[Dorji Tshezom] - Fab Futures - Data Science

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Data Visualization

➡️ Data visualization is the process of turning numbers and information into charts, graphs, and images so we can understand patterns, trends, and comparisons quickly.

Very easy example:

If you have fruit sales for 5 dzongkhags, instead of looking at a long table of numbers, you draw a bar graph. Just by looking, you can see which dzongkhag sold the most or the least.

Why data visualization is useful?

Makes complex data simple

Helps you see trends (increase/decrease)

Helps compare categories

Makes reports easy to explain

Helps in decision-making

Common types of visualizations:

Bar graph

Line graph

Pie chart

Histogram

Scatter plot

Maps

Drwan followig information from youtube: here

TOOLs:

-. They help us find patterns and trends

-They improve reports and presentations

-They allow interactive exploration

1 Excel 2. Google Sheets 3. Python Matplotlib & Seaborn in Jupyter Notebook

Python Libraies

I came to learned lots of python data visualization libraries , however i loved to explore the matplotlib sinch i saw many advantages of using iy. Not only that eveen our professor recommend and mostly made us familiar with this labrary. As i started exploring on it, i came to learn form tutorial and other platform that this libraries is benificial for the begginer learner like me.

Anatomy of plot

First of all before we do any data visualization or use matplotlib to visualize our data we need to know the anatomy of plot. So that it will be eaisier for me to give label to my plot as follow

I have followed the following steps

Matplotlib is a Python library used to create graphs and charts

Example use in Python:

import matplotlib.pyplot as plt

plt.plot([1, 2, 3], [4, 5, 6]) plt.show() This will draw a simple line graph.

import matplotlib.pyplot as plt
from matplotlib import style
%matplotlib inline
import pandas as pd
import numpy as np

randomnumber = np.random.rand(10)
print (randomnumber)

[0.78759321 0.53782427 0.56924464 0.47910728 0.73808527 0.56013932
 0.56247922 0.3603106  0.02800028 0.5325992 ]

plt.style.use (`ggplot`)
plt. plot (randomnumber,(`gg`), label = `line one` linewidth=2)
plt.xlabel(`rang`)
plt.ylabel(`numbers`)
plt.title(First plot)
plt.legend()
plt.show()

  Cell In[1], line 1
    plt.style.use (`ggplot`)
                   ^
SyntaxError: invalid syntax

x = [1,2,3,4]
y = [4,5,6,7]

plt.plot(x,y)
plt.show

<function matplotlib.pyplot.show(close=None, block=None)>

pyplot Api

univariate - Numerical

dorji-tshezom/datasets/Job Posting.csv
import matplotlib.pyplot as plt
import seaborn as sns

dorji-tshezom/datasets/Job Posting.csv

df.head()

	Unnamed: 0	Unnamed: 1	Unnamed: 2	Unnamed: 3	Unnamed: 4	Unnamed: 5	Unnamed: 6	Unnamed: 7	Unnamed: 8	Unnamed: 9	...	Unnamed: 15	Unnamed: 16	Unnamed: 17	Unnamed: 18	Unnamed: 19	Unnamed: 20	Unnamed: 21	Unnamed: 22	Unnamed: 23	Unnamed: 24
0	NaN	Irrigated Paddy	NaN	NaN	Upland Paddy	NaN	NaN	Maize	NaN	NaN	...	NaN	Barley	NaN	NaN	Millet	NaN	NaN	Quinoa	NaN	NaN
1	Dzongkhag	Sown Area (Acre)	Harvested Area (Acre)	Production (MT)	Sown Area (Acre)	Harvested Area (Acre)	Production (MT)	Sown Area (Acre)	Harvested Area (Acre)	Production (MT)	...	Production (MT)	Sown Area (Acre)	Harvested Area (Acre)	Production (MT)	Sown Area (Acre)	Harvested Area (Acre)	Production (MT)	Sown Area (Acre)	Harvested Area (Acre)	Production (MT)
2	Bumthang	112.732886	108.73793	164.98479	0	0	0	0.95405	0.477025	0.276172	...	303.913949	322.13337	284.32811	148.17781	2.950391	1.229329	0.983464	0.318016	0.318016	0.19081
3	Chukha	1047.123288	907.49028	1539.711124	55.618929	45.462151	30.29093	1494.45484	1153.47943	1446.529806	...	121.949055	47.090664	41.795427	20.235387	362.342557	323.000712	147.827759	4.241636	3.50086	1.423413
4	Dagana	2067.202705	1862.639608	2450.662581	30.711222	29.497675	7.568256	2364.611317	1717.412537	2001.271484	...	86.097623	50.407894	44.798978	19.547031	181.607689	163.633424	79.192313	0.020625	0.020625	0.010313

