To count DataFrame rows that meet a condition, build a Boolean mask and sum it: count = int(mask.sum()). For example, mask = df["score"].ge(80) counts rows with scores of at least 80. If you also need the matching records, filter with df.loc[mask] and count them with len(...) or .shape[0].
Count rows that meet one condition
A comparison such as df["Age"] > 35 returns a Boolean Series with one value per DataFrame row. Use that Series as a mask, then sum its true values:
mask = df["score"].ge(80)
count = int(mask.sum())The int() conversion makes the result a regular Python integer. You can also filter the DataFrame and count the resulting rows:
matching_rows = df.loc[mask]
count = len(matching_rows)
# Equivalent:
count = matching_rows.shape[0]Summing the mask is compact when you only need the count. Filtering first is useful when you also want to inspect, export, or process the matching rows.
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Combine multiple conditions
Use & for AND, | for OR, and ~ to negate a condition. Put parentheses around each comparison so Python evaluates the conditions as intended.
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())Match any value in a list
Use .isin() when a row qualifies if a column contains one of several values:
mask = df["status"].isin(["active", "pending"])
count = int(mask.sum())Count rows by group or value
For a count per group, filter to qualifying records and use GroupBy.size(). It counts rows in each group, including rows where other columns have missing values.
mask = df["score"].ge(80)
counts = df.loc[mask].groupby("department").size()Choose the operation according to what you mean by “count”:
| What you want to count | Use | How missing values are handled |
|---|---|---|
| Rows meeting a condition | int(mask.sum()), len(df.loc[mask]), or df.loc[mask].shape[0] |
The mask has one Boolean value per row. |
| Rows in each group | df.groupby("category").size() |
Counts group rows, even if other columns contain missing values. |
| Non-missing values per group and column | df.groupby("category").count() |
Counts non-missing values separately in each column. |
| Frequency of each value in one column | df["category"].value_counts() |
By default, missing values are omitted; use dropna to control this. |
| Frequency of unique row combinations | df.value_counts(subset=["a", "b"], dropna=False) |
By default, combinations containing missing values are omitted; dropna=False includes them. |
Understand missing values and count behavior
Comparisons involving missing values do not make those rows true matches. If the condition is that a value is missing, test it explicitly:
mask = df["col"].isna()
count = int(mask.sum())DataFrame.count() is not a general row counter: it counts non-missing cells, by column by default, and excludes None, NaN, NaT, and pandas.NA. To get the DataFrame’s dimensions regardless of missing entries, use df.shape; its first element is the number of rows.
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row_count = df.shape[0]For a Series sum, pandas returns zero by default for an empty or all-missing Series. If the intended meaning is “no valid count available” rather than zero, min_count=1 makes the sum return a missing value when there are no valid values:
total = series.sum(min_count=1)These API descriptions and examples are documented in the pandas DataFrame.count documentation, indexing and selection guide, Boolean indexing guide, pandas comparison with SQL guide, GroupBy.count documentation, Series.value_counts documentation, DataFrame.value_counts documentation, and Series.sum documentation. The cited API pages were surfaced as pandas 3.0.6 documentation; check the documentation for your installed pandas version if version-specific behavior matters.
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