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* Implement smart suggestions for the visualize flow. * Address JS comments. * Implement caravel dataframe wrapper.
114 lines
3.2 KiB
Python
114 lines
3.2 KiB
Python
""" Caravel wrapper around pandas.DataFrame.
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TODO(bkyryliuk): add support for the conventions like: *_dim or dim_*
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dimensions, *_ts, ts_*, ds_*, *_ds - datetime, etc.
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TODO(bkyryliuk): recognize integer encoded enums.
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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import pandas as pd
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import numpy as np
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INFER_COL_TYPES_THRESHOLD = 95
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INFER_COL_TYPES_SAMPLE_SIZE = 100
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# http://pandas.pydata.org/pandas-docs/stable/internals.html#
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# subclassing-pandas-data-structures
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class CaravelDataFrame(object):
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def __init__(self, df):
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self.__df = df.where((pd.notnull(df)), None)
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@property
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def size(self):
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return len(self.__df.index)
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@property
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def data(self):
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return self.__df.to_dict(orient='records')
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@property
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def columns_dict(self):
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"""Provides metadata about columns for data visualization.
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:return: dict, with the fields name, type, is_date, is_dim and agg.
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"""
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if self.__df.empty:
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return None
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columns = []
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sample_size = min(INFER_COL_TYPES_SAMPLE_SIZE, len(self.__df.index))
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sample = self.__df
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if sample_size:
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sample = self.__df.sample(sample_size)
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for col in self.__df.dtypes.keys():
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column = {
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'name': col,
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'type': self.__df.dtypes[col].name,
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'is_date': is_date(self.__df.dtypes[col]),
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'is_dim': is_dimension(self.__df.dtypes[col], col),
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}
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agg = agg_func(self.__df.dtypes[col], col)
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if agg_func:
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column['agg'] = agg
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if column['type'] == 'object':
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# check if encoded datetime
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if (datetime_conversion_rate(sample[col]) >
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INFER_COL_TYPES_THRESHOLD):
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column.update({
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'type': 'datetime_string',
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'is_date': True,
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'is_dim': False,
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'agg': None
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})
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# 'agg' is optional attribute
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if not column['agg']:
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column.pop('agg', None)
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columns.append(column)
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return columns
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# It will give false positives on the numbers that are stored as strings.
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# It is hard to distinguish integer numbers and timestamps
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def datetime_conversion_rate(data_series):
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success = 0
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total = 0
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for value in data_series:
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total = total + 1
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try:
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pd.to_datetime(value)
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success = success + 1
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except Exception:
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continue
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return 100 * success / total
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def is_date(dtype):
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return dtype.name.startswith('datetime')
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def is_dimension(dtype, column_name):
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if is_id(column_name):
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return False
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return dtype == np.object or dtype == np.bool
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def is_id(column_name):
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return column_name.startswith('id') or column_name.endswith('id')
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def agg_func(dtype, column_name):
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# consider checking for key substring too.
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if is_id(column_name):
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return 'count_distinct'
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if np.issubdtype(dtype, np.number):
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return 'sum'
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return None
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