My Visualisations Academia¶

hero4.jpg

Go Beyond! Plus Ultra!¶

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1. Import¶

In [1]:
import pandas as pd
from pandas import DataFrame
import numpy as np
import matplotlib.pyplot as plt
In [2]:
hero = pd.read_csv("mha_v2.csv")
hero.head()
Out[2]:
Name Occupation Quirk Power Speed Technique Intelligence Score Rank
0 All For One Villain All for One 6.5 6.5 6.5 6.5 26.0 1
1 All Might Hero One for All 6.5 6.5 6.0 6.0 25.0 2
2 Best Jeanist Hero Fiber Master 2.0 3.0 5.0 2.0 12.0 94
3 Bubble Girl Hero Bubble 1.0 2.0 4.5 3.0 10.5 111
4 Camie Utsushimi Student Glamour 1.5 2.5 5.0 2.5 11.5 105

2. Description¶

In [3]:
hero.shape
Out[3]:
(120, 9)
In [4]:
hero.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 120 entries, 0 to 119
Data columns (total 9 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   Name          120 non-null    object 
 1   Occupation    120 non-null    object 
 2   Quirk         120 non-null    object 
 3   Power         120 non-null    float64
 4   Speed         120 non-null    float64
 5   Technique     120 non-null    float64
 6   Intelligence  120 non-null    float64
 7   Score         120 non-null    float64
 8   Rank          120 non-null    int64  
dtypes: float64(5), int64(1), object(3)
memory usage: 8.6+ KB
In [5]:
hero.describe()
Out[5]:
Power Speed Technique Intelligence Score Rank
count 120.000000 120.000000 120.000000 120.000000 120.000000 120.000000
mean 3.425000 3.512500 4.270833 3.791667 15.000000 55.666667
std 1.682398 1.461642 1.309140 1.371351 3.856131 34.226712
min 1.000000 1.000000 1.000000 1.000000 8.000000 1.000000
25% 2.000000 3.000000 3.875000 3.000000 13.000000 29.000000
50% 3.000000 3.000000 4.250000 4.000000 14.500000 59.000000
75% 5.000000 4.500000 5.000000 5.000000 16.500000 80.000000
max 10.000000 10.000000 10.000000 10.000000 40.000000 119.000000
In [6]:
hero["Name"] = hero["Name"].astype("category")
hero["Occupation"] = hero["Occupation"].astype("category")
hero["Quirk"] = hero["Quirk"].astype("category")
In [7]:
hero.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 120 entries, 0 to 119
Data columns (total 9 columns):
 #   Column        Non-Null Count  Dtype   
---  ------        --------------  -----   
 0   Name          120 non-null    category
 1   Occupation    120 non-null    category
 2   Quirk         120 non-null    category
 3   Power         120 non-null    float64 
 4   Speed         120 non-null    float64 
 5   Technique     120 non-null    float64 
 6   Intelligence  120 non-null    float64 
 7   Score         120 non-null    float64 
 8   Rank          120 non-null    int64   
dtypes: category(3), float64(5), int64(1)
memory usage: 16.3 KB
In [8]:
hero.describe(include = "category")
Out[8]:
Name Occupation Quirk
count 120 120 120
unique 120 4 119
top All For One Student One for All
freq 1 53 2

Rename columns.

In [9]:
hero = hero.rename(columns = {"Name": "NAME"})
hero.head()
Out[9]:
NAME Occupation Quirk Power Speed Technique Intelligence Score Rank
0 All For One Villain All for One 6.5 6.5 6.5 6.5 26.0 1
1 All Might Hero One for All 6.5 6.5 6.0 6.0 25.0 2
2 Best Jeanist Hero Fiber Master 2.0 3.0 5.0 2.0 12.0 94
3 Bubble Girl Hero Bubble 1.0 2.0 4.5 3.0 10.5 111
4 Camie Utsushimi Student Glamour 1.5 2.5 5.0 2.5 11.5 105

3. Visualisations¶

3.1 Histogram¶

In [10]:
import seaborn as sns
In [11]:
sns.histplot(x = hero["Power"])
Out[11]:
<AxesSubplot:xlabel='Power', ylabel='Count'>

Use ";" at the end to remove the AxesSubplot. Change the number of bins.

In [12]:
sns.histplot(x = hero["Speed"], bins = 20);
In [13]:
histogram = sns.histplot(x = hero["Technique"], bins = 20, stat = "probability", color = "purple");

histogram.set_xlabel("Technique", fontsize = 15);
histogram.set_ylabel("Probability", fontsize = 15);

3.2 Column¶

In [14]:
sns.barplot(x = "Occupation", y = "Intelligence", data = hero, color = "green");
In [15]:
count = sns.countplot(x = "Occupation", data = hero, color = "red");

count.set_xlabel("Occupation", fontsize = 15);
count.set_ylabel("Number", fontsize = 15);
In [16]:
bar = sns.barplot(x = "Occupation", y = "Score", data = hero, color = "blue", estimator = sum);

bar.set_xlabel("Occupation", fontsize = 15);
bar.set_ylabel("Total Score", fontsize = 15);
In [17]:
bar = sns.barplot(x = "Occupation", y = "Speed", data = hero, color = "lightblue", estimator = max);

bar.set_xlabel("Occupation", fontsize = 15);
bar.set_ylabel("Maximum Speed", fontsize = 15);

Alternatively from numpy import median

In [18]:
import numpy as np
bar = sns.barplot(x = "Occupation", y = "Speed", data = hero, color = "lightblue", estimator = np.median, ci = None);

bar.set_xlabel("Occupation", fontsize = 15);
bar.set_ylabel("Median Speed", fontsize = 15);

Convert to a horizontal orientation.

