总结：
1. Series是一维数据，代表了DateFrame的一行数据或一列数据。是有多个不同类型的标量构成的一行数据
2. DateFrame是二维数据，是有多行构成的，可以理解为一个表。
3. data.loc 函数用来通过表名取表中指定数据的,第一个参数是行名称，第二个参数是列名称
4. data.iloc 函数用来通过下标取表中指定数据的,第一个参数行下标，第二个参数是列下标
数据类型：
1. Series:带标签的一维同构数组
(1).用值列表生成Series对象，Pandas 默认自动生成整数索引：
s = pd.Series([1, 3, 5, np.nan, 6, 8]) # 一列数据
>>> s:
0 1.0
1 3.0
2 5.0
3 NaN
4 6.0
5 8.0
dtype: float64
2. DataFrame:带标签的，大小可变的，二维异构表格
(1. )生成DataFrame对象
# DataFrame 表格数据，包含index（行）或 columns（列）
# 用这种方式迭代 DataFrame 的列
for colum in df.columns:
series = df[colum]
# do something with series
1. 用含日期时间索引与标签的 NumPy 数组生成DataFrame:
dates = pd.date_range('20130101', periods=6)
>>>> dates:
DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',
'2013-01-05', '2013-01-06'],
dtype='datetime64[ns]', freq='D')
df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list('ABCD'))
>>>> df:
A B C D
2013-01-01 0.469112 -0.282863 -1.509059 -1.135632
2013-01-02 1.212112 -0.173215 0.119209 -1.044236
2013-01-03 -0.861849 -2.104569 -0.494929 1.071804
2013-01-04 0.721555 -0.706771 -1.039575 0.271860
2013-01-05 -0.424972 0.567020 0.276232 -1.087401
2013-01-06 -0.673690 0.113648 -1.478427 0.524988
用 Series 字典对象生成 DataFrame:
df2 = pd.DataFrame({'A': 1.,
...: 'B': pd.Timestamp('20130102'),
...: 'C': pd.Series(1, index=list(range(4)), dtype='float32'),
...: 'D': np.array([3] * 4, dtype='int32'),
...: 'E': pd.Categorical(["test", "train", "test", "train"]),
...: 'F': 'foo'})
>>>>: df2
A B C D E F
0 1.0 2013-01-02 1.0 3 test foo
1 1.0 2013-01-02 1.0 3 train foo
2 1.0 2013-01-02 1.0 3 test foo
3 1.0 2013-01-02 1.0 3 train foo
DataFrame 的列有不同数据类型：
>>>>:df2.dtypes
A float64
B datetime64[ns]
C float32
D int32
E category
F object
dtype: object
(2.)查看数据:
1. 查看 DataFrame 头部和尾部数据:
>>>> df.head()
A B C D
2013-01-01 0.469112 -0.282863 -1.509059 -1.135632
2013-01-02 1.212112 -0.173215 0.119209 -1.044236
2013-01-03 -0.861849 -2.104569 -0.494929 1.071804
2013-01-04 0.721555 -0.706771 -1.039575 0.271860
2013-01-05 -0.424972 0.567020 0.276232 -1.087401
>>>> df.tail(3)
A B C D
2013-01-04 0.721555 -0.706771 -1.039575 0.271860
2013-01-05 -0.424972 0.567020 0.276232 -1.087401
2013-01-06 -0.673690 0.113648 -1.478427 0.524988
2. 显示索引与列名:
>>> df.index
DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',
'2013-01-05', '2013-01-06'],
dtype='datetime64[ns]', freq='D')
>>>> df.columns
Index(['A', 'B', 'C', 'D'], dtype='object')
(3.) 转化为低层 NumPy 对象：输出不包含行索引和列标签
>>>> df.to_numpy()
array([[ 0.4691, -0.2829, -1.5091, -1.1356],
[ 1.2121, -0.1732, 0.1192, -1.0442],
[-0.8618, -2.1046, -0.4949, 1.0718],
[ 0.7216, -0.7068, -1.0396, 0.2719],
