def main(self):
reader = agate.reader(self.input_file, **self.reader_kwargs)
if self.args.dryrun:
checker = RowChecker(reader)
for row in checker.checked_rows():
pass
if checker.errors:
for e in checker.errors:
self.output_file.write('Line %i: %s\n' %
(e.line_number, e.msg))
else:
self.output_file.write('No errors.\n')
if checker.joins:
self.output_file.write(
'%i rows would have been joined/reduced to %i rows after eliminating expected internal line breaks.\n'
% (checker.rows_joined, checker.joins))
else:
base, ext = splitext(self.input_file.name)
with open('%s_out.csv' % base, 'w') as f:
clean_writer = agate.writer(f, **self.writer_kwargs)
checker = RowChecker(reader)
clean_writer.writerow(checker.column_names)
for row in checker.checked_rows():
clean_writer.writerow(row)
if checker.errors:
error_filename = '%s_err.csv' % base
with open(error_filename, 'w') as f:
error_writer = agate.writer(f, **self.writer_kwargs)
error_header = ['line_number', 'msg']
error_header.extend(checker.column_names)
error_writer.writerow(error_header)
error_count = len(checker.errors)
for e in checker.errors:
error_writer.writerow(self._format_error_row(e))
self.output_file.write(
'%i error%s logged to %s\n' %
(error_count, '' if error_count == 1 else 's',
error_filename))
else:
self.output_file.write('No errors.\n')
if checker.joins:
self.output_file.write(
'%i rows were joined/reduced to %i rows after eliminating expected internal line breaks.\n'
% (checker.rows_joined, checker.joins))
def main(self):
reader = agate.reader(self.input_file, **self.reader_kwargs)
if self.args.dryrun:
checker = RowChecker(reader)
for row in checker.checked_rows():
pass
if checker.errors:
for e in checker.errors:
self.output_file.write('Line %i: %s\n' % (e.line_number, e.msg))
else:
self.output_file.write('No errors.\n')
if checker.joins:
self.output_file.write('%i rows would have been joined/reduced to %i rows after eliminating expected internal line breaks.\n' % (checker.rows_joined, checker.joins))
else:
if self.input_file == sys.stdin:
base = 'stdin' # "<stdin>_out.csv" is invalid on Windows
else:
base = splitext(self.input_file.name)[0]
with open('%s_out.csv' % base, 'w') as f:
clean_writer = agate.writer(f, **self.writer_kwargs)
checker = RowChecker(reader)
clean_writer.writerow(checker.column_names)
for row in checker.checked_rows():
clean_writer.writerow(row)
if checker.errors:
error_filename = '%s_err.csv' % base
with open(error_filename, 'w') as f:
error_writer = agate.writer(f, **self.writer_kwargs)
error_header = ['line_number', 'msg']
error_header.extend(checker.column_names)
error_writer.writerow(error_header)
error_count = len(checker.errors)
for e in checker.errors:
error_writer.writerow(self._format_error_row(e))
self.output_file.write('%i error%s logged to %s\n' % (error_count, '' if error_count == 1 else 's', error_filename))
else:
self.output_file.write('No errors.\n')
if checker.joins:
self.output_file.write('%i rows were joined/reduced to %i rows after eliminating expected internal line breaks.\n' % (checker.rows_joined, checker.joins))
def main(self):
if self.args.names_only:
self.print_column_names()
return
if self.input_file.name != '<stdin>':
# Use filename as table name
table_name = os.path.splitext(
os.path.split(self.input_file.name)[1])[0]
else:
table_name = 'csvsql_table'
tab = table.Table.from_csv(self.input_file,
name=table_name,
snifflimit=self.args.snifflimit,
no_header_row=self.args.no_header_row,
infer_types=(not self.args.no_inference),
**self.reader_kwargs)
column_ids = parse_column_identifiers(self.args.columns, tab.headers(),
self.args.zero_based)
rows = tab.to_rows(serialize_dates=True)
sorter = lambda r: [(r[c] is not None, r[c]) for c in column_ids]
rows.sort(key=sorter, reverse=self.args.reverse)
rows.insert(0, tab.headers())
output = agate.writer(self.output_file, **self.writer_kwargs)
for row in rows:
output.writerow(row)
def main(self):
if self.args.names_only:
self.print_column_names()
return
rows = agate.reader(self.input_file, **self.reader_kwargs)
if self.args.no_header_row:
row = next(rows)
column_names = make_default_headers(len(row))
# Put the row back on top
rows = itertools.chain([row], rows)
else:
column_names = next(rows)
column_ids = parse_column_identifiers(self.args.columns, column_names, self.args.zero_based, self.args.not_columns)
output = agate.writer(self.output_file, **self.writer_kwargs)
output.writerow([column_names[c] for c in column_ids])
for row in rows:
out_row = [row[c] if c < len(row) else None for c in column_ids]
if self.args.delete_empty:
if ''.join(out_row) == '':
continue
output.writerow(out_row)
def main(self):
if self.args.names_only:
self.print_column_names()
return
if not self.args.columns:
self.argparser.error('You must specify at least one column to search using the -c option.')
