Apply and Values

Value Counts

Count occurrences of each distinct value in a column. By hand, a running dict accumulates the tally via .get. With pandas, Series.value_counts() returns a sorted frequency Series in one call.

By hand

With pandas

df['cat'].value_counts() returns a Series with category values as the index and counts as values, sorted by frequency descending. The snapshot shows the default frequency order; result re-sorts by key to match the naive.

naive.py 
cats = ['A', 'B', 'A', 'C', 'B', 'A']
counts = {}
for c in cats:
    counts[c] = counts.get(c, 0) + 1
result = {k: counts[k] for k in sorted(counts)}
print('RESULT:', result)
library.py 
import pandas as pd
from dalib.display import set_display
set_display()

cats = ['A', 'B', 'A', 'C', 'B', 'A']
df = pd.DataFrame({'cat': cats})
vc = df['cat'].value_counts()
result = {k: int(vc[k]) for k in sorted(vc.index)}
print('index:', vc.index.tolist())
print('values:', vc.tolist())
print('dtype:', vc.dtype)
print('RESULT:', result)

index: ['A', 'B', 'C']
values: [3, 2, 1]
dtype: int64
RESULT: {'A': 3, 'B': 2, 'C': 1}

Implementation notes

  • value_counts() sorts by frequency descending by default. Chain .sort_index() (or pass sort=False) to get values in key order instead.
  • Cross-reference: frequency-count (python-data-basics) for the pure-Python version; groupby-count (ch04) for the same operation via groupby.