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Count rows that meet one condition
A comparison on a DataFrame column produces a Boolean Series with one value for each row. For example, to count rows where score is at least 80:
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mask = df["score"].ge(80)
count = int(mask.sum())The mask contains True for matching rows and False otherwise. Summing it counts the true values; wrapping the result in int() gives you a regular Python integer.
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matching_rows = df.loc[mask]
count = len(matching_rows)
# Equivalent:
count = matching_rows.shape[0]Boolean indexing keeps rows where the condition is true. See pandas’ selection and indexing guide.
Combine conditions
Use & for AND, | for OR, and ~ for NOT. Put parentheses around each comparison so Python evaluates the comparisons before combining their masks.
Require both conditions
mask = (df["age"] >= 18) & (df["country"] == "US")
count = int(mask.sum())Match either condition
mask = (df["status"] == "active") | (df["priority"] == "high")
count = len(df.loc[mask])Exclude a condition
mask = ~(df["status"] == "cancelled")
count = int(mask.sum())For a column that should match one of several values, use .isin():
mask = df["country"].isin(["US", "CA"])
count = int(mask.sum())The pandas indexing guide covers selecting rows by conditions and values.
Count matching rows within each group
To count qualifying records by category, filter first and then use groupby(...).size():
mask = df["score"].ge(80)
counts = df.loc[mask].groupby("department").size()size() counts rows in each group, including rows whose other columns contain missing values. This makes it the appropriate choice when the question is how many records are in each group.
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Choose the right counting method
| Question | Use | What it counts |
|---|---|---|
| How many rows match a condition? | int(mask.sum()), len(df.loc[mask]), or df.loc[mask].shape[0] |
Rows selected by the Boolean mask. |
| How many non-missing values are in each column? | df.count() |
Non-NA cells in each column, not total rows. It excludes None, NaN, NaT, and pandas.NA. See the DataFrame.count documentation. |
| How many non-missing values are in each row? | df.count(axis="columns") |
Non-NA cells per row. |
| How many rows are in each group? | df.groupby("category").size() |
Rows per group, regardless of missing values in other columns. |
| How many non-missing values per group and column? | df.groupby("category").count() |
Non-NA values in each column, separately for each group. |
| How often does each value in one column appear? | df["category"].value_counts() |
Frequency of each value; set dropna to control how missing values are handled. |
| How often does each distinct combination of columns appear? | df.value_counts(subset=["a", "b"], dropna=False) |
Frequency of unique row combinations. By default, combinations containing NA are omitted. |
For SQL-style group record counts, pandas’ comparison with SQL uses groupby("sex").size() and explains why count() gives a different result.
Handle missing values and empty results
A comparison involving a missing value does not make that row a true match. If missingness is the condition you want to count, test for it explicitly:
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mask = df["col"].isna()
count = int(mask.sum())Use df.shape[0] when you need the DataFrame’s total row count regardless of missing entries. Unlike DataFrame.count(), it does not count non-missing cells.
For frequency counts, check the missing-value defaults: DataFrame.value_counts() omits combinations containing NA unless you pass dropna=False, while a column’s Series.value_counts() also has a dropna option. See the DataFrame.value_counts documentation and Series.value_counts documentation.
If you are summing numeric data rather than a Boolean mask, an empty or all-NA Series sums to zero by default. Set min_count=1 when you want an empty or all-NA input to produce NA instead of zero; the Series.sum documentation describes this behavior.
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The linked API pages are from the pandas 3.0.6 documentation surfaced for this topic. If your project depends on version-specific behavior, consult the documentation for its installed pandas version.
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