sort has been replaced in v0.20 by DataFrame.sort_values and DataFrame.sort_index. Aside from this, we also have argsort.
Here are some common use cases in sorting, and how to solve them using the sorting functions in the current API. First, the setup.
# Setup
np.random.seed(0)
df = pd.DataFrame({'A': list('accab'), 'B': np.random.choice(10, 5)})
df
A B
0 a 7
1 c 9
2 c 3
3 a 5
4 b 2
For example, to sort df by column "A", use sort_values with a single column name:
df.sort_values(by='A')
A B
0 a 7
3 a 5
4 b 2
1 c 9
2 c 3
If you need a fresh RangeIndex, use DataFrame.reset_index.
For example, to sort by both col "A" and "B" in df, you can pass a list to sort_values:
df.sort_values(by=['A', 'B'])
A B
3 a 5
0 a 7
4 b 2
2 c 3
1 c 9
df2 = df.sample(frac=1)
df2
A B
1 c 9
0 a 7
2 c 3
3 a 5
4 b 2
You can do this using sort_index:
df2.sort_index()
A B
0 a 7
1 c 9
2 c 3
3 a 5
4 b 2
df.equals(df2)
# False
df.equals(df2.sort_index())
# True
Here are some comparable methods with their performance:
%timeit df2.sort_index()
%timeit df2.iloc[df2.index.argsort()]
%timeit df2.reindex(np.sort(df2.index))
605 µs ± 13.6 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
610 µs ± 24.2 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
581 µs ± 7.63 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
For example,
idx = df2.index.argsort()
idx
# array([0, 7, 2, 3, 9, 4, 5, 6, 8, 1])
This "sorting" problem is actually a simple indexing problem. Just passing integer labels to iloc will do.
df.iloc[idx]
A B
1 c 9
0 a 7
2 c 3
3 a 5
4 b 2