Preprocessing Data with NumPy

NumPy, ndarrays, Slicing, Random Generators, Importing and Saving Data, Statistics, Data Manipulation, Preprocessing

Created by: 365 Careers
Creating opportunities for Data Science and Finance students

What Will I Learn?

• Arrays.
• The definition of a package/library.
• Installing and Upgrading a package.
• Navigating the documentation.
• A history of NumPy.
• The relationship between arrays and vectors.
• Arrays vs Lists.
• Indexing.
• Assigning values to arrays.
• Elementwise properties and operations.
• Datatypes supported by ndarrays.
• Broadcasting and type casting.
• Running a function or method over a given axis.
• Slicing, Stepwise Slicing, Conditional Slicing
• Dimensionality reduction in arrays.
• Generating arrays full of identical values.
• Generating non-random sequences of data.
• Generating random data with Random Generators.
• Generating random samples from a random probability distribution.
• Importing and exporting data with and from NumPy.
• NPY and NPZ files.
• Maximums and Minimums.
• Percentiles and Quantiles.
• Mean and Variance.
• Covariance and Correlation.
• Calculating histograms.
• Higher dimension histograms.
• Finding and filling up missing values.
• Substituting "filler" values.
• Reshaping arrays.
• Removing parts of arrays.
• Removing parts of individual elements within arrays. (Stripping)
• Sorting and Shuffling.
• Argument Functions.
• Stacking and Concatenating.
• Finding the unique values within an array.
• A comprehensive practical example of data cleaning and preprocessing.

Requirements

• You'll need to install Python.
• No prior experience with NumPy is required.
• Some general understanding of coding languages is preferred, but not required.

Target audience

• Aspiring data analysts.
• Programming beginners.
• People interested in analyzing data through Python.
• Analysts who wish to specialize in Python.
• Finance graduates and professionals who need to better apply their knowledge in Python.