KevsRobots Learning Platform
Python for beginners
45% Percent Complete
Introduction
Real world Python
Working with Data in Python
Learn about Python's capabilities for data manipulation and analysis, focusing on using lists, dictionaries, and introducing Pandas.
By Kevin McAleer, 2 Minutes
Introduction
Python is a powerful tool for working with data. In this lesson, we will learn about Python’s built-in data types and structures, as well as a brief introduction to the popular data analysis library Pandas.
Learning Objectives
- Understand how to use lists and dictionaries for data manipulation.
- Learn the basics of the Pandas library for data analysis.
Lists
A list is a collection which is ordered and changeable. Lists are written with square brackets.
# Create a list
fruits = ["apple", "banana", "cherry"]
# Access items in a list
print(fruits[0]) # Prints 'apple'
# Modify items in a list
fruits[1] = "blueberry"
print(fruits) # Prints '['apple', 'blueberry', 'cherry']'
Dictionaries
A dictionary is a collection which is unordered, changeable and indexed. Dictionaries are written with curly brackets, and have keys and values.
# Create a dictionary
fruit_colors = {"apple": "red", "banana": "yellow", "cherry": "red"}
# Access items in a dictionary
print(fruit_colors["apple"]) # Prints 'red'
# Modify items in a dictionary
fruit_colors["banana"] = "green"
print(fruit_colors) # Prints '{"apple": "red", "banana": "green", "cherry": "red"}'
Introduction to Pandas
Pandas is a powerful library for data manipulation and analysis. It provides data structures and functions needed to manipulate structured data, including functionality for manipulating tables, time series data and more.
# Import the pandas library
import pandas as pd
# Create a DataFrame
df = pd.DataFrame({
"Fruit": ["apple", "banana", "cherry"],
"Color": ["red", "yellow", "red"],
})
# Display the DataFrame
print(df)
# Access columns in a DataFrame
print(df["Fruit"])
# Access rows in a DataFrame
print(df.loc[0])
Summary
In this lesson, you’ve learned about Python’s capabilities for data manipulation and analysis. We’ve covered lists and dictionaries, two of Python’s built-in data structures, and we’ve introduced the powerful Pandas library for more complex data tasks. Understanding these concepts is crucial for doing data science in Python.
You can use the arrows ← → on your keyboard to navigate between lessons.
Comments