Seaborn: Where Data Visualization Meets Scientific Excellence!

Seaborn use case.


Seaborn is a special visualization library for making statistical graphics and charts in Python.
Seaborn was created on top of matplotlib and integrated closely with Pandas dataset structures. So you can guess tha it's visual and statistial oriented features are nicer and more advanced than matplotlib has.

Visualization with Seaborn.
Seaborn for Data Science meme.

Python Knowledge Base: Make coding great again.
- Updated: 2024-12-01 by Andrey BRATUS, Senior Data Analyst.




    Seaborn practices data driven approach and helps us explore and understand available data in a smart way. Its plotting functions operate on Pandas dataframes and NumPy arrays and internally perform the necessary semantic mapping and statistical aggregation to produce enlightening graphic plots, that help us focus on better data understanding, rather than on the details of how to draw them.


  1. Plot creation algorithm.


  2. The main steps for creating the plots with Seaborn are:

    1 Preparing the data
    2 Controlling the plot
    3 Plotting
    4 Customizing the plot
    5 Showing and saving the plot


    
    import matplotlib.pyplot as plt
    import seaborn as sns
    
    tips = sns.load_dataset("tips") # Step 1
    sns.set_style("whitegrid") # Step 2
    g = sns.lmplot(x="tip", # Step 3
    y="total_bill",
    data=tips,
    aspect=2)
    g = (g.set_axis_labels("Tip","Total bill(USD)").
    set(xlim=(0,10),ylim=(0,100)))  # Step 4
    plt.title("title")
    plt.show(g)  # Step 5
    
    Seaborn visualization example.


  3. Preparing the data.


  4. 
    import pandas as pd
    import numpy as np
    uniform_data = np.random.rand(10, 12)
    data = pd.DataFrame({'x':np.arange(1,101),
    'y':np.random.normal(0,4,100)})
    
    # you can also use built-in datasets
    titanic = sns.load_dataset("titanic")
    iris = sns.load_dataset("iris")
    
  5. Controlling the plot - one subplot.


  6. 
    f, ax = plt.subplots(figsize=(5,6))
    
  7. Controlling the plot - Seaborn styles.


  8. 
    sns.set() #(Re) set the seaborn default
    sns.set_style("whitegrid") #Set the matplotlib parameters
    sns.set_style("ticks",
    {"xtick.major.size":8,
    "ytick.major.size":8})
    sns.axes_style("whitegrid")
    

  9. Controlling the plot - Context Functions.


  10. 
    sns.set_context("talk") #Set context to "talk"
    sns.set_context("notebook", #Set context to "notebook",
    font_scale=1.5, #Scale font elements and override param mapping
    rc={"lines.linewidth":2.5})
    
  11. Controlling the plot - Color Palette.


  12. 
    sns.set_palette("husl",3) #Define the color palette
    sns.color_palette("husl") #Use with with to temporarily set palette
    flatui = ["#9b59b6","#3498db","#95a5a6","#e74c3c","#34495e","#2ecc71"]
    sns.set_palette(flatui) #Set your own color palette
    
  13. Plotting With Seaborn - Axis Grids.


  14. 
    g = sns.FacetGrid(titanic, #Subplot grid for plotting conditional relationships
    col="survived",
    row="sex")
    g = g.map(plt.hist,"age")
    sns.factorplot(x="pclass", #Draw a categorical plot onto a Facetgrid
    y="survived",
    hue="sex",
    data=titanic)
    sns.lmplot(x="sepal_width", #Plot data and regression model fits across a FacetGrid
    y="sepal_length",
    hue="species",
    data=iris)
    
    h = sns.PairGrid(iris) #Subplot grid for plotting pairwise relationships
    h = h.map(plt.scatter)
    sns.pairplot(iris) #Plot pairwise bivariate distributions
    i = sns.JointGrid(x="x", #Grid for bivariate plot with marginal univariate plots
    y="y",
    data=data)
    i = i.plot(sns.regplot,
    sns.distplot)
    sns.jointplot("sepal_length", #Plot bivariate distribution
    "sepal_width",
    data=iris,
    kind='kde')
    
  15. Plotting With Seaborn - Categorical Plots.


  16. 
    #Scatterplot
    sns.stripplot(x="species", #Scatterplot with one categorical variable
    y="petal_length",
    data=iris)
    sns.swarmplot(x="species", #Categorical scatterplot with non-overlapping points
    y="petal_length",
    data=iris)
    
    #Bar Chart
    sns.barplot(x="sex", #Show point estimates and confidence intervals with scatterplot glyphs
    y="survived",
    hue="class",
    data=titanic)
    
    #Count Plot
    sns.countplot(x="deck", #Show count of observations
    data=titanic,
    palette="Greens_d")
    
    #Point Plot
    sns.pointplot(x="class", #Show point estimates and confidence intervals as rectangular bars
    y="survived",
    hue="sex",
    data=titanic,
    palette={"male":"g",
    "female":"m"},
    markers=["^","o"],
    linestyles=["-","--"])
    
    #Boxplot
    sns.boxplot(x="alive", #Boxplot
    y="age",
    hue="adult_male",
    data=titanic)
    sns.boxplot(data=iris,orient="h") #Boxplot with wide-form data
    
    #Violinplot
    sns.violinplot(x="age", #Violin plot
    y="sex",
    hue="survived",
    data=titanic)
    
  17. Plotting With Seaborn - Regression Plots.


  18. 
    sns.regplot(x="sepal_width", #Plot data and a linear regression model fit
    y="sepal_length",
    data=iris,
    ax=ax)
    
  19. Plotting With Seaborn - Distribution Plots.


  20. 
    plot = sns.distplot(data.y, #Plot univariate distribution
    kde=False,
    color="b")
    
  21. Plotting With Seaborn - Matrix Plots.


  22. 
    sns.heatmap(uniform_data,vmin=0,vmax=1) #Heatmap
    
  23. Customizing the plot.


  24. 
    g.despine(left=True) #Remove left spine
    g.set_ylabels("Survived") #Set the labels of the y-axis
    g.set_xticklabels(rotation=45) #Set the tick labels for x
    g.set_axis_labels("Survived", #Set the axis labels
    "Sex")
    h.set(xlim=(0,5), #Set the limit and ticks of the x-and y-axis
    ylim=(0,5),
    xticks=[0,2.5,5],
    yticks=[0,2.5,5])
    
    plt.title("A Title") #Add plot title
    plt.ylabel("Survived") #Adjust the label of the y-axis
    plt.xlabel("Sex") #Adjust the label of the x-axis
    plt.ylim(0,100) #Adjust the limits of the y-axis
    plt.xlim(0,10) #Adjust the limits of the x-axis
    plt.setp(ax,yticks=[0,5]) #Adjust a plot property
    plt.tight_layout() #Adjust subplot params
    
  25. Showing and Saving the Plot.


  26. 
    plt.show() #Show the plot
    plt.savefig("foo.png") #Save the plot as a figure
    plt.savefig("foo.png", #Save transparent figure
    transparent=True)
    
  27. Close & Clear.


  28. 
    plt.cla() #Clear an axis
    plt.clf() #Clear an entire figure
    plt.close() #Close a window
    



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