Tag Archives: data visualisation

Fitting polynomial or exponential curves to biological data in Python

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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Matplotlib: How to plot subplots of unequal sizes

Sometimes we would like to focus more on some data and less on others, but still provide a visual display. The matplotlib function gridspec allows subplots of unequal size to be plotted on the same figure. How this function can be applied will be demonstrated using simulated data. Let’s simulate some common probability distributions of different statistics using Python’s numpy.random

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Why showing raw data matters

Bar and line graphs with standard deviation (SD) or standard error (SE) error bars are often used to visually present continuous data from laboratory studies with small sample sizes. Last year the journal PloS Biology published a paper showing that presenting such data this way is problematic for the following reasons: bar and line graphs conceal how data are distributed

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