Python How to convert float as hex to decimal
I was reading some data from a csv file with pandas. The data is incomplete and therefore contains many nan values. I want to add a column to data that converts hex values to decimal values. Unfortunately, a column with hex values is read as floats, not strings, because they just have those values. Sample data
val
0 20.0
1 nan
2 20.0
The easy way to convert hex to decimal in python seems to be:, int('20.0',16)
which should give 32
.
However, since this is pandas, I cannot convert the values to int, or at least I keep getting the error. My current code:
df['valdec'] = np.where(np.isnan(df['val']),
df['val'],
int(df['val'].astype(int).astype(str), 16))
Error with error:
ValueError: Cannot convert NA to integer
without a astype(int)
value "20.0"
that cannot be converted. Is there any other way to interpret the float value as a hex value and convert to decimal when working with a pandas dataframe?
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You can mask the lines of interest and double click and call apply
:
In [126]:
df['valdec'] = df['val'].dropna().astype(int).astype(str).apply(lambda x: int(x, 16))
df
Out[126]:
val valdec
0 20.0 32.0
1 NaN NaN
2 20.0 32.0
So first we call dropna
to delete NaN
, this allows us to use int
with .astype(int)
, then convert to str
by calling .astype(str)
.
Then we call apply
this to convert to hex and assign the result of all this to a new column
Note that there dtype
will be a new column float
, since presence NaN
enforces this, you cannot have a mixture int
and float
s
As @jasonharper pointed out, casting to int
here will lose any fractional so a higher precision method would use float.fromhex
:
In [128]:
df['valdec'] = df['val'].astype(str).dropna().apply(lambda x: float.fromhex(x))
df
Out[128]:
val valdec
0 20.0 32.0
1 NaN NaN
2 20.0 32.0
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