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I want to change a boolean variable based on other boolean variables for every observation in a Python data frame

A data summary looks like this from the data frame. This just shows a subset where multiple A, B and C variables are True. I only want one of them to be true. I created the Multiples variable to select the ones to change and a summary looks like this table.
A B C Multiples
ID
197 True True False True
215 True True False True
225 True False True True
234 True True False True
265 True True False True
321 False True True True
Here is an example of what the improved data would look like. In every row only one of A, B or C is True and it's the rightmost one. I need to know how make that change in Python. Don't worry about the Multiples variable. When I re-compute that, with A, B and C as they are below, all the Multiples will be False. I already have the code to do that.
A B C Multiples
ID
197 False True False False
215 False True False False
225 False False True False
234 False True False False
265 False True False False
321 False False True False
I've searched the web and this site and can't find anything that works, at least that I understand.
Here is my current Python code:
for index, item in enumerate(df['A']):
if ((df['Multiples'] == True) & (df['C'] == True | df['B'] == True)):
df['Multiples'] = False

If you want to have Multiples as True if only one column is True:
import pandas as pd
df = pd.DataFrame({
"A": [True, True, True, False],
"B": [True, True, False, False],
"C": [True, False, False, False]
})
df['Multiples'] = 0+df['A']+df['B']+df['C'] == 1
print(df)
That will output:
A B C Multiples
0 True True True False
1 True True False False
2 True False False True
3 False False False False
To check for more than one True column use > 1 instead of == 1.

I managed to solve the problem myself with the following code:
def clear_redundant(row):
# no need to change rows with only one True value
if not row['Multiples']:
return
# if the last column is true, both earlier should be false
if row['C']:
row['A'] = False
row['B'] = False
# if the middle column is true, the first should be false
elif row['B']:
row['A'] = False
df.apply(clear_redundant, axis="columns")

Merge the True False Value based On Condition

i have df like this
a b c
0 True False True
1 False False False
2 True True True
i want this
a b c Result
0 True False True True
1 False False False False
2 True True True True
if any one Value True then Result True ele false

You can use any():
df['result'] = df.any(1)
# or with pd.assign
df = df.assign(result = df.any(1))
both will print:
a b c result
0 True False True True
1 False False False False
2 True True True True
Note that 1 is short for axis=1, i.e. perform operation row-wise

It's quite easy...
if a or b or c:
#do stuff
or you could also use
if a | b | c:
#do stuff

Use any with (axis=1) to check the existance of any True in each row.
df['result'] = df.any(axis=1)
If values are string rather than boolean then:
df['result'] = df.eq('True').any(axis=1)

How to check if column A row 1 and column B in row 1 has value true in pandas Datarame?

how can i imitate following excel formula in python DataFrame?
=IF(AND(A1=TRUE,B1=TRUE),TRUE,FALSE)
A B C
TRUE FALSE FALSE
TRUE TRUE TRUE
FALSE FALSE FALSE
FALSE TRUE FALSE
i tried this,
def check(sig1,sig2):
if sig1 == True and sig2 == True:
return True
else:
return False
df['chk'] = df.apply(check,df['up_signal1',df['up_signal2']],axis=1)

You can do this:
# DataFrame that checks all possible combinations
df = pd.DataFrame({
'up_signal1': [False, False, True, True],
'up_signal2': [False, True, False, True]
})
df['chk'] = df.up_signal1 & df.up_signal2
df
up_signal1 up_signal2 chk
0 False False False
1 False True False
2 True False False
3 True True True

How to test all possible combinations with True/False Statement in python?

I have two DataFrames where each column contain True/False statements. I am looking for a way to test all possible combinations and find out where "True" for each row in df1 also is "True" in the corresponding row in df2.
In reference to the data below, the logic would be something like this:
For each row, starting in column "Main1", test if row is equal to True and if row in column "Sub1" also is True. Next, test if row in "Main1" is equal to true and if rows in column "Sub1" is True and column "sub2" also is True. In this case, if all values are True, the output would be True. Then repeat for all columns and all possible combinations.
df1:
Main1 Main2 Main3
0 True False True
1 False False False
2 False True True
3 False False True
4 False True True
5 True True True
6 True False False
df2:
Sub1 Sub2 Sub3
0 False False True
1 False True False
2 True False True
3 False False False
4 True True False
5 False False False
6 True True True
The output would be similar to something like this.
Of course, I could do this manually but it would be timely as well as there would be rooms for errors.
Main1Sub1 Main1Sub1Sub2 ... Main3Sub2Sub3 Main3Sub3
0 False False ... False True
1 False False ... False False
2 False False ... False True
3 False False ... False False
4 False False ... False False
5 False False ... False False
6 True True ... False False
[7 rows x 18 columns]
Any help on how to tackle this problem is appreciated!

You can use the combinations() function in itertools to extract all the possible combinations of the columns of the 2 data frames, and then use the product() function in pandas to identify the rows where all the columns in the considered combination are equal to True. I included an example below, which considers all combinations of either 2 or 3 columns.
import pandas as pd
from itertools import combinations
df1 = pd.DataFrame({"Main1": [True, False, False, False, False, True, True],
"Main2": [False, False, True, False, True, True, False],
"Main3": [True, False, True, True, True, True, False]})
df2 = pd.DataFrame({"Sub1": [False, False, True, False, True, False, True],
"Sub2": [False, True, False, False, True, False, True],
"Sub3": [True, False, True, False, False, False, True]})
df3 = df1.join(df2)
all_combinations = list(combinations(df3.columns, 2)) + \
list(combinations(df3.columns, 3))
for combination in all_combinations:
df3["".join(list(combination))] = df3[list(combination)].product(axis=1).astype(bool)
df3.drop(labels=["Main1", "Main2", "Main3", "Sub1", "Sub2", "Sub3"], axis=1, inplace=True)
df3
Main1Main2 Main1Main3 ... Main3Sub2Sub3 Sub1Sub2Sub3
0 False True ... False False
1 False False ... False False
2 False False ... False False
3 False False ... False False
4 False False ... False False
5 True True ... False False
6 False False ... False True

How to change the first occurrence of 'True' in a row to false in pandas

I'm trying to change the first instance of True to False in my DataFrame dependent on row:
A B C
Number
1 True True True
2 False True True
3 False False True
A B C
Number
1 False True True
2 False False True
3 False False False
Every time I try using the for index, row in target_df.iterrows(): line it ends up never finding any 'True' when I look through the row.
Thanks in advance!

You can use the cumulative sum of the Boolean values (False corresponds to 0; True to 1) for each row, along with DataFrame.mask():
>>> condition = df.cumsum(axis=1) == 1
>>> df.mask(condition, False)
a b c
0 False True True
1 False False True
2 False False False
df.mask(self, cond, other=nan)
Return an object of same shape as self and whose corresponding entries
are from self where cond is False and otherwise are from other.
In this case, condition is False everywhere except the points at which you want to switch True -> False:
>>> condition
a b c
0 True False False
1 False True False
2 False False True
One other option would be to use NumPy:
>>> row, col = np.where(df.cumsum(axis=1) == 1)
>>> df.values[row, col] = False
>>> df
a b c
0 False True True
1 False False True
2 False False False