Common dtype errors and fixes in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this dtype conversion code?

Consider the following pandas code that attempts to convert a column to numeric. What will be the output of df['A'] after running this code?

Pandas

import pandas as pd

df = pd.DataFrame({'A': ['1', '2', 'three', '4']})
df['A'] = pd.to_numeric(df['A'], errors='coerce')
print(df['A'])

A Raises ValueError due to 'three' string

B

0       1
1       2
2    three
3       4
Name: A, dtype: object

C

0    1.0
1    2.0
2    NaN
3    4.0
Name: A, dtype: float64

D

0    1
1    2
2    0
3    4
Name: A, dtype: int64

💡 Hint

Look at how errors='coerce' handles non-numeric values.

The pd.to_numeric function converts values to numbers. When errors='coerce' is set, any value that cannot be converted becomes NaN. So 'three' becomes NaN, and the column dtype becomes float64 because NaN is a float.

❓ data_output

intermediate

How many unique values are in the column after conversion?

Given this code, how many unique values does the column 'B' have after conversion?

Pandas

import pandas as pd

df = pd.DataFrame({'B': ['10', '20', '30', '20', 'ten']})
df['B'] = pd.to_numeric(df['B'], errors='coerce')
unique_count = df['B'].nunique(dropna=True)
print(unique_count)

A 3

B 4

C 5

D Raises TypeError

💡 Hint

Remember that nunique(dropna=True) excludes NaN values.

The values after conversion are 10.0, 20.0, 30.0, 20.0, and NaN (from 'ten'). The unique non-NaN values are 10.0, 20.0, and 30.0, so the count is 3.

🔧 Debug

advanced

Why does this code raise a ValueError?

This code tries to convert a column to datetime but raises a ValueError. What is the cause?

Pandas

import pandas as pd

df = pd.DataFrame({'dates': ['2023-01-01', 'not_a_date', '2023-03-01']})
df['dates'] = pd.to_datetime(df['dates'], format='%Y-%m-%d')

A Because the format string is incorrect for the given dates

B Because 'not_a_date' does not match the format and no error handling is set

C Because pd.to_datetime cannot convert strings to datetime

D Because the DataFrame column is not of string type

💡 Hint

Check how pd.to_datetime behaves when a string does not match the format and no errors parameter is given.

The format parameter enforces strict parsing. The string 'not_a_date' does not match the format '%Y-%m-%d', so pd.to_datetime raises a ValueError unless errors='coerce' or errors='ignore' is set.

🚀 Application

advanced

Which option correctly converts a mixed-type column to numeric with invalid values as zero?

You have a pandas column with numbers as strings and some invalid strings. You want to convert it to numeric, replacing invalid values with zero. Which code does this correctly?

Pandas

import pandas as pd

df = pd.DataFrame({'vals': ['5', '10', 'abc', '20']})

A df['vals'] = pd.to_numeric(df['vals'], errors='raise')

B df['vals'] = pd.to_numeric(df['vals'], errors='ignore').fillna(0)

C df['vals'] = df['vals'].astype(int)

D df['vals'] = pd.to_numeric(df['vals'], errors='coerce').fillna(0).astype(int)

💡 Hint

Think about how to handle invalid strings and convert the column to integers.

Option D converts invalid strings to NaN with errors='coerce', then replaces NaN with 0, and finally converts the column to integers.

Option D does not convert invalid strings and fillna(0) will not work properly. Option D raises an error because 'abc' cannot be cast to int. Option D raises an error on invalid strings.

🧠 Conceptual

expert

What is the main reason pandas converts integer columns with NaN to float dtype?

In pandas, when you convert a column with integers but some values are missing (NaN), the column dtype changes from int to float. Why does this happen?

A Because NaN is a float value and integer dtype cannot represent NaN

B Because pandas automatically converts all columns with missing values to float for performance

C Because integers are stored as floats internally in pandas

D Because NaN is treated as zero in integer columns

💡 Hint

Think about how missing values are represented in pandas and numpy.

NaN is a special floating-point value. Integer types in pandas (and numpy) cannot represent NaN. So when a column has integers and NaN, pandas upcasts the column to float to accommodate NaN.

