使用条件列从另一个框架更新 Pandas dataframe 列

Question

I have a main dataframe that I want to update periodically with an update frame. The main frame has a column that determines which column in the update column to update from. Currently, I'm able to do it as follows:

import pandas as pd
import numpy as np

##### Test data
# Not unique Name but still index
df_main = pd.DataFrame({
        "Name": ["a", "b", "c", "b", "d"],
        "Flip": [True, True, False, False, True],
        "Value": [1.0, 2.0, 3.0, 2.5, 4.0]
    }, columns=["Name", "Flip", "Value"])
df_main.set_index('Name', inplace=True)

#        Flip  Value
# Name              
# a      True    1.0
# b      True    2.0
# c     False    3.0
# b     False    2.5
# d      True    4.0

# Unique index
df_update_data = pd.DataFrame({
        "Name": ["a", "b", "c", "d", "f"],
        "Value_True":  [1.1, 2.1, 3.1, 4.1, 5.1],
        "Value_False": [1.2, 2.2, 3.2, 4.2, 5.2]
    }, columns=["Name", "Value_True", "Value_False"])
df_update_data.set_index('Name', inplace=True)

#       Value_True  Value_False
# Name                         
# a            1.1          1.2
# b            2.1          2.2
# c            3.1          3.2
# d            4.1          4.2
# f            5.1          5.2

df_main = df_main.join(df_update_data, how='inner')
df_main["Value"] = np.where(df_main['Flip'].values, df_main['Value_True'].values, df_main['Value_False'].values)
df_main = df_main.drop(['Value_True', 'Value_False'], axis=1)

print(df_main)
#        Flip  Value
# Name              
# a      True    1.1
# b      True    2.1
# b     False    2.2
# c     False    3.2
# d      True    4.1

This is done quite often and I actually have Name_{1,2,3}, Flop_{1,2,3}, Value_{1,2,3} so I'm doing the join, update and drop 3 times. I'm trying to be as efficient as possible as I'm chasing time. Is this the best way to do it? I did not really find a speed improvement using merge rather than join.

Answer 1

Note that your result is sorted on the index, so my solution starts from explicit sorting (on the index).

I think, creation of an intermediate DataFrame is unavoidable. But then you can compute values for Value column and save them just in this column.

I also noticed that how='left' (default) works a bit faster and in your case is also acceptable.

So the code can be:

df_main.sort_index(inplace=True)
wrk = df_main.join(df_update_data)
df_main.Value = np.where(wrk.Flip, wrk.Value_True, wrk.Value_False)

At least you avoid dropping 2 columns.