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1
- Introduction and agenda
2
- Local environment setup
3
- Import libraries and data
4
- Explore and understand data
5
- Define goal of the data exploration
6
- Convert months from strings to ints
7
- Convert month and day to datetime
8
- Question: Why might you manipulate one column at a time?
9
- Continue converting month and day to datetime
10
- Question: How does Pandas datetime conversion handle cyclical data?
11
- Convert hemisphere strings to ints
12
- Convert moon phase strings to decimals
13
- Cleanse data by dropping unneeded columns
14
- Fill NaN values in moon phase column
15
- Cleanse data by dropping unneeded columns
16
- Review cleansed data
17
- Begin creating the prediction function
18
- Check to see if city is in list
19
- Get the latitude of the city
20
- List of constellations that are viewable
21
- Return if no constellations are viewable
22
- Determine which meteor showers are viewable
23
- Gather moon phases for desired dates
24
- Question: Why use an "&" operator and not "|"
25
- Determine best date based on moon phases
26
- Test the new code
27
- Add in Chang'e's meteor shower data
28
- Recap
Description:
Explore the fascinating world of meteor shower prediction using Python and Visual Studio Code in this 50-minute tutorial inspired by the Netflix Original "Over the Moon." Dive into data science techniques for forecasting celestial events, learning how to manipulate and analyze astronomical data. Set up a local environment, import necessary libraries, and work with real-world datasets to predict viewable constellations and meteor showers. Gain insights into data cleansing, datetime conversions, and creating prediction functions. Follow along as the instructor demonstrates how to determine optimal viewing dates based on moon phases and city locations. No prior coding experience is required, making this an accessible introduction to applying data science in astronomy.

Predicting Meteor Showers Using Python and Visual Studio Code

Microsoft
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