Crosstab Counts - Pandas Step by Step

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
                        
Replay: real traced execution (multi-file project)

                            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)

                        
1region ← ['N', 'S', 'N', 'N', 'S', 'S']1region = ['N', 'S', 'N', 'N', 'S', 'S']2status = ['A', 'B', 'B', 'A', 'A', 'B']⋯values this step['N', 'S', 'N', 'N', 'S', 'S']region
2status ← ['A', 'B', 'B', 'A', 'A', 'B']1region = ['N', 'S', 'N', 'N', 'S', 'S']2status = ['A', 'B', 'B', 'A', 'A', 'B']3counts = {}⋯values this step['A', 'B', 'B', 'A', 'A', 'B']status
3counts ← {}⋯2status = ['A', 'B', 'B', 'A', 'A', 'B']3counts = {}4for i in range(len(region)):⋯values this step{}counts
4i ← 0⋯3counts = {}4for i in range(len(region)):5    r = region[i]⋯values this step0i
5r ← 'N'⋯4for i in range(len(region)):5    r = region[i]6    s = status[i]⋯values this step'N'r
6s ← 'A'⋯5r = region[i]6s = status[i]7if r not in counts:⋯values this step'A's
7if r not in counts:⋯6s = status[i]7if r not in counts:8    counts[r] = {}⋯
8counts ← {'N': {}}⋯7if r not in counts:8    counts[r] = {}9if s not in counts[r]:⋯values this step{} → {'N': {}}counts
9if s not in counts[r]:⋯8    counts[r] = {}9if s not in counts[r]:10    counts[r][s] = 0⋯
10counts ← {'N': {'A': 0}}⋯9if s not in counts[r]:10    counts[r][s] = 011counts[r][s] += 1⋯values this step{'N': {}} → {'N': {'A': 0}}counts
11counts ← {'N': {'A': 1}}⋯10        counts[r][s] = 011    counts[r][s] += 112result = {k: dict(sorted(counts[k].items())) for k in sorted(counts)}⋯values this step{'N': {'A': 0}} → {'N': {'A': 1}}counts
12i ← 1⋯3counts = {}4for i in range(len(region)):5    r = region[i]⋯values this step0 → 1i
13r ← 'S'⋯4for i in range(len(region)):5    r = region[i]6    s = status[i]⋯values this step'N' → 'S'r
14s ← 'B'⋯5r = region[i]6s = status[i]7if r not in counts:⋯values this step'A' → 'B's
15if r not in counts:⋯6s = status[i]7if r not in counts:8    counts[r] = {}⋯
16counts ← {'N': {'A': 1}, 'S': {}}⋯7if r not in counts:8    counts[r] = {}9if s not in counts[r]:⋯values this step{'N': {'A': 1}} → {'N': {'A': 1}, 'S': {}}counts
17if s not in counts[r]:⋯8    counts[r] = {}9if s not in counts[r]:10    counts[r][s] = 0⋯
18counts ← {'N': {'A': 1}, 'S': {'B': 0}}⋯9if s not in counts[r]:10    counts[r][s] = 011counts[r][s] += 1⋯values this step{'N': {'A': 1}, 'S': {}} → {'N': {'A': 1}, 'S': {'B': 0}}counts
19counts ← {'N': {'A': 1}, 'S': {'B': 1}}⋯10        counts[r][s] = 011    counts[r][s] += 112result = {k: dict(sorted(counts[k].items())) for k in sorted(counts)}⋯values this step{'N': {'A': 1}, 'S': {'B': 0}} → {'N': {'A': 1}, 'S': {'B': 1}}counts
20i ← 2⋯3counts = {}4for i in range(len(region)):5    r = region[i]⋯values this step1 → 2i
21r ← 'N'⋯4for i in range(len(region)):5    r = region[i]6    s = status[i]⋯values this step'S' → 'N'r
22s = status[i]⋯5r = region[i]6s = status[i]7if r not in counts:⋯
23if r not in counts:⋯6s = status[i]7if r not in counts:8    counts[r] = {}⋯
24if s not in counts[r]:⋯8    counts[r] = {}9if s not in counts[r]:10    counts[r][s] = 0⋯
25counts ← {'N': {'A': 1, 'B': 0}, 'S': {'B': 1}}⋯9if s not in counts[r]:10    counts[r][s] = 011counts[r][s] += 1⋯values this step{'N': {'A': 1}, 'S': {'B': 1}} → {'N': {'A': 1, 'B': 0}, 'S': {'B': 1}}counts
