Troubleshooting Common Errors

Troubleshooting Common Errors#

This guide helps you understand and fix common Python and pandas errors you might encounter in this course.

How to Read Error Messages#

Python error messages follow a pattern. Here’s an example:

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-5-abc123def456> in <module>
      1 # Select the age column
----> 2 data['Age']

KeyError: 'Age'

Reading the error:

  1. Error Type (top): KeyError - tells you what went wrong

  2. Traceback (middle): Shows which line caused the error (marked with ---->)

  3. Error Message (bottom): 'Age' - gives specific details

The arrow (---->) points to the exact line that caused the problem.

Common Error Types#

KeyError#

What it means: You’re trying to access a dictionary key or DataFrame column that doesn’t exist.

Common causes:

# ❌ Wrong column name (case sensitive!)
data['Age']  # Column is actually 'age' (lowercase)

# ❌ Typo in column name
data['ART_regimen']  # Column is actually 'ART'

# ❌ Column doesn't exist at all
data['nonexistent_column']

How to fix:

# 1. Check available columns
print(data.columns)

# 2. Use the exact column name (case matters!)
data['age']  # ✅ CORRECT

# 3. For complex names, copy-paste from the columns list
print(data.columns.tolist())

Real example from our course:

# ❌ WRONG
result = data['Processing_Domain_Z']

# ✅ CORRECT (check the actual column name)
print(data.columns)  # Shows: 'processing_domain_z'
result = data['processing_domain_z']

TypeError#

What it means: You’re using an operation on the wrong type of data.

Common causes:

Example 1: Using + on incompatible types

# ❌ Can't add string and number
age = '25' + 5  # TypeError: can only concatenate str (not "int") to str

# ✅ Convert to same type first
age = int('25') + 5  # Returns 30
age = '25' + str(5)  # Returns '255'

Example 2: Wrong bracket type

# ❌ Using () instead of [] for indexing
data('age')  # TypeError: 'DataFrame' object is not callable

# ✅ Use square brackets
data['age']

Example 3: Missing quotes on strings

# ❌ Treating variable as string
data[data['sex'] == Male]  # TypeError or NameError

# ✅ Add quotes for string values
data[data['sex'] == 'Male']

How to fix:

  1. Check if you’re using the right bracket type ([] vs ())

  2. Ensure strings have quotes

  3. Check data types: type(variable) or data.dtypes

SyntaxError#

What it means: Python can’t understand your code because of a typo or missing punctuation.

Common causes:

Missing closing bracket/parenthesis

# ❌ Missing closing bracket
subset = data[data['age'] > 30
# SyntaxError: unexpected EOF while parsing

# ✅ Add the closing bracket
subset = data[data['age'] > 30]

Missing comma

# ❌ Missing comma between list items
columns = ['age' 'sex' 'education']
# SyntaxError: invalid syntax

# ✅ Add commas
columns = ['age', 'sex', 'education']

Mismatched quotes

# ❌ Starting with " but ending with '
name = "Alice'
# SyntaxError: EOL while scanning string literal

# ✅ Match your quotes
name = "Alice"
name = 'Alice'  # Both work, just be consistent

How to fix:

  1. Look at the line indicated by the arrow (---->)

  2. Count your brackets: each ( needs a ), each [ needs a ]

  3. Check for missing commas in lists

  4. Ensure quotes match (both single ' or both double ")

AttributeError#

What it means: You’re trying to use a method or attribute that doesn’t exist for that object type.

