In [1]:

# 首先导入库
import pandas as pd

1、Series¶

由一组数据（各种Numpy数据类型），以及一组与之相关的标签数据（即索引）组成。仅由一组数据即可产生最简单的Series，可以通过传递一个list对象来创建一个Series，Pandas会默认创建整型索引。

创建一个Series：

In [2]:

s = pd.Series([1,3,5,np.nan,6,8])
s

Out[2]:

0    1.0
1    3.0
2    5.0
3    NaN
4    6.0
5    8.0
dtype: float64

获取 Series 的索引:

In [3]:

s.index

Out[3]:

RangeIndex(start=0, stop=6, step=1)

2、DataFrame¶

DataFrame是一个表格型的数据结构，它含有一组有序的列，每一列的数据结构都是相同的，而不同的列之间则可以是不同的数据结构（数值、字符、布尔值等）。或者以数据库进行类比，DataFrame中的每一行是一个记录，名称为Index的一个元素，而每一列则为一个字段，是这个记录的一个属性。DataFrame既有行索引也有列索引，可以被看做由Series组成的字典（共用同一个索引）。

2.1 创建一个DataFrame，包括一个numpy array, 时间索引和列名字：¶

In [4]:

dates = pd.date_range('20130101',periods=6)
dates

Out[4]:

DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',
               '2013-01-05', '2013-01-06'],
              dtype='datetime64[ns]', freq='D')

In [5]:

df = pd.DataFrame(np.random.randn(6,4),index=dates,columns=list('ABCD'))
df

Out[5]:

	A	B	C	D
2013-01-01	-1.971026	-1.149504	-0.886253	-1.423737
2013-01-02	1.248603	0.158524	0.047291	0.834145
2013-01-03	0.572176	-0.093579	0.892276	0.376732
2013-01-04	0.343039	-0.820224	0.220198	-0.026627
2013-01-05	0.835942	-0.209759	-0.740630	0.234057
2013-01-06	-1.240034	1.753316	0.209350	0.027492

2.2 查看数据¶

我们以平台获取的数据为例进行讲解：

In [6]:

# 获取沪深300的数据
df = D.history_data(instruments=['000300.SHA'], start_date='2017-01-01', end_date='2017-01-20',
               fields=['open', 'high', 'low', 'close', 'volume', 'amount'])
df

Out[6]:

	date	instrument	open	high	low	close	volume	amount
0	2017-01-03	000300.SHA	3313.953369	3345.262939	3313.953369	3342.227295	8656789600	8.761277e+10
1	2017-01-04	000300.SHA	3340.557617	3369.565918	3338.152588	3368.311768	9005082700	9.801436e+10
2	2017-01-05	000300.SHA	3368.340088	3373.411865	3361.461914	3367.789307	8833635700	9.118934e+10
3	2017-01-06	000300.SHA	3366.852295	3368.870605	3346.891357	3347.666504	8745911400	9.107226e+10
4	2017-01-09	000300.SHA	3345.748291	3366.221924	3344.347168	3363.901367	8370794200	8.791612e+10
5	2017-01-10	000300.SHA	3361.639404	3370.505127	3354.087891	3358.271484	8381916000	8.253397e+10
6	2017-01-11	000300.SHA	3355.800781	3366.631592	3334.297852	3334.495361	9107143800	8.675045e+10
7	2017-01-12	000300.SHA	3332.685791	3343.127930	3315.962891	3317.624023	8123913500	7.503189e+10
8	2017-01-13	000300.SHA	3317.962402	3338.174072	3309.374023	3319.912109	8418058900	8.455610e+10
9	2017-01-16	000300.SHA	3314.150635	3322.781982	3264.207031	3319.445557	15777913500	1.532625e+11
10	2017-01-17	000300.SHA	3305.600098	3327.376709	3292.204346	3326.356201	6945332800	7.475451e+10
11	2017-01-18	000300.SHA	3323.338867	3350.961914	3320.406250	3339.365234	7062322700	7.568068e+10
12	2017-01-19	000300.SHA	3330.952148	3346.495850	3324.683594	3329.289062	6615980800	6.386971e+10
13	2017-01-20	000300.SHA	3326.977295	3358.412109	3326.374023	3354.889160	6530046200	7.083133e+10

