Updating a DataFrame row with a Pandas Series

Question

I am currently working on a problem and now got stuck to implement one of it's steps. I made up the following example which is a simple scenario in order to explore possibilities. The length of the text might be scary but it was only my attempt to better explain it.

This simpler scenario works like the following: I have an look-up table where I store the values which contains the optimal parameters combination for each situation ( df1 ). Afterwards, I perform a simulation to check update the parameters of my look-up table for a given situation, storing everything on df2 , then, I proceed by extracting the row which returns the highest outcome. Now, what I want is to get the values maximum values extracted for the parameters 1 and 2, and subsequently, update the df1 for that specific situation. So please, consider:

import pandas as pd 
import numpy as np 
import random

np.random.seed(1)

a = np.linspace(0,10,11)
b = np.tile(0,11)
c = np.tile(0,11)

df1 = pd.DataFrame([a,b,c]).T
df1.columns = ['Situation','Parameter 1','Parameter 2']

d = np.tile(8,11)
e = abs(np.random.randn(11))
f = abs(np.random.randn(11))
g = abs(100*np.random.randn(11))
df2 = pd.DataFrame([d,e,f,g]).T
df2.columns = ['Situation', 'Parameter 1', 'Parameter 2', 'Outcome']

x = df2.loc[df2['Outcome'].idxmax()]     

At first, my df1 looks like:

    Situation  Parameter 1  Parameter 2
0         0.0          0.0          0.0
1         1.0          0.0          0.0
2         2.0          0.0          0.0
3         3.0          0.0          0.0
4         4.0          0.0          0.0
5         5.0          0.0          0.0
6         6.0          0.0          0.0
7         7.0          0.0          0.0
8         8.0          0.0          0.0
9         9.0          0.0          0.0
10       10.0          0.0          0.0

For the maximum of df2 , I will get a series like this:

Situation       8.000000
Parameter 1     2.301539
Parameter 2     0.172428
Outcome        93.576943

What I want is to df1 to look like this:

    Situation  Parameter 1  Parameter 2
0         0.0          0.0          0.0
1         1.0          0.0          0.0
2         2.0          0.0          0.0
3         3.0          0.0          0.0
4         4.0          0.0          0.0
5         5.0          0.0          0.0
6         6.0          0.0          0.0
7         7.0          0.0          0.0
8         8.0       2.301539     0.172428
9         9.0          0.0          0.0
10       10.0          0.0          0.0

Hope that I managed to be succinct and precise albeit the length of the explanation. I would really appreciate your help on this one!

Answer 1

It looks to me that your Situation column should be an index, I would do the following:

df1 = df1.set_index('Situation')
df1.loc[df2.iloc[0], :] = df2.iloc[1:]

This will do what you want considering the rows order in df2 is exactly as you described. Your other option, would be to turn df2 into a DF like df1 :

df2 = pd.DataFrame(df2).T.set_index('Situation')

Then you can assign by the indexes:

df1.loc[df2.index[0], :] = df2.iloc[0, :]

Answer 2

You could replace a row by

1. deleting the row in question
2. appending the Series w/o the not needed 'Outcome'
3. sorting the DataFrame according to the 'Situation'
4. resetting the index

Not sure if 3. and 4. are actually needed, but with Pandas it's still a one-liner.

df1 = (
    df1[df1.Situation != x.Situation]
   .append(x.drop(labels=['Outcome']), ignore_index=True)
   .sort_values('Situation')
   .reset_index(drop=True)
)
print(df1)

    Situation  Parameter 1  Parameter 2
0         0.0     0.000000     0.000000
1         1.0     0.000000     0.000000
2         2.0     0.000000     0.000000
3         3.0     0.000000     0.000000
4         4.0     0.000000     0.000000
5         5.0     0.000000     0.000000
6         6.0     0.000000     0.000000
7         7.0     0.000000     0.000000
8         8.0     2.301539     0.172428
9         9.0     0.000000     0.000000
10       10.0     0.000000     0.000000