根据另一列包含的单词有条件地为一列赋值

Question

I have a dataframe "df":

Patient    Condition
John       Adductor tear left
Mary       Adductor sprain left
Henry      Hamstring sprain
Lucy       Hamstring tear

Answer 1

You do not need to split the condition column. You can use the following code given the assumptions:

• every row in df contains either "Adductor" or "Hamstring" in the Condition column, case sensitive.
• same thing with the words "tear" and "sprain" .

df["Muscle"] = df["Condition"].apply(lambda x: 1 if "Adductor" in x else 2)

Same thing with the Injury column. You can try it and let me know if you need help.

If you do not want to worry about words being in upper or lower cases, you can use:

df["Muscle"] = df["Condition"].apply(lambda x: 1 if "adductor" in x.lower() else 2)

Answer 2

You could also use str.contains<\/code><\/a> to search for specific strings in Condition column and assign values using np.select<\/code><\/a> .

import numpy as np
df['Muscle'] = np.select([df['Condition'].str.contains('Adductor'), df['Condition'].str.contains('Hamstring')], [1,2], np.nan)
df['Injury'] = np.select([df['Condition'].str.contains('tear'), df['Condition'].str.contains('sprain')], [1,2], np.nan)

Answer 3

Here is another way using enumerate()<\/code> and assign()<\/code>

m = {j:i for i,j in enumerate(['adductor','hamstring'],1)}
i = {j:i for i,j in enumerate(['tear','sprain'],1)}

col = df['Condition'].str.split()
df.assign(Muscle = col.str[0].str.lower().map(m), Injury = col.str[1].str.lower().map(i))