import numpy as np
import pandas as pd
import dtale.global_state as global_state
[docs]class ColumnBuilder(object):
def __init__(self, data_id, column_type, name, cfg):
self.data_id = data_id
if column_type == 'numeric':
self.builder = NumericColumnBuilder(name, cfg)
elif column_type == 'datetime':
self.builder = DatetimeColumnBuilder(name, cfg)
elif column_type == 'bins':
self.builder = BinsColumnBuilder(name, cfg)
else:
raise NotImplementedError('{} column builder not implemented yet!'.format(column_type))
[docs] def build_column(self):
data = global_state.get_data(self.data_id)
return self.builder.build_column(data)
[docs] def build_code(self):
return self.builder.build_code()
[docs]class NumericColumnBuilder(object):
def __init__(self, name, cfg):
self.name = name
self.cfg = cfg
[docs] def build_column(self, data):
left, right, operation = (self.cfg.get(p) for p in ['left', 'right', 'operation'])
left = data[left['col']] if 'col' in left else float(left['val'])
right = data[right['col']] if 'col' in right else float(right['val'])
if operation == 'sum':
return left + right
if operation == 'difference':
return left - right
if operation == 'multiply':
return left * right
if operation == 'divide':
return left / right
return np.nan
[docs] def build_code(self):
left, right, operation = (self.cfg.get(p) for p in ['left', 'right', 'operation'])
operations = dict(sum='+', difference='-', multiply='*', divide='/')
return "df.loc[:, '{name}'] = {left} {operation} {right}".format(
name=self.name,
operation=operations.get(operation),
left="df['{}']".format(left['col']) if 'col' in left else left['val'],
right="df['{}']".format(right['col']) if 'col' in right else right['val']
)
FREQ_MAPPING = dict(month='M', quarter='Q', year='Y')
[docs]class DatetimeColumnBuilder(object):
def __init__(self, name, cfg):
self.name = name
self.cfg = cfg
[docs] def build_column(self, data):
col = self.cfg['col']
if 'property' in self.cfg:
return getattr(data[col].dt, self.cfg['property'])
conversion_key = self.cfg['conversion']
[freq, how] = conversion_key.split('_')
freq = FREQ_MAPPING[freq]
conversion_data = data[[col]].set_index(col).index.to_period(freq).to_timestamp(how=how).normalize()
return pd.Series(conversion_data, index=data.index, name=self.name)
[docs] def build_code(self):
if 'property' in self.cfg:
return "df.loc[:, '{name}'] = df['{col}'].dt.{property}".format(name=self.name, **self.cfg)
conversion_key = self.cfg['conversion']
[freq, how] = conversion_key.split('_')
freq = FREQ_MAPPING[freq]
return (
"{name}_data = data[['{col}']].set_index('{col}').index.to_period('{freq}')'"
".to_timestamp(how='{how}').normalize()\n"
"df.loc[:, '{name}'] = pd.Series({name}_data, index=df.index, name='{name}')"
).format(name=self.name, col=self.cfg['col'], freq=freq, how=how)
[docs]class BinsColumnBuilder(object):
def __init__(self, name, cfg):
self.name = name
self.cfg = cfg
[docs] def build_column(self, data):
col, operation, bins, labels = (self.cfg.get(p) for p in ['col', 'operation', 'bins', 'labels'])
bins = int(bins)
if operation == 'cut':
bin_data = pd.cut(data[col], bins=bins)
else:
bin_data = pd.qcut(data[col], q=bins)
if labels:
cats = {idx: str(cat) for idx, cat in enumerate(labels.split(','))}
else:
cats = {idx: str(cat) for idx, cat in enumerate(bin_data.cat.categories)}
return pd.Series(bin_data.cat.codes.map(cats), index=data.index, name=self.name)
[docs] def build_code(self):
col, operation, bins, labels = (self.cfg.get(p) for p in ['col', 'operation', 'bins', 'labels'])
bins_code = []
if operation == 'cut':
bins_code.append("{name}_data = pd.cut(df['{col}'], bins={bins})".format(
name=self.name, col=col, bins=bins
))
else:
bins_code.append("{name}_data = pd.qcut(df['{col}'], bins={bins})".format(
name=self.name, col=col, bins=bins
))
if labels:
labels_str = ', '.join(['{}: {}'.format(idx, cat) for idx, cat in enumerate(labels.split(','))])
labels_str = '{' + labels_str + '}'
bins_code.append('{name}_cats = {labels}'.format(name=self.name, labels=labels_str))
else:
bins_code.append(
'{name}_cats = {idx: str(cat) for idx, cat in enumerate({name}_data.cat.categories)}'
)
s_str = "df.loc[:, '{name}'] = pd.Series({name}_data.cat.codes.map({name}_cats), index=df.index, name='{name}')"
bins_code.append(s_str.format(name=self.name))
return '\n'.join(bins_code)