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Data Science Quiz

Check what you learned in this data science tutorial. Choose an answer for each question to see if you are right.

Before You Start

Many questions use this DataFrame:

Example

python

import pandas as pd

df = pd.DataFrame({
    "name": ["Ali", "Ayesha", "Bilal", "Sana"],
    "city": ["Karachi", "Lahore", "Karachi", "Lahore"],
    "marks": [70, 88, 64, 90],
})
print(df.groupby("city")["marks"].mean())

Output

city
Karachi    67.0
Lahore     89.0
Name: marks, dtype: float64

If you miss a question, go back to the matching chapter:

Topic	Chapter
DataFrames, loc and iloc	Pandas Basics
Cleaning	Cleaning Data
groupby and filters	Analysis
Charts	Visualization
Statistics and models	Statistics and ML

Questions

Exercise

What does df.shape return for the DataFrame above?

(4, 3)(3, 4)12["name", "city", "marks"]

Exercise

Which code selects only the students from Karachi?

df["city"] == "Karachi"df[df["city"] == "Karachi"]df.loc["Karachi"]df.groupby("Karachi")

Exercise

Which code keeps students from Lahore with marks above 85?

df[df["city"] == "Lahore" and df["marks"] > 85]df[(df["city"] == "Lahore") & (df["marks"] > 85)]df[(df["city"] == "Lahore") + (df["marks"] > 85)]df.filter("Lahore", 85)

Exercise

What does df["marks"].fillna(0) do?

Deletes rows with missing marksReplaces missing marks with 0Counts the missing marksSets every mark to 0

Exercise

Which method removes repeated identical rows?

df.dropna()df.unique()df.drop_duplicates()df.remove()

Exercise

What is the result of df.groupby("city")["marks"].mean() for Lahore?

67.089.088.0178.0

Exercise

Which chart is best for showing the relationship between study hours and marks?

Pie chartScatter plotBar chart of city namesLine chart of student names

Exercise

A correlation of -0.9 between two columns means:

There is no relationshipWhen one goes up, the other also goes upWhen one goes up, the other tends to go down stronglyOne column causes the other

Exercise

In scikit-learn, which method trains a model on data?

model.predict()model.fit()model.train_test_split()model.score_data()

Exercise

Why do we split data into training and test sets?

To make the dataset biggerTo make training fasterTo check the model on data it has not seen beforeBecause pandas requires it

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