def _table_info(self, buf=None):
"""
Similar to pandas DataFrame.info. Show column names, types, and null
counts. Output to stdout by default
"""
metrics = [self.count().name('nrows')]
for col in self.columns:
metrics.append(self[col].count().name(col))
metrics = self.aggregate(metrics).execute().loc[0]
names = ['Column', '------'] + self.columns
types = ['Type', '----'] + [repr(x) for x in self.schema().types]
counts = ['Non-null #', '----------'] + [str(x) for x in metrics[1:]]
col_metrics = util.adjoin(2, names, types, counts)
if buf is None:
import sys
buf = sys.stdout
result = ('Table rows: {0}\n\n'
'{1}'
.format(metrics[0], col_metrics))
buf.write(result)
def _table_info(self, buf=None):
"""
Similar to pandas DataFrame.info. Show column names, types, and null
counts. Output to stdout by default
"""
metrics = [self.count().name('nrows')]
for col in self.columns:
metrics.append(self[col].count().name(col))
metrics = self.aggregate(metrics).execute().loc[0]
names = ['Column', '------'] + self.columns
types = ['Type', '----'] + [repr(x) for x in self.schema().types]
counts = ['Non-null #', '----------'] + [str(x) for x in metrics[1:]]
col_metrics = util.adjoin(2, names, types, counts)
if buf is None:
import sys
buf = sys.stdout
result = ('Table rows: {0}\n\n'
'{1}'
.format(metrics[0], col_metrics))
buf.write(result)