查看前几条数据：

In [7]:

df.head() # 默认是查询5条

Out[7]:

	date	instrument	open	high	low	close	volume	amount
0	2017-01-03	000300.SHA	3313.953369	3345.262939	3313.953369	3342.227295	8656789600	8.761277e+10
1	2017-01-04	000300.SHA	3340.557617	3369.565918	3338.152588	3368.311768	9005082700	9.801436e+10
2	2017-01-05	000300.SHA	3368.340088	3373.411865	3361.461914	3367.789307	8833635700	9.118934e+10
3	2017-01-06	000300.SHA	3366.852295	3368.870605	3346.891357	3347.666504	8745911400	9.107226e+10
4	2017-01-09	000300.SHA	3345.748291	3366.221924	3344.347168	3363.901367	8370794200	8.791612e+10

查看后几条数据：

In [8]:

df.tail()

Out[8]:

	date	instrument	open	high	low	close	volume	amount
9	2017-01-16	000300.SHA	3314.150635	3322.781982	3264.207031	3319.445557	15777913500	1.532625e+11
10	2017-01-17	000300.SHA	3305.600098	3327.376709	3292.204346	3326.356201	6945332800	7.475451e+10
11	2017-01-18	000300.SHA	3323.338867	3350.961914	3320.406250	3339.365234	7062322700	7.568068e+10
12	2017-01-19	000300.SHA	3330.952148	3346.495850	3324.683594	3329.289062	6615980800	6.386971e+10
13	2017-01-20	000300.SHA	3326.977295	3358.412109	3326.374023	3354.889160	6530046200	7.083133e+10

查看 DataFrame 的索引

In [9]:

df.index

Out[9]:

RangeIndex(start=0, stop=14, step=1)

查看 DataFrame 的列名

In [10]:

df.columns

Out[10]:

Index(['date', 'instrument', 'open', 'high', 'low', 'close', 'volume',
       'amount'],
      dtype='object')

查看 DataFrame 的值

In [11]:

df.values

Out[11]:

array([[Timestamp('2017-01-03 00:00:00'), '000300.SHA', 3313.953369140625,
        3345.262939453125, 3313.953369140625, 3342.227294921875,
        8656789600, 87612768256.0],
       [Timestamp('2017-01-04 00:00:00'), '000300.SHA', 3340.5576171875,
        3369.56591796875, 3338.152587890625, 3368.311767578125, 9005082700,
        98014355456.0],
       [Timestamp('2017-01-05 00:00:00'), '000300.SHA', 3368.340087890625,
        3373.411865234375, 3361.4619140625, 3367.789306640625, 8833635700,
        91189338112.0],
       [Timestamp('2017-01-06 00:00:00'), '000300.SHA', 3366.852294921875,
        3368.87060546875, 3346.891357421875, 3347.66650390625, 8745911400,
        91072258048.0],
       [Timestamp('2017-01-09 00:00:00'), '000300.SHA', 3345.748291015625,
        3366.221923828125, 3344.34716796875, 3363.9013671875, 8370794200,
        87916118016.0],
       [Timestamp('2017-01-10 00:00:00'), '000300.SHA', 3361.639404296875,
        3370.505126953125, 3354.087890625, 3358.271484375, 8381916000,
        82533965824.0],
       [Timestamp('2017-01-11 00:00:00'), '000300.SHA', 3355.80078125,
        3366.631591796875, 3334.2978515625, 3334.495361328125, 9107143800,
        86750453760.0],
       [Timestamp('2017-01-12 00:00:00'), '000300.SHA', 3332.685791015625,
        3343.1279296875, 3315.962890625, 3317.6240234375, 8123913500,
        75031887872.0],
       [Timestamp('2017-01-13 00:00:00'), '000300.SHA', 3317.96240234375,
        3338.174072265625, 3309.3740234375, 3319.912109375, 8418058900,
        84556103680.0],
       [Timestamp('2017-01-16 00:00:00'), '000300.SHA', 3314.150634765625,
        3322.781982421875, 3264.20703125, 3319.445556640625, 15777913500,
        153262522368.0],
       [Timestamp('2017-01-17 00:00:00'), '000300.SHA', 3305.60009765625,
        3327.376708984375, 3292.204345703125, 3326.356201171875,
        6945332800, 74754506752.0],
       [Timestamp('2017-01-18 00:00:00'), '000300.SHA', 3323.3388671875,
        3350.9619140625, 3320.40625, 3339.365234375, 7062322700,
        75680677888.0],
       [Timestamp('2017-01-19 00:00:00'), '000300.SHA', 3330.9521484375,
        3346.495849609375, 3324.68359375, 3329.2890625, 6615980800,
        63869710336.0],
       [Timestamp('2017-01-20 00:00:00'), '000300.SHA', 3326.977294921875,
        3358.412109375, 3326.3740234375, 3354.88916015625, 6530046200,
        70831325184.0]], dtype=object)

