how to check if value in a DataFrame is a type Decimal

Question

I am writing a data test for some api calls that return a DataFrame with a date type and a type Decimal. I can't find a way to verify the Decimal

the DataFrame is returned as 2022-01-18 12:35:00 2856.8430

So I have

result = ds.DataService.get_recent(5, 24)
assert not result.empty
assert ptypes.is_datetime64_any_dtype(result['foo'])

but if I try

assert all(ptypes.is_float_dtype(result[col]) for col in result["foo1"])

 raise KeyError(key) from err
KeyError: Decimal('2873.6408')

Answer 1

So given a series like result["foo1"], you can check that with

from decimal import Decimal

import pandas as pd

is_decimal: bool = pd.core.dtypes.common.is_dtype_equal(result["foo1"], Decimal)

Answer 2

Sadly the usage of is_dtype_equal function as suggested yields True if the Series is of another object types. What worked for me was using isinstance(x, Decimal) on of the the Series elements.

See example:

from decimal import Decimal

import pandas as pd

df = pd.DataFrame(
    {
        'a': ['A','B','C'] ,
        'b': [11.1, 23.3,55.5],
        'c': [Decimal(10.0), Decimal(11.1), Decimal(12.1)]
    }
)
print("Using isinstance of")
print(isinstance(df['a'].values[0], Decimal))
print(isinstance(df['b'].values[0], Decimal))
print(isinstance(df['c'].values[0], Decimal))

print("Using is_dtype_equal")
print(pd.core.dtypes.common.is_dtype_equal(df['a'], Decimal))
print(pd.core.dtypes.common.is_dtype_equal(df['b'], Decimal))
print(pd.core.dtypes.common.is_dtype_equal(df['c'], Decimal))