如何让 Pandas 的 groupby 命令返回 DataFrame 而不是 Series? [英] How can I get pandas' groupby command to return a DataFrame instead of a Series?

问题描述

我不明白pandas 的groupby 的输出.我从一个包含 5 个字段/列(邮编、城市、位置、人口、州)的 DataFrame (df0) 开始.

I don't understand the output of pandas' groupby. I started with a DataFrame (df0) with 5 fields/columns (zip, city, location, population, state).

 >>> df0.info()
 <class 'pandas.core.frame.DataFrame'>
 RangeIndex: 29467 entries, 0 to 29466
 Data columns (total 5 columns):
 zip      29467 non-null object
 city     29467 non-null object
 loc      29467 non-null object
 pop      29467 non-null int64
 state    29467 non-null object
 dtypes: int64(1), object(4)
 memory usage: 1.1+ MB

我想得到每个城市的总人口，但由于几个城市有多个邮政编码，我想我会使用 groupby.sum 如下:

I wanted to get the total population of each city, but since several cities have multiple zip codes, I thought I would use groupby.sum as follows:

  df6 = df0.groupby(['city','state'])['pop'].sum()

然而，这返回了一个系列而不是一个数据帧:

However, this returned a Series instead of a DataFrame:

 >>> df6.info()
 Traceback (most recent call last):
   File "<stdin>", line 1, in <module>
   File "/usr/local/lib/python2.7/dist-packages/pandas/core/generic.py", line 2672, in __getattr__
     return object.__getattribute__(self, name)
  AttributeError: 'Series' object has no attribute 'info'
 >>> type(df6)
 <class 'pandas.core.series.Series'>

我希望能够使用类似于

 df0[df0['city'].isin(['ALBANY'])]

但是由于我有一个系列而不是数据帧，所以我不能.我也无法强制转换为 DataFrame.

but since I have a Series instead of a DataFrame, I can't. I haven't been able to force a conversion into a DataFrame either.

我现在想知道的是:

1. 为什么我没有得到 DataFrame 而不是 Series?
2. 我怎样才能得到一个可以让我查询一个城市人口的表格?我可以使用从 groupby 获得的系列，还是应该采取不同的方法?

推荐答案

需要groupby 或 reset_index 用于将 MultiIndex 转换为列:

Need parameter as_index=False in groupby or reset_index for convert MultiIndex to columns:

df6 = df0.groupby(['city','state'], as_index=False)['pop'].sum()

或者:

df6 = df0.groupby(['city','state'])['pop'].sum().reset_index()

示例:

df0 = pd.DataFrame({'city':['a','a','b'],
                   'state':['t','t','n'],
                   'pop':[7,8,9]})

print (df0)
  city  pop state
0    a    7     t
1    a    8     t
2    b    9     n

df6 = df0.groupby(['city','state'], as_index=False)['pop'].sum()
print (df6)
  city state  pop
0    a     t   15
1    b     n    9

<小时>

df6 = df0.groupby(['city','state'])['pop'].sum().reset_index()
print (df6)
  city state  pop
0    a     t   15
1    b     n    9

最后选择 loc，对于标量添加 item():

print (df6.loc[df6.state == 't', 'pop'])
0    15
Name: pop, dtype: int64

print (df6.loc[df6.state == 't', 'pop'].item())
15

但如果只需要查找表，则可以使用 Series 和 MultiIndex:

But if need only lookup table is possible use Series with MultiIndex:

s = df0.groupby(['city','state'])['pop'].sum()
print (s)
city  state
a     t        15
b     n         9
Name: pop, dtype: int64

#select all cities by : and state by string like 't'
#output is Series of len 1
print (s.loc[:, 't'])
city
a    15
Name: pop, dtype: int64

#if need output as scalar add item()
print (s.loc[:, 't'].item())
15

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