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lynda Python Functions for Data Science

Video Introducing this tutorial


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