In today's data-driven world, we are overwhelmed with vast amounts of information. To make sense of this deluge, Data Visualization has emerged as a critical tool. It transforms complex datasets into intuitive visuals, enabling better comprehension, decision-making, and insights. Dive in with us as we explore the power and potential of visualizing data!In this post,First we will learn what is Data Visualization and why do we need to be an expert in data visualization in order to jump into the field of Data Science/Analytic?Secondly, we will learn about popular data visualization toolsThirdly, we will see about the types of plotsLastly, we will learn how to make those plots on dummy dataset using python libraries (pandas, matplotlib, seaborn, plotly, etc.).Let's have a look into it: What is Data Visualization? Data Visualization is the representation of data in a graphical format. It allows users to see and understand patterns, trends, and insights in data. From simple bar charts to intricate 3D models, data visualization encompasses a wide range of techniques. Why is Data Visualization Important? Quick Insights: A well-crafted visual can communicate complex data in seconds, saving time and effort.Informed Decision Making: Visual data aids in making evidence-based decisions, reducing risks.Engagement: Visuals are more engaging than spreadsheets, making it easier to present and share findings.Data Exploration: Visualization tools enable users to delve deep into data, uncovering hidden insights. Key Principles of Effective Data Visualization: Simplicity: Less is more. Avoid clutter and focus on the essentials.Consistency: Use consistent colors, fonts, and symbols to avoid confusion.Accuracy: Ensure the visual accurately represents the data.Interactivity: Modern tools allow for interactive visuals, enhancing user engagement and understanding. Popular Data Visualization Tools: Tableau: A powerful tool offering a wide range of visualization options.Power BI: Microsoft's solution for data visualization and business intelligence.D3.js: A JavaScript library for creating custom, dynamic visuals.Python Libraries: Libraries like Matplotlib, Seaborn, and Plotly are popular among data scientists.R Programming: ggplot2, plotly Data Visualization in Action: Imagine you're a retailer looking to understand sales trends. A spreadsheet with thousands of rows of sales data might be overwhelming. However, a heat map showing sales density or a line chart depicting monthly sales trends can provide clear insights in moments.For our readers, here's a glimpse of the kind of visuals we're talking about: Different Plots-1Geographical MapsBar plotsDashboard Previous slide Next slide The images above perfectly encapsulate the essence of data visualization. From analyzing pie charts to understanding sales trends through heat maps and line charts, these visuals demonstrate the power and versatility of data visualization tools. Moreover, the intricacy of a comprehensive dashboard showcases how various visualizations can come together to provide a holistic view of data.Incorporating such visuals into your content not only enhances the reader's experience but also solidifies their understanding of the topic. As the saying goes, "A picture is worth a thousand words," and in the realm of data, a well-crafted visualization might be worth a million data points!Harness the potential of data visualization, and let your data come alive. Happy visualizing! 