Try Except in Context

Error Handling for Data Analysts in Python - Deep Dive

A Closer Look at Exception Handling in Data Preprocessing

In the previous post, we touched upon using error handling in various stages of the data analysis pipeline. Now, let's take a deeper look into an example of exception handling during data preprocessing, particularly when converting data types.

import pandas as pd

def preprocess_dataframe(df, column):
    This function attempts to convert a specified column of a dataframe
    to integer data type.
        df[column] = df[column].astype(int)
    except ValueError:
        print(f"ValueError: could not convert string to int in column '{column}'")
        df[column] = pd.to_numeric(df[column], errors='coerce')  # Convert to numeric and set unconvertible values to NaN
    return df

def main():
    # Create a sample dataframe
    data = {
        'A': ['1', '2', '3', 'four'],
        'B': ['5', '6', '7', '8'],
    df = pd.DataFrame(data)

    print("Before preprocessing:")

    # Attempt to preprocess the dataframe
    df = preprocess_dataframe(df, 'A')

    print("After preprocessing:")

if __name__ == "__main__":

In this Python script, we create a simple dataframe with a column 'A' that contains an entry 'four', which cannot be converted to an integer. When we run the main() function, it attempts to preprocess this dataframe by converting column 'A' to integer type. When it encounters the 'four' entry, a ValueError is raised, and the exception handling code inside preprocess_dataframe() catches this error and instead converts the unconvertible values to NaN using pd.to_numeric() with errors='coerce'. The function then returns the preprocessed dataframe.

The main() function is often used in Python scripts as an entry point that orchestrates the higher-level logic of the script, calling other functions and methods as necessary. Here, main() creates the dataframe, calls the preprocessing function, and displays the dataframe before and after preprocessing.


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