Descriptive statistics review in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the mean of the list 'data'.

Data Analysis Python

mean_value = sum(data) / [1]

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

Amin(data)

Bsum(data)

Cmax(data)

Dlen(data)

Attempts:

3 left

💡 Hint

Common Mistakes

Dividing by sum(data) instead of the number of elements.

Using max or min instead of the count of elements.

The mean is the sum of all values divided by the number of values, which is given by len(data).

2fill in blank

medium

Complete the code to calculate the median of the sorted list 'data_sorted'.

Data Analysis Python

n = len(data_sorted)
median = (data_sorted[n // 2] if n % 2 != 0 else (data_sorted[n // 2 - 1] + data_sorted[[1]]) / 2)

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

An // 2

Bn // 2 + 1

Cn // 2 - 1

Dn // 2 - 2

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong index for the second middle element.

Confusing integer division with float division.

For even number of elements, the median is the average of the two middle values at positions n//2 - 1 and n//2.

3fill in blank

hard

Fix the error in the code to calculate the variance of the list 'data'.

Data Analysis Python

mean = sum(data) / len(data)
variance = sum((x - mean)[1] for x in data) / len(data)

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

A* 2

B** 2

C^ 2

D* * 2

Attempts:

3 left

💡 Hint

Common Mistakes

Using '^' which is bitwise XOR, not exponentiation.

Using '* 2' which doubles instead of squares.

To calculate variance, subtract the mean from each value, square the result using ** 2, then average.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 characters.

Data Analysis Python

lengths = {word: [1] for word in words if len(word) [2] 3}

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

Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the value instead of its length.

Using '<' instead of '>' in the condition.

The dictionary comprehension maps each word to its length using len(word). The condition filters words longer than 3 using >.

5fill in blank

hard

Fill all three blanks to create a dictionary of uppercase words and their lengths for words longer than 4 characters.

Data Analysis Python

result = [1]: [2] for w in words if len(w) [3] 4

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

Aw.upper()

Blen(w)

C>

Dw.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase instead of uppercase for keys.

Using '<' instead of '>' in the condition.

The dictionary comprehension uses the uppercase word as the key with w.upper(), the length as the value with len(w), and filters words longer than 4 with >.

Practice

1. Which of the following best describes the median in a dataset?

easy

A. The middle value when data is sorted from smallest to largest

B. The most frequently occurring value in the data

C. The average of all values in the data

D. The difference between the largest and smallest values

Solution

1. Step 1: Understand what median means

   The median is the value that lies in the middle of a sorted list of numbers.

2. Step 2: Compare with other options

   Mode is the most frequent value, mean is the average, and range is max-min.

3. Final Answer:

   The middle value when data is sorted from smallest to largest -> Option A

4. Quick Check:

   Median = middle value [OK]

Hint: Median splits sorted data in half [OK]

Common Mistakes:

• Confusing median with mean
• Thinking median is the most frequent value
• Mixing median with range

2. Which of the following is the correct way to get descriptive statistics of a pandas DataFrame df?

easy

A. describe(df)

B. df.summary()

C. df.describe()

D. df.stats()

Solution

1. Step 1: Recall pandas method for summary

   In pandas, describe() is the method to get descriptive stats.

2. Step 2: Check other options

   Methods like summary(), stats() do not exist in pandas, and describe(df) is not valid syntax.

3. Final Answer:

   df.describe() -> Option C

4. Quick Check:

   Use df.describe() for stats [OK]

Hint: Use df.describe() for quick stats [OK]

Common Mistakes:

• Using non-existent methods like summary()
• Calling describe as a function with df argument
• Confusing with other libraries

3. Given the following code, what will be the output of print(df.mean())?

import pandas as pd
data = {'A': [1, 2, 3], 'B': [4, 5, 6]}
df = pd.DataFrame(data)

medium

A. A 2.0
B 5.0
dtype: float64

B. A 3.0
B 6.0
dtype: int64

C. A 1.5
B 4.5
dtype: float64

D. Error: mean() not defined

Solution

1. Step 1: Calculate mean for each column

   Column A values: 1, 2, 3; mean = (1+2+3)/3 = 2.0. Column B values: 4, 5, 6; mean = (4+5+6)/3 = 5.0.

2. Step 2: Understand pandas output format

   Printing df.mean() shows means per column with dtype float64.

3. Final Answer:

   A 2.0 B 5.0 dtype: float64 -> Option A

4. Quick Check:

   Mean = sum/number of values [OK]

Hint: Mean = sum of values divided by count [OK]

Common Mistakes:

• Calculating median instead of mean
• Mixing up column values
• Expecting integer dtype instead of float

4. The following code is intended to calculate the standard deviation of column 'X' in DataFrame df. What is the error?

import pandas as pd
data = {'X': [10, 20, 30, 40]}
df = pd.DataFrame(data)
std_dev = df.std('X')
print(std_dev)

medium

A. DataFrame std() method requires axis=1 argument

B. The std() method does not take any arguments; column must be selected first

C. The code should use df['X'].variance() instead

D. No error; code runs correctly

Solution

1. Step 1: Check std() method usage

   In pandas, std() calculates standard deviation for all numeric columns or Series. It does not accept a column name as argument.

2. Step 2: Correct way to get std of column 'X'

   We must select the column first: df['X'].std().

3. Final Answer:

   The std() method does not take any arguments; column must be selected first -> Option B

4. Quick Check:

   Use df['X'].std() to get column std [OK]

Hint: Select column before std() call [OK]

Common Mistakes:

• Passing column name as argument to std()
• Using variance() instead of std()
• Assuming std() needs axis=1

5. You have a DataFrame df with columns 'Age' and 'Salary'. You want to find the average salary for people older than 30. Which code correctly does this?

hard

A. df['Salary'].mean(df['Age'] > 30)

B. df['Salary'].mean().filter(df['Age'] > 30)

C. df.mean()['Salary'][df['Age'] > 30]

D. df[df['Age'] > 30]['Salary'].mean()

Solution

1. Step 1: Filter rows where Age > 30

   Use boolean indexing: df[df['Age'] > 30] selects rows with Age greater than 30.

2. Step 2: Select 'Salary' column and calculate mean

   From filtered rows, select 'Salary' column and call mean() to get average salary.

3. Final Answer:

   df[df['Age'] > 30]['Salary'].mean() -> Option D

4. Quick Check:

   Filter then mean for conditional average [OK]

Hint: Filter rows first, then calculate mean [OK]

Common Mistakes:

• Passing condition inside mean()
• Trying to filter after mean()
• Using mean() on entire DataFrame