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By Kevin McAleer, 2 Minutes
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.
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']'
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"}'
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])
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.
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