CSV¶
Odo interacts with local CSV files through Pandas.
URIs¶
CSV URI’s are their paths/filenames
Simple examples of CSV uris:
myfile.csv
/path/to/myfile.csv.gz
Keyword Arguments¶
The standard csv dialect terms are usually supported:
has_header=True/False/None
encoding
delimiter
doublequote
escapechar
lineterminator
quotechar
quoting
skipinitialspace
However these or others may be in effect depending on what library is
interacting with your file. Oftentimes this is the pandas.read_csv
function, which has an extensive list of keyword arguments
Conversions¶
The default paths in and out of CSV files is through Pandas DataFrames.
Because CSV files might be quite large it is dangerous to read them directly
into a single DataFrame. Instead we convert them to a stream of medium sized
DataFrames. We call this type chunks(DataFrame)
.:
chunks(DataFrame) <-> CSV
CSVs can also be efficiently loaded into SQL Databases:
CSV -> SQL