def column_summary(series, column_props, delta=0.01):
"""Summarise a numeric column.
Parameters
----------
series : pd.Series
Numeric column.
column_props : TODO
TODO
delta : float
TODO
Returns
-------
TODO
"""
col = series.name
if not column_props[col]["numeric"] or column_props[col]["notnulls"] == 0:
# Series is not numeric or is all NaNs.
return None
logger.debug("column_summary - " + col)
# select non-nulls from column
data = series.dropna()
colresult = {}
for m in ["mean", "min", "max", "std", "sum"]:
val = getattr(data, m)()
if type(val) is np.int64:
colresult[m] = int(val)
else:
colresult[m] = val
colresult["n"] = column_props[col]["notnulls"]
percentiles = [0.1, 1, 10, 25, 50, 75, 90, 99, 99.9]
colresult["percentiles"] = {
perc: np.nanpercentile(series, perc)
for perc in percentiles
}
colresult["median"] = colresult["percentiles"][50]
colresult["iqr"] = (colresult["percentiles"][75] -
colresult["percentiles"][25])
# Compute the t-digest.
logger.debug("column_summary - {} - creating TDigest...".format(col))
digest = TDigest(delta)
digest.batch_update(data)
logger.debug("column_summary - {} - testing log trans...".format(col))
try:
colresult["logtrans"] = bool(_test_logtrans(digest))
except Exception as e:
# Hard to pinpoint problems with the logtrans TDigest.
logger.warning("test_logtrans has failed for column `{}`: {}".format(
col, e))
colresult["logtrans"] = False
if colresult["logtrans"]:
logdigest = TDigest()
for c in digest.C.values():
logdigest.update(np.log(c.mean), c.count)
colresult["logtrans_mean"] = _tdigest_mean(logdigest)
colresult["logtrans_std"] = _tdigest_std(logdigest)
colresult["logtrans_IQR"] = logdigest.percentile(
75) - logdigest.percentile(25)
logger.debug("column_summary - {} - should {}be log-transformed".format(
col, "NOT " if not colresult["logtrans"] else ""))
# Compress and store the t-digest.
digest.delta = delta
digest.compress()
colresult["tdigest"] = [(c.mean, c.count) for c in digest.C.values()]
# Compute histogram
logger.debug("column_summary - {} - computing histogram...".format(col))
if column_props[col]["is_categorical"]:
# Compute frequency table and store as histogram
counts, edges = _compute_histogram_from_frequencies(data)
else:
if colresult["logtrans"]:
counts, log_edges = np.histogram(np.log10(data),
density=False,
bins="fd")
edges = 10**log_edges
else:
counts, edges = np.histogram(data, density=False, bins="fd")
colresult["histogram"] = {
"counts": counts.tolist(),
"bin_edges": edges.tolist(),
}
# Compute KDE
logger.debug("column_summary - {} - computing KDE...".format(col))
bw = _bw_scott(colresult, colresult["n"], colresult["logtrans"], 1)
logger.debug("column_summary - {} - KDE bw: {:.4g}".format(col, bw))
if column_props[col]["is_categorical"]:
kde_x, kde_y = np.zeros(1), np.zeros(1)
else:
coord_range = colresult["min"], colresult["max"]
kde_x, kde_y = _compute_smoothed_histogram(
data, bw, coord_range, logtrans=colresult["logtrans"])
colresult["kde"] = {"x": kde_x.tolist(), "y": kde_y.tolist()}
return {col: colresult, "_columns": [col]}
def column_summary(series, column_props, delta=0.01):
"""Summarise a numeric column.
Parameters
----------
series : pd.Series
Numeric column.
column_props : TODO
TODO
delta : float
TODO
Returns
-------
TODO
"""
col = series.name
if not column_props[col]['numeric'] or column_props[col]['notnulls'] == 0:
# Series is not numeric or is all NaNs.
return None
logger.debug('column_summary - ' + col)
# select non-nulls from column
data = series.dropna()
colresult = {}
for m in ['mean', 'min', 'max', 'std', 'sum']:
val = getattr(data, m)()
if type(val) is np.int64:
colresult[m] = int(val)
else:
colresult[m] = val
colresult['n'] = column_props[col]['notnulls']
percentiles = [0.1, 1, 10, 25, 50, 75, 90, 99, 99.9]
colresult['percentiles'] = {
perc: np.nanpercentile(series, perc)
for perc in percentiles
}
colresult['median'] = colresult['percentiles'][50]
colresult['iqr'] = (colresult['percentiles'][75] -
colresult['percentiles'][25])
# Compute the t-digest.
logger.debug('column_summary - {} - creating TDigest...'.format(col))
digest = TDigest(delta)
digest.batch_update(data)
logger.debug('column_summary - {} - testing log trans...'.format(col))
try:
colresult['logtrans'] = bool(_test_logtrans(digest))
except Exception as e:
# Hard to pinpoint problems with the logtrans TDigest.
logger.warning('test_logtrans has failed for column `{}`: {}'.format(
col, e))
colresult['logtrans'] = False
if colresult['logtrans']:
logdigest = TDigest()
for c in digest.C.values():
logdigest.update(np.log(c.mean), c.count)
colresult['logtrans_mean'] = _tdigest_mean(logdigest)
colresult['logtrans_std'] = _tdigest_std(logdigest)
colresult['logtrans_IQR'] = (logdigest.percentile(75) -
logdigest.percentile(25))
logger.debug('column_summary - {} - should {}be log-transformed'.format(
col, 'NOT ' if not colresult['logtrans'] else ''))
# Compress and store the t-digest.
digest.delta = delta
digest.compress()
colresult['tdigest'] = [(c.mean, c.count) for c in digest.C.values()]
# Compute histogram
logger.debug('column_summary - {} - computing histogram...'.format(col))
if column_props[col]['is_categorical']:
# Compute frequency table and store as histogram
counts, edges = _compute_histogram_from_frequencies(data)
else:
if colresult['logtrans']:
counts, log_edges = np.histogram(np.log10(data),
density=False,
bins='fd')
edges = 10**log_edges
else:
counts, edges = np.histogram(data, density=False, bins='fd')
colresult['histogram'] = {
'counts': counts.tolist(),
'bin_edges': edges.tolist()
}
# Compute KDE
logger.debug('column_summary - {} - computing KDE...'.format(col))
bw = _bw_scott(colresult, colresult['n'], colresult['logtrans'], 1)
logger.debug('column_summary - {} - KDE bw: {:.4g}'.format(col, bw))
if column_props[col]['is_categorical']:
kde_x, kde_y = np.zeros(1), np.zeros(1)
else:
coord_range = colresult['min'], colresult['max']
kde_x, kde_y = _compute_smoothed_histogram(
data, bw, coord_range, logtrans=colresult['logtrans'])
colresult['kde'] = {'x': kde_x.tolist(), 'y': kde_y.tolist()}
return {col: colresult, '_columns': [col]}
