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

btd
8 min readNov 14, 2023

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 position. Make sure to specify rows and columns as integers or slices.
# Correct
df.iloc[0, 1]

# Incorrect
df.iloc['row_label', 'column_label']

5. Using == for DataFrame Comparison:

  • Using the == operator for DataFrame comparison. Pandas uses element-wise comparison, so it's better to use equals().
# Correct
df1.equals(df2)

# Incorrect
df1 == df2

6. Not Handling Missing Values:

  • Ignoring missing values without proper handling. Use functions like dropna(), fillna(), or interpolate() to manage missing data.
# Correct
df.dropna()

# Incorrect
df[~df.isnull()]

7. Modifying Original DataFrame:

  • Modifying the original DataFrame unintentionally. If you need to create a new DataFrame, use the copy() method.

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