How can I round a number of decimals based on an assigned series?

My sample data is like this:

```
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.uniform(1,5,size=(10,1)), columns=['Results'])
df['groups'] = ['A', 'B', 'C', 'D']
df['decimal'] = [1, 0, 2, 3]
```

This produces a dataframe like:

```
Results decimal groups
0 2.851325 A 1
1 1.397018 B 0
2 3.522660 C 2
3 1.995171 D 3
```

Next: each result number needs to be rounded the number of decimals shown in `decimal`

. What I tried below resulted in an error of `TypeError: cannot convert the series to <class 'int'>`

```
df['new'] = df['Results'].round(df['decimal'])
```

I want the results like:

```
Results decimal groups new
0 2.851325 A 1 2.9
1 1.397018 B 0 1
2 3.522660 C 2 3.52
3 1.995171 D 3 1.995
```

### >Solution :

You can pass a dict-like object to `DataFrame.round`

to set different precision levels for different columns. So you need to transpose a single column DataFrame (constructed from `Results`

column) twice:

```
df['Results'] = df[['Results']].T.round(df['decimal']).T
```

Another option is a list comprehension:

```
df['Results'] = [round(num, rnd) for num, rnd in zip(df['Results'], df['decimal'])]
```

Output:

```
Results groups decimal
0 2.500 A 1
1 2.000 B 0
2 2.190 C 2
3 1.243 D 3
```

Note that since it’s a single column, it’s decimal places is determined by the highest decimal; but if you look at the constructor of this DataFrame, you’ll see that the precisions have indeed changed:

```
>>> df[['Results']].to_dict('list')
{'Results': [2.5, 2.0, 2.19, 1.243]}
```