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The pandas package is the most important tool at the disposal of Data Scientists and Analysts working in Python today. The powerful machine learning and glamorous visualization tools may get all the attention, but pandas is the backbone of most data projects.
[pandas] is derived from the term "panel data", an econometrics term for data sets that include observations over multiple time periods for the same individuals. — Wikipedia
If you're thinking about data science as a career, then it is imperative that one of the first things you do is learn pandas. In this post, we will go over the essential bits of information about pandas, including how to install it, its uses, and how it works with other common Python data analysis packages such as matplotlib and scikit-learn.
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- What's Pandas for?
- How does pandas fit into the data science toolkit?
- When should you start using pandas?
- Pandas First Steps
- Install and import
- Core components of pandas: Series and DataFrames
- Creating DataFrames from scratch
- How to read in data
- Reading data from CSVs
- Reading data from JSON
- Reading data from a SQL database
- Converting back to a CSV, JSON, or SQL
- Most important DataFrame operations
- Viewing your data
- Getting info about your data
- Handling duplicates
- Column cleanup
- How to work with missing values
- Removing null values
- Imputation
- Understanding your variables
- DataFrame slicing, selecting, extracting
- By column
- By rows
- Conditional selections
- Applying functions
- Brief Plotting
- Wrapping up
Contents
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Try it out:
https://www.learndatasci.com/tutorials/python-pandas-tutorial-complete-introduction-for-beginners/
