The python visualization world can be a frustrating place for a new user. There are many different options and choosing the right one is a challenge. For example, even after 2 years, this article is one of the top posts that lead people to this site. In that article, I threw some shade at matplotlib and dismissed it during the analysis. However, after using tools such as pandas, scikit-learn, seaborn and the rest of the data science stack in python - I think I was a little premature in dismissing matplotlib. To be honest, I did not quite understand it and how to use it effectively in my workflow.

Now that I have taken the time to learn some of these tools and how to use them with matplotlib, I have started to see matplotlib as an indispensable tool. This post will show how I use matplotlib and provide some recommendations for users getting started or users who have not taken the time to learn matplotlib. I do firmly believe matplotlib is an essential part of the python data science stack and hope this article will help people understand how to use it for their own visualizations.

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One of the compelling features of pandas is that it has a rich library of methods for manipulating data. However, there are times when it is not clear what the various functions do and how to use them. If you are approaching a problem from an Excel mindset, it can be difficult to translate the planned solution into the unfamiliar pandas command. One of those “unknown” functions is the transform method. Even after using pandas for a while, I have never had the chance to use this function so I recently took some time to figure out what it is and how it could be helpful for real world analysis. This article will walk through an example where transform can be used to efficiently summarize data.

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A common business analytics task is trying to forecast the future based on known historical data. Forecasting is a complicated topic and relies on an analyst knowing the ins and outs of the domain as well as knowledge of relatively complex mathematical theories. Because the mathematical concepts can be complex, a lot of business forecasting approaches are “solved” with a little linear regression and “intuition.” More complex models would yield better results but are too difficult to implement.

Given that background, I was very interested to see that Facebook recently open sourced a python and R library called prophet which seeks to automate the forecasting process in a more sophisticated but easily tune-able model. In this article, I’ll introduce prophet and show how to use it to predict the volume of traffic in the next year for Practical Business Python. To make this a little more interesting, I will post the prediction through the end of March so we can take a look at how accurate the forecast is.

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In a previous post, I covered one approach for generating documents using HTML templates to create a PDF. While PDF is great, the world still relies on Microsoft Word for document creation. In reality, it will be much simpler for a business user to create the desired template that supports all the custom formatting they need in Word versus trying to use HTML+CSS. Fortunately, there is a a package that supports doing a MS Word mailmerge purely within python. This approach has the advantage of running on any system - even if Word is not installed. The benefit to using python for the merge (vs. an Excel sheet) is that you are not limited in how you retrieve or process the data. The full flexibility and power of the python ecosystem is at your finger tips. This should be a useful tool to keep in mind any time you need to automate document creation.

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In many practical Data Science activities, the data set will contain categorical variables. These variables are typically stored as text values which represent various traits. Some examples include color (“Red”, “Yellow”, “Blue”), size (“Small”, “Medium”, “Large”) or geographic designations (State or Country). Regardless of what the value is used for, the challenge is determining how to use this data in the analysis. Many machine learning algorithms can support categorical values without further manipulation but there are many more algorithms that do not. Therefore, the analyst is faced with the challenge of figuring out how to turn these text attributes into numerical values for further processing.

As with many other aspects of the Data Science world, there is no single answer on how to approach this problem. Each approach has trade-offs and has potential impact on the outcome of the analysis. Fortunately, the python tools of pandas and scikit-learn provide several approaches that can be applied to transform the categorical data into suitable numeric values. This article will be a survey of some of the various common (and a few more complex) approaches in the hope that it will help others apply these techniques to their real world problems.

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