使用 describe() 函数对于数据的快速统计汇总：

In [12]:

df.describe()

Out[12]:

	open	high	low	close	volume	amount
count	14.000000	14.000000	14.000000	14.000000	1.400000e+01	1.400000e+01
mean	3336.040283	3353.414062	3324.742920	3342.110596	8.612489e+09	8.736257e+10
std	20.855013	16.710600	25.615406	18.314083	2.248143e+09	2.110282e+10
min	3305.600098	3322.781982	3264.207031	3317.624023	6.530046e+09	6.386971e+10
25%	3319.306519	3343.661682	3314.455750	3327.089417	7.327720e+09	7.519409e+10
50%	3331.818970	3354.687012	3325.528809	3340.796265	8.399987e+09	8.565328e+10
75%	3353.287659	3368.310852	3342.798523	3357.425903	8.811705e+09	9.028322e+10
max	3368.340088	3373.411865	3361.461914	3368.311768	1.577791e+10	1.532625e+11

对数据的转置：

In [13]:

df.T

Out[13]:

	0	1	2	3	4	5	6	7	8	9	10	11	12	13
date	2017-01-03 00:00:00	2017-01-04 00:00:00	2017-01-05 00:00:00	2017-01-06 00:00:00	2017-01-09 00:00:00	2017-01-10 00:00:00	2017-01-11 00:00:00	2017-01-12 00:00:00	2017-01-13 00:00:00	2017-01-16 00:00:00	2017-01-17 00:00:00	2017-01-18 00:00:00	2017-01-19 00:00:00	2017-01-20 00:00:00
instrument	000300.SHA	000300.SHA	000300.SHA	000300.SHA	000300.SHA	000300.SHA	000300.SHA	000300.SHA	000300.SHA	000300.SHA	000300.SHA	000300.SHA	000300.SHA	000300.SHA
open	3313.95	3340.56	3368.34	3366.85	3345.75	3361.64	3355.8	3332.69	3317.96	3314.15	3305.6	3323.34	3330.95	3326.98
high	3345.26	3369.57	3373.41	3368.87	3366.22	3370.51	3366.63	3343.13	3338.17	3322.78	3327.38	3350.96	3346.5	3358.41
low	3313.95	3338.15	3361.46	3346.89	3344.35	3354.09	3334.3	3315.96	3309.37	3264.21	3292.2	3320.41	3324.68	3326.37
close	3342.23	3368.31	3367.79	3347.67	3363.9	3358.27	3334.5	3317.62	3319.91	3319.45	3326.36	3339.37	3329.29	3354.89
volume	8656789600	9005082700	8833635700	8745911400	8370794200	8381916000	9107143800	8123913500	8418058900	15777913500	6945332800	7062322700	6615980800	6530046200
amount	8.76128e+10	9.80144e+10	9.11893e+10	9.10723e+10	8.79161e+10	8.2534e+10	8.67505e+10	7.50319e+10	8.45561e+10	1.53263e+11	7.47545e+10	7.56807e+10	6.38697e+10	7.08313e+10

按列对 DataFrame 进行排序

In [14]:

df.sort(columns='open')

