I have a dataframe like this :
Patch Last reward First reward Difference Name Block_No.
group_id
1 3 0.0 0.0 0.0 XYZ 1
2 4 43.0 54.0 11.0 XYZ 1
3 5 0.0 0.0 0.0 XYZ 2
4 6 40.0 65.0 25.0 XYZ 2
5 7 0.0 0.0 0.0 XYZ 3
6 0 0.0 0.0 0.0 XYZ 3
I want to create a new column called 'Rep_rate' based on the following condition: if block_no. = 1 and if patch = 3 , then Rep_rate = 4 , else Rep_rate = 0.
I tried doing this :
if (df_last['Block_No.']) == 1:
for i in range (len(df_last)):
if df_last['Patch'][i] == 1:
rep = 8
else:
rep = 0
df_last['Rep_Rate'] = rep
if (df_last['Block_No.']) == 2:
for i in range (len(df_last)):
if df_last['Patch'][i] == 1:
rep = 4
else:
rep = 0
df_last['Rep_Rate'] = rep
if (df_last['Block_No.']) == 3:
for i in range (len(df_last)):
if df_last['Patch'][i] == 1:
rep = 8
else:
rep = 0
df_last['Rep_Rate'] = rep
However when i try this i get the following 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 understand the logic you are using to populate the RR column, but one approach might be:
df['RR'] = 0
if (df['Block'] == 1).all():
for i, row in df.iterrows():
if row['Patch'] == 3:
df.loc[i,'RR'] = 4 # note using df.loc to directly edit the dataframe
elif (df['Block'] == x).all():
for i, row in df.iterrows():
if row['Patch'] == y:
df.loc[i,'RR'] = z
# more if statements as needed
Replace x, y, and z with whatever values you need.
The issue you are having is with if df["Block"] == 1. The code df["Block"] == 1 results in a boolean Series of True/False for whether or not each value in the Series is equal to 1. Using an if statement against a Series is not supported because the meaning is ambiguous. Pandas provides ser.any() and ser.all() for unambiguous boolean evaluation. EG
if (df["Block"] ==1).all():
# more code ...
would evaluate the code in the if statement if all of the values in the Block column were equal to 1.