if self.args.regex is None and self.args.pattern is None and self.args.matchfile is None:
self.argparser.error('One of -r, -m or -f must be specified, unless using the -n option.')
reader_kwargs = self.reader_kwargs
writer_kwargs = self.writer_kwargs
if writer_kwargs.pop('line_numbers', False):
reader_kwargs = {'line_numbers': True}
rows, column_names, column_ids = self.get_rows_and_column_names_and_column_ids(**reader_kwargs)
if self.args.regex:
pattern = re.compile(self.args.regex)
elif self.args.matchfile:
lines = set(line.rstrip() for line in self.args.matchfile)
pattern = lambda x: x in lines
else:
pattern = self.args.pattern
patterns = dict((column_id, pattern) for column_id in column_ids)
filter_reader = FilteringCSVReader(rows, header=False, patterns=patterns, inverse=self.args.inverse)
output = agate.writer(self.output_file, **writer_kwargs)
output.writerow(column_names)
for row in filter_reader:
output.writerow(row)
def main(self):
if self.args.names_only:
self.print_column_names()
return
rows = agate.reader(self.input_file, **self.reader_kwargs)
if self.args.no_header_row:
row = next(rows)
column_names = make_default_headers(len(row))
# Put the row back on top
rows = itertools.chain([row], rows)
else:
column_names = next(rows)
column_ids = parse_column_identifiers(self.args.columns, column_names,
self.args.zero_based,
self.args.not_columns)
output = agate.writer(self.output_file, **self.writer_kwargs)
output.writerow([column_names[c] for c in column_ids])
for row in rows:
out_row = [row[c] if c < len(row) else None for c in column_ids]
if self.args.delete_empty:
if ''.join(out_row) == '':
continue
output.writerow(out_row)
def main(self):
try:
engine, metadata = sql.get_connection(self.args.connection_string)
except ImportError:
raise ImportError('You don\'t appear to have the necessary database backend installed for connection string you\'re trying to use.. Available backends include:\n\nPostgresql:\tpip install psycopg2\nMySQL:\t\tpip install MySQL-python\n\nFor details on connection strings and other backends, please see the SQLAlchemy documentation on dialects at: \n\nhttp://www.sqlalchemy.org/docs/dialects/\n\n')
conn = engine.connect()
if self.args.query:
query = self.args.query.strip()
else:
query = ""
for line in self.args.file:
query += line
rows = conn.execute(query)
output = agate.writer(self.output_file, **self.writer_kwargs)
if not self.args.no_header_row:
output.writerow(rows._metadata.keys)
for row in rows:
output.writerow(row)
conn.close()
def main(self):
if self.args.names_only:
self.print_column_names()
return
if not self.args.columns:
self.argparser.error('You must specify at least one column to search using the -c option.')
if self.args.regex is None and self.args.pattern is None and self.args.matchfile is None:
self.argparser.error('One of -r, -m or -f must be specified, unless using the -n option.')
rows = agate.reader(self.input_file, **self.reader_kwargs)
column_names = next(rows)
column_ids = parse_column_identifiers(self.args.columns, column_names, self.args.zero_based)
if self.args.regex:
pattern = re.compile(self.args.regex)
elif self.args.matchfile:
lines = set(line.rstrip() for line in self.args.matchfile)
pattern = lambda x: x in lines
else:
pattern = self.args.pattern
patterns = dict((c, pattern) for c in column_ids)
output = agate.writer(self.output_file, **self.writer_kwargs)
output.writerow(column_names)
filter_reader = FilteringCSVReader(rows, header=False, patterns=patterns, inverse=self.args.inverse)
for row in filter_reader:
output.writerow(row)
def main(self):
try:
engine, metadata = sql.get_connection(self.args.connection_string)
except ImportError:
raise ImportError(
'You don\'t appear to have the necessary database backend installed for connection string you\'re trying to use.. Available backends include:\n\nPostgresql:\tpip install psycopg2\nMySQL:\t\tpip install MySQL-python\n\nFor details on connection strings and other backends, please see the SQLAlchemy documentation on dialects at: \n\nhttp://www.sqlalchemy.org/docs/dialects/\n\n'
)
conn = engine.connect()
if self.args.query:
query = self.args.query.strip()
else:
query = ""
for line in self.args.file:
query += line
# Must escape '%'.
# @see https://github.com/onyxfish/csvkit/issues/440
# @see https://bitbucket.org/zzzeek/sqlalchemy/commits/5bc1f17cb53248e7cea609693a3b2a9bb702545b
rows = conn.execute(query.replace('%', '%%'))
output = agate.writer(self.output_file, **self.writer_kwargs)
if rows.returns_rows:
if not self.args.no_header_row:
output.writerow(rows._metadata.keys)
for row in rows:
output.writerow(row)
conn.close()
def main(self):
try:
engine, metadata = sql.get_connection(self.args.connection_string)
except ImportError:
raise ImportError('You don\'t appear to have the necessary database backend installed for connection string you\'re trying to use.. Available backends include:\n\nPostgresql:\tpip install psycopg2\nMySQL:\t\tpip install MySQL-python\n\nFor details on connection strings and other backends, please see the SQLAlchemy documentation on dialects at: \n\nhttp://www.sqlalchemy.org/docs/dialects/\n\n')
conn = engine.connect()
if self.args.query:
query = self.args.query.strip()
else:
query = ""
for line in self.args.file:
