Python has many libraries for data science, statistical analysis, and machine learning. In this article, I will introduce you to the four most important python libraries for statistics that you should learn.
Statistics cover most of the fundamentals of data learning. All machine learning algorithms and data analysis concepts are derived from the fundamentals of statistics. In the section below, I’ll introduce you to the most important Python libraries for statistics.
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Python libraries for statistics
Here are the four most important python libraries for statistical analysis.
Now, let’s go through all of the above libraries to understand what features they contribute to statistics using python.
NumPy stands for Numerical Python. It is one of the most widely-used Python libraries for statistics and data science. Numpy is mainly used to create multidimensional arrays for mathematical and logical operations. Numpy does so many math operations and the best part about using NumPy for statistics is that it can also be used to convert pictures and sound waves into multidimensional arrays.
Below are some of the most important operations provided by NumPy for statistics.
- Simple and complex mathematical and scientific computations
- Multidimensional arrays
- Fourier transformations and data manipulations
- Creating and implementing machine learning algorithms
SciPy is also one of the best python libraries for statistics. It is built on NumPy and is mainly used to solve basic statistics problems. It is also used to calculate mathematical equations which cannot be performed using NumPy.
Here are some of the most important features provided by SciPy by statistics:
- Vector quantization
- Fourier transformation and interpolation
- linear algebra
- signal processing and data structures
- Numerical Integration and optimization
Pandas is one of the most important Python libraries for statistics for the task of preparing and processing data. It is also based on NumPy. Pandas are mainly used for a wide range of operations such as finance, economics, data analysis, etc.
Here are some of the important features provided by Pandas for statistics:
- Creating DataFrames
- Slicing, manipulation and transformation for data
- Inbuild features for creating excel charts
- Can also be used Time Series data
StatsModels is also one of the important Python libraries for statistics, primarily used for building statistical models, managing data, and evaluating models. StatsModels is built on Scipy and Numpy. It can also be integrated with Pandas.
Below are some of the most important features provided by StatsModels for Statistics:
- Statistical tests and Hypotheses Testing
- Generalised Linear Models
- Ordinary least square Linear Regression Models
So these were the most important Python libraries for Statistical Analysis. I hope you liked this article on the most important Python libraries for Statistical Analysis. Feel free to ask your valuable questions in the comments section below.