To apply a function to a list of dataframes in pandas, you can use a list comprehension or the `map()`

function.

First, create a list of dataframes that you want to apply the function to. Then, use a list comprehension or the `map()`

function to apply the desired function to each dataframe in the list by iterating over them.

For example, if you have a list of dataframes called `df_list`

and you want to apply a function called `my_function`

to each dataframe in the list, you can do so using a list comprehension like this:

```
1
``` |
```
results = [my_function(df) for df in df_list]
``` |

Alternatively, you can use the `map()`

function to achieve the same result:

```
1
``` |
```
results = list(map(my_function, df_list))
``` |

This will return a list of results after applying the function to each dataframe in the original list.

## How to apply a function that requires a condition to a dataframe in pandas?

You can apply a function that requires a condition to a dataframe in pandas using the `apply()`

method along with a lambda function to check the condition. Here's an example:

Suppose you have a dataframe `df`

and you want to apply a function `my_function()`

to a column based on a condition (e.g., when the value is greater than 10). You can do the following:

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import pandas as pd # Define the dataframe df = pd.DataFrame({ 'A': [5, 15, 20, 8, 12], 'B': [10, 5, 25, 7, 9] }) # Define the function def my_function(x): return x * 2 # Apply the function based on a condition df['A'] = df['A'].apply(lambda x: my_function(x) if x > 10 else x) print(df) |

In this example, the lambda function checks if the value in column 'A' is greater than 10. If the condition is met, the function `my_function()`

is applied to that value; otherwise, the original value is returned. The modified dataframe is then printed out.

You can customize the condition inside the lambda function to match the specific requirements of your use case.

## What is the easiest way to apply a function to all rows in a dataframe in pandas?

The easiest way to apply a function to all rows in a dataframe in pandas is by using the `apply()`

method.

You can use the `apply()`

method along with `axis=1`

parameter to apply a function to all rows in a dataframe. Here is an example:

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import pandas as pd # Create a sample dataframe df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}) # Define a function that you want to apply to all rows def sum_values(row): return row['A'] + row['B'] # Use the apply() method with axis=1 to apply the function to all rows df['sum'] = df.apply(sum_values, axis=1) print(df) |

This will create a new column in the dataframe called `'sum'`

which contains the sum of the values in columns `'A'`

and `'B'`

for each row.

## What is the effect of applying functions across columns versus rows in pandas?

Applying functions across columns and rows in pandas can have different effects based on the operation being performed.

When applying functions across columns, the function is applied to each column individually. This allows for column-wise calculations or transformations, and the output will typically depend on the values within each column.

On the other hand, when applying functions across rows, the function is applied to each row individually. This allows for row-wise calculations or transformations, and the output will typically depend on the values within each row.

It is important to consider the desired outcome and the structure of the data when deciding whether to apply functions across columns or rows in pandas.