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
```