15.1. EDA With Python Part 2

Read the following articles:

15.1.1. pandas Function

1. 20 Must-Know Pandas Function for Exploratory Data Analysis.

15.1.2. Outlier Detection

1. A Quick Guide to the Different Types of Outliers.

15.1.3. Missing Data

1. How to… deal with Nulls.

2. The best way to handle missing data.

15.1.4. Univariate and Multivariate Charts

Code along with the article below using the DataCleaning-Heart-Data repo:

1. Understand the Data With Univariate And Multivariate Charts and Plots in Python.

15.1.5. Check Your Understanding

Question

The following plot graphs a user’s Spotify recommended song length (in milliseconds) with a song’s energy score (a perceptual measure of intensity and activity between 0 and 1).

True or False: This graph contains no outliers.

1. True

2. False

Question

The National Park Service records the number of visitors to the Gateway Arch in St Louis every day. Occasionally, concerts are held on the park grounds and the number of visitors soars. On concert days, what type of anomaly is the NPS see in their visitor data?

1. Global anomaly

2. Contextual anomaly

3. Collective anomaly

Question

If a dataset is missing values, it is absolutely useless.

1. True

2. False

Question

How can data analysts leverage the presence of null values in a data set?

1. We can create an additional column with a binary type, indicating if any information is missing in the column in question

2. We can clean the data by removing any missing entries.

3. We can clean the data by removing any columns with missing entries.

4. We can’t, Null values serve no purpose in analysis.

Question

Data sets with missing values have been improperly assembled and are rare to encounter in the professional field of data analysis.

1. True

2. False

Question

There is no best method for addressing missing data.

1. True

2. False