问题
For a simple program below, I was expecting the 2nd output to be same as first..
Why is this not happening?
It's just a order change in data1 and data2
columnList = ["PID", "Sec", "Util", "random"]
data1 = [('67123', 12, '85' , '100'),
('67123', 112, '15', '100'),
('87878', 23, "95", '100'),
]
df1 = pd.DataFrame(data1, columns=columnList)
df1 = df1.set_index(["PID", "Sec"])
print df1
Util random
PID Sec
67123 12 85 100
112 15 100
87878 23 95 100
data2 = [('67123', 12, '85' , '100'),
('87878', 23, "95", '100'),
('67123', 112, '15', '100'),
]
df2 = pd.DataFrame(data2, columns=columnList)
df2 = df2.set_index(["PID", "Sec"])
print df2
Util random
PID Sec
67123 12 85 100
87878 23 95 100
67123 112 15 100
回答1:
It is default displaying of Multiindex. You can temporary change it by set parameter display.multi_sparse to False with with.
Option: display.multi_sparse
Default: True
Function: “Sparsify” MultiIndex display (don’t display repeated elements in outer levels within groups)
Available options of Pandas.
#default options
#set temporary multi_sparse to True
with pd.option_context('display.multi_sparse', True):
print df1
Util random
PID Sec
67123 12 85 100
112 15 100
87878 23 95 100
#same as
print df1
Util random
PID Sec
67123 12 85 100
112 15 100
87878 23 95 100
#set temporary multi_sparse to False
with pd.option_context('display.multi_sparse', False):
print df1
Util random
PID Sec
67123 12 85 100
67123 112 15 100
87878 23 95 100
If you want set it, use:
pd.set_option('display.multi_sparse', False)
And reset:
pd.reset_option('multi_sparse')
来源：https://stackoverflow.com/questions/35961320/set-index-not-indexing-in-pandas