from platform import python_version

python_version()

'3.6.9'

import pandas as pd
import numpy as np

pd.__version__, np.__version__

('1.0.5', '1.19.0')

df = pd.DataFrame({
    'name': ['alice','bob','charlie','david'],
    'age': [25,26,27,22],
})[['name', 'age']]
df

	name	age
0	alice	25
1	bob	26
2	charlie	27
3	david	22

apply example

import pandas as pd

df = pd.DataFrame({
    'name': ['alice','bob','charlie','david'],
    'age': [25,26,27,22],
})[['name', 'age']]

# each element of the age column is a string
# so you can call .upper() on it
df['name_uppercase'] = df['name'].apply(lambda element: element.upper())

df

	name	age	name_uppercase
0	alice	25	ALICE
1	bob	26	BOB
2	charlie	27	CHARLIE
3	david	22	DAVID

custom function

import pandas as pd

df = pd.DataFrame({
    'name': ['alice','bob','charlie','david'],
    'age': [25,26,27,22],
})[['name', 'age']]


def first_letter(input_str):
    return input_str[:1]

# each element of the age column is a string
# so you can call .upper() on it
df['first_letter'] = df['name'].apply(first_letter)

df

	name	age	first_letter
0	alice	25	a
1	bob	26	b
2	charlie	27	c
3	david	22	d

Take multiple columns as parameters

import pandas as pd

df = pd.DataFrame({
    'name': ['alice','bob','charlie','david'],
    'age': [25,26,27,22],
})[['name', 'age']]


def concatenate(value_1, value_2):
    return str(value_1)+ "--" + str(value_2) 

# note the use of DOUBLE SQUARE BRACKETS!
df['concatenated'] = df[['name','age']].apply(lambda row: concatenate(row['name'], row['age']) , axis=1)

df

	name	age	concatenated
0	alice	25	alice--25
1	bob	26	bob--26
2	charlie	27	charlie--27
3	david	22	david--22

Apply function to row

import pandas as pd

df = pd.DataFrame({
    'value1': [1,2,3,4,5],
    'value2': [5,4,3,2,1],
    'value3': [10,20,30,40,50],
    'value4': [99,99,99,99,np.nan],
})

def sum_all(row):
    return np.sum(row)

# note that apply was called on the dataframe itself, not on columns
df['sum_all'] = df.apply(lambda row: sum_all(row), axis=1)

df

	value1	value2	value3	value4	sum_all
0	1	5	10	99.0	115.0
1	2	4	20	99.0	125.0
2	3	3	30	99.0	135.0
3	4	2	40	99.0	145.0
4	5	1	50	NaN	56.0

return multiple values

df = pd.DataFrame({
    'name': ['alice','bob','charlie','david','edward'],
    'age': [25,26,27,22,np.nan],
})[['name', 'age']]
df

	name	age
0	alice	25.0
1	bob	26.0
2	charlie	27.0
3	david	22.0
4	edward	NaN

import pandas as pd

df = pd.DataFrame({
    'name': ['alice','bob','charlie','david','edward'],
    'age': [25,26,27,22,np.nan],
})[['name', 'age']]

def times_two_times_three(value):
    value_times_2 = value*2
    value_times_3 = value*3

    return pd.Series([value_times_2,value_times_3])

# note that apply was called on age column
df[['times_2','times_3']]= df['age'].apply(times_two_times_three)
df

	name	age	times_2	times_3
0	alice	25.0	50.0	75.0
1	bob	26.0	52.0	78.0
2	charlie	27.0	54.0	81.0
3	david	22.0	44.0	66.0
4	edward	NaN	NaN	NaN

df

	name	age	times_2	times_3
0	alice	25.0	50.0	75.0
1	bob	26.0	52.0	78.0
2	charlie	27.0	54.0	81.0
3	david	22.0	44.0	66.0
4	edward	NaN	NaN	NaN