Try and Except in Python

Error Handling for Data Analysts

Error Handling for Data Analysts in Python


As a data analyst working in Python, one crucial skill you can develop to improve the robustness of your code is exception handling. It helps you anticipate and manage problems before they escalate into larger issues. In this post, we'll explore various categories and provide examples of how exceptions can be effectively used in your data analysis pipeline.

1. Handling Data Ingestion Errors

Errors during data import from sources such as Oracle databases can occur due to network problems, authentication issues, or incorrect query syntax. Here's an example of handling these errors:

import cx_Oracle
try:
    connection = cx_Oracle.connect(user, pwd, dsn)
    df = pd.read_sql_query(query, connection)
except cx_Oracle.DatabaseError as e:
    error, = e.args
    print(f'Error code: {error.code}')
    print(f'Error message: {error.message}')

2. Handling Data Cleaning and Preprocessing Errors

Unexpected data types, missing values, or inconsistent string formats during data cleaning and preprocessing can cause errors. Here's an example:

try:
    df['column'] = df['column'].astype(int)
except ValueError:
    print("ValueError: could not convert string to int")

3. Handling Calculation Errors

Calculation errors can occur due to issues such as division by zero or type errors. Here's how you might handle them:

try:
    df['ratio'] = df['numerator'] / df['denominator']
except ZeroDivisionError:
    df['ratio'] = 0
    print("ZeroDivisionError: division by zero")

4. Handling Data Transformation Errors

Errors could occur while performing operations like groupby or merging dataframes, due to reasons like missing keys or a mismatch in dimensions. Here's an example of handling such errors:

try:
    df_grouped = df.groupby('key_column').sum()
except KeyError:
    print("KeyError: The key column is not in the dataframe")

5. Handling Output Errors

Errors might occur when saving your results to a file or database, due to issues like insufficient permissions or lack of disk space. Here's an example of handling these errors:

try:
    df.to_csv('output.csv', index=False)
except PermissionError:
    print("PermissionError: Do not have write permissions for the directory")

In conclusion, understanding and effectively handling exceptions can make your code more robust and reliable. It allows the code to continue running even in the face of errors, and ensures that you are notified of issues that need to be addressed.

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