5 rows × 25 columns

import pandas as pd

import pandas as pd
pd.set_option('display.max_rows',None) 
pd.set_option('display.max_columns',None)

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 23 entries, 0 to 22
Data columns (total 25 columns):
 #   Column       Non-Null Count  Dtype 
---  ------       --------------  ----- 
 0   Unnamed: 0   22 non-null     object
 1   Unnamed: 1   23 non-null     object
 2   Unnamed: 2   22 non-null     object
 3   Unnamed: 3   22 non-null     object
 4   Unnamed: 4   23 non-null     object
 5   Unnamed: 5   22 non-null     object
 6   Unnamed: 6   22 non-null     object
 7   Unnamed: 7   23 non-null     object
 8   Unnamed: 8   22 non-null     object
 9   Unnamed: 9   22 non-null     object
 10  Unnamed: 10  23 non-null     object
 11  Unnamed: 11  22 non-null     object
 12  Unnamed: 12  22 non-null     object
 13  Unnamed: 13  23 non-null     object
 14  Unnamed: 14  22 non-null     object
 15  Unnamed: 15  22 non-null     object
 16  Unnamed: 16  23 non-null     object
 17  Unnamed: 17  22 non-null     object
 18  Unnamed: 18  22 non-null     object
 19  Unnamed: 19  23 non-null     object
 20  Unnamed: 20  22 non-null     object
 21  Unnamed: 21  22 non-null     object
 22  Unnamed: 22  23 non-null     object
 23  Unnamed: 23  22 non-null     object
 24  Unnamed: 24  22 non-null     object
dtypes: object(25)
memory usage: 4.6+ KB

df.describe(include='all')

	Unnamed: 0	Unnamed: 1	Unnamed: 2	Unnamed: 3	Unnamed: 4	Unnamed: 5	Unnamed: 6	Unnamed: 7	Unnamed: 8	Unnamed: 9	...	Unnamed: 15	Unnamed: 16	Unnamed: 17	Unnamed: 18	Unnamed: 19	Unnamed: 20	Unnamed: 21	Unnamed: 22	Unnamed: 23	Unnamed: 24
count	22	23	22	22	23	22	22	23	22	22	...	22	23	22	22	23	22	22	23	22	22
unique	22	23	22	22	22	21	19	23	22	22	...	22	23	22	22	23	22	22	21	20	19
top	Dzongkhag	Irrigated Paddy	Harvested Area (Acre)	Production (MT)	0	0	0	Maize	Harvested Area (Acre)	Production (MT)	...	Production (MT)	Barley	Harvested Area (Acre)	Production (MT)	Millet	Harvested Area (Acre)	Production (MT)	0	0	0
freq	1	1	1	1	2	2	4	1	1	1	...	1	1	1	1	1	1	1	3	3	4

4 rows × 25 columns

plt.title("fruits production")
plt.plot(df['Apple, Arecanut,Mandarin,Watermelon,Dragonfruit,kiwi'], df['Bumthang,chukha,Dagana,Gasa,Haa'])
plt.xlabel("plt.plot(df['Column25'], df['Column25'])

