pandas 将集合计数器变成字典
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turning a collections counter into dictionary
提问by Blue Moon
I have a collection outcome resulting from the function:
我有一个由函数产生的收集结果：
Counter(df.email_address)
it returns each individual email address with the count of its repetitions.
它返回每个单独的电子邮件地址及其重复次数。
Counter({nan: 1618, '[email protected]': 265, '[email protected]': 1})
what I want to do is to use it as if it was a dictionary and create a pandas dataframe out of it with two columns one for email addresses and one for the value associated.
我想要做的是像使用字典一样使用它，并从中创建一个 Pandas 数据框，其中有两列，一列用于电子邮件地址，另一列用于关联的值。
I tried with:
我试过：
dfr = repeaters.from_dict(repeaters, orient='index')
but i got the following error:
但我收到以下错误：
AttributeError: 'Counter' object has no attribute 'from_dict'
It makes thing that Counter is not a dictionary as it looks like. Any idea on how to append it to a df?
这使得 Counter 不像它看起来的那样是一本字典。关于如何将它附加到 df 的任何想法？
回答by doru
d = {}
cnt = Counter(df.email_address)
for key, value in cnt.items():
d[key] = value
EDIT
编辑
Or, how @Trif Nefzger suggested:
或者，@Trif Nefzger 如何建议：
d = dict(Counter(df.email_address))
回答by omri_saadon
as ajcr wrote at the comment, from_dictis a method that belongs to dataframe and thus you can write the following to achieve your goal:
正如ajcr在评论中所写的那样，from_dict是一种属于数据框的方法，因此您可以编写以下内容来实现您的目标：
from collections import Counter
import pandas as pd
repeaters = Counter({"nan": 1618, '[email protected]': 265, '[email protected]': 1})
dfr = pd.DataFrame.from_dict(repeaters, orient='index')
print dfr
Output:
输出：
[email protected] 1
nan 1618
[email protected] 265
回答by ldirer
Alternatively you could use pd.Series.value_counts, which returns a Seriesobject.
或者，您可以使用pd.Series.value_counts，它返回一个Series对象。
df.email_address.value_counts(dropna=False)
Sample output:
示例输出：
[email protected] 2
[email protected] 1
NaN 1
dtype: int64
This is not exactly what you asked for but looks like what you'd like to achieve.
这不完全是您所要求的，但看起来像是您想要实现的。
