Say I have the following df called df_trading_pair which contains the following data:
Start Date Open Price High Price Low Price Close Price Volume End Date
0 2022-08-06 05:30:00 0.3738 0.3741 0.3737 0.3739 13767.0 2022-08-06 05:32:59.999
1 2022-08-06 05:33:00 0.3739 0.3742 0.3738 0.3741 28212.0 2022-08-06 05:35:59.999
2 2022-08-06 05:36:00 0.3740 0.3743 0.3739 0.3740 47274.0 2022-08-06 05:38:59.999
3 2022-08-06 05:39:00 0.3740 0.3740 0.3737 0.3739 55859.0 2022-08-06 05:41:59.999
After running df_trading_pair["Volume"], you get:
0 13767.0
1 28212.0
2 47274.0
3 55859.0
Name: Volume, dtype: float64
How can I know if every subsequent value is greater than the preceding ones in df_trading_pair["Volume"]
Initially I thought of coding something like this:
if df_trading_pair["Volume"][3] > df_trading_pair["Volume"][2] > df_trading_pair["Volume"][1] > f_trading_pair["Volume"][0]:
print(True)
But that doesn’t look very Pythonic
So I came here to learn a better approach to do that.
May I get some help here?
>Solution :
Use:
df_trading_pair["Volume"].diff().iloc[1:].gt(0).all()
output: True
explanation:
(df_trading_pair["Volume"]
.diff() # compute pairwise difference
.iloc[1:] # remove first row
.gt(0) # are the differences positive?
.all() # are ALL differences positive?
)
numpy alternative:
a = df_trading_pair["Volume"].to_numpy()
(a[1:]>a[:-1]).all()
# True