Applying Functions

df.get(column_name).apply(function_name)

Applies a function of one parameter to every entry in the column.

Input:

function_name: a python function

The function to apply to every entry in the column. This function should take a single parameter and return a value.

Returns:

A Series of the same size containing the results of the function application.

Return Type:

Series:

The returned Series will have the same index as the input Series and will contain the transformed values based on the applied function.

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The diagram below provides an example of applying a function to a column of a dataframe. For additional helpful visual guides, please visit the Diagrams site.

(Source)

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pets

Index	Species	Color	Weight	Age
0	dog	black	40	5
1	cat	golden	15	8
2	cat	black	20	9
3	dog	white	80	2
4	dog	black	25	0.5
5	hamster	black	1	3
6	hamster	golden	0.25	0.2

pets.get('Species').apply(is_dog)

• 0"True"
• 1"False"
• 2"False"
• 3"True"
• 4"True"
• 5"False"
• 6"False"

Name: Species, dtype: bool

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pets.get('Weight').apply(np.sqrt)

• 0"6.324555"
• 1"3.872983"
• 2"4.472136"
• 3"8.944272"
• 4"5.000000"
• 5"1.000000"
• 6"0.500000"

Name: Weights, dtype: float64

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(Refer back to Writing Functions for categorize_animal.)

pets.get('ID').apply(categorize_animal)

• 0"Adult Normal"
• 1"Kitten Underweight"
• 2"Adult Overweight"
• 3"Adult Overweight"
• 4"Puppy Normal"
• 5"Senior Overweight"
• 6"Young Normal"
• 7"Kitten Normal"

Name: description, dtype: object

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