query += line
# Must escape '%'.
# @see https://github.com/onyxfish/csvkit/issues/440
# @see https://bitbucket.org/zzzeek/sqlalchemy/commits/5bc1f17cb53248e7cea609693a3b2a9bb702545b
rows = conn.execute(query.replace('%', '%%'))
output = agate.writer(self.output_file, **self.writer_kwargs)
if rows.returns_rows:
if not self.args.no_header_row:
output.writerow(rows._metadata.keys)
for row in rows:
output.writerow(row)
conn.close()
def main(self):
if self.args.names_only:
self.print_column_names()
return
if self.input_file.name != '<stdin>':
# Use filename as table name
table_name = os.path.splitext(os.path.split(self.input_file.name)[1])[0]
else:
table_name = 'csvsql_table'
tab = table.Table.from_csv(
self.input_file,
name=table_name,
snifflimit=self.args.snifflimit,
no_header_row=self.args.no_header_row,
infer_types=(not self.args.no_inference),
**self.reader_kwargs
)
column_ids = parse_column_identifiers(self.args.columns, tab.headers(), self.args.zero_based)
rows = tab.to_rows(serialize_dates=True)
sorter = lambda r: [(r[c] is not None, r[c]) for c in column_ids]
rows.sort(key=sorter, reverse=self.args.reverse)
rows.insert(0, tab.headers())
output = agate.writer(self.output_file, **self.writer_kwargs)
for row in rows:
output.writerow(row)
def main(self):
self.input_files = []
for path in self.args.input_paths:
self.input_files.append(self._open_input_file(path))
if not self.input_files:
self.argparser.error('You must specify at least one file to stack.')
if self.args.group_by_filenames:
groups = [os.path.split(f.name)[1] for f in self.input_files]
elif self.args.groups:
groups = self.args.groups.split(',')
if len(groups) != len(self.input_files):
self.argparser.error('The number of grouping values must be equal to the number of CSV files being stacked.')
else:
groups = None
group_name = self.args.group_name if self.args.group_name else 'group'
output = agate.writer(self.output_file, **self.writer_kwargs)
for i, f in enumerate(self.input_files):
rows = agate.reader(f, **self.reader_kwargs)
# If we have header rows, use them
if not self.args.no_header_row:
headers = next(rows, [])
if i == 0:
if groups:
headers.insert(0, group_name)
output.writerow(headers)
# If we don't generate simple column names based on first row
else:
row = next(rows, [])
headers = make_default_headers(len(row))
if i == 0:
if groups:
headers.insert(0, group_name)
output.writerow(headers)
if groups:
row.insert(0, groups[i])
output.writerow(row)
for row in rows:
if groups:
row.insert(0, groups[i])
output.writerow(row)
f.close()
def geojson2csv(f, key=None, **kwargs):
"""
Convert a GeoJSON document into CSV format.
"""
js = json.load(f, object_pairs_hook=OrderedDict)
if not isinstance(js, dict):
raise TypeError('JSON document is not valid GeoJSON: Root element is not an object.')
if 'type' not in js:
raise TypeError('JSON document is not valid GeoJSON: No top-level "type" key.')
if js['type'] != 'FeatureCollection':
raise TypeError('Only GeoJSON with root FeatureCollection type is supported. Not %s' % js['type'])
if 'features' not in js:
raise TypeError('JSON document is not a valid FeatureCollection: No top-level "features" key.')
features = js['features']
features_parsed = [] # tuples in the format (id, properties, geometry)
property_fields = []
for feature in features:
geoid = feature.get('id', None)
properties = feature.get('properties') or {}
for prop in properties.keys():
if prop not in property_fields:
property_fields.append(prop)
geometry = json.dumps(feature['geometry'])
features_parsed.append((geoid, properties, geometry))
header = ['id']
header.extend(property_fields)
header.append('geojson')
o = six.StringIO()
writer = agate.writer(o)
writer.writerow(header)
for geoid, properties, geometry in features_parsed:
row = [geoid]
for field in property_fields:
row.append(properties.get(field, None))
row.append(geometry)
writer.writerow(row)
output = o.getvalue()
o.close()
return output
def xlsx2csv(f, output=None, **kwargs):
"""
Convert an Excel .xlsx file to csv.
Note: Unlike other convertor's, this one allows output columns to contain mixed data types.
Blank headers are also possible.
