# How to know if every subsequent value is greater than the preceding ones in a pandas column? Python related

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
``````