🌟📊🖼️ Key Terms used in Data Visualization Here's a list of key terms in data visualization, along with explanations and examples for each:Bar Chart:Explanation: A graphical representation of data using bars of varying heights or lengths.Example: Comparing the sales of different products in a month.Pie Chart:Explanation: A circular chart divided into slices to illustrate numerical proportions.Example: Showing the market share of various smartphone brands.Histogram:Explanation: A representation of the distribution of a dataset, similar to a bar chart but for frequency distribution.Example: Displaying the age distribution of employees in a company.Scatter Plot:Explanation: A graph with points plotted to show the relationship between two sets of data.Example: Comparing advertising spend with sales revenue.Line Chart:Explanation: A chart that displays information as a series of data points connected by straight line segments.Example: Tracking stock market prices over a week.Heat Map:Explanation: A data visualization technique where values in a matrix are represented as colors.Example: Showing website activity, where darker colors represent more clicks.Box Plot (or Whisker Plot):Explanation: A standardized way of displaying the dataset based on a five-number summary: minimum, first quartile, median, third quartile, and maximum.Example: Comparing exam scores of different classes.Area Chart:Explanation: Similar to a line chart, but the area between the axis and the line is filled with color or shading.Example: Displaying the total revenue of a company over several years.Radar (or Spider) Chart:Explanation: A chart that displays multivariate data on axes starting from the same point.Example: Comparing the features of different products.Treemap:Explanation: A visualization of hierarchical data using nested rectangles.Example: Showing storage used by different types of files on a computer.Geographical Map:Explanation: A map that displays data based on geographical areas or locations.Example: Highlighting areas with high crime rates in a city.Time Series:Explanation: A sequence of data points indexed in time order.Example: Analyzing the daily temperature of a place over a year.There are several other plots, such as bubble chart, violin plots and much more. Plotting in Python We can plot in python using libraries such as pandas, matplotlib, seaborn, plotly, altair, bokeh 1. Bar Chart # import pandas and matplotlib.pyplot libraries import pandas as pd import matplotlib.pyplot as plt # create a dictionary with sample data data = {'Products': ['Product A', 'Product B', 'Product C'], 'Sales': [100, 150, 80]} # create a pandas dataframe from the dictionary df = pd.DataFrame(data) # plot the dataframe as a bar chart df.plot(x='Products', y='Sales', kind='bar', legend=False) # set the y-axis label plt.ylabel('Sales') # set the title of the plot plt.title('Sales of different products') # save the plot to a file plt.savefig('bar_chart.png', dpi=300, bbox_inches='tight') # display the plot plt.show() 2. Pie Chart # import pandas and matplotlib.pyplot libraries import pandas as pd import matplotlib.pyplot as plt # create a dictionary with sample data data = {'Products': ['Product A', 'Product B', 'Product C'], 'Sales': [100, 150, 80]} # create a pandas dataframe from the dictionary df = pd.DataFrame(data) # plot the dataframe as a pie chart df.set_index('Products')['Sales'].plot(kind='pie', autopct='%1.1f%%', startangle=90) # set the title of the plot plt.title('Sales distribution of different products') # save the plot to a file plt.savefig('pie_chart.png', dpi=300, bbox_inches='tight') # display the plot plt.ylabel('') # This is to remove the 'Sales' ylabel which is unnecessary in a pie chart plt.show() 3. Histogram # import pandas and matplotlib.pyplot libraries import pandas as pd import matplotlib.pyplot as plt # create a dictionary with sample data data = {'Sales': [30,50,120,150,200,300,100, 150, 80, 120, 140, 130, 50,50,30,20,10,50,35,45,55,50,55,65,56,58,58,54,55,50,5, 110, 145, 105, 90, 115, 125, 135, 85, 95]} # create a pandas dataframe from the dictionary df = pd.DataFrame(data) # plot the dataframe as a