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pandas基础使用
Series是一种类似于一维数组的对象，它由一组数据（各种NumPy数据类型）以及一组与之相关的数据标签（即索引）组成。仅由一组数据即可产生最简单的Series：
In [11]: obj = pd.Series([4, 7, -5, 3]) In [12]: obj Out[12]: 0 4 1 7 2 -5 3 3 dtype: int64 In [13]: obj.values Out[13]: array([ 4, 7, -5, 3]) In [14]: obj.index # like range(4) Out[14]: RangeIndex(start=0, stop=4, step=1) In [15]: obj2 = pd.Series([4, 7, -5, 3], index=['d', 'b', 'a', 'c']) In [16]: obj2 Out[16]: d 4 b 7 a -5 c 3 dtype: int64 In [17]: obj2.index Out[17]: Index(['d', 'b', 'a', 'c'], dtype='object') In [18]: obj2['a'] Out[18]: -5 In [19]: obj2['d'] = 6 In [20]: obj2[['c', 'a', 'd']] Out[20]: c 3 a -5 d 6 dtype: int64 In [21]: obj2[obj2 > 0] Out[21]: d 6 b 7 c 3 dtype: int64 In [22]: obj2 * 2 Out[22]: d 12 b 14 a -10 c 6 dtype: int64 In [23]: np.exp(obj2) Out[23]: d 403.428793 b 1096.633158 a 0.006738 c 20.085537 dtype: float64 In [24]: 'b' in obj2 Out[24]: True In [25]: 'e' in obj2 Out[25]: False
pandas的isnull和notnull函数可用于检测缺失数据：
In [32]: pd.isnull(obj4) Out[32]: California True Ohio False Oregon False Texas False dtype: bool In [33]: pd.notnull(obj4) Out[33]: California False Ohio True Oregon True Texas True dtype: bool In [34]: obj4.isnull()
DataFrame是一个表格型的数据结构，它含有一组有序的列，每列可以是不同的值类型（数值、字符串、布尔值等）。DataFrame既有行索引也有列索引，它可以被看做由Series组成的字典（共用同一个索引）。DataFrame中的数据是以一个或多个二维块存放的（而不是列表、字典或别的一维数据结构）。
data = {'state': ['Ohio', 'Ohio', 'Ohio', 'Nevada', 'Nevada', 'Nevada'],
'year': [2000, 2001, 2002, 2001, 2002, 2003],
'pop': [1.5, 1.7, 3.6, 2.4, 2.9, 3.2]}
frame = pd.DataFrame(data)
In [45]: frame
Out[45]:
pop state year
0 1.5 Ohio 2000
1 1.7 Ohio 2001
2 3.6 Ohio 2002
3 2.4 Nevada 2001
4 2.9 Nevada 2002
5 3.2 Nevada 2003
head方法会选取前五行：
In [46]: frame.head() Out[46]: pop state year 0 1.5 Ohio 2000 1 1.7 Ohio 2001 2 3.6 Ohio 2002 3 2.4 Nevada 2001 4 2.9 Nevada 2002
如果传入的列在数据中找不到，就会在结果中产生缺失值：
In [48]: frame2 = pd.DataFrame(data, columns=['year', 'state', 'pop', 'debt'],
....: index=['one', 'two', 'three', 'four',
....: 'five', 'six'])
In [49]: frame2
Out[49]:
year state pop debt
one 2000 Ohio 1.5 NaN
two 2001 Ohio 1.7 NaN
three 2002 Ohio 3.6 NaN
four 2001 Nevada 2.4 NaN
five 2002 Nevada 2.9 NaN
six 2003 Nevada 3.2 NaN
In [50]: frame2.columns
Out[50]: Index(['year', 'state', 'pop', 'debt'], dtype='object')
行也可以通过位置或名称的方式进行获取，比如用loc属性
In [53]: frame2.loc['three'] Out[53]: year 2002 state Ohio pop 3.6 debt NaN Name: three, dtype: object
列可以通过赋值的方式进行修改
In [54]: frame2['debt'] = 16.5
In [55]: frame2
Out[55]:
year state pop debt
one 2000 Ohio 1.5 16.5
two 2001 Ohio 1.7 16.5
three 2002 Ohio 3.6 16.5
four 2001 Nevada 2.4 16.5
five 2002 Nevada 2.9 16.5
six 2003 Nevada 3.2 16.5
In [56]: frame2['debt'] = np.arange(6.)
In [57]: frame2
Out[57]:
year state pop debt
one 2000 Ohio 1.5 0.0
two 2001 Ohio 1.7 1.0
three 2002 Ohio 3.6 2.0
four 2001 Nevada 2.4 3.0
five 2002 Nevada 2.9 4.0
six 2003 Nevada 3.2 5.0
丢弃某条轴上的一个或多个项很简单，只要有一个索引数组或列表即可。由于需要执行一些数据整理和集合逻辑，所以drop方法返回的是一个在指定轴上删除了指定值的新对象：
In [105]: obj = pd.Series(np.arange(5.), index=['a', 'b', 'c', 'd', 'e'])
In [106]: obj
Out[106]:
a 0.0
b 1.0
c 2.0
d 3.0
e 4.0
dtype: float64
In [107]: new_obj = obj.drop('c')
In [108]: new_obj
Out[108]:
a 0.0
b 1.0
d 3.0
e 4.0
dtype: float64
In [109]: obj.drop(['d', 'c'])
Out[109]:
a 0.0
b 1.0
e 4.0
dtype: float64
对于DataFrame，可以删除任意轴上的索引值。
In [110]: data = pd.DataFrame(np.arange(16).reshape((4, 4)),
.....: index=['Ohio', 'Colorado', 'Utah', 'New York'],
.....: columns=['one', 'two', 'three', 'four'])
In [111]: data
Out[111]:
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
用标签序列调用drop会从行标签（axis 0）删除值：
In [112]: data.drop(['Colorado', 'Ohio'])
Out[112]:
one two three four
Utah 8 9 10 11
New York 12 13 14 15
通过传递axis=1或axis='columns'可以删除列的值：
In [113]: data.drop('two', axis=1)
Out[113]:
one three four
Ohio 0 2 3
Colorado 4 6 7
Utah 8 10 11
New York 12 14 15
In [114]: data.drop(['two', 'four'], axis='columns')
Out[114]:
one three
Ohio 0 2
Colorado 4 6
Utah 8 10
New York 12 14
索引、选取和过滤
Series索引（obj[...]）的工作方式类似于NumPy数组的索引，只不过Series的索引值不只是整数。下面是几个例子：
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 In [120]: obj[1] Out[120]: 1.0 In [121]: obj[2:4] Out[121]: c 2.0 d 3.0 dtype: float64 In [122]: obj[['b', 'a', 'd']] Out[122]: b 1.0 a 0.0 d 3.0 dtype: float64 In [123]: obj[[1, 3]] Out[123]: b 1.0 d 3.0 dtype: float64 In [124]: obj[obj < 2] Out[124]: a 0.0 b 1.0 dtype: float64
用一个值或序列对DataFrame进行索引其实就是获取一个或多个列：
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
这种索引方式有几个特殊的情况。首先通过切片或布尔型数组选取数据： 选取行的语法data[:2]十分方便。向[ ]传递单一的元素或列表，就可选择列。
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
另一种用法是通过布尔型DataFrame（比如下面这个由标量比较运算得出的）进行索引：
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
用loc和iloc进行选取
使用轴标签（loc）或整数索引（iloc），从DataFrame选择行和列的子集。
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
