df = pd.DataFrame([['A', 'A1', 'label1', 1],
['A', 'A2', 'label2', 2],
['B', 'A1', 'label1', 3],
['B', 'A2', 'label2', 4]], columns=['index_1', 'index_2', 'label', 'value'])
df = df.set_index(['index_1', 'index_2'])
pivoted_df = df.pivot(index=None,
columns='label',
values = 'value')
枢轴函数给出错误NotImplementedError: isna is not defined for MultiIndex
。 当index设置为None
。
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
<ipython-input-84-54426dadf31d> in <module>()
2 pivoted_df = df.pivot(index=None,
3 columns='label',
----> 4 values = 'value')
~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\frame.py in pivot(self, index, columns, values)
5192 """
5193 from pandas.core.reshape.reshape import pivot
-> 5194 return pivot(self, index=index, columns=columns, values=values)
5195
5196 _shared_docs['pivot_table'] = """
~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\reshape\reshape.py in pivot(self, index, columns, values)
404 else:
405 index = self[index]
--> 406 index = MultiIndex.from_arrays([index, self[columns]])
407
408 if is_list_like(values) and not isinstance(values, tuple):
~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\indexes\multi.py in from_arrays(cls, arrays, sortorder, names)
1272 from pandas.core.arrays.categorical import _factorize_from_iterables
1273
-> 1274 labels, levels = _factorize_from_iterables(arrays)
1275 if names is None:
1276 names = [getattr(arr, "name", None) for arr in arrays]
~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\arrays\categorical.py in _factorize_from_iterables(iterables)
2541 # For consistency, it should return a list of 2 lists.
2542 return [[], []]
-> 2543 return map(list, lzip(*[_factorize_from_iterable(it) for it in iterables]))
~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\arrays\categorical.py in <listcomp>(.0)
2541 # For consistency, it should return a list of 2 lists.
2542 return [[], []]
-> 2543 return map(list, lzip(*[_factorize_from_iterable(it) for it in iterables]))
~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\arrays\categorical.py in _factorize_from_iterable(values)
2513 codes = values.codes
2514 else:
-> 2515 cat = Categorical(values, ordered=True)
2516 categories = cat.categories
2517 codes = cat.codes
~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\arrays\categorical.py in __init__(self, values, categories, ordered, dtype, fastpath)
359
360 # we're inferring from values
--> 361 dtype = CategoricalDtype(categories, dtype.ordered)
362
363 elif is_categorical_dtype(values):
~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\dtypes\dtypes.py in __init__(self, categories, ordered)
136
137 def __init__(self, categories=None, ordered=None):
--> 138 self._finalize(categories, ordered, fastpath=False)
139
140 <strong i="12">@classmethod</strong>
~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\dtypes\dtypes.py in _finalize(self, categories, ordered, fastpath)
161 if categories is not None:
162 categories = self.validate_categories(categories,
--> 163 fastpath=fastpath)
164
165 self._categories = categories
~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\dtypes\dtypes.py in validate_categories(categories, fastpath)
318 if not fastpath:
319
--> 320 if categories.hasnans:
321 raise ValueError('Categorial categories cannot be null')
322
pandas\_libs\properties.pyx in pandas._libs.properties.CachedProperty.__get__()
~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\indexes\base.py in hasnans(self)
2237 """ return if I have any nans; enables various perf speedups """
2238 if self._can_hold_na:
-> 2239 return self._isnan.any()
2240 else:
2241 return False
pandas\_libs\properties.pyx in pandas._libs.properties.CachedProperty.__get__()
~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\indexes\base.py in _isnan(self)
2218 """ return if each value is nan"""
2219 if self._can_hold_na:
-> 2220 return isna(self)
2221 else:
2222 # shouldn't reach to this condition by checking hasnans beforehand
~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\dtypes\missing.py in isna(obj)
104 Name: 1, dtype: bool
105 """
--> 106 return _isna(obj)
107
108
~\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\dtypes\missing.py in _isna_new(obj)
115 # hack (for now) because MI registers as ndarray
116 elif isinstance(obj, ABCMultiIndex):
--> 117 raise NotImplementedError("isna is not defined for MultiIndex")
118 elif isinstance(obj, (ABCSeries, np.ndarray, ABCIndexClass,
119 ABCExtensionArray)):
NotImplementedError: isna is not defined for MultiIndex
