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Add WIP draft of diverse filetypes lesson #2
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@@ -67,6 +67,7 @@ contact: '[email protected]' | |
# Order of episodes in your lesson | ||
episodes: | ||
- introduction.md | ||
- working-with-diverse-filetypes.md | ||
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# Information for Learners | ||
learners: | ||
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--- | ||
title: "Handling diverse filetypes in Pandas" | ||
teaching: 10 | ||
exercises: 2 | ||
--- | ||
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:::::::::::::::::::::::::::::::::::::: questions | ||
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- How can I read in different tabular data types to a familiar format in Python? | ||
- What are some common errors that occur when importing data, and how can I troubleshoot them? | ||
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:::::::::::::::::::::::::::::::::::::::::::::::: | ||
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::::::::::::::::::::::::::::::::::::: objectives | ||
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- Import tabular data from XML, JSON, and Parquet formats to pandas dataframes using the `pandas` library | ||
- Import a table from a SQL database using the `pandas` library | ||
- Implement strategies to handle common errors on data import | ||
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:::::::::::::::::::::::::::::::::::::::::::::::: | ||
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::::::::::::::::::::::::::::::::::::: keypoints | ||
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- `pandas` has functionality to read in many data formats (e.g., XML, JSON, SQL, | ||
Parquet) into the same kind of DataFrame in Python. We can take advantage of this to | ||
transform many kinds of data with similar functions in Python. | ||
- `pandas` accepts both relative and absolute file paths on read-in. | ||
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They said to set this to 0!