Have different sorting orders in single column in pandas

Question

I’m trying to sort a small df by values in two columns, but I need different sorting orders (ascending/descending) for column A (price) depending on the values in column B (action).

So the df looks like this:

dfindex	price	action
4	0.9	‘sell’
7	0.7	‘buy’
2	0.9	‘buy’
3	0.4	‘sell’
6	0.6	‘sell’
5	0.8	‘buy’
1	0.7	‘buy’
8	0.9	‘buy’

My current sorting is via pandas:

tx_hist = tx_hist.sort_values(by=['dfindex', 'price'], ascending=[True, False], ignore_index=True)

I’ve also tried a separate sorting function, which takes the ‘buy’ prices separately, sorts them and inserts them back in the df, but I can’t make it work as intended either.

def sorttxhist(tx_hist):
    for i in range(len(tx_hist)):
        w = i
        buytxs = []
        selltx = []
        if tx_hist['action'].iloc[w] == 'buy':
            while tx_hist['action'].iloc[w] == 'buy':
                print(buytxs)
                buytxs.append(tx_hist['price'].iloc[w])
                w = w + 1
            buytxs = buytxs.sort(reverse=True)
            tx_hist['price'].iloc[i:w] = buytxs
            #buytxs.clear()

        elif tx_hist['action'].iloc[w] == 'sell':
            while tx_hist['action'].iloc[w] == 'sell':
                print(selltx)
                selltx.append(tx_hist['price'].iloc[w])
                w = w + 1
            selltx = selltx.sort(reverse=False)
            tx_hist['price'].iloc[i:w] = selltx
            print(type(selltx))
    print(tx_hist)
    return tx_hist

But it doesn’t sort it how I need it, which is a first sort by ‘dfindex’, and a secondary sort in descending order (price-wise) for ‘buy’ (in the ‘action’ col) and in ascending order (price-wise) for ‘sell’ (in the ‘action’ col).

So the outcome should look like this:

dfindex	price	action	Sorting order (not in the df)
2	0.9	‘buy’	desc
1	0.7	‘buy’	desc
3	0.4	‘sell’	asc
4	0.9	‘sell’	asc
5	0.8	‘buy’	– / desc
6	0.6	‘sell’	– / asc
8	0.9	‘buy’	desc
7	0.7	‘buy’	desc

Any help is much appreciated!
Cheers

Asked By: TheRealOrange

||

Source

Answer 1

make 2 columns named sort1 and sort2 for sorting

sort1 column divide between group buy and group sell

buy group have negative price in sort2 column

then sort by sort1 and sort2

(df
 .sort_values('dfindex')
 .assign(sort1=df1['action'].ne(df1['action'].shift(1)).cumsum())
 .assign(sort2=df1['price'].mask(df1['action']=="buy", -df1['price']))
 .sort_values(['sort1', 'sort2'])
)

output before drop sort1 and sort2

    dfindex price   action  sort1   sort2
2   2       0.90    buy     1       -0.90
6   1       0.70    buy     1       -0.70
3   3       0.40    sell    2       0.40
0   4       0.90    sell    2       0.90
5   5       0.80    buy     3       -0.80
4   6       0.60    sell    4       0.60
7   8       0.90    buy     5       -0.90
1   7       0.70    buy     5       -0.70

finally drop sort1 and sort2

Answered By: Panda Kim

Have different sorting orders in single column in pandas

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