[-0.425 , 0.567 , 0.2762, -1.0874],
[-0.6737, 0.1136, -1.4784, 0.525 ]])
>>>> df2.to_numpy()
array([[1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'test', 'foo'],
[1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'train', 'foo'],
[1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'test', 'foo'],
[1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'train', 'foo']], dtype=object)
(3.)摘要:
// 快速查看数据的统计
>>>> df.describe()
A B C D
count 6.000000 6.000000 6.000000 6.000000
mean 0.073711 -0.431125 -0.687758 -0.233103
std 0.843157 0.922818 0.779887 0.973118
min -0.861849 -2.104569 -1.509059 -1.135632
25% -0.611510 -0.600794 -1.368714 -1.076610
50% 0.022070 -0.228039 -0.767252 -0.386188
75% 0.658444 0.041933 -0.034326 0.461706
max 1.212112 0.567020 0.276232 1.071804
// 转置数据：
>>>> df.T
2013-01-01 2013-01-02 2013-01-03 2013-01-04 2013-01-05 2013-01-06
A 0.469112 1.212112 -0.861849 0.721555 -0.424972 -0.673690
B -0.282863 -0.173215 -2.104569 -0.706771 0.567020 0.113648
C -1.509059 0.119209 -0.494929 -1.039575 0.276232 -1.478427
D -1.135632 -1.044236 1.071804 0.271860 -1.087401 0.524988
// 按轴排序：
>>>> df.sort_index(axis=1, ascending=False)
D C B A
2013-01-01 -1.135632 -1.509059 -0.282863 0.469112
2013-01-02 -1.044236 0.119209 -0.173215 1.212112
2013-01-03 1.071804 -0.494929 -2.104569 -0.861849
2013-01-04 0.271860 -1.039575 -0.706771 0.721555
2013-01-05 -1.087401 0.276232 0.567020 -0.424972
2013-01-06 0.524988 -1.478427 0.113648 -0.673690
// 按值排序:
>>>> df.sort_values(by='B')
A B C D
2013-01-03 -0.861849 -2.104569 -0.494929 1.071804
2013-01-04 0.721555 -0.706771 -1.039575 0.271860
2013-01-01 0.469112 -0.282863 -1.509059 -1.135632
2013-01-02 1.212112 -0.173215 0.119209 -1.044236
2013-01-06 -0.673690 0.113648 -1.478427 0.524988
2013-01-05 -0.424972 0.567020 0.276232 -1.087401
(4.)数据访问:.at、.iat、.loc 和 .iloc
// 获取数据:
>>>> df['A']
2013-01-01 0.469112
2013-01-02 1.212112
2013-01-03 -0.861849
2013-01-04 0.721555
2013-01-05 -0.424972
2013-01-06 -0.673690
// 用 [ ] 切片行:
>>>> df[0:3]
A B C D
2013-01-01 0.469112 -0.282863 -1.509059 -1.135632
2013-01-02 1.212112 -0.173215 0.119209 -1.044236
2013-01-03 -0.861849 -2.104569 -0.494929 1.071804
>>>> df['20130102':'20130104']
A B C D
2013-01-02 1.212112 -0.173215 0.119209 -1.044236
2013-01-03 -0.861849 -2.104569 -0.494929 1.071804
2013-01-04 0.721555 -0.706771 -1.039575 0.271860
// 用标签提取一行数据:返回了Series
>>>> df.loc[dates[0]]
A 0.469112
B -0.282863
C -1.509059
D -1.135632
Name: 2013-01-01 00:00:00, dtype: float64
// 用标签选择多列数据：
>>>> df.loc[:, ['A', 'B']]
A B
2013-01-01 0.469112 -0.282863
2013-01-02 1.212112 -0.173215
