我正在通过以下操作创建数据框:
months = [ 'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec' ]
monthyAmounts = [ "actual", "budgeted", "difference" ]
income = []
names = []
for x in range( incomeIndex + 1, expensesIndex ):
amounts = [ randint( -1000, 15000 ) for x in range( 0, len( months ) * len( monthyAmounts ) ) ]
income.append( amounts )
names.append( f"name_{x}" )
index = pd.Index( names, name = 'category' )
columns = pd.MultiIndex.from_product( [ months, monthyAmounts ], names = [ 'month', 'type' ] )
incomeDF = pd.DataFrame( income, index = index, columns = columns )
数据框如下所示: (3月-12月删除的月份)
Jan Feb ...
actual budgeted difference actual budgeted difference
name_13 14593 -260 10165 9767 629 10054
name_14 6178 1398 13620 1821 10986 -663
name_15 2432 3279 7545 8196 1052 7386
name_16 9964 13098 10342 5564 4631 7422
我想要的是每一行,以对1月至5月的月份的差异列进行切片。我可以通过以下方法将所有月份的差异列切成薄片:
incomeDifferenceDF = incomeDF.loc[ :, idx[ :, 'difference' ] ]
这给了我一个像这样的数据框: (3月-12月删除了几个月)
Jan Feb ....
difference difference
name_13 10165 10054
name_14 13620 -663
name_15 7545 7386
name_16 10342 7422
我尝试过的是:
incomeDifferenceDF = incomeDF.loc[ :, idx[ 'Jan' : 'May', 'difference' ] ]
但这给了我错误:
UnsortedIndexError: 'MultiIndex slicing requires the index to be lexsorted: slicing on levels [0], lexsort depth 0'
所以,这似乎很接近,但是我不确定如何解决该问题。
我也尝试过:
incomeDifferenceDF = incomeDF.loc[ :, idx[ ['Jan':'May'], 'difference' ] ]
但这只会产生错误:
SyntaxError: invalid syntax
( Points at ['Jan':'May'] )
最好的方法是什么?
答案 0 :(得分:1)
如果需要通过MultiIndex
选择,则需要布尔掩码:
index = pd.Index( [1,2,3,4], name = 'category' )
budgetMonths = pd.date_range( "January, 2018", periods = 12, freq = 'BM' )
months = [ 'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',
'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec' ]
monthyAmounts = [ "actual", "budgeted", "difference" ]
columns = pd.MultiIndex.from_product( [ months, monthyAmounts ], names = [ 'month', 'type' ])
incomeDF = pd.DataFrame( 10, index = index, columns = columns )
#trick for get values between
idx = pd.Series(0,index=months).loc['Jan' : 'May'].index
print (idx)
Index(['Jan', 'Feb', 'Mar', 'Apr', 'May'], dtype='object')
mask1 = incomeDF.columns.get_level_values(0).isin(idx)
mask2 = incomeDF.columns.get_level_values(1) == 'difference'
incomeDifferenceDF = incomeDF.loc[:, mask1 & mask2]
print (incomeDifferenceDF)
month Jan Feb Mar Apr May
type difference difference difference difference difference
category
1 10 10 10 10 10
2 10 10 10 10 10
3 10 10 10 10 10
4 10 10 10 10 10