How do you use value_counts() and pd.crosstab() to understand categorical data?

Categorical columns are understood by counting their frequencies and cross-tabulating them against other variables. These two tools answer the questions 'what values exist and how often?' and 'how are two categorical variables related?'

import pandas as pd
tips = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/tips.csv')

# --- value_counts ---
tips['day'].value_counts()
# Sat     87  Fri    19  Sun     76  Thur    62

tips['day'].value_counts(normalize=True).round(3)
# proportions: Sat 0.357, Sun 0.312, Thur 0.255, Fri 0.078

tips['day'].value_counts(dropna=False)  # includes NaN count if any

# Count unique values
tips['day'].nunique()   # 4

# Histogram of numeric with bins
pd.cut(tips['total_bill'], bins=5).value_counts().sort_index()

# --- pd.crosstab ---
# Frequency cross-table: how many smokers vs non-smokers per day
ct = pd.crosstab(tips['day'], tips['smoker'])
# smoker   No  Yes
# day
# Fri       4   15
# Sat      45   42
# Sun      57   19
# Thur     45   17

# Proportions within rows (what % of each day are smokers)
pd.crosstab(tips['day'], tips['smoker'], normalize='index').round(3)

# With aggregation (mean tip by day and smoker)
pd.crosstab(tips['day'], tips['smoker'],
            values=tips['tip'], aggfunc='mean').round(2)

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