Rules - EDA with Python, Pandas and Jupyter

Rules in EDA with Python, Pandas and Jupyter

IDE rules exported with workflows to your editor

Rules 37

Title	Slug	Activities
Always check r together with its p-value	`always-check-r-together-with-its-p-value`	1
A non-significant linear correlation doesn't rule out a relationship	`a-non-significant-linear-correlation-doesnt-rule-out-a-relationship`	1	Read the 'A non-significant linear correlation doesn't rule out a relationship' rule from playbook 'EDA with Python, Pandas and Jupyter'. Slug: a-non-significant-linear-correlation-doesnt-rule-out-a-relationship. always_apply: false. Absence of significant linear correlation may just mean the relationship is non-linear — investigate alternative relation models such as clustering instead of concluding "no relationship."
Avoid glyph-based multivariate displays	`avoid-glyph-based-multivariate-displays`	1
Bar-graph zero baseline	`bar-graph-zero-baseline`	0
Box plots beat all else for comparing distributions	`box-plots-beat-all-else-for-comparing-distributions`	1
Check for spurious or missed patterns before concluding	`check-for-spurious-or-missed-patterns-before-concluding`	0	Read the 'Check for spurious or missed patterns before concluding' rule from playbook 'EDA with Python, Pandas and Jupyter'. Slug: check-for-spurious-or-missed-patterns-before-concluding. always_apply: true. Before committing to a finding — statistical or visual — verify it against the underlying detail data, and deliberately re-examine with fresh eyes for a pattern that was missed or isn't really there.
Choose the right accuracy metric for the use case	`choose-the-right-accuracy-metric-for-the-use-case`	1
Colorblind-safe, non-black-midpoint heatmap coloring	`colorblind-safe-non-black-midpoint-heatmap-coloring`	1	Read the 'Colorblind-safe, non-black-midpoint heatmap coloring' rule from playbook 'EDA with Python, Pandas and Jupyter'. Slug: colorblind-safe-non-black-midpoint-heatmap-coloring. always_apply: false. In multivariate/heatmap displays, use a colorblind-safe diverging palette (e.g., blue↔gray↔red) rather than red/green, and avoid using a highly salient color like black to represent "near zero/average" values.
Confirm statistics with a visualization	`confirm-statistics-with-a-visualization`	0
Correlation is not causation	`correlation-is-not-causation`	0
Data science is science first	`data-science-is-science-first`	0
Design for working-memory limits	`design-for-working-memory-limits`	1
Dot plot instead of narrowed bars	`dot-plot-instead-of-narrowed-bars`	1
Justify the imputation method by domain knowledge	`justify-the-imputation-method-by-domain-knowledge`	1
k is an analyst's judgment call	`k-is-an-analysts-judgment-call`	1
Log scale or % change for comparing growth rates	`log-scale-or-change-for-comparing-growth-rates`	1
map() is not vectorized	`map-is-not-vectorized`	1
Merge for complementary data, concat for similar data	`merge-for-complementary-data-concat-for-similar-data`	1
Missing values can't be dropped cell-by-cell	`missing-values-cant-be-dropped-cell-by-cell`	1
Model quality must be measured, not assumed	`model-quality-must-be-measured-not-assumed`	1
Never plot missing values as zero	`never-plot-missing-values-as-zero`	0
Never touch test data while building a model	`never-touch-test-data-while-building-a-model`	0
Over-plotting remedy order	`over-plotting-remedy-order`	1	Read the 'Over-plotting remedy order' rule from playbook 'EDA with Python, Pandas and Jupyter'. Slug: over-plotting-remedy-order. always_apply: false. To fix over-plotted scatter/point charts, try in order: reduce point size → remove fill color (outline only) → change point shape → jitter → add transparency → encode density via contours/heatmap cells → reduce the data itself (aggregate, filter, trellis, or sample).
pandas correlation lacks significance testing	`pandas-correlation-lacks-significance-testing`	1
Pie charts underperform bar graphs	`pie-charts-underperform-bar-graphs`	1
Precision hierarchy of pre-attentive attributes	`precision-hierarchy-of-pre-attentive-attributes`	1
Prefer scriptable, reproducible pipelines	`prefer-scriptable-reproducible-pipelines`	1
Recommended train/test split is 70:30	`recommended-traintest-split-is-7030`	1
Resistant statistics for skewed/outlier-prone data	`resistant-statistics-for-skewedoutlier-prone-data`	1
Round numbers on axes	`round-numbers-on-axes`	0
Running averages over single trend lines for time series	`running-averages-over-single-trend-lines-for-time-series`	1	Read the 'Running averages over single trend lines for time series' rule from playbook 'EDA with Python, Pandas and Jupyter'. Slug: running-averages-over-single-trend-lines-for-time-series. always_apply: false. Smooth a volatile time series with a running (moving) average rather than a single linear trend line, because a trend line's slope depends heavily and misleadingly on the exact start/end dates chosen.
Scale variables before clustering	`scale-variables-before-clustering`	1
Square scatterplot aspect ratio	`square-scatterplot-aspect-ratio`	1
Trellis consistency	`trellis-consistency`	1
Trend-line model matching	`trend-line-model-matching`	1
Validate regression by comparing train vs. test fit	`validate-regression-by-comparing-train-vs-test-fit`	1
Watch for duplicate keys after vertical concat	`watch-for-duplicate-keys-after-vertical-concat`	1