# Pandas: Explore Datasets by Visualization – Exploring the Holland Code (RIASEC) Test – Part IV

## What will we cover in this tutorial?

We will continue our journey to explore a big dataset of 145,000+ respondents to a RIASEC test. If you want to explore the full journey, we recommend you read this tutorial first as well as the second part of the tutorial, and finally, the third part before continuing.

In this part we will investigate if we can see any correlation between the major of education and the 6 dimensions of the personality types in RIASEC.

## Step 1: Group into major of educations

This is getting tricky, as the majors are typed in by the respondent. We will be missing some connections between them.

But let’s start by exploring them.

import pandas as pd

major = data.loc[:,['major']]

print(major.groupby('major').size().sort_values(ascending=False))

The output is given here.

major
psychology                6861
Psychology                5763
English                   2342
Biology                   1289
...
Sociology, Social work       1
Sociology, Psychology        1
Sociology, Math              1
Sociology, Linguistics       1
Nuerobiology                 1
Length: 15955, dtype: int64

Where we identify one problem, that some write with lowercase and others with uppercase.

## Step 2: Clean up a few ambiguities

The first step would be to lowercase everything.

import pandas as pd

major = data.loc[:,['major']]
major['major'] = major['major'].str.lower()
print(major.groupby('major').size().sort_values(ascending=False).iloc[:10])

Now printing the 10 first lines.

major
psychology          12766
english              3042
nursing              2142
biology              1961
education            1800
engineering          1353
accounting           1186
computer science     1159
psychology           1098
dtype: int64

Where we notice that psychology is the first and last. Inspecting it further, it seems the the last one has a space after it. Hence, we can try to remove whitespaces around all educations.

import pandas as pd
import numpy as np

major = data.loc[:,['major']]
major['major'] = major['major'].str.lower()
major['major'] = major.apply(lambda row: row['major'].strip() if row['major'] is not np.nan else np.nan, axis=1)

print(major.groupby('major').size().sort_values(ascending=False).iloc[:10])

Now the output is as follows.

major
psychology          13878
english              3240
nursing              2396
biology              2122
education            1954
engineering          1504
accounting           1292
computer science     1240
law                  1111
dtype: int64

Introducing law at the bottom of the list.

This process could continue, but let’s keep the focus on these 10 highest representative educations in the dataset. Obviously, further data points could be added if investigating it further.

## Step 3: See if education correlates to known words

First let’s explore the dataset a bit more. The respondents are asked if they know the definitions of the following words.

• boat
• incoherent
• pallid
• robot
• audible
• cuivocal
• paucity
• epistemology
• florted
• decide
• pastiche
• verdid
• abysmal
• lucid
• betray
• funny

Each word they know they mark. Hence, we can count the number of words each respondent knows and calculate an average per major group.

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

data['VCL'] = data['VCL1'] + data['VCL2'] + data['VCL3'] + data['VCL4'] + data['VCL5'] + data['VCL6'] + data['VCL7'] + data['VCL8'] + data['VCL9'] + data['VCL10'] + data['VCL11'] + data['VCL12'] + data['VCL13'] + data['VCL14'] + data['VCL15'] + data['VCL16']

view = data.loc[:, ['VCL', 'major']]
view['major'] = view['major'].str.lower()
view['major'] = view.apply(lambda row: row['major'].strip() if row['major'] is not np.nan else np.nan, axis=1)

view = view.groupby('major').aggregate(['mean', 'count'])
view = view[view['VCL','count'] > 1110]
view.loc[:,('VCL','mean')].plot(kind='barh', figsize=(14,5))
plt.show()

Which results in the following output.

The Engineers seem to score lower than nursing. Well, I am actually surprised that Computer Science scores that high.

## Step 4: Adding it all up together

Let’s use what we did in previous tutorial and use the calculations from there.

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

def sum_dimension(data, letter):
return data[letter + '1'] + data[letter + '2'] + data[letter + '3'] + data[letter + '4'] + data[letter + '5'] + data[letter + '6'] + data[letter + '7'] + data[letter + '8']

data['R'] = sum_dimension(data, 'R')
data['I'] = sum_dimension(data, 'I')
data['A'] = sum_dimension(data, 'A')
data['S'] = sum_dimension(data, 'S')
data['E'] = sum_dimension(data, 'E')
data['C'] = sum_dimension(data, 'C')
data['VCL'] = data['VCL1'] + data['VCL2'] + data['VCL3'] + data['VCL4'] + data['VCL5'] + data['VCL6'] + data['VCL7'] + data['VCL8'] + data['VCL9'] + data['VCL10'] + data['VCL11'] + data['VCL12'] + data['VCL13'] + data['VCL14'] + data['VCL15'] + data['VCL16']

view = data.loc[:, ['R', 'I', 'A', 'S', 'E', 'C', 'VCL', 'major']]
view['major'] = view['major'].str.lower()
view['major'] = view.apply(lambda row: row['major'].strip() if row['major'] is not np.nan else np.nan, axis=1)

view = view.groupby('major').aggregate(['mean', 'count'])
view = view[view['VCL','count'] > 1110]
view.loc[:,[('R','mean'), ('I','mean'),('A','mean'), ('S','mean'),('C','mean'), ('C','mean')]].plot(kind='barh', figsize=(14,5))
plt.show()

Which results in the following diagram.

Biology has high I (Investigative, people that prefer to work with data). While the R (Realistic, People who like to work with things) is dominated by Engineers and Computer Scientist.

Hmm… I should have noticed that many have major education.