In [1]: import numpy as np
In [2]: import pandas as pd
Series
Series是一维带label和index的数组。我们使用下面的方法来创建一个Series：
>>> s = pd.Series(data, index=index)
这里的data可以是Python的字典，np的ndarray，或者一个标量。
index是一个横轴label的list。接下来我们分别来看下怎么创建Series。
从ndarray创建
s = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e'])
s
Out[67]:
a -1.300797
b -2.044172
c -1.170739
d -0.445290
e 1.208784
dtype: float64
s[0]
Out[72]: -1.3007972194268396
s[:3]
Out[73]:
a -1.300797
b -2.044172
c -1.170739
dtype: float64
s[s > s.median()]
Out[74]:
d -0.445290
e 1.208784
dtype: float64
s[[4, 3, 1]]
Out[75]:
e 1.208784
d -0.445290
b -2.044172
dtype: float64
Series和dict
如果使用label来访问Series，那么它的表现就和dict很像：
s['a']
Out[80]: -1.3007972194268396
s['e'] = 12.
s
Out[82]:
a -1.300797
b -2.044172
c -1.170739
d -0.445290
e 12.000000
dtype: float64
矢量化操作和标签对齐
Series可以使用更加简单的矢量化操作：
s + s
Out[83]:
a -2.601594
b -4.088344
c -2.341477
d -0.890581
e 24.000000
dtype: float64
s * 2
Out[84]:
a -2.601594
b -4.088344
c -2.341477
d -0.890581
e 24.000000
dtype: float64
np.exp(s)
Out[85]:
a 0.272315
b 0.129487
c 0.310138
d 0.640638
e 162754.791419
dtype: float64
Name属性
Series还有一个name属性，我们可以在创建的时候进行设置：
s = pd.Series(np.random.randn(5), name='something')
s
Out[88]:
0 0.192272
1 0.110410
2 1.442358
3 -0.375792
4 1.228111
Name: something, dtype: float64
d = {'one': pd.Series([1., 2., 3.], index=['a', 'b', 'c']),'two': pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])}
df = pd.DataFrame(d)
df
Out[92]:
one two
a 1.0 1.0
b 2.0 2.0
c 3.0 3.0
d NaN 4.0
进行index重排：
pd.DataFrame(d, index=['d', 'b', 'a'])
Out[93]:
one two
d NaN 4.0
b 2.0 2.0
a 1.0 1.0
进行列重排：
pd.DataFrame(d, index=['d', 'b', 'a'], columns=['two', 'three'])
Out[94]:
two three
d 4.0 NaN
b 2.0 NaN
a 1.0 NaN
从ndarrays 和 lists创建
d = {'one': [1., 2., 3., 4.],'two': [4., 3., 2., 1.]}
pd.DataFrame(d)
Out[96]:
one two
0 1.0 4.0
1 2.0 3.0
2 3.0 2.0
3 4.0 1.0
pd.DataFrame(d, index=['a', 'b', 'c', 'd'])
Out[97]:
one two
a 1.0 4.0
b 2.0 3.0
c 3.0 2.0
d 4.0 1.0
从结构化数组创建
可以从结构化数组中创建DF：
In [47]: data = np.zeros((2, ), dtype=[('A', 'i4'), ('B', 'f4'), ('C', 'a10')])
In [48]: data[:] = [(1, 2., 'Hello'), (2, 3., "World")]
In [49]: pd.DataFrame(data)
Out[49]:
A B C
0 1 2.0 b'Hello'
1 2 3.0 b'World'
In [50]: pd.DataFrame(data, index=['first', 'second'])
Out[50]:
A B C
first 1 2.0 b'Hello'
second 2 3.0 b'World'
In [51]: pd.DataFrame(data, columns=['C', 'A', 'B'])
Out[51]:
C A B
0 b'Hello' 1 2.0
1 b'World' 2 3.0
从字典list创建
In [52]: data2 = [{'a': 1, 'b': 2}, {'a': 5, 'b': 10, 'c': 20}]
In [53]: pd.DataFrame(data2)
Out[53]:
a b c
0 1 2 NaN
1 5 10 20.0
In [54]: pd.DataFrame(data2, index=['first', 'second'])
Out[54]:
a b c
first 1 2 NaN
second 5 10 20.0
In [55]: pd.DataFrame(data2, columns=['a', 'b'])
Out[55]:
a b
0 1 2
1 5 10
从元组中创建
可以从元组中创建更加复杂的DF：
In [56]: pd.DataFrame({('a', 'b'): {('A', 'B'): 1, ('A', 'C'): 2},
....: ('a', 'a'): {('A', 'C'): 3, ('A', 'B'): 4},
....: ('a', 'c'): {('A', 'B'): 5, ('A', 'C'): 6},
....: ('b', 'a'): {('A', 'C'): 7, ('A', 'B'): 8},
....: ('b', 'b'): {('A', 'D'): 9, ('A', 'B'): 10}})
....:
Out[56]:
a b
b a c a b
A B 1.0 4.0 5.0 8.0 10.0
C 2.0 3.0 6.0 7.0 NaN
D NaN NaN NaN NaN 9.0
列选择，添加和删除
可以像操作Series一样操作DF：
In [64]: df['one']
Out[64]:
a 1.0
b 2.0
c 3.0
d NaN
Name: one, dtype: float64
In [65]: df['three'] = df['one'] * df['two']
In [66]: df['flag'] = df['one'] > 2
In [67]: df
Out[67]:
one two three flag
a 1.0 1.0 1.0 False
b 2.0 2.0 4.0 False
c 3.0 3.0 9.0 True
d NaN 4.0 NaN False
可以删除特定的列，或者pop操作：
In [68]: del df['two']
In [69]: three = df.pop('three')
In [70]: df
Out[70]:
one flag
a 1.0 False
b 2.0 False
c 3.0 True
d NaN False
如果插入常量，那么会填满整个列：
In [71]: df['foo'] = 'bar'
In [72]: df
Out[72]:
one flag foo
a 1.0 False bar
b 2.0 False bar
c 3.0 True bar
d NaN False bar
默认会插入到DF中最后一列，可以使用insert来指定插入到特定的列：
In [75]: df.insert(1, 'bar', df['one'])
In [76]: df
Out[76]:
one bar flag foo one_trunc
a 1.0 1.0 False bar 1.0
b 2.0 2.0 False bar 2.0
c 3.0 3.0 True bar NaN
d NaN NaN False bar NaN