Which Seaborn function would you use to visualize a bivariate distribution of two variables?
- sns.barplot()
- sns.distplot()
- sns.jointplot()
- sns.plot()
To visualize a bivariate distribution of two variables in Seaborn, you should use the sns.jointplot() function. It creates a scatter plot with marginal histograms and can also display a regression line or a kernel density estimate.
Which sorting algorithm is best suited for large datasets?
- Bubble Sort
- Insertion Sort
- Quick Sort
- Selection Sort
Quick Sort is typically the best choice for sorting large datasets due to its average-case time complexity of O(n log n). Bubble Sort, Insertion Sort, and Selection Sort have worse time complexities and are less efficient for large datasets.
Which status code indicates that a request was successful in HTTP?
- 200 OK
- 401 Unauthorized
- 404 Not Found
- 500 Internal Server Error
The HTTP status code 200 OK indicates that a request was successful. It is used to signal that the request has been successfully received, understood, and accepted by the server. Other codes (404, 500, 401) indicate various error conditions.
Which type of tree would you use to implement an ordered map?
- AVL Tree
- Binary Search Tree (BST)
- Heap
- Red-Black Tree
To implement an ordered map, you would typically use a Binary Search Tree (BST). A BST ensures that elements are stored in sorted order, making it efficient for operations like search, insert, and delete in O(log n) time.
You are asked to create a new column in a DataFrame that is the sum of two other columns. How would you create this new column in Pandas?
- df.create_column('new_column', df.column1 + df.column2)
- df.new_column = df.column1 + df.column2
- df['new_column'] = df['column1'] + df['column2']
- df['new_column'] = df['column1'].add(df['column2'])
To create a new column in a Pandas DataFrame that is the sum of two existing columns, you would use the syntax df['new_column'] = df['column1'] + df['column2']. This operation will perform element-wise addition and create the new column.
You are asked to create a plot comparing the distribution of a variable across different categories, highlighting the median and interquartile range. Which Seaborn plot would you choose?
- Box Plot
- Line Plot
- Swarm Plot
- Violin Plot
To compare the distribution of a variable across categories while highlighting the median and interquartile range, a Violin Plot in Seaborn is a suitable choice. It combines a box plot with a kernel density estimation to provide a richer visualization of the data distribution.
Which Python library is specifically designed for creating static, interactive, and real-time graphs and plots?
- Matplotlib
- NumPy
- Pandas
- Seaborn
Matplotlib is specifically designed for creating static, interactive, and real-time graphs and plots in Python. It is a widely-used plotting library for data visualization.
Which Matplotlib function allows plotting data points in the form of a two-dimensional density plot?
- contour()
- heatmap()
- hist2d()
- scatter()
The heatmap() function in Matplotlib allows you to create a two-dimensional density plot. It is useful for visualizing the distribution and density of data points in a heatmap-like format.
Which method can be used to get the value for a given key from a dictionary, and if the key is not found, it returns a default value?
- fetch()
- get()
- retrieve()
- value()
The get() method of a dictionary allows you to retrieve the value associated with a given key. If the key is not found, it returns a default value, which can be specified as a second argument to get(). This is a useful way to avoid KeyError exceptions. The other options are not valid methods for this purpose.
Which method in Scikit-learn would you use to tune hyperparameters of a model?
- fit()
- gradient_boosting()
- GridSearchCV
- predict()
GridSearchCV is used in Scikit-learn for hyperparameter tuning. It performs an exhaustive search over specified hyperparameter values to find the best combination for the model.