Learning from Mistakes: 50 Common Mistakes in Pandas and Solutions to Overcome Them

While mistakes are a natural part of the learning process, here are some common pandas mistakes that beginners might make when working with the Python pandas library for data manipulation and analysis:

1. Not Importing Pandas:

• Forgetting to import the pandas library at the beginning of the script or notebook. This often results in ‘NameError’ when using pandas functions.

import pandas as pd

2. Using Incorrect DataFrame or Series Names:

• Referring to DataFrame or Series names incorrectly. Ensure that you use the correct variable names when performing operations.

# Correct
df['column_name']

# Incorrect
dataframe['column_name']

3. Not Checking Data Types:

• Neglecting to check and convert data types. Pandas may not always infer the correct data types, and it’s crucial to ensure that columns have the appropriate types.

df['column_name'] = pd.to_numeric(df['column_name'], errors='coerce')

4. Using iloc Incorrectly:

• Misusing the iloc indexer for selecting rows and columns by…