Data Week Continued: Intro to Pandas
Corey Schafer - Pandas Tutorials
Explain the purpose and basic functionality of the Pandas library. What are some common operations that can be performed on data using Pandas, and how do they contribute to data analysis and manipulation?
The Pandas library provides several mathematical operations available in NumPy, but also contains additional tools for organizing and visualizing data with features like labels and indices. Each column can be a different type of data and you can use sort methods to perform operations on subsets of data.
What are the primary data structures in Pandas, and how do they differ in terms of use cases?
Pandas uses Series and DataFrames. Series are one dimensional arrays, whereas DataFrames are two dimensional with rows and columns - the columns are individual Series. As noted above, each column can be a different type of data so it allows for greater depth of data processing and calculating/describing relationships across data-types
Describe the process of loading a dataset into a Pandas DataFrame. What are some common file formats that can be used, and which Pandas functions are utilized to read these formats?
*The DataFrame class function appears to be able to take values in at least a few formats… NumPy arrays, dictionaries, other Panda DataFrames and Series, strings, and numbers/float/integers. Some common file formats used to pass this data include Common File Formats: CSV, Excel, JSON, and SQL.
When do we get to start making pretty graphs?