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#How to use statistical tools for data analysis how to#
#How to use statistical tools for data analysis series#

The built-in Python statistics library has a relatively small number of the most important statistics functions. Getting Started With Python Statistics Libraries

#How to use statistical tools for data analysis series#

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:

#How to use statistical tools for data analysis how to#

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.

Change in the behavior of the observed systemĭata collection errors are a particularly prominent cause of outliers.

There are many possible causes of outliers, but here are a few to start you off: OutliersĪn outlier is a data point that differs significantly from the majority of the data taken from a sample or population. That way, you’ll be able to use the sample to glean conclusions about the population. Ideally, the sample should preserve the essential statistical features of the population to a satisfactory extent. This subset of a population is called a sample. That’s why statisticians usually try to make some conclusions about a population by choosing and examining a representative subset of that population. Populations are often vast, which makes them inappropriate for collecting and analyzing data. In statistics, the population is a set of all elements or items that you’re interested in. You’ll learn how to understand and calculate these measures with Python. Useful measures include covariance and the correlation coefficient.

Correlation or joint variability tells you about the relation between a pair of variables in a dataset.

Useful measures include variance and standard deviation.

Variability tells you about the spread of the data.

Useful measures include the mean, median, and mode.

Central tendency tells you about the centers of the data.

In this tutorial, you’ll learn about the following types of measures in descriptive statistics: