def _list_kernel_vars():
_nms = NamespaceMagics()
_Jupyter = get_ipython()
_nms.shell = _Jupyter.kernel.shell
variables = [
v for v in _nms.who_ls() if _get_var_type(v) in PYTHON_TYPE_TO_IOT_TYPE
]
return json.dumps([{
'varName': v,
'varType': PYTHON_TYPE_TO_IOT_TYPE[_get_var_type(v)]
} for v in variables])
import json
from IPython import get_ipython
from IPython.core.magics.namespace import NamespaceMagics
_nms = NamespaceMagics()
_Jupyter = get_ipython()
_nms.shell = _Jupyter.kernel.shell
def dataframes_info():
values = _nms.who_ls()
info = {
v: (eval(v).columns.tolist())
for v in values if type(eval(v)).__name__ == 'DataFrame'
}
return json.dumps(info)
import json
from sys import getsizeof
from IPython import get_ipython
from IPython.core.magics.namespace import NamespaceMagics
_nms = NamespaceMagics()
_Jupyter = get_ipython()
_nms.shell = _Jupyter.kernel.shell
try:
import numpy as np
except ImportError:
pass
def _getsizeof(x):
# return the size of variable x. Amended version of sys.getsizeof
# which also supports ndarray, Series and DataFrame
if type(x).__name__ in ['ndarray', 'Series']:
return x.nbytes
elif type(x).__name__ == 'DataFrame':
return x.memory_usage().sum()
else:
return getsizeof(x)
def _getshapeof(x):
#returns the shape of x if it has one
#returns None otherwise - might want to return an empty string for an empty column
try:
return x.shape
except AttributeError: #x does not have a shape
return None
def _deepnote_get_var_list():
import json
from sys import getsizeof
import numpy as np
import pandas as pd
from IPython import get_ipython
from IPython.core.magics.namespace import NamespaceMagics
MAX_CONTENT_LENGTH = 500
_nms = NamespaceMagics()
_Jupyter = get_ipython()
_nms.shell = _Jupyter.kernel.shell
def _getsizeof(x):
# return the size of variable x. Amended version of sys.getsizeof
# which also supports ndarray, Series and DataFrame
try:
if isinstance(x, (np.ndarray, pd.Series)):
return x.nbytes
elif isinstance(x, pd.DataFrame):
return x.memory_usage().sum()
else:
return getsizeof(x)
except:
None
def _getshapeof(x):
# returns the shape of x if it has one
# returns None otherwise - might want to return an empty string for an empty collum
try:
return x.shape
except AttributeError: # x does not have a shape
return None
def _getunderlyingdatatype(x):
# returns the underlying datatype of x if it has one
# returns None otherwise
try:
return x.dtype.name
except AttributeError: # x does not have an underlying dtype
return None
def _getcontentof(x):
try:
if type(x).__name__ == 'DataFrame':
colnames = ', '.join(x.columns.map(str))
content = "Column names: %s" % colnames
elif type(x).__name__ == 'Series':
content = "Series [%d rows]" % x.shape
elif type(x).__name__ == 'ndarray':
content = x.__repr__()
else:
if hasattr(x, '__len__') and len(x) > MAX_CONTENT_LENGTH:
content = str(x[:MAX_CONTENT_LENGTH]) + "…"
else:
content = str(x)
if len(content) > MAX_CONTENT_LENGTH:
return content[:MAX_CONTENT_LENGTH] + "…"
else:
return content
except:
return None
def _get_number_of_elements(x):
try:
return len(x)
except:
return None
def _get_number_of_columns(x):
try:
if isinstance(x, pd.DataFrame):
return len(x.columns)
else:
return None
except:
return None
def to_int(x):
# for JSON serialization purposes, we need to convert numpy integers to standard integers
return int(x) if x else None
def _get_dict_entry(var_name):
try:
v = eval(var_name)
shape = _getshapeof(v)
underlying_data_type = _getunderlyingdatatype(v)
return {'varName': var_name,
'varType': type(v).__name__,
'varSize': to_int(_getsizeof(v)),
'varShape': str(shape) if shape else '',
'varContent': _getcontentof(v) or '',
'varUnderlyingType': str(underlying_data_type) if underlying_data_type else '',
'numElements': to_int(_get_number_of_elements(v)),
'numColumns': to_int(_get_number_of_columns(v)) }
except LookupError as e:
return None
variables = _nms.who_ls()
variables = filter(lambda v: v not in ['_html', '_nms', 'NamespaceMagics', '_Jupyter'], variables)
variables = filter(lambda v: type(eval(v)).__name__ not in ['module', 'function', 'builtin_function_or_method', 'instance', '_Feature', 'type', 'ufunc'], variables)
variables_dic = {v: _get_dict_entry(v) for v in variables}
variables_dic = {k: v for k, v in variables_dic.items() if v is not None}
return json.dumps({'variables': variables_dic})