Out[14]:

	date	instrument	open	high	low	close	volume	amount
10	2017-01-17	000300.SHA	3305.600098	3327.376709	3292.204346	3326.356201	6945332800	7.475451e+10
0	2017-01-03	000300.SHA	3313.953369	3345.262939	3313.953369	3342.227295	8656789600	8.761277e+10
9	2017-01-16	000300.SHA	3314.150635	3322.781982	3264.207031	3319.445557	15777913500	1.532625e+11
8	2017-01-13	000300.SHA	3317.962402	3338.174072	3309.374023	3319.912109	8418058900	8.455610e+10
11	2017-01-18	000300.SHA	3323.338867	3350.961914	3320.406250	3339.365234	7062322700	7.568068e+10
13	2017-01-20	000300.SHA	3326.977295	3358.412109	3326.374023	3354.889160	6530046200	7.083133e+10
12	2017-01-19	000300.SHA	3330.952148	3346.495850	3324.683594	3329.289062	6615980800	6.386971e+10
7	2017-01-12	000300.SHA	3332.685791	3343.127930	3315.962891	3317.624023	8123913500	7.503189e+10
1	2017-01-04	000300.SHA	3340.557617	3369.565918	3338.152588	3368.311768	9005082700	9.801436e+10
4	2017-01-09	000300.SHA	3345.748291	3366.221924	3344.347168	3363.901367	8370794200	8.791612e+10
6	2017-01-11	000300.SHA	3355.800781	3366.631592	3334.297852	3334.495361	9107143800	8.675045e+10
5	2017-01-10	000300.SHA	3361.639404	3370.505127	3354.087891	3358.271484	8381916000	8.253397e+10
3	2017-01-06	000300.SHA	3366.852295	3368.870605	3346.891357	3347.666504	8745911400	9.107226e+10
2	2017-01-05	000300.SHA	3368.340088	3373.411865	3361.461914	3367.789307	8833635700	9.118934e+10

In [15]:

df.sort_values(by='open')

Out[15]:

	date	instrument	open	high	low	close	volume	amount
10	2017-01-17	000300.SHA	3305.600098	3327.376709	3292.204346	3326.356201	6945332800	7.475451e+10
0	2017-01-03	000300.SHA	3313.953369	3345.262939	3313.953369	3342.227295	8656789600	8.761277e+10
9	2017-01-16	000300.SHA	3314.150635	3322.781982	3264.207031	3319.445557	15777913500	1.532625e+11
8	2017-01-13	000300.SHA	3317.962402	3338.174072	3309.374023	3319.912109	8418058900	8.455610e+10
11	2017-01-18	000300.SHA	3323.338867	3350.961914	3320.406250	3339.365234	7062322700	7.568068e+10
13	2017-01-20	000300.SHA	3326.977295	3358.412109	3326.374023	3354.889160	6530046200	7.083133e+10
12	2017-01-19	000300.SHA	3330.952148	3346.495850	3324.683594	3329.289062	6615980800	6.386971e+10
7	2017-01-12	000300.SHA	3332.685791	3343.127930	3315.962891	3317.624023	8123913500	7.503189e+10
1	2017-01-04	000300.SHA	3340.557617	3369.565918	3338.152588	3368.311768	9005082700	9.801436e+10
4	2017-01-09	000300.SHA	3345.748291	3366.221924	3344.347168	3363.901367	8370794200	8.791612e+10
6	2017-01-11	000300.SHA	3355.800781	3366.631592	3334.297852	3334.495361	9107143800	8.675045e+10
5	2017-01-10	000300.SHA	3361.639404	3370.505127	3354.087891	3358.271484	8381916000	8.253397e+10
3	2017-01-06	000300.SHA	3366.852295	3368.870605	3346.891357	3347.666504	8745911400	9.107226e+10
2	2017-01-05	000300.SHA	3368.340088	3373.411865	3361.461914	3367.789307	8833635700	9.118934e+10

2.3 选择数据¶

2.3.1 通过下标选取数据:¶

df['open']，df.open

以上两个语句是等效的，都是返回 df 名称为 open 列的数据，返回的为一个 Series。

df[0:3], df['2016-07-05':'2016-07-08']

