Having a dataframe with a single row, I need to filter it into a smaller one with filtered columns based on a value in a row.
What's the most effective way?
df = pd.DataFrame({'a':[1], 'b':[10], 'c':[3], 'd':[5]})
| a | b | c | d |
|---|---|---|---|
| 1 | 10 | 3 | 5 |
For example top-3 features:
| b | c | d |
|---|---|---|
| 10 | 3 | 5 |
CodePudding user response：
Use sorting per row and select first 3 values:
df1 = df.sort_values(0, axis=1, ascending=False).iloc[:, :3]
print (df1)
b d c
0 10 5 3
Solution with Series.nlargest:
df1 = df.iloc[0].nlargest(3).to_frame().T
print (df1)
b d c
0 10 5 3
CodePudding user response：
You can transpose T, and use nlargest():
new = df.T.nlargest(columns = 0, n = 3).T
print(new)
b d c
0 10 5 3