Text(0.5, 1.0, 'fruits production')

My work

import matplotlib.pyplot as plt
from matplotlib import style
%matplotlib inline
import pandas as pd
import numpy as np

Data import

import pandas as pd

# Load the dataset
datasets = pd.read_excel("datasets/data 1.xlsx")

# Check the shape
print("Data shape:", datasets.shape)

# Display the first 5 rows
datasets.head()

Data shape: (11, 7)

	Unnamed: 0	Name	Subject	Unnamed: 3	Unnamed: 4	Unnamed: 5	Unnamed: 6
0	Sl.No	NaN	Math	sci	Eng	Dzo	Total
1	1	Dorji	20	54	67	93	234
2	2	Tashi	35	60	76	59	230
3	3	Pema	70	54	55	76	255
4	4	Dawa	40	34	45	77	196

display all the rows and columns

pd.set_option('display.max_rows',None)
pd.set_option('display.max_columns',None)
datasets 

	Unnamed: 0	Name	Subject	Unnamed: 3	Unnamed: 4	Unnamed: 5	Unnamed: 6
0	Sl.No	NaN	Math	sci	Eng	Dzo	Total
1	1	Dorji	20	54	67	93	234
2	2	Tashi	35	60	76	59	230
3	3	Pema	70	54	55	76	255
4	4	Dawa	40	34	45	77	196
5	5	Nima	50	36	34	59	179
6	6	Karma	67	67	25	47	206
7	7	Dema	88	89	78	29	284
8	8	Dechen	46	90	47	39	222
9	9	Kelzang	67	57	67	71	262
10	10	Zam	46	67	76	62	251

Data Visualizaton

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

df.head()

	Unnamed: 0	Name	Subject	Unnamed: 3	Unnamed: 4	Unnamed: 5	Unnamed: 6
0	Sl.No	NaN	Math	sci	Eng	Dzo	Total
1	1	Dorji	20	54	67	93	234
2	2	Tashi	35	60	76	59	230
3	3	Pema	70	54	55	76	255
4	4	Dawa	40	34	45	77	196

# Check column names
print(df.columns.tolist())

# Clean column names (remove spaces)
df.columns = df.columns.str.strip()

# Check again after cleaning
print(df.columns.tolist())

['Unnamed: 0', 'Name ', 'Subject', 'Unnamed: 3', 'Unnamed: 4', 'Unnamed: 5', 'Unnamed: 6']
['Unnamed: 0', 'Name', 'Subject', 'Unnamed: 3', 'Unnamed: 4', 'Unnamed: 5', 'Unnamed: 6']

# Check the shape
print("Data shape:", df.shape)

# Check for missing values
print(df.isnull().sum())

Data shape: (11, 7)
Unnamed: 0    0
Name          1
Subject       0
Unnamed: 3    0
Unnamed: 4    0
Unnamed: 5    0
Unnamed: 6    0
dtype: int64

numeric_cols = df.select_dtypes(include='number').columns.tolist()
print("Numeric columns:", numeric_cols)

Numeric columns: []

# Example: compare first numeric column with first column (like student names)
plt.figure(figsize=(12,6))
sns.barplot(x=df[df.columns[0]], y=df[numeric_cols[0]])
plt.xticks(rotation=45)
plt.title(f'{numeric_cols[0]} by {df.columns[0]}')
plt.show()

import pandas as pd
import matplotlib.pyplot as plt

# ---------- Step 1: Create the Dataset ----------
data = {
    "Sl.No": [1,2,3,4,5,6,7,8,9,10],
    "Name": ["Dorji","Tashi","Pema","Dawa","Nima","Karma","Dema","Dechen","Kelzang","Zam"],
    "Math": [20,35,70,40,50,67,88,46,67,46],
    "Sci":  [54,60,54,34,36,67,89,90,57,67],
    "Eng":  [67,76,55,45,34,25,78,47,67,76],
    "Dzo":  [93,59,76,77,59,47,29,39,71,62],
    "Total":[234,230,255,196,179,206,284,222,262,251]
}

df = pd.DataFrame(data)

