Pandas loop and anonymise data

Question

I need to read an excel file and loop over rows, retain value for a column Location, anonymise the other values for the same column, and output the result for all combinations to seperate pdf files

Original DF

| Location  | Patients | Deceased | Rank |
|-----------|----------|----------|------|
| Leicester | 2000     | 13       | 1    |
| Coventry  | 2200     | 24       | 2    |
| Norwich   | 3000     | 56       | 3    |
| Sheffield | 2800     | 65       | 4    |
| Luton     | 1800     | 90       | 5    |

Desired DF #1

| Location  | Patients | Deceased | Rank |
|-----------|----------|----------|------|
| Leicester | 2000     | 13       | 1    |
| ########  | 2200     | 24       | 2    |
| ########  | 3000     | 56       | 3    |
| ########  | 2800     | 65       | 4    |
| ########  | 1800     | 90       | 5    |

Desired DF #2

| Location  | Patients | Deceased | Rank |
|-----------|----------|----------|------|
| ########  | 2000     | 13       | 1    |
| Coventry  | 2200     | 24       | 2    |
| ########  | 3000     | 56       | 3    |
| ########  | 2800     | 65       | 4    |
| ########  | 1800     | 90       | 5    |

I want to write out each DF using pd.to_html and then convert.

I'm finding it hard to figure out how to implement this in pandas or how best use iterrows and faker to achieve this or whether a replace() would do in each iteration even.

Answer 1

The easiest way to do it, it's to play with the index to change one value by one value

import pandas as pd

import pandas as pd

# Create the fake data
data = {
    'Location': ['Leicester', 'Coventry', 'Norwich', 'Sheffield', 'Luton'],
    'Patients': [2000, 2200, 3000, 2800, 1800],
    'Deceased': [13, 24, 56, 65, 90],
    'Rank': [1, 2, 3, 4, 5]
}

# Create a DataFrame
df = pd.DataFrame(data)

# Step 2: Function to create a new DataFrame with anonymized Locations except for one row
def anonymize_locations(df, index_to_keep):
    # Create a copy of the original DataFrame to avoid modifying it
    df_copy = df.copy()
    # Replace all Location values with '########' except for the index_to_keep
    df_copy['Location'] = df_copy['Location'].apply(lambda x: '########')
    df_copy.at[index_to_keep, 'Location'] = df.at[index_to_keep, 'Location']
    return df_copy


# Step 3: Loop over each row and generate the desired DataFrames and save to html
for index in df.index:
    anonymized_df = anonymize_locations(df, index)
    file_name = f"output_{index + 1}.html"
    anonymized_df.to_html(file_name)
    print(f"Saved {file_name}")