下标索引选取的是 DataFrame 的记录，与 List 相同 DataFrame 的下标也是从0开始，区间索引的话，为一个左闭右开的区间，即[0：3]选取的为0-2三条记录。

与此等价，还可以用起始的索引名称和结束索引名称选取数据,如：df['a':'b']。有一点需要注意的是使用起始索引名称和结束索引名称时，也会包含结束索引的数据。具体看下方示例：

以上两种方式返回的都是DataFrame。

选择一列数据：

In [16]:

df['open'] # 返回Series

Out[16]:

0     3313.953369
1     3340.557617
2     3368.340088
3     3366.852295
4     3345.748291
5     3361.639404
6     3355.800781
7     3332.685791
8     3317.962402
9     3314.150635
10    3305.600098
11    3323.338867
12    3330.952148
13    3326.977295
Name: open, dtype: float32

In [17]:

df[['open']] # 返回DataFrame

Out[17]:

	open
0	3313.953369
1	3340.557617
2	3368.340088
3	3366.852295
4	3345.748291
5	3361.639404
6	3355.800781
7	3332.685791
8	3317.962402
9	3314.150635
10	3305.600098
11	3323.338867
12	3330.952148
13	3326.977295

选择多列

In [18]:

df[['open', 'high']]

Out[18]:

	open	high
0	3313.953369	3345.262939
1	3340.557617	3369.565918
2	3368.340088	3373.411865
3	3366.852295	3368.870605
4	3345.748291	3366.221924
5	3361.639404	3370.505127
6	3355.800781	3366.631592
7	3332.685791	3343.127930
8	3317.962402	3338.174072
9	3314.150635	3322.781982
10	3305.600098	3327.376709
11	3323.338867	3350.961914
12	3330.952148	3346.495850
13	3326.977295	3358.412109

选择多行

In [19]:

df[0:3]

Out[19]:

	date	instrument	open	high	low	close	volume	amount
0	2017-01-03	000300.SHA	3313.953369	3345.262939	3313.953369	3342.227295	8656789600	8.761277e+10
1	2017-01-04	000300.SHA	3340.557617	3369.565918	3338.152588	3368.311768	9005082700	9.801436e+10
2	2017-01-05	000300.SHA	3368.340088	3373.411865	3361.461914	3367.789307	8833635700	9.118934e+10

2.3.2 使用标签选取数据：¶

df.loc[行标签,列标签]

df.loc['a':'b'] #选取 ab 两行数据

df.loc[:,'open'] #选取 open 列的数据

df.loc 的第一个参数是行标签，第二个参数为列标签（可选参数，默认为所有列标签），两个参数既可以是列表也可以是单个字符，如果两个参数都为列表则返回的是 DataFrame，否则，则为 Series。

PS：loc为location的缩写。

In [20]:

df.loc[3,'open']

Out[20]:

3366.8523

In [21]:

df.loc[1:3]

Out[21]:

	date	instrument	open	high	low	close	volume	amount
1	2017-01-04	000300.SHA	3340.557617	3369.565918	3338.152588	3368.311768	9005082700	9.801436e+10
2	2017-01-05	000300.SHA	3368.340088	3373.411865	3361.461914	3367.789307	8833635700	9.118934e+10
3	2017-01-06	000300.SHA	3366.852295	3368.870605	3346.891357	3347.666504	8745911400	9.107226e+10

In [22]:

df.loc[:, 'open']

Out[22]:

0     3313.953369
1     3340.557617
2     3368.340088
3     3366.852295
4     3345.748291
5     3361.639404
6     3355.800781
7     3332.685791
8     3317.962402
9     3314.150635
10    3305.600098
11    3323.338867
12    3330.952148
13    3326.977295
Name: open, dtype: float32

