pandas get the min/max value of a row in a dataframe of only those rows that contain a certain string in another column

I feel really stupid now, this should be easy.

I got good help here how-to-keep-the-index-of-my-pandas-dataframe-after-normalazation-json

I need to get the min/max value in the column 'price' only where the value in the column 'type' is buy/sell. Ultimately I want to get back the 'id' also for that specific order.

So first of I need the price value and second I need to get back the value of 'id' corresponding.

You can find the dataframe that I'm working with in the link.

What I can do is find the min/max value of the whole column 'price' like so :

x = df['price'].max() # = max price

and I can sort out all the "buy" type like so:

d = df[['type', 'price']].value_counts(ascending=True).loc['buy']

but I still can't do both at the same time.

Solution

I' m still not sure how your line isin works. buy_and_sell not specified ;)

How I did it --> I now first found the highest buy, then found the 'txid' for that price, then I had to remove the index from the returned series. And finally I had to remove a whitespace before my string. no idea how it came there

def get_highest_sell_txid():
hs = df.loc[df.type == 'sell', :].max()['price'] 
hsid = df.loc[df.price == hs, :] 
xd = hsid['txid'] 
return xd.to_string(index=False)




 xd = get_highest_sell_txid()
    sd = xd.strip()
    cancel_order = 'python -m krakenapi CancelOrder txid=' + sd  #
    subprocess.run(cancel_order)