def value_counts(values,
sort=True,
ascending=False,
normalize=False,
bins=None,
dropna=True):
"""
Compute a histogram of the counts of non-null values.
Parameters
----------
values : ndarray (1-d)
sort : boolean, default True
Sort by values
ascending : boolean, default False
Sort in ascending order
normalize: boolean, default False
If True then compute a relative histogram
bins : integer, optional
Rather than count values, group them into half-open bins,
convenience for pd.cut, only works with numeric data
dropna : boolean, default True
Don't include counts of NaN
Returns
-------
value_counts : Series
"""
from pandas.core.series import Series
name = getattr(values, 'name', None)
if bins is not None:
try:
from pandas.tools.tile import cut
values = Series(values).values
cat, bins = cut(values, bins, retbins=True)
except TypeError:
raise TypeError("bins argument only works with numeric data.")
values = cat.codes
if is_extension_type(values) and not is_datetimetz(values):
# handle Categorical and sparse,
# datetime tz can be handeled in ndarray path
result = Series(values).values.value_counts(dropna=dropna)
result.name = name
counts = result.values
else:
# ndarray path. pass original to handle DatetimeTzBlock
keys, counts = _value_counts_arraylike(values, dropna=dropna)
from pandas import Index, Series
if not isinstance(keys, Index):
keys = Index(keys)
result = Series(counts, index=keys, name=name)
if bins is not None:
# TODO: This next line should be more efficient
result = result.reindex(np.arange(len(cat.categories)), fill_value=0)
result.index = bins[:-1]
if sort:
result = result.sort_values(ascending=ascending)
if normalize:
result = result / float(counts.sum())
return result
def value_counts(values, sort=True, ascending=False, normalize=False,
bins=None, dropna=True):
"""
Compute a histogram of the counts of non-null values.
Parameters
----------
values : ndarray (1-d)
sort : boolean, default True
Sort by values
ascending : boolean, default False
Sort in ascending order
normalize: boolean, default False
If True then compute a relative histogram
bins : integer, optional
Rather than count values, group them into half-open bins,
convenience for pd.cut, only works with numeric data
dropna : boolean, default True
Don't include counts of NaN
Returns
-------
value_counts : Series
"""
from pandas.core.series import Series
name = getattr(values, 'name', None)
if bins is not None:
try:
from pandas.tools.tile import cut
values = Series(values).values
cat, bins = cut(values, bins, retbins=True)
except TypeError:
raise TypeError("bins argument only works with numeric data.")
values = cat.codes
if is_extension_type(values) and not is_datetimetz(values):
# handle Categorical and sparse,
# datetime tz can be handeled in ndarray path
result = Series(values).values.value_counts(dropna=dropna)
result.name = name
counts = result.values
else:
# ndarray path. pass original to handle DatetimeTzBlock
keys, counts = _value_counts_arraylike(values, dropna=dropna)
from pandas import Index, Series
if not isinstance(keys, Index):
keys = Index(keys)
result = Series(counts, index=keys, name=name)
if bins is not None:
# TODO: This next line should be more efficient
result = result.reindex(np.arange(len(cat.categories)),
fill_value=0)
result.index = bins[:-1]
if sort:
result = result.sort_values(ascending=ascending)
if normalize:
result = result / float(counts.sum())
return result
