

[5, 6, 7, 8]

stringList = ['avocado', 'mango', 'papaya']
stringList

['avocado', 'mango', 'papaya']

Caution! While in R, all items in a vector have to be the same data type, in Python, lists can mix data types. Usually, we don’t really want to do this, so it is good to be careful.

mixList = [2, '2']
mixList

[2, '2']

To find the length of a list we can use the len() function.

len(numList)

4

We can also add items onto the ends of lists with the append() function.

numList.append(2)
numList

[5, 6, 7, 8, 2]

List Indexing

  • One VERY IMPORTANT difference between R and Python is that list indexing in Python begins at 0.
  • So, item 0 is always the first item in the list.
  • List indexing in Python is also done with square brackets [ ].
numList[0]

5

Using a negative number n will return the item n spots from the end.

stringList[-1]

'papaya'

We can index a range of items too by using the :.

  • This will return the items including the first number, up until (but not including) the second.
stringList[0:2]

['avocado', 'mango']

What happens if we go off the end of the list?

numList[4]

2

We don’t just use list indexing to print out specific parts of lists though. We can use this to replace values (overwrite them) in existing lists.

stringList[0] = 'guacamole'
stringList

['guacamole', 'mango', 'papaya']

And, we can remove items from lists entirely with del().

del(stringList[0])
stringList

['mango', 'papaya']

In Python, we also want to make sure we’re careful about what it is we’re indexing. Strings can also be indexed in the same manner as lists, and will return the characters inside them at the respective indices.

print(var3)
var3[2:8]

I like to boogie





'like t'

Relational Operators

Most of the time, when you want to do things in Python, you want to do them in some conditions but not others.

Relational operators are the first key to making this happen. These are essentially inequality operators like the ones you would encounter in algebra, and return either True or False.

Relational operators work largely the same way in Python as in R.

2>1

True

2<1

False

2>=1

True

2<=1

False

2 == 2

True

'hi' == 'hi'

True

2 == '2'

False

Just as in R, the exclamation point is used to indicate ’not’.

2 != 3

True

We can use relational operators with variables as well.

a = 2
b = 3
a > b

False

A useful relational operator is to see whether a variable is in an object containing multiple pieces of data. We can use in for this.

2 in [1,3,4]

False

'banana' in ['fruit', 'banana', 'antelope']

True

Logical Operators

In Python we can use and, or, and not to define conditions based on multiple relations.

(1 == 1) & (2 == 2)

True

(1 == 1) and (2 == 2)

True

(1 == 1) or (2 == 3)

True

(1 == 1) and not (2 == 3)

True

Next in Fall 2020 Intro Scientific Computing Workshop: Data Cleaning

Next in Complete Tutorial Series: Control Flow