NumPy Arrays
In the life of a data scientist, reading and manipulating arrays is of prime importance, and it is also the most frequently encountered task. These arrays could be a one-dimensional list or a multi-dimensional table or a matrix full of numbers.
The array could be filled with integers, floating-point numbers, Booleans, strings, or even mixed types. However, in the majority of cases, numeric data types are predominant.
Some example scenarios where you will need to handle numeric arrays are as follows:
To read a list of phone numbers and postal codes and extract a certain pattern
To create a matrix with random numbers to run a Monte Carlo simulation on some statistical process
To scale and normalize a sales figure table, with lots of financial and transactional data
To create a smaller table of key descriptive statistics (for example, mean, median, min/max range, variance, inter-quartile ranges) from a large raw data table
To read in and analyze time series data in a one-dimensional array...
