Quickstart

Analysing and highlighting data problems

import pandas as pd
from zaps.eda import UniStat, Dist, Olrs, NumAna, CatAna

# loading sample dataset
df = pd.read_csv('...')

# univariate stats - highlighting skewed numeric features and rare categories and
u_s = UniStat(df)

num_cols, cat_cols, dup_df = u_s.peek()

# visualizing data problems
u_s.stats_plot()

# skewed features goodness of fit: Normal distribution
u_s.skew_plot()

Visualizing distributions

# plotting numeric distributions with no user input
dsts = Dist(df = df, cols = num_cols, silent = True)

# Histograms iterative plotting
dsts.hs()

# analysing best fitting distribution
dsts.best_fit(
              cols = num_cols,
              distr = ['norm', 'expon', 'lognorm', 'uniform']
             )

# visualize best fitting distribution
dsts.best_vis()

Identifying and handling outliers

# outliers: identifying and capping using different methods
lrs = Olrs(num_cols,
           mapping = {'column_name': ('iqr', 1.5)},
           method = 'q',
           hide_p_bar = True
          )

trans_df = lrs.fit_transform(df)

Numeric features analysis

# numeric analysis - fitting regression models and displaying results
n_a = NumAna(df, num_cols, 'numeric_target_column_name',
             hide_p_bar = True).fit_models()

# target and feature correlation - with filtered display
corr_df, feat_corr = n_a.corr(plot = True, thresh = .5)

# visualizing trend lines and overlaying outliers on a selected subset
n_a.vis_fit(olrs_mapping = lrs.z_olrs_)

# assess fit results visually - OLS
n_a.vis_ols_fit()

# check fit results
n_a.z_fit_results_['column_name'].summary()

# interactive multivariate analysis
n_a.vis_multi(col = 'column_name1', color = 'column_name2', symbol = 'column_name3',
             trendline = 'ols', olrs_idx = lrs.z_olrs_idx_)

Categorical features analysis

# categorical analysis - cats vs num
c_a = CatAna(df, cat_cols, 'numeric_target_column_name', hide_p_bar = True)

# ANOVA with assumptions and mutual info scores
anova = c_a.ana_owva()

# Post-hoc displaying groups that could be merged
post_hoc = c_a.ana_post(multi_tst_corrc = 'bonf')

# categorical analysis - cats vs cat
c_a = CatAna(df, cat_cols, 'categorical_target_column_name', hide_p_bar = True)

# chi2 test of independence
chi2 = c_a.ana_chi2()

Preprocessing Pipeline

# sklearn pipline integration
from sklearn.preprocessing import PolynomialFeatures
from sklearn.pipeline import Pipeline

# setup
feats = ['column_name1', 'column_name2']

lrs = Olrs(cols = feats, hide_p_bar = True)
poly = PolynomialFeatures(interaction_only = True,
                          include_bias = False).set_output(transform = "pandas")

# pipline
pl = Pipeline([
               ('pf', poly),
               ('olrs', lrs),
             ])

pl.fit_transform(df[feats])