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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Use df.apply(function, axis=1) to call a function once for each row in a pandas DataFrame. By default, the function receives that row as a Series, so you can read values by column name. For simple calculations across columns, a vectorized expression is usually clearer and faster.
Apply a function to each row
Here is a complete example that multiplies each row’s price by its quantity and stores the result in a new column:
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import pandas as pd
df = pd.DataFrame({"price": [10, 20], "quantity": [2, 3]})
def line_total(row):
return row["price"] * row["quantity"]
df["total"] = df.apply(line_total, axis=1)The axis=1 argument selects rows. The equivalent spelling is axis="columns". The default, axis=0, applies the function to each column instead. See the pandas.DataFrame.apply API reference.
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What the function receives and returns
By default, each input is a Series
With the default raw=False, pandas passes the function a Series for each row. Its index contains the DataFrame’s column labels, so access fields by name—for example, row["price"]. This is generally the clearest choice when your logic depends on named columns.
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With raw=True, the function instead receives an ndarray. That can suit some NumPy operations, but the input no longer carries column labels; access is positional. Consult the API reference for the behavior and options supported by your installed pandas version.
One scalar per row creates a Series
If the function returns one value for every row, apply returns a Series indexed by the DataFrame’s original row index. That is why the example’s result can be assigned directly to df["total"].
Multiple values per row create multiple columns
To return several named results, return a Series from the function. The returned Series index supplies the output column names:
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def summarize(row):
return pd.Series({
"total": row["price"] * row["quantity"],
"is_bulk": row["quantity"] >= 3,
})
result = df.apply(summarize, axis=1)Alternatively, a list-like return can be split into columns with result_type="expand". result_type="broadcast" attempts to broadcast returned values while retaining the original columns and shape. These result_type options apply only to row-wise application; see the API documentation for details.
Prefer vectorized operations for straightforward calculations
When an operation can be expressed over whole columns, use pandas or NumPy operations rather than calling a Python function for every row. The example’s total can be calculated directly:
df["total"] = df["price"] * df["quantity"]Vectorized expressions avoid Python-level per-row function calls and are often more efficient. In its own getting-started guide, pandas compares a user-defined-function example taking 5.6435 seconds with a vectorized version taking 0.0043 seconds. Those are timings for that documentation example, not a general benchmark; results vary with the data, hardware, pandas version and implementation. See Getting started with pandas.
Choose the right approach
- Look for an operation over whole columns. Use a pandas or NumPy expression when it naturally describes the calculation.
- Use row-wise apply when the logic genuinely needs a row. It is appropriate when several fields must be considered together and no suitable vectorized operation expresses the rule.
- Keep label access when names matter. Use the default
raw=Falsefor a Series indexed by column labels; considerraw=Trueonly when an ndarray is appropriate. - Measure your actual workload. If performance matters, time representative data and code rather than assuming one method will always be faster.
Do not mutate the row passed to the function
Avoid changing the Series object that pandas passes into your function. The pandas user guide warns that mutating data passed to a user-defined function can cause unexpected behavior or errors and is unsupported. Return the value or values you want instead. See User-Defined Functions (UDFs).
Check your pandas version before using JIT engines
The stable DataFrame.apply reference for pandas 3.0.5 documents engine options, including Numba and Bodo decorators, with limitations on type stability and API support. JIT compilation is most suitable when the function itself does substantial work; a fast function may not benefit. Engine interfaces have changed across versions—the pandas 2.2 reference shows an earlier interface—so check the documentation matching your installed version before copying engine-specific syntax. The usual apply(function, axis=1) pattern does not require an engine option.
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