def upload(self,
with_lnglat=None,
if_exists=FAIL,
table_name=None,
schema=None,
context=None):
if table_name:
self.table_name = normalize_name(table_name)
if context:
self.cc = context
self.schema = context.get_default_schema()
if schema:
self.schema = schema
if self.table_name is None or self.cc is None:
raise ValueError(
'You should provide a table_name and context to upload data.')
if self.gdf is None and self.df is None and self.query is None:
raise ValueError(
'Nothing to upload.'
'We need data in a DataFrame or GeoDataFrame or a query to upload data to CARTO.'
)
already_exists_error = CartoException(
'Table with name {t} and schema {s} already exists in CARTO.'
'Please choose a different `table_name` or use'
'if_exists="replace" to overwrite it'.format(t=self.table_name,
s=self.schema))
# priority order: gdf, df, query
if self.gdf is not None:
warn(
'GeoDataFrame option is still under development. We will try the upload with DataFrame'
)
# TODO: uncomment when we support GeoDataFrame
# self.normalized_column_names = _normalize_column_names(self.gdf)
if self.df is not None:
self.normalized_column_names = _normalize_column_names(self.df)
if if_exists == Dataset.REPLACE or not self.exists():
self._create_table(with_lnglat)
elif if_exists == Dataset.FAIL:
raise already_exists_error
self._copyfrom(with_lnglat)
elif self.query is not None:
if if_exists == Dataset.APPEND:
raise CartoException(
'Error using append with a query Dataset.'
'It is not possible to append data to a query')
elif if_exists == Dataset.REPLACE or not self.exists():
self._create_table_from_query()
elif if_exists == Dataset.FAIL:
raise already_exists_error
return self
def send(self, relative_path, http_method, **requests_args):
try:
return super(EtoolsCartoNoAuthClient,
self).send(relative_path, http_method.lower(),
**requests_args)
except Exception as e:
raise CartoException(e)
def send(self, relative_path, http_method, **requests_args):
try:
return super().send(
relative_path,
http_method.lower(),
**requests_args
)
except Exception as e: # pragma: no cover
raise CartoException(e)
def get(self, method_name, mocker):
"""
Returns a mock request for a given `method_name`
:param method_name: The test method name
:return:
A `requests_mock` object
:raise:
CartoException
"""
try:
if method_name in self.requests:
r = self.requests[method_name]
mocker.get(r['url'], text=r['text'])
else:
raise CartoException('method_name not found: ' + method_name)
except Exception as e:
raise CartoException(e)
def _create_table(self, with_lnglat=None):
job = self.cc.batch_sql_client \
.create_and_wait_for_completion(
'''BEGIN; {drop}; {create}; {cartodbfy}; COMMIT;'''
.format(drop=self._drop_table_query(),
create=self._create_table_query(with_lnglat),
cartodbfy=self._cartodbfy_query()))
if job['status'] != 'done':
raise CartoException('Cannot create table: {}.'.format(
job['failed_reason']))
def query_with_retries(self, query, offset, max_retries=5):
"""
Query CartoDB with retries
"""
retries = 0
while retries < max_retries:
time.sleep(0.1)
retries += 1
try:
sites = self.sql_client.send(query)
except CartoException:
if retries < max_retries:
logger.warning(
'Retrying again table page at offset {}'.format(
offset))
if 'error' in sites:
raise CartoException('Invalid CartoDBTable')
return sites['rows']
raise CartoException('Cannot connect to CartoDB')
def _handle_import(self, import_job, table_name):
"""Handle state of import job"""
if import_job['state'] == 'failure':
if import_job['error_code'] == 8001:
raise CartoException('Over CARTO account storage limit for '
'user `{}`. Try subsetting your '
'DataFrame or dropping columns to reduce '
'the data size.'.format(self.username))
elif import_job['error_code'] == 6668:
raise CartoException('Too many rows in DataFrame. Try '
'subsetting DataFrame before writing to '
'CARTO.')