2013-01-03 -0.861849 -2.104569
2013-01-04 0.721555 -0.706771
2013-01-05 -0.424972 0.567020
2013-01-06 -0.673690 0.113648
// 标签切片，包含行与列结束点:
>>>> df.loc['20130102':'20130104', ['A', 'B']]
A B
2013-01-02 1.212112 -0.173215
2013-01-03 -0.861849 -2.104569
2013-01-04 0.721555 -0.706771
// 对象降维: DataFrame返回了Series(一行数据)
>>>> df.loc['20130102', ['A', 'B']]
A 1.212112
B -0.173215
Name: 2013-01-02 00:00:00, dtype: float64
// 提取标量值：返回一个横纵坐标下准确标量值
>>>> df.loc[dates[0], 'A']
0.46911229990718628
# 快速访问标量，与上述方法等效：
>>>> df.at[dates[0], 'A']
0.46911229990718628
// 按位置选择:
>>>> df.iloc[3]
A 0.721555
B -0.706771
C -1.039575
D 0.271860
Name: 2013-01-04 00:00:00, dtype: float64
>>>> df.iloc[3:5, 0:2] # 意思是返回index从3到5(不包含5) colum从0到2（不包含2）的DateFrame
A B
2013-01-04 0.721555 -0.706771
2013-01-05 -0.424972 0.567020
>>>> df.iloc[[1, 2, 4], [0, 2]] # 意思是返回index = 1 and 2 and 4 colum = 0 and 2 的DateFrame
A C
2013-01-02 1.212112 0.119209
2013-01-03 -0.861849 -0.494929
2013-01-05 -0.424972 0.276232
>>>> df.iloc[1:3, :] # 意思是返回index从1到3(不包含3)的所有列的DateFrame
A B C D
2013-01-02 1.212112 -0.173215 0.119209 -1.044236
2013-01-03 -0.861849 -2.104569 -0.494929 1.071804
>>>> df.iloc[:, 1:3] # 意思是返回colum从1到3(不包含3)的所有行的DateFrame
B C
2013-01-01 -0.282863 -1.509059
2013-01-02 -0.173215 0.119209
2013-01-03 -2.104569 -0.494929
2013-01-04 -0.706771 -1.039575
2013-01-05 0.567020 0.276232
2013-01-06 0.113648 -1.478427
// 提取标量值：返回一个横纵坐标下准确标量值
>>>> df.iloc[1, 1]
-0.17321464905330858
# 快速访问标量，与上述方法等效：
>>>> df.iat[1, 1]
-0.17321464905330858
布尔索引:
// 单列的值选择数据:
>>>> df[df.A > 0] # 筛选'A' 这一列大于0的数据然后返回DateFrame
A B C D
2013-01-01 0.469112 -0.282863 -1.509059 -1.135632
2013-01-02 1.212112 -0.173215 0.119209 -1.044236
2013-01-04 0.721555 -0.706771 -1.039575 0.271860
>>>> df[df > 0] # 筛选出标中的正数
A B C D
2013-01-01 0.469112 NaN NaN NaN
2013-01-02 1.212112 NaN 0.119209 NaN
2013-01-03 NaN NaN NaN 1.071804
2013-01-04 0.721555 NaN NaN 0.271860
2013-01-05 NaN 0.567020 0.276232 NaN
2013-01-06 NaN 0.113648 NaN 0.524988
// 用 isin筛选：
>>>> df2 = df.copy()
>>>> df2['E'] = ['one', 'one', 'two', 'three', 'four', 'three']
>>>> df2
A B C D E
2013-01-01 0.469112 -0.282863 -1.509059 -1.135632 one
2013-01-02 1.212112 -0.173215 0.119209 -1.044236 one
2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 two
2013-01-04 0.721555 -0.706771 -1.039575 0.271860 three
2013-01-05 -0.424972 0.567020 0.276232 -1.087401 four
2013-01-06 -0.673690 0.113648 -1.478427 0.524988 three
>>>> df2[df2['E'].isin(['two', 'four'])] # 筛选df2这个表中名为‘E’的这一列中值在['two', 'four']中的Series然后组成新的DataFrame返回
A B C D E
2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 two
