Sorting by absolute value without changing the data

Question

I\'m looking for a simple way to sort a pandas dataframe by the absolute value of a particular column, but without actually changing the values within the dataframe. Somethi

Answer 1

UPDATE

Since 0.17.0 order and sort have been deprecated (thanks @Ruggero Turra), you can use sort_values to achieve this now:

In[16]:

df.reindex(df.b.abs().sort_values().index)
Out[16]: 
   a  b
2  3 -1
3  4  2
0  1 -3
1  2  5
4  5 -9

Answer 2

Towards more idiomatic pandas: Use argsort

A cleaner approach would be to call Series.argsort on the absolute values, and then index:

df.iloc[df['b'].abs().argsort()]

   a  b
2  3 -1
3  4  2
0  1 -3
1  2  5
4  5 -9

If you need to reset the index, use Series.reset_index,

df.iloc[df['b'].abs().argsort()].reset_index(drop=True)

   a  b
0  3 -1
1  4  2
2  1 -3
3  2  5
4  5 -9

Lastly, since argsort does not have an ascending parameter to specify ascending/descending order, you will need to negate df['b'].abs() to sort by descending order.

df.iloc[(-df['b'].abs()).argsort()]

   a  b
4  5 -9
1  2  5
0  1 -3
3  4  2
2  3 -1

---

You can do this with NumPy as well—use np.abs and ndarray.argsort.

df.iloc[np.abs(df['b'].values).argsort()]

   a  b
2  3 -1
3  4  2
0  1 -3
1  2  5
4  5 -9

Or, for descending order,

df.iloc[(-np.abs(df['b'].values)).argsort()]

   a  b
4  5 -9
1  2  5
0  1 -3
3  4  2
2  3 -1