Common causes:

Wrong method name (typo)

# ❌ Method doesn't exist (typo: groupby vs groupBy)
data.groupBy('ART')
# AttributeError: 'DataFrame' object has no attribute 'groupBy'

# ✅ Correct method name
data.groupby('ART')

Using DataFrame method on Series (or vice versa)

# ❌ .query() only works on DataFrames, not Series
ages = data['age']
young_ages = ages.query('age < 30')
# AttributeError: 'Series' object has no attribute 'query'

# ✅ Filter the DataFrame first, then select column
young_data = data.query('age < 30')
young_ages = young_data['age']

# OR use boolean indexing
young_ages = data[data['age'] < 30]['age']

Calling a result instead of a method

# ❌ Missing parentheses to call the method
data_copy = data.copy
# This assigns the function itself, not the result

# ✅ Add () to call the method
data_copy = data.copy()

How to fix:

  1. Check spelling of method names (case matters!)

  2. Verify you’re using a DataFrame vs Series method correctly

  3. Don’t forget parentheses () when calling methods

  4. Check the documentation: help(data.groupby) or online docs

NameError#

What it means: You’re using a variable or function name that Python doesn’t recognize.

Common causes:

Typo in variable name

# ❌ Variable name misspelled
my_data = pd.read_csv('file.csv')
result = mydata['age']
# NameError: name 'mydata' is not defined

# ✅ Use exact variable name
result = my_data['age']

Forgot to import a library

# ❌ Using pd before importing pandas
data = pd.read_csv('file.csv')
# NameError: name 'pd' is not defined

# ✅ Import first
import pandas as pd
data = pd.read_csv('file.csv')

Variable not defined yet

# ❌ Using variable before creating it
print(result)
result = data['age'].mean()
# NameError: name 'result' is not defined

# ✅ Create variable first
result = data['age'].mean()
print(result)

How to fix:

  1. Check spelling of variable names

  2. Ensure you’ve run all necessary cells (especially imports!)

  3. Make sure you’ve run cells in order (top to bottom)

  4. In Colab, use Runtime → Restart and Run All to start fresh

IndexError#

What it means: You’re trying to access a position in a list or array that doesn’t exist.

Common causes:

# ❌ List only has 3 items (indices 0, 1, 2)
my_list = ['a', 'b', 'c']
print(my_list[3])
# IndexError: list index out of range

# ✅ Use valid indices
print(my_list[2])  # Last item
print(my_list[-1])  # Also gets last item

How to fix:

  1. Remember Python uses 0-based indexing (first item is [0])

  2. Check the length: len(my_list)

  3. Use negative indices to count from the end: [-1] is the last item

ValueError#

What it means: You passed the right type of argument, but an invalid value.

Common causes:

Wrong value in function argument

# ❌ 'median' is not a valid method for normality test
pg.normality(data['age'], method='median')
# ValueError: method must be 'normaltest' or 'jarque_bera'

# ✅ Use a valid method
pg.normality(data['age'], method='normaltest')

Incompatible array shapes

# ❌ Arrays have different lengths
x = [1, 2, 3]
y = [1, 2, 3, 4, 5]
plt.plot(x, y)
# ValueError: x and y must have same first dimension

# ✅ Make sure arrays match
x = [1, 2, 3, 4, 5]
y = [1, 2, 3, 4, 5]
plt.plot(x, y)

How to fix:

  1. Read the error message carefully - it often tells you valid options

  2. Check function documentation: help(function_name)

  3. Verify data dimensions match when required

Troubleshooting Workflow#

When you encounter an error, follow these steps:

  1. Read the error type - What kind of error is it?

  2. Find the line - Look for the arrow ----> in the traceback

  3. Check the error message - What specific detail does it give?

  4. Consult this guide - Find your error type above

  5. Try the fixes - Work through the suggested solutions

  6. Still stuck?

    • Print intermediate values: print(variable)

    • Check types: type(variable) or data.dtypes

    • Review earlier cells - did they all run successfully?

    • Restart and run all: Runtime → Restart and Run All

    • Ask for help in office hours or discussion board

Prevention Tips#

  1. Run cells in order - Start from the top and work down

  2. Check column names - Use data.columns frequently

  3. Use tab completion - Start typing and press Tab to autocomplete

  4. Match the examples - Follow the pattern from walkthroughs

  5. Test as you go - Don’t write lots of code before running it

  6. Read error messages - They often tell you exactly what’s wrong

Additional Resources#

Contents