Slicing Series in pandas

Question

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?

Answer 1

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