In addition, you can get the unlabeled data from a Series or DataFrame as a np.ndarray object by calling. Often, you might just pass them to a NumPy or SciPy statistical function. Note that, in many cases, Series and DataFrame objects can be used in place of NumPy arrays. It works well in combination with NumPy, SciPy, and Pandas. Matplotlib is a third-party library for data visualization. It excels in handling labeled one-dimensional (1D) data with Series objects and two-dimensional (2D) data with DataFrame objects. Pandas is a third-party library for numerical computing based on NumPy. It offers additional functionality compared to NumPy, including scipy.stats for statistical analysis. SciPy is a third-party library for scientific computing based on NumPy. This library contains many routines for statistical analysis. Its primary type is the array type called ndarray. NumPy is a third-party library for numerical computing, optimized for working with single- and multi-dimensional arrays. You can use it if your datasets are not too large or if you can’t rely on importing other libraries. Python’s statistics is a built-in Python library for descriptive statistics. There are many Python statistics libraries out there for you to work with, but in this tutorial, you’ll be learning about some of the most popular and widely used ones: You have to rely on experience, knowledge about the subject of interest, and common sense to determine if a data point is an outlier and how to handle it. There isn’t a precise mathematical definition of outliers. Other errors can be caused by miscalculations, data contamination, human error, and more. For example, the limitations of measurement instruments or procedures can mean that the correct data is simply not obtainable.
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