In this step by step guide, we will walk through the python packages for statistics and we also covering some important and basic statistical modules. In this specific module we will covering the NumPy package that is used for numerical and scientific computing.
Introduction
The Anaconda distribution of Python includes suitable
packages and libraries that can readily be used for statistical
tasks. This libraries will be used right through this module.
- NumPy
- Pandas
- Statistics
- Matplotlib
- SciPy.stats
- Statsmodels
- PyMC
NumPy — For Mathematical Functions
NumPy or Numerical Python is a Python library that supports
multi-dimensional arrays and matrices. It provides a sizable
collection of fast numeric functions to work with these arrays and to perform operations of linear algebra on them.
In most programming languages including Python, arrays are
used to store multiple values in a variable. An array is a variable
that is able to hold several values. Arrays are commonly used
to store statistical data. In standard Python, lists are used as
arrays. However, lists are slow to work with. In case speed is
an important factor so that we use NumPy’s array object also called
ndarray that is significantly faster than a list.
To create and use nd arrays we first need to import the NumPy
library. Often, we use an alias to refer to the name of different
libraries. NumPy is usually replaced with its defined alias np.
The scalar values are considered as 0-dimensional arrays. An
array of scalar or 0-dimensional values is called a 1-dimensional
array. The variable my_arr0 in the aforementioned program is a 0-dimensional array whereas variable my_arr1 is a 1-dimensional array. We can create a 2-dimensional array by placing 1-dimensional arrays as elements of another array. A 2-dimensional array is used to store and process matrices.
As you can see in the above mentioned example, a matrix of two rows and four columns is created. We can also create three or higher dimensional NumPy arrays.
This code generates two matrices of 2 rows and 3 columns each. This program uses a few of the NumPy
mathematical functions on arrays.
Conclusion
In this step by step guide, we will covering the python outclass libraries for numerical and scientific computing with python. NumPy is very useful when we work with all complex mathematical functions and arrays. It provides a rich set of built-in methods that help us in some specific tasks.
