Slicing Series in pandas
Consider the Series obj:
In [50]: obj = Series(np.arange(6,10), index = ['a', 'b', 'c', 'd'])
In [51]: obj
Out[51]:
a 6
b 7
c 8
d 9
dtype: int64
I'd like to take a slice of obj, and I can do that in a couple of ways:
In [52]: obj[1:3]
Out[52]:
b 7
c 8
dtype: int64
In [53]: obj['b' : 'c']
Out[53]:
b 7
c 8
dtype: int64
Now consider the DataFrame param_estimates_good:
In [54]: param_estimates_good
Out[54]:
a b sigma a_se b_se sigma_se success
1968 0.648508 1.803889 0.498017 0.784340 0.082366 0.529649 1
1972 0.539485 1.733304 0.451311 1.084170 0.174030 0.677134 1
1973 1.205704 2.054114 1.465606 0.095780 0.052851 0.090562 1
1974 1.398968 2.105287 2.029865 0.451929 0.056154 0.428696 1
1975 1.570900 1.877486 2.016978 0.186177 0.052413 0.183577 1
1976 0.688932 1.651232 0.874860 0.065038 0.099080 0.055247 1
1977 0.816918 1.949563 0.691899 0.516742 0.083973 0.385799 1
1980 0.730454 2.569974 2.297921 0.619403 0.157950 0.439383 1
1986 1.053362 1.770256 1.115229 0.235353 0.063867 0.202970 1
1993 2.531327 1.235418 2.005588 0.107785 0.011513 0.060647 1
1994 -0.759318 2.556910 0.175695 0.052099 0.078433 0.044315 1
1998 1.007787 1.548161 0.911332 2.538235 0.040285 2.001148 1
2000 -0.693261 1.518839 -0.290453 3.763934 1.329302 0.868444 1
2001 0.662391 0.650003 0.854752 0.550188 0.547999 0.376354 1
2002 0.652630 0.424864 0.524909 0.413478 0.334703 0.251172 1
2004 -0.169553 1.290054 -0.040504 0.279700 0.093937 0.115120 1
2005 0.146209 1.610219 -0.233461 0.171832 0.083844 0.123676 1
2007 -0.301397 0.822584 0.309423 1.119639 0.860818 0.377673 1
2008 1.334283 0.065856 1.704950 0.462811 0.489639 0.427041 1
2009 2.082782 -0.727128 1.072343 0.464726 0.093574 0.472603 1
2010 2.309353 -1.202509 0.906165 0.037950 0.080356 0.031981 1
2013 3.490101 -2.033734 1.468027 0.251317 0.030869 0.259732 1
2014 1.820431 -1.961015 -0.050831 0.262710 0.176057 0.266525 1
2016 1.818855 -0.580492 0.312369 0.450659 0.065661 0.474896 1
I take a slice of this to form a Series g:
In [55]: g = param_estimates_good['a']
In [56]: g
Out[56]:
1968 0.648508
1972 0.539485
1973 1.205704
1974 1.398968
1975 1.570900
1976 0.688932
1977 0.816918
1980 0.730454
1986 1.053362
1993 2.531327
1994 -0.759318
1998 1.007787
2000 -0.693261
2001 0.662391
2002 0.652630
2004 -0.169553
2005 0.146209
2007 -0.301397
2008 1.334283
2009 2.082782
2010 2.309353
2013 3.490101
2014 1.820431
2016 1.818855
Name: a, dtype: float64
The Index of g are ints...
In [57]: g.index
Out[57]:
Int64Index([1968, 1972, 1973, 1974, 1975, 1976, 1977, 1980, 1986, 1993, 1994,
1998, 2000, 2001, 2002, 2004, 2005, 2007, 2008, 2009, 2010, 2013,
2014, 2016],
dtype='int64')
... so I try to slice g in an analagous way to to obj:
In [58]: g[0:7]
Out[58]:
1968 0.648508
1972 0.539485
1973 1.205704
1974 1.398968
1975 1.570900
1976 0.688932
1977 0.816918
Name: a, dtype: float64
In [59]: g[1968 : 1977]
Out[59]: Series([], Name: a, dtype: float64)
Why does the latter method return an empty Series?
I think it wants to find rows from position 1968 to 1977, because it selects rows by positions - Slicing ranges [] in docs:
With Series, the syntax works exactly as with an ndarray, returning a slice of the values and the corresponding labels
It is same as Selection by position in docs with iloc.
print g.iloc[1968 : 1977]
Series([], Name: a, dtype: float64)
With loc it works perfectly Selection By Label in docs:
print g.loc[1968 : 1977]
1968 0.648508
1972 0.539485
1973 1.205704
1974 1.398968
1975 1.570900
1976 0.688932
1977 0.816918
Name: a, dtype: float64
it's simple cause the index function is trying to call "label of index" not the "position of index" unless you specified it like g.index(1968). turns out the correct one you should call it by g[0:7] / your previous answer.