[Python] pandas/numpy conversion of values based on values of several other columns (multile...

I have a pandas dataframe which looks like this (columns names and values are not representative):

A | B | C | D | E | F | G ...| X | Z
a0|a0 |c0 |d0 |e0 |f0 | g0 | x0| z0
a1|b1 |c1 |d1 |e1 |f1 | g1 | x1| z1
a2|b2 |c2 |d2 |e2 |f2 | g2 | x2| z2
a3|b3 |c3 |d3 |e3 |f3 | g3 | x3| z3
a4|b4 |c4 |d4 |e4 |f4 | g4 | x4| z4
.
.
axx|bxx|cxx|dxx|exx|fxx|gxx | xx| zx


And then I have a series of rules which are the mapping requirements. The number of rules will change over time

Something like this:

A | B | C | D | E | F | G ...| X | Z
a0| |c0 |d0 | | | | ns| wb
a0|b1 | |d1 | |f4 | | cp| qk
| |c1 |d0 |e0 |f0 | | bq| mc
a0|b0 | | | | | j0 | os| mw
a4| | |d4 | |f4 | | tw| px



In other words if certain columns hold certain values for a row, then change the value of this row for either the same columns or other ones.

What would be an efficient way to handle this problem?

This is an AI generated answer:

import pandas as pd
import numpy as np

# Your original dataframe
df = pd.DataFrame({
'A': ['a0', 'a1', 'a2', 'a3', 'a4'],
'B': ['b0', 'b1', 'b2', 'b3', 'b4'],
'C': ['c0', 'c1', 'c2', 'c3', 'c4'],
'D': ['d0', 'd1', 'd2', 'd3', 'd4'],
'E': ['e0', 'e1', 'e2', 'e3', 'e4'],
'F': ['f0', 'f1', 'f2', 'f3', 'f4'],
'X': ['x0', 'x1', 'x2', 'x3', 'x4'],
'Z': ['z0', 'z1', 'z2', 'z3', 'z4']
})

# Define your rules as a list of dictionaries
rules = [
{'condition': {'A': 'a0', 'C': 'c0', 'D': 'd0'}, 'action': {'X': 'ns', 'Z': 'wb'}},
{'condition': {'A': 'a0', 'B': 'b1', 'D': 'd1', 'F': 'f4'}, 'action': {'X': 'cp', 'Z': 'qk'}},
{'condition': {'C': 'c1', 'D': 'd0', 'E': 'e0', 'F': 'f0'}, 'action': {'X': 'bq', 'Z': 'mc'}},
{'condition': {'A': 'a0', 'B': 'b0'}, 'action': {'X': 'os', 'Z': 'mw'}},
{'condition': {'A': 'a4', 'D': 'd4', 'F': 'f4'}, 'action': {'X': 'tw', 'Z': 'px'}}
]

# Function to apply rules
def apply_rules(df, rules):
for rule in rules:
# Create a boolean mask for the condition
mask = pd.Series(True, index=df.index)
for col, val in rule['condition'].items():
mask &= df[col] == val

# Apply the action where the condition is met
for col, val in rule['action'].items():
df.loc[mask, col] = val

return df

# Apply the rules
df = apply_rules(df, rules)

print(df)

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