How can I stricly enforce a dtype Decimal in a pandas DataFrame?

To clarify: I am not looking for weak workarounds, such as rounding every time I write to or read from a column (and hope that no other operations happend elsewhere that might lead to unwanted results).

I really want to be 100% sure that whatever is written in that column, no matter where it might have come from, will always have exactly 2 significant digits behind the decimal point, end of story. And if a user intends to write something that's not in agreement, the whole thing should blow up (either producing a TypeError or ValueError). --> To avoid theoretical dicussions and motivate the usage a bit: I am dealing with a trading system, that's why saving anything other than 2 decimal points in that frame would be a hard error, always.

I have tried to assign a dtype, but without success:

from decimal import Decimal
df[col].astype(Decimal)

Pydantic comes to mind: but if I bake the df into a class (say class MyDfType), then do I need to write my own setter/getter functions into MyDfType for the contained dataframe (MyDFType().df) ensure that all values to/from certain cols are manually enforced to be Decimal?

CodePudding user response：

To strictly enforce the Decimal data type for a column in a pandas DataFrame, you can use the pd.DataFrame.astype() method to convert the column to the Decimal type.

import pandas as pd
from decimal import Decimal

# create a sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4.0, 5.0, 6.0]})

# convert column 'B' to the Decimal type
df['B'] = df['B'].astype(Decimal)

# this will raise an error because the value in the first row cannot be converted to a Decimal
df['B'][0] = 'abc'

This method will raise an error if any of the values in the column cannot be converted to the specified data type.

CodePudding user response：

importing pandas as pd

import pandas as pd

importing numpy as np

import numpy as np

setting the seed to re-create the dataframe

np.random.seed(25)

Creating a 5 * 4 dataframe

df = pd.DataFrame(np.random.random([5, 4]), columns =["A", "B", "C", "D"])

importing pandas as pd

importing numpy as np

setting the seed to re-create the dataframe

Creating a 5 * 4 dataframe

Print the dataframe