Convert a Decimal column in pandas into a float column

Question

I have the following dataframe:

df2 = pd.DataFrame({'price': [Decimal(1.4334), Decimal(1.4335), Decimal(1.4336), Decimal(1.4337)], 'tpo_count': [1, 2, 3, 1], 'tpo': ['A', 'BC', 'BCD', 'D']})
print(df2.dtypes)

price        object
tpo_count     int64
tpo          object
dtype: object

If want ot create an additional column named "price_float" which is the same column as "price" but with floats (to be later draw using matplotlib which does not support Decimal).

I have tried:

df2['price_float']=float(df2['price'])

but I get:

TypeError: cannot convert the series to <class 'float'>

df2['price_float'] = df2['price_float'].astype(float). Did you actually try researching how to change types? — roganjosh
– roganjosh, Commented Aug 11, 2019 at 13:47

Karn Kumar · Accepted Answer · 2019-08-11 15:41:42Z

4

Just simulating your sample DataFrame:

Sample DataFarme:

>>> df2
    price  tpo  tpo_count
0  1.4334    A          1
1  1.4335   BC          2
2  1.4336  BCD          3
3  1.4337    D          1
>>> print(df2.dtypes)
price        object
tpo          object
tpo_count     int64
dtype: object

Solution..

>>> df2['price_float'] = df2['price'].astype(float)
>>> df2
    price  tpo  tpo_count  price_float
0  1.4334    A          1       1.4334
1  1.4335   BC          2       1.4335
2  1.4336  BCD          3       1.4336
3  1.4337    D          1       1.4337

now you got new float column created..

>>> print(df2.dtypes)
price           object
tpo             object
tpo_count        int64
price_float    float64
dtype: object

answered Aug 11, 2019 at 15:41

Karn Kumar

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2 Comments

Gathide Over a year ago

Will the numbers in decimal.Decimal be ALWAYS retained during conversion to float? i.e. any possible changes of values, like round up or round down?

fantabolous Over a year ago

@Gathide Decimal and float are different beasts, so yes the numbers may change very slightly, which is the whole reason for Decimal type existing in the first place. Good summary: docs.python.org/3/library/decimal.html

gCoh · Accepted Answer · 2019-08-11 13:47:57Z

1

You can use astype function:

 df.astype({'col1': 'int32'}).dtypes

answered Aug 11, 2019 at 13:47

gCoh

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