In [19]:
bar = sns.barplot(x = "Speed", y = "Occupation", data = hero, color = "purple", estimator = np.median);

bar.set_ylabel("Occupation", fontsize = 15);
bar.set_xlabel("Median Speed", fontsize = 15);

3.3 Boxplot¶

In [20]:
sns.boxplot(x = hero["Power"]);
In [21]:
box = sns.boxplot(x = hero["Intelligence"], showmeans = True, color = "yellow");

box.set_xlabel("Intelligence", fontsize = 15);

3.4 Line¶

Just for illustration. Does not make sense here.

In [22]:
line_1 = sns.lineplot(x = "Technique", y = "Score", data = hero)
plt.show()

Just for illustration. Does not make sense here.

In [23]:
hero_line_df = hero[["Technique", "Score"]]
line_2 = sns.lineplot(data = hero_line_df)

line_2.set_xlabel("Records", fontsize = 15);
line_2.set_ylabel("Value", fontsize = 15);

plt.show()

Just for illustration. Does not make sense here.

In [24]:
line_3 = sns.lineplot(x = "Score", y = "Technique", hue = "Occupation", data = hero);

line_3.set_xlabel("Score", fontsize = 15);
line_3.set_ylabel("Technique", fontsize = 15);

line_3.legend(title = "Occupation", title_fontsize = "10");

Create a new column from index 0. Just for illustration. Does not make much sense here.

In [25]:
hero_copy = hero.copy()
hero_copy.insert(0, "Year", range(1980, 1980 + len(hero_copy)))
hero_copy.head()
Out[25]:
Year NAME Occupation Quirk Power Speed Technique Intelligence Score Rank
0 1980 All For One Villain All for One 6.5 6.5 6.5 6.5 26.0 1
1 1981 All Might Hero One for All 6.5 6.5 6.0 6.0 25.0 2
2 1982 Best Jeanist Hero Fiber Master 2.0 3.0 5.0 2.0 12.0 94
3 1983 Bubble Girl Hero Bubble 1.0 2.0 4.5 3.0 10.5 111
4 1984 Camie Utsushimi Student Glamour 1.5 2.5 5.0 2.5 11.5 105
In [26]:
line_4 = sns.lineplot(x = "Year", y = "Score", data = hero_copy);

line_4.set_xlabel("Year", fontsize = 15);
line_4.set_ylabel("Score", fontsize = 15);

A multi-line plot requires a long format Again, it does not make much sense here

In [27]:
hero_copy_2 = hero_copy[["Year", "Power", "Speed", "Technique", "Intelligence"]]
hero_copy_2.head()
Out[27]:
Year Power Speed Technique Intelligence
0 1980 6.5 6.5 6.5 6.5
1 1981 6.5 6.5 6.0 6.0
2 1982 2.0 3.0 5.0 2.0
3 1983 1.0 2.0 4.5 3.0
4 1984 1.5 2.5 5.0 2.5
In [28]:
# import pandas as pd
hero_copy_long = pd.melt(hero_copy_2, ["Year"])
hero_copy_long.head()
Out[28]:
Year variable value
0 1980 Power 6.5
1 1981 Power 6.5
2 1982 Power 2.0
3 1983 Power 1.0
4 1984 Power 1.5

Rename columns if desired

In [29]:
hero_copy_long.rename({"variable": "Attribute", "value": "Value"}, axis=1, inplace=True)
hero_copy_long.head()
Out[29]:
Year Attribute Value
0 1980 Power 6.5
1 1981 Power 6.5
2 1982 Power 2.0
3 1983 Power 1.0
4 1984 Power 1.5

Terrible plot. Just for illustration only.

In [30]:
line_5 = sns.lineplot(x = "Year", y = "Value", hue = "Attribute", data = hero_copy_long);

line_5.set_xlabel("Year", fontsize = 15);
line_5.set_ylabel("Score", fontsize = 15);

line_5.legend(title = "The 4 Attributes", title_fontsize = "10");

3.5 Scatter plot¶

Someone's over-powered :-)

In [31]:
scatter = sns.scatterplot(x = "Power",
                          y = "Speed",
                          hue = "Occupation",
                          data = hero);

scatter.set_xlabel("Power", fontsize = 20);
scatter.set_ylabel("Speed", fontsize = 20);

scatter.legend(title = "Occupation", title_fontsize = "10");
In [32]:
scatter_2 = sns.scatterplot(x = "Technique",
                            y = "Intelligence",
                            hue = "Occupation",
                            size = "Score",
                            data = hero);

scatter_2.set_xlabel("Technique", fontsize = 20);
scatter_2.set_ylabel("Intelligence", fontsize = 20);

scatter_2.legend(title = "The Legends", title_fontsize = "10");

plt.text(6.6, 9.88, "Master Yeoda :-) -->", horizontalalignment = "left", size = "medium", color = "red", weight = "bold")
Out[32]:
Text(6.6, 9.88, 'Master Yeoda :-) -->')

Show distributions with the scatter plot.

In [33]:
sns.jointplot(x = "Power", y = "Speed", data = hero);
In [34]:
sns.jointplot(x = "Technique", y = "Intelligence", data = hero, kind = "reg");
In [35]:
sns.jointplot(x = "Power", y = "Technique", data = hero, kind = "hex");
In [36]:
sns.jointplot(x = "Speed", y = "Intelligence", data = hero, kind = "kde", color = "purple");

3.6 Violin plot¶

In [37]:
sns.catplot(x = "Occupation", y = "Score", kind = "violin", data = hero);

Go Beyond! Plus Ultra!¶

hero5.webp