更改熊猫数据框列中的值

Question

I have a dataframe filled with several columns. I need to change the values of a column for data normalization like in the following example:

User_id   
751730951     
751730951
0
163526844
...and so on

I need to replace every value in the column that is not 0 (string) in a into something like "is not empty". I have tried it now for hours but still cannot change every value that is not 0 into something else. Replace()-function don't work really good for that. Some good ideas?

EDIT (my solution):

finalResult.loc[finalResult['update_user'] == '0', 'update_user'] = 'empty'
finalResult.loc[finalResult['update_user'] != 'empty', 'update_user'] = 'not empty'

Answer 1

df.loc[df['mycolumn'] != '0', 'mycolumn'] = 'not empty'

or if the value is an int,

df.loc[df['mycolumn'] != 0, 'mycolumn'] = 'not empty'

df.loc[rows, cols] allows you to get or set a range of values in your DataFrame. First parameter is rows, in which case I'm using a boolean mask to get all rows that don't have a 0 in mycolumn . The second parameter is the column you want to get/set. Since I'm replacing the same column I queried from, it is also mycolumn .

I then simply using the assignment operator to assign the value of 'not empty' like you wanted.

New column containing 'not empty'

If you want a new column to contain the 'not empty' so you're not contaminating your original data in mycolumn , you can do:

df.loc[df['mycolumn'] != 0, 'myNewColumnsName'] = 'not empty'

Answer 2

Simpliest is use:

df['User_id'] = df['User_id'].replace('0', 'is not empty')

If 0 is int :

df['User_id'] = df['User_id'].replace(0, 'is not empty')

Answer 3

Suppose we use a Series with the data specified in the question, named user_id, with a single line you do what you need:

user_id.where(user_id == 0).fillna('is not empty')

I don't like loc very much since I think it complicates the reading.

It might be better than replace because it allows the opposite case:

user_id.where(user_id != 0).fillna('is empty')