Data Analysis with Python Coursera Quiz Answers &#8211; 100% Correct Answers

All Weeks Data Analysis with Python Coursera Quiz Answers

Week 01: Importing Datasets

Practice Quiz: Understanding the Data

Q1. Each column contains a:

attribute or feature
different used car

Q2. How many columns does the dataset have?

Practice Quiz: Python Packages for Data Science

Q1. What description best describes the library Pandas?

Includes functions for some advanced math problems as listed in the slide as well as data visualization.
Uses arrays as their inputs and outputs. It can be extended to objects for matrices, and with a little change of coding, developers perform fast array processing.
Offers data structure and tools for effective data manipulation and analysis. It provides fast access to structured data. The primary instrument of Pandas is a two-dimensional table consisting of columns and rows labels which are called a DataFrame. It is designed to provide an easy indexing function.

Q2. What is a Python library?

A file that contains data.
A collection of functions and methods that allows you to perform lots of actions without writing your code.

Practice Quiz: Importing and Exporting Data in Python

Q1. What does the following method do to the data frame? df : df.head(12)1 point

Show the first 12 rows of dataframe.
Shows the bottom 12 rows of dataframe.

Q2. What task does the following lines of code perform?

path=’C:\Windows\…\ automobile.csv’

df.to_csv(path)

Exports your Pandas dataframe to a new csv file, in the location specified by the variable path.
Loads a csv file.

Practice Quiz: Getting Started Analyzing Data in Python

Q1. To enable a summary of all the columns, what must the parameter include be set to for the method described?

df.describe(include=“all”)
df.describe(include=“None”)

Graded Quiz: Importing Datasets

Q1. What do we want to predict from the dataset?

price
colour
make

Q2. What library is primarily used for machine learning

scikit-learn
Python
matplotlib

Q3. We have the list headers_list:

headers_list=['A','B','C']

We also have the data frame df that contains three columns, what is the correct syntax to replace the headers of the data frame df with values in the list headers_list?

df.columns = headers_list
df.head()
df.tail()

Q4. What attribute or method will give you the data type of each column?

describe()
columns
dtypes

Q5. How would you generate descriptive statistics for all the columns for the data frame df?

df.describe()
df.describe(include = “all”)
df.info

Practice Quiz: Dealing with Missing Values in Python

Q1. How would you access the column ”body-style” from the data frame df?

df[ “body-style”]
df==”bodystyle”

Q2. What is the correct symbol for missing data?

nan
no-data

Practice Quiz: Data Formatting in Python

Q1. How would you rename the column “city_mpg” to “city-L/100km”?

df.rename(columns={”city_mpg”: “city-L/100km”}, inplace=True)
df.rename(columns={”city_mpg”: “city-L/100km”})

Practice Quiz: Data Normalization in Python

Q1. Which of the following is the correct formula for z -score or data standardization?

Q2. What is the maximum value for feature scaling?

Practice Quiz: Turning categorical variables into quantitative variables in Python

Q1. Consider the column ‘diesel’; what should the value for Car B be?

Graded Quiz: Data Wrangling

Q1. What task do the following lines of code perform?

avg=df['horsepower'].mean(axis=0)
df['horsepower'].replace(np.nan, avg)

calculate the mean value for the ‘horsepower’ column and replace all the NaN values of that column by the mean value
nothing; because the parameter inplace is not set to true
replace all the NaN values with the mean

Q2. Consider the dataframe df; convert the column df[“city-mpg”] to df[“city-L/100km’] by dividing 235 by each element in the column ‘city-mpg’.

Q3. What data type is the following set of numbers? 666, 1.1,232,23.12

Q4. Consider the two columns ‘horsepower’, and ‘horsepower-binned’; from the data frame df; how many categories are there in the ‘horsepower-binned’ column?