Learn basic statistics within NumPy and SciPy in this video tutorial by Charles Kelly. These are explained in the context of computer science and data science to technologists and students in preparation for machine learning, applied statistics, neural ne
- [Narrator] The statistics file in your…Exercises file folder is pre-populated…with an import statement and a sample data set.…This video presents a very brief illustration…of statistics using NumPy and SciPy.…The purpose of this video is to illustrate…how to import statistical methods,…find information about statistical methods,…perform rudimentary statistical operations,…and plot output from statistical operations.…Note the import statement.…From SciPy.stats import norm.…
This enables us to refer to the function as…N-O-R-M rather than SciPy.stats.norm.…First we'll note that the sample data contains…99 normally distributed values.…This is the case because we're parameterizing…the number of data points,…then passing this parameterized value…as an argument to SciPy's random number generator.…We can look at the type of the random data set…and it's a NumPy ndarray.…
Before you proceed with using this notebook,…remember to go to the Cell menu…and select Run All.…What this does for you, is it ensures…that each of these import statements…
- Using Jupyter Notebook
- Creating NumPy arrays from Python structures
- Slicing arrays
- Using Boolean masking and broadcasting techniques
- Plotting in Jupyter notebooks
- Joining and splitting arrays
- Rearranging array elements
- Creating universal functions
- Finding patterns
- Building magic squares and magic cubes with NumPy and Python
Skill Level Intermediate
2. Create NumPy Arrays
3. Index, Slice, and Iterate
4. Plots: Matplotlib and Pyplot
5. Manipulate Arrays
6. Short Examples
7. Extended Examples
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