Tutorial Playlist
200 Lessons1. Introduction to Python
2. Features of Python
3. How to install python in windows
4. How to Install Python on macOS
5. Install Python on Linux
6. Hello World Program in Python
7. Python Variables
8. Global Variable in Python
9. Python Keywords and Identifiers
10. Assert Keyword in Python
11. Comments in Python
12. Escape Sequence in Python
13. Print In Python
14. Python-if-else-statement
15. Python for Loop
16. Nested for loop in Python
17. While Loop in Python
18. Python’s do-while Loop
19. Break in Python
20. Break Pass and Continue Statement in Python
21. Python Try Except
22. Data Types in Python
23. Float in Python
24. String Methods Python
25. List in Python
26. List Methods in Python
27. Tuples in Python
28. Dictionary in Python
29. Set in Python
30. Operators in Python
31. Boolean Operators in Python
32. Arithmetic Operators in Python
33. Assignment Operator in Python
34. Bitwise operators in Python
35. Identity Operator in Python
36. Operator Precedence in Python
37. Functions in Python
38. Lambda and Anonymous Function in Python
39. Range Function in Python
40. len() Function in Python
41. How to Use Lambda Functions in Python?
42. Random Function in Python
43. Python __init__() Function
44. String Split function in Python
45. Round function in Python
46. Find Function in Python
47. How to Call a Function in Python?
48. Python Functions Scope
49. Method Overloading in Python
50. Method Overriding in Python
51. Static Method in Python
52. Python List Index Method
53. Python Modules
54. Math Module in Python
55. Module and Package in Python
56. OS module in Python
57. Python Packages
58. OOPs Concepts in Python
59. Class in Python
60. Abstract Class in Python
61. Object in Python
62. Constructor in Python
63. Inheritance in Python
64. Multiple Inheritance in Python
65. Encapsulation in Python
66. Data Abstraction in Python
67. Opening and closing files in Python
68. How to open JSON file in Python
69. Read CSV Files in Python
70. How to Read a File in Python
71. How to Open a File in Python?
72. Python Write to File
73. JSON Python
74. Python JSON – How to Convert a String to JSON
75. Python JSON Encoding and Decoding
76. Exception Handling in Python
77. Recursion in Python
78. Python Decorators
79. Python Threading
80. Multithreading in Python
81. Multiprocеssing in Python
82. Python Regular Expressions
83. Enumerate() in Python
84. Map in Python
85. Filter in Python
86. Eval in Python
87. Difference Between List, Tuple, Set, and Dictionary in Python
88. List to String in Python
89. Linked List in Python
90. Length of list in Python
91. Reverse a List in Python
92. Python List remove() Method
93. How to Add Elements in a List in Python
94. How to Reverse a List in Python?
95. Difference Between List and Tuple in Python
96. List Slicing in Python
97. Sort in Python
98. Merge Sort in Python
99. Selection Sort in Python
100. Sort Array in Python
101. Sort Dictionary by Value in Python
102. Datetime Python
103. Random Number in Python
104. 2D Array in Python
105. Abs in Python
106. Advantages of Python
107. Anagram Program in Python
108. Append in Python
109. Applications of Python
110. Armstrong Number in Python
111. Assert in Python
112. Binary Search in Python
113. Binary to Decimal in Python
114. Bool in Python
115. Calculator Program in Python
116. chr in Python
117. Control Flow Statements in Python
118. Convert String to Datetime Python
119. Count in python
120. Counter in Python
121. Data Visualization in Python
Now Reading
122. Datetime in Python
123. Extend in Python
124. F-string in Python
125. Fibonacci Series in Python
126. Format in Python
127. GCD of Two Numbers in Python
128. How to Become a Python Developer
129. How to Run Python Program
130. In Which Year Was the Python Language Developed?
131. Indentation in Python
132. Index in Python
133. Interface in Python
134. Is Python Case Sensitive?
135. Isalpha in Python
136. Isinstance() in Python
137. Iterator in Python
138. Join in Python
139. Leap Year Program in Python
140. Lexicographical Order in Python
141. Literals in Python
142. Matplotlib
143. Matrix Multiplication in Python
144. Memory Management in Python
145. Modulus in Python
146. Mutable and Immutable in Python
147. Namespace and Scope in Python
148. OpenCV Python
149. Operator Overloading in Python
150. ord in Python
151. Palindrome in Python
152. Pass in Python
153. Pattern Program in Python
154. Perfect Number in Python
155. Permutation and Combination in Python
156. Prime Number Program in Python
157. Python Arrays
