10  Basics

10.1 Importing Libraries

Importing libraries is essential for accessing additional Python features. We import them at the top of the file for clarity and efficiency. An alias (a short name) makes it easier to reference them throughout your code.

Insert the following code in a new code cell directly under your title and execute it:

# Importing libraries (at the top)
import numpy as np
import pandas as pd
import altair as alt

10.2 Variables and Data Types

Variables are used to store different kinds of data in Python. Data types define the nature of this data and influence how it can be used.

Add a new code cell, then copy and paste the code provided below into that cell. Make sure to run the cell afterward.

Example Code:

# Assigning variables (using descriptive names)
name = "Alice"  # String for a person's name
age = 30  # Integer for age
height = 5.5  # Float for height in feet
numbers = [1, 2, 3]  # List of integers
person = {"name": "Alice", "age": 30}  # Dictionary for person data

Explanation:

  • name is a string that stores a sequence of characters representing a name.
  • age is an integer variable representing a whole number.
  • height is a float representing a number with a decimal point.
  • numbers is a list, which is an ordered collection of items.
  • person is a dictionary consisting of key-value pairs that can store multiple attributes.

To inspect the values stored in these variables, you can use the print function:

print(name)  # Output: Alice
print(age)  # Output: 30
print(numbers)  # Output: [1, 2, 3]
print(person)  # Output: {'name': 'Alice', 'age': 30}
Alice
30
[1, 2, 3]
{'name': 'Alice', 'age': 30}

10.3 Basic Operators and Calculations

Operators allow you to perform calculations, comparisons, and logical operations.

Example Code:

# Arithmetic operations with multiple variables
length = 10
width = 5
# Calculate the area and perimeter
area = length * width  # Multiplication
perimeter = 2 * (length + width)  # Addition and multiplication

# Display perimeter
perimeter
30
# Comparison operations
is_square = length == width  # Check if length and width are equal

is_square
False
# Logical operations
is_large_rectangle = (area > 20) and (perimeter > 10)  # Logical AND

is_large_rectangle
True

Explanation:

  • length and width are multiplied to calculate the area.
  • perimeter is calculated by adding length and width together and then multiplying by 2.
  • is_square checks if the two sides are of equal length.
  • is_large_rectangle uses logical operators to determine if the rectangle is both large in area and perimeter.

10.4 Working with DataFrames

A DataFrame is a two-dimensional, tabular data structure provided by Pandas. It allows you to efficiently manipulate data, similar to a spreadsheet or SQL table.

Example Code:

# Prepare data
data = { "Numbers": [1, 2, 3, 4, 5], 
         "Squares": np.array([1, 2, 3, 4, 5]) ** 2}

# Creating a DataFrame with Pandas
df = pd.DataFrame(data)

# Inspecting the DataFrame
df
Numbers Squares
0 1 1
1 2 4
2 3 9
3 4 16
4 5 25

Explanation:

  • The dictionary data contains two lists: numbers and their squares.
  • The DataFrame df is created from this dictionary using the pd.DataFrame function, which organizes the data into rows and columns.
  • Typing just df in a Jupyter Notebook cell displays the DataFrame in a tabular format.

10.5 Understanding Methods

A method is a function that is associated with an object (like a DataFrame, list, or string) and is accessed using dot notation (.). This makes it easy to manipulate the object and retrieve information.

Example Code:

df.head() is a method that displays the first few rows of a DataFrame to preview the data:

df.head()
Numbers Squares
0 1 1
1 2 4
2 3 9
3 4 16
4 5 25

df.describe() provides summary statistics like mean, count, and standard deviation. Additionally, we use the method .round(2) to round the values:

df.describe().round(2)
Numbers Squares
count 5.00 5.00
mean 3.00 11.00
std 1.58 9.67
min 1.00 1.00
25% 2.00 4.00
50% 3.00 9.00
75% 4.00 16.00
max 5.00 25.00

General Method Concept:

  • Object: An entity containing data and functionality (e.g., DataFrame, list, or string).
  • Method: A function associated with an object.
  • Dot Notation (.): Used to call a method on an object.

10.6 Plotting Data

After understanding the structure of a DataFrame, you can visualize its data using Altair.

Example Code:

# Creating the chart
alt.Chart(df).mark_point().encode(
    x='Numbers',
    y='Squares'
)

Explanation:

  • Altair Library: A declarative visualization library that simplifies the process of creating charts and graphs.
  • alt.Chart(df): Creates a new chart object from the DataFrame df.
  • mark_bar(): Specifies the type of chart (scatter plot in this case).
  • encode: Maps DataFrame columns to chart attributes.
    • x='Numbers': Sets the horizontal axis to use the Numbers column.
    • y='Squares': Sets the vertical axis to use the Squares column.

This concise syntax allows for rapid creation and customization of visualizations.