This code defines a function `clean_data` that performs data cleaning...

March 16, 2025 at 03:14 AM

def clean_data(frame):
    df = frame.copy()
    df.replace(['None', 'N/A', '?', 'n/a'], numpy.nan, inplace=True)

    def cleanup_nums(cell):
        if pandas.isna(cell):
            return numpy.nan
        if isinstance(cell, str):
            cleaned = re.sub(r'[^\d\.\-]', '', cell)
            try:
                return float(cleaned)
            except ValueError:
                return numpy.nan
        return cell
        
    mixed_cols = ['col_00', 'col_04']
    pure_num_cols = ['label', 'col_01', 'col_05', 'col_06', 'col_07', 'col_08', 'col_09', 'col_10']
    num_cols = mixed_cols + pure_num_cols
    
    for col in num_cols:
        df[col] = df[col].apply(cleanup_nums)
    
    text_cols = ['col_02', 'col_03', 'col_11']
    for col in text_cols:
        df[col] = df[col].astype(str).str.lower().str.strip()

    return df

unique_data = clean_data(unique_data)
unique_data

This code defines a function clean_data that performs data cleaning on a given dataframe frame. Here's the breakdown of what the function does:

1. Copy the original dataframe:

df = frame.copy() creates a copy of the input dataframe frame to ensure no modifications are made to the original dataframe.

2. Replace specified placeholders for missing data:

df.replace(['None', 'N/A', '?', 'n/a'], numpy.nan, inplace=True) replaces certain placeholders for missing values ('None', 'N/A', '?', 'n/a') in the dataframe with numpy.nan.

3. Define `cleanup_nums` function for numeric cleaning:

This nested function processes each cell in specified numeric-like columns:

If the cell is NaN, it returns a numpy.nan.
If the cell is a string, it removes all non-numeric characters (excluding . and -) using a regular expression and attempts to convert the cleaned string to a float.
If the conversion fails, it returns numpy.nan.
If the cell is already numeric, it remains unchanged.

4. Apply numeric cleaning:

mixed_cols (e.g., ['col_00', 'col_04']) contains columns that may include mixed string and numeric content.
pure_num_cols (e.g., ['label', 'col_01', 'col_05', ..., 'col_10']) includes columns expected to hold only numeric values.
num_cols is a combined list of all columns to be treated as numeric.
The cleanup_nums function is applied to all these columns to process and clean numeric values:
```
for col in num_cols:
    df[col] = df[col].apply(cleanup_nums)
```

5. Apply text cleaning:

text_cols (e.g., ['col_02', 'col_03', 'col_11']) identifies columns that are treated as text/string.
These columns are converted to lowercase, stripped of leading/trailing spaces, and forced to be string type using:
```
for col in text_cols:
    df[col] = df[col].astype(str).str.lower().str.strip()
```

6. Return the cleaned dataframe:

The cleaned copy of the dataframe (df) is returned at the end of the function.

Final Usage:

The function is called on unique_data to clean it, and the result replaces the original unique_data variable with the cleaned version.

Summary:

This code:

Cleans numeric and text data in specific dataframe columns.
Converts placeholders for missing values into numpy.nan.
Handles mixed columns (both text and numeric).
Applies transformations like lowercasing and trimming spaces to textual columns.
Outputs a cleaned dataframe that is ready for further analysis or processing.

Generate your own explanations

Download our vscode extension

Read other generated explanations

Built by @thebuilderjr