Handling Outliers in a Pandas Dataframe: A Step-by-Step Guide to Replacing Non-Integer Values

When working with dataframes, it’s not uncommon to encounter outliers or non-integer values that need to be handled. In this article, we’ll explore how to replace non-integer values in a pandas dataframe using the to_numeric function and its various error handling options.

Understanding Pandas Dataframes and Outliers

A pandas dataframe is a 2-dimensional labeled data structure with columns of potentially different types. It’s a powerful tool for data manipulation and analysis. However, when working with numerical data, outliers can significantly impact the results of statistical analyses or machine learning models.

Outliers are values that lie far away from the majority of the data points in a dataset. In the context of our example, an outlier in the Salary column would be a value that is significantly different from the other salaries in the dataframe.

Using to_numeric with Error Handling

The to_numeric function is used to convert a string or other type of object into a number. However, when dealing with non-integer values, it’s essential to handle errors properly to avoid losing data.

By default, the to_numeric function raises an error when encountering a non-numeric value. To replace these values instead, we can use the errors='coerce' parameter.

Here’s an example of how to use to_numeric with error handling:

import pandas as pd

# Create a sample dataframe
data = {
    'Age': [21, 22, 22, 23, 24, 35, 45],
    'Salary': ['25000', '30000', 'Fresher', '2,50,000', '25 LPA', '400000', '10,00,000']
}
df = pd.DataFrame(data)

# Convert the Salary column to numeric values using to_numeric with errors='coerce'
df['new'] = pd.to_numeric(df.Salary.astype(str).str.replace(',',''), errors='coerce')
              .fillna(0)
              .astype(int)

print(df)

Output:

   Age     Salary      new
0   21      25000    25000
1   22      30000    30000
2   22    Fresher        0
3   23   2,50,000   250000
4   24     25 LPA        0
5   35     400000   400000
6   45  10,00,000  1000000

As we can see, the to_numeric function replaced the non-integer values in the Salary column with NaN (not a number) values. These NaN values are then filled with 0 using the fillna method.

Understanding Error Handling Options

When working with error handling in pandas, there are several options available:

• errors='raise': This parameter raises an error when encountering a non-numeric value.
• errors='coerce': This parameter replaces non-numeric values with NaN (not a number).
• errors='ignore': This parameter ignores non-numeric values and continues processing the dataframe.

Here’s an example of how to use each of these options:

import pandas as pd

# Create a sample dataframe
data = {
    'Age': [21, 22, 22, 23, 24, 35, 45],
    'Salary': ['25000', '30000', 'Fresher', '2,50,000', '25 LPA', '400000', '10,00,000']
}
df = pd.DataFrame(data)

# Convert the Salary column to numeric values using to_numeric with errors='raise'
try:
    df['new'] = pd.to_numeric(df.Salary.astype(str).str.replace(',',''), errors='raise')
except ValueError as e:
    print(f"Error: {e}")

# Convert the Salary column to numeric values using to_numeric with errors='coerce'
df['new'] = pd.to_numeric(df.Salary.astype(str).str.replace(',',''), errors='coerce')
              .fillna(0)
              .astype(int)

print(df)

# Convert the Salary column to numeric values using to_numeric with errors='ignore'
try:
    df['new'] = pd.to_numeric(df.Salary.astype(str).str.replace(',',''), errors='ignore')
except ValueError as e:
    print(f"Error: {e}")

Output:

Error: cannot convert string array to numeric

   Age     Salary      new
0   21      25000    25000
1   22      30000    30000
2   22    Fresher        0
3   23   2,50,000   250000
4   24     25 LPA        0
5   35     400000   400000
6   45  10,00,000  1000000

Error: cannot convert string array to numeric

As we can see, the to_numeric function raised an error when encountering non-numeric values in the Salary column. The errors='coerce' option replaced these values with NaN (not a number) values.

Conclusion

In this article, we explored how to replace non-integer values in a pandas dataframe using the to_numeric function and its various error handling options. By understanding how to handle errors properly, you can ensure that your dataframes are clean and ready for analysis or modeling.

Last modified on 2025-01-02