# ---------- Step 2: Display the Data ----------
print("Dataset:")
df

Dataset:

	Sl.No	Name	Math	Sci	Eng	Dzo	Total
0	1	Dorji	20	54	67	93	234
1	2	Tashi	35	60	76	59	230
2	3	Pema	70	54	55	76	255
3	4	Dawa	40	34	45	77	196
4	5	Nima	50	36	34	59	179
5	6	Karma	67	67	25	47	206
6	7	Dema	88	89	78	29	284
7	8	Dechen	46	90	47	39	222
8	9	Kelzang	67	57	67	71	262
9	10	Zam	46	67	76	62	251

import matplotlib.pyplot as plt
import seaborn as sns

plt.style.use("ggplot")

plt.figure(figsize=(14,6))
for subject in ["Math", "Sci", "Eng", "Dzo"]:
    plt.plot(df["Name"], df[subject], marker="o", label=subject)

plt.title("Subject-wise Score Comparison")
plt.xlabel("Student Name")
plt.ylabel("Marks")
plt.xticks(rotation=45)
plt.legend()
plt.show()

Sangky Graph

import plotly.graph_objects as go

# Data
students = ["Dorji", "Tashi", "Pema", "Dawa", "Nima", "Karma", "Dema", "Dechen", "Kelzang", "Zam"]
math_scores = [20, 35, 70, 40, 50, 67, 88, 46, 67, 46]
sci_scores = [54, 60, 54, 34, 36, 67, 89, 90, 57, 67]
eng_scores = [67, 76, 55, 45, 34, 25, 78, 47, 67, 76]
dzo_scores = [93, 59, 76, 77, 59, 47, 29, 39, 71, 62]

# Nodes (students + subjects)
labels = students + ["Math", "Science", "English", "Dzongkha"]

# Links for Sankey diagram
source = []
target = []
value = []

# Connect each student to each subject with the score as value
for i, student in enumerate(students):
    source.extend([i, i, i, i])             # student index
    target.extend([10, 11, 12, 13])         # subject indices
    value.extend([math_scores[i], sci_scores[i], eng_scores[i], dzo_scores[i]])

# Create Sankey diagram
fig = go.Figure(data=[go.Sankey(
    node=dict(
        pad=15,
        thickness=20,
        line=dict(color="black", width=0.5),
        label=labels,
        color="blue"
    ),
    link=dict(
        source=source,
        target=target,
        value=value,
        color="lightblue"
    )
)])

fig.update_layout(title_text="Student Scores Sankey Diagram", font_size=10)
fig.show()

import numpy as np
from sklearn.linear_model import LinearRegression

# Data
Math = np.array([20, 35, 70, 40, 50, 67, 88, 46, 67, 46])
Sci  = np.array([54, 60, 54, 34, 36, 67, 89, 90, 57, 67])
Eng  = np.array([67, 76, 55, 45, 34, 25, 78, 47, 67, 76])
Dzo  = np.array([93, 59, 76, 77, 59, 47, 29, 39, 71, 62])
Total = np.array([234, 230, 255, 196, 179, 206, 284, 222, 262, 251])

# Combine independent variables
X = np.column_stack((Math, Sci, Eng, Dzo))
y = Total

# Create linear regression model
model = LinearRegression()
model.fit(X, y)

# Get coefficients
coefficients = model.coef_
intercept = model.intercept_

print("Fitted function:")
print(f"Total = {intercept:.2f} + ({coefficients[0]:.2f}*Math) + ({coefficients[1]:.2f}*Sci) + ({coefficients[2]:.2f}*Eng) + ({coefficients[3]:.2f}*Dzo)")

# Predict Total using the model
Total_pred = model.predict(X)
print("\nPredicted Total:", Total_pred)