6-1: Visualizations

Streamlit and seaborn

• histplot, lmplot, boxplot, violinplot

Datasets we will use

• “tips” One waiter collected 244 data points about his tipping customers. https://rdrr.io/cran/reshape2/man/tips.html

• Fast Food Nutrition https://www.kaggle.com/datasets/joebeachcapital/fast-food

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
tips = sns.load_dataset('tips')
nut = pd.read_csv("./data/fast_food_nutrition_cleaned.csv")

Histograms

A histogram is a graphical representation of the distribution of numerical data. It is an estimate of the probability distribution of a continuous variable.

To construct a histogram, the first step is to “bin” the range of values—that is, divide the entire range of values into a series of intervals—and then count how many values fall into each interval. The bins are usually specified as consecutive, non-overlapping intervals of a variable. The bins (intervals) must be adjacent, and are often (but not required to be) of equal size.

In seaborn we create a histogram using the histplot function.

• The bins named argument specifies the number of bins to use.

• The kde named argument specifies whether to plot a gaussian kernel density estimate. which is a best-fit line for the data.

# distribution of tips amount 9 bins
sns.histplot(data=tips, x='tip', bins=9)

<Axes: xlabel='tip', ylabel='Count'>

# distribution of tips amount 9 bins
sns.histplot(data=tips, x='tip', bins=20, kde=True)

<Axes: xlabel='tip', ylabel='Count'>

Challenge 6-2-1

Create a streamlit with a histogram of the calories column in the fast food dataset.

fast_food_nutrition_cleaned.csv

the input is the number of bins as a slider. between 1 and 100 bins.

What happens to the data shape when you increase the number of bins?

LM Plots

A lmplot is a scatterplot with a linear regression line.

Linear regression is a linear approach to modeling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables). The case of one explanatory variable is called simple linear regression; for more than one, the process is called multiple linear regression.

# let's compare total bill to tip amount, separated by the diner's sex
sns.lmplot(data=tips, x='total_bill', y='tip', hue='sex')

<seaborn.axisgrid.FacetGrid at 0x270d36c4ef0>

# let's compare total bill to tip amount, separated by if the diner is a smoker
sns.lmplot(data=tips, x='total_bill', y='tip', hue='smoker')

<seaborn.axisgrid.FacetGrid at 0x270d3742870>

Challenge 6-2-2

Create a streamlit with the fast food dataset.

fast_food_nutrition_cleaned.csv

Let’s plot calories vs. sodium broken down by the type fast food restaurant.

Create a streamlit to allow the user to select two restaurants to compare.

Which restaurant is the worst?

Box Plots and Violin Plots

A box plot is a method for graphically depicting groups of numerical data through their quartiles. The box extends from the Q1 to Q3 quartile values of the data, with a line at the median (Q2). The whiskers extend from the edges of the box to show the range of the data.

• They extend no more than 1.5 * IQR (Inter-quartile-range) (IQR = Q3 - Q1) from the edges of the box, ending at the farthest data point within that interval.

• The line through the box is the median value of the data.

• The box represents the interquartile range. Q3 - Q1

• The whiskers represent the range of the data, defaults to 1.5 * (Q3 - Q1), a general rule for outliers.

• Outliers are plotted as separate points.

Think of a box plot as a must more detailed bar chart, since it tells you much more about the data than just totals.

A violin plot is like the combination of a box plot and a histogram. It uses a kernel density estimate to show the probability density of the data at different values.

# this will help us to see what the box plot and violin plot are showing us
tips.describe()

	total_bill	tip	size
count	244.000000	244.000000	244.000000
mean	19.785943	2.998279	2.569672
std	8.902412	1.383638	0.951100
min	3.070000	1.000000	1.000000
25%	13.347500	2.000000	2.000000
50%	17.795000	2.900000	2.000000
75%	24.127500	3.562500	3.000000
max	50.810000	10.000000	6.000000

# Here's a box plot of total bill by day
# median = 17.79
# 25% = 13.3475
# 75% = 24.1275
sns.boxplot(data=tips,y='total_bill')

<Axes: ylabel='total_bill'>

# Here's a box plot of total bill by day
# median = 17.79
# 25% = 13.3475
# 75% = 24.1275
sns.violinplot(data=tips,y='total_bill')

<Axes: ylabel='total_bill'>

# Let's boxplot of total bill by day and smoker
sns.boxplot(data=tips, x='day', y='total_bill', hue="smoker")

<Axes: xlabel='day', ylabel='total_bill'>

sns.violinplot(data=tips, x='day', y='total_bill', hue="smoker")

<Axes: xlabel='day', ylabel='total_bill'>

Interpreting Box / Violin Plots

• short boxes and whiskers indicate that the data is dense concentrated near the median value.

• long whiskers and boxes indicate that the data is spread out.

• long whiskers and short boxes indicate that the data is spread out but concentrated near the median value.

• short whiskers and long boxes indicate that the data is concentrated but spread out.

Heatmaps

Heatmaps are a way to visualize data in a 2D space. They are often used to show the correlation between variables in a dataset. Its called a heatmap color gradients are used to represent the magnitude of data points

Heatmaps require 3 variables:

• x-axis variable, usually categorical

• y-axis variable

• value variable

In seaborn, you need to filter or pivot your dataframe into a smaller dataframe that contains the x, y, and value variables only.

# pivot first 
tp = tips.pivot_table(values='total_bill', index="day", columns='time', aggfunc='mean')

# then heatmap
sns.heatmap(tp, annot=True)

C:\Users\mafud\AppData\Local\Temp\ipykernel_16584\2516421358.py:2: FutureWarning: The default value of observed=False is deprecated and will change to observed=True in a future version of pandas. Specify observed=False to silence this warning and retain the current behavior
  tp = tips.pivot_table(values='total_bill', index="day", columns='time', aggfunc='mean')

<Axes: xlabel='time', ylabel='day'>

Challenge 6-2-3

Create a streamlit with the fast food dataset.

fast_food_nutrition_cleaned.csv

Let’ input the macro nutrient: protein, fat, carbs, and sugar as a dropdown.

Create a heatmap of the calories vs. the macro nutrient broken down by the type fast food restaurant.

You’ll have to pivot the data to create the heatmap. So use a mean aggregation to create the heatmap… average carbs, etc…

How can you interpret the heatmap you see?