How can I manage units in pandas data?

Question

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:

1. column selection is awkward -- df['width (m)'] vs. df['width']
2. 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?

Answer 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)' 

Answer 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