Reputation: 23
I have a df where I have several columns, that, based on the value (1-6) in these columns, I want to assign a value (0-1) to its corresponding column. I can do it on a column by column basis but would like to make it a single function. Below is some example code:
import pandas as pd
df = pd.DataFrame({'col1': [1,3,6,3,5,2], 'col2': [4,5,6,6,1,3], 'col3': [3,6,5,1,1,6],
'colA': [0,0,0,0,0,0], 'colB': [0,0,0,0,0,0], 'colC': [0,0,0,0,0,0]})
(col1 corresponds with colA, col2 with colB, col3 with colC)
This code works on a column by column basis:
df.loc[(df.col1 != 1) & (df.col1 < 6), 'colA'] = (df['colA']+ 1)
But I would like to be able to have a list of columns, so to speak, and have it correspond with another. Something like this, (but that actually works):
m = df['col1' : 'col3'] != 1 & df['col1' : 'col3'] < 6
df.loc[m, 'colA' : 'colC'] += 1
Thank You!
Upvotes: 2
Views: 84
Reputation: 863236
Idea is filter both DataFrame
s by DataFrame.loc
, then filter columns by mask and rename columns by another df2
and last use DataFrame.add
only for df.columns
:
df1 = df.loc[:, 'col1' : 'col3']
df2 = df.loc[:, 'colA' : 'colC']
d = dict(zip(df1.columns,df2.columns))
df1 = ((df1 != 1) & (df1 < 6)).rename(columns=d)
df[df2.columns] = df[df2.columns].add(df1)
print (df)
col1 col2 col3 colA colB colC
0 1 4 3 0 1 1
1 3 5 6 1 1 0
2 6 6 5 0 0 1
3 3 6 1 1 0 0
4 5 1 1 1 0 0
5 2 3 6 1 1 0
Upvotes: 2
Reputation: 1516
Here's what I would do:
# split up dataframe
sub_df = df.iloc[:,:3]
abc = df.iloc[:,3:]
# make numpy array truth table
truth_table = (sub_df.to_numpy() > 1) & (sub_df.to_numpy() < 6)
# redefine abc based on numpy truth table
new_abc = pd.DataFrame(truth_table.astype(int), columns=['colA', 'colB', 'colC'])
# join the updated dataframe subgroups
new_df = pd.concat([sub_df, new_abc], axis=1)
Upvotes: 0