Description
Several puzzling behaviors appear below wherein calling Series.iloc[int] for a Series with a MultiIndex gives confusing results. Namely, I see unexpected indices mutated. Unexpected indices read and then added to others. As well as more than one index being mutated. Contrast all of the behaviors below with indexing a vanilla numpy array where none of the mystery occurs. Full recreates below along with why I find them surprising. I've seen similar behavior back in the Pandas 0.19.x series.
Code Sample 1
The length-2 case with duplicated keys at the first level.
In [1]: import pandas as pd
In [2]: z = pd.DataFrame()
...: z['i'] = [1, 1]
...: z['j'] = [22, 33]
...: z['k'] = [555, 666]
...: z.set_index(['i', 'j'], inplace=True)
...: v = z.k
...:
In [3]: v
Out[3]:
i j
1 22 555
33 666
Name: k, dtype: int64
In [4]: v.values
Out[4]: array([555, 666])
In [5]: v.iloc[0] += 200; v
Out[5]:
i j
1 22 755
33 666
Name: k, dtype: int64
In [6]: v.iloc[-1] += 300; v
Out[6]:
i j
1 22 755
33 966
Name: k, dtype: int64
In [7]: v.iloc[1] += 100; v
Out[7]:
i j
1 22 1066
33 1066
Name: k, dtype: int64Code Sample 1 Problem / Expected Result
Final statement incremented key (1, 33) then wrote the result to be (1, 22) and (1, 33)?
Expected (1, 22) to remain 755 and (1, 33) to become 1066.
Code Sample 2
The length-2 case with unique keys at each level.
In [1]: import pandas as pd
In [2]: z = pd.DataFrame()
...: z['i'] = [1, 2]
...: z['j'] = [3, 4]
...: z['k'] = [7, 8]
...: z.set_index(['i', 'j'], inplace=True)
...: v = z.k
...:
In [3]: v
Out[3]:
i j
1 3 7
2 4 8
Name: k, dtype: int64
In [4]: v.values
Out[4]: array([7, 8])
In [5]: v.iloc[0] += 10; v
Out[5]:
i j
1 3 17
2 4 8
Name: k, dtype: int64
In [6]: v.iloc[-1] += 10; v
Out[6]:
i j
1 3 17
2 4 18
Name: k, dtype: int64
In [7]: v.iloc[1] += 1000; v
Out[7]:
i j
1 3 1018
2 4 18
Name: k, dtype: int64Code Sample 2 Problem / Expected Result
Above iloc[0] incremented key (1, 3) but then iloc[1] does?
Do not expect 2 different iloc indices differing by 1 to point to the same location in a length 2 array.
Code Sample 3
The length-3 case.
In [1]: import pandas as pd
In [2]: x = pd.DataFrame()
...: x['i'] = [1, 2, 3]
...: x['j'] = [11, 22, 33]
...: x['k'] = [4, 5, 6]
...: x.set_index(['i', 'j'], inplace=True)
...: v = x.k
...:
In [3]: v
Out[3]:
i j
1 11 4
2 22 5
3 33 6
Name: k, dtype: int64
In [4]: v.values
Out[4]: array([4, 5, 6])
In [5]: v.iloc[0] += 4; v
Out[5]:
i j
1 11 8
2 22 5
3 33 6
Name: k, dtype: int64
In [6]: # Expected (3, 33) to be incremented but instead (2, 22) is?
...: v.iloc[2] += 7; v
...:
Out[6]:
i j
1 11 8
2 22 13
3 33 6
Name: k, dtype: int64
In [7]: # Expected (2, 22) to be incremented but instead (1, 11) is set to (2, 22) + increment?
...: v.iloc[1] += 10; v
...:
Out[7]:
i j
1 11 23
2 22 13
3 33 6
Name: k, dtype: int64Code Sample 3 Problem / Expected Result
Comments inline above
Output of pd.show_versions()
pandas: 0.20.3
pytest: None
pip: 9.0.1
setuptools: 27.2.0
Cython: None
numpy: 1.13.1
scipy: None
xarray: None
IPython: 6.1.0
sphinx: None
patsy: None
dateutil: 2.6.1
pytz: 2017.2
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
pandas_gbq: None
pandas_datareader: None