histogram df['Sales'].plot(kind='hist', bins=5, edgecolor='black') # set the x-axis and y-axis labels plt.xlabel('Sales Value') plt.ylabel('Number of Transactions') # set the title of the plot plt.title('Distribution of Sales Transactions') # save the plot to a file plt.savefig('histogram.png', dpi=300, bbox_inches='tight') # display the plot plt.show() 4. Scatter plot # import pandas and matplotlib.pyplot libraries import pandas as pd import matplotlib.pyplot as plt # create a dictionary with sample data data = { 'Sales': [100, 150, 80, 120, 140, 130, 110, 145, 105, 90], 'Profit': [50, 70, 40, 60, 75, 65, 55, 72, 52, 45] } # create a pandas dataframe from the dictionary df = pd.DataFrame(data) # plot the dataframe as a scatter plot df.plot(x='Sales', y='Profit', kind='scatter') # set the x-axis and y-axis labels plt.xlabel('Sales Value') plt.ylabel('Profit Value') # set the title of the plot plt.title('Relationship between Sales and Profit') # save the plot to a file plt.savefig('scatter_plot.png', dpi=300, bbox_inches='tight') # display the plot plt.show() 5. Line Chart # import pandas and matplotlib.pyplot libraries import pandas as pd import matplotlib.pyplot as plt # create a dictionary with sample data data = { 'Month': ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'], 'Monthly Sales': [100, 110, 105, 115, 120, 125, 130, 135, 140, 145, 150, 155] } # create a pandas dataframe from the dictionary df = pd.DataFrame(data) # plot the dataframe as a line chart df.plot(x='Month', y='Monthly Sales', kind='line', marker='o') # set the x-axis and y-axis labels plt.xlabel('Month') plt.ylabel('Sales Value') # set the title of the plot plt.title('Monthly Sales Trend over a Year') # save the plot to a file plt.savefig('line_chart.png', dpi=300, bbox_inches='tight') # display the plot plt.show() 6. Heatmap # import required libraries import pandas as pd import matplotlib.pyplot as plt import seaborn as sns # Sample correlation matrix data = { 'Product A': [1, 0.6, 0.8, 0.3], 'Product B': [0.6, 1, 0.5, 0.7], 'Product C': [0.8, 0.5, 1, 0.4], 'Product D': [0.3, 0.7, 0.4, 1] } # Create a pandas dataframe from the dictionary df = pd.DataFrame(data) # Plotting the heatmap plt.figure(figsize=(8, 6)) sns.heatmap(df, annot=True, cmap='viridis', vmin=0, vmax=1) # Set the title of the plot plt.title('Product Correlation Heatmap') # Save the plot to a file plt.savefig('heatmap.png', dpi=300, bbox_inches='tight') # Display the plot plt.show() 7. Boxplot # import required libraries import pandas as pd import matplotlib.pyplot as plt # Sample data data = { 'Product A': [95, 85, 90, 100, 105, 110], 'Product B': [100, 105, 95, 90, 115, 120], 'Product C': [105, 110, 115, 120, 125, 130] } # Create a pandas dataframe from the dictionary df = pd.DataFrame(data) # Plotting the boxplot df.boxplot(grid=False, vert=True, fontsize=10) # Set the title of the plot plt.title('Sales Distribution by Product') # Set the y-axis label plt.ylabel('Sales Value') # Save the plot to a file plt.savefig('boxplot.png', dpi=300, bbox_inches='tight') # Display the plot plt.show() Box plot can also be created using seaborn library as follows # we can do the same thing with seaborn library as follows # import required libraries import pandas as pd import matplotlib.pyplot as plt import seaborn as sns # Sample data data = { 'Product A': [95, 85, 90, 100, 105, 110], 'Product B': [100, 105, 95, 90, 115, 120], 'Product C': [105, 110, 115, 120, 125, 130] } # Create a pandas dataframe from the dictionary df = pd.DataFrame(data) # Plotting the boxplot using seaborn plt.figure(figsize=(10, 6)) sns.boxplot(data=df) # Set the title of the plot plt.title('Sales Distribution by Product') # Set the y-axis label plt.ylabel('Sales Value') # Save the plot to a file plt.savefig('sns_boxplot.png', dpi=300, bbox_inches='tight') # Display