else:
raise CartoException('Error code: `{}`. See CARTO Import '
'API error documentation for more '
'information: https://carto.com/docs/'
'carto-engine/import-api/import-errors'
''.format(import_job['error_code']))
elif import_job['state'] == 'complete':
self._debug_print(final_table=import_job['table_name'])
if import_job['table_name'] != table_name:
try:
res = self.sql_client.send('''
DROP TABLE IF EXISTS {orig_table};
ALTER TABLE {dupe_table}
RENAME TO {orig_table};
'''.format(
orig_table=table_name,
dupe_table=import_job['table_name']))
self._debug_print(res=res)
except Exception as err:
self._debug_print(err=err)
raise Exception('Cannot overwrite table `{table_name}` '
'({err}). DataFrame was written to '
'`{new_table}` instead.'.format(
table_name=table_name,
err=err,
new_table=import_job['table_name']))
return table_name
def sync(self):
try:
with transaction.atomic():
old2new, to_deactivate = get_remapping(self.sql_client,
self.carto)
self.handle_obsolete_locations(to_deactivate)
self.apply_remap(old2new)
new, updated, skipped, error = self.create_or_update_locations(
)
self.clean_upper_level()
return new, updated, skipped, error
except CartoException as e:
logger.error(str(e))
raise CartoException(str(e))
def _send_dataframe(self, df, table_name, temp_dir, geom_col):
"""Send a DataFrame to CARTO to be imported as a SQL table
Args:
df (pandas.DataFrame): DataFrame that is will be sent to CARTO
table_name (str): Name on CARTO for the table that will have the
data from ``df``
temp_dir (str): Name of directory used for temporarily storing the
DataFrame file to sent to CARTO
geom_col (str): Name of geometry column
Returns:
final_table_name (str): Name of final table. This method will
overwrite the table `table_name` if it already exists.
"""
def remove_tempfile(filepath):
"""removes temporary file"""
os.remove(filepath)
tempfile = '{temp_dir}/{table_name}.csv'.format(temp_dir=temp_dir,
table_name=table_name)
self._debug_print(tempfile=tempfile)
df.drop(geom_col, axis=1, errors='ignore').to_csv(tempfile)
with open(tempfile, 'rb') as f:
res = self._auth_send('api/v1/imports', 'POST',
files={'file': f},
params={'type_guessing': 'false'},
stream=True)
self._debug_print(res=res)
if not res['success']:
remove_tempfile(tempfile)
raise CartoException('Failed to send DataFrame')
import_id = res['item_queue_id']
remove_tempfile(tempfile)
final_table_name = table_name
while True:
import_job = self._check_import(import_id)
self._debug_print(import_job=import_job)
final_table_name = self._handle_import(import_job, table_name)
if import_job['state'] == 'complete':
break
# Wait a second before doing another request
time.sleep(1.0)
return final_table_name
def get_cartodb_locations(self, cartodb_id_col='cartodb_id'):
"""
returns locations referenced by cartodb_table
"""
rows = []
try:
row_count = self.sql_client.send(
f'select count(*) from {self.carto.table_name}'
)['rows'][0]['count']
max_id = self.sql_client.send(
f'select MAX({cartodb_id_col}) from {self.carto.table_name}'
)['rows'][0]['max']
except CartoException: # pragma: no-cover
message = f"Cannot fetch pagination prerequisites from CartoDB for table {self.carto.table_name}"
logger.exception(message)
raise CartoException(message)
offset, limit = 0, 100
# failsafe in the case when cartodb id's are too much off compared to the nr. of records
if max_id > (5 * row_count):
limit = max_id + 1
logger.warning(
"The CartoDB primary key seems off, pagination is not possible"
)
parent_qry = f', {self.carto.parent_code_col}' if self.carto.parent_code_col and self.carto.parent else ''
base_qry = f'select st_AsGeoJSON(the_geom) as the_geom, {self.carto.name_col}, ' \
f'{self.carto.pcode_col}{parent_qry} from {self.carto.table_name}'
while offset <= max_id:
logger.info(
f'Requesting rows between {offset} and {offset + limit} for {self.carto.table_name}'
)
paged_qry = base_qry + f' WHERE {cartodb_id_col} > {offset} AND {cartodb_id_col} <= {offset + limit}'
time.sleep(0.1) # do not spam Carto with requests
new_rows = self.query_with_retries(paged_qry, offset)
rows += new_rows
offset += limit
return rows
def data_augment(self, table_name, metadata):
"""Augment an existing CARTO table with `Data Observatory
<https://carto.com/data-observatory>`__ measures. See the full `Data
Observatory catalog
<https://cartodb.github.io/bigmetadata/index.html>`__ for all available
measures. The result of this operation is:
1. It updates `table_name` by adding columns from the Data Observatory
2. It returns a pandas DataFrame representation of that newly augmented
table.