"""
streaming = True if output else False
if not streaming:
output = six.StringIO()
writer = agate.writer(output)
book = load_workbook(f, use_iterators=True, data_only=True)
if 'sheet' in kwargs:
sheet = book.get_sheet_by_name(kwargs['sheet'])
else:
sheet = book.get_active_sheet()
for i, row in enumerate(sheet.iter_rows()):
if i == 0:
writer.writerow([c.value for c in row])
continue
out_row = []
for c in row:
value = c.value
if value.__class__ is datetime.datetime:
# Handle default XLSX date as 00:00 time
if value.date() == datetime.date(1904, 1, 1) and not has_date_elements(c):
value = value.time()
value = normalize_datetime(value)
elif value.time() == NULL_TIME:
value = value.date()
else:
value = normalize_datetime(value)
elif value.__class__ is float:
if value % 1 == 0:
value = int(value)
if value.__class__ in (datetime.datetime, datetime.date, datetime.time):
value = value.isoformat()
out_row.append(value)
writer.writerow(out_row)
if not streaming:
data = output.getvalue()
return data
# Return empty string when streaming
return ''
def to_csv(self, output, **kwargs):
"""
Serializes the table to CSV and writes it to any file-like object.
"""
rows = self.to_rows(serialize_dates=True)
# Insert header row
rows.insert(0, self.headers())
csv_writer = agate.writer(output, **kwargs)
csv_writer.writerows(rows)
def to_csv(self, output, **kwargs):
"""
Serializes the table to CSV and writes it to any file-like object.
"""
rows = self.to_rows(serialize_dates=True)
# Insert header row
rows.insert(0, self.headers())
csv_writer = agate.writer(output, **kwargs)
csv_writer.writerows(rows)
def main(self):
if self.args.names_only:
self.print_column_names()
return
rows, column_names, column_ids = self.get_rows_and_column_names_and_column_ids(**self.reader_kwargs)
output = agate.writer(self.output_file, **self.writer_kwargs)
output.writerow([column_names[column_id] for column_id in column_ids])
for row in rows:
out_row = [row[column_id] if column_id < len(row) else None for column_id in column_ids]
if not self.args.delete_empty or ''.join(out_row):
output.writerow(out_row)
def main(self):
if self.args.names_only:
self.print_column_names()
return
if not self.args.columns:
self.argparser.error(
'You must specify at least one column to search using the -c option.'
)
if self.args.regex is None and self.args.pattern is None and self.args.matchfile is None:
self.argparser.error(
'One of -r, -m or -f must be specified, unless using the -n option.'
)
rows = agate.reader(self.input_file, **self.reader_kwargs)
if self.args.no_header_row:
row = next(rows)
column_names = make_default_headers(len(row))
# Put the row back on top
rows = itertools.chain([row], rows)
else:
column_names = next(rows)
column_ids = parse_column_identifiers(self.args.columns, column_names,
self.args.zero_based)
if self.args.regex:
pattern = re.compile(self.args.regex)
elif self.args.matchfile:
lines = set(line.rstrip() for line in self.args.matchfile)
pattern = lambda x: x in lines
else:
pattern = self.args.pattern
patterns = dict((c, pattern) for c in column_ids)
output = agate.writer(self.output_file, **self.writer_kwargs)
output.writerow(column_names)
filter_reader = FilteringCSVReader(rows,
header=False,
patterns=patterns,
inverse=self.args.inverse)
for row in filter_reader:
output.writerow(row)
def json2csv(f, key=None, **kwargs):
"""
Convert a JSON document into CSV format.
The top-level element of the input must be a list or a dictionary. If it is a dictionary, a key must be provided which is an item of the dictionary which contains a list.
"""
js = json.load(f, object_pairs_hook=OrderedDict)
if isinstance(js, dict):
if not key:
raise TypeError(
'When converting a JSON document with a top-level dictionary element, a key must be specified.'
)
js = js[key]
fields = []
flat = []
for obj in js:
parsed_object = parse_object(obj)
flat.append(parsed_object)
for key in parsed_object.keys():
if key not in fields:
fields.append(key)
o = six.StringIO()
writer = agate.writer(o)
writer.writerow(fields)
for i in flat:
row = []
for field in fields:
row.append(i.get(field, None))
writer.writerow(row)
output = o.getvalue()
o.close()
return output
def main(self):
if self.args.names_only:
self.print_column_names()
return
if not self.args.columns:
self.argparser.error(
'You must specify at least one column to search using the -c option.'
)
if self.args.regex is None and self.args.pattern is None and self.args.matchfile is None:
self.argparser.error(
'One of -r, -m or -f must be specified, unless using the -n option.'
)
reader_kwargs = self.reader_kwargs
writer_kwargs = self.writer_kwargs
if writer_kwargs.pop('line_numbers', False):
reader_kwargs = {'line_numbers': True}
rows, column_names, column_ids = self.get_rows_and_column_names_and_column_ids(
**reader_kwargs)
if self.args.regex:
pattern = re.compile(self.args.regex)
elif self.args.matchfile:
lines = set(line.rstrip() for line in self.args.matchfile)
pattern = lambda x: x in lines
else:
pattern = self.args.pattern
patterns = dict((column_id, pattern) for column_id in column_ids)
filter_reader = FilteringCSVReader(rows,
header=False,
patterns=patterns,
inverse=self.args.inverse)
output = agate.writer(self.output_file, **writer_kwargs)
output.writerow(column_names)
for row in filter_reader:
output.writerow(row)
def json2csv(f, key=None, **kwargs):
"""
Convert a JSON document into CSV format.
