Introduction to Python Programming

1. Introduction to Python Programming#

1.1. Learning objectives#

understand the basics of Python programming
Understand data types and objects
understand Python functions

1.2. Basics of Python#

In the previous section, we showed the layout of RStudio. For this lesson, you will write all the code in the scripts/source and see the output in the console. To comment on the code, you will use the hashtag (#) to tell R not to execute the line as a code.

R can be used as a calculator:

print(4 + 7) 

print(9 * 6) 

11
54

1.2.1. Arithmetic Operators#

Description	Operator	Example
Addition	+	1 + 3
Subtract	-	90 - 5
Multiplication	*	6 * 7
Exponentiation	**	3 ^ 6
Division	/	54 / 7

Type in and run the above examples in the script or console.

1.2.2. Assigning variables#

Notice that we have been running previous codes without assigning them to a variable. We can assign anything to a variable (=); this can be a plot, a variable, a table, etc.

Say we ages of two individuals: Thembi’s age is 30 and Sipho’s age is 20. You can assign their ages to a variable:

thembi_age = 30

sipho_age = 20

You can use print() function to get the results

print(thembi_age)

print(sipho_age)

30
20

Because you have stored/assigned these into variables, you can calculate, let’s say, total, differences, multiplication, etc. Get the sum of ages:

sipho_age + thembi_age

Get age differences:

thembi_age - sipho_age

Multiply these ages:

thembi_age * sipho_age

Basically, we have crated variables: sipho_age and thembi_age, and stored their ages in numbers there. always remember this:

a variable name is case sensitive: if you have named it sipho_age, when you type Sipho_age there will be an error:

#| error: true
print(Sipho_age)

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Input In [7], in <cell line: 2>()
      1 #| error: true
----> 2 print(Sipho_age)

NameError: name 'Sipho_age' is not defined

1.3. Data types in Python#

While there many data types in Python, in this course, we will focus on the most common:

There 3 basic data types in R

numeric:
- int: integers eg. (1, 2, 6, 90)
- float: with decimal e.g. (1.2, 5.3, 8.4)
string: str. This is text data, for axample, “My name is Aubrey”
boolean: bool. TRUE/FALSE

1.3.1. Strings#

Strings are character data types, alwways surrounded by quoates: either single quotes ('') or double quotes ("")

Example of a string variables:

river = "Tugela river"

print(river)

country  = "South Africa"

print(country)

Notice that a string need to be surrounded by (“”) every time, otherwise Python will return an error

#| error: true

river = Tugela_river

There are various basic analysis we can apply to string. For example, you may add two string:

name = "Peter"

surname = "Zulu"

### Add to strings

print(name + surname)

You can leave spaces between when adding two string variables:

print(name + " " + surname)

You can convert to strings to upper or lower cases by using .upper() and .lower() functions, respectively:

name.upper() 

surname.lower()

How long is the string? You can find out by using len() function:

len(name)

len(surname)

1.3.2. Numeric data#

Numeric data represent numerical values, they can be either:

interger: whole numbers (e.g. 1, 30, 5, 90) or
float: numbers with decimals (e.g. 1.2, 4.7…)

For example we may create a variables of river lenght and depth in kilometers

river_length_km = 531
print(river_length_km)

river_depth_km = 0.15
print(river_depth_km)

There are many functions that can be used to analyse numeric data, for example, we may add two numeric variables:

print(river_length_km + river_depth_km)

You can multiply:

print(river_depth_km * river_depth_km)

Numeric data does not to need to be surrounded by " ", if you do, they will be stored as a string.

1.3.3. Boolean#

Boolean data takes two possible values:True or False. An example of a logical data type:

is_male = True

print(is_male) 

is_female = False

print(is_female)

In many cases, booleans are used to evaluate conditions:

==: Equal to
!=: Not equal to
<: Less than
>: Greater than
<=: Less than or equal to
>=: Greater than or equal to

age = 45

print(age == 45)

age = 45
print(age > 50)

age = 45
print(age > 50)

age = 45

if age > 34:
    print("This person is an adult")
else: 
        print("You are young")

1.3.4. What type?#

You can ask python to tell you the type of the data structure by using type() function:

river_length_km = 531
print(type(river_length_km))

The river_lenght_km variable is an integer

river_name = "Tugela"

print(type(river_name))

is_female = False

print(type(is_female))

1.4. Conclusion#

In this section, you have learnt basic data types, functions and operators. Next, we learn different type of data structures.