2.3.3. 使用位置选取数据：¶

df.iloc[行位置,列位置]

df.iloc[1,1] #选取第二行，第二列的值，返回的为单个值

df.iloc[[0,2],:] #选取第一行及第三行的数据

df.iloc[0:2,:] #选取第一行到第三行（不包含）的数据

df.iloc[:,1] #选取所有记录的第二列的值，返回的为一个Series

df.iloc[1,:] #选取第一行数据，返回的为一个Series

PS：iloc 则为 integer & location 的缩写

In [23]:

df

Out[23]:

	date	instrument	open	high	low	close	volume	amount
0	2017-01-03	000300.SHA	3313.953369	3345.262939	3313.953369	3342.227295	8656789600	8.761277e+10
1	2017-01-04	000300.SHA	3340.557617	3369.565918	3338.152588	3368.311768	9005082700	9.801436e+10
2	2017-01-05	000300.SHA	3368.340088	3373.411865	3361.461914	3367.789307	8833635700	9.118934e+10
3	2017-01-06	000300.SHA	3366.852295	3368.870605	3346.891357	3347.666504	8745911400	9.107226e+10
4	2017-01-09	000300.SHA	3345.748291	3366.221924	3344.347168	3363.901367	8370794200	8.791612e+10
5	2017-01-10	000300.SHA	3361.639404	3370.505127	3354.087891	3358.271484	8381916000	8.253397e+10
6	2017-01-11	000300.SHA	3355.800781	3366.631592	3334.297852	3334.495361	9107143800	8.675045e+10
7	2017-01-12	000300.SHA	3332.685791	3343.127930	3315.962891	3317.624023	8123913500	7.503189e+10
8	2017-01-13	000300.SHA	3317.962402	3338.174072	3309.374023	3319.912109	8418058900	8.455610e+10
9	2017-01-16	000300.SHA	3314.150635	3322.781982	3264.207031	3319.445557	15777913500	1.532625e+11
10	2017-01-17	000300.SHA	3305.600098	3327.376709	3292.204346	3326.356201	6945332800	7.475451e+10
11	2017-01-18	000300.SHA	3323.338867	3350.961914	3320.406250	3339.365234	7062322700	7.568068e+10
12	2017-01-19	000300.SHA	3330.952148	3346.495850	3324.683594	3329.289062	6615980800	6.386971e+10
13	2017-01-20	000300.SHA	3326.977295	3358.412109	3326.374023	3354.889160	6530046200	7.083133e+10

In [24]:

df.iloc[1,1] # 选取第二行，第二列的值，返回的为单个值

Out[24]:

'000300.SHA'

In [25]:

df.iloc[[0,2],:] # 选取第一行及第三行的数据

Out[25]:

	date	instrument	open	high	low	close	volume	amount
0	2017-01-03	000300.SHA	3313.953369	3345.262939	3313.953369	3342.227295	8656789600	8.761277e+10
2	2017-01-05	000300.SHA	3368.340088	3373.411865	3361.461914	3367.789307	8833635700	9.118934e+10

In [26]:

df.iloc[0:2,:] # 选取第一行到第三行（不包含）的数据

Out[26]:

	date	instrument	open	high	low	close	volume	amount
0	2017-01-03	000300.SHA	3313.953369	3345.262939	3313.953369	3342.227295	8656789600	8.761277e+10
1	2017-01-04	000300.SHA	3340.557617	3369.565918	3338.152588	3368.311768	9005082700	9.801436e+10

In [27]:

df.iloc[:,1] # 选取所有记录的第一列的值，返回的为一个Series

Out[27]:

0     000300.SHA
1     000300.SHA
2     000300.SHA
3     000300.SHA
4     000300.SHA
5     000300.SHA
6     000300.SHA
7     000300.SHA
8     000300.SHA
9     000300.SHA
10    000300.SHA
11    000300.SHA
12    000300.SHA
13    000300.SHA
Name: instrument, dtype: object

In [28]:

df.iloc[1,:] # 选取第一行数据，返回的为一个Series

Out[28]:

date          2017-01-04 00:00:00
instrument             000300.SHA
open                      3340.56
high                      3369.57
low                       3338.15
close                     3368.31
volume                 9005082700
amount                9.80144e+10
Name: 1, dtype: object

2.3.4. 更广义的切片方式是使用.ix，它自动根据给到的索引类型判断是使用位置还是标签进行切片¶

df.ix[1,1]

df.ix['a':'b']

