Essential libraries overview (Pandas, NumPy, Matplotlib) in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the Pandas library with the common alias.

Data Analysis Python

import [1] as pd

Drag options to blanks, or click blank then click option'

Anumpy

Bmatplotlib

Cpandas

Dseaborn

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'numpy' instead of 'pandas'.

Using 'matplotlib' which is for plotting.

Using 'seaborn' which is another plotting library.

The Pandas library is imported using the name pandas and commonly aliased as pd.

2fill in blank

medium

Complete the code to create a NumPy array from a Python list.

Data Analysis Python

import numpy as np
arr = np.[1]([1, 2, 3, 4])

Drag options to blanks, or click blank then click option'

Aarray

Bseries

Cmatrix

Dlist

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'list' which is a Python type, not a NumPy function.

Using 'matrix' which is a different NumPy type.

Using 'series' which is from Pandas.

The array function in NumPy converts a list into a NumPy array.

3fill in blank

hard

Fix the error in the code to plot a simple line graph using Matplotlib.

Data Analysis Python

import matplotlib.pyplot as plt
plt.[1]([1, 2, 3], [4, 5, 6])
plt.show()

Drag options to blanks, or click blank then click option'

Ascatter

Bplot

Chist

Dbar

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'scatter' which draws points, not lines.

Using 'hist' which draws histograms.

Using 'bar' which draws bar charts.

The plot function draws a line graph with x and y values.

4fill in blank

hard

Fill both blanks to create a Pandas DataFrame from a dictionary and show its first rows.

Data Analysis Python

import pandas as pd
data = {'name': ['Anna', 'Bob'], 'age': [25, 30]}
df = pd.[1](data)
print(df.[2]())

Drag options to blanks, or click blank then click option'

ADataFrame

BSeries

Chead

Dtail

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'Series' instead of 'DataFrame' to create the table.

Using 'tail()' instead of 'head()' to show rows.

DataFrame creates a table from a dictionary. head() shows the first rows.

5fill in blank

hard

Fill all three blanks to create a NumPy array, calculate its mean, and print the result.

Data Analysis Python

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

Drag options to blanks, or click blank then click option'

Aarray

Bmean

Cmean_value

Dmedian

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'median' instead of 'mean' to calculate average.

Printing 'arr' instead of the mean value.

Using 'Series' instead of 'array' to create the array.

array creates the NumPy array, mean() calculates the average, and mean_value holds the result to print.

Practice

1. Which library is mainly used to organize data in tables for easy analysis?

easy

A. Seaborn

B. NumPy

C. Matplotlib

D. Pandas

Solution

1. Step 1: Understand library purposes

   Pandas is designed to organize data in tables called DataFrames.

2. Step 2: Match library to task

   Since the question asks about organizing data in tables, Pandas fits best.

3. Final Answer:

   Pandas -> Option D

4. Quick Check:

   Tables = Pandas [OK]

Hint: Tables mean Pandas, numbers mean NumPy, charts mean Matplotlib [OK]

Common Mistakes:

• Confusing NumPy with Pandas for table data
• Thinking Matplotlib organizes data
• Choosing Seaborn which is for visualization, not tables

2. Which of the following is the correct way to import NumPy in Python?

easy

A. import matplotlib.pyplot as plt

B. import pandas as pd

C. import numpy as np

D. import numpy as pd

Solution

1. Step 1: Recall standard import conventions

   NumPy is commonly imported as 'np' to shorten code.

2. Step 2: Check each option

   import numpy as np uses 'import numpy as np' which is correct; others are for different libraries or wrong aliases.

3. Final Answer:

   import numpy as np -> Option C

4. Quick Check:

   NumPy alias = np [OK]

Hint: NumPy is always imported as np [OK]

Common Mistakes:

• Using wrong alias like pd for NumPy
• Mixing import statements of different libraries
• Forgetting 'as' keyword

3. What will be the output of this code?

import numpy as np
arr = np.array([1, 2, 3])
print(arr * 2)

medium

A. [2 4 6]

B. [1 2 3 1 2 3]

C. [3 4 5]

D. Error

Solution

1. Step 1: Understand NumPy array multiplication

   Multiplying a NumPy array by a number multiplies each element by that number.

2. Step 2: Calculate element-wise multiplication

   Each element in [1, 2, 3] multiplied by 2 gives [2, 4, 6].

3. Final Answer:

   [2 4 6] -> Option A

4. Quick Check:

   Array * 2 doubles each element [OK]

Hint: NumPy * number multiplies each element [OK]

Common Mistakes:

• Thinking * duplicates the array
• Expecting element-wise addition instead of multiplication
• Assuming syntax error

4. Find the error in this code snippet:

import matplotlib.pyplot as plt
plt.plot([1, 2, 3], [4, 5])
plt.show()

medium

A. plt.show() is missing

B. The lists have different lengths causing an error

C. Wrong import statement for matplotlib

D. No error, code runs fine

Solution

1. Step 1: Check input lists length

   Plotting requires x and y lists to have the same length.

2. Step 2: Identify mismatch

   Here, x has 3 elements, y has 2, causing a ValueError.

3. Final Answer:

   The lists have different lengths causing an error -> Option B

4. Quick Check:

   Plot lists length must match [OK]

Hint: x and y lists must be same length for plot [OK]

Common Mistakes:

• Ignoring length mismatch
• Thinking plt.show() is missing
• Assuming import is wrong

5. You have a Pandas DataFrame with a column 'Age'. Which library and method would you use to quickly calculate the average age?

hard

A. Pandas' mean() method on the 'Age' column

B. Matplotlib's plot() on the 'Age' column

C. NumPy's np.mean() on the 'Age' column

D. NumPy's array() to convert 'Age' column

Solution

1. Step 1: Identify the data structure

   The 'Age' column is part of a Pandas DataFrame.

2. Step 2: Choose the best method for average

   Pandas columns have a built-in mean() method to calculate average quickly.

3. Step 3: Compare options

   NumPy's np.mean() works but requires extra step; Matplotlib is for plotting, not calculation.

4. Final Answer:

   Pandas' mean() method on the 'Age' column -> Option A

5. Quick Check:

   DataFrame column average = mean() [OK]

Hint: Use Pandas mean() directly on DataFrame columns [OK]

Common Mistakes:

• Using Matplotlib for calculations
• Converting column unnecessarily with NumPy
• Not using built-in Pandas methods