Python Functions for Data Science

Save time, and make your code more readable and reusable, by learning the most powerful Python functions for data science.

Introduction

• Python functions you should know
• Getting the most from this course

1. Fundamental Built-In Python Functions for Data Science

• Python print() function
• Python input() function
• Python abs() function
• Python round() function
• Python min() function
• Python max() function
• Python sorted() function
• Python sum() function
• Python len() function
• Python type() function

2. Advanced Built-In Python Functions for Data Science

• Python map() function
• Python zip() function
• Python filter() function

3. Functions from NumPy Library for Manipulation of Numerical Data

• Create NumPy arrays in Python
• Minimum and maximum values in NumPy arrays
• Indices of min and max values in NumPy arrays
• Find shapes of NumPy arrays and reshape
• Select items or groups of items from NumPy arrays
• Arithmetic operations on NumPy arrays
• Scalar operations on NumPy arrays
• Statistical operations on NumPy arrays
• Other operations on NumPy arrays

4. Functions from SciPy Library for Scientific Computing

• Linear algebra operations with SciPy
• Statistical functions with SciPy

5. Functions from pandas Library for Data Manipulation and Data Analysis

• Create a pandas series
• Create a pandas DataFrame
• Select data subsets from pandas objects
• Modify pandas objects
• Combine data from pandas objects
• Group data from pandas objects

6. Functions from Matplotlib for Data Visualization

• Matplotlib line plots
• Matplotlib scatter plots
• Matplotlib bar plots
• Matplotlib pie charts
• Matplotlib histograms
• Matplotlib subplots

7. Functions from Seaborn for Data Visualization

• Seaborn box plots
• Seaborn kernel density estimate plots
• Seaborn violin plots
• Seaborn heatmaps

Conclusion

• Get started using Python functions