Update index after sorting data-frame

Question

Take the following data-frame:

x = np.tile(np.arange(3),3)
y = np.repeat(np.arange(3),3)
df = pd.DataFrame({"x": x, "y": y})

I need to sort it by x first, and only second by y:

df2 = df.sort(["x", "y"])

How can I change the index such that it is ascending again. I.e. how do I get this:

I have tried the following. Unfortunately, it doesn’t change the index at all:

df2.reindex(np.arange(len(df2.index)))

Asked By: Lemming

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Source

Answer 1

You can reset the index using reset_index to get back a default index of 0, 1, 2, …, n-1 (and use drop=True to indicate you want to drop the existing index instead of adding it as an additional column to your dataframe):

In [19]: df2 = df2.reset_index(drop=True)

In [20]: df2
Out[20]:
   x  y
0  0  0
1  0  1
2  0  2
3  1  0
4  1  1
5  1  2
6  2  0
7  2  1
8  2  2

Answered By: joris

Answer 2

You can set new indices by using set_index:

df2.set_index(np.arange(len(df2.index)))

Output:

Answered By: ilyakhov

Answer 3

df.sort() is deprecated, use df.sort_values(...): https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.sort_values.html

Then follow joris’ answer by doing df.reset_index(drop=True)

Answered By: aaronpenne

Answer 4

Since pandas 1.0.0 df.sort_values has a new parameter ignore_index which does exactly what you need:

In [1]: df2 = df.sort_values(by=['x','y'],ignore_index=True)

In [2]: df2
Out[2]:
   x  y
0  0  0
1  0  1
2  0  2
3  1  0
4  1  1
5  1  2
6  2  0
7  2  1
8  2  2

Answered By: David

Answer 5

The following works!

If you want to change the existing dataframe itself, you may directly use

 df.sort_values(by=['col1'], inplace=True)
 df.reset_index(drop=True, inplace=True)

 df
 >>     col1  col2  col3 col4
     0    A     2     0    a
     1    A     1     1    B
     2    B     9     9    c
     5    C     4     3    F
     4    D     7     2    e
     3  NaN     8     4    D

Else, if you don’t want to change the existing dataframe but want to store the sorted dataframe into another variable separately, you may use:

df_sorted = df.sort_values(by=['col1']).reset_index(drop=True)

df_sorted
>>     col1  col2  col3 col4
    0    A     2     0    a
    1    A     1     1    B
    2    B     9     9    c
    3    C     4     3    F
    4    D     7     2    e
    5  NaN     8     4    D

df
>>       col1  col2  col3 col4
      0    A     2     0    a
      1    A     1     1    B
      2    B     9     9    c
      3  NaN     8     4    D
      4    D     7     2    e
      5    C     4     3    F

Answered By: vagdevi k

Update index after sorting data-frame

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