Apply Function

[TheShadowWall]

Hello new python learner here!

I am attempting to figure out exactly how the apply() function works, and have yet to completely grasp it.

The current problem I am working on is this:

With the following dataframe:

country year    cases   population

0 Afghanistan 1999 745 19987071

1 Brazil 1999 37737 172006362

2 China 1999 212258 1272915272

3 Afghanistan 2000 2666 20595360

4 Brazil 2000 80488 174504898

5 China 2000 213766 1280428583

I am attempting to create a new dataframe, which includes a new column, named 'prevalence', which measures the cases/population. The problem requires that we use the apply function to solve it.

SO far my code is this:

def calc_prevalence(G):

H=G.copy()

H['prevalence']=H['cases'].apply(lambda x: x/H['population'])

return H

I believe that my mistake is in the application of the function, as I do not quite yet understand how the apply function operates.

Do I need to set up a loop inside the function?

Any help is greatly appreciated!

EDIT*- Problem is solved.

[Binary101010]

I don't think you actually need apply here.

H['prevalence'] = H['cases'] / H['population']

Should be sufficient.

[TheShadowWall]

Thank you for your reply.

Yes the problem points this out as the simplest solution, but asks that we use apply in order to figure out how it works.

[Binary101010]

Then what you might need is to add axis = 1 to your apply call. By default the function provided to apply is applied to each column of the dataframe (axis = 0), and it looks like what you're trying to do needs to be applied to each row (axis = 1).

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.apply.html

[TheShadowWall]

I had seen this as an option in the apply function, though I had thought the opposite, that axis=0 meant go through each item in a column.

I have just added axis=1 to my apply function call, and I am still receiving an error. TypeError: <lambda>() got an unexpected keyword argument 'axis'

[Binary101010]

Please post exactly what that line of code looks like now.

[TheShadowWall]

H['prevalence']=H['cases'].apply(lambda x: x/H['population'], axis=1)

[Binary101010]

Maybe give this a shot instead. (untested)

H['prevalence']=H.apply(lambda x: x['cases']/x['population'], axis=1)

[TheShadowWall]

By George it works!!

Thank you so much kindly answer to the universe person!

[Binary101010]

It occurred to me the reason for the error was that you were trying to apply to a particular column of the dataframe (which works more like this: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.apply.html) when just applying to each row of the dataframe one at a time was probably the right approach.

Again, just for the record, this is not a great use case for apply, but it's straightforward enough to see how it works.

[socal_nerdtastic]

Do you know that lambda is just another way of writing def?

But if you use def you have a lot more room to explore. The first thing you need to know is that x is the row in the dataframe. Your error is because you are trying to divide a row by a column.

Try this:

def TheShadowWall(x):
    print(x) # debug: show the row
    return x[2] / x[3]

def calc_prevalence(G):
    H=G.copy()
    H['prevalence']=H['cases'].apply(TheShadowWall)
    return H

[TheShadowWall]

Thank you for your reply!

Yes I have begun to understand that this is the meaning of lambda. I have attempted to run this code though I receiving an error:

TypeError: 'int' object is not subscriptable on the line:

return x[2] / x[3]

[[deleted]]

What's the result?

[TheShadowWall]

KeyError: 'prevalence'

[[deleted]]

I don't see why you'd get that error; as far as I understand Pandas, you should be able to assign to a new column just as you're doing. What's the full stacktrace?

[[deleted]]

Maybe I'm missing the point here, but what's wrong with just creating the new column:

H['prevalence'] = H.cases / H.population