Pandas logical indexing on a single column of a dataframe to assign values

Question

I am an R programmer and looking for a similar way to do something like this in R:

data[data$x > value, y] <- 1

(basically, take all rows where the x column is greater than some value and assign the y column at those rows the value of 1)

In pandas it would seem the equivalent would go something like:

data['y'][data['x'] > value] = 1

But this gives a SettingWithCopyWarning.

Equivalent statements I've tried are:

condition = data['x']>value
data.loc(condition,'x')=1

But I'm seriously confused. Maybe I'm thinking too much in R terms and can't wrap my head around what's going on in Python. What would be equivalent code for this in Python, or workarounds?

Answer 1

Your statement is incorrect it should be:

data.loc[condition, 'x'] = 1

Example:

In [3]:

df = pd.DataFrame({'a':np.random.randn(10)})
df
Out[3]:
          a
0 -0.063579
1 -1.039022
2 -0.011687
3  0.036160
4  0.195576
5 -0.921599
6  0.494899
7 -0.125701
8 -1.779029
9  1.216818
In [4]:

condition = df['a'] > 0
df.loc[condition, 'a'] = 20
df
Out[4]:
           a
0  -0.063579
1  -1.039022
2  -0.011687
3  20.000000
4  20.000000
5  -0.921599
6  20.000000
7  -0.125701
8  -1.779029

As you are subscripting the df you should use square brackets [] rather than parentheses () which is a function call. See the docs