26counts ← {'N': {'A': 1, 'B': 1}, 'S': {'B': 1}}⋯10        counts[r][s] = 011    counts[r][s] += 112result = {k: dict(sorted(counts[k].items())) for k in sorted(counts)}⋯values this step{'N': {'A': 1, 'B': 0}, 'S': {'B': 1}} → {'N': {'A': 1, 'B': 1}, 'S': {'B': 1}}counts
27i ← 3⋯3counts = {}4for i in range(len(region)):5    r = region[i]⋯values this step2 → 3i
28r = region[i]⋯4for i in range(len(region)):5    r = region[i]6    s = status[i]⋯
29s ← 'A'⋯5r = region[i]6s = status[i]7if r not in counts:⋯values this step'B' → 'A's
30if r not in counts:⋯6s = status[i]7if r not in counts:8    counts[r] = {}⋯
31if s not in counts[r]:⋯8    counts[r] = {}9if s not in counts[r]:10    counts[r][s] = 0⋯
32counts ← {'N': {'A': 2, 'B': 1}, 'S': {'B': 1}}⋯10        counts[r][s] = 011    counts[r][s] += 112result = {k: dict(sorted(counts[k].items())) for k in sorted(counts)}⋯values this step{'N': {'A': 1, 'B': 1}, 'S': {'B': 1}} → {'N': {'A': 2, 'B': 1}, 'S': {'B': 1}}counts
33i ← 4⋯3counts = {}4for i in range(len(region)):5    r = region[i]⋯values this step3 → 4i
34r ← 'S'⋯4for i in range(len(region)):5    r = region[i]6    s = status[i]⋯values this step'N' → 'S'r
35s = status[i]⋯5r = region[i]6s = status[i]7if r not in counts:⋯
36if r not in counts:⋯6s = status[i]7if r not in counts:8    counts[r] = {}⋯
37if s not in counts[r]:⋯8    counts[r] = {}9if s not in counts[r]:10    counts[r][s] = 0⋯
38counts ← {'N': {'A': 2, 'B': 1}, 'S': {'B': 1, 'A': 0}}⋯9if s not in counts[r]:10    counts[r][s] = 011counts[r][s] += 1⋯values this step{'N': {'A': 2, 'B': 1}, 'S': {'B': 1}} → {'N': {'A': 2, 'B': 1}, 'S': {'B': 1, 'A': 0}}counts
39counts ← {'N': {'A': 2, 'B': 1}, 'S': {'B': 1, 'A': 1}}⋯10        counts[r][s] = 011    counts[r][s] += 112result = {k: dict(sorted(counts[k].items())) for k in sorted(counts)}⋯values this step{'N': {'A': 2, 'B': 1}, 'S': {'B': 1, 'A': 0}} → {'N': {'A': 2, 'B': 1}, 'S': {'B': 1, 'A': 1}}counts
40i ← 5⋯3counts = {}4for i in range(len(region)):5    r = region[i]⋯values this step4 → 5i
41r = region[i]⋯4for i in range(len(region)):5    r = region[i]6    s = status[i]⋯
42s ← 'B'⋯5r = region[i]6s = status[i]7if r not in counts:⋯values this step'A' → 'B's
43if r not in counts:⋯6s = status[i]7if r not in counts:8    counts[r] = {}⋯
44if s not in counts[r]:⋯8    counts[r] = {}9if s not in counts[r]:10    counts[r][s] = 0⋯
45counts ← {'N': {'A': 2, 'B': 1}, 'S': {'B': 2, 'A': 1}}⋯10        counts[r][s] = 011    counts[r][s] += 112result = {k: dict(sorted(counts[k].items())) for k in sorted(counts)}⋯values this step{'N': {'A': 2, 'B': 1}, 'S': {'B': 1, 'A': 1}} → {'N': {'A': 2, 'B': 1}, 'S': {'B': 2, 'A': 1}}counts
46for i in range(len(region)):⋯3counts = {}4for i in range(len(region)):5    r = region[i]⋯
47result ← {'N': {'A': 2, 'B': 1}, 'S': {'A': 1, 'B': 2}}⋯11    counts[r][s] += 112result = {k: dict(sorted(counts[k].items())) for k in sorted(counts)}13print('RESULT:', result)values this step{'N': {'A': 2, 'B': 1}, 'S': {'A': 1, 'B': 2}}result
48stdout ← RESULT: {'N': {'A': 2, 'B': 1}, 'S': {'A': 1, 'B': 2}}⋯12result = {k: dict(sorted(counts[k].items())) for k in sorted(counts)}13print('RESULT:', result)values this stepRESULT: {'N': {'A': 2, 'B': 1}, 'S': {'A': 1, 'B': 2}}stdout

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.