
Welcome to my class!
I'll explain how the course is structured. Also, please download the following two files:
worldcities.csv This is our main dataset. We will apply each function on this dataset.
top100cities_weather_data.csv We will need this additional dataset in the lecture Function 10 merge
I'll explain the course requirement (setup of Python).
Furthermore, I'll provide a document with links to tutorials that show you step by step how to setup Python either with VS Code or in Anaconda.
I'll explain to you what Pandas is and why it is needed for Data Analysis in Python.
After this class, students will be able to import data from CSV files into Python using the read_csv() function.
After this class, students will be able to quickly view the first few rows of their dataset with the head() function for easy inspection.
After this class, students will be able to retrieve an overview of their dataset, including data types and missing values, using the info() function.
After this class, students will be able to generate descriptive statistics for their numerical data using the describe() function.
After this class, students will be able to clean their dataset by removing rows or columns with missing values using the dropna() function.
After this class, students will be able to fill missing values in their dataset with a specified value using the fillna() function.
After this class, students will be able to group data by one or more columns and apply aggregation functions using the groupby() function.
After this class, students will be able to sort their dataset based on one or more columns using the sort_values() function.
After this class, students will be able to filter their dataset using string-based queries with the query() function.
After this class, students will be able to combine two or more datasets by matching on columns or indexes using the merge() function.
Some last words from my side.
Welcome to "10 Awesome Functions in Python to Analyze Data"!
Who this Course is for
This course is tailored for anyone eager to step into the world of data analysis using Python, whether you have coding experience or not. There’s no need for prior knowledge—just a computer, an internet connection, and a willingness to learn.
What You Need
To start analyzing data with Python, you will need to set up a Python environment on your computer. But don't worry — I'm here to help every step of the way. We’ll be using tools like Anaconda (which includes Jupyter Notebooks) or Visual Studio Code, both of which are free and widely used for data analysis.
What You’ll Learn
In this class, you’ll dive into 10 of the most powerful and practical functions in Python that are essential for data analysis. Each lesson focuses on a specific function, explaining its purpose and demonstrating how to use it with real-world datasets. By the end of the course, you'll have a solid toolkit of Python skills that you can apply directly to your own data projects. Here’s what you’ll cover:
How to load and view data with read_csv() and head()
Summarizing your data with info() and describe()
Cleaning and handling missing data using dropna() and fillna()
Grouping and sorting data with groupby() and sort_values()
Filtering data with query()
Combining datasets using merge()
By the end of this class you will not only understand the methods presented but also be able to apply the 10 functions on your own datasets and have gained great skills regarding data analysis.