SciPy with Pandas for data handling - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of scipy.stats.ttest_ind with pandas DataFrames

Given two pandas DataFrames representing two groups of data, what is the output of the following code snippet?

SciPy

import pandas as pd
from scipy import stats

# Create two groups of data
data1 = pd.DataFrame({'score': [88, 92, 85, 90, 87]})
data2 = pd.DataFrame({'score': [78, 81, 79, 83, 80]})

# Perform independent t-test
result = stats.ttest_ind(data1['score'], data2['score'])
print((round(result.statistic, 2), round(result.pvalue, 3)))

A (-6.32, 0.000)

B (6.32, 0.000)

C (-1.23, 0.234)

D (1.23, 0.234)

💡 Hint

Remember that the t-statistic sign depends on which group mean is larger.

The first group has a higher mean score than the second, so the t-statistic is positive. The p-value is very small, indicating a significant difference.

❓ data_output

intermediate

Result of scipy.stats.linregress on pandas Series

What is the output of the linear regression performed on the following pandas Series using scipy.stats.linregress?

SciPy

import pandas as pd
from scipy import stats

x = pd.Series([1, 2, 3, 4, 5])
y = pd.Series([2, 4, 5, 4, 5])

result = stats.linregress(x, y)
print((round(result.slope, 2), round(result.intercept, 2)))

A (0.6, 2.2)

B (0.7, 1.6)

C (0.8, 1.4)

D (0.5, 2.5)

💡 Hint

Slope is the change in y per unit change in x.

The slope is approximately 0.7 and intercept about 1.6, fitting the data points best in a linear model.

❓ visualization

advanced

Visualizing correlation matrix with SciPy and Pandas

You have a pandas DataFrame with multiple numeric columns. You calculate the Pearson correlation matrix using SciPy. Which code snippet correctly produces a heatmap visualization of this correlation matrix using matplotlib?

SciPy

import pandas as pd
import numpy as np
from scipy.stats import pearsonr
import matplotlib.pyplot as plt

# Sample data
np.random.seed(0)
data = pd.DataFrame(np.random.rand(5, 4), columns=list('ABCD'))

# Calculate correlation matrix
corr = data.corr()

# Visualization code here

A

plt.bar(corr.columns, corr.iloc[0])
plt.show()

B

plt.plot(corr)
plt.show()

C

plt.imshow(corr, cmap='coolwarm', interpolation='none')
plt.colorbar()
plt.xticks(range(len(corr)), corr.columns)
plt.yticks(range(len(corr)), corr.columns)
plt.show()

D

plt.scatter(corr.columns, corr.columns)
plt.show()

💡 Hint

A heatmap uses imshow with a color map to show matrix values.

Option C correctly uses imshow to display the correlation matrix as a heatmap with colorbar and axis labels.

🔧 Debug

advanced

Identify the error in applying scipy.stats.kstest on pandas Series

What error will the following code raise when running the Kolmogorov-Smirnov test on a pandas Series?

SciPy

import pandas as pd
from scipy import stats

sample = pd.Series([0.1, 0.4, 0.35, 0.8, 0.9])

result = stats.kstest(sample, 'uniform')
print(result)

A TypeError: 'Series' object is not iterable

B AttributeError: 'Series' object has no attribute 'shape'

C ValueError: Data must be 1-dimensional array-like

D No error, outputs KstestResult(statistic=..., pvalue=...)

💡 Hint

Check if scipy.stats.kstest accepts pandas Series directly.

scipy.stats.kstest accepts array-like inputs including pandas Series, so it runs without error and returns the test result.

🚀 Application

expert

Using SciPy and Pandas to find the most correlated feature

Given a pandas DataFrame with numeric columns, which code snippet correctly finds the column most positively correlated with column 'target' using SciPy's pearsonr function?

SciPy

import pandas as pd
from scipy.stats import pearsonr

data = pd.DataFrame({
    'target': [1, 2, 3, 4, 5],
    'feat1': [5, 4, 3, 2, 1],
    'feat2': [2, 3, 4, 5, 6],
    'feat3': [5, 5, 5, 5, 5]
})

# Find most correlated feature with 'target'

A

corrs = {col: pearsonr(data['target'], data[col])[0] for col in data.columns if col != 'target'}
most_corr = max(corrs, key=corrs.get)
print(most_corr)

B

corrs = data.corr()['target'].drop('target')
most_corr = corrs.idxmax()
print(most_corr)

C

most_corr = data.corrwith(data['target']).idxmax()
print(most_corr)

D

most_corr = max(data.columns, key=lambda col: pearsonr(data['target'], data[col])[1])
print(most_corr)

💡 Hint

Use pearsonr to get correlation coefficient, not p-value.

Option A correctly uses pearsonr to compute correlation coefficients and finds the column with the highest positive correlation.