DataFrame 类型类似于数据库表结构的数据结构，其含有行索引和列索引，可以将DataFrame 想成是由相同索引的Series组成的Dict类型。在其底层是通过二维以及一维的数据块实现。
1. DataFrame 对象的构建
1.1 用包含等长的列表或者是NumPy数组的字典创建DataFrame对象
In [68]: import pandas as pd In [69]: from pandas import Series,DataFrame
# 建立包含等长列表的字典类型
In [70]: data = {'state': ['Ohio', 'Ohio', 'Ohio', 'Nevada', 'Nevada'],'year': [2000, 2001, 20
...: 02, 2001, 2002],'pop': [1.5, 1.7, 3.6, 2.4, 2.9]}
In [71]: data
Out[71]:
{'pop': [1.5, 1.7, 3.6, 2.4, 2.9],
'state': ['Ohio', 'Ohio', 'Ohio', 'Nevada', 'Nevada'],
'year': [2000, 2001, 2002, 2001, 2002]}
# 建立DataFrame对象
In [72]: frame1 = DataFrame(data)
# 红色部分为自动生成的索引
In [73]: frame1
Out[73]:
pop state year
0 1.5 Ohio 2000
1.7 Ohio 2001
3.6 Ohio 2002
2.4 Nevada 2001
2.9 Nevada 2002
在建立过程中可以指点列的顺序：
In [74]: frame1 = DataFrame(data,columns=['year', 'state', 'pop']) In [75]: frame1
Out[75]:
year state pop
0 2000 Ohio 1.5
1 2001 Ohio 1.7
2 2002 Ohio 3.6
3 2001 Nevada 2.4
4 2002 Nevada 2.9
和Series一样，DataFrame也是可以指定索引内容：
In [76]: ind = ['one', 'two', 'three', 'four', 'five']
In [77]: frame1 = DataFrame(data,index = ind) In [78]: frame1
Out[78]:
pop state year
one 1.5 Ohio 2000
two 1.7 Ohio 2001
three 3.6 Ohio 2002
four 2.4 Nevada 2001
five 2.9 Nevada 2002
1.2. 用由字典类型组成的嵌套字典类型来生成DataFrame对象
当由嵌套的字典类型生成DataFrame的时候，外部的字典索引会成为列名，内部的字典索引会成为行名。生成的DataFrame会根据行索引排序
In [84]: pop = {'Nevada': {2001: 2.4, 2002: 2.9},'Ohio': {2000: 1.5, 2001: 1.7, 2002: 3.6}}
In [85]: frame3 = DataFrame(pop)
In [86]: frame3
Out[86]:
Nevada Ohio
2000 NaN 1.5
2001 2.4 1.7
2002 2.9 3.6
除了使用默认的按照行索引排序之外，还可以指定行序列：
In [95]: frame3 = DataFrame(pop,[2002,2001,2000]) In [96]: frame3
Out[96]:
Nevada Ohio
2002 2.9 3.6
2001 2.4 1.7
2000 NaN 1.5
1.3 其它构造方法：
2. DataFrame 内容访问
从DataFrame中获取一列的结果为一个Series，可以通过以下两种方式获取：
# 以字典索引方式获取
In [100]: frame1["state"]
Out[100]:
one Ohio
two Ohio
three Ohio
four Nevada
five Nevada
Name: state, dtype: object
# 以属性方式获取
In [101]: frame1.state
Out[101]:
one Ohio
two Ohio
three Ohio
four Nevada
five Nevada
Name: state, dtype: object
也可以通过ix获取一行数据：
In [109]: frame1.ix["one"] # 或者是 frame1.ix[0]
Out[109]:
pop 1.5
state Ohio
year 2000
Name: one, dtype: object
# 获取多行数据
In [110]: frame1.ix[["tow","three","four"]]
Out[110]:
pop state year
tow NaN NaN NaN
three 3.6 Ohio 2002.0
four 2.4 Nevada 2001.0
# 还可以通过默认数字行索引来获取数据
In [111]: frame1.ix[range(3)]
Out[111]:
pop state year
one 1.5 Ohio 2000
two 1.7 Ohio 2001
three 3.6 Ohio 2002
获取指定行，指定列的交汇值：
In [119]: frame1["state"]
Out[119]:
one Ohio
two Ohio
three Ohio
four Nevada
five Nevada
Name: state, dtype: object In [120]: frame1["state"][0]
Out[120]: 'Ohio' In [121]: frame1["state"]["one"]
Out[121]: 'Ohio'
先指定列再指定行：
In [125]: frame1.ix[0]
Out[125]:
pop 1.5
state Ohio
year 2000
Name: one, dtype: object In [126]: frame1.ix[0]["state"]
Out[126]: 'Ohio' In [127]: frame1.ix["one"]["state"]
Out[127]: 'Ohio' In [128]: frame1.ix["one"][0]
Out[128]: 1.5 In [129]: frame1.ix[0][0]
Out[129]: 1.5
3. DataFrame 对象的修改
增加一列，并所有赋值为同一个值：
# 增加一列值
In [131]: frame1["debt"] = 10 In [132]: frame1
Out[132]:
pop state year debt
one 1.5 Ohio 2000 10
two 1.7 Ohio 2001 10
three 3.6 Ohio 2002 10
four 2.4 Nevada 2001 10
five 2.9 Nevada 2002 10
# 更改一列的值
In [133]: frame1["debt"] = np.arange(5) In [134]: frame1
Out[134]:
pop state year debt
one 1.5 Ohio 2000 0
two 1.7 Ohio 2001 1
three 3.6 Ohio 2002 2
four 2.4 Nevada 2001 3
five 2.9 Nevada 2002 4
追加类型为Series的一列
# 判断是否为东部区
In [137]: east = (frame1.state == "Ohio") In [138]: east
Out[138]:
one True
two True
three True
four False
five False
Name: state, dtype: bool
# 赋Series值
In [139]: frame1["east"] = east In [140]: frame1
Out[140]:
pop state year debt east
one 1.5 Ohio 2000 0 True
two 1.7 Ohio 2001 1 True
three 3.6 Ohio 2002 2 True
four 2.4 Nevada 2001 3 False
five 2.9 Nevada 2002 4 False
DataFrame 的行可以命名，同时多列也可以命名：
In [145]: frame3.columns.name = "state" In [146]: frame3.index.name = "year" In [147]: frame3
Out[147]:
state Nevada Ohio
year
2002 2.9 3.6
2001 2.4 1.7
2000 NaN 1.5