158. Python Automation Projects Ideas
159. Python Frameworks
160. Python Graphical User Interface GUI
161. Python IDE
162. Python input and output
163. Python Installation on Windows
164. Python Object-Oriented Programming
165. Python PIP
166. Python Seaborn
167. Python Slicing
168. type() function in Python
169. Queue in Python
170. Replace in Python
171. Reverse a Number in Python
172. Reverse a string in Python
173. Reverse String in Python
174. Stack in Python
175. scikit-learn
176. Selenium with Python
177. Self in Python
178. Sleep in Python
179. Speech Recognition in Python
180. Split in Python
181. Square Root in Python
182. String Comparison in Python
183. String Formatting in Python
184. String Slicing in Python
185. Strip in Python
186. Subprocess in Python
187. Substring in Python
188. Sum of Digits of a Number in Python
189. Sum of n Natural Numbers in Python
190. Sum of Prime Numbers in Python
191. Switch Case in Python
192. Python Program to Transpose a Matrix
193. Type Casting in Python
194. What are Lists in Python?
195. Ways to Define a Block of Code
196. What is Pygame
197. Why Python is Interpreted Language?
198. XOR in Python
199. Yield in Python
200. Zip in Python
Every day, an astonishing volume of data is created, quantified in zettabytes, where 1 zettabyte represents an astonishing 1,000,000,000,000,000,000,000 bytes. Given the colossal quantity of data generated daily, attempting to understand it in its unprocessed format becomes overwhelming. To decipher the messages hidden within this vast sea of data and to prepare it for analysis and modeling, the data must first be visualized and transformed into a more intuitive, graphical format. Data visualization unlocks the insights, patterns, correlations, and trends that lie dormant within the data. It empowers individuals to grasp the underlying stories that data has to offer. This comprehensive guide will walk you through the fascinating data visualization in the Python domain, providing a clear understanding of its significance, the databases used, and in-depth explorations of popular Python libraries - Matplotlib, Seaborn, and Bokeh.
To comprehend the information your data holds and the stories it encapsulates and to enable proper data cleaning for modeling, it's imperative to first visualize and represent it in a graphic format. Using visual formats such as charts, this depiction of your data is commonly known as data visualization. Python offers a multitude of libraries for data visualization. Some of the notable libraries for data analysis, decision-making, and communication include Matplotlib, Seaborn, Bokeh, and Plotly.
Data visualization in Python is the graphical representation of data to facilitate understanding. It is indispensable in various fields, including business, science, research, and communication.
Examples of data visualization in Python
1. Bar Chart
A bar chart is a common visualization for showing categorical data. It uses rectangular bars of varying heights to represent data values.
2. Scatter Plot
A scatter plot displays individual data points on a two-dimensional plane. It's useful for showing the relationship between two variables.
3. Line Chart
A line chart connects data points with lines, making it ideal for visualizing trends over time.
4. Histogram
Histograms are used to represent the distribution of a single variable. They group data into bins and show their frequencies.
Its significance lies in its ability to -
Several tools and libraries are used for data visualization, including:
Data visualization in Python starts with structured data stored in databases. Common types include:
The database choice depends on data complexity and accessibility requirements.
Databases are the repositories for structured data, simplifying data retrieval and analysis. It stores and organizes the data used to create charts, graphs, and dashboards.
Let's explore the concept of databases using a practical example, the "Tips Database."
The "Tips Database" is a collection of data related to customer transactions at a restaurant. It includes the following columns:
Here's an example entry from the "Tips Database":
Total Bill | Tip | Sex | Smoker | Day | Time | Size |
16.99 | 1.01 | Female | No | Sunday | Dinner | 2 |
Matplotlib is a Python library for creating a wide range of visualizations, from simple line charts to complex, customized plots. It offers full control over plot elements to data scientists and analysts. Let's explore an example of creating a simple line chart using Matplotlib.
