Data Manipulation
Now that we have deconstructed the structure of the pandas DataFrame down to its basics, the remainder of the wrangling tasks, that is, creating new DataFrames, selecting or slicing a DataFrame into its parts, filtering DataFrames for some values, joining different DataFrames, and so on, will become very intuitive. Let's start by selecting and filtering in the following section.
Note
Jupyter notebooks for the code examples listed in this chapter can be found at the following links: https://packt.link/xTvR2 and https://packt.link/PGIzK.
Selecting and Filtering in pandas
If you wanted to access a particular cell in a spreadsheet, you would do so by addressing that cell in the familiar format of (column name, row name). For example, when you call cell A63, A refers to the column and 63 refers to the row. Data is stored similarly in pandas, but as (row name, column name) and we can use the same convention to access cells in a DataFrame.
For example, look...
