Equivalent of R/ifelse in Python/Pandas? Compare string columns?

Question

My goal is comparing between two columns and add the result column. R uses ifelse but I need to know pandas’s way.

R

> head(mau.payment)
  log_month user_id install_month payment
1   2013-06       1       2013-04       0
2   2013-06       2       2013-04       0
3   2013-06       3       2013-04   14994

> mau.payment$user.type <-ifelse(mau.payment$install_month == mau.payment$log_month, "install", "existing")
> head(mau.payment)
  log_month user_id install_month payment user.type
1   2013-06       1       2013-04       0  existing
2   2013-06       2       2013-04       0  existing
3   2013-06       3       2013-04   14994  existing
4   2013-06       4       2013-04       0  existing
5   2013-06       6       2013-04       0  existing
6   2013-06       7       2013-04       0  existing

Pandas

>>> maupayment
user_id  log_month  install_month
1        2013-06    2013-04              0
         2013-07    2013-04              0
2        2013-06    2013-04              0
3        2013-06    2013-04          14994

I tried some cases but did not work. It seems that string comparison does not work.

>>>np.where(maupayment['log_month'] == maupayment['install_month'], 'install', 'existing')

TypeError: 'str' object cannot be interpreted as an integer

Could you help me please?

Pandas and numpy version.

>>> pd.version.version
'0.16.2'
>>> np.version.full_version
'1.9.2'

After update the versions, it worked!

>>> np.where(maupayment['log_month'] == maupayment['install_month'], 'install', 'existing')
array(['existing', 'install', 'existing', ..., 'install', 'install',
       'install'], 
      dtype='<U8')

Asked By: zono

||

Source

Answer 1

You have to upgrade pandas to last version, because in version 0.17.1 it works very well.

Sample (first value in column install_month is changed for matching):

print maupayment
  log_month  user_id install_month  payment
1   2013-06        1       2013-06        0
2   2013-06        2       2013-04        0
3   2013-06        3       2013-04    14994

print np.where(maupayment['log_month'] == maupayment['install_month'], 'install', 'existing')
['install' 'existing' 'existing']

Answered By: jezrael

Answer 2

One option is to use an anonymous function in combination with Pandas’s apply function:

Setup some branching logic in a function:

def if_this_else_that(x, list_of_checks, yes_label, no_label):
    if x in list_of_checks:
        res = yes_label
    else: 
        res = no_label
    return(res)

This takes the x from lambda (see below), a list of things to look for, the yes label, and the no label.

For example, say we are looking at the IMDB dataset (imdb_df):

…and I want to add a new column called “new_rating” that shows whether the movie is mature or not.

I can use Pandas apply function along with my branching logic above:

imdb_df['new_rating'] = imdb_df['Rated'].apply(lambda x: if_this_else_that(x, ['PG', 'PG-13'], 'not mature', 'mature'))

There are also times we need to combine this with another check. For example, some entries in the IMDB dataset are NaN. I can check for both NaN and the maturity rating as follows:

imdb_df['new_rating'] = imdb_df['Rated'].apply(lambda x: 'not provided' if x in ['nan'] else if_this_else_that(x, ['PG', 'PG-13'], 'not mature', 'mature'))

In this case my NaN was first converted to a string, but you can obviously do this with genuine NaNs as well.

Answered By: Cybernetic

Equivalent of R/ifelse in Python/Pandas? Compare string columns?

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