“Bullshit” and Exploratory Data Analysis
By: Kexin Zhai
Tags: Google Play Store, Info3402s19ma1, Lifehacks, Information Science, Exploratory Data Analysis

About “Bullshit”

Our lives are filled with so much “bullshit”. “Bullshit” exists in the interaction between people, in reality, exists in the network. “Bullshit” can be found everywhere. According to On Bullshit by Harry Frankfurt, “The expression bullshit is often employed quite loosely — simply as a generic term of abuse, with no very specific literal meaning.[…]Bullshit is disconnected from a concern with the truth.” However, bullshit is not the same as misinformation.

From On Bullshit by Harry Frankfurt:

“Deceptive misrepresentation: This may sound pleonastic. No doubt what Black has in mind is that humbug is necessarily designed or intended to deceive, that its misrepresentation is not merely inadvertent. In other words, it is deliberate misrepresentation. […]A person may be lying even if the statement he makes is true, as long as he himself believes that the statement is false and intends by making it to deceive.”

Although bullshit is not about truth, it is not necessarily false which is a fundamental aspect of the nature of bullshitIt. This points to that it is easier to get away with bullshit than with lies. It is true that people are more tolerant of bullshit than lies, perhaps because we are more likely to get hurt from a lie. Then, what about bluffing?

From On Bullshit by Harry Frankfurt:

“Bullshit is just this lack of connection to a concern with truth — this indifference to how things really are. […]It does seem that bullshitting involves a kind of bluff. It is closer to bluffing, surely than to telling a lie. Lying and bluffing are both modes of misrepresentation or deception. Now the concept most central to the distinctive nature of a lie is that of falsity: the liar is essentially someone who deliberately promulgates a falsehood. Bluffing too is typically devoted to conveying something false. Unlike plain lying, however, it is more especially a matter not of falsity but of fakery. This is what accounts for its nearness to bullshit.”

Lying requires a certain level of skill, and the liar must submit to the objective constraints imposed by what he considers to be the truth. A liar is inevitably concerned with true value. To make up a lie, he must think he knows what is true. In order to fabricate an effective lie, he must design his lie under the guidance of truth. For the bullshitter, He does not care if what he says accurately describes reality. He just picks them out, or makes them up, for his purposes.

Application of Exploratory Data Analysis on “Bullshit”

Exploratory data analysis contributes to limiting bullshit. For this project, I found a Google Play Store Apps dataset on Kaggle and applied the steps from Exploratory Data Analysis Checklist on this dataset by using Jupyter notebook. My question to this dataset is “how do ratings differ between categories?”.

From Professor Brian Keegan’s slide

First 6 rows of Google Play Store Apps dataset

Last 5 rows of Google Play Store Apps dataset

For step 5, checking the “n”s, I used .value_counts() on “Type” column and “Content Rating” column, and I found out there is a “0” value nither belongs to “Free” nor “Paid”. The row having “0” value on “Type” also has unknown value on “Content Rating” column. Since originally there were 10840 rows in this dataset, I deleted the row that has incomplete information. Now, this dataset has 13 columns and 10839 rows.

Counts of Content ratings for apps and games

This is what “Content Rating” column looks like after deleting the incomplete row.

However, I found out another problem after using .count() on the whole dataFrame.

Counts for each column

We can see the “Rating” column only has 9366 rows having a value which is far away from 10839 rows. So I could not directly delete the rows with a blank value.

The incomplete rows described above will cause data bias in this project.

Next, I analyzed which category has the most apps, and the result shows that in this dataset most of the apps are the family apps and secondly, game.