MLS-C01 Exploratory Data Analysis Practice Question
A data scientist runs a SageMaker notebook and uses pandas to explore a dataset. The dataset contains 500,000 rows and 20 columns, including a 'timestamp' column. After loading the data into a DataFrame, the memory usage is unexpectedly high. What is the most likely cause?
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 default data types inferred by pandas are unnecessarily large for the actual data ranges.
When pandas reads a CSV without explicit dtypes, it infers data types. For numeric columns, it defaults to int64 (8 bytes per value) or float64 (8 bytes per value), even if the actual values could fit in smaller types (e.g., int8, int16). With 500,000 rows and 20 columns, such large types significantly increase memory usage. Option A is wrong because pandas does not automatically create an index from the timestamp column; it assigns a default integer index. Option C is wrong because the entire dataset was loaded, not just a sample. Option D is wrong because the CSV is uncompressed; compression would not cause memory inflation after loading.
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 DataFrame created an index column on the timestamp field, doubling memory usage.
Why it's wrong here
Pandas does not automatically create an index column; the index is just row numbers.
- ✓
The default data types inferred by pandas are unnecessarily large for the actual data ranges.
Why this is correct
Pandas uses int64/float64 by default, which can be optimized by downcasting.
- ✗
The DataFrame only loaded a sample of the data, but the sample size was too large.
Why it's wrong here
Pandas loads the entire dataset by default unless specified.
- ✗
The CSV file was compressed, and pandas inflated it in memory.
Why it's wrong here
Pandas can read compressed files directly without extra memory overhead.
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