the plot plt.show() 8. Area Chart # import required libraries import pandas as pd import matplotlib.pyplot as plt import seaborn as sns # Sample data data = { 'Month': ['Jan', 'Feb', 'Mar', 'Apr', 'May'], 'Sales': [100, 150, 180, 220, 270] # Accumulated sales for demonstration } # Create a pandas dataframe from the dictionary df = pd.DataFrame(data) # Plotting the area chart plt.figure(figsize=(10, 6)) sns.set_style("whitegrid") plt.fill_between(df['Month'], df['Sales'], color="skyblue", alpha=0.4) plt.plot(df['Month'], df['Sales'], color="Slateblue", alpha=0.6) # Set the title, x-axis and y-axis labels plt.title('Monthly Accumulated Sales') plt.xlabel('Month') plt.ylabel('Accumulated Sales Value') # Save the plot to a file plt.savefig('area_chart.png', dpi=300, bbox_inches='tight') # Display the plot plt.show() 9. Spider or Radar Plot # import required libraries import pandas as pd import matplotlib.pyplot as plt import numpy as np # Sample data data = { 'Metrics': ['Metric A', 'Metric B', 'Metric C', 'Metric D', 'Metric E'], 'Product 1': [3, 4, 5, 4, 2], 'Product 2': [4, 5, 4, 3, 3] } # Create a pandas dataframe from the dictionary df = pd.DataFrame(data) # Number of variables categories = list(df['Metrics']) N = len(categories) # Set the angle for each metric's axis angles = [n / float(N) * 2 * np.pi for n in range(N)] angles += angles[:1] # Initialize the spider plot plt.figure(figsize=(8, 6)) ax = plt.subplot(111, polar=True) # Plotting for Product 1 values = df['Product 1'].tolist() values += values[:1] # Repeat the first value to close the circular graph ax.plot(angles, values, linewidth=2, linestyle='solid', label='Product 1') ax.fill(angles, values, 'b', alpha=0.1) # Plotting for Product 2 values = df['Product 2'].tolist() values += values[:1] # Repeat the first value to close the circular graph ax.plot(angles, values, linewidth=2, linestyle='solid', label='Product 2') ax.fill(angles, values, 'r', alpha=0.1) # Add the axis labels plt.xticks(angles[:-1], categories, color='grey', size=12) ax.set_rlabel_position(30) plt.yticks([1,2,3,4,5], ["1","2","3","4","5"], color="grey", size=8) plt.ylim(0, 5) # Add title and legend plt.title('Comparison between Product 1 and Product 2 on various metrics') ax.legend(loc='upper right', bbox_to_anchor=(0.1, 0.1)) # Save the plot to a file plt.savefig('radar_chart.png', dpi=300, bbox_inches='tight') # Display the plot plt.show() 10. Tree Map # Import required libraries import pandas as pd import matplotlib.pyplot as plt import squarify # install if you do not have it # Sample data data = { 'File Type': ['Documents', 'Videos', 'Images', 'Audio', 'Other'], 'Storage Used (GB)': [50, 150, 80, 60, 10] } # Create a pandas dataframe from the dictionary df = pd.DataFrame(data) # Plotting the treemap plt.figure(figsize=(12, 8)) colors = ['red', 'green', 'blue', 'yellow', 'grey'] squarify.plot(sizes=df['Storage Used (GB)'], label=df['File Type'], color=colors, alpha=0.6) # Add title plt.title('Storage Used by File Type') plt.axis('off') # Turn off the axis # Save the plot to a file plt.savefig('treemap.png', dpi=300, bbox_inches='tight') # Display the plot plt.show() 11. Geographical Maps # Import required libraries import plotly.graph_objects as go # Data for cities and their coordinates cities = { 'New York': [40.7128, -74.0060], 'London': [51.5074, -0.1278], 'Sydney': [-33.8688, 151.2093], 'Tokyo': [35.6895, 139.6917], 'Rio de Janeiro': [-22.9068, -43.1729] } # Extracting latitudes, longitudes, and city names for plotting lats = [coords[0] for coords in cities.values()] longs = [coords[1] for coords in cities.values()] names = list(cities.keys()) # Creating the Scattergeo plot fig = go.Figure(data=go.Scattergeo( lon = longs, lat = lats, text = names, mode = 'markers', marker = dict( size = 10, opacity = 0.8, reversescale = True, autocolorscale = False, symbol = 'circle', line = dict( width=1, color='rgba(102, 102, 102)' ), ) )) # Setting the layout for the map fig.update_layout( title = 'Cities on World Map using Plotly', geo = dict( scope='world', showland = True, ) ) # Display the map fig.show() # Save the map to an HTML file fig.write_html("world_map_plotly.html") 12. Time Series plots # Import required libraries import plotly.graph_objects as go import pandas as pd # Sample time series data data = { 'Date': ['2022-01-01', '2022-01-02', '2022-01-03', '2022-01-04', '2022-01-05'], 'Value': [10, 15, 7, 12, 10] } # Create a pandas dataframe from the dictionary df = pd.DataFrame(data) # Convert the 'Date' column to a datetime object for better handling df['Date'] = pd.to_datetime(df['Date']) # Creating the time series plot fig = go.Figure(data=go.Scatter(x=df['Date'], y=df['Value'], mode='lines+markers')) # Setting the layout for the plot fig.update_layout( title='Time Series Plot using Plotly', xaxis_title='Date', yaxis_title='Value', xaxis=dict(showline=True, showgrid=False, showticklabels=True), yaxis=dict(zeroline=False, showgrid=True, showline=False, showticklabels=True), ) # Display the plot fig.show() # Save the plot to an HTML file fig.write_html("time_series_plotly.html") # save as png fig.write_image("time_series_plotly.png") 13. Bubble Chart # Import required libraries import plotly.graph_objects as go # Sample data data = { 'X': [10, 20, 30, 40, 50], 'Y': [15, 25, 35, 45, 55], 'Size': [30, 60, 90, 120, 150], # Determines the size of bubbles 'Labels': ['A', 'B', 'C', 'D', 'E'] } # Creating the bubble chart fig = go.Figure(data=go.Scatter( x=data['X'], y=data['Y'], mode='markers', text=data['Labels'], marker=dict( size=data['Size'], sizemode='diameter', # 'diameter' ensures that the size values correspond to the diameter of the bubbles opacity=0.6, color=data['Y'], # Coloring the bubbles based on Y values colorscale='Viridis', colorbar=dict(title='Colorbar') ) )) # Setting the layout for the plot fig.update_layout( title='Bubble Chart using Plotly', xaxis_title='X Values', yaxis_title='Y Values', showlegend=False ) # Display the plot fig.show() # Save the plot to an HTML file fig.write_html("bubble_chart_plotly.html") # save png file with 300 dpi fig.write_image("bubble_chart_plotly.png", scale=3) 14. Violin plots A violin plot is similar to a box plot but also includes a rotated kernel density plot on each side. It provides a visualization of the distribution of the data, its probability density, and its cumulative distribution.Here's how to create a violin plot using Plotly: # Import required libraries import plotly.express as px # Sample data data = { 'Category': ['A', 'B', 'C', 'A', 'B', 'C', 'A', 'B', 'C', 'A', 'B', 'C'], 'Values': [10, 15, 5, 25, 20, 10, 30, 25, 5, 20, 15, 10] } # Creating the violin plot using plotly express fig = px.violin(data, y="Values", x="Category", box=True, points="all") # if box=False then no Boxplots will be there inside violins # Setting the layout for the plot fig.update_layout( title='Violin Plot using Plotly', yaxis_title='Values', xaxis_title='Category' ) # Display the plot fig.show() # Save the plot to an HTML file fig.write_html("violin_plot_plotly.html") #save png file with 300 dpi fig.write_image("violin_plot_plotly.png", scale=3) Data Visualization tutorial in Urdu/Hindi https://youtu.be/gVpGpmwDXkshttps://youtu.be/ZuiGxlkROb4https://youtu.be/uDdMTXh3CUw Learn from our free courses here: 4.97 (118) Six months of AI and Data Science Mentorship Program By Dr. Aammar Tufail Start Learning 4.91 (22) Python ka Chilla for Data Science (40 Days of Python for Data Science) By Dr. Aammar Tufail Download Certificate Our recent blogs Let us know in your comment section which other plots would you like to make?
