Sorting a column in pandas dataframe using two other columns

Question

I have a dataframe that looks like this -

   P_id Sim no_of_occurance
0   1   0.00    4
1   2   0.25    3
2   3   0.25    5
3   4   0.25    5
4   5   0.25    6
5   6   0.25    4
6   7   0.50    6
7   8   0.50    2
8   9   0.50    5
9   10  0.25    4
10  11  0.25    6
11  12  0.50    4
12  13  0.50    4
13  14  0.25    4

I want to sort P_id based on the corresponding descending values in Sim' column for each row and for rows where Sim values are same, I want that P_id to come first whose no_of_occurance is higher.

Desired output-

P_id  Sim    no_occurance
 7    0.50    6
 9    0.50    5
 12   0.50    4
 13   0.50    4
 8    0.50    2
 5    0.25    6
 11   0.25    6
 .
 .
 .

You are looking for pandas.DataFrame.sort_values. Here is a link — lcorag
– lcorag, Commented Jul 12, 2021 at 16:05

Andrej Kesely · Accepted Answer · 2021-07-12 16:05:06Z

2

Try:

df = df.sort_values(by=["Sim", "no_of_occurance"], ascending=[False, False])
print(df)

Prints:

    P_id   Sim  no_of_occurance
6      7  0.50                6
8      9  0.50                5
11    12  0.50                4
12    13  0.50                4
7      8  0.50                2
4      5  0.25                6
10    11  0.25                6
2      3  0.25                5
3      4  0.25                5
5      6  0.25                4
9     10  0.25                4
13    14  0.25                4
1      2  0.25                3
0      1  0.00                4

answered Jul 12, 2021 at 16:05

Andrej Kesely

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2 Comments

Karn Kumar Over a year ago

i think df.sort_values(by =["Sim", "no_of_occurance"],ascending=False ) , do we need False two times?

Andrej Kesely Over a year ago

@KarnKumar Yes, one False is enought. I wanted to be just explicit.

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