我正在尝试对包含字符串作为结果的表进行数据透视。
import pandas as pd
df1 = pd.DataFrame({'index' : range(8),
'variable1' : ["A","A","B","B","A","B","B","A"],
'variable2' : ["a","b","a","b","a","b","a","b"],
'variable3' : ["x","x","x","y","y","y","x","y"],
'result': ["on","off","off","on","on","off","off","on"]})
df1.pivot_table(values='result',rows='index',cols=['variable1','variable2','variable3'])
但我得到:DataError: No numeric types to aggregate
。
当我将结果值更改为数字时,这会按预期工作:
df2 = pd.DataFrame({'index' : range(8),
'variable1' : ["A","A","B","B","A","B","B","A"],
'variable2' : ["a","b","a","b","a","b","a","b"],
'variable3' : ["x","x","x","y","y","y","x","y"],
'result': [1,0,0,1,1,0,0,1]})
df2.pivot_table(values='result',rows='index',cols=['variable1','variable2','variable3'])
我得到了我需要的:
variable1 A B
variable2 a b a b
variable3 x y x y x y
index
0 1 NaN NaN NaN NaN NaN
1 NaN NaN 0 NaN NaN NaN
2 NaN NaN NaN NaN 0 NaN
3 NaN NaN NaN NaN NaN 1
4 NaN 1 NaN NaN NaN NaN
5 NaN NaN NaN NaN NaN 0
6 NaN NaN NaN NaN 0 NaN
7 NaN NaN NaN 1 NaN NaN
我知道我可以将字符串映射为数值然后反转操作,但也许有更优雅的解决方案?
最佳答案
我最初的回复是基于 Pandas 0.14.1,从那时起,pivot_table 函数发生了很多变化(行 --> 索引,列 --> 列...)
此外,我发布的原始 lambda 技巧似乎不再适用于 Pandas 0.18。您必须提供一个归约函数(即使它是最小值、最大值或平均值)。但即使那样似乎也不合适——因为我们没有减少数据集,只是对其进行转换……所以我更仔细地研究了 unstack……
import pandas as pd
df1 = pd.DataFrame({'index' : range(8),
'variable1' : ["A","A","B","B","A","B","B","A"],
'variable2' : ["a","b","a","b","a","b","a","b"],
'variable3' : ["x","x","x","y","y","y","x","y"],
'result': ["on","off","off","on","on","off","off","on"]})
# these are the columns to end up in the multi-index columns.
unstack_cols = ['variable1', 'variable2', 'variable3']
首先,使用索引+要堆叠的列在数据上设置索引,然后使用级别参数调用 unstack。
df1.set_index(['index'] + unstack_cols).unstack(level=unstack_cols)
结果数据框如下。
关于python - pandas - 具有非数值的 pivot_table? (数据错误 : No numeric types to aggregate),我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/19279229/