Data Science Essentials - Quiz

Create a NumPy array of 10 zeros for initializing a weight vector

Select the second column from a 2D NumPy array for further analysis

Calculate the mean of all values in a NumPy array of daily temperatures

Which statement best describes a key advantage of NumPy over Python lists for numerical data?

Given arr = np.array([[1, 2, 3], [4, 5, 6]]), what is arr.shape?

You have a large numeric dataset in a CSV file. Which Pandas function is typically used to load it into a DataFrame?

In a Pandas DataFrame df, which syntax selects the 'age' column as a Series?

Which Matplotlib function displays the plot window after creating a figure?

Which plot type is most appropriate to compare total sales across product categories?

Given this DataFrame: df, which expression filters rows where 'Revenue' is greater than 1000?

What does the axis=0 argument mean in np.mean(data, axis=0) for a 2D array?

Which DataFrame method is commonly used to compute summary statistics like mean and std for numeric columns?

You want to color bars differently for each category in a Matplotlib bar chart. Which parameter should you use?

Which Pandas method combines rows from two DataFrames based on a common key column (like a database join)?

Order the steps to load a CSV file into a Pandas DataFrame and plot a line chart of a 'sales' column over 'day'.

Drag and drop to reorder, or use the arrows.

Order the steps to compute the average rating per product using a Pandas DataFrame and a groupby operation.

Drag and drop to reorder, or use the arrows.

What is the output of this NumPy code?

1import numpy as np
2arr = np.array([10, 20, 30, 40])
3print(arr[1] + arr[-1])

What is the output of this Pandas code?

1import pandas as pd
2s = pd.Series([3, 5, 7], index=['a', 'b', 'c'])
3print(s['b'])

What does this code print?

1import numpy as np
2m = np.array([[1, 2], [3, 4]])
3print(m.reshape((4,))[2])

What is the output of this Matplotlib-related code?

1import matplotlib.pyplot as plt
2values = [1, 2, 3]
3plt.plot(values)
4print(len(values))

What is the output of this Pandas DataFrame code?

1import pandas as pd
2data = {'A': [1, 2], 'B': [3, 4]}
3df = pd.DataFrame(data)
4print(df['A'].mean())

Find the bug in this NumPy code that normalizes a vector of features.

Click on the line(s) that contain the bug.

import numpy as np

def normalize_vector(features):

    total = np.sum(features)

    normalized = features / total

    return normalized

Find the bug in this Pandas code that filters high-value transactions.

Click on the line(s) that contain the bug.

import pandas as pd

def filter_high_value(df_transactions):

    condition = df_transactions['amount'] > 1000

    result = df_transactions.loc[condition, 'amount', 'customer_id']

    return result

Match the NumPy function to what it creates or computes.

Click an item on the left, then click its match on the right. Click a matched item to unmatch.

Match the visualization type to the most suitable use case.

Click an item on the left, then click its match on the right. Click a matched item to unmatch.

Complete the code to create a NumPy array of 100 evenly spaced values between 0 and 1 and compute their average.

Complete the code to read a CSV file into a DataFrame and show the first 3 rows.

Complete the code to filter rows where 'Profit' is positive and compute the average profit.

Complete the code to create a bar chart of product sales using Matplotlib.

Click the line that sets the x-axis label in this Matplotlib plot.

Click on the line to select.

import matplotlib.pyplot as plt

x = [1, 2, 3]

y = [2, 4, 6]

plt.plot(x, y)

plt.xlabel('Time (days)')

plt.ylabel('Value')

plt.title('Trend over time')

plt.show()

Click the line that performs the grouping operation in this Pandas code.

Click on the line to select.

import pandas as pd

sales = pd.read_csv('sales.csv')

by_region = sales.groupby('region')

summary = by_region['revenue'].sum()

print(summary)