Seaborn Visualization in Python

Exhibit 25.61 provides the Python code for the statistical analysis and visualization of the tips.csv dataset which contains information about tips given in a restaurant, including total bill, tip amount, gender of the payer, whether the payer is a smoker, day of the week, time of day, size of the party, and the table number. The Tips dataset is included in the seaborn library and is sourced from github. A complete list of seaborn datasets such as tips can be obtained via this code: print(sns.get_dataset_names()).

Here are relevant details of the charts in the Seaborn visualization code:

Histogram and Continuous Probability Density Function

Kernel Density Estimation (KDE) is a method for estimating the probability density function (PDF) of a continuous variable. When the kde=True parameter is used in Seaborn, the plot not only displays the histogram bars but also fits a continuous PDF estimated using KDE.

The KDE curve adds valuable insight into the underlying data distribution. It provides a smoother visualization of the data’s overall shape, peaks, and variations compared to a traditional histogram.

By default, Seaborn’s histplot function uses Gaussian kernels for KDE estimation, though you can change the kernel type using the kernel parameter.

Gaussian kernels, also known as normal kernels, are centered around each data point and follow a bell-shaped curve. These kernels are defined by the Gaussian probability density function:
\( f(x) = \frac{1}{\sqrt{2\pi\sigma^2}} e^{-\frac{(x-\mu)^2}{2\sigma^2}} \)

Bar Plot

In a Seaborn bar plot, the vertical lines often represent error bars, which graphically depict the variability or uncertainty of a data point or group of data points. By default, Seaborn uses errorbar="bootstrapped", which calculates the 95% confidence interval using bootstrapping. Alternatively, you can use "sd" for standard deviation or "SEM" for the standard error of the mean.

Box Plot

A box plot visualizes the distribution of a dataset using quartiles, making it easy to identify the median, data spread, and potential outliers. Key components of a Seaborn box plot include:

• Box: Represents the interquartile range (IQR), covering the range between the first quartile (25th percentile) and the third quartile (75th percentile), containing 50% of the data. The line inside the box marks the median (50th percentile).
• Whiskers: Extend from the box to the minimum and maximum values within a specified range from the quartiles, typically 1.5 times the IQR by default. Values beyond this range are considered outliers and plotted individually.
• Outliers: Data points beyond the whiskers, plotted as individual dots, indicating significant deviations from the rest of the data.

Heatmap

Matplotlib provides several colormaps (cmap) commonly used in Seaborn heatmaps:

• Sequential Colormaps: Gradually change in lightness and saturation, often from low to high. Examples: viridis, plasma, inferno, magma, cividis, rocket, mako, YlGnBu, Greens.
• Diverging Colormaps: Feature a central color that changes in lightness and saturation in two directions. Examples: RdBu, coolwarm, bwr, seismic.
• Qualitative Colormaps: Vary rapidly in color with no specific sequence or order. Examples: tab10, tab20, tab20b, tab20c.
• Miscellaneous Colormaps: Colormaps that don’t fit well into other categories. Examples: binary, gray, gist_earth, ocean, cubehelix.

import seaborn as sns
import matplotlib.pyplot as plt

# Load tips dataset and view top/bottom 5 records
tips = sns.load_dataset("tips")
print(tips)

  total_bill   tip     sex smoker   day    time  size
0         16.99  1.01  Female     No   Sun  Dinner     2
1         10.34  1.66    Male     No   Sun  Dinner     3
2         21.01  3.50    Male     No   Sun  Dinner     3
3         23.68  3.31    Male     No   Sun  Dinner     2
4         24.59  3.61  Female     No   Sun  Dinner     4
..          ...   ...     ...    ...   ...     ...   ...
239       29.03  5.92    Male     No   Sat  Dinner     3
240       27.18  2.00  Female    Yes   Sat  Dinner     2
241       22.67  2.00    Male    Yes   Sat  Dinner     2
242       17.82  1.75    Male     No   Sat  Dinner     2
243       18.78  3.00  Female     No  Thur  Dinner     2

[244 rows x 7 columns]

# 1: Scatter Plot to visually examine the relationship between total_bill and tip
sns.scatterplot(x="total_bill", y="tip", data=tips)

# Add labels and title
plt.xlabel("Total Bill")
plt.ylabel("Tip")
plt.title("Scatter Plot of Total Bill vs Tip")

# Show plot
plt.show()

# 2: Histogram
sns.histplot(tips["total_bill"], bins=20, kde=True)

# Add labels and title
plt.xlabel("Total Bill")
plt.ylabel("Frequency")
plt.title("Histogram of Total Bill")

# Show plot
plt.show()

# 3: Bar plot
sns.barplot(x="day", y="total_bill", data=tips)  

# Add labels and title
plt.xlabel("Day of the Week")
plt.ylabel("Total Bill")
plt.title("Average Total Bill by Day of the Week")

