本文整理汇总了Python中pandas.core.series.Series.value_counts方法的典型用法代码示例。如果您正苦于以下问题：Python Series.value_counts方法的具体用法？Python Series.value_counts怎么用？Python Series.value_counts使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类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个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: describe
# 需要导入模块: from pandas.core.series import Series [as 别名]
# 或者: from pandas.core.series.Series import value_counts [as 别名]
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
# 需要导入模块: from pandas.core.series import Series [as 别名]
# 或者: from pandas.core.series.Series import value_counts [as 别名]
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
# 需要导入模块: from pandas.core.series import Series [as 别名]
# 或者: from pandas.core.series.Series import value_counts [as 别名]
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
# 需要导入模块: from pandas.core.series import Series [as 别名]
# 或者: from pandas.core.series.Series import value_counts [as 别名]
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')