👀
Crash Visualization
  • Welcome
  • Preface
    • Who the book is written for
    • How the book is organized
  • 1. Introduction of Data Visualization
    • 1.1 What is data visualization?
    • 1.2 Why does visualization matter?
  • 2. Tricks in Visualization
    • 2.1 Choose Appropriate Chart
    • 2.2 Features of Charts
      • 2.2.1 Table
      • 2.2.2 Column Chart
      • 2.2.3 Line Chart
      • 2.2.4 Pie Chart
      • 2.2.5 Scatter Chart
      • 2.2.6 Map Chart
    • 2.3 Misused Graph
    • 2.4 Tips in Visualization
  • 3. Matplotlib
    • 3.1 Basic Concepts
    • 3.2 Line Chart
    • 3.3 Area Chart
    • 3.4 Column Chart
    • 3.5 Histogram Chart
    • 3.6 Scatter Chart
    • 3.7 Lollipop Chart
    • 3.8 Pie Chart
    • 3.9 Venn Chart
    • 3.10 Waffle Chart
    • 3.11 Animation
  • 4. Seaborn
    • 4.1 Trends
    • 4.2 Ranking
      • 4.2.1 Barplot
      • 4.2.2 Boxplot
    • 4.3 Composition
      • 4.3.1 Stacked Chart
    • 4.4 Correlation
      • 4.4.1 Scatter Plot
      • 4.4.2 Linear Relationship
      • 4.4.3 Heatmap
      • 4.4.4 Pairplot
    • 4.5 Distribution
      • 4.5.1 Boxplot
      • 4.5.2 Violin plot
      • 4.5.3 Histogram plot
      • 4.5.4 Density plot
      • 4.5.5 Joint plot
  • 5. Bokeh
    • 5.1 Basic Plotting
    • 5.2 Data Sources
    • 5.3 Annotations
    • 5.4 Categorical Data
    • 5.5 Presentation and Layouts
    • 5.6 Linking and Interactions
    • 5.7 Network Graph
    • 5.8 Widgets
  • 6. Plotly
    • 6.1 Fundamental Concepts
      • 6.1.1 Plotly Express
      • 6.1.2 Plotly Graph Objects
    • 6.2 Advanced Charts
      • 6.2.1 Advanced Scatter Chart
      • 6.2.2 Advanced Bar Chart
      • 6.2.3 Advanced Pie Chart
      • 6.2.4 Advanced Heatmap
      • 6.2.5 Sankey Chart
      • 6.2.6 Tables
    • 6.3 Statistical Charts
      • 6.3.1 Common Statistical Charts
      • 6.3.2 Dendrograms
      • 6.3.3 Radar Chart
      • 6.3.4 Polar Chart
      • 6.3.5 Streamline Chart
    • 6.4 Financial Charts
      • 6.4.1 Funnel Chart
      • 6.4.2 Candlestick Chart
      • 6.4.3 Waterfall Chart
  • Support
    • Donation
Powered by GitBook
On this page

Was this helpful?

  1. 3. Matplotlib

3.2 Line Chart

Previous3.1 Basic ConceptsNext3.3 Area Chart

Last updated 4 years ago

Was this helpful?

A line chart or line graph is a type of chart that displays information as a series of data points called ‘markers’ connected by straight line segments. It is a basic type of chart common in many fields.

What to learn:

  • Add multiple lines on one figure

  • Set x-label name and y-label

  • Set title name

  • Set and show legend

x = np.linspace(0, 3, 100)
plt.plot(x, x, label='linear')       # Plot  'linear line' on axes.
plt.plot(x, x**2, label='quadratic') # plot 'quadratic line' on the same axes
plt.plot(x, x**3, label='cubic')     #etc
plt.xlabel('this is x label')                      # set the xlabel
plt.ylabel('this is y label')                      # set the ylabel
plt.title('Multi-lines in one figure')             # set the title
plt.legend()                         # show legend

What to learn:

  • change width of a line

  • change color of a line

  • add markers on a line

  • add annotation on line

  • change location of the legend

x = np.arange(1,11)
y1 = np.random.random(10)
y2 = np.random.random(10)

# plot line 1
plt.plot(x, y1,linewidth =5, color = 'c', label = 'line')
# plot line 2
plt.plot(x, y2, marker = 'o',color = 'orange',label = 'line + marker')

# add annotation for line 2
for i in range(len(x)):
    xi = "{:.1f}".format(x[i])
    yi = "{:.1f}".format(y2[i])
    s = str("P(" + str(xi) + ',' + str(yi) + ')' )
    plt.text(x[i] + 0.03, y2[i] + 0.03, s)
    
plt.legend(loc='upper right')    # set legend location
plt.title('Line and line marks') # set title name
plt.show()

What to learn:

  • set different type of lines ( solid, dashed, dash-dot, and dotted)

plt.plot(x, x + 1, linestyle='-',label = 'solid line')  # solid
plt.plot(x, x + 3, linestyle='--',label = 'dashed line') # dashed
plt.plot(x, x + 5 , linestyle='-.',label = 'dashdot line') # dashdot
plt.plot(x, x + 7, linestyle=':',label = 'dotted')         # dotted
plt.title('Different type of line')                       # title
plt.legend()                                             # show legend
plt.show()

If you find the font size is too small, you can customize it like this:

plt.rcParams.update({'font.size': 20}) # You can change 20 to other values

Lineplot
Line and Line marks
Four types of line