Find column and row of partial string match in entire pandas dataframe

I'm trying to locate the row and the column in a pandas dataframe that partially match a given string.
So far, my approach is based on an iteration over all the columns (and the rows in every column) to return boolean "True" values:

rowindex = []
columnindex = []

i = 0

for i in range (0, len(df.columns)):
    ask = df.iloc[:, i].str.contains('string')
    
    for j in range (0, len(ask)):
        ask2 = np.equal(ask, True) 
        if ask2 == True:
            columnindex.append(i)
            rowindex.append(j)
            
        j + 1
            
    i + 1

The problem is that I always get this error message for the "if ask2 == True:" statement:
>The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

Thank you for your help on this!

Your original error came from the fact that the numpy.equal() method returns an array of booleans rather than a single one.

If you want to locate the rows and columns in a pandas DataFrame that partially match a given string, you can use the vectorized operations provided by pandas instead of iterating over all the columns and rows. This approach is more efficient and recommended for working with pandas DataFrames. Here's an example of how you can achieve this:

import pandas as pd

# Create a sample DataFrame
data = {
    'Name': ['John Doe', 'Jane Smith', 'Mike Johnson'],
    'Age': [25, 30, 35],
    'City': ['New York', 'London', 'Paris']
}

df = pd.DataFrame(data)

# Define the partial string you want to match
partial_string = 'Jo'

# Use the `str.contains` method to check if each element in the DataFrame contains the partial string
matches = df.apply(lambda col: col.astype(str).str.contains(partial_string, case=False))

# Get the row and column indices where the partial string matches
rows, cols = matches.values.nonzero()

# Print the matched rows and columns
for row, col in zip(rows, cols):
    print(f"Match found at Row: {row}, Column: {df.columns[col]}")