本文整理匯總了Python中pandas.core.series.Series.value_counts方法的典型用法代碼示例。如果您正苦於以下問題：Python Series.value_counts方法的具體用法？Python Series.value_counts怎麽用？Python Series.value_counts使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類pandas.core.series.Series的用法示例。
在下文中一共展示了Series.value_counts方法的4個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: describe
# 需要導入模塊: from pandas.core.series import Series [as 別名]
# 或者: from pandas.core.series.Series import value_counts [as 別名]
def describe(self):
"""
Describes this Categorical
Returns
-------
description: `DataFrame`
A dataframe with frequency and counts by category.
"""
counts = self.value_counts(dropna=False)
freqs = counts / float(counts.sum())
from pandas.core.reshape.concat import concat
result = concat([counts, freqs], axis=1)
result.columns = ['counts', 'freqs']
result.index.name = 'categories'
return result 示例2: describe
# 需要導入模塊: from pandas.core.series import Series [as 別名]
# 或者: from pandas.core.series.Series import value_counts [as 別名]
def describe(self):
""" Describes this Categorical
Returns
-------
description: `DataFrame`
A dataframe with frequency and counts by category.
"""
counts = self.value_counts(dropna=False)
freqs = counts / float(counts.sum())
from pandas.core.reshape.concat import concat
result = concat([counts, freqs], axis=1)
result.columns = ['counts', 'freqs']
result.index.name = 'categories'
return result 示例3: value_counts
# 需要導入模塊: from pandas.core.series import Series [as 別名]
# 或者: from pandas.core.series.Series import value_counts [as 別名]
def value_counts(self, dropna=True):
"""
Returns a Series containing counts of each category.
Every category will have an entry, even those with a count of 0.
Parameters
----------
dropna : boolean, default True
Don't include counts of NaN.
Returns
-------
counts : Series
See Also
--------
Series.value_counts
"""
from numpy import bincount
from pandas import Series, CategoricalIndex
code, cat = self._codes, self.categories
ncat, mask = len(cat), 0 <= code
ix, clean = np.arange(ncat), mask.all()
if dropna or clean:
obs = code if clean else code[mask]
count = bincount(obs, minlength=ncat or None)
else:
count = bincount(np.where(mask, code, ncat))
ix = np.append(ix, -1)
ix = self._constructor(ix, dtype=self.dtype,
fastpath=True)
return Series(count, index=CategoricalIndex(ix), dtype='int64') 示例4: value_counts
# 需要導入模塊: from pandas.core.series import Series [as 別名]
# 或者: from pandas.core.series.Series import value_counts [as 別名]
def value_counts(self, dropna=True):
"""
Returns a Series containing counts of each category.
Every category will have an entry, even those with a count of 0.
Parameters
----------
dropna : boolean, default True
Don't include counts of NaN, even if NaN is a category.
Returns
-------
counts : Series
See Also
--------
Series.value_counts
"""
from numpy import bincount
from pandas import isna, Series, CategoricalIndex
obj = (self.remove_categories([np.nan]) if dropna and
isna(self.categories).any() else self)
code, cat = obj._codes, obj.categories
ncat, mask = len(cat), 0 <= code
ix, clean = np.arange(ncat), mask.all()
if dropna or clean:
obs = code if clean else code[mask]
count = bincount(obs, minlength=ncat or None)
else:
count = bincount(np.where(mask, code, ncat))
ix = np.append(ix, -1)
ix = self._constructor(ix, dtype=self.dtype,
fastpath=True)
return Series(count, index=CategoricalIndex(ix), dtype='int64')