In [29]:

df

Out[29]:

	date	instrument	open	high	low	close	volume	amount
0	2017-01-03	000300.SHA	3313.953369	3345.262939	3313.953369	3342.227295	8656789600	8.761277e+10
1	2017-01-04	000300.SHA	3340.557617	3369.565918	3338.152588	3368.311768	9005082700	9.801436e+10
2	2017-01-05	000300.SHA	3368.340088	3373.411865	3361.461914	3367.789307	8833635700	9.118934e+10
3	2017-01-06	000300.SHA	3366.852295	3368.870605	3346.891357	3347.666504	8745911400	9.107226e+10
4	2017-01-09	000300.SHA	3345.748291	3366.221924	3344.347168	3363.901367	8370794200	8.791612e+10
5	2017-01-10	000300.SHA	3361.639404	3370.505127	3354.087891	3358.271484	8381916000	8.253397e+10
6	2017-01-11	000300.SHA	3355.800781	3366.631592	3334.297852	3334.495361	9107143800	8.675045e+10
7	2017-01-12	000300.SHA	3332.685791	3343.127930	3315.962891	3317.624023	8123913500	7.503189e+10
8	2017-01-13	000300.SHA	3317.962402	3338.174072	3309.374023	3319.912109	8418058900	8.455610e+10
9	2017-01-16	000300.SHA	3314.150635	3322.781982	3264.207031	3319.445557	15777913500	1.532625e+11
10	2017-01-17	000300.SHA	3305.600098	3327.376709	3292.204346	3326.356201	6945332800	7.475451e+10
11	2017-01-18	000300.SHA	3323.338867	3350.961914	3320.406250	3339.365234	7062322700	7.568068e+10
12	2017-01-19	000300.SHA	3330.952148	3346.495850	3324.683594	3329.289062	6615980800	6.386971e+10
13	2017-01-20	000300.SHA	3326.977295	3358.412109	3326.374023	3354.889160	6530046200	7.083133e+10

In [30]:

df.ix[1,1]

Out[30]:

'000300.SHA'

In [31]:

df.ix[1,'open']

Out[31]:

3340.5576

2.3.5 通过逻辑指针进行数据切片：¶

df[逻辑条件]

df[df.one >= 2] #单个逻辑条件

df[(df.one >=1 ) & (df.one < 3) ] #多个逻辑条件组合

In [32]:

df

Out[32]:

	date	instrument	open	high	low	close	volume	amount
0	2017-01-03	000300.SHA	3313.953369	3345.262939	3313.953369	3342.227295	8656789600	8.761277e+10
1	2017-01-04	000300.SHA	3340.557617	3369.565918	3338.152588	3368.311768	9005082700	9.801436e+10
2	2017-01-05	000300.SHA	3368.340088	3373.411865	3361.461914	3367.789307	8833635700	9.118934e+10
3	2017-01-06	000300.SHA	3366.852295	3368.870605	3346.891357	3347.666504	8745911400	9.107226e+10
4	2017-01-09	000300.SHA	3345.748291	3366.221924	3344.347168	3363.901367	8370794200	8.791612e+10
5	2017-01-10	000300.SHA	3361.639404	3370.505127	3354.087891	3358.271484	8381916000	8.253397e+10
6	2017-01-11	000300.SHA	3355.800781	3366.631592	3334.297852	3334.495361	9107143800	8.675045e+10
7	2017-01-12	000300.SHA	3332.685791	3343.127930	3315.962891	3317.624023	8123913500	7.503189e+10
8	2017-01-13	000300.SHA	3317.962402	3338.174072	3309.374023	3319.912109	8418058900	8.455610e+10
9	2017-01-16	000300.SHA	3314.150635	3322.781982	3264.207031	3319.445557	15777913500	1.532625e+11
10	2017-01-17	000300.SHA	3305.600098	3327.376709	3292.204346	3326.356201	6945332800	7.475451e+10
11	2017-01-18	000300.SHA	3323.338867	3350.961914	3320.406250	3339.365234	7062322700	7.568068e+10
12	2017-01-19	000300.SHA	3330.952148	3346.495850	3324.683594	3329.289062	6615980800	6.386971e+10
13	2017-01-20	000300.SHA	3326.977295	3358.412109	3326.374023	3354.889160	6530046200	7.083133e+10

