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Any ideas on how to convert a series (column) from float to decimal? I am on Python 3.6. I have read the Decimal documentation, but it offers no help.
df['rate'].dtype
Out[158]: dtype('float64')
Decimal(df['rate'])
Traceback (most recent call last):
File "C:\Users\user\Anaconda3\lib\site-packages\IPython
\core\interactiveshell.py", line 2862, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-159-88710e11f7cd>", line 1, in <module>
Decimal(df['rate'])
TypeError: conversion from Series to Decimal is not supported
846
Alternatively, you can convert all string columns to float type using pandas. to_numeric() . For example use df['Discount'] = pd. to_numeric(df['Discount']) function to convert 'Discount' column to float.
There is two methods: float_number = float ( decimal_number ) float_number = decimal_number * 1.0.
A float value can be converted to an int value no larger than the input by using the math. floor() function, whereas it can also be converted to an int value which is the smallest integer greater than the input using math. ceil() function.
You can't cast like this, you will need to do
df['rate'] = df['rate'].apply(Decimal)
pandas does support Decimal but you can't cast like that
Example:
In[28]:
from decimal import *
df = pd.DataFrame(np.random.randn(5,3), columns=list('abc'))
df['a'] = df['a'].apply(Decimal)
df
Out[28]:
a b c
0 -1.6122557830197199457700207858579233288764953... -1.865243 -0.836893
1 0.96962430214434858211092205237946473062038421... -0.105823 -0.842267
2 -0.9113389075755260471112251252634450793266296... -0.351389 -0.183489
3 1.22765470106414120721183280693367123603820800... -1.232627 -0.067909
4 -0.0376339704393285762185072940155805554240942... -0.445606 -0.080623
the dtype will still show object but the dtype really is Decimal:
In[29]:
type(df['a'].iloc[0])
Out[29]: decimal.Decimal
If you use astype(Decimal) it will look like it worked but it doesn't:
In[38]:
df['b'].astype(Decimal)
Out[38]:
0 -1.86524
1 -0.105823
2 -0.351389
3 -1.23263
4 -0.445606
Name: b, dtype: object
If we try to assign this back:
In[39]:
df['b'] = df['b'].astype(Decimal)
type(df['b'].iloc[0])
Out[39]: float
As pointed out by @JonClements and I agree it is ill-advised to use non-native numpy types as you lose any vectorisation in particular with arithmetic operations, additionally the dtype may be converted when you perform some operation on it which then loses your original intention
116
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