SciPy with Pandas for data handling - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the scipy.stats module.

SciPy

from scipy import [1]

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

Apandas

Bscipy

Cstats

Dnumpy

Attempts:

3 left

💡 Hint

Common Mistakes

Importing only scipy without .stats

Importing pandas or numpy instead

The correct import is from scipy import stats. This imports the scipy.stats module as stats.

2fill in blank

medium

Complete the code to calculate the Pearson correlation coefficient between two pandas Series, x and y.

SciPy

corr, p_value = stats.pearsonr([1], y)

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

Ax

By

Cx.values

Dx.to_list()

Attempts:

3 left

💡 Hint

Common Mistakes

Passing y instead of x

Passing x.values or x.to_list() is optional but not required

The pearsonr function accepts array-like inputs. Passing the pandas Series x directly works fine.

3fill in blank

hard

Fix the error in the code to perform a t-test between two pandas Series, sample1 and sample2.

SciPy

t_stat, p_val = stats.ttest_ind(sample1, [1])

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

Asample1

Bsample2

Csample2.values

Dsample1.values

Attempts:

3 left

💡 Hint

Common Mistakes

Passing sample1 twice

Passing sample2.values unnecessarily

The t-test compares two independent samples. The second argument should be the second sample, sample2.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps each word to its length only if the length is greater than 3.

SciPy

{word: [1] for word in words if [2]

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

Alen(word)

Bword

Clen(word) > 3

Dword > 3

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as value

Using word > 3 which is invalid

The dictionary maps each word to its length, so the value is len(word). The condition filters words with length greater than 3, so the if condition is len(word) > 3.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps each uppercase word to its frequency only if frequency is greater than 1.

SciPy

{ [1]: [2] for [3], [2] in freq_dict.items() if [2] > 1 }

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

Aword.upper()

Bfreq

Cword

Dcount

Attempts:

3 left

💡 Hint

Common Mistakes

Using count instead of freq

Using word instead of word.upper() as key

The dictionary keys are uppercase words, so word.upper(). The values are frequencies, so freq. The loop variables are word and freq.

Practice

1. What is the main reason to use SciPy together with Pandas in data analysis?

easy

A. SciPy provides advanced math and stats functions, while Pandas organizes data in tables.

B. Pandas is used only for visualization, SciPy handles all data storage.

C. SciPy replaces Pandas for data cleaning tasks.

D. Pandas is used to write code, SciPy runs the code faster.

Solution

1. Step 1: Understand roles of Pandas and SciPy

   Pandas organizes data into tables called DataFrames, making it easy to handle data.

2. Step 2: Identify SciPy's role

   SciPy offers math and statistics tools to analyze data prepared by Pandas.

3. Final Answer:

   SciPy provides advanced math and stats functions, while Pandas organizes data in tables. -> Option A

4. Quick Check:

   Data organization = Pandas, Analysis = SciPy [OK]

Hint: Remember: Pandas for tables, SciPy for math [OK]

Common Mistakes:

• Thinking Pandas does advanced stats alone
• Confusing SciPy as a data storage tool
• Believing SciPy replaces Pandas for cleaning

2. Which of the following is the correct way to import SciPy's stats module and Pandas in Python?

easy

A. from scipy import stats; import pandas as pd

B. import scipy.stats; import pandas as pandas

C. from scipy.stats import stats; import pandas as pd

D. import scipy.stats as sp; import pandas as pd

Solution

1. Step 1: Check common import styles

   Using 'from scipy import stats' imports the stats module directly, which is common and clear.

2. Step 2: Verify Pandas import

   Importing pandas as 'pd' is the standard alias used in data science.

3. Final Answer:

   from scipy import stats; import pandas as pd -> Option A

4. Quick Check:

   Standard imports = from scipy import stats, import pandas as pd [OK]

Hint: Use 'from scipy import stats' and 'import pandas as pd' [OK]

Common Mistakes:

• Using wrong alias for pandas
• Importing scipy.stats without alias or direct import
• Mixing import styles incorrectly

3. Given the code below, what will be the output?

import pandas as pd
from scipy import stats

data = {'score': [10, 20, 20, 30, 40]}
df = pd.DataFrame(data)
mode_result = stats.mode(df['score'])
print(mode_result.mode[0])

medium

A. 10

B. 30

C. 20

D. 40

Solution

1. Step 1: Understand the data

   The 'score' column has values [10, 20, 20, 30, 40]. The number 20 appears twice, others once.

2. Step 2: Apply stats.mode

   stats.mode finds the most frequent value, which is 20 here.

3. Final Answer:

   20 -> Option C

4. Quick Check:

   Most frequent value = 20 [OK]

Hint: Mode is the most frequent value in the list [OK]

Common Mistakes:

• Choosing the first value instead of mode
• Confusing mean or median with mode
• Not accessing .mode[0] correctly

4. Identify the error in the following code snippet:

import pandas as pd
from scipy import stats

data = {'values': [1, 2, 3, 4, 5]}
df = pd.DataFrame(data)
result = stats.mean(df['values'])
print(result)

medium

A. DataFrame creation syntax is incorrect.

B. stats.mean does not exist; use numpy.mean or pandas mean method instead.

C. The print statement is missing parentheses.

D. The import statement for pandas is wrong.

Solution

1. Step 1: Check function availability in SciPy

   SciPy's stats module does not have a 'mean' function; mean is in numpy or pandas.

2. Step 2: Identify correct function usage

   Use df['values'].mean() or numpy.mean(df['values']) instead.

3. Final Answer:

   stats.mean does not exist; use numpy.mean or pandas mean method instead. -> Option B

4. Quick Check:

   stats.mean missing, use pandas or numpy mean [OK]

Hint: Use pandas or numpy for mean, not stats.mean [OK]

Common Mistakes:

• Assuming all stats functions exist in SciPy
• Ignoring error messages about missing attributes
• Confusing pandas and SciPy function locations

5. You have a Pandas DataFrame with a column 'height' containing some missing values (NaN). You want to fill these missing values with the median height calculated using SciPy. Which code snippet correctly does this?

hard

A. from scipy import stats
median_height = stats.median(df['height'])
df['height'] = df['height'].fillna(median_height)

B. from scipy import stats
median_height = stats.median(df['height'].dropna())
df['height'] = df['height'].fillna(median_height)

C. from scipy import stats
median_height = stats.mode(df['height'].dropna()).mode[0]
df['height'] = df['height'].fillna(median_height)

D. from scipy import stats
median_height = stats.scoreatpercentile(df['height'].dropna(), 50)
df['height'] = df['height'].fillna(median_height)

Solution

1. Step 1: Identify correct SciPy function for median

   SciPy's stats module does not have 'median', but 'scoreatpercentile' can find the 50th percentile (median).

2. Step 2: Handle missing values correctly

   Drop NaN values before calculating median, then fill NaNs with this median.

3. Final Answer:

   from scipy import stats median_height = stats.scoreatpercentile(df['height'].dropna(), 50) df['height'] = df['height'].fillna(median_height) -> Option D

4. Quick Check:

   Median via scoreatpercentile, fillna with median [OK]

Hint: Use scoreatpercentile for median in SciPy [OK]

Common Mistakes:

• Using stats.median which does not exist
• Not dropping NaN before median calculation
• Using mode instead of median