dframe is a Python implementation of indexless dataframe data structure. dframe was built to favor ease-of-use over computational speed. It’s specifically aimed to be simple and unambiguous for interactive use.
- No index/No rownames. dframe provides indexless dataframes. There is no index and no rownames. Rows can only be indexed by row numbers (
int) and by logicals (coming soon). This means there is no ambiguity whether row “index” or row “number” is used. There is no
.loc; use familiar, regular indexing, for example,
- String column names only. Dataframes in dframe can only have have column names that are string type (
unicode). You can never have a column named with an
- No duplicate column names. Dataframes in dframe cannot have duplicate column names. This means there can only be one column named
df['colname']will always return exactly one column without any ambiguity.
- Simple stacking operations. Since there is no index, there is no ambiguity when performing simple matrix-like horizontal and vertical stacking operations. You will not have to reindex dataframes to horizontal stack them. No index cleanup required after a stacking operation. Database-style merge operations are completely separate from matrix-like stacking operations – use whichever one suits the task at hand. No need to cast matrix-like stacking operations into database-style merge operations.
- Almost first-class missing value support. dframe handles missing data using Python’s in-built
Noneinstead of defining a new missing value type. You can have missing values in any
dtype, not just in
float. Marking one element of a column as missing value will not change the
dtypeof that column.
- No Series vs DataFrame. There is no object like
Seriesin pandas. In dframe,
Arrayis sub-classed from
listand behaves like a
listis most ways. If you would rather have a dataframe with only one column, use