Raw data are not always pretty. Data can have different patterns, be noisy, and vary from trial to trial. We usually collect data to measure, or more accurately, estimate an effect or phenomenon. At times, it is necessary to fit a mathematical expression to raw data in order to estimate the underlying effect or phenomenon. We can then use this
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In our previous post we reviewed how to apply the DRY principle (don’t repeat yourself) to our code and we learned how to create Python modules to avoid repeating ourselves (i.e. cutting-and-pasting code) across scripts and programs. In this post we will see how we can use our modules as a stand-alone program as well as useful functions that can
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We previously learned to create our own Python functions to reduce how much we repeat ourselves in our code. In this post we see another example of the DRY principle (don’t repeat yourself) and we will learn how to ensure we don’t repeat ourselves between the different programs we write. A typical (bad) script to process data Below is an
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Learning to program is not easy. We have to learn a new language and a new way of thinking. We have to learn the grammatical rules of the programming language we are learning, and the logic of our program, how it will work its way through the code and hopefully give us the answer we expect. In this post we
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Previously, we read in CSV data and summarised it. Here, we will learn how to locate data using labels and tabulate categorical data. The CSV file data_pandas.csv (available here) contains simulated data of age, height and score from 20 subjects. Read in and store the CSV data in the dataframe df, using the header names. Also create and assign subjects
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Previously, we used Pandas to read and write data to CSV files, reshape data from wide to long format, and used the Seaborn package to plot paired data. Here, we will summarise some data and write the results to a CSV file. The CSV file data_pandas.csv (available here) contains simulated data of age, height and score from 20 subjects. To
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