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Module 2 - Data Wrangling.md

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Module 2 - Data Wrangling.md

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Learning Objectives

  • Describe how to handle missing values
  • Describe data formatting techniques
  • Describe data normalization
  • Demonstrate the use of binning
  • Demonstrate the use of categorical variables

Practice Quiz: Dealing with Missing Values in Python

Question 1: How would you access the column ”body-style" from the dataframe df?

  • A. [X] df["body-style"]
  • B. [ ] df=="bodystyle"

Question 2: What is the correct symbol for missing data?

  • A. [X] nan
  • B. [ ] no-data

Practice Quiz: Data Formatting in Python

Question 1: How would you rename the column "city_mpg" to "city-L/100km"?

  • A. [ ] df.rename(columns={”city_mpg”: “city-L/100km”})
  • B. [X] df.rename(columns={”city_mpg”: “city-L/100km”}, inplace=True)

Practice Quiz: Data Normalization in Python

Question 1: Which of the following is the correct formula for z -score or data standardization?

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c

Practice Quiz: Turning Categorical Variables into Quantitative Variables in Python

Question 1: Why do we convert values of Categorical Variables into numerical values?

  • A. [X] Most statistical models cannot take in objects or strings as inputs
  • B. [ ] To save memory

Lesson Summary

In this lesson, you have learned how to:

  • Identify and Handle Missing Values: Drop rows with incomplete information and impute missing data using the mean values.
  • Understand Data Formatting: Wrangle features in a dataset and make them meaningful for data analysis.
  • Apply normalization to a data set: By understanding the relevance of using feature scaling on your data and how normalization and standardization have varying effects on your data analysis.

Data Wrangling

Data Wrangling

Graded Quiz: Data Wrangling

Question 1: What task do the following lines of code perform?

avg=df['horsepower'].mean(axis=0)

df['horsepower'].replace(np.nan, avg)
  • A. [ ] calculate the mean value for the 'horsepower' column and replace all the NaN values of that column by the mean value
  • B. [X] nothing; because the parameter inplace is not set to true
  • C. [ ] replace all the NaN values with the mean.

Question 2: 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'.

  • A. [ ] df['city-L/100km'] = df["city-mpg"].diV(235)
  • B. [X] df['city-L/100km'] = 235/df["city-mpg"]

Question 3: What data type is the following set of numbers? 666, 1.1,232,23.12

  • A. [X] float
  • B. [ ] int
  • C. [ ] object

Question 4: The following code is an example of:

(df["length"]-df["length"].mean())/df["length"].std()
  • A. [ ] simple feature scaling
  • B. [X] z-score
  • C. [ ] min-max scaling

Question 5: Consider the two columns 'horsepower', and 'horsepower-binned'; from the dataframe df; how many categories are there in the 'horsepower-binned' column?

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