Pandas for Everyone: Python Data Analysis by Nazmul Rajib

Author:Nazmul Rajib
Language: eng
Format: epub
Publisher: Addison-Wesley
Published: 2018-04-14T04:00:00+00:00

9.3.2.1 Column-wise Operations

Use the axis=0 parameter (the default value) in apply when working with functions in a column-wise manner.

Click here to view code image

df.apply(print_me, axis=0)

0 10

1 20

2 30

Name: a, dtype: int64

0 20

1 30

2 40

Name: b, dtype: int64

a None

b None

dtype: object

Compare this output to the following:

Click here to view code image

print(df['a'])

0 10

1 20

2 30

Name: a, dtype: int64

print(df['b'])

0 20

1 30

2 40

Name: b, dtype: int64

You can see that the outputs are exactly the same. When you apply a function across a DataFrame (in this case, column-wise with axis=0), the entire axis (e.g., column) is passed into the first argument of the function. To illustrate this further, let’s write a function that calculates the mean (average) of three numbers (each column in our data set contains values).

def avg_3(x, y, z):

return (x + y + z) / 3

If we try to apply this function across our columns, we get an error.

Click here to view code image

# will cause an error

print(df.apply(avg_3))

Traceback (most recent call last):

File "<ipython-input-1-5ebf32ddae32>", line 2, in <module>

print(df.apply(avg_3))

TypeError: ("avg_3() missing 2 required positional arguments: 'y' and

'z'", 'occurred at index a')

From the (last line of the) error message, you can see that the function takes three arguments, but we failed to pass in the y and z (i.e., the second and third) arguments. Again, when we use apply, the entire column is passed into the first argument. For this function to work with the apply method, we will have to rewrite parts of it.

Click here to view code image

def avg_3_apply(col):

x = col[0]

y = col[1]

z = col[2]

return (x + y + z) / 3

print(df.apply(avg_3_apply))

a 20.0

b 30.0

dtype: float64

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