The top-level element of the input must be a list or a dictionary. If it is a dictionary, a key must be provided which is an item of the dictionary which contains a list.
"""
js = json.load(f, object_pairs_hook=OrderedDict)
if isinstance(js, dict):
if not key:
raise TypeError('When converting a JSON document with a top-level dictionary element, a key must be specified.')
js = js[key]
fields = []
flat = []
for obj in js:
parsed_object = parse_object(obj)
flat.append(parsed_object)
for key in parsed_object.keys():
if key not in fields:
fields.append(key)
o = six.StringIO()
writer = agate.writer(o)
writer.writerow(fields)
for i in flat:
row = []
for field in fields:
row.append(i.get(field, None))
writer.writerow(row)
output = o.getvalue()
o.close()
return output
def ndjson2csv(f, key=None, **kwargs):
"""
Convert a JSON document into CSV format.
Supports both JSON and "Newline-delimited JSON".
The top-level element of the input must be a list or a dictionary. If it is a dictionary, a key must be provided which is an item of the dictionary which contains a list.
"""
first_line = f.readline()
first_row = json.loads(first_line, object_pairs_hook=OrderedDict)
js = itertools.chain(
(first_row, ),
(json.loads(l, object_pairs_hook=OrderedDict) for l in f))
fields = []
flat = []
for obj in js:
flat.append(parse_object(obj))
for key in obj.keys():
if key not in fields:
fields.append(key)
o = six.StringIO()
writer = agate.writer(o)
writer.writerow(fields)
for i in flat:
row = []
for field in fields:
row.append(i.get(field, None))
writer.writerow(row)
output = o.getvalue()
o.close()
return output
def ndjson2csv(f, key=None, **kwargs):
"""
Convert a JSON document into CSV format.
Supports both JSON and "Newline-delimited JSON".
The top-level element of the input must be a list or a dictionary. If it is a dictionary, a key must be provided which is an item of the dictionary which contains a list.
"""
first_line = f.readline()
first_row = json.loads(first_line, object_pairs_hook=OrderedDict)
js = itertools.chain((first_row, ), (json.loads(l, object_pairs_hook=OrderedDict) for l in f))
fields = []
flat = []
for obj in js:
flat.append(parse_object(obj))
for key in obj.keys():
if key not in fields:
fields.append(key)
o = six.StringIO()
writer = agate.writer(o)
writer.writerow(fields)
for i in flat:
row = []
for field in fields:
row.append(i.get(field, None))
writer.writerow(row)
output = o.getvalue()
o.close()
return output
def fixed2csv(f, schema, output=None, **kwargs):
"""
Convert a fixed-width file to csv using a CSV-formatted schema description.
A schema CSV must start with a header row with (at least) columns labeled
"column","start", and "length". (Other columns will be ignored.) For each
subsequent row, therefore, those columns will be used to identify a column
name, the starting index of the column (an integer), and the length of the
column (also an integer).
Values in the 'start' column are assumed to be zero-based, unless the first
value for 'start' is 1, in which case all values are assumed to be
one-based.
If output is specified, rows will be written to that object, otherwise the
complete data will be returned.
"""
streaming = True if output else False
if not streaming:
output = six.StringIO()
try:
encoding = kwargs['encoding']
except KeyError:
encoding = None
writer = agate.writer(output)
reader = FixedWidthReader(f, schema, encoding=encoding)
writer.writerows(reader)
if not streaming:
data = output.getvalue()
return data
# Return empty string when streaming
return ''
def main(self):
connection_string = self.args.connection_string
do_insert = self.args.insert
query = self.args.query
self.input_files = []
for path in self.args.input_paths:
self.input_files.append(self._open_input_file(path))
if self.args.table_names:
table_names = self.args.table_names.split(',')
else:
table_names = []
# If one or more filenames are specified, we need to add stdin ourselves (if available)
if sys.stdin not in self.input_files:
try:
if not sys.stdin.isatty():
self.input_files.insert(0, sys.stdin)
except:
pass
# Create an SQLite database in memory if no connection string is specified
if query and not connection_string:
connection_string = "sqlite:///:memory:"
do_insert = True
if self.args.dialect and connection_string:
self.argparser.error('The --dialect option is only valid when --db is not specified.')
if do_insert and not connection_string:
self.argparser.error('The --insert option is only valid when --db is also specified.')
if self.args.no_create and not do_insert:
self.argparser.error('The --no-create option is only valid --insert is also specified.')
# Establish database validity before reading CSV files
if connection_string:
try:
engine, metadata = sql.get_connection(connection_string)
except ImportError:
raise ImportError('You don\'t appear to have the necessary database backend installed for connection string you\'re trying to use. Available backends include:\n\nPostgresql:\tpip install psycopg2\nMySQL:\t\tpip install MySQL-python\n\nFor details on connection strings and other backends, please see the SQLAlchemy documentation on dialects at: \n\nhttp://www.sqlalchemy.org/docs/dialects/\n\n')
conn = engine.connect()
trans = conn.begin()
for f in self.input_files:
try:
# Try to use name specified via --table
table_name = table_names.pop(0)
except IndexError:
if f == sys.stdin:
table_name = "stdin"
else:
# Use filename as table name
table_name = os.path.splitext(os.path.split(f.name)[1])[0]
csv_table = table.Table.from_csv(
f,
name=table_name,
snifflimit=self.args.snifflimit,
blanks_as_nulls=(not self.args.blanks),
infer_types=(not self.args.no_inference),
no_header_row=self.args.no_header_row,
**self.reader_kwargs
)
f.close()
if connection_string:
sql_table = sql.make_table(
csv_table,
table_name,
self.args.no_constraints,
self.args.db_schema,
metadata
)
# Create table
if not self.args.no_create:
sql_table.create()
# Insert data
if do_insert and csv_table.count_rows() > 0:
insert = sql_table.insert()
headers = csv_table.headers()
conn.execute(insert, [dict(zip(headers, row)) for row in csv_table.to_rows()])
# Output SQL statements
else:
sql_table = sql.make_table(csv_table, table_name, self.args.no_constraints)
self.output_file.write('%s\n' % sql.make_create_table_statement(sql_table, dialect=self.args.dialect))
if connection_string:
if query:
# Execute specified SQL queries
queries = query.split(';')
rows = None
for q in queries:
if q:
rows = conn.execute(q)
# Output result of last query as CSV
try:
output = agate.writer(self.output_file, **self.writer_kwargs)
if not self.args.no_header_row:
output.writerow(rows._metadata.keys)
for row in rows:
output.writerow(row)
except AttributeError:
pass
trans.commit()
conn.close()
def main(self):
self.input_files = []
for path in self.args.input_paths:
self.input_files.append(self._open_input_file(path))
if not self.input_files:
self.argparser.error(
'You must specify at least one file to stack.')
if self.args.group_by_filenames:
groups = [os.path.split(f.name)[1] for f in self.input_files]
elif self.args.groups:
groups = self.args.groups.split(',')
if len(groups) != len(self.input_files):
self.argparser.error(
'The number of grouping values must be equal to the number of CSV files being stacked.'
)
else:
groups = None
group_name = self.args.group_name if self.args.group_name else 'group'
output = agate.writer(self.output_file, **self.writer_kwargs)
for i, f in enumerate(self.input_files):
rows = agate.reader(f, **self.reader_kwargs)
# If we have header rows, use them
if not self.args.no_header_row:
headers = next(rows, [])
if i == 0:
if groups:
headers.insert(0, group_name)
output.writerow(headers)
# If we don't generate simple column names based on first row
else:
row = next(rows, [])
headers = make_default_headers(len(row))
if i == 0:
if groups:
headers.insert(0, group_name)
output.writerow(headers)
if groups:
row.insert(0, groups[i])
output.writerow(row)
for row in rows:
if groups:
row.insert(0, groups[i])
output.writerow(row)
f.close()
def main(self):
# Determine the file type.
if self.args.filetype:
filetype = self.args.filetype
if filetype not in SUPPORTED_FORMATS:
self.argparser.error('"%s" is not a supported format' %
self.args.filetype)
elif self.args.schema:
filetype = 'fixed'
elif self.args.key:
filetype = 'json'
else:
if not self.args.input_path or self.args.input_path == '-':
self.argparser.error(
'You must specify a format when providing data via STDIN (pipe).'
)
filetype = convert.guess_format(self.args.input_path)
if not filetype:
self.argparser.error(
'Unable to automatically determine the format of the input file. Try specifying a format with --format.'
)
# Set the input file.
if filetype in ('xls', 'xlsx'):
self.input_file = open(self.args.input_path, 'rb')
else:
self.input_file = self._open_input_file(self.args.input_path)
# Set the reader's arguments.
kwargs = {}
if self.args.schema:
schema = self._open_input_file(self.args.schema)
elif filetype == 'fixed':
raise ValueError('schema must not be null when format is "fixed"')
if self.args.sheet:
kwargs['sheet'] = self.args.sheet
if filetype == 'csv':
kwargs.update(self.reader_kwargs)
# Streaming CSV musn't set sniff_limit, but non-streaming should.
if not self.args.no_inference:
kwargs['sniff_limit'] = self.args.sniff_limit
if self.args.no_header_row:
kwargs['header'] = False
elif self.args.no_inference:
# Streaming CSV musn't set column_types, but other formats should.
kwargs['column_types'] = agate.TypeTester(limit=0)
