Coding Ref

To apply a function to multiple columns in Pandas, you can use the `apply`

method. This method allows you to apply a function to a DataFrame or a specific column or set of columns.

For example, consider the following DataFrame:

main.py

```
import pandas as pd
df = pd.DataFrame({
'A': [1, 2, 3, 4, 5],
'B': [10, 20, 30, 40, 50],
'C': [100, 200, 300, 400, 500]
})
```

This DataFrame has three columns `A`

, `B`

, and `C`

, with five rows of data.

To apply a function to all the columns in this DataFrame, you could do the following:

main.py

```
import pandas as pd
df = pd.DataFrame({
'A': [1, 2, 3, 4, 5],
'B': [10, 20, 30, 40, 50],
'C': [100, 200, 300, 400, 500]
})
# Define the function to apply
def square(x):
return x ** 2
# Apply the function to the DataFrame
df_squared = df.apply(square)
# Print the resulting DataFrame
print(df_squared)
```

In the code above, the `apply`

method is applied to the DataFrame, and the `square`

function is passed as an argument.

This tells the `apply`

method to apply the `square`

function to each column in the DataFrame. The result is a new `DataFrame`

object containing the squared values for each column.

You can also use the `apply`

method to apply a function to a specific column or set of columns in a DataFrame. For example, if you wanted to apply the `square`

function to only column `B`

, you could do the following:

main.py

```
import pandas as pd
df = pd.DataFrame({
'A': [1, 2, 3, 4, 5],
'B': [10, 20, 30, 40, 50],
'C': [100, 200, 300, 400, 500]
})
# Define the function to apply
def square(x):
return x ** 2
# Apply the function to column B
df_squared = df['B'].apply(square)
# Print the resulting Series
print(df_squared)
```

In the code above, the `apply`

method is applied to the `B`

column of the DataFrame, and the `square`

function is passed as an argument.

This tells the `apply`

method to apply the `square`

function to the `B`

column.

The result is a new `Series`

object containing the squared values for the `B`

column.

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