Python Tutorial : Slicing DataFrames

Key Takeaways

let's look now at slicing data frames remember we're still using our sales data frame we assume that pandas has already been imported as PD the basic indexing here picks a column by default there's a result returned is actually a pandas series a series is a one-dimensional array with a labelled index like a hybrid between an umpire array and a dictionary another way to think of a data frame is a labelled two-dimensional array with series four columns sharing common row labels slicing can be performed with or without accesses having worked with Python lists we're familiar with the colon syntax for slicing for instance we remember positional slicing is half open so slicing one colon for extracts positions one two and three indexed from 0 thus here we extract the eggs column as a series and then select those 3 elements from the series pandas extends this colon syntax to allow labels in slices here the first colon selects all rows the slice eggs colon salt selects both columns eggs and salt this is a potential gutter with slicing with labels and the dot lowkick sesor it includes the right endpoint unlike positional slicing seen so far this example is similar in using the dot Lok accessor to slice all columns in some rows the first slice jam : april extracts all four rows corresponding to January February March and April inclusive the second bare colon is a universal slice selecting all columns this example extracts a block with a proper subset of rows and columns from March to May inclusive and from salt to spam inclusive using dot Iloka is very similar to using dot Lok simply with positional integers specifying slices rather than labels here we extract the same slices before using dot I lock from row 2 up to but not including row 5 and from column 1 to the last column remember omitting the explicit start or end in a slice means we slice from the beginning or to the end respectively both the dot Iloka and dot lok excesses can use lists in place of slices here is an example of using dot Lok and a list of two columns here's another using dot ILOG and a list of three rows remember with ILOG the column slice 0 colon 2 selects only two columns here's an important subtle distinction to understand selecting DF left bracket eggs right bracket yields a series from the column labeled eggs selecting DF left bracket left bracket eggs right bracket right bracket returns the data frame consisting of a single column namely eggs many but not all operations are shared between data frames in series and a series is always only one dimension of labeled data now that you've learned pandas slicing idioms here are some exercises for you to work