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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesUse pandas’ .pipe() to send a whole DataFrame or Series through a function while keeping transformations in a readable, left-to-right chain. For example, df.assign(...).pipe(clean_data) first creates the assigned column, then passes that resulting DataFrame to clean_data. It improves how a sequence reads; it does not make the operations faster.
How to use .pipe() in pandas
The basic form is object.pipe(func, *args, **kwargs). pandas passes the current object to func along with any additional positional or keyword arguments, and pipe() returns whatever the function returns. The callable therefore determines the output: it might return a transformed DataFrame, a Series, or another result.
For example, define a function that accepts the DataFrame first, then call it after any ordinary pandas methods that should run before it:
def add_country_name(df, country_name):
df["city_and_country"] = df["city_name"] + country_name
return df
result = (
df.assign(city_name=lambda x: x["city_and_code"].str.split(",").str[0])
.pipe(add_country_name, country_name="US")
)Read the chain from top to bottom: assign() creates city_name, then pipe() sends that updated DataFrame to add_country_name() with country_name="US". The function must return the value you want the chain to continue with.
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When the function’s data parameter is not first
Some functions expect the DataFrame under a named parameter that appears later in their signature. Pass a pair containing the callable and the name of that parameter: .pipe((func, "data"), ...). pandas then supplies the current object as the data keyword argument, leaving other arguments for the function.
result = df.query("h > 0").pipe((some_function, "data"), "formula")In this example, some_function must accept a parameter named data. The current DataFrame is routed to that parameter, and "formula" is passed as another argument. pandas documents this pattern with statsmodels.ols; see the DataFrame.pipe API reference and the method-chaining guide.
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What belongs in a pipe—and what does not
Use pipe() when a function is designed to receive the whole object at once, such as a DataFrame or Series, and you want that operation to remain in a method chain. It is not a substitute for methods that operate on a different input shape.
| Method | What the callable or operation receives | Use it when |
|---|---|---|
pipe() |
The whole Series, DataFrame, or supported group-like object | A function transforms or otherwise uses the entire object in a chain. |
map() |
Scalar values | You want to map or transform individual values. |
apply() |
A row or column, depending on how it is called | The operation is defined over rows or columns rather than the whole DataFrame. |
agg() and other aggregation methods |
Values to summarize according to the aggregation | You want summary results rather than a whole-object transformation. |
These methods can produce different output shapes, so choose based on what the function should receive and return. pandas explains the distinction in its guide to user-defined functions and pipes.
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Combine pipes with pandas methods and GroupBy
pipe() can sit between standard pandas operations, letting a chain alternate between built-in methods and your own functions. It is also documented for GroupBy workflows: a function can receive a group-like object as a whole, rather than being applied separately to individual values. Consult the GroupBy pipe documentation for that use.
The pandas user-defined-functions guide describes readability as the main advantage: chaining can make the order of multiple function calls clearer. Keep a pipe when that sequence is easier to follow; use a direct function call when a chain would obscure what the code does. The current pandas documentation identifies its version as 3.0.6, dated September 17, 2026; consult the official documentation for version-specific details.
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