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
How to pivot a table with duplicate key pairs in Pandas (with Code)
Use pivot_table with an explicit aggregation function when repeated row-key and column-key pairs represent legitimate repeated measurements.
How to reshape wide data to long format in Pandas (with Code)
Use melt with explicit identifier and measurement columns to turn each selected wide cell into one long-format row.
How to validate a many-to-one merge in Pandas (with Code)
Use validate="many_to_one" in a left merge to require unique join keys in the right-hand lookup table.
How to aggregate columns with named groupby outputs in Pandas (with Code)
Pass output_name=(input_column, aggregation) pairs to DataFrameGroupBy.agg to create explicitly named summary columns.
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How to flag missing values before imputation in Pandas (with Code)
Capture an isna mask before filling values, then store it as a Boolean or zero-one indicator beside the original column.
How to fill missing values within groups in Pandas (with Code)
Use groupby with transform("median") to return each group median to its original rows, then fill only missing numeric values with that aligned Series.
How to run Tukey pairwise comparisons after ANOVA in Statsmodels (with Code)
Use pairwise_tukeyhsd with the numeric outcomes, their group labels and use_var="equal" to compare all pairs of means with Tukey-adjusted inference.
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How to calculate power for an independent-samples t-test in Statsmodels (with Code)
Use TTestIndPower.solve_power with a planned standardized mean difference, significance level, target power and group allocation ratio to solve for the first group size.
How to compare two proportions with a z-test in Statsmodels (with Code)
Pass two success counts and their corresponding trial counts to proportions_ztest with value=0 to test equal proportions in independent groups.
How to calculate a Wilson interval for a binomial proportion in Statsmodels (with Code)
Call proportion_confint with the observed success count, total trial count and method="wilson" to calculate a Wilson score interval for one binomial proportion.
How to adjust p-values with the Benjamini-Hochberg method in Statsmodels (with Code)
Pass the complete planned family of p-values to multipletests with method="fdr_bh" to obtain Benjamini-Hochberg adjusted p-values and rejection decisions.
How to bootstrap a confidence interval for the median in SciPy (with Code)
Call scipy.stats.bootstrap with np.median, a one-element data tuple and a recorded random generator seed to estimate a median confidence interval.
How to run a permutation test for a difference in means in SciPy (with Code)
Pass a difference-of-means function to scipy.stats.permutation_test with permutation_type="independent" for two exchangeable independent samples.
How to test variance homogeneity with the Levene test in SciPy (with Code)
Call scipy.stats.levene with center="median" to compare the spreads of independent groups using median-centered absolute deviations.
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How to calculate a Spearman rank correlation in SciPy (with Code)
Use scipy.stats.spearmanr to calculate the correlation of average ranks, keeping each pair of observations aligned.
How to calculate a Pearson correlation and confidence interval in SciPy (with Code)
Call scipy.stats.pearsonr on matched numeric observations, then use its confidence_interval method for a Fisher-transformed interval.
How to run Fisher’s exact test in SciPy (with Code)
Call scipy.stats.fisher_exact with a two-by-two table of nonnegative integer counts and an explicit alternative.
How to test independence with a chi-square contingency test in SciPy (with Code)
Pass a table of observed counts to scipy.stats.chi2_contingency and inspect the returned expected counts, degrees of freedom and p-value.
How to run a Wilcoxon signed-rank test in SciPy (with Code)
Calculate the paired differences in the intended direction, then pass them to scipy.stats.wilcoxon with an explicit alternative and method.
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How to run a Mann-Whitney U test in SciPy (with Code)
Use scipy.stats.mannwhitneyu for two independent samples and state the alternative and p-value method explicitly.
How to run a Kruskal-Wallis test in SciPy (with Code)
Pass independent groups as separate arguments to scipy.stats.kruskal to obtain a tie-corrected H statistic and an approximate p-value.
How to run a one-way ANOVA in SciPy (with Code)
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.