Let's delve into data visualization in Python using Matplotlib examples for creating a simple line chart using Matplotlib. Here, we will use Matplotlib to visualize a set of data points as a line chart. We'll plot the change in temperature over several days.
code
import matplotlib.pyplot as plt
# Sample data: Days and Temperature
days = [1, 2, 3, 4, 5]
temperature = [78, 82, 80, 85, 88]
# Create a line chart
plt.plot(days, temperature, marker='o', linestyle='-')
# Add labels and a title
plt.xlabel("Days")
plt.ylabel("Temperature (°F)")
plt.title("Temperature Change Over Days")
# Display the plot
plt.show()
A scatter plot is an excellent choice to visualize the relationship between two numerical variables. Here's an example illustrating the correlation between a student's study time and their test score:
code
import matplotlib.pyplot as plt
study_hours = [2, 3, 4, 5, 6, 7, 8]
test_scores = [50, 55, 60, 70, 75, 80, 85]
plt.scatter(study_hours, test_scores)
plt.xlabel('Study Hours')
plt.ylabel('Test Scores')
plt.title('Scatter Plot: Study Hours vs. Test Scores')
plt.show()
Line charts are ideal for showing trends over time. In this data visualization in Python using matplotlib examples, we visualize the daily temperature fluctuations in a city over a week:
code
import matplotlib.pyplot as plt
days = ['Day 1', 'Day 2', 'Day 3', 'Day 4', 'Day 5', 'Day 6', 'Day 7']
temperatures = [75, 78, 82, 77, 73, 79, 80]
plt.plot(days, temperatures)
plt.xlabel('Days')
plt.ylabel('Temperature (°F)')
plt.title('Line Chart: Daily Temperature Trends')
plt.show()
Bar charts are suitable for comparing categories or groups. They use rectangular bars of varying heights to represent data values. Bar charts are often used for visualizing categorical data, making comparisons, and showing distribution. Here's an example illustrating the sales of various products in a store:
code
import matplotlib.pyplot as plt
products = ['Product A,' 'Product B,' 'Product C,' 'Product D']
sales = [450, 600, 800, 550]
plt.bar(products, sales)
plt.xlabel('Products')
plt.ylabel('Sales')
plt.title('Bar Chart: Product Sales')
plt.show()
Histograms are used to visualize the distribution of a single variable. They group data into bins and show the frequency or count of data points within each bin. They are ideal for understanding the data's distribution and identifying patterns. In this example, we depict the distribution of ages in a population:
code
import matplotlib.pyplot as plt
population_ages = [25, 30, 32, 35, 38, 40, 42, 45, 48, 50, 55, 60, 65, 70]
plt.hist(population_ages, bins=5, edgecolor='black,' alpha=0.7)
plt.xlabel('Age')
plt.ylabel('Frequency')
plt.title('Histogram: Age Distribution')
plt.show()
Seaborn is a Python library built on Matplotlib that simplifies data visualization and provides a higher-level interface.
