pandas Indexing, Selection, and

Series indexing (obj[...]) works analogously to NumPy array indexing, except you
can use the Series’s index values instead of only integers.

In [117]: obj = pd.Series(np.arange(4.), index=['a', 'b', 'c', 'd'])
In [118]: obj
Out[118]:
a 0.0
b 1.0
c 2.0
d 3.0
dtype: float64
In [119]: obj['b']
Out[119]: 1.0

Slicing with labels behaves differently than normal Python slicing in that the end‐
point is inclusive:

In [125]: obj['b':'c']
Out[125]:
b 1.0
c 2.0
dtype: float64

In [128]: data = pd.DataFrame(np.arange(16).reshape((4, 4)),
.....: index=['Ohio', 'Colorado', 'Utah', 'New York'],
.....: columns=['one', 'two', 'three', 'four'])
In [129]: data
Out[129]:
one two three four
Ohio 0 1 2 3
Colorado 4 5 6 7
Utah 8 9 10 11
New York 12 13 14 15

In [130]: data['two']
Out[130]:
Ohio 1
Colorado 5
Utah 9
New York 13
Name: two, dtype: int64

In [131]: data[['three', 'one']]
Out[131]:
three one
Ohio 2 0
Colorado 6 4
Utah 10 8
New York 14 12

In [133]: data[data['three'] > 5]
Out[133]:
one two three four
Colorado 4 5 6 7
Utah 8 9 10 11
New York 12 13 14 15

In [132]: data[:2]
Out[132]:
one two three four
Ohio 0 1 2 3
Colorado 4 5 6 7
In [133]: data[data['three'] > 5]
Out[133]:
one two three four
Colorado 4 5 6 7
Utah 8 9 10 11
New York 12 13 14 15

In [134]: data < 5
Out[134]:
one two three four
Ohio True True True True
Colorado True False False False
Utah False False False False
New York False False False False
In [135]: data[data < 5] = 0
In [136]: data
Out[136]:
one two three four
Ohio 0 0 0 0
Colorado 0 5 6 7
Utah 8 9 10 11
New York 12 13 14 15

In [137]: data.loc['Colorado', ['two', 'three']]
Out[137]:
two 5
three 6
Name: Colorado, dtype: int64

In [138]: data.iloc[2, [3, 0, 1]]
Out[138]:
four 11
one 8
two 9
Name: Utah, dtype: int64
In [139]: data.iloc[2]
Out[139]:
one 8
two 9
three 10
four 11
Name: Utah, dtype: int64

In [140]: data.iloc[[1, 2], [3, 0, 1]]
Out[140]:
four one two
Colorado 7 0 5
Utah 11 8 9

In [141]: data.loc[:'Utah', 'two']
Out[141]:
Ohio 0
Colorado 5
Utah 9
Name: two, dtype: int64
In [142]: data.iloc[:, :3][data.three > 5]
Out[142]:
one two three
Colorado 0 5 6
Utah 8 9 10
New York 12 13 14