What are the sorting order of a Pandas dataframe?

Question

input1

import pandas as pd
import numpy as np
np.random.seed(0)
data = {'item': np.random.choice(['skirt', 'shirt', 'coat'], 6),
        'size': np.random.choice(['S', 'M', 'L', 'XL'], 6)}
df1 = pd.DataFrame(data)

df1:

    item    size
0   skirt   S
1   shirt   XL
2   skirt   L
3   shirt   S
4   shirt   S
5   coat    S

when i sort by size

df1.sort_values('size')

out:

    item    size
2   skirt   L
0   skirt   S
3   shirt   S
4   shirt   S
5   coat    S
1   shirt   XL

The data is sorted by the size column, and when the values of the size column are the same, the rows that were originally higher remain higher.

---

input2

import pandas as pd
import numpy as np
pd.options.display.max_rows = 6
np.random.seed(0)
data1 = {'item': np.random.choice(['skirt', 'shirt', 'coat'], 1000000),
        'size': np.random.choice(['S', 'M', 'L', 'XL'], 1000000)}
df2 = pd.DataFrame(data1)

df2

         item size
0       skirt    M
1       shirt    L
2       skirt    M
...       ...  ...
999997   coat    S
999998  shirt    S
999999  skirt    L

[1000000 rows x 2 columns]

df2 has 1M rows

when i sort by size

df2.sort_values('size')

out:

         item size
999999  skirt    L   <- why top?
645704  shirt    L
645714  shirt    L
...       ...  ...
822256   coat   XL
699230   coat   XL
400737  skirt   XL

[1000000 rows x 2 columns]

I don't know why 999999 row is at the top in df2.

Shouldn't the existing order be followed if size is the same?

Answer 1

What you want is a "stable" sort. "Stable" means it maintains the current order when the keys are identical. The default algorithm, quicksort, is not stable.

df2.sort_values('size',kind='stable')