2013-01-05 -0.424972 0.567020 0.276232 -1.087401 four
(5.) 赋值
// 用位置赋值
>>>> s1 = pd.Series([1, 2, 3, 4, 5, 6], index=pd.date_range('20130102', periods=6)) # 创建一个标量为[1, 2, 3, 4, 5, 6]，多行一列的一组数据，s1 就是一个新增列数据
2013-01-02 1
2013-01-03 2
2013-01-04 3
2013-01-05 4
2013-01-06 5
2013-01-07 6
Freq: D, dtype: int64
>>>> df['F'] = s1 # 意思是名为df的dataFrame表，新增一列，列名为'F'的数据。
A B C D F
2013-01-01 -0.861849 -2.104569 -1.509059 -1.135632 NaN
2013-01-02 1.212112 -0.173215 0.119209 -1.044236 1.0
2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 2.0
2013-01-04 0.721555 -0.706771 -1.039575 0.271860 3.0
2013-01-05 -0.424972 0.567020 0.276232 -1.087401 4.0
2013-01-06 -0.673690 0.113648 -1.478427 0.524988 5.0
>>>> df.at[dates[0], 'A'] = 0 # 改变的是上面的 2013-01-01 'A' 对应的值-0.861849
>>>> df.iat[0, 1] = 0 # 改变的是上面的 2013-01-01 'B' 对应的值-2.104569
>>>> df.loc[:, 'D'] = np.array([5] * len(df)) # 按 NumPy 数组赋值，改变的是‘D’这一列， len(df)就是所有行，改成5
>>>> df
A B C D F
2013-01-01 0.000000 0.000000 -1.509059 5 NaN
2013-01-02 1.212112 -0.173215 0.119209 5 1.0
2013-01-03 -0.861849 -2.104569 -0.494929 5 2.0
2013-01-04 0.721555 -0.706771 -1.039575 5 3.0
2013-01-05 -0.424972 0.567020 0.276232 5 4.0
2013-01-06 -0.673690 0.113648 -1.478427 5 5.0
// 条件赋值：
>>>> df2 = df.copy()
>>>> df2[df2 > 0] = -df2 # df2中所有正数都变成负数
>>>> df2
A B C D F
2013-01-01 0.000000 0.000000 -1.509059 -5 NaN
2013-01-02 -1.212112 -0.173215 -0.119209 -5 -1.0
2013-01-03 -0.861849 -2.104569 -0.494929 -5 -2.0
2013-01-04 -0.721555 -0.706771 -1.039575 -5 -3.0
2013-01-05 -0.424972 -0.567020 -0.276232 -5 -4.0
2013-01-06 -0.673690 -0.113648 -1.478427 -5 -5.0
(6.)缺失值:np.nan 表示缺失数据
>>>> df1 = df.reindex(index=dates[0:4], columns=list(df.columns) + ['E'])
>>>> df1.loc[dates[0]:dates[1], 'E'] = 1
>>>> df1
A B C D F E
2013-01-01 0.000000 0.000000 -1.509059 5 NaN 1.0
2013-01-02 1.212112 -0.173215 0.119209 5 1.0 1.0
2013-01-03 -0.861849 -2.104569 -0.494929 5 2.0 NaN
2013-01-04 0.721555 -0.706771 -1.039575 5 3.0 NaN
// 删除所有含缺失值的行：
>>>> df1.dropna(how='any')
A B C D F E
2013-01-02 1.212112 -0.173215 0.119209 5 1.0 1.0
// 填充缺失值：
>>>> df1.fillna(value=5)
A B C D F E
2013-01-01 0.000000 0.000000 -1.509059 5 5.0 1.0
2013-01-02 1.212112 -0.173215 0.119209 5 1.0 1.0
2013-01-03 -0.861849 -2.104569 -0.494929 5 2.0 5.0
2013-01-04 0.721555 -0.706771 -1.039575 5 3.0 5.0
// 提取 nan 值的布尔掩码：
>>>> pd.isna(df1)
A B C D F E
2013-01-01 False False False False True False
2013-01-02 False False False False False False
2013-01-03 False False False False False True
2013-01-04 False False False False False True
(7.)统计
// 描述性统计
>>>> df.mean() # 纵轴统计
A -0.004474
B -0.383981
