Automated Data Analysis
What automated analysis actually does to a dataset, where it helps, and where it quietly goes wrong.
Automated data analysis covers everything a system does between receiving a dataset and producing an interpretation: profiling the columns, choosing a method, fitting it, checking it, and describing the result. The parts that are easy to automate — counting, summarising, plotting — are largely solved. The parts that decide whether an analysis is any good are not.
What can be automated well
Structural description: types, cardinality, missingness, distributions, obvious outliers, correlations, and simple trend and seasonality indicators. A profiler will do this faster and more consistently than a person, and will not get bored on the fortieth column.
What resists automation
Framing the question. Deciding which relationships are plausible rather than merely present. Knowing which missing values are missing at random and which encode something. Recognising that a change in a metric is a change in how the metric is collected. These require knowledge that is not in the file.
The useful middle
The most valuable systems are the ones that automate the description and then hand back something a person can argue with — a stated structure, an interval, and a note of where the model and the data disagree. That is the standard the original research set, and it remains a good one.
In this section
Automated Data Analysis
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Call scipy.stats.f_oneway with the independent groups as separate arguments and equal_var=True for the classical equal-variance ANOVA.
How to calculate a confidence interval for a sample mean in SciPy (with Code)
Pass the sample mean and its standard error to scipy.stats.t.interval, using one fewer degree of freedom than the sample size.
How to run a one-sample t-test in SciPy (with Code)
Call scipy.stats.ttest_1samp with popmean set to the fixed reference mean you want to test.
How to run a paired-sample t-test in SciPy (with Code)
Use scipy.stats.ttest_rel(after, before) when each after measurement is matched to the same subject in before.
How to run a Welch two-sample t-test in SciPy (with Code)
Call scipy.stats.ttest_ind with equal_var=False to compare the means of two independent samples without assuming equal population variances.
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How to display prediction intervals around a line in Plotly (with Code)
Draw lower and upper Scatter traces in increasing x order, then set fill="tonexty" on the upper trace to shade the interval between them.
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Pass range_color=(lower, upper) to px.imshow for a numeric two-dimensional heatmap.
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Pass hover_name to choose a tooltip heading and hover_data to include or format additional columns in a Plotly Express scatterplot.
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Set ci=95 in sns.regplot to draw a 95% bootstrap confidence band around the fitted regression estimate.
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How to draw paired observations with a point plot in Seaborn (with Code)
Map each subject identifier to hue in sns.pointplot and set errorbar=None to connect that subject's observed values across conditions.
How to order categorical levels in a bar plot in Seaborn (with Code)
Pass an explicit list to the order argument of sns.barplot to set the displayed category sequence.
How to plot a box plot without hiding observations in Seaborn (with Code)
Draw a Seaborn boxplot and overlay a stripplot using the same data and category order.
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How to compare distributions with an empirical cumulative distribution plot in Seaborn (with Code)
Use sns.ecdfplot with a numeric x column and hue for the groups you want to compare.
How to draw a diverging image with a zero-centered normalization in Matplotlib (with Code)
Pass TwoSlopeNorm(vmin=..., vcenter=0, vmax=...) as the norm of imshow with a diverging colormap.
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How to plot a histogram with unequal bin widths in Matplotlib (with Code)
Pass increasing bin edges to hist and use density=True when bar areas should represent proportions.
How to format dates on a time-series axis in Matplotlib (with Code)
Give the axis an AutoDateLocator and a ConciseDateFormatter that uses the same locator.
How to share a colorbar across subplots in Matplotlib (with Code)
Give every subplot the same Normalize object and colormap, then pass the subplot axes to fig.colorbar.
How to save a figure with a transparent background in Matplotlib (with Code)
Pass transparent=True to fig.savefig and choose a format such as PNG that supports an alpha channel.
How to annotate heatmap values in Seaborn (with Code)
Set annot=True to print values inside Seaborn heatmap cells, and use fmt to control their display precision.
How to create small-multiple scatterplots in Seaborn (with Code)
Use sns.relplot with kind="scatter" and col set to the grouping column to draw one scatterplot per group.
How to mask the upper triangle of a correlation heatmap in Seaborn (with Code)
Pass a Boolean upper-triangle mask to sns.heatmap; cells whose mask is True are hidden.
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