# Convert the file.
if filetype == 'csv' and self.args.no_inference:
reader = agate.reader(self.input_file, **self.reader_kwargs)
writer = agate.writer(self.output_file, **self.writer_kwargs)
writer.writerows(reader)
elif filetype == 'fixed':
self.output_file.write(
fixed2csv(self.input_file,
schema,
output=self.output_file,
**kwargs))
elif filetype == 'geojson':
self.output_file.write(geojson2csv(self.input_file, **kwargs))
elif filetype in ('csv', 'dbf', 'json', 'ndjson', 'xls', 'xlsx'):
if filetype == 'csv':
table = agate.Table.from_csv(self.input_file, **kwargs)
elif filetype == 'json':
table = agate.Table.from_json(self.input_file,
key=self.args.key,
**kwargs)
elif filetype == 'ndjson':
table = agate.Table.from_json(self.input_file,
key=self.args.key,
newline=True,
**kwargs)
elif filetype == 'xls':
table = agate.Table.from_xls(self.input_file,
sheet=kwargs.get('sheet', None))
elif filetype == 'xlsx':
table = agate.Table.from_xlsx(self.input_file,
sheet=kwargs.get('sheet', None))
elif filetype == 'dbf':
if not hasattr(self.input_file, 'name'):
raise ValueError(
'DBF files can not be converted from stdin. You must pass a filename.'
)
table = agate.Table.from_dbf(self.input_file.name, **kwargs)
table.to_csv(self.output_file)
def main(self):
# Determine the file type.
if self.args.filetype:
filetype = self.args.filetype
if filetype not in SUPPORTED_FORMATS:
self.argparser.error('"%s" is not a supported format' % self.args.filetype)
elif self.args.schema:
filetype = 'fixed'
elif self.args.key:
filetype = 'json'
else:
if not self.args.input_path or self.args.input_path == '-':
self.argparser.error('You must specify a format when providing data via STDIN (pipe).')
filetype = convert.guess_format(self.args.input_path)
if not filetype:
self.argparser.error('Unable to automatically determine the format of the input file. Try specifying a format with --format.')
# Set the input file.
if filetype in ('xls', 'xlsx'):
self.input_file = open(self.args.input_path, 'rb')
else:
self.input_file = self._open_input_file(self.args.input_path)
# Set the reader's arguments.
kwargs = {}
if self.args.schema:
schema = self._open_input_file(self.args.schema)
elif filetype == 'fixed':
raise ValueError('schema must not be null when format is "fixed"')
if self.args.sheet:
kwargs['sheet'] = self.args.sheet
if filetype == 'csv':
kwargs.update(self.reader_kwargs)
# Streaming CSV musn't set sniff_limit, but non-streaming should.
if not self.args.no_inference:
kwargs['sniff_limit'] = self.args.sniff_limit
if self.args.no_header_row:
kwargs['header'] = False
elif self.args.no_inference:
# Streaming CSV musn't set column_types, but other formats should.
kwargs['column_types'] = agate.TypeTester(limit=0)
# Convert the file.
if filetype == 'csv' and self.args.no_inference:
reader = agate.reader(self.input_file, **self.reader_kwargs)
writer = agate.writer(self.output_file, **self.writer_kwargs)
writer.writerows(reader)
elif filetype == 'fixed':
self.output_file.write(fixed2csv(self.input_file, schema, output=self.output_file, **kwargs))
elif filetype == 'geojson':
self.output_file.write(geojson2csv(self.input_file, **kwargs))
elif filetype in ('csv', 'dbf', 'json', 'ndjson', 'xls', 'xlsx'):
if filetype == 'csv':
table = agate.Table.from_csv(self.input_file, **kwargs)
elif filetype == 'json':
table = agate.Table.from_json(self.input_file, key=self.args.key, **kwargs)
elif filetype == 'ndjson':
table = agate.Table.from_json(self.input_file, key=self.args.key, newline=True, **kwargs)
elif filetype == 'xls':
table = agate.Table.from_xls(self.input_file, sheet=kwargs.get('sheet', None))
elif filetype == 'xlsx':
table = agate.Table.from_xlsx(self.input_file, sheet=kwargs.get('sheet', None))
elif filetype == 'dbf':
if not hasattr(self.input_file, 'name'):
raise ValueError('DBF files can not be converted from stdin. You must pass a filename.')
table = agate.Table.from_dbf(self.input_file.name, **kwargs)
table.to_csv(self.output_file)
def main(self):
reader = agate.reader(self.input_file, **self.reader_kwargs)
writer = agate.writer(self.output_file, **self.writer_kwargs)
writer.writerows(reader)
def main(self):
reader = agate.reader(self.input_file, **self.reader_kwargs)
writer = agate.writer(self.output_file, **self.writer_kwargs)
writer.writerows(reader)
def main(self):
connection_string = self.args.connection_string
do_insert = self.args.insert
query = self.args.query
self.input_files = []
for path in self.args.input_paths:
self.input_files.append(self._open_input_file(path))
if self.args.table_names:
table_names = self.args.table_names.split(',')
else:
table_names = []
# If one or more filenames are specified, we need to add stdin ourselves (if available)
if sys.stdin not in self.input_files:
try:
if not sys.stdin.isatty():
self.input_files.insert(0, sys.stdin)
except:
pass
# Create an SQLite database in memory if no connection string is specified
if query and not connection_string:
connection_string = "sqlite:///:memory:"
do_insert = True
if self.args.dialect and connection_string:
self.argparser.error(
'The --dialect option is only valid when --db is not specified.'
