القائمة
Screen psychology datasets before Pingouin analysis: variable types, missingness, duplicates, long/wide shape, assumption checks, and safer preprocessing.
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استنادا إلى تصنيف SOC المهني
Run or generate Pingouin code for multivariate comparisons — Hotelling's T-squared test (multivariate_ttest) — plus the multivariate assumption checks box_m (equal covariance) and multivariate_normality, for designs with several dependent variables.
Route psychology statistics requests to the smallest Pingouin workflow. Use when the user asks what analysis to run, gives a psychology design, asks to analyze data with Pingouin, or needs lower-token guidance before code generation.
Compute or generate Pingouin Bayes factors for t tests, correlations, and proportions (bayesfactor_ttest, bayesfactor_pearson, bayesfactor_binom), and read the BF10 already returned by pg.ttest and pg.corr.
Run or generate Pingouin code for categorical / contingency-table analyses — chi-square test of independence, McNemar's paired test, 2x2 crosstabs, and chi-square power — for psychology data with nominal variables.
Run or generate Pingouin code for rank-based non-parametric tests — Mann-Whitney U, Wilcoxon signed-rank, Kruskal-Wallis, Friedman, and Cochran Q — when outcomes are ordinal or parametric assumptions fail.
Review and approve Pingouin psychology analyses before final reporting. Use after data screening or statistical output and before interpretation to catch common problems: wrong test, ignored repeated measures, missing assumption checks, multiple-comparison errors, unsupported causal claims, incomplete reporting, or output-format mismatches.
| name | pg-data-screening |
| description | Screen psychology datasets before Pingouin analysis: variable types, missingness, duplicates, long/wide shape, assumption checks, and safer preprocessing. |
Use before analysis when data has not been inspected in the current thread, or when a test fails because of missing values, shape, categories, or assumptions.
Read:
../../references/supervision-gates.md../../references/pingouin-api-quickref.mdimport pandas as pd
import pingouin as pg
df = pd.read_csv("data.csv")
print(df.info())
print(df.describe(include="all"))
print(df.isna().mean().sort_values(ascending=False).head(20))
print(df.duplicated().sum())
For group balance:
print(df.groupby("group", dropna=False).size())
For normality by group:
pg.normality(data=df, dv="score", group="group")
For homogeneity:
pg.homoscedasticity(data=df, dv="score", group="group")
For repeated-measures sphericity:
pg.sphericity(data=df, dv="score", within="condition", subject="id")
For robust outliers (MAD-median rule) and a normality Q-Q plot:
mask = pg.madmedianrule(df["score"].to_numpy()) # True = flagged outlier
print({"n_outliers": int(mask.sum())})
import matplotlib.pyplot as plt # qqplot needs matplotlib
pg.qqplot(df["score"], dist="norm")
plt.savefig("qqplot.png", dpi=150, bbox_inches="tight")
Report: