# Create an apply function for pandas

In Pandas, the apply() method allows you to apply a function to each row or column of a DataFrame or a Series. The apply() method takes a function as an argument and returns a new DataFrame or Series that contains the results of applying the function.

In Pandas, the `apply()`

method allows you to apply a function to each row or column of a DataFrame or a Series. The `apply()`

method takes a function as an argument and returns a new DataFrame or Series that contains the results of applying the function.

Here's an example of how to use the `apply()`

method to apply a function to each row of a DataFrame:

```
import pandas as pd
# Create a sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]})
# Define a function to add a prefix to a value
def add_prefix(x, prefix):
return prefix + str(x)
# Apply the function to each value in the DataFrame
df = df.applymap(lambda x: add_prefix(x, 'value_'))
# Print the modified DataFrame
print(df)
```

In this example, the `row_sum()`

function takes a row of the DataFrame as an argument and returns the sum of the values in that row. The `apply()`

method is used to apply the `row_sum()`

function to each row of the DataFrame, using the `axis=1`

parameter to indicate that the function should be applied to each row. The `apply()`

method returns a new DataFrame with a new column called `'sum'`

that contains the result of applying the `row_sum()`

function to each row.

You can also use the `apply()`

method to apply a function to each column of a DataFrame or a Series. To do this, simply use `axis=0`

instead of `axis=1`

. Here's an example:

```
# Create a sample Series
s = pd.Series([1, 2, 3])
# Define a function to square a value
def square(x):
return x ** 2
# Apply the function to each element of the Series
s = s.apply(square)
# Print the modified Series
print(s)
```

In this example, the `square()`

function takes a value as an argument and returns the square of that value. The `apply()`

method is used to apply the `square()`

function to each element of the Series, returning a new Series that contains the result of applying the `square()`

function to each element.

The `apply()`

method is one of the most powerful tools in the Pandas library. It allows you to apply a custom function to each row or column of a DataFrame or a Series, and then return the results in a new DataFrame or Series.

The syntax for the `apply()`

method is as follows:

```
DataFrame.apply(func, axis=0, raw=False, result_type=None, args=(), **kwds)
```

The `apply()`

method takes several parameters:

`func`

: The function to apply to each row or column.`axis`

: The axis to apply the function along. Use`axis=1`

to apply the function to each row, or`axis=0`

to apply the function to each column.`raw`

: If`True`

, the function is applied to a numpy array of values. If`False`

, the function is applied to a Series or DataFrame.`result_type`

: The type of the result returned by the function. Use`'expand'`

to return a DataFrame,`'reduce'`

to return a Series, or`None`

to infer the result type automatically.`args`

: Additional arguments to pass to the function.`**kwds`

: Additional keyword arguments to pass to the function.

The function you pass to the `apply()`

method should take a Series or DataFrame as an argument and return a value. For example, here's a function that takes a Series and returns the square of each value:

```
def square(series):
return series ** 2
```

To apply this function to each row of a DataFrame, you can use the following code:

```
df.apply(square, axis=1)
```

This will return a new DataFrame with the squares of each value in each row.

You can also use lambda functions with the `apply()`

method. For example, here's a lambda function that takes a Series and returns the sum of its values:

`lambda series: series.sum()`

To apply this lambda function to each column of a DataFrame, you can use the following code:

`df.apply(lambda series: series.sum(), axis=0)`

This will return a new Series with the sum of each column.

In summary, the `apply()`

method in Pandas is a powerful tool that allows you to apply custom functions to each row or column of a DataFrame or a Series. By using this method, you can transform your data in a flexible and efficient way.