Reshaping

Crosstab Counts

Count co-occurrences of two categorical lists into a nested dict-of-dicts (a contingency table). With pandas, pd.crosstab builds this frequency table in one call, with sorted row and column labels.

By hand

Loop over both parallel lists at once by index. For each (region, status) pair, create the inner dict when a region is first seen, initialize the count to zero when a status is first seen for that region, then increment.

naive.py 
region = ['N', 'S', 'N', 'N', 'S', 'S']
status = ['A', 'B', 'B', 'A', 'A', 'B']
counts = {}
for i in range(len(region)):
    r = region[i]
    s = status[i]
    if r not in counts:
        counts[r] = {}
    if s not in counts[r]:
        counts[r][s] = 0
    counts[r][s] += 1
result = {k: dict(sorted(counts[k].items())) for k in sorted(counts)}
print('RESULT:', result)

With pandas

pd.crosstab(df['region'], df['status']) returns a DataFrame where each cell is the count of rows with that (region, status) combination. Row and column labels are sorted automatically.

library.py 
import pandas as pd
from dalib.display import set_display
set_display()

region = ['N', 'S', 'N', 'N', 'S', 'S']
status = ['A', 'B', 'B', 'A', 'A', 'B']
df = pd.DataFrame({'region': region, 'status': status})
ct = pd.crosstab(df['region'], df['status'])
result = {r: {c: int(ct.loc[r, c]) for c in ct.columns} for r in ct.index}
print('index:', ct.index.tolist())
print('columns:', ct.columns.tolist())
print('A:', [int(ct.loc[r, 'A']) for r in ct.index])
print('B:', [int(ct.loc[r, 'B']) for r in ct.index])
print('RESULT:', result)

index: ['N', 'S']
columns: ['A', 'B']
A: [2, 1]
B: [1, 2]
RESULT: {'N': {'A': 2, 'B': 1}, 'S': {'A': 1, 'B': 2}}

Implementation notes

  • pd.crosstab is a frequency-count pivot — it counts rows, not aggregates values. For value aggregation, use pivot_table with aggfunc='sum' etc.
  • Pass normalize='index' to get row-fraction proportions (each row sums to 1), normalize='columns' for column fractions, or normalize='all' (same as normalize=True) to normalize over all cells.
  • Cross-reference: groupby-count (ch04) for single-column frequency counts; pivot-table-simple (this chapter) for value aggregation into a grid.