How to put the max of 3 separate columns in a new column in python/pandas
Question:
for example we have:
a
b
c
1
1
3
2
4
1
3
2
1
And now using python I’m trying to create this:
a
b
c
max
1
1
3
3c
2
4
1
4b
3
2
1
3a
Answers:
If need match first maximal column join max converted to strings with DataFrame.idxmax for columns names by max:
cols = ['a','b','c']
df['max'] = df[cols].max(axis=1).astype(str).str.cat(df[cols].idxmax(axis=1))
print (df)
a b c max
0 1 1 3 3c
1 2 4 1 4b
2 3 2 1 3a
If possible multiple max values and need all matched columns use DataFrame.dot trick with separator ,:
print (df)
a b c
0 3 1 3
1 2 4 1
2 3 2 1
cols = ['a','b','c']
max1 = df[cols].max(axis=1)
s = df[cols].eq(max1, axis=0).dot(pd.Index(cols) + ',').str[:-1]
df['max'] = max1.astype(str).str.cat(s)
print (df)
a b c max
0 3 1 3 3a,c
1 2 4 1 4b
2 3 2 1 3a
You can also use this:
cols = ['a', 'b', 'c']
df['max'] = df[cols].apply(lambda row: f"{row.max()}{row.idxmax()}", axis=1)
idxmax(axis=1), method returns a Series with the index of the maximum value for each row.
assuming your dataframe is df.
df['max'] = df.idxmax(axis =1) + df.max(axis=1).map(str)
for example we have:
| a | b | c |
|---|---|---|
| 1 | 1 | 3 |
| 2 | 4 | 1 |
| 3 | 2 | 1 |
And now using python I’m trying to create this:
| a | b | c | max |
|---|---|---|---|
| 1 | 1 | 3 | 3c |
| 2 | 4 | 1 | 4b |
| 3 | 2 | 1 | 3a |
If need match first maximal column join max converted to strings with DataFrame.idxmax for columns names by max:
cols = ['a','b','c']
df['max'] = df[cols].max(axis=1).astype(str).str.cat(df[cols].idxmax(axis=1))
print (df)
a b c max
0 1 1 3 3c
1 2 4 1 4b
2 3 2 1 3a
If possible multiple max values and need all matched columns use DataFrame.dot trick with separator ,:
print (df)
a b c
0 3 1 3
1 2 4 1
2 3 2 1
cols = ['a','b','c']
max1 = df[cols].max(axis=1)
s = df[cols].eq(max1, axis=0).dot(pd.Index(cols) + ',').str[:-1]
df['max'] = max1.astype(str).str.cat(s)
print (df)
a b c max
0 3 1 3 3a,c
1 2 4 1 4b
2 3 2 1 3a
You can also use this:
cols = ['a', 'b', 'c']
df['max'] = df[cols].apply(lambda row: f"{row.max()}{row.idxmax()}", axis=1)
idxmax(axis=1), method returns a Series with the index of the maximum value for each row.
assuming your dataframe is df.
df['max'] = df.idxmax(axis =1) + df.max(axis=1).map(str)