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')
ピボット関数でエラーNotImplementedError: isna is not defined for MultiIndex
ます。 インデックスがNone
設定されている場合。
---------------------------------------------------------------------------
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 | label1 | label2
-| -| -| -
A | A1 | 1.0 | NaN
|| A2 | NaN | 2.0
B | A1 | 3.0 | NaN
|| A2 | NaN | 4.0 4.0
pd.show_versions()
出力コミット:なし
python:3.6.5.final.0
python-ビット:64
OS:Windows
OSリリース:10
マシン:AMD64
プロセッサー:Intel64ファミリー6モデル85ステッピング4、GenuineIntel
バイトオーダー:少し
LC_ALL:なし
言語:なし
ローカル:なし。なし
パンダ:0.23.4
pytest:3.5.1
ピップ:10.0.1
setuptools:39.1.0
Cython:0.28.2
numpy:1.15.4
scipy:1.1.0
pyarrow:なし
xarray:なし
IPython:6.4.0
スフィンクス:1.7.4
patsy:0.5.0
dateutil:2.7.3
pytz:2018.4
blosc:なし
ボトルネック:1.2.1
表:3.4.3
numexpr:2.6.5
フェザー:なし
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:なし
psycopg2:なし
jinja2:2.10
s3fs:なし
fastparquet:なし
pandas_gbq:なし
pandas_datareader:なし
これに関する更新はありますか? 私が理解しているように、現在、 pivot()
メソッドは複数のインデクサーでは機能せず、 index
引数はリストを受け入れません。また、 None
しようとすると、実際に失敗します。既存のMultiIndexを使用します。
今のところ、元のインデックスの複数のレベルの連結として単一のインデックスを生成し、ピボットしてから、連結された単一のインデックスを分割してMultiIndexのさまざまなレベルを再構築することにより、これをハックな方法で解決します。 @srajanpaliwalの例に続いて:
(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']))
出力:
| | ラベル| label1 | label2 |
| --------- | --------- | -------- | -------- |
| index_1 | index_1 | | |
| A | A1 | 1.0 | NaN |
| | A2 | NaN | 2.0 | |
| B | A1 | 3.0 | NaN |
|| A2 | NaN | 4.0 | |
いずれにせよ、MultiIndexがpivot()
操作で受け入れられない理由はありますか?
ソリューションhttps://github.com/pandas-dev/pandas/issues/23955#issuecomment-480804068をありがとう。 それが誰かのトラブルを救うなら、ここに一般化があります
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
ピボットAPIとの統一性のために@gmacarioコメントをわずかに調整
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
使用法:
df.pipe(multiindex_pivot, index=['idx_column1', 'idx_column2'], columns='foo', values='bar')
複数のcolumns=
も許可するもう1つのわずかな機能強化(徹底的にテストされていませんが、私の例では機能します):
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
使用法:
df.pipe(multiindex_pivot,
index=['idx_column1', 'idx_column2'],
columns=['col_column1', 'col_column2'],
values='bar')
さらにもう1つのわずかに改善されたバージョン:
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
使用法:
df.pipe(multiIndex_pivot, index = ['idx_column1', 'idx_column2'], columns = ['col_column1', 'col_column2'], values = 'bar')
最も参考になるコメント
ソリューションhttps://github.com/pandas-dev/pandas/issues/23955#issuecomment-480804068をありがとう。 それが誰かのトラブルを救うなら、ここに一般化があります