Learn how to create Plotly point maps.
- [Instructor] To keep things brief,…I'm only going to spell out the important differences…between this method and the method we used…to generate a choropleth.…First of all, as we already discussed,…for point maps, you need precise X/Y position data…for each observation you plot.…A list of state abbreviations won't work,…so we need a new data set.…For this map, I got some data from NOAA Snow Inventory Data…from 1981 to 2010.…Let's read that data into our Jupyter notebook.…This is a huge data set…from every weather station in America.…
If we tried to plot all of that out,…we wouldn't be able to see anything…but a mess of dots on top of each other.…For that reason, I'm going to take…a small random sample of only 200 observations…from the data set,…and use this to generate our point map.…To do that, we say snow_sample.…This is going to be the name of our sample.…And we'll call this sample method and pass in n=200.…We want 200 observations.…
We'll set our seed, random_state=25, and axis=0.…Let's just print out the first few records…
AuthorLillian Pierson, P.E.
- Getting started with Jupyter Notebooks
- Visualizing data: basic charts, time series, and statistical plots
- Preparing for analysis: treating missing values and data transformation
- Data analysis basics: arithmetic, summary statistics, and correlation analysis
- Outlier analysis: univariate, multivariate, and linear projection methods
- Introduction to machine learning
- Basic machine learning methods: linear and logistic regression, Naïve Bayes
- Reducing dataset dimensionality with PCA
- Clustering and classification: k-means, hierarchical, and k-NN
- Simulating a social network with NetworkX
- Creating Plot.ly charts
- Scraping the web with Beautiful Soup
Skill Level Beginner
1. Data Munging Basics
2. Data Visualization Basics
3. Basic Math and Statistics
4. Dimensionality Reduction
Explanatory factor analysis6m 39s
5. Outlier Analysis
6. Cluster Analysis
7. Network Analysis with NetworkX
8. Basic Algorithmic Learning
9. Web-based Data Visualizations with Plotly
10. Web Scraping with Beautiful Soup
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