Data Analysis with Python Flashcards
6 cards from real Data Science with Python Certification practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 6 Data Analysis with Python flashcards as text
Choose a non-indexed object.
Answer: None of these
In pandas, SparseDataFrame, SparseSeries, and SparsePanel are all indexed data structures, allowing label- or position-based access. Since none of the listed options is a non-indexed object, 'None of these' is the correct answer.
What serves as the fundamental building block for all sparse indexed data structures?
Answer: SparseArray
Explanation: The base layer for all sparse indexed data structures is typically an array or a similar data structure that provides a contiguous block of memory. In the case of sparse indexed data structures, this base layer is often referred to as a "sparse array" or a "sparse matrix."
What are things like pandas?
Answer: NumPy
Explanation: NumPy (Numerical Python) is a powerful library in Python that provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently. NumPy is often used for numerical computations and data manipulation tasks.
What plot evaluates the unpredictability of time series?
Answer: Autocorrelation
Explanation: An autocorrelation plot is a plot that helps check randomness in a time series. It is commonly used to identify patterns or dependencies in the data over different lags (time intervals).
To import Pandas, select the appropriate statement.
Answer: Import pandas as pd
Explanation: The statement "import pandas as pd" is a valid way to import the Pandas library in Python. It is a common convention to import Pandas using the alias "pd" to make it easier to refer to the library when using its functions and classes throughout the code.
What kind of data can be used with Pandas?
Answer: All of these
Explanation: All of these can be used as data in Pandas. Pandas provide various data structures, such as Series and DataFrame, that can hold and manipulate data. These data structures can be initialized with different types of data.
How this page was made: written with AI tools and published by Nathan Beja, founder of Practice Test Geeks, who is responsible for its content. We have not yet matched this exam's facts to an official source, so confirm fees, dates and rules with the exam body. Not reviewed by a subject-matter expert. Editorial policy