Converting column of dataframe based on scaling factor from another dataframe-CodePudding

I have a dataframe that has a column length (numeric) and another column units (string). For example,

df2convert =
length    units
1.0       "m"       # meters
1.4       "in"      # inches
0.5       "km"      # kilometers
....

I have another dataframe that maps all the units to meters. For example

units2meters:
units   scaling
"m"     1.0
"in"    0.0254
"km"    1000

I want to convert everything in df2convert to meters.

Currently, I do it without joining/merging the 2 dataframes. I convert units2meters to a dict with key as units and value as scaling and then pass in df2.convert.units as a key into the hash table.

Is there a better/more efficient way to do this?

CodePudding user response：

Not sure whether or not this is the method you're already using, but I would use:

lookup = units2meters.set_index("units")["scaling"]
df2convert["length"] * df2convert["units"].map(lookup)

0      1.00000
1      0.03556
2    500.00000
dtype: float64

CodePudding user response：

import pandas as pd

df2convert = pd.DataFrame(data={
    "length":[1.0, 1.4, 0.5],
    "units":["m","in","km"]})

units2meters = pd.DataFrame(data={
    "units":["m","km","in"],
    "scaling":[1.0, 1000, 0.0254]})

merge = pd.merge(units2meters, df2convert, on="units")
merge["result"] = merge["length"] * merge["scaling"]