Question

我有一个数据框，如下所示。 period-1(1998-01-01:1998-02-01) period 2(1998-02-02-1998-05-02)有两个时间段。我想将其转换为另一个数据帧，如图2所示。第二个数据帧的每个条目都是相应时间段内ID的平均值。

根据一位朋友的建议，这里是代码

import pandas as pd

df=pd.DataFrame({"ID": ["1002", "2001", "1002", "2003", "1002"],
                "Time": ["1998-01-02", "1998-02-03", "1998-04-05", "1998-02-03", "1998-01-20"],
                 "Price": ["34", "45", "23", "30","60"]})
print(df)

df.Time=pd.to_datetime(df.Time)
period2=pd.Interval(pd.Timestamp('1998-02-02'), pd.Timestamp('1998-05-02'), closed='both')
df['Price'].apply(pd.to_numeric)
df['New']='period1'

df.loc[df.Time.apply(lambda x : x in period2),'New']='period2'
df.pivot_table(index='ID',columns='New',values='Price',aggfunc='mean')

但是我收到以下错误消息，这可能是什么原因？

---------------------------------------------------------------------------
DataError                                 Traceback (most recent call last)
<ipython-input-62-1d40b807c09c> in <module>()
----> 1 df.pivot_table(index='ID',columns='New',values='Price',aggfunc='mean')

~\Anaconda3\lib\site-packages\pandas\core\frame.py in pivot_table(self, values, index, columns, aggfunc, fill_value, margins, dropna, margins_name)
   5298                            aggfunc=aggfunc, fill_value=fill_value,
   5299                            margins=margins, dropna=dropna,
-> 5300                            margins_name=margins_name)
   5301 
   5302     def stack(self, level=-1, dropna=True):

~\Anaconda3\lib\site-packages\pandas\core\reshape\pivot.py in pivot_table(data, values, index, columns, aggfunc, fill_value, margins, dropna, margins_name)
     81 
     82     grouped = data.groupby(keys, observed=dropna)
---> 83     agged = grouped.agg(aggfunc)
     84 
     85     table = agged

~\Anaconda3\lib\site-packages\pandas\core\groupby\groupby.py in aggregate(self, arg, *args, **kwargs)
   4656         axis=''))
   4657     def aggregate(self, arg, *args, **kwargs):
-> 4658         return super(DataFrameGroupBy, self).aggregate(arg, *args, **kwargs)
   4659 
   4660     agg = aggregate

~\Anaconda3\lib\site-packages\pandas\core\groupby\groupby.py in aggregate(self, arg, *args, **kwargs)
   4087 
   4088         _level = kwargs.pop('_level', None)
-> 4089         result, how = self._aggregate(arg, _level=_level, *args, **kwargs)
   4090         if how is None:
   4091             return result

~\Anaconda3\lib\site-packages\pandas\core\base.py in _aggregate(self, arg, *args, **kwargs)
    346         if isinstance(arg, compat.string_types):
    347             return self._try_aggregate_string_function(arg, *args,
--> 348                                                        **kwargs), None
    349 
    350         if isinstance(arg, dict):

~\Anaconda3\lib\site-packages\pandas\core\base.py in _try_aggregate_string_function(self, arg, *args, **kwargs)
    302         if f is not None:
    303             if callable(f):
--> 304                 return f(*args, **kwargs)
    305 
    306             # people may try to aggregate on a non-callable attribute

~\Anaconda3\lib\site-packages\pandas\core\groupby\groupby.py in mean(self, *args, **kwargs)
   1304         nv.validate_groupby_func('mean', args, kwargs, ['numeric_only'])
   1305         try:
-> 1306             return self._cython_agg_general('mean', **kwargs)
   1307         except GroupByError:
   1308             raise

~\Anaconda3\lib\site-packages\pandas\core\groupby\groupby.py in _cython_agg_general(self, how, alt, numeric_only, min_count)
   3972                             min_count=-1):
   3973         new_items, new_blocks = self._cython_agg_blocks(
-> 3974             how, alt=alt, numeric_only=numeric_only, min_count=min_count)
   3975         return self._wrap_agged_blocks(new_items, new_blocks)
   3976 

~\Anaconda3\lib\site-packages\pandas\core\groupby\groupby.py in _cython_agg_blocks(self, how, alt, numeric_only, min_count)
   4044 
   4045         if len(new_blocks) == 0:
-> 4046             raise DataError('No numeric types to aggregate')
   4047 
   4048         # reset the locs in the blocks to correspond to our

DataError: No numeric types to aggregate

Answer 1

有一个您忘记分配回去的问题：

df['Price'] = df['Price'].apply(pd.to_numeric)

更好的是：

df['Price'] = df['Price'].astype(int)

或者：

df['Price'] = pd.to_numeric(df['Price'])

此外，应使用numpy.where创建的带有掩码Series.between的代码来简化代码：

df.Time=pd.to_datetime(df.Time)

df['Price']= df['Price'].astype(int)
df['New'] = np.where(df['Time'].between('1998-02-02','1998-05-02'), 'period2', 'period1')
df1 = df.pivot_table(index='ID',columns='New',values='Price',aggfunc='mean')
print (df1)
New   period1  period2
ID                    
1002     47.0     23.0
2001      NaN     45.0
2003      NaN     30.0

DataError：在熊猫中创建数据透视表时，没有任何数字类型可聚合

1 个答案: