Current File : //opt/alt/python35/lib64/python3.5/site-packages/playhouse/csv_utils.py
"""
Peewee helper for loading CSV data into a database.
Load the users CSV file into the database and return a Model for accessing
the data:
from playhouse.csv_loader import load_csv
db = SqliteDatabase(':memory:')
User = load_csv(db, 'users.csv')
Provide explicit field types and/or field names:
fields = [IntegerField(), IntegerField(), DateTimeField(), DecimalField()]
field_names = ['from_acct', 'to_acct', 'timestamp', 'amount']
Payments = load_csv(db, 'payments.csv', fields, field_names)
"""
import csv
import datetime
import os
import re
from contextlib import contextmanager
try:
from StringIO import StringIO
except ImportError:
from io import StringIO
from peewee import *
from peewee import Database
from peewee import Func
from peewee import PY3
if PY3:
basestring = str
decode_value = False
else:
decode_value = True
class _CSVReader(object):
@contextmanager
def get_reader(self, file_or_name, **reader_kwargs):
is_file = False
if isinstance(file_or_name, basestring):
fh = open(file_or_name, 'r')
elif isinstance(file_or_name, StringIO):
fh = file_or_name
fh.seek(0)
else:
fh = file_or_name
fh.seek(0)
is_file = True
reader = csv.reader(fh, **reader_kwargs)
yield reader
if is_file:
fh.close()
def convert_field(field_class, **field_kwargs):
def decorator(fn):
fn.field = lambda: field_class(**field_kwargs)
return fn
return decorator
class RowConverter(_CSVReader):
"""
Simple introspection utility to convert a CSV file into a list of headers
and column types.
:param database: a peewee Database object.
:param bool has_header: whether the first row of CSV is a header row.
:param int sample_size: number of rows to introspect
"""
date_formats = [
'%Y-%m-%d',
'%m/%d/%Y']
datetime_formats = [
'%Y-%m-%d %H:%M:%S',
'%Y-%m-%d %H:%M:%S.%f']
def __init__(self, database, has_header=True, sample_size=10):
self.database = database
self.has_header = has_header
self.sample_size = sample_size
def matches_date(self, value, formats):
for fmt in formats:
try:
datetime.datetime.strptime(value, fmt)
except ValueError:
pass
else:
return True
@convert_field(IntegerField, default=0)
def is_integer(self, value):
return value.isdigit()
@convert_field(FloatField, default=0)
def is_float(self, value):
try:
float(value)
except (ValueError, TypeError):
pass
else:
return True
@convert_field(DateTimeField, null=True)
def is_datetime(self, value):
return self.matches_date(value, self.datetime_formats)
@convert_field(DateField, null=True)
def is_date(self, value):
return self.matches_date(value, self.date_formats)
@convert_field(BareField, default='')
def default(self, value):
return True
def extract_rows(self, file_or_name, **reader_kwargs):
"""
Extract `self.sample_size` rows from the CSV file and analyze their
data-types.
:param str file_or_name: A string filename or a file handle.
:param reader_kwargs: Arbitrary parameters to pass to the CSV reader.
:returns: A 2-tuple containing a list of headers and list of rows
read from the CSV file.
"""
rows = []
rows_to_read = self.sample_size
with self.get_reader(file_or_name, **reader_kwargs) as reader:
if self.has_header:
rows_to_read += 1
for i, row in enumerate(reader):
rows.append(row)
if i == self.sample_size:
break
if self.has_header:
header, rows = rows[0], rows[1:]
else:
header = ['field_%d' % i for i in range(len(rows[0]))]
return header, rows
def get_checks(self):
"""Return a list of functions to use when testing values."""
return [
self.is_date,
self.is_datetime,
self.is_integer,
self.is_float,
self.default]
def analyze(self, rows):
"""
Analyze the given rows and try to determine the type of value stored.
:param list rows: A list-of-lists containing one or more rows from a
csv file.
:returns: A list of peewee Field objects for each column in the CSV.
"""
transposed = zip(*rows)
checks = self.get_checks()
column_types = []
for i, column in enumerate(transposed):
# Remove any empty values.
col_vals = [val for val in column if val != '']
for check in checks:
results = set(check(val) for val in col_vals)
if all(results):
column_types.append(check.field())
break
return column_types
class Loader(_CSVReader):
"""
Load the contents of a CSV file into a database and return a model class
suitable for working with the CSV data.
:param db_or_model: a peewee Database instance or a Model class.
:param file_or_name: the filename of the CSV file *or* a file handle.
:param list fields: A list of peewee Field() instances appropriate to
the values in the CSV file.
:param list field_names: A list of names to use for the fields.
:param bool has_header: Whether the first row of the CSV file is a header.
