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dataframe.py
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dataframe.py
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# -*- coding: utf-8 -*-
# pylint: disable=C,R,W
""" Superset wrapper around pandas.DataFrame.
TODO(bkyryliuk): add support for the conventions like: *_dim or dim_*
dimensions, *_ts, ts_*, ds_*, *_ds - datetime, etc.
TODO(bkyryliuk): recognize integer encoded enums.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from datetime import date, datetime
import logging
import numpy as np
import pandas as pd
from pandas.core.common import _maybe_box_datetimelike
from pandas.core.dtypes.dtypes import ExtensionDtype
from past.builtins import basestring
from superset.utils import JS_MAX_INTEGER
INFER_COL_TYPES_THRESHOLD = 95
INFER_COL_TYPES_SAMPLE_SIZE = 100
def dedup(l, suffix='__'):
"""De-duplicates a list of string by suffixing a counter
Always returns the same number of entries as provided, and always returns
unique values.
>>> print(','.join(dedup(['foo', 'bar', 'bar', 'bar'])))
foo,bar,bar__1,bar__2
"""
new_l = []
seen = {}
for s in l:
if s in seen:
seen[s] += 1
s += suffix + str(seen[s])
else:
seen[s] = 0
new_l.append(s)
return new_l
class SupersetDataFrame(object):
# Mapping numpy dtype.char to generic database types
type_map = {
'b': 'BOOL', # boolean
'i': 'INT', # (signed) integer
'u': 'INT', # unsigned integer
'l': 'INT', # 64bit integer
'f': 'FLOAT', # floating-point
'c': 'FLOAT', # complex-floating point
'm': None, # timedelta
'M': 'DATETIME', # datetime
'O': 'OBJECT', # (Python) objects
'S': 'BYTE', # (byte-)string
'U': 'STRING', # Unicode
'V': None, # raw data (void)
}
def __init__(self, data, cursor_description, db_engine_spec):
column_names = []
if cursor_description:
column_names = [col[0] for col in cursor_description]
self.column_names = dedup(
db_engine_spec.get_normalized_column_names(cursor_description))
data = data or []
self.df = (
pd.DataFrame(list(data), columns=column_names).infer_objects())
self._type_dict = {}
try:
# The driver may not be passing a cursor.description
self._type_dict = {
col: db_engine_spec.get_datatype(cursor_description[i][1])
for i, col in enumerate(self.column_names)
if cursor_description
}
except Exception as e:
logging.exception(e)
@property
def size(self):
return len(self.df.index)
@property
def data(self):
# work around for https://github.com/pandas-dev/pandas/issues/18372
data = [dict((k, _maybe_box_datetimelike(v))
for k, v in zip(self.df.columns, np.atleast_1d(row)))
for row in self.df.values]
for d in data:
for k, v in list(d.items()):
# if an int is too big for Java Script to handle
# convert it to a string
if isinstance(v, int):
if abs(v) > JS_MAX_INTEGER:
d[k] = str(v)
return data
@classmethod
def db_type(cls, dtype):
"""Given a numpy dtype, Returns a generic database type"""
if isinstance(dtype, ExtensionDtype):
return cls.type_map.get(dtype.kind)
elif hasattr(dtype, 'char'):
return cls.type_map.get(dtype.char)
@classmethod
def datetime_conversion_rate(cls, data_series):
success = 0
total = 0
for value in data_series:
total += 1
try:
pd.to_datetime(value)
success += 1
except Exception:
continue
return 100 * success / total
@classmethod
def is_date(cls, dtype):
if dtype.name:
return dtype.name.startswith('datetime')
@classmethod
def is_dimension(cls, dtype, column_name):
if cls.is_id(column_name):
return False
return dtype.name in ('object', 'bool')
@classmethod
def is_id(cls, column_name):
return column_name.startswith('id') or column_name.endswith('id')
@classmethod
def agg_func(cls, dtype, column_name):
# consider checking for key substring too.
if cls.is_id(column_name):
return 'count_distinct'
if (hasattr(dtype, 'type') and issubclass(dtype.type, np.generic) and
np.issubdtype(dtype, np.number)):
return 'sum'
return None
@property
def columns(self):
"""Provides metadata about columns for data visualization.
:return: dict, with the fields name, type, is_date, is_dim and agg.
"""
if self.df.empty:
return None
columns = []
sample_size = min(INFER_COL_TYPES_SAMPLE_SIZE, len(self.df.index))
sample = self.df
if sample_size:
sample = self.df.sample(sample_size)
for col in self.df.dtypes.keys():
col_db_type = (
self._type_dict.get(col) or
self.db_type(self.df.dtypes[col])
)
column = {
'name': col,
'agg': self.agg_func(self.df.dtypes[col], col),
'type': col_db_type,
'is_date': self.is_date(self.df.dtypes[col]),
'is_dim': self.is_dimension(self.df.dtypes[col], col),
}
if column['type'] in ('OBJECT', None):
v = sample[col].iloc[0] if not sample[col].empty else None
if isinstance(v, basestring):
column['type'] = 'STRING'
elif isinstance(v, int):
column['type'] = 'INT'
elif isinstance(v, float):
column['type'] = 'FLOAT'
elif isinstance(v, (datetime, date)):
column['type'] = 'DATETIME'
column['is_date'] = True
column['is_dim'] = False
# check if encoded datetime
if (
column['type'] == 'STRING' and
self.datetime_conversion_rate(sample[col]) >
INFER_COL_TYPES_THRESHOLD):
column.update({
'is_date': True,
'is_dim': False,
'agg': None,
})
# 'agg' is optional attribute
if not column['agg']:
column.pop('agg', None)
columns.append(column)
return columns