)
if do_insert and not connection_string:
self.argparser.error(
'The --insert option is only valid when --db is also specified.'
)
if self.args.no_create and not do_insert:
self.argparser.error(
'The --no-create option is only valid --insert is also specified.'
)
# Establish database validity before reading CSV files
if connection_string:
try:
engine, metadata = sql.get_connection(connection_string)
except ImportError:
raise ImportError(
'You don\'t appear to have the necessary database backend installed for connection string you\'re trying to use. Available backends include:\n\nPostgresql:\tpip install psycopg2\nMySQL:\t\tpip install MySQL-python\n\nFor details on connection strings and other backends, please see the SQLAlchemy documentation on dialects at: \n\nhttp://www.sqlalchemy.org/docs/dialects/\n\n'
)
conn = engine.connect()
trans = conn.begin()
for f in self.input_files:
try:
# Try to use name specified via --table
table_name = table_names.pop(0)
except IndexError:
if f == sys.stdin:
table_name = "stdin"
else:
# Use filename as table name
table_name = os.path.splitext(os.path.split(f.name)[1])[0]
csv_table = table.Table.from_csv(
f,
name=table_name,
sniff_limit=self.args.sniff_limit,
blanks_as_nulls=(not self.args.blanks),
infer_types=(not self.args.no_inference),
no_header_row=self.args.no_header_row,
**self.reader_kwargs)
f.close()
if csv_table:
if connection_string:
sql_table = sql.make_table(csv_table, table_name,
self.args.no_constraints,
self.args.db_schema, metadata)
# Create table
if not self.args.no_create:
sql_table.create()
# Insert data
if do_insert and csv_table.count_rows() > 0:
insert = sql_table.insert()
headers = csv_table.headers()
conn.execute(insert, [
dict(zip(headers, row))
for row in csv_table.to_rows()
])
# Output SQL statements
else:
sql_table = sql.make_table(csv_table, table_name,
self.args.no_constraints)
self.output_file.write(
'%s\n' % sql.make_create_table_statement(
sql_table, dialect=self.args.dialect))
if connection_string:
if query:
# Execute specified SQL queries
queries = query.split(';')
rows = None
for q in queries:
if q:
rows = conn.execute(q)
# Output result of last query as CSV
try:
output = agate.writer(self.output_file,
**self.writer_kwargs)
if not self.args.no_header_row:
output.writerow(rows._metadata.keys)
for row in rows:
output.writerow(row)
except AttributeError:
pass
trans.commit()
conn.close()
def main(self):
self.input_files = []
for path in self.args.input_paths:
self.input_files.append(self._open_input_file(path))
if len(self.input_files) < 2:
self.argparser.error('You must specify at least two files to join.')
if self.args.columns:
join_column_names = self._parse_join_column_names(self.args.columns)
if len(join_column_names) == 1:
join_column_names = join_column_names * len(self.input_files)
if len(join_column_names) != len(self.input_files):
self.argparser.error('The number of join column names must match the number of files, or be a single column name that exists in all files.')
if (self.args.left_join or self.args.right_join or self.args.outer_join) and not self.args.columns:
self.argparser.error('You must provide join column names when performing an outer join.')
if self.args.left_join and self.args.right_join:
self.argparser.error('It is not valid to specify both a left and a right join.')
tables = []
for f in self.input_files:
tables.append(list(agate.reader(f, **self.reader_kwargs)))
f.close()
join_column_ids = []
if self.args.columns:
for i, t in enumerate(tables):
join_column_ids.append(match_column_identifier(t[0], join_column_names[i]))
jointab = []
if self.args.left_join:
# Left outer join
jointab = tables[0]
for i, t in enumerate(tables[1:]):
jointab = join.left_outer_join(jointab, join_column_ids[0], t, join_column_ids[i + 1])
elif self.args.right_join:
# Right outer join
jointab = tables[-1]
remaining_tables = tables[:-1]
remaining_tables.reverse()
for i, t in enumerate(remaining_tables):
jointab = join.right_outer_join(t, join_column_ids[-(i + 2)], jointab, join_column_ids[-1])
elif self.args.outer_join:
# Full outer join
jointab = tables[0]
for i, t in enumerate(tables[1:]):
jointab = join.full_outer_join(jointab, join_column_ids[0], t, join_column_ids[i + 1])
else:
if self.args.columns:
# Inner join
jointab = tables[0]
for i, t in enumerate(tables[1:]):
jointab = join.inner_join(jointab, join_column_ids[0], t, join_column_ids[i + 1])
else:
jointab = tables[0]
# Sequential join
for t in tables[1:]:
jointab = join.sequential_join(jointab, t)
output = agate.writer(self.output_file, **self.writer_kwargs)
for row in jointab:
output.writerow(row)