Pandasdrillvalue_counts
df["col"].value_counts() returns how many times each distinct value appears, sorted from most to least frequent. normalize=True gives shares instead of counts and dropna=False includes a row for missing values.
Syntax
Series.value_counts(normalize=False, sort=True, ascending=False, bins=None, dropna=True)| Part | What it is |
|---|---|
normalize | True returns proportions that sum to 1 |
sort / ascending | Sort by count (default, largest first) |
bins | For numeric data, count into this many equal-width intervals |
dropna | False adds a count for NaN |
Examples
import pandas as pd
import numpy as np
df = pd.DataFrame({"plan": ["free", "pro", "free", "team", "free", np.nan]})
df["plan"].value_counts()Result: plan free 3 pro 1 team 1 Name: count, dtype: int64
df["plan"].value_counts(normalize=True).round(2)Result: plan free 0.6 pro 0.2 team 0.2 Name: proportion, dtype: float64
Shares are computed over the five non-missing values.
df["plan"].value_counts(dropna=False)Result: plan free 3 pro 1 team 1 NaN 1 Name: count, dtype: int64
Common mistakes
- The result is a Series, not a DataFrame: call .reset_index() to get columns named plan and count.
- Missing values are excluded by default, so proportions may not reflect the full table; pass dropna=False.
- Counts split across variants like "Free" and "free ": normalize text with str.strip().str.lower() first.
Practice value_counts
These drills check your answer on live data, free, no account needed.
- Shares with value_countsReturn the share of orders for each product as a fraction between 0 and 1, largest share first.
- Count rows per groupReturn the number of orders each rep has, as a Series indexed by rep in alphabetical order.
Questions
How do I sort value_counts by value instead of count?
Append .sort_index(): df["col"].value_counts().sort_index().
How do I get value counts as a DataFrame with a percentage column?
counts = s.value_counts(); pd.DataFrame({"count": counts, "pct": (counts / counts.sum() * 100).round(1)}).
value_counts or groupby size?
They give the same numbers. value_counts is shorter for one column and sorts by frequency; groupby().size() is better when you group by several columns or want the keys sorted.
Related
- groupbyHow groupby works in pandas: group by one or more columns, apply sum, mean, count or size, get a flat table with as_index=False, and iterate over groups.
- unique and nuniqueHow to get distinct values in pandas with unique, count them with nunique, count distinct per group with groupby, handle NaN, and see counts for all columns.
- pd.crosstabHow to build a contingency table in pandas with pd.crosstab: counts of one column against another, totals with margins, row or column percentages, sums.
- cut and qcutHow to bin numeric data in pandas: pd.cut for fixed ranges with labels like age bands, pd.qcut for equal-sized quantile groups, right-closed edges and labels.