MLS-C01 Exploratory Data Analysis Practice Question
After loading a dataset into a pandas DataFrame, a data scientist runs df.info() and sees that a column 'income' has object dtype. What does this indicate, and what EDA step should be taken?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The column contains strings; convert to numeric using pd.to_numeric() and investigate non-convertible values.
'object' dtype in pandas typically indicates string or mixed types. The appropriate EDA step is to attempt conversion to numeric using pd.to_numeric() and investigate non-convertible values to handle data quality issues. Option A is incorrect because object dtype does not specifically indicate missing values; missing values can appear in any dtype. Option C is premature; conversion should precede normalization. Option D is incorrect because object dtype is not numeric.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The column has missing values; impute them.
Why it's wrong here
dtype object does not directly indicate missing values.
- ✓
The column contains strings; convert to numeric using pd.to_numeric() and investigate non-convertible values.
Why this is correct
Conversion to numeric is necessary for analysis; non-convertible values may indicate errors.
- ✗
Normalize the column to a 0-1 range.
Why it's wrong here
Normalization requires numeric data first.
- ✗
The column is already numeric; proceed.
Why it's wrong here
Object dtype means the column contains strings or mixed types.
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