Note:
This method alters `table_name` in the user's CARTO database by
adding additional columns. To avoid this, create a copy of the
table first and use the new copy instead.
Example:
Add new measures to a CARTO table and pass it to a pandas DataFrame.
Using the "Median Household Income in the past 12 months" measure
from the `Data Observatory Catalog
<https://cartodb.github.io/bigmetadata/united_states/income.html#median-household-income-in-the-past-12-months>`__.
::
import cartoframes
cc = cartoframes.CartoContext(BASEURL, APIKEY)
median_income = [{'numer_id': 'us.census.acs.B19013001',
'geom_id': 'us.census.tiger.block_group',
'numer_timespan': '2011 - 2015'}]
df = cc.data_augment('transaction_events',
median_income)
Args:
table_name (str): Name of table on CARTO account that Data
Observatory measures are to be added to.
metadata (list of dicts): List of all measures to add to
`table_name`. Each `dict` has the following keys:
- `numer_id` (str): The identifier for the desired measurement
- `geom_id` (str, optional): Identifier for a desired
geographic boundary level to use when calculating measures.
Will be automatically assigned if undefined
- `normalization` (str, optional): The desired normalization. One
of 'area', 'prenormalized', or 'denominated'. 'Area' will
normalize the measure per square kilometer, 'prenormalized'
will return the original value, and 'denominated' will
normalize by a denominator.
- `denom_id` (str, optional): Measure ID from DO catalog
- `numer_timespan` (str, optional): The desired timespan for the
measurement. Defaults to most recent timespan available if
left unspecified.
- `geom_timespan` (str, optional): The desired timespan for the
geometry. Defaults to timespan matching `numer_timespan` if
left unspecified.
- `target_area` (str, optional): Instead of aiming to have
`target_geoms` in the area of the geometry passed as extent,
fill this area. Unit is square degrees WGS84. Set this to
`0` if you want to use the smallest source geometry for this
element of metadata, for example if you're passing in points.
- `target_geoms` (str, optional): Override global `target_geoms`
for this element of metadata
- `max_timespan_rank` (str, optional): Override global
`max_timespan_rank` for this element of metadata
- `max_score_rank` (str, optional): Override global
`max_score_rank` for this element of metadata
Returns:
pandas.DataFrame: A DataFrame representation of `table_name` which
has new columns for each measure in `metadata`.
"""
try:
with open(os.path.join(os.path.dirname(__file__),
'assets/data_obs_augment.sql'), 'r') as f:
augment_functions = f.read()
self.sql_client.send(augment_functions)
except Exception as err:
raise CartoException("Could not install `obs_augment_table` onto "
"user account ({})".format(err))
# augment with data observatory metadata
augment_query = '''
select obs_augment_table('{username}.{tablename}',
'{cols_meta}');
'''.format(username=self.username,
tablename=table_name,
cols_meta=json.dumps(metadata))
resp = self.sql_client.send(augment_query)
# read full augmented table
return self.read(table_name)
def _send_batches(self, df, table_name, temp_dir, geom_col):
"""Batch sending a dataframe
Args:
df (pandas.DataFrame): DataFrame that will be batched up for
sending to CARTO
table_name (str): Name of table to send DataFrame to
temp_dir (str): Local directory for temporary storage of DataFrame
written to file that will be sent to CARTO
geom_col (str): Name of encoded geometry column (if any) that will
