You've learned how to pull data from a SQLite3 database. Now we'd like to take that data and use it in the module that you created from section 2: earthquake data. How many earthquakes happen in each hour of our data set?
- [Narrator] Working with regular expressions…in our SQLite3 database is no different…than working with a CSV file.…In this video, we will demonstrate how to filter our data…using regular expressions, using the timestamp data,…from a SQLite3 database, in a similar manner…to our last video.…So we're going to be loading the data from a SQLite3 database,…sifting through that data using a regular expression,…and analyzing the data gleaned from that regular expression.…Now the problem that I would like to solve in this video,…is to determine how many earthquakes happen, by hour,…in our seven day database.…
Let's go over to our virtual machine,…and let's create a new Haskell notebook.…I'm going to name this notebook RegexLearning-SQLite3,…and I will be importing our libraries.…I don't plan on using any descriptive statistics…in this video, so there's no need…to load the descriptive statistics module.…There is one import that you see there that…we haven't covered yet, and that's Text.Printf,…there's one little problem which I hope to solve…
Author
James Church
Released
7/7/2017Note: This course was created by Packt Publishing. We are pleased to host this training in our library.
- Data ranges, means, and medians
- Standard deviation
- SQLite3 command line
- Slices of data
- Regular expressions
- Atoms and modifiers
- Character classes
- Line plots of a single variable
- Plotting a moving average
- Feature scaling
- Scatter plots
- Normal distribution
- Kernel density estimation (KDE)
- Visualizations
Skill Level Intermediate
Duration
Views
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1. Descriptive Statistics
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Course overview2m 37s
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CSV files11m 32s
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Data range5m 2s
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Data median5m 43s
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Data mode6m 2s
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2. SQLite3
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SQLite3 command line9m 25s
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Slices of data6m 56s
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3. Regular Expressions
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Atoms and modifiers7m 29s
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Character classes7m 40s
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4. Visualizations
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Plotting a moving average6m 45s
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Publication-ready plots7m 22s
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Feature scaling5m 38s
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Scatter plots5m 49s
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5. Kernel Density Estimation
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What is normal distribution?10m 26s
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Application of the KDE9m 22s
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6. Course Review
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CSV variations to SQLite38m 39s
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Visualizations5m 24s
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KDE11m 51s
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Video: SQLite3 and regular expressions