2.3 Attribute Access
You may access an index on a Series, column on a DataFrame, and an item on a Panel directly
as an attribute:
In [1]: sa
Out[1]:
a 1
b 2
c 3
dtype: int64
In [2]: sa.b
Out[2]: 2
In [3]: dfa
Out[3]:
A B C D
2000-01-01 0.4691 -0.2829 -1.5091 -1.1356
2000-01-02 1.2121 -0.1732 0.1192 -1.0442
2000-01-03 -0.8618 -2.1046 -0.4949 1.0718
2000-01-04 0.7216 -0.7068 -1.0396 0.2719
2000-01-05 -0.4250 0.5670 0.2762 -1.0874
2000-01-06 -0.6737 0.1136 -1.4784 0.5250
2000-01-07 0.4047 0.5770 -1.7150 -1.0393
2000-01-08 -0.3706 -1.1579 -1.3443 0.8449
In [4]: dfa.A
Out[4]:
2000-01-01 0.4691
2000-01-02 1.2121
2000-01-03 -0.8618
2000-01-04 0.7216
2000-01-05 -0.4250
2000-01-06 -0.6737
2000-01-07 0.4047
2000-01-08 -0.3706
Freq: D, Name: A, dtype: float64
In [5]: panel
Out[5]:
<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 8 (major_axis) x 4 (minor_axis)
Items axis: one to two
Major_axis axis: 2000-01-01 00:00:00 to 2000-01-08 00:00:00
Minor_axis axis: A to D
In [6]: panel.one
Out[6]:
A B C D
2000-01-01 0.4691 -0.2829 -1.5091 -1.1356
2000-01-02 1.2121 -0.1732 0.1192 -1.0442
2000-01-03 -0.8618 -2.1046 -0.4949 1.0718
2000-01-04 0.7216 -0.7068 -1.0396 0.2719
2000-01-05 -0.4250 0.5670 0.2762 -1.0874
2000-01-06 -0.6737 0.1136 -1.4784 0.5250
2000-01-07 0.4047 0.5770 -1.7150 -1.0393
2000-01-08 -0.3706 -1.1579 -1.3443 0.8449
You can use attribute access to modify an existing element of a Series or column of a DataFrame, but be careful; if you try to use attribute access to create a new column, it fails silently, creating a new attribute rather than a new column.
In [7]: sa
Out[7]:
a 1
b 2
c 3
dtype: int64
In [8]: sa.a = 5
In [9]: sa
Out[9]:
a 5
b 2
c 3
dtype: int64
In [10]: dfa
Out[10]:
A B C D
2000-01-01 0.4691 -0.2829 -1.5091 -1.1356
2000-01-02 1.2121 -0.1732 0.1192 -1.0442
2000-01-03 -0.8618 -2.1046 -0.4949 1.0718
2000-01-04 0.7216 -0.7068 -1.0396 0.2719
2000-01-05 -0.4250 0.5670 0.2762 -1.0874
2000-01-06 -0.6737 0.1136 -1.4784 0.5250
2000-01-07 0.4047 0.5770 -1.7150 -1.0393
2000-01-08 -0.3706 -1.1579 -1.3443 0.8449
In [11]: dfa.A = list(range(len(dfa.index))) # ok if A already exists
In [12]: dfa
Out[12]:
A B C D
2000-01-01 0 -0.2829 -1.5091 -1.1356
2000-01-02 1 -0.1732 0.1192 -1.0442
2000-01-03 2 -2.1046 -0.4949 1.0718
2000-01-04 3 -0.7068 -1.0396 0.2719
2000-01-05 4 0.5670 0.2762 -1.0874
2000-01-06 5 0.1136 -1.4784 0.5250
2000-01-07 6 0.5770 -1.7150 -1.0393
2000-01-08 7 -1.1579 -1.3443 0.8449
In [13]: dfa['A'] = list(range(len(dfa.index))) # use this form to create a new column
In [14]: dfa
Out[14]:
A B C D
2000-01-01 0 -0.2829 -1.5091 -1.1356
2000-01-02 1 -0.1732 0.1192 -1.0442
2000-01-03 2 -2.1046 -0.4949 1.0718
2000-01-04 3 -0.7068 -1.0396 0.2719
2000-01-05 4 0.5670 0.2762 -1.0874
2000-01-06 5 0.1136 -1.4784 0.5250
2000-01-07 6 0.5770 -1.7150 -1.0393
2000-01-08 7 -1.1579 -1.3443 0.8449
Warning
- You can use this access only if the index element is a valid python identifier, e.g.
s.1is not allowed. See here for an explanation of valid identifiers. - The attribute will not be available if it conflicts with an existing method name, e.g.
s.minis not allowed. - Similarly, the attribute will not be available if it conflicts with any of the following list:
index,major_axis,minor_axis,items,labels. - In any of these cases, standard indexing will still work, e.g.
s['1'],s['min'], ands['index']will access the corresponding element or column. - The
Series/Panelaccesses are available starting in 0.13.0.
If you are using the IPython environment, you may also use tab-completion to see these accessible attributes.
You can also assign a dict to a row of a DataFrame:
In [15]: x = pd.DataFrame({'x': [1, 2, 3], 'y': [3, 4, 5]})
In [16]: x
Out[16]:
x y
0 1 3
1 2 4
2 3 5
In [17]: x.iloc[1] = dict(x=9, y=99)
In [18]: x
Out[18]:
x y
0 1 3
1 9 99
2 3 5