# Show plot
plt.show()

'''
In a Seaborn bar plot, the vertical lines typically represent error bars. Error bars are graphical 
representations of the variability or uncertainty in a data point or group of data points.
By default, errorbar="bootstrapped" is used, which calculates the 95% confidence interval using bootstrapping. 
Alternatively "sd" is used for standard deviation or "SEM" for standard error of the mean.
To disable error bars set errorbar=None. 
'''
# sns.barplot(x="day", y="total_bill", data=tips, errorbar="sd")  
sns.barplot(x="day", y="total_bill", data=tips, errorbar=None)

# 4: Box plot
sns.boxplot(x="day", y="total_bill", data=tips)

# Add labels and title
plt.xlabel("Day of the Week")
plt.ylabel("Total Bill")
plt.title("Box Plot of Total Bill by Day of the Week")

# Show plot
plt.show()

# 5: Heatmap

# Load the flights dataset
flights = sns.load_dataset("flights")
'''
Flights Dataset:
URL: https://github.com/mwaskom/seaborn-data/blob/master/flights.csv
Description: The "flights" dataset contains information about the number of passengers on flights for each month 
from 1949 to 1960.
'''
print("1) Flights data:\n", flights)

# pivot the data for the heatmap and save the pivot table in dataframe flights_pivot
flights_pivot = flights.pivot_table(
    index="month", columns="year", values="passengers"
)
print("\n2) Pivot table:\n", flights_pivot)

# Create the heatmap using cmap="YlGnBu"
sns.heatmap(flights_pivot, 
            annot=True,
            fmt=".0f",
            cmap="plasma",
            linewidths=1)

# Customize the plot (optional)
plt.title("Monthly Passenger Numbers by Year")
plt.xlabel("Year")
plt.ylabel("Month")
print("\n3) Heatmap:")
plt.show()

1) Flights data:
      year month  passengers
0    1949   Jan         112
1    1949   Feb         118
2    1949   Mar         132
3    1949   Apr         129
4    1949   May         121
..    ...   ...         ...
139  1960   Aug         606
140  1960   Sep         508
141  1960   Oct         461
142  1960   Nov         390
143  1960   Dec         432

[144 rows x 3 columns]

2) Pivot table:
 year   1949  1950  1951  1952  1953  1954  1955  1956  1957  1958  1959  1960
month                                                                        
Jan     112   115   145   171   196   204   242   284   315   340   360   417
Feb     118   126   150   180   196   188   233   277   301   318   342   391
Mar     132   141   178   193   236   235   267   317   356   362   406   419
Apr     129   135   163   181   235   227   269   313   348   348   396   461
May     121   125   172   183   229   234   270   318   355   363   420   472
Jun     135   149   178   218   243   264   315   374   422   435   472   535
Jul     148   170   199   230   264   302   364   413   465   491   548   622
Aug     148   170   199   242   272   293   347   405   467   505   559   606
Sep     136   158   184   209   237   259   312   355   404   404   463   508
Oct     119   133   162   191   211   229   274   306   347   359   407   461
Nov     104   114   146   172   180   203   237   271   305   310   362   390
Dec     118   140   166   194   201   229   278   306   336   337   405   432

3) Heatmap:
    

print(sns.get_dataset_names())

['anagrams', 'anscombe', 'attention', 'brain_networks', 'car_crashes', 'diamonds', 'dots', 'dowjones', 'exercise', 'flights', 'fmri', 'geyser', 'glue', 'healthexp', 'iris', 'mpg', 'penguins', 'planets', 'seaice', 'taxis', 'tips', 'titanic', 'anagrams', 'anagrams', 'anscombe', 'anscombe', 'attention', 'attention', 'brain_networks', 'brain_networks', 'car_crashes', 'car_crashes', 'diamonds', 'diamonds', 'dots', 'dots', 'dowjones', 'dowjones', 'exercise', 'exercise', 'flights', 'flights', 'fmri', 'fmri', 'geyser', 'geyser', 'glue', 'glue', 'healthexp', 'healthexp', 'iris', 'iris', 'mpg', 'mpg', 'penguins', 'penguins', 'planets', 'planets', 'seaice', 'seaice', 'taxis', 'taxis', 'tips', 'tips', 'titanic', 'titanic', 'anagrams', 'anscombe', 'attention', 'brain_networks', 'car_crashes', 'diamonds', 'dots', 'dowjones', 'exercise', 'flights', 'fmri', 'geyser', 'glue', 'healthexp', 'iris', 'mpg', 'penguins', 'planets', 'seaice', 'taxis', 'tips', 'titanic']

Exhibit 25.61   Python code for the statistical analysis and visualization of the tips.csv dataset using the Seaborn library. Jupyter notebook