be dropped or converted to `the_geom` column
Returns:
final_table_name (str): Final table name on CARTO that the
DataFrame is stored in
Exceptions:
* TODO: add more (Out of storage)
"""
subtables = []
# send dataframe chunks to carto
for chunk_num, chunk in tqdm(df.groupby([i // MAX_IMPORT_ROWS
for i in range(df.shape[0])]),
desc='Uploading in batches: '):
temp_table = '{orig}_cartoframes_temp_{chunk}'.format(
orig=table_name[:40],
chunk=chunk_num)
try:
# send dataframe chunk, get new name if collision
temp_table = self._send_dataframe(chunk, temp_table,
temp_dir, geom_col)
except CartoException as err:
self._drop_tables(subtables)
raise CartoException(err)
if temp_table:
subtables.append(temp_table)
self._debug_print(chunk_num=chunk_num,
chunk_shape=str(chunk.shape),
temp_table=temp_table)
# combine chunks into final table
try:
select_base = ('SELECT %(schema)s '
'FROM "{table}"') % dict(schema=_df2pg_schema(df))
unioned_tables = '\nUNION ALL\n'.join([select_base.format(table=t)
for t in subtables])
self._debug_print(unioned=unioned_tables)
query = '''
DROP TABLE IF EXISTS "{table_name}";
CREATE TABLE "{table_name}" As {unioned_tables};
ALTER TABLE {table_name} DROP COLUMN IF EXISTS cartodb_id;
{drop_tables}
SELECT CDB_CartoDBFYTable('{org}', '{table_name}');
'''.format(table_name=table_name,
unioned_tables=unioned_tables,
org=self.username if self.is_org else 'public',
drop_tables=_drop_tables_query(subtables))
self._debug_print(query=query)
_ = self.sql_client.send(query)
except CartoException as err:
try:
self._drop_tables(subtables)
except CartoException as err:
warn('Failed to drop the following subtables from CARTO '
'account: {}'.format(', '.join(subtables)))
finally:
raise Exception('Failed to upload dataframe: {}'.format(err))
return table_name
def to_carto(dataframe,
table_name,
credentials=None,
if_exists='fail',
geom_col=None,
index=False,
index_label=None,
cartodbfy=True,
log_enabled=True,
retry_times=3,
max_upload_size=MAX_UPLOAD_SIZE_BYTES,
skip_quota_warning=False):
"""Upload a DataFrame to CARTO. The geometry's CRS must be WGS 84 (EPSG:4326) so you can use it on CARTO.
Args:
dataframe (pandas.DataFrame, geopandas.GeoDataFrame`): data to be uploaded.
table_name (str): name of the table to upload the data.
credentials (:py:class:`Credentials <cartoframes.auth.Credentials>`, optional):
instance of Credentials (username, api_key, etc).
if_exists (str, optional): 'fail', 'replace', 'append'. Default is 'fail'.
geom_col (str, optional): name of the geometry column of the dataframe.
index (bool, optional): write the index in the table. Default is False.
index_label (str, optional): name of the index column in the table. By default it
uses the name of the index from the dataframe.
cartodbfy (bool, optional): convert the table to CARTO format. Default True. More info
`here <https://carto.com/developers/sql-api/guides/creating-tables/#create-tables>`.
log_enabled (bool, optional): enable the logging mechanism. Default is True.
retry_times (int, optional):
Number of time to retry the upload in case it fails. Default is 3.
max_upload_size (int, optional): defines the maximum size of the dataframe to be uploaded.
Default is 2GB.
skip_quota_warning (bool, optional): skip the quota exceeded check and force the upload.
(The upload will still fail if the size of the dataset exceeds the remaining DB quota).
Default is False.
Returns:
string: the table name normalized.
Raises:
ValueError: if the dataframe or table name provided are wrong or the if_exists param is not valid.