C -0.687758
D 5.000000
F 3.000000
dtype: float64
>>>> df.mean(1) #另一个轴(即，行)上执行同样的操作
2013-01-01 0.872735
2013-01-02 1.431621
2013-01-03 0.707731
2013-01-04 1.395042
2013-01-05 1.883656
2013-01-06 1.592306
Freq: D, dtype: float64
// 不同维度对象运算时，要先对齐， 此外，Pandas 自动沿指定维度广播。
>>>> s = pd.Series([1, 3, 5, np.nan, 6, 8], index=dates).shift(2) # shift(2)： 偏移2行对齐
2013-01-01 NaN
2013-01-02 NaN
2013-01-03 1.0
2013-01-04 3.0
2013-01-05 5.0
2013-01-06 NaN
Freq: D, dtype: float64
>>>> df.sub(s, axis='index') # 啥意思是按列运算，NaN+ 标量 = NaN，按列为组运算，index相同的做和,返回一个新的dataframe，不会改变df，只是用df运算。
A B C D F
2013-01-01 NaN NaN NaN NaN NaN
2013-01-02 NaN NaN NaN NaN NaN
2013-01-03 -1.861849 -3.104569 -1.494929 4.0 1.0 # -2.861849+1 = -1.861849
2013-01-04 -2.278445 -3.706771 -4.039575 2.0 0.0
2013-01-05 -5.424972 -4.432980 -4.723768 0.0 -1.0
2013-01-06 NaN NaN NaN NaN NaN
// Apply 函数处理数据
>>>> df.apply(np.cumsum) # np.cumsum 是一个函数，列累加的函数,apply和.sub一样,返回一个新的dataframe，不会改变df，只是用df运算。
A B C D F
2013-01-01 0.000000 0.000000 -1.509059 5 NaN # 0.000000 = df.iat[0,0]
2013-01-02 1.212112 -0.173215 -1.389850 10 1.0 # 1.212112 = df.iat[0,0] + df.iat[0,1]
2013-01-03 0.350263 -2.277784 -1.884779 15 3.0 # -0.350263 = df.iat[0,1] + df.iat[0,2]
2013-01-04 1.071818 -2.984555 -2.924354 20 6.0 # ...
2013-01-05 0.646846 -2.417535 -2.648122 25 10.0 # ...
2013-01-06 -0.026844 -2.303886 -4.126549 30 15.0 # ...
>>>> df.apply(lambda x: x.max() - x.min()) # 先找到df每列的最大值和最小值，然后做差,然后返回一个Series，列统计。
A 2.073961 # 1.212112 - (-0.861849) = 2.073961
B 2.671590
C 1.785291
D 0.000000
F 4.000000
dtype: float64
（8.）直方图
>>>> test = np.random.randint(0, 7, size=10)
>>>> test
array([4, 2, 1, 2, 6, 4, 4, 6, 4, 4])
>>>>s = pd.Series(test)
0 4
1 2
2 1
3 2
4 6
5 4
6 4
7 6
8 4
9 4
dtype: int64
>>>> s.value_counts() # 4 出现了5次 6 出现了2次 。。。。。
4 5
6 2
2 2
1 1
dtype: int64
（9.）字符串方法：Series 的 str 属性包含一组字符串处理功能
>>>> s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])
>>>> s.str.lower()
0 a
1 b
2 c
3 aaba
4 baca
5 NaN
6 caba
7 dog
8 cat
dtype: object
（10.）合并
>>> test = np.random.randn(10, 4)
>>> test
array([[-0.81741352, 1.84903603, 0.49850415, 0.44634968],
[-0.36911923, -0.62801459, 1.17384241, 0.10961811],
[-2.02391694, -1.13128739, 0.23789725, -0.34718204],
[ 0.48488279, -1.99957237, -0.11090257, -0.00836397],
[ 1.12524972, -1.06405041, 0.23928546, 1.49336223],
[-0.72348469, -0.47378583, -0.34307547, -0.35879956],
[-1.34686762, 1.24929498, 0.66233861, 0.262106 ],
[-0.38460465, -0.02032119, -1.16112247, 0.11717697],
[-0.22309295, -0.14498124, 0.01653987, -0.92529958],
[ 0.78572275, 1.85298325, -0.85724784, 0.08748715]])