:param int sample_size: Number of rows to introspect if fields are not
defined.
:param converter: A RowConverter instance to use.
:param str db_table: Name of table to store data in (if not specified, the
table name will be derived from the CSV filename).
:param reader_kwargs: Arbitrary arguments to pass to the CSV reader.
"""
def __init__(self, db_or_model, file_or_name, fields=None,
field_names=None, has_header=True, sample_size=10,
converter=None, db_table=None, pk_in_csv=False,
**reader_kwargs):
self.file_or_name = file_or_name
self.fields = fields
self.field_names = field_names
self.has_header = has_header
self.sample_size = sample_size
self.converter = converter
self.reader_kwargs = reader_kwargs
if isinstance(file_or_name, basestring):
self.filename = file_or_name
elif isinstance(file_or_name, StringIO):
self.filename = 'data.csv'
else:
self.filename = file_or_name.name
if isinstance(db_or_model, Database):
self.database = db_or_model
self.model = None
self.db_table = (
db_table or
os.path.splitext(os.path.basename(self.filename))[0])
else:
self.model = db_or_model
self.database = self.model._meta.database
self.db_table = self.model._meta.db_table
self.fields = self.model._meta.sorted_fields
self.field_names = self.model._meta.sorted_field_names
# If using an auto-incrementing primary key, ignore it unless we
# are told the primary key is included in the CSV.
if self.model._meta.auto_increment and not pk_in_csv:
self.fields = self.fields[1:]
self.field_names = self.field_names[1:]
def clean_field_name(self, s):
return re.sub('[^a-z0-9]+', '_', s.lower())
def get_converter(self):
return self.converter or RowConverter(
self.database,
has_header=self.has_header,
sample_size=self.sample_size)
def analyze_csv(self):
converter = self.get_converter()
header, rows = converter.extract_rows(
self.file_or_name,
**self.reader_kwargs)
if rows:
self.fields = converter.analyze(rows)
else:
self.fields = [converter.default.field() for _ in header]
if not self.field_names:
self.field_names = [self.clean_field_name(col) for col in header]
def get_model_class(self, field_names, fields):
if self.model:
return self.model
attrs = dict(zip(field_names, fields))
if 'id' not in attrs:
attrs['_auto_pk'] = PrimaryKeyField()
elif isinstance(attrs['id'], IntegerField):
attrs['id'] = PrimaryKeyField()
klass = type(self.db_table.title(), (Model,), attrs)
klass._meta.database = self.database
klass._meta.db_table = self.db_table
return klass
def load(self):
if not self.fields:
self.analyze_csv()
if not self.field_names and not self.has_header:
self.field_names = [
'field_%d' % i for i in range(len(self.fields))]
reader_obj = self.get_reader(self.file_or_name, **self.reader_kwargs)
with reader_obj as reader:
if not self.field_names:
row = next(reader)
self.field_names = [self.clean_field_name(col) for col in row]
elif self.has_header:
next(reader)
ModelClass = self.get_model_class(self.field_names, self.fields)
with self.database.transaction():
ModelClass.create_table(True)
for row in reader:
insert = {}
for field_name, value in zip(self.field_names, row):
if value:
if decode_value:
value = value.decode('utf-8')
insert[field_name] = value
if insert:
ModelClass.insert(**insert).execute()
return ModelClass
def load_csv(db_or_model, file_or_name, fields=None, field_names=None,
has_header=True, sample_size=10, converter=None,
db_table=None, pk_in_csv=False, **reader_kwargs):
loader = Loader(
db_or_model=db_or_model,
file_or_name=file_or_name,
fields=fields,
field_names=field_names,
has_header=has_header,
sample_size=sample_size,
converter=converter,
db_table=db_table,
pk_in_csv=pk_in_csv,
**reader_kwargs)
return loader.load()
load_csv.__doc__ = Loader.__doc__
def dump_csv(query, file_or_name, include_header=True, close_file=True,
append=True, csv_writer=None):
"""
Create a CSV dump of a query.
"""
if isinstance(file_or_name, basestring):
fh = open(file_or_name, append and 'a' or 'w')
else:
fh = file_or_name
if append:
fh.seek(0, 2)
writer = csv_writer or csv.writer(
fh,
delimiter=',',
quotechar='"',
quoting=csv.QUOTE_MINIMAL)
if include_header:
header = []
for idx, node in enumerate(query._select):
if node._alias:
header.append(node._alias)
elif isinstance(node, (Field, Func)):
header.append(node.name)
else:
header.append('col_%s' % idx)
writer.writerow(header)
for row in query.tuples().iterator():
writer.writerow(row)
if close_file:
fh.close()
return fh
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