Question 1

How to handle missing data in pandas?

Accepted Answer

Use methods like `df.dropna()` to remove rows/columns with missing values or `df.fillna(value)` to fill them with specific values. Pandas supports NaN, NA, and NaT for flexible missing data management across data types.

Question 2

Pandas vs NumPy: which one should I use?

Accepted Answer

Use NumPy for low-level numerical computations on arrays without labels, and pandas for structured, labeled data manipulation like DataFrames. Pandas builds on NumPy and is better for tabular data analysis with column names and indices.

Question 3

How to merge two DataFrames in pandas?

Accepted Answer

Use the `pd.merge()` function or DataFrame's `merge` method, specifying keys and join types (e.g., inner, outer). It supports database-style joins, making data combination intuitive for relational datasets.

Question 4

What's the best way to read a large CSV file in pandas?

Accepted Answer

Use `pd.read_csv()` with the chunksize parameter to read data in chunks for memory efficiency. For out-of-core processing, consider integrating with Dask or using alternative formats like Parquet via pandas' I/O tools.

Question 5

How to group data and calculate statistics in pandas?

Accepted Answer

Use the `groupby()` method followed by aggregation functions like `sum()` or `mean()`. This implements split-apply-combine operations, enabling efficient data transformations and summaries.

Question 6

Is pandas good for big data or should I use PySpark?

Accepted Answer

Pandas is ideal for datasets that fit in memory; for larger data, PySpark or Dask are better as they support distributed computing. Pandas can be used alongside these tools for smaller, in-memory subsets or prototyping.

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