>>> df = pd.DataFrame(test)
>>> df
0 1 2 3
0 -0.817414 1.849036 0.498504 0.446350
1 -0.369119 -0.628015 1.173842 0.109618
2 -2.023917 -1.131287 0.237897 -0.347182
3 0.484883 -1.999572 -0.110903 -0.008364
4 1.125250 -1.064050 0.239285 1.493362
5 -0.723485 -0.473786 -0.343075 -0.358800
6 -1.346868 1.249295 0.662339 0.262106
7 -0.384605 -0.020321 -1.161122 0.117177
8 -0.223093 -0.144981 0.016540 -0.925300
9 0.785723 1.852983 -0.857248 0.087487
>>>> pieces = [df[:3], df[3:7], df[7:]]# 分解为多组，从第0行到第3行（不包括3），。。。pieces是dataframe数组
>>>> pd.concat(pieces)
0 1 2 3
0 -0.817414 1.849036 0.498504 0.446350
1 -0.369119 -0.628015 1.173842 0.109618
2 -2.023917 -1.131287 0.237897 -0.347182
3 0.484883 -1.999572 -0.110903 -0.008364
4 1.125250 -1.064050 0.239285 1.493362
5 -0.723485 -0.473786 -0.343075 -0.358800
6 -1.346868 1.249295 0.662339 0.262106
7 -0.384605 -0.020321 -1.161122 0.117177
8 -0.223093 -0.144981 0.016540 -0.925300
9 0.785723 1.852983 -0.857248 0.087487
(11.)join连接 ：SQL 风格的合并
>>>> left = pd.DataFrame({'key': ['foo', 'foo'], 'lval': [1, 2]})
>>>> right = pd.DataFrame({'key': ['foo', 'foo'], 'rval': [4, 5]})
>>> left
key lval
0 foo 1
1 foo 2
>>> right
key rval
0 foo 4
1 foo 5
>>>> pd.merge(left, right, on='key') # 14 15 24 25
key lval rval
0 foo 1 4
1 foo 1 5
2 foo 2 4
3 foo 2 5
>>>> left = pd.DataFrame({'key': ['foo', 'bar'], 'lval': [1, 2]})
>>>> right = pd.DataFrame({'key': ['foo', 'bar'], 'rval': [4, 5]})
>>>> left
key lval
0 foo 1
1 bar 2
>>>> right
key rval
0 foo 4
1 bar 5
>>>> pd.merge(left, right, on='key')
key lval rval
0 foo 1 4
1 bar 2 5
（12.）Append 追加：
>>> df = pd.DataFrame(np.random.randn(8, 4), columns=['A', 'B', 'C', 'D'])
>>> s = df.iloc[3] # index = 3 的一行数据，一个Series
>>> df.append(s, ignore_index=True) # 添加了一条数据(index = 8的那条)，index = 3 和 index = 8
A B C D
0 1.346061 1.511763 1.627081 -0.990582
1 -0.441652 1.211526 0.268520 0.024580
2 -1.577585 0.396823 -0.105381 -0.532532
3 1.453749 1.208843 -0.080952 -0.264610
4 -0.727965 -0.589346 0.339969 -0.693205
5 -0.339355 0.593616 0.884345 1.591431
6 0.141809 0.220390 0.435589 0.192451
7 -0.096701 0.803351 1.715071 -0.708758
8 1.453749 1.208843 -0.080952 -0.264610
（13.）分组（Grouping）
>>> df = pd.DataFrame({'A': ['foo', 'bar', 'foo', 'bar',
....: 'foo', 'bar', 'foo', 'foo'],
....: 'B': ['one', 'one', 'two', 'three',
....: 'two', 'two', 'one', 'three'],
....: 'C': np.random.randn(8),
....: 'D': np.random.randn(8)})
A B C D
0 foo one -1.202872 -0.055224
1 bar one -1.814470 2.395985
2 foo two 1.018601 1.552825
3 bar three -0.595447 0.166599
4 foo two 1.395433 0.047609
5 bar two -0.392670 -0.136473
