Top Python Interview Questions for Data Science Roles (NumPy & Pandas)
Landing a data science role requires more than just knowing algorithms; it demands mastery of the core tools—especially NumPy and Pandas. These libraries are the bedrock of data manipulation and analysis in Python. If you’re preparing for your next interview, focusing on the efficiency, structure, and specialized features of these libraries is key. Here are some top python interview questions centered around NumPy and Pandas that you should be ready to ace.
NumPy: The Foundation of Numerical Computing
NumPy (Numerical Python) is essential for high-performance array operations and mathematical computation. Interviewers test your understanding of why NumPy is faster than native Python lists and how its arrays work.
1. Why is NumPy faster than native Python lists for numerical operations?
- Vectorization: NumPy operations are vectorized, meaning they operate on entire arrays at once, eliminating the need for explicit loops, which are slow in Python.
- Contiguous Memory: NumPy arrays store data in contiguous blocks of memory. This locality is highly optimized for modern CPUs (CPU cache utilization).
- C/C++ Back-end: The core logic of NumPy is implemented in highly optimized C and C++ code, which is executed directly, bypassing the Python interpreter overhead.
- Homogeneous Data: NumPy arrays must contain elements of the same data type (e.g., all integers or all floats), simplifying the memory layout and operations.
2. Explain the concept of NumPy Broadcasting.
- Broadcasting is a set of rules that governs how NumPy handles arrays of different shapes during arithmetic operations.
- It allows smaller arrays to be “stretched” or “broadcast” across larger arrays so that they have compatible shapes without actually creating multiple copies of the data (which would be memory-inefficient).
- Rule: The dimensions are compared starting from the trailing (rightmost) dimension. Two dimensions are compatible when:
- They are equal, OR
- One of them is 1.
Example: Adding a scalar (shape ()) to an array (shape (3, 3)) is broadcasting. Adding an array of shape (3,) to an array of shape (3, 3) is also valid broadcasting.
3. What is the difference between numpy.array.view() and numpy.array.copy()?
- view() (Shallow Copy): Creates a new array object that looks at the same data in memory as the original array. Changes to the view will affect the original data.
- copy() (Deep Copy): Creates a completely new array object with its own, distinct copy of the data. Changes to the copy will not affect the original array.
Pandas: Data Wrangling and Analysis
Pandas is built on top of NumPy and provides the indispensable Series and DataFrame structures for handling labeled data. A significant part of top python interview questions for data science revolves around efficient data manipulation using Pandas.
4. Differentiate between a Pandas Series and a DataFrame.
| Feature | Series | DataFrame |
| Structure | 1-dimensional labeled array | 2-dimensional labeled data structure |
| Data Type | Homogeneous (all elements must be of the same type) | Heterogeneous (columns can have different data types) |
| Analogy | A single column in a spreadsheet or SQL table | An entire spreadsheet or SQL table |
| Indexing | Has a single index (the row index) | Has two indices (row index and column index) |
5. Explain the significance of the groupby() operation in Pandas.
- The groupby() method is the core tool for the “Split-Apply-Combine” strategy:
- Split: Divides the data into groups based on some criterion (e.g., grouping sales data by region).
- Apply: Applies a function to each individual group (e.g., calculating the mean, sum, or max).
- Combine: Combines the results into a new data structure (usually a DataFrame or Series).
- Common methods used after groupby(): agg(), sum(), mean(), count(), apply(), and transform().
6. What is the difference between DataFrame.loc[] and DataFrame.iloc[]?
These are the primary methods for indexing and selecting data in a DataFrame:
- loc[] (Label-based indexing): Selects data primarily by row and column labels (names).
- Syntax: df.loc[row_label, column_label]
- Crucial Detail: Includes the endpoint label when slicing (e.g., df.loc[‘A’:’C’] includes row ‘C’).
- iloc[] (Integer-based indexing): Selects data primarily by integer position (from 0 to $N-1$).
- Syntax: df.iloc[row_position, column_position]
- Crucial Detail: Excludes the endpoint position when slicing (standard Python slicing rules: df.iloc[0:3] includes rows 0, 1, and 2).
7. How do you handle missing values in Pandas?
Handling missing data (NaN or None) is a critical step in the data science pipeline.
- Identification: Use df.isnull() or df.isna() to get a Boolean DataFrame showing missing values. Use df.isnull().sum() to get counts per column.
- Removal: Use df.dropna() to drop rows or columns with missing values.
- Imputation: Use df.fillna() to replace missing values. Common strategies include:
- Scalar Imputation: Replacing with a single value (e.g., df.fillna(0)).
- Statistical Imputation: Replacing with the mean, median, or mode of the column (e.g., df[‘col’].fillna(df[‘col’].mean())).
- Forward/Backward Fill: Using method=’ffill’ (forward fill) or method=’bfill’ (backward fill) to propagate the previous or next valid observation.
8. Compare and contrast merge() and join() in Pandas.
Both are used to combine DataFrames, similar to SQL joins:
- pd.merge():
- Primary Tool: The primary function for combining DataFrames.
- Explicit: Requires you to explicitly specify the column(s) to join on using the on parameter (e.g., on=[‘key1’, ‘key2’]).
- Join Types: Supports all standard SQL join types (how=’left’, ‘right’, ‘inner’, ‘outer’).
- df.join():
- Convenience Method: A method on the DataFrame object.
- Implicit: By default, it joins on the index of the calling DataFrame.
Key-to-Index Join: If joining on a column of the right DataFrame, you use the on parameter on the left DataFrame, and the right DataFrame must be set to use that column as its index.