"""
if not isinstance(dataframe, DataFrame):
raise ValueError(
'Wrong dataframe. You should provide a valid DataFrame instance.')
if isinstance(dataframe, GeoDataFrame):
if is_reprojection_needed(dataframe):
dataframe = reproject(dataframe)
if not is_valid_str(table_name):
raise ValueError(
'Wrong table name. You should provide a valid table name.')
if if_exists not in IF_EXISTS_OPTIONS:
raise ValueError(
'Wrong option for the `if_exists` param. You should provide: {}.'.
format(', '.join(IF_EXISTS_OPTIONS)))
context_manager = ContextManager(credentials)
if not skip_quota_warning:
me_data = context_manager.credentials.me_data
if me_data is not None and me_data.get('user_data'):
n = min(SAMPLE_ROWS_NUMBER, len(dataframe))
estimated_byte_size = len(dataframe.sample(n=n).to_csv(header=False)) * len(dataframe) \
/ n / CSV_TO_CARTO_RATIO
remaining_byte_quota = me_data.get('user_data').get(
'remaining_byte_quota')
if remaining_byte_quota is not None and estimated_byte_size > remaining_byte_quota:
raise CartoException(
'DB Quota will be exceeded. '
'The remaining quota is {} bytes and the dataset size is {} bytes.'
.format(remaining_byte_quota, estimated_byte_size))
gdf = GeoDataFrame(dataframe, copy=True)
if index:
index_name = index_label or gdf.index.name
if index_name is not None and index_name != '':
# Append the index as a column
gdf[index_name] = gdf.index
else:
raise ValueError(
'Wrong index name. You should provide a valid index label.')
if geom_col in gdf:
set_geometry(gdf, geom_col, inplace=True, drop=True)
elif has_geometry(dataframe):
gdf.set_geometry(dataframe.geometry.name, inplace=True)
if has_geometry(gdf):
if GEOM_COLUMN_NAME in gdf and dataframe.geometry.name != GEOM_COLUMN_NAME:
gdf.drop(columns=[GEOM_COLUMN_NAME], inplace=True)
# Prepare geometry column for the upload
gdf.rename_geometry(GEOM_COLUMN_NAME, inplace=True)
elif isinstance(dataframe, GeoDataFrame):
log.warning('Geometry column not found in the GeoDataFrame.')
chunk_count = math.ceil(estimate_csv_size(gdf) / max_upload_size)
chunk_row_size = int(math.ceil(len(gdf) / chunk_count))
chunked_gdf = [
gdf[i:i + chunk_row_size]
for i in range(0, gdf.shape[0], chunk_row_size)
]
for i, chunk in enumerate(chunked_gdf):
if i > 0:
if_exists = 'append'
table_name = context_manager.copy_from(chunk, table_name, if_exists,
cartodbfy, retry_times)
if log_enabled:
log.info('Success! Data uploaded to table "{}" correctly'.format(
table_name))
return table_name
def create_or_update_locations(self):
"""
Create or update locations based on p-code (only active locations are considerate)
"""
logging.info('Create/Update new locations')
rows = self.get_cartodb_locations()
new, updated, skipped, error = 0, 0, 0, 0
for row in rows:
pcode = row[self.carto.pcode_col]
name = row[self.carto.name_col]
geom = row['the_geom']
if all([name, pcode, geom]):
geom_key = 'point' if 'Point' in row['the_geom'] else 'geom'
default_dict = {
'admin_level': self.carto.admin_level,
'admin_level_name': self.carto.admin_level_name,
'name': name,
geom_key: geom,
}
parent_pcode = row[
self.carto.
parent_code_col] if self.carto.parent_code_col in row else None
if parent_pcode:
try:
parent = get_location_model().objects.get(
p_code=parent_pcode, is_active=True)
default_dict['parent'] = parent
except (get_location_model().DoesNotExist,
get_location_model().MultipleObjectsReturned):
skipped += 1
logger.info(f"Skipping row pcode {pcode}")
continue
try:
location, created = get_location_model(
).objects.get_or_create(p_code=pcode,
is_active=True,
defaults=default_dict)
if created:
new += 1
else:
for attr, value in default_dict.items():
setattr(location, attr, value)
location.save()
updated += 1
except get_location_model().MultipleObjectsReturned:
message = f"Multiple locations found for: {self.carto.admin_level}, {name} ({pcode})"
logger.exception(message)
raise CartoException(message)
except IntegrityError:
message = f"Duplicate Creation {name} {pcode} {self.carto.location_type.name}"
logger.exception(message)
raise CartoException(message)
else:
skipped += 1
logger.info(f"Skipping row pcode {pcode}")
return new, updated, skipped, error