index_1 | index_2 | 标签1 | 标签2
-| -| -| -
A | A1 | 1.0 | N
|| A2 | NaN | 2.0
B | A1 | 3.0 | N
|| A2 | NaN | 4.0
pd.show_versions()
提交:无
的Python:3.6.5.final.0
python位:64
操作系统:Windows
操作系统版本:10
机器:AMD64
处理器:Intel64家族6型号85 Stepping 4,原装Intel
字节序:小
LC_ALL:无
朗:无
地点:无。
熊猫:0.23.4
pytest的:3.5.1
点:10.0.1
设置工具:39.1.0
Cython:0.28.2
numpy的:1.15.4
scipy:1.1.0
pyarrow:无
xarray:无
IPython:6.4.0
狮身人面像:1.7.4
麻痹:0.5.0
dateutil的:2.7.3
pytz:2018.4
blosc:无
瓶颈:1.2.1
表格:3.4.3
numexpr:2.6.5
羽毛:无
matplotlib:2.2.2
openpyxl:2.5.3
xlrd:1.1.0
xlwt:1.3.0
xlsxwriter:1.0.4
lxml:4.2.1
bs4:4.6.0
html5lib:1.0.1
sqlalchemy:1.2.7
pymysql:无
psycopg2:无
jinja2:2.10
s3fs:无
fastparquet:无
pandas_gbq:无
pandas_datareader:无
有任何更新吗? 据我了解,当前pivot()
方法不适用于多个索引器, index
参数不接受列表,并且当None
确实会失败,因为它试图使用现有的MultiIndex。
到目前为止,我通过生成单个索引作为原始索引的多个级别的串联,然后通过拆分串联的单个索引来构造MultiIndex的不同级别,以一种怪诞的方式解决了这一问题。 以下@srajanpaliwal示例:
(df.reset_index()
.assign(new_index=lambda dd: dd['index_1'].str.cat(dd['index_2'], sep='_'))
.pivot(index='new_index', columns='label', values='value')
.assign(index_1=lambda dd: dd.index.str.split('_').str.get(0),
index_2=lambda dd: dd.index.str.split('_').str.get(1))
.set_index(['index_1', 'index_2']))
输出:
| | 标签| 标签1 | 标签2 |
| --------- | --------- | -------- | -------- |
| index_1 | index_1 | | |
| A | A1 | 1.0 | NaN |
| | A2 | NaN | 2.0 | |
| B | A1 | 3.0 | NaN |
|| A2 | NaN | 4.0 | |
无论哪种方式,是否有原因为什么pivot()
操作不接受MultiIndex?
感谢您的解决方案https://github.com/pandas-dev/pandas/issues/23955#issuecomment -480804068。 如果它免除了麻烦,这是一个概括
def multiindex_pivot(df, columns=None, values=None):
#https://github.com/pandas-dev/pandas/issues/23955
names = list(df.index.names)
df = df.reset_index()
list_index = df[names].values
tuples_index = [tuple(i) for i in list_index] # hashable
df = df.assign(tuples_index=tuples_index)
df = df.pivot(index="tuples_index", columns=columns, values=values)
tuples_index = df.index # reduced
index = pd.MultiIndex.from_tuples(tuples_index, names=names)
df.index = index
return df
为了与数据透视图API保持一致,对@gmacario注释进行了少许调整
def multiindex_pivot(df, index=None, columns=None, values=None):
#https://github.com/pandas-dev/pandas/issues/23955
if index is None:
names = list(df.index.names)
df = df.reset_index()
else:
names = index
list_index = df[names].values
tuples_index = [tuple(i) for i in list_index] # hashable
df = df.assign(tuples_index=tuples_index)
df = df.pivot(index="tuples_index", columns=columns, values=values)
tuples_index = df.index # reduced
index = pd.MultiIndex.from_tuples(tuples_index, names=names)
df.index = index
return df
用法:
df.pipe(multiindex_pivot, index=['idx_column1', 'idx_column2'], columns='foo', values='bar')
另一个细微的增强,它也允许多个columns=
(未经彻底测试,但在我的示例中有效):
def multiindex_pivot(df, index=None, columns=None, values=None):
# https://github.com/pandas-dev/pandas/issues/23955
if index is None:
names = list(df.index.names)
df = df.reset_index()
else:
names = index
df = df.assign(tuples_index=[tuple(i) for i in df[names].values]) # hashable
df = df.assign(tuples_columns=[tuple(i) for i in df[columns].values]) # hashable
df = df.pivot(index='tuples_index', columns='tuples_columns', values=values)
df.index = pd.MultiIndex.from_tuples(df.index, names=names) # reduced
df.columns = pd.MultiIndex.from_tuples(df.columns, names=columns) # reduced
return df
用法:
df.pipe(multiindex_pivot,
index=['idx_column1', 'idx_column2'],
columns=['col_column1', 'col_column2'],
values='bar')
另一个稍微改进的版本:
def multiIndex_pivot(df, index = None, columns = None, values = None):
# https://github.com/pandas-dev/pandas/issues/23955
output_df = df.copy(deep = True)
if index is None:
names = list(output_df.index.names)
output_df = output_df.reset_index()
else:
names = index
output_df = output_df.assign(tuples_index = [tuple(i) for i in output_df[names].values])
if isinstance(columns, list):
output_df = output_df.assign(tuples_columns = [tuple(i) for i in output_df[columns].values]) # hashable
output_df = output_df.pivot(index = 'tuples_index', columns = 'tuples_columns', values = values)
output_df.columns = pd.MultiIndex.from_tuples(output_df.columns, names = columns) # reduced
else:
output_df = output_df.pivot(index = 'tuples_index', columns = columns, values = values)
output_df.index = pd.MultiIndex.from_tuples(output_df.index, names = names)
return output_df
用法:
df.pipe(multiIndex_pivot, index = ['idx_column1', 'idx_column2'], columns = ['col_column1', 'col_column2'], values = 'bar')
最有用的评论
感谢您的解决方案https://github.com/pandas-dev/pandas/issues/23955#issuecomment -480804068。 如果它免除了麻烦,这是一个概括