I have a DataFrame like this:
col1 col2
1 0
0 1
0 0
0 0
3 3
2 0
0 4
I'd like to add a column that is a 1 if col2 is > 0 or 0 otherwise. If I was using R I'd do something like
df1[,'col3'] <- ifelse(df1$col2 > 0, 1, 0)
How would I do this in python / pandas?
You could convert the boolean series df.col2 > 0 to an integer series (True becomes 1 and False becomes 0):
df['col3'] = (df.col2 > 0).astype('int')
(To create a new column, you simply need to name it and assign it to a Series, array or list of the same length as your DataFrame.)
This produces col3 as:
col2 col3
0 0 0
1 1 1
2 0 0
3 0 0
4 3 1
5 0 0
6 4 1
Another way to create the column could be to use np.where, which lets you specify a value for either of the true or false values and is perhaps closer to the syntax of the R function ifelse. For example:
>>> np.where(df['col2'] > 0, 4, -1)
array([-1, 4, -1, -1, 4, -1, 4])
I assume that you're using Pandas (because of the 'df' notation). If so, you can assign col3 a boolean flag by using .gt (greater than) to compare col2 against zero. Multiplying the result by one will convert the boolean flags into ones and zeros.
df1 = pd.DataFrame({'col1': [1, 0, 0, 0, 3, 2, 0],
'col2': [0, 1, 0, 0, 3, 0, 4]})
df1['col3'] = df1.col2.gt(0) * 1
>>> df1
Out[70]:
col1 col2 col3
0 1 0 0
1 0 1 1
2 0 0 0
3 0 0 0
4 3 3 1
5 2 0 0
6 0 4 1
You can also use a lambda expression to achieve the same result, but I believe the method above is simpler for your given example.
df1['col3'] = df1['col2'].apply(lambda x: 1 if x > 0 else 0)
var = 1 if condition else 0syntax?