问题
I'm trying to figure out if there is a good way to manage units in my pandas data. For example, I have a DataFrame
that looks like this:
length (m) width (m) thickness (cm)
0 1.2 3.4 5.6
1 7.8 9.0 1.2
2 3.4 5.6 7.8
Currently, the measurement units are encoded in column names. Downsides include:
- column selection is awkward --
df['width (m)']
vs.df['width']
- things will likely break if the units of my source data change
If I wanted to strip the units out of the column names, is there somewhere else that the information could be stored?
回答1:
There isn't any great way to do this right now, see github issue here for some discussion.
As a quick hack, could do something like this, maintaining a separate dict with the units.
In [3]: units = {}
In [5]: newcols = []
...: for col in df:
...: name, unit = col.split(' ')
...: units[name] = unit
...: newcols.append(name)
In [6]: df.columns = newcols
In [7]: df
Out[7]:
length width thickness
0 1.2 3.4 5.6
1 7.8 9.0 1.2
2 3.4 5.6 7.8
In [8]: units['length']
Out[8]: '(m)'
回答2:
As I was searching for this, too. Here is what pint and the (experimental) pint_pandas is capable of today:
import pandas as pd
import pint
import pint_pandas
ureg = pint.UnitRegistry()
ureg.Unit.default_format = "~P"
pint_pandas.PintType.ureg.default_format = "~P"
df = pd.DataFrame({
"length": pd.Series([1.2, 7.8, 3.4], dtype="pint[m]"),
"width": pd.Series([3.4, 9.0, 5.6], dtype="pint[m]"),
"thickness": pd.Series([5.6, 1.2, 7.8], dtype="pint[cm]"),
})
print(df.pint.dequantify())
length width thickness
unit m m cm
0 1.2 3.4 5.6
1 7.8 9.0 1.2
2 3.4 5.6 7.8
df['width'] = df['width'].pint.to("inch")
print(df.pint.dequantify())
length width thickness
unit m in cm
0 1.2 133.858268 5.6
1 7.8 354.330709 1.2
2 3.4 220.472441 7.8
来源:https://stackoverflow.com/questions/39419178/how-can-i-manage-units-in-pandas-data