Python Functions for Data Science (2020)
1h 32mIntermediate2020-10-02
Authors
Lavanya Vijayan
Coding instructor who is passionate about STEM education and diversity
Madecraft
Full-Service Learning Content Company
Course details
Functions are a core building block of programming and working with data and an essential approach to making code more readable and reusable. In this course, discover how to save time and enhance your code using common functions for data science projects. Explore fundamental built-in Python functions for data science, as well as more advanced built-in functions such as the filter function in Python. Learn how to leverage functions from the NumPy and SciPy libraries to manipulate numerical data and perform statistical operations. Learn how to store, modify, and group data efficiently using functions from the pandas library. Get tips on using functions from the Matplotlib and Seaborn libraries for data visualization. Plus, get insights into common pitfalls and mistakes developers make when using functions as part of a data science initiative.
Skills covered
PythonPersonaData AnalysisProgramming LanguagesData ScienceBusiness Analysis and StrategyBusiness Software and ToolsOpen SourceSoftware Development
Concepts
0. Introduction
- 01 - Python functions you should know
- 02 - Getting the most from this course
1. Fundamental Built-In Python Functions for Data Science
- 03 - Python print() function
- 04 - Python input() function
- 05 - Python abs() function
- 06 - Python round() function
- 07 - Python min() function
- 08 - Python max() function
- 09 - Python sorted() function
- 10 - Python sum() function
- 11 - Python len() function
- 12 - Python type() function
2. Advanced Built-In Python Functions for Data Science
- 13 - Python map() function
- 14 - Python zip() function
- 15 - Python filter() function
3. Functions from NumPy Library for Manipulation of Numerical Data
- 16 - Create NumPy arrays in Python
- 17 - Minimum and maximum values in NumPy arrays
- 18 - Indices of min and max values in NumPy arrays
- 19 - Find shapes of NumPy arrays and reshape
- 20 - Select items or groups of items from NumPy arrays
- 21 - Arithmetic operations on NumPy arrays
- 22 - Scalar operations on NumPy arrays
- 23 - Statistical operations on NumPy arrays
- 24 - Other operations on NumPy arrays
4. Functions from SciPy Library for Scientific Computing
- 25 - Linear algebra operations with SciPy
- 26 - Statistical functions with SciPy
5. Functions from pandas Library for Data Manipulation and Data Analysis
- 27 - Create a pandas series
- 28 - Create a pandas DataFrame
- 29 - Select data subsets from pandas objects
- 30 - Modify pandas objects
- 31 - Combine data from pandas objects
- 32 - Group data from pandas objects
6. Functions from Matplotlib for Data Visualization
- 33 - Matplotlib line plots
- 34 - Matplotlib scatter plots
- 35 - Matplotlib bar plots
- 36 - Matplotlib pie charts
- 37 - Matplotlib histograms
- 38 - Matplotlib subplots
7. Functions from Seaborn for Data Visualization
- 39 - Seaborn box plots
- 40 - Seaborn kernel density estimate plots
- 41 - Seaborn violin plots
- 42 - Seaborn heatmaps
Conclusion
- 43 - Get started using Python functions