6 foo one 0.007207 -0.561757
7 foo three 1.928123 -1.623033
>>>> df.groupby('A').sum() # 根据'A'分组，然后用sum计算值，返回新的dataframe
C D
A
bar -2.802588 2.42611
foo 3.146492 -0.63958
>>>> df.groupby(['A', 'B']).sum() # 多列分组后,根据'A'and 'B' 分组，然后用sum计算值，生成多层索引，返回新的dataframe
C D
A B
bar one -1.814470 2.395985
three -0.595447 0.166599
two -0.392670 -0.136473
foo one -1.195665 -0.616981
three 1.928123 -1.623033
two 2.414034 1.600434
(14.)重塑（Reshaping） + 数据透视表（Pivot Tables）+ 堆叠（Stack）: 。。。不看了不看了，感觉用不上啊（https://www.pypandas.cn/docs/getting_started/10min.html#%E9%87%8D%E5%A1%91-reshaping）
(16.) 时间序列:
>>> rng = pd.date_range('1/1/2012', periods=100, freq='S')
>>> ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng)
>>> ts.resample('5Min').sum() # 意思是把 将秒级的数据转换为 5 分钟为频率的数据,用sum()把数据合并
2012-01-01 25083
Freq: 5T, dtype: int64
>>> rng = pd.date_range('3/6/2012 00:00', periods=5, freq='D')
>>> ts = pd.Series(np.random.randn(len(rng)), rng)
>>> ts
2012-03-06 0.464000
2012-03-07 0.227371
2012-03-08 -0.496922
2012-03-09 0.306389
2012-03-10 -2.290613
>>>ts_utc = ts.tz_localize('UTC')
>>>ts_utc
2012-03-06 00:00:00+00:00 0.464000
2012-03-07 00:00:00+00:00 0.227371
2012-03-08 00:00:00+00:00 -0.496922
2012-03-09 00:00:00+00:00 0.306389
2012-03-10 00:00:00+00:00 -2.290613
Freq: D, dtype: float64
// 转换成其它时区
>>>ts_utc.tz_convert('US/Eastern')
2012-03-05 19:00:00-05:00 0.464000
2012-03-06 19:00:00-05:00 0.227371
2012-03-07 19:00:00-05:00 -0.496922
2012-03-08 19:00:00-05:00 0.306389
2012-03-09 19:00:00-05:00 -2.290613
// 转换时间段：
>>>rng = pd.date_range('1/1/2012', periods=5, freq='M')
>>>ts = pd.Series(np.random.randn(len(rng)), index=rng)
>>>ts
2012-01-31 -1.134623
2012-02-29 -1.561819
2012-03-31 -0.260838
2012-04-30 0.281957
2012-05-31 1.523962
Freq: M, dtype: float64
>>>ps = ts.to_period()
2012-01 -1.134623
2012-02 -1.561819
2012-03 -0.260838
2012-04 0.281957
2012-05 1.523962
>>>ps.to_timestamp()
2012-01-01 -1.134623
2012-02-01 -1.561819
2012-03-01 -0.260838
2012-04-01 0.281957
2012-05-01 1.523962
eg:
// Pandas 函数可以很方便地转换时间段与时间戳。下例把以 11 月为结束年份的季度频率转换为下一季度月末上午 9 点：
>>> prng = pd.period_range('1990Q1', '2000Q4', freq='Q-NOV')
>>> ts = pd.Series(np.random.randn(len(prng)), prng)
>>> ts.index = (prng.asfreq('M', 'e') + 1).asfreq('H', 's') + 9
>>> ts.head()
1990-03-01 09:00 -0.902937
1990-06-01 09:00 0.068159
1990-09-01 09:00 -0.057873
1990-12-01 09:00 -0.368204
1991-03-01 09:00 -1.144073
Freq: H, dtype: float64
（17.）类别型（Categoricals） + 可视化 + HDF5+Excel 都先不看了 用到在说
（18.）数据输入 / 输出
// 写入csv文件
>>> df.to_csv('foo.csv')
// 读取 CSV 文件数据
>>> pd.read_csv('foo.csv')
✨✨✨✨：DataFrame 是 Series 的容器，Series 则是标量的容器
