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Check if a value is present in each group - Menthix - May-13-2022
Hi,
I have a dataframe with two columns : an 'ID' column and a column 'V1' with different values between 1 and 3.
I'd like to mark each ID with 1 if value 3 is present for this group and 0 otherwise.
The attachment is more clear. I'd like to obtain the 'Result'.
Thank you by advance !
RE: Check if a value is present in each group - deanhystad - May-13-2022
What have you tried?
RE: Check if a value is present in each group - Menthix - May-13-2022
I havn't tried much...
I 'd like to combine df.groupby('ID') and np.where(df['V1']==3, 1, 0) but I don't think it's possible...
RE: Check if a value is present in each group - Menthix - May-15-2022
Any idea ?
Thank you by advance for your responses
RE: Check if a value is present in each group - Pedroski55 - May-16-2022
Caveat: I practically never use pandas, so my answer may not be too great.
I recently created some online polls to keep the students occupied. The csv file has for example 3 rows of Q1, 3 rows of Q2 and so on, similar to your 3 rows AAA, BBB.
So I adapted the poll csv, made 3 columns, id, Qnr, Result
Result contains nothing to start with, shows up in pandas as NaN.
If you search online, you can very quickly find answers for your particular problem. There is always more than 1 way to skin a cat!
import pandas as pd csv_file = '/home/pedro/myPython/pandas/test1.csv' df = pd.read_csv(csv_file) # try with these 2 I can't see how to combine them into 1 line! df.loc[df['Qnr'] == 'Q3', 'Result'] = 1 df.loc[df['Qnr'] != 'Q3', 'Result'] = 0 # or like this df['Result'] = df['Qnr'].apply(lambda x: 1 if x == 'Q3' else 0)
RE: Check if a value is present in each group - Menthix - May-16-2022
I tried two things :
1) df['Result'] = df.groupby(df['ID']).apply(lambda x: np.where(df['Var1']==3,0,1))
It keeps running, but i'm pretty sure it won't work
2) df['Result'] = df['Var1'].groupby(df['ID']).apply(lambda x: 1 if x == '3' else 0)
I get the error : ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
I don't get why
Any idea how to sort one of these two ideas ?
Thank you by advance
RE: Check if a value is present in each group - Menthix - May-16-2022
(May-16-2022, 01:01 AM)Pedroski55 Wrote: Caveat: I practically never use pandas, so my answer may not be too great.
I recently created some online polls to keep the students occupied. The csv file has for example 3 rows of Q1, 3 rows of Q2 and so on, similar to your 3 rows AAA, BBB.
So I adapted the poll csv, made 3 columns, id, Qnr, Result
Result contains nothing to start with, shows up in pandas as NaN.
If you search online, you can very quickly find answers for your particular problem. There is always more than 1 way to skin a cat!
import pandas as pd csv_file = '/home/pedro/myPython/pandas/test1.csv' df = pd.read_csv(csv_file) # try with these 2 I can't see how to combine them into 1 line! df.loc[df['Qnr'] == 'Q3', 'Result'] = 1 df.loc[df['Qnr'] != 'Q3', 'Result'] = 0 # or like this df['Result'] = df['Qnr'].apply(lambda x: 1 if x == 'Q3' else 0)
It doesn't wok because the "result" column is not grouped by ID
RE: Check if a value is present in each group - Pedroski55 - May-16-2022
Haha, what a wonderful thing the Internet is: instant answers
I could never have got to this on my own!
df['Result'] = df['Type'].isin(df.loc[df['Var1'].eq(3), 'Type']).astype(int)
df['Result'] = np.where(df['Type'].isin(df.loc[df['Var1'].eq(3), 'Type']), 1, 0)
df['Result'] = df['Var1'].eq(3).groupby(df['Type']).transform('any').astype(int)
RE: Check if a value is present in each group - Menthix - May-16-2022
(May-16-2022, 10:24 AM)Pedroski55 Wrote: Haha, what a wonderful thing the Internet is: instant answers
I could never have got to this on my own!
df['Result'] = df['Type'].isin(df.loc[df['Var1'].eq(3), 'Type']).astype(int) df['Result'] = np.where(df['Type'].isin(df.loc[df['Var1'].eq(3), 'Type']), 1, 0) df['Result'] = df['Var1'].eq(3).groupby(df['Type']).transform('any').astype(int)
Thank you so much, it works !! :D