giorgio-p :
Suppose I have the following lists:
cond_1 = [1,2]
cond_2 = [3,5]
And also the following dataframe df:
|----------|
| Column_1 |
|----------|
| x |
|----------|
| y |
|----------|
| y |
|----------|
| x |
|----------|
What I want to do, is to add a second column Column_2. following these criteria:
1) if Column_1 contains a x, add a value in Column_2 from cond_1;
2) if Column_1 contains a y, add a value in Column_2 from cond_2
The desired output should be like this:
|----------|----------|
| Column_1 | Column_2 |
|----------|----------|
| x | 1 |
|----------|----------|
| y | 3 |
|----------|----------|
| y | 5 |
|----------|----------|
| x | 2 |
|----------|----------|
I have been trying to do this using pd.Series:
df_x = df.loc[df['Column_1'] == "x"] #first I create a dataframe only with the x values
df_x['Column_2'] = pd.Series(cond_1)
Then I would repeat the same thing for the y values, obtaining df_y.
However, this doesn't succeed. Then, I would need to append again the two dataframes (df_x and df_y) and I lose information on the original index that I want to maintain from df.
Jon Clements :
You can create a helper class and use it in an .apply, eg:
class ReplaceWithNext:
def __init__(self, **kwargs):
self.lookup = {k: iter(v) for k, v in kwargs.items()}
def __call__(self, value):
return next(self.lookup[value])
Then use it as:
df['Column_2' ] = df['Column_1'].apply(ReplaceWithNext(x=cond_1, y=cond_2))
Which'll give you:
Column_1 Column_2
0 x 1
1 y 3
2 y 5
3 x 2
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