Practice

1. What is the main reason to fix data types (dtypes) in a pandas DataFrame?

easy

A. To change column names automatically

B. To make the DataFrame look colorful

C. To increase the number of rows

D. To avoid errors and get correct analysis results

Solution

1. Step 1: Understand the role of dtypes

   Dtypes tell pandas how to treat data, like numbers or text.

2. Step 2: Recognize the impact of wrong dtypes

   If dtypes are wrong, calculations or filters may fail or give wrong answers.

3. Final Answer:

   To avoid errors and get correct analysis results -> Option D

4. Quick Check:

   Fixing dtypes = correct results [OK]

Hint: Fix dtypes to prevent errors and wrong answers [OK]

Common Mistakes:

• Ignoring dtype issues and expecting correct results
• Thinking dtype fixes change data values
• Confusing dtype fixes with styling

2. Which of the following is the correct syntax to convert a pandas column named 'age' to integer type?

easy

A. df['age'].astype(int)

B. df.astype('age', int)

C. astype(df['age'], 'int')

D. df['age'].convert(int)

Solution

1. Step 1: Recall the correct method to change dtype

   Use the astype() method on the DataFrame column.

2. Step 2: Check the syntax for astype()

   The correct syntax is df['column'].astype(type).

3. Final Answer:

   df['age'].astype(int) -> Option A

4. Quick Check:

   astype() method syntax = df['age'].astype(int) [OK]

Hint: Use df['col'].astype(type) to fix dtype [OK]

Common Mistakes:

• Using astype on DataFrame instead of column
• Wrong method names like convert()
• Passing arguments in wrong order

3. Given the code:

import pandas as pd
df = pd.DataFrame({'score': ['10', '20', '30']})
df['score'] = df['score'].astype(int)
print(df['score'].sum())

What is the output?

medium

A. '102030'

B. TypeError

C. 60

D. None

Solution

1. Step 1: Convert 'score' column from string to int

   The astype(int) changes strings like '10' to integer 10.

2. Step 2: Calculate the sum of integer values

   Sum of 10 + 20 + 30 = 60.

3. Final Answer:

   60 -> Option C

4. Quick Check:

   Sum of integers = 60 [OK]

Hint: Convert strings to int before sum to get numeric result [OK]

Common Mistakes:

• Not converting strings to int before sum
• Expecting string concatenation instead of sum
• Confusing sum output with error

4. You run this code:

df = pd.DataFrame({'price': ['100', '200', '300']})
df['price'] = df['price'].astype(float)
df['price'] = df['price'].astype(int)

But get an error. What is the likely cause?

medium

A. DataFrame missing import statement

B. Strings cannot be converted to float

C. Cannot convert float to int directly if float has decimals

D. astype() cannot be chained

Solution

1. Step 1: Examine the code snippet

   The code uses pd.DataFrame but no import statement is shown.

2. Step 2: Identify the runtime error

   Without import pandas as pd, pd raises NameError: name 'pd' is not defined.

3. Final Answer:

   DataFrame missing import statement -> Option A

4. Quick Check:

   Missing import causes NameError [OK]

Hint: Always import pandas as pd before using pd.DataFrame [OK]

Common Mistakes:

• Forgetting the import pandas as pd statement
• Assuming pd is predefined
• Focusing on dtype conversions instead of basic syntax

5. You have a DataFrame with a column 'date' containing strings like '2023-01-01'. You want to analyze by month but get errors when grouping. What is the best way to fix the dtype before grouping?

hard

A. Convert 'date' column to integer using astype(int)

B. Convert 'date' column to datetime using pd.to_datetime()

C. Leave 'date' as string and group directly

D. Drop the 'date' column before grouping

Solution

1. Step 1: Identify the problem with string dates

   Grouping by string dates does not allow easy extraction of months.

2. Step 2: Convert strings to datetime type

   Use pd.to_datetime() to convert strings to datetime objects.

3. Step 3: Group by month after conversion

   Datetime dtype allows grouping by month using dt.month.

4. Final Answer:

   Convert 'date' column to datetime using pd.to_datetime() -> Option B

5. Quick Check:

   Datetime dtype needed for date grouping [OK]

Hint: Use pd.to_datetime() to fix date strings before grouping [OK]

Common Mistakes:

• Trying to group by string dates directly
• Converting dates to int which loses meaning
• Dropping date column instead of fixing dtype