Overview
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Save time, and make your code more readable and reusable, by learning the most powerful Python functions for data science.
Syllabus
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Introduction
- Python functions you should know
- Getting the most from this course
- 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
- Python map() function
- Python zip() function
- Python filter() function
- 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
- Linear algebra operations with SciPy
- Statistical functions with SciPy
- 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
- Matplotlib line plots
- Matplotlib scatter plots
- Matplotlib bar plots
- Matplotlib pie charts
- Matplotlib histograms
- Matplotlib subplots
- Seaborn box plots
- Seaborn kernel density estimate plots
- Seaborn violin plots
- Seaborn heatmaps
- Get started using Python functions
