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Data visualization in Python
Welcome!
Welcome & introduction (4:25)
Setting up the environment (1:37)
Matplotlib
A brief introduction to matplotlib (2:13)
Line Plot (1:29)
Our first graph (3:35)
Anatomy of a figure in matplotlib (3:27)
The Figure and Axes classes (2:26)
Pyplot (6:39)
Object-oriented interface (7:09)
Add annotations to the graph (7:06)
Draw shapes on the graph (5:43)
Draw lines on the graph (2:12)
Manipulate the axes of the graph (7:17)
Data exploration with matplotlib: Iris dataset
Dataset presentation (3:34)
Loading the dataset (5:18)
Pie chart (1:34)
How many records of each class do we have? (4:55)
Modifying the style of the chart (3:19)
Scatter Plot (1:45)
Can we differentiate the species by their petals? (6:21)
3D Plots (1:37)
Does it help to know the length of the sepal? (6:15)
Box Plot (3:29)
What is the range of values for each feature? (2:29)
Violin Plot (2:19)
Feature value distribution (9:05)
Bar chart (1:11)
Multiple graphs (1:51)
Do different species have different features? (11:28)
Global styles (2:58)
Image manipulation with matplotlib
Using images in matplotlib (3:00)
Grayscale vs RGB (3:00)
Color maps (6:15)
Creating complex graph structures (6:22)
Creating color histograms for an image (8:16)
Increasing image resolution (2:05)
Saving images or graphs to a local file (0:45)
Seaborn
Introduction to Seaborn (0:41)
Figure level and axes level graphs (4:05)
Pandas DataFrames (2:02)
Modifying the style of the graphs (7:33)
Data exploration with Seaborn: Titanic dataset
Dataset presentation (1:49)
Loading the dataset (2:40)
Density plots (1:13)
Who was on the Titanic? - Part 1 (6:14)
Who was on the Titanic? - Part 2 (8:30)
How much did the guests pay? (2:28)
Does paying more improve the odds of surviving? (4:13)
Where did the guests stay? (3:27)
What are the odds of surviving? (5:12)
Data exploration with Seaborn: Penguin species
Presentation and loading of the dataset (1:09)
The PairGrid class (2:01)
How can we differentiate between penguin species? (4:30)
The JointPlot class and marginal plots (2:04)
Identifying penguins with joint plots (10:09)
Data exploration with Seaborn: monthly number of flights
Presentation and loading of the dataset (0:50)
Long vs wide data format (1:11)
Evolution of the number of flights (4:12)
Plotly
Introduction to Plotly (2:10)
Plotly express (1:30)
Data exploration with Plotly: Wind dataset
Polar chart (1:19)
Presentation and loading of the dataset (2:56)
Which way is the wind blowing? With what intensity? (9:36)
Data exploration with Plotly: FMRI
Presentation and loading of the dataset (2:26)
How does the brain react to certain events? (5:51)
Data exploration with Plotly: Stocks
Presentation and loading of the dataset (4:32)
How have the stock prices evolved? (2:33)
Data exploration with Plotly: price of diamonds
Dataset loading and presentation (1:02)
How many diamonds do we have (by color, cut and clarity)? (3:54)
What are the most common features of diamonds? (1:38)
How does cut, color and clarity affect the price? (1:56)
Which continuous variables affect the price? (4:06)
Explore the relationship between carats, cut, clarity and price (3:10)
Data exploration in Plotly: car sharing in Montreal
Working with maps in Plotly (1:15)
Data exploration in Plotly: car sharing in Montreal (5:00)
Data exploration in Plotly: car accidents in the US
Choropleths (2:26)
Data exploration in Plotly: car accidents in the US (5:42)
Dash
Introduction to Dash (1:33)
Elements of a Dash application (2:46)
Results presentation: World Bank data
Creating the dash application (5:27)
Dataset loading and presentation (1:40)
Creating the application title (2:00)
Creating the scatterplot (7:15)
Adding a selector for the scatterplot (3:25)
Connecting the scatterplot selector with a callback (5:14)
Adding a selector for the map (2:42)
Creating the map (3:10)
Connecting the map selector with a callback (3:27)
Creating the trend chart (4:20)
Adding a selector to the trend chart (2:04)
Connecting the selector to the trend chart (3:18)
Creating the distribution chart (3:15)
Adding a selector to the distribution chart (2:19)
Connecting the selector to the distribution chart (4:07)
Exploring the resulting dashboard (4:00)
Results presentation: e-commerce website traffic
Dashboard overview (2:35)
Creating the dashboard (2:42)
Dataset loading and presentation (1:06)
Dashboard styling (3:30)
Creating the dashboard heading (2:25)
First chart: evolution of website visits (5:53)
Second chart: sales funnel (13:43)
Third chart: proportion of visits by category (5:56)
Fourth chart: visit distribution by day and hour (10:46)
Fifth chart: visits per country (2:51)
Dashboard analysis (2:01)
Dataset loading and presentation
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