csv

csv

csv parses and writes comma-separated values files (csv).

Every function has a try_-prefixed variant (try_read_all, try_read_dict, try_write_all, try_write_dict) that never aborts the script: it returns a (value, error) pair where exactly one side is None, the same shape as the json module's try_* functions.

Functions

read_all(source, comma=",", comment="", lazy_quotes=False, trim_leading_space=False, fields_per_record=0, skip=0, limit=0) [][]string

read all rows from a source string, returning a list of string lists

Rows are read one at a time: a positive limit stops parsing at that many rows (malformed content beyond the limit is never reached), and rows consumed by skip do not pin the expected field count when fields_per_record is 0 — the first kept row does.

Parameters

Examples

basic

read a csv string into a list of string lists

load("csv", "read_all")
data_str = """type,name,number_of_legs
dog,spot,4
cat,spot,3
spider,samantha,8
"""
data = read_all(data_str)
print(data)
# Output: [["type", "name", "number_of_legs"], ["dog", "spot", "4"], ["cat", "spot", "3"], ["spider", "samantha", "8"]]

skip_and_limit

read a csv string with skip and limit

load("csv", "read_all")
data_str = """type,name,number_of_legs
dog,spot,4
cat,spot,3
spider,samantha,8
"""
data = read_all(data_str, skip=1, limit=1)
print(data)
# Output: [["dog", "spot", "4"]]

read_dict(source, comma=",", comment="", lazy_quotes=False, trim_leading_space=False, fields_per_record=0, skip=0, limit=0) []dict

read csv data whose first row (after skip) is the header, returning a list of dicts keyed by the header fields

Takes the same parameters as read_all. limit counts data rows (the header is not included). A duplicate header field is an error; an empty source yields an empty list. With the default fields_per_record=0 every data row must have as many fields as the header.

Examples

basic

read a csv string into a list of dicts

load("csv", "read_dict")
data_str = """type,name,number_of_legs
dog,spot,4
cat,spot,3
"""
data = read_dict(data_str)
print(data)
# Output: [{"type": "dog", "name": "spot", "number_of_legs": "4"}, {"type": "cat", "name": "spot", "number_of_legs": "3"}]

write_all(source, comma=",") string

write all rows from source to a csv-encoded string

Cell values are rendered per type: strings as-is; ints and floats in plain decimal notation (never scientific, e.g. 1000000.0 → 1000000); True/False as true/false; None as an empty cell; time values as RFC 3339. Nested lists/dicts and non-finite floats (nan, inf) are reported as errors instead of being silently written in Go syntax.

Parameters

Examples

basic

write a list of string lists to a csv string

load("csv", "write_all")
data = [
["type", "name", "number_of_legs"],
["dog", "spot", "4"],
["cat", "spot", "3"],
["spider", "samantha", "8"],
]
csv_str = write_all(data)
print(csv_str)
# Output: "type,name,number_of_legs\ndog,spot,4\ncat,spot,3\nspider,samantha,8\n"

write_dict(data, header, comma=",") string

write a list of dictionaries to a csv string based on the provided header

Cell values are rendered with the same per-type rules as write_all; a key missing from a row produces an empty cell, the same as an explicit None.

Parameters

Examples

basic

write a list of dictionaries to a csv string based on header

load("csv", "write_dict")
data = [
{"type": "dog", "name": "spot", "number_of_legs": 4},
{"type": "cat", "name": "spot", "number_of_legs": 3},
{"type": "spider", "name": "samantha", "number_of_legs": 8},
]
csv_str = write_dict(data, header=["type", "name", "number_of_legs"])
print(csv_str)
# Output: "type,name,number_of_legs\ndog,spot,4\ncat,spot,3\nspider,samantha,8\n"