将 pandas.Series.value_counts 返回的系列转换为字典
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convert series returned by pandas.Series.value_counts to a dictionary
提问by swati saoji
I am trying to use pandas.Series.value_counts to get the frequency of values in a dataframe, so I go through each column and get values_count , which gives me a series:
我正在尝试使用 pandas.Series.value_counts 来获取数据框中值的频率，因此我遍历每一列并获取 values_count ，这给了我一个系列：
I am struggling to convert this resultant series to a dict:
我正在努力将这个结果系列转换为字典：
groupedData = newData.groupby('class')
for k, group in groupedData:
dictClass[k] = {}
for eachlabel in dataLabels:
myobj = group[eachlabel].value_counts()
for eachone in myobj:
print type(myobj)
print myobj
what I need is a dict :
我需要的是一个字典：
{'high': 3909 , 'average': 3688, 'less': '182 , 'veryless' : 62}
{'high'：3909，'average'：3688，'less'：'182，'veryless'：62}
回答by DSM
If you want to convert a Seriesto a dict, you could call dictor .to_dict():
如果要将 a 转换Series为 a dict，可以调用dict或.to_dict()：
>>> s
high 3909
average 3688
less 182
veryless 62
dtype: int64
>>> type(s)
<class 'pandas.core.series.Series'>
>>> dict(s)
{'high': 3909, 'average': 3688, 'veryless': 62, 'less': 182}
>>> s.to_dict()
{'high': 3909, 'average': 3688, 'veryless': 62, 'less': 182}
回答by Martin Thoma
values = df['your_column'].value_counts(dropna=False).keys().tolist()
counts = df['your_column'].value_counts(dropna=False).tolist()
value_dict = dict(zip(values, counts))
