Looking at different techniques to organize and locate data in a DataFrame
The content of this repository is from the book "Python: Machine Learning" by Wei-Meng Lee
In this repository, you can see the use of Pandas to represent tabular data. I demonstrate the two main Pandas data structures: Series and DataFrame. I attempted to keep things simple, and show some of the most common operations that would be performed on these data structures. As extracting rows and columns from DataFrams is so common, the section is summarized is a few areas.
Below is a quick list of common DataFrame Operations:
Extract a range of rows using row numbers df[2:4] df.iloc[2:4]
Extract a single row using row number df.iloc[2]
Extract a range of rows and range of columns df.iloc[2:4, 1:4]
Extract a range of rows and specific columns using positional values df.iloc[[2:4, [1,3]]
Extract specific row(s) and column(s) df.iloc[[2,4], [1,3]]
Extract a range of rows using labels df['20910601':'20910601']
Extract a single row based on its label df.loc['20910601']
Extract specific row(s) using their labels df.loc[[date1,date2]]
Extract specific row(s) and column(s) using their labels df.loc[[date1, date2], ['A','C']] df.loc[[date1, date2], 'A':'C']]
Extract a range of rows and columns using their labels df.loc[[date1:date2, 'A':'C']