Applied Data Science Coding in Python: histogram plots

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Applied Data Science Coding in Python: histogram plots

A histogram is a graphical representation of the distribution of a dataset. It is an estimate of the probability distribution of a continuous variable. In other words, it shows how often certain values appear in a dataset. The histogram groups the values into bins, and the height of each bar represents the number of observations in that bin.

In Python, the most common library to generate histograms is matplotlib. The pyplot sublibrary of matplotlib provides a function called hist() that can be used to generate histograms. This function takes in a data array, and it returns a histogram plot.

The hist() function has a few parameters that can be used to customize the plot. For example, the number of bins in the histogram can be changed by passing the bins parameter to the function. The default value is 10, but it can be set to any integer value. The range parameter can be used to specify the range of values to include in the histogram.

Another library that can be used for Histograms is seaborn library, it has a function displot() can be used to generate histograms.

In summary, histograms are used to visualize the distribution of a dataset. In Python, the most common library to generate histograms is matplotlib with pyplot sublibrary, it provides a function called hist() that can be used to generate histograms. The seaborn library also provides a function displot() to generate histograms. These functions can be customized using parameters such as number of bins and range of values to include in the histogram.

 

In this Applied Machine Learning & Data Science Recipe, the reader will learn: How to generate histogram plots.

Applied Data Science Coding in Python: histogram plots

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