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I have dataframe whose one column is cost column and it's data goes from negative to positive values. Ex:-

1,0,-2,3,-2,5,1,-4,0

I want it to be sorted like 0,0,1,1,3,5,-2,-2,-4 Same valued column can be kept in order of their encounter.

So basic sorting should be done so that we have positive values in ascending and then negative in descending.

I don't want to copy or create new dataframe because I'll sort dataframe and pick a row based on cost and delete it and then again sort by changing cost factor.

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  • Problem solved?? Commented Feb 23, 2021 at 15:11

2 Answers 2

Reset to default
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Assuming this to be your df:

In [312]: l = [1,0,-2,3,-2,5,1,-4,0]
In [315]: df = pd.DataFrame(l, columns=['a'])

In [316]: df
Out[316]: 
   a
0  1
1  0
2 -2
3  3
4 -2
5  5
6  1
7 -4
8  0

Use pd.concat with df.sort_values:

In [327]: mask = df['a'].ge(0) # Get all values which are >= 0

# sort positive and negative values separately, and then concat both
In [329]: df['a'] = pd.concat([df[mask].sort_values('a'), df[~mask].sort_values('a', ascending=False)], ignore_index=True) 

In [330]: df
Out[330]: 
   a
0  0
1  0
2  1
3  1
4  3
5  5
6 -2
7 -2
8 -4
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2 Comments

Could you please explain this....pd.concat([df[mask].sort_values('a', ascending=True), df[~mask].sort_values('a', ascending=False)], ignore_index=True)
@Brainiac I've put explanation in comments. Please check.
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Method 1:

using sort_values:

df = pd.concat((df.cost[df.cost >= 0].sort_values(), df.cost[df.cost < 0].sort_values(ascending=False)))

Method 2:

Pythonic way:

so = sorted([1,0,-2,3,-2,5,1,-4,0], key=lambda x: (x<0, abs(x)))

so

[0, 0, 1, 1, 3, 5, -2, -2, -4]

Complete Solution:

li = [1,0,-2,3,-2,5,1,-4,0]
df = pd.DataFrame(li, columns=['cost'])
so = sorted(df.cost, key=lambda x: (x<0, abs(x)))
sorterIndex = dict(zip(so, range(len(so))))
df = df.sort_values(by=['cost'], key=lambda x: x.map(sorterIndex))

df:

cost
1   0
8   0
0   1
6   1
3   3
5   5
2   -2
4   -2
7   -4
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4 Comments

How can it be used for Dataframe, I'm guessing this is for array
My dataframe have other columns too and when I tried sort method it only copied cost column and dropped or deleted or ignored other columns.
@Brainiac: Use second solution then
Yes, made few changes in first solution only and hopefully it's working.. here's the code ` df = pd.concat((df[df['cost'] >= 0].sort_values(by=['cost']), df[df['cost'] < 0].sort_values(by=['cost'],ascending=False))) ` and then reindexed for cleaner format.

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