Python Datatypes and Basic Charts .ipynb

Kernel: Python 3 (ipykernel)

Advantages of Data Visualization

Data visualization is essential for understanding complex data and making informed decisions.

Here are its key advantages:

• 1. Makes Data Easier to Understand 📊 Converts raw data into visual formats like charts, graphs, and maps, making it easier to comprehend patterns and trends.

✔ Example: A line chart showing sales trends over time is easier to interpret than raw numbers in a table.

• 2. Identifies Trends and Patterns Quickly 🔍 Helps in detecting patterns, trends, and outliers that might not be obvious in spreadsheets or raw data.

✔ Example: A heatmap of customer activity can reveal peak engagement times.

• 3. Improves Decision-Making 📈 Enables businesses and analysts to make data-driven decisions faster and more accurately.

✔ Example: A dashboard showing real-time sales and inventory levels helps in making quick supply chain decisions.

• 4. Enhances Communication and Storytelling 🗣 Helps present complex data to stakeholders in a compelling and clear manner.

✔ Example: A bar chart comparing monthly revenue makes financial reports more engaging.

• 5. Saves Time in Data Analysis ⏳ Analyzing thousands of rows in a dataset takes time, but a well-designed chart can provide insights in seconds.

✔ Example: A scatter plot showing the correlation between marketing spend and revenue can provide immediate insights.

• 6. Helps in Detecting Errors and Anomalies ⚠ Quickly highlights inconsistencies, missing data, or unusual trends in datasets.

✔ Example: A box plot can show unexpected outliers in employee performance ratings.

• 7. Engages and Persuades Audience Effectively 📢 Makes reports, presentations, and pitches more engaging, leading to better audience retention.

✔ Example: A pie chart showing market share can be more persuasive than just listing numbers.

• 8. Supports Predictive Analysis 🔮 Helps in forecasting future trends based on historical data using techniques like regression analysis and time series forecasting.

✔ Example: A forecasting graph for stock prices using past data trends.

• 9. Enhances Accessibility for Non-Technical Users 👩‍💼 Enables business managers, executives, and non-data professionals to understand complex data without needing advanced analytical skills.

✔ Example: An interactive Tableau dashboard allows users to filter data dynamically.

• 10. Facilitates Competitive Analysis 🏆 Helps organizations compare their performance with competitors by visualizing industry trends.

Python Variables

# Defining variables
name = "Ashi"  # String
age = 25  # Integer
height = 5.5  # Float
is_student = True  # Boolean
x='1234'
y= 1234

x[0],x[1],x[2],x[3]
len(x)
max(x)

print("her name is,",name)
print("%s is her name and her age is %d and height is %f" %(name,age,height))
print()

## del x[0]

her name is, Ashi
Ashi is her name and her age is 25 and height is 5.500000

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[24], line 17
     14 print("%s is her name and her age is %d and height is %f" %(name,age,height))
     15 print()
---> 17 del x[0]
TypeError: 'str' object doesn't support item deletion

##List 
l=[]
fruits = ["apple", "banana", 9,10.2 ]
fruits[0]  # Access first element


fruits[-1]

fruits[0]="banana"
fruits[1]="apple"
fruits
#fruits.append("lilly")
##fruits.remove("apple")
fruits
#del fruits[1]
fruits

['banana', 'apple', 9, 10.2]

##Tuples 

d=()
d=("a",4,4.5)
d[0]
del d[0]

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[28], line 6
      4 d=("a",4,4.5)
      5 d[0]
----> 6 del d[0]
TypeError: 'tuple' object doesn't support item deletion

##  Sets

s={}

b = {1, 2, 3, 4, 2, 3,4,5}

#unique_numbers.add(5)  # Add element
#unique_numbers.remove(2)  # Remove element

b
b.add(8)
b
b.remove(4)
b 

{1, 2, 3, 5, 8}

#dictionary 

s={}
s={"a":1,"b":2,"c":3}
s
s.keys()
s.values()
s["d"]=4
s["a"]=12
s["c"]=12
s

{'a': 12, 'b': 2, 'c': 12, 'd': 4}

Type Conversion

type(4),type(4.5),type("ashi"),type((1,3)),type({1,2}),type([1,4]),type({2:3})

(int, float, str, tuple, set, list, dict)

int(4.3)
float(8)
str(34)
int("45")
list("ashi")
tuple([1,23])

(1, 23)

Input Functions

# Create an interface name"Learning platform"
##ask user for input their name, age, exp
##also if age is less than 18 then they are not eligible to create a user and password. 
## If age is greater than 18 ask user to create User Name and Password,
## "Account created"
## " Welcome to login page"
## "Enter user name and password"
##"use can use interface Now"

print("WELCOME TO ABSA LEARNING Platform.\n To register enter following details:")

name=input("Name please:")
Age=int(input("Age:"))
WorkExp=float(input("TOTAL Exp:"))

if Age < 18:
    print("Not eligible to create a account")
else:
    print("Welcome you can create an account")

WELCOME TO ABSA LEARNING Platform.
 To register enter following details:
Name please:Abha
Age:32
TOTAL Exp:13.5
Welcome you can create an account

Basic Charts

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

# Generate Sample Data
data = {
    'Category': ['A', 'B', 'C', 'D', 'E'],
    'Values': [10, 20, 15, 25, 30]
}
df = pd.DataFrame(data)

# Basic Bar Chart using Matplotlib
plt.figure(figsize=(8,5))
plt.bar(df['Category'], df['Values'], color='skyblue')
plt.xlabel('Category')
plt.ylabel('Values')
plt.title('Basic Bar Chart')
plt.show()

# Line Chart using Matplotlib
x = np.linspace(0, 10, 100)
y = np.sin(x)
plt.figure(figsize=(8,5))
plt.plot(x, y, label='Sine Wave', color='r')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Line Chart')
plt.legend()
plt.show()

# Scatter Plot using Seaborn
np.random.seed(42)
data = pd.DataFrame({
    'X': np.random.rand(50),
    'Y': np.random.rand(50)
})
sns.scatterplot(x='X', y='Y', data=data)
plt.title('Scatter Plot')
plt.show()

# Histogram using Seaborn
sns.histplot(data['X'], bins=10, kde=True)
plt.title('Histogram')
plt.show()