In [33]:

df[df.open > 3340]

Out[33]:

	date	instrument	open	high	low	close	volume	amount
1	2017-01-04	000300.SHA	3340.557617	3369.565918	3338.152588	3368.311768	9005082700	9.801436e+10
2	2017-01-05	000300.SHA	3368.340088	3373.411865	3361.461914	3367.789307	8833635700	9.118934e+10
3	2017-01-06	000300.SHA	3366.852295	3368.870605	3346.891357	3347.666504	8745911400	9.107226e+10
4	2017-01-09	000300.SHA	3345.748291	3366.221924	3344.347168	3363.901367	8370794200	8.791612e+10
5	2017-01-10	000300.SHA	3361.639404	3370.505127	3354.087891	3358.271484	8381916000	8.253397e+10
6	2017-01-11	000300.SHA	3355.800781	3366.631592	3334.297852	3334.495361	9107143800	8.675045e+10

In [34]:

df[(df.open > 3310) & (df.close < 3350)]

Out[34]:

	date	instrument	open	high	low	close	volume	amount
0	2017-01-03	000300.SHA	3313.953369	3345.262939	3313.953369	3342.227295	8656789600	8.761277e+10
3	2017-01-06	000300.SHA	3366.852295	3368.870605	3346.891357	3347.666504	8745911400	9.107226e+10
6	2017-01-11	000300.SHA	3355.800781	3366.631592	3334.297852	3334.495361	9107143800	8.675045e+10
7	2017-01-12	000300.SHA	3332.685791	3343.127930	3315.962891	3317.624023	8123913500	7.503189e+10
8	2017-01-13	000300.SHA	3317.962402	3338.174072	3309.374023	3319.912109	8418058900	8.455610e+10
9	2017-01-16	000300.SHA	3314.150635	3322.781982	3264.207031	3319.445557	15777913500	1.532625e+11
11	2017-01-18	000300.SHA	3323.338867	3350.961914	3320.406250	3339.365234	7062322700	7.568068e+10
12	2017-01-19	000300.SHA	3330.952148	3346.495850	3324.683594	3329.289062	6615980800	6.386971e+10

In [35]:

df[df>3360]

Out[35]:

	date	instrument	open	high	low	close	volume	amount
0	2017-01-03	000300.SHA	NaN	NaN	NaN	NaN	8656789600	8.761277e+10
1	2017-01-04	000300.SHA	NaN	3369.565918	NaN	3368.311768	9005082700	9.801436e+10
2	2017-01-05	000300.SHA	3368.340088	3373.411865	3361.461914	3367.789307	8833635700	9.118934e+10
3	2017-01-06	000300.SHA	3366.852295	3368.870605	NaN	NaN	8745911400	9.107226e+10
4	2017-01-09	000300.SHA	NaN	3366.221924	NaN	3363.901367	8370794200	8.791612e+10
5	2017-01-10	000300.SHA	3361.639404	3370.505127	NaN	NaN	8381916000	8.253397e+10
6	2017-01-11	000300.SHA	NaN	3366.631592	NaN	NaN	9107143800	8.675045e+10
7	2017-01-12	000300.SHA	NaN	NaN	NaN	NaN	8123913500	7.503189e+10
8	2017-01-13	000300.SHA	NaN	NaN	NaN	NaN	8418058900	8.455610e+10
9	2017-01-16	000300.SHA	NaN	NaN	NaN	NaN	15777913500	1.532625e+11
10	2017-01-17	000300.SHA	NaN	NaN	NaN	NaN	6945332800	7.475451e+10
11	2017-01-18	000300.SHA	NaN	NaN	NaN	NaN	7062322700	7.568068e+10
12	2017-01-19	000300.SHA	NaN	NaN	NaN	NaN	6615980800	6.386971e+10
13	2017-01-20	000300.SHA	NaN	NaN	NaN	NaN	6530046200	7.083133e+10