Seaborn extends Matplotlib's capabilities by introducing specialized plots for visualizing complex data relationships. Some advanced visualizations include:
Let's explore Seaborn with data visualization projects in Python with source code:
Seaborn enhances scatter plots with regression lines. In this example, we visualize the relationship between a total bill and tips in a restaurant dataset:
code
import seaborn as sns
import matplotlib.pyplot as plt
tips = sns.load_dataset("tips")
sns.scatterplot(x="total_bill," y="tip," data=tips)
plt.title('Seaborn Scatter Plot: Total Bill vs. Tips')
plt.show()
Seaborn's line plots include confidence intervals, making them ideal for showing uncertain trends. In this data visualization project in Python with source code, we visualize the response signal over different time points:
code
import seaborn as sns
import matplotlib.pyplot as plt
fmri = sns.load_dataset("fmri")
sns.lineplot(x="timepoint," y="signal," data=fmri, ci="sd")
plt.title('Seaborn Line Plot: Timepoint vs. Signal')
plt.show()
Seaborn simplifies the creation of bar plots with additional statistical estimation. In this example, we depict the survival rate in different passenger classes:
code
import seaborn as sns
import matplotlib.pyplot as plt
titanic = sns.load_dataset("titanic")
sns.barplot(x="class," y="survived," data=titanic, ci=None)
plt.title('Seaborn Bar Plot: Passenger Class vs. Survival Rate')
plt.show()
Seaborn's histograms include kernel density estimation for a smoother representation of data distributions. In this example, we visualize the distribution of diamond carat weights:
code
import seaborn as sns
import matplotlib.pyplot as plt
diamonds = sns.load_dataset("diamonds")
sns.histplot(data=diamonds, x="carat," kde=True)
plt.title('Seaborn Histogram: Carat Weight Distribution')
plt.show()
Here's a comparison of Seaborn and Matplotlib:
Aspect | Seaborn | Matplotlib |
---|---|---|
Ease of Use | Built on top of Matplotlib, offering a higher-level interface with simpler syntax. | Provides lower-level customization, which can be more complex for beginners. |
Aesthetics | Employs stylish default themes and color palettes, resulting in attractive visualizations. | It requires more manual configuration for aesthetics but offers full customization. |
Default Visuals | Simplifies, creating statistical plots like violin plots, pair plots, and heatmaps. | Primarily focuses on basic plot types and requires additional coding for complex visuals. |
Integration | Seamlessly integrates with Pandas DataFrames, simplifying data handling. | Works well with Pandas but may require more manual data manipulation. |
Plot Types | Specialized for statistical and information-rich visualizations. | Offers a wide range of plot types for various use cases, such as data visualization in data science. |
Code Length | Requires fewer lines of code for common statistical visualizations. | Often requires more lines of code for similar visualizations. |
Customization Options | Provides some customization options but excels in simplifying aesthetics. | Offers extensive customization possibilities, allowing full control over plot details. |
Learning Curve | Beginner-friendly due to simplified syntax and elegant defaults. | It may have a steeper learning curve, especially for those new to data visualization. |
Community & Resources: | Has a growing community with resources and tutorials available. | Has a well-established community with extensive documentation and resources. |
Bokeh is a Python library specializing in interactive and web-based data visualizations. It empowers you to create interactive dashboards.
Bokeh data visualization projects in Python with source code:
code
from bokeh.plotting import figure, show
p = figure(title="Bokeh Line Chart")
p.line([1, 2, 3, 4, 5], [10, 15, 13, 18, 21], line_width=2)
show(p)
Data visualization in Python is a robust tool to convey complex information in a comprehensible and engaging manner. Visualization can provide valuable insights, whether you're exploring trends in data, comparing categories, or understanding data distributions. The choice of the right library, such as Matplotlib, Seaborn, or Bokeh, depends on your specific needs, from static charts to interactive dashboards.
1. When should I use a scatter plot?
Use a scatter plot when you want to visualize the relationship between two numerical variables to identify correlations or patterns.
2. What is the advantage of using Seaborn over Matplotlib?
Seaborn simplifies data visualization and offers a higher-level interface, making creating aesthetically pleasing statistical graphics easier with less code.
3. How can I create interactive visualizations using Bokeh?
Bokeh allows you to create interactive visualizations for web applications. You can incorporate features like tooltips, zooming, and panning for user interactivity.
4. What is the difference between data visualization and data exploration?
Data visualization focuses on representing data visually, while data exploration involves analyzing and discovering patterns in the data.
5. How can I choose the right chart type for my data?
To select the right chart type, consider the data's nature and your goal. Use bar charts for category comparisons, line charts for trends, scatter plots for relationships, and histograms for data distributions.
6. Can data visualization be used for storytelling?
Data visualization is an excellent tool for crafting data-driven narratives, enabling storytellers to convey insights and findings effectively.
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upGrad does not grant credit; credits are granted, accepted or transferred at the sole discretion of the relevant educational institution offering the diploma or degree. We advise you to enquire further regarding the suitability of this program for your academic, professional requirements and job prospects before enr...