在删除选定的行后，为什么大熊猫数据框仅显示NaN值？

Question

Using the pandas library v. 17.1, I am trying to remove the rows from a large (882504 rows) dataframe named productDataNat where parName =='rt', but then all the other rows become NaN :

productDataNat = pd.read_csv('https://lobianco.org/temp/productData_P0-Mi-Ei.csv',sep=';',  dtype={'value': np.float64})
productDataNat = productDataNat.drop(['Unnamed: 8'],axis=1)
productDataNat.set_index(['scen','country','region','prod','freeDim','year','parName'], inplace=True)
productDataNat.head()

productDataNat.drop('rt', level='parName', axis=0)

When instead I play with an example dataframe it works as expected:

midx = pd.MultiIndex(levels=[['one', 'two'], ['x','y']], labels=[[1,1,1,0],[1,0,1,0]])
dfmix = pd.DataFrame({'A' : [1, 2, 3, 4], 'B': [5, 6, 7, 8]}, index=midx)
dfmix

dfmix.drop('x',level=1,axis=0)

Bug in pandas or something wrong (what?) with my dataframe ?

Answer 1

Correction: It works exactly the same for me (i'm using Pandas v0.18.0):

In [4]: df.drop('rt', level='parName', axis=0)
Out[4]:
                                                          value
scen     country region prod        freeDim year parName
P0-Mi-Ei 11000   11042  hardWRoundW NaN     2005 dl         NaN
                        softWRoundW NaN     2005 dl         NaN
                        pulpWFuelW  NaN     2005 dl         NaN
                        ashRoundW   NaN     2005 dl         NaN
                        fuelW       NaN     2005 dl         NaN
                        hardWSawnW  NaN     2005 dl         NaN
                        softWSawnW  NaN     2005 dl         NaN
                        plyW        NaN     2005 dl         NaN
                        pulpW       NaN     2005 dl         NaN
                        pannels     NaN     2005 dl         NaN
                        ashSawnW    NaN     2005 dl         NaN
                        ashPlyW     NaN     2005 dl         NaN
                 11061  hardWRoundW NaN     2005 dl         NaN
                        softWRoundW NaN     2005 dl         NaN
                        pulpWFuelW  NaN     2005 dl         NaN
                        ashRoundW   NaN     2005 dl         NaN
                        fuelW       NaN     2005 dl         NaN
                        hardWSawnW  NaN     2005 dl         NaN
                        softWSawnW  NaN     2005 dl         NaN
                        plyW        NaN     2005 dl         NaN
                        pulpW       NaN     2005 dl         NaN
                        pannels     NaN     2005 dl         NaN
                        ashSawnW    NaN     2005 dl         NaN
                        ashPlyW     NaN     2005 dl         NaN
                 11072  hardWRoundW NaN     2005 dl         NaN
                        softWRoundW NaN     2005 dl         NaN
                        pulpWFuelW  NaN     2005 dl         NaN
                        ashRoundW   NaN     2005 dl         NaN
                        fuelW       NaN     2005 dl         NaN
                        hardWSawnW  NaN     2005 dl         NaN

as a workaround you can get rid of rt s before setting multiindex:

cols = 'scen;parName;country;region;prod;freeDim;year;value'.split(';')
url = 'https://lobianco.org/temp/productData_P0-Mi-Ei.csv'
productDataNat = pd.read_csv(url, sep=';', dtype={'value': np.float64}, usecols=cols)

df = productDataNat.ix[productDataNat.parName != 'rt']

df.set_index(['scen','country','region','prod','freeDim','year','parName'], inplace=True)