Pandas 精简实例入门

import pandas as pd
import numpy as np

0. 案例引入

# 由np直接生成的ndarray
stock_change = np.random.normal(0, 1, (10, 8))

stock_change

array([[ 0.74057955,  0.78604657, -0.15264135,  0.05680483,  0.09388135,
         0.7313751 , -1.52338443,  1.71156505],
       [ 0.42204925,  0.62541715, -1.41583042, -0.27434654,  0.98587136,
        -0.55797884,  0.31026482, -0.47964535],
       [ 0.99741102, -0.94397298, -0.40782973, -1.33631227, -0.0124836 ,
         1.1873408 , -0.25430393, -0.74264106],
       [ 0.34156662, -0.40621262,  0.82861416,  0.1272128 ,  1.04101412,
         0.79061324, -0.60325544,  1.29954581],
       [-1.23289547,  0.83789748,  1.19276989,  0.45092868, -1.7418129 ,
        -0.65362211, -0.17752493,  1.87679286],
       [-0.4268705 ,  1.14017572,  0.18261009, -0.28947877,  0.82489897,
         0.11566058, -0.53191371, -0.96065812],
       [ 0.92792797,  0.26086313,  0.08316582, -0.94533007, -0.77956139,
         0.23006703, -0.81971461, -1.36742474],
       [ 0.82241768,  0.54201367, -0.19331564,  0.50576697, -0.42545839,
        -0.24247517, -0.03526651, -0.02268451],
       [ 1.67480093, -1.23265948, -2.88199942, -1.07761987, -1.37844497,
        -0.13581683,  2.06013919,  1.18986057],
       [ 0.60744357,  0.52348326,  0.76418263, -0.73385554,  0.54857341,
         0.27310645, -0.26464179,  0.77370496]])

# 通过pd.DataFrame生成    (pd.DataFrame(ndarray))
stock_df = pd.DataFrame(stock_change)

stock_df

	0	1	2	3	4	5	6	7
0	0.740580	0.786047	-0.152641	0.056805	0.093881	0.731375	-1.523384	1.711565
1	0.422049	0.625417	-1.415830	-0.274347	0.985871	-0.557979	0.310265	-0.479645
2	0.997411	-0.943973	-0.407830	-1.336312	-0.012484	1.187341	-0.254304	-0.742641
3	0.341567	-0.406213	0.828614	0.127213	1.041014	0.790613	-0.603255	1.299546
4	-1.232895	0.837897	1.192770	0.450929	-1.741813	-0.653622	-0.177525	1.876793
5	-0.426871	1.140176	0.182610	-0.289479	0.824899	0.115661	-0.531914	-0.960658
6	0.927928	0.260863	0.083166	-0.945330	-0.779561	0.230067	-0.819715	-1.367425
7	0.822418	0.542014	-0.193316	0.505767	-0.425458	-0.242475	-0.035267	-0.022685
8	1.674801	-1.232659	-2.881999	-1.077620	-1.378445	-0.135817	2.060139	1.189861
9	0.607444	0.523483	0.764183	-0.733856	0.548573	0.273106	-0.264642	0.773705

stock_df.shape

(10, 8)

# 添加行索引
stock_name = ['股票{}'.format(i+1) for i in range(stock_df.shape[0])]
stock_df = pd.DataFrame(data=stock_change, index=stock_name)

stock_df

	0	1	2	3	4	5	6	7
股票1	0.740580	0.786047	-0.152641	0.056805	0.093881	0.731375	-1.523384	1.711565
股票2	0.422049	0.625417	-1.415830	-0.274347	0.985871	-0.557979	0.310265	-0.479645
股票3	0.997411	-0.943973	-0.407830	-1.336312	-0.012484	1.187341	-0.254304	-0.742641
股票4	0.341567	-0.406213	0.828614	0.127213	1.041014	0.790613	-0.603255	1.299546
股票5	-1.232895	0.837897	1.192770	0.450929	-1.741813	-0.653622	-0.177525	1.876793
股票6	-0.426871	1.140176	0.182610	-0.289479	0.824899	0.115661	-0.531914	-0.960658
股票7	0.927928	0.260863	0.083166	-0.945330	-0.779561	0.230067	-0.819715	-1.367425
股票8	0.822418	0.542014	-0.193316	0.505767	-0.425458	-0.242475	-0.035267	-0.022685
股票9	1.674801	-1.232659	-2.881999	-1.077620	-1.378445	-0.135817	2.060139	1.189861
股票10	0.607444	0.523483	0.764183	-0.733856	0.548573	0.273106	-0.264642	0.773705

# 添加列索引
# 引入df.date_range(),start-开始日期, end: 结束日期,  periods - 持续时间, frep- B:工作日, M:月, D:天
date = pd.date_range(start='2020-3-30', periods=stock_df.shape[1], freq='d')
date

DatetimeIndex(['2020-03-30', '2020-03-31', '2020-04-01', '2020-04-02',
               '2020-04-03', '2020-04-04', '2020-04-05', '2020-04-06'],
              dtype='datetime64[ns]', freq='D')

stock_df = pd.DataFrame(stock_change, index=stock_name, columns=date)
stock_df

	2020-03-30	2020-03-31	2020-04-01	2020-04-02	2020-04-03	2020-04-04	2020-04-05	2020-04-06
股票1	0.740580	0.786047	-0.152641	0.056805	0.093881	0.731375	-1.523384	1.711565
股票2	0.422049	0.625417	-1.415830	-0.274347	0.985871	-0.557979	0.310265	-0.479645
股票3	0.997411	-0.943973	-0.407830	-1.336312	-0.012484	1.187341	-0.254304	-0.742641
股票4	0.341567	-0.406213	0.828614	0.127213	1.041014	0.790613	-0.603255	1.299546
股票5	-1.232895	0.837897	1.192770	0.450929	-1.741813	-0.653622	-0.177525	1.876793
股票6	-0.426871	1.140176	0.182610	-0.289479	0.824899	0.115661	-0.531914	-0.960658
股票7	0.927928	0.260863	0.083166	-0.945330	-0.779561	0.230067	-0.819715	-1.367425
股票8	0.822418	0.542014	-0.193316	0.505767	-0.425458	-0.242475	-0.035267	-0.022685
股票9	1.674801	-1.232659	-2.881999	-1.077620	-1.378445	-0.135817	2.060139	1.189861
股票10	0.607444	0.523483	0.764183	-0.733856	0.548573	0.273106	-0.264642	0.773705

1. Pandas 主要数据结构

1.1 DataFrame

stock_df

	2020-03-30	2020-03-31	2020-04-01	2020-04-02	2020-04-03	2020-04-04	2020-04-05	2020-04-06
股票1	0.740580	0.786047	-0.152641	0.056805	0.093881	0.731375	-1.523384	1.711565
股票2	0.422049	0.625417	-1.415830	-0.274347	0.985871	-0.557979	0.310265	-0.479645
股票3	0.997411	-0.943973	-0.407830	-1.336312	-0.012484	1.187341	-0.254304	-0.742641
股票4	0.341567	-0.406213	0.828614	0.127213	1.041014	0.790613	-0.603255	1.299546
股票5	-1.232895	0.837897	1.192770	0.450929	-1.741813	-0.653622	-0.177525	1.876793
股票6	-0.426871	1.140176	0.182610	-0.289479	0.824899	0.115661	-0.531914	-0.960658
股票7	0.927928	0.260863	0.083166	-0.945330	-0.779561	0.230067	-0.819715	-1.367425
股票8	0.822418	0.542014	-0.193316	0.505767	-0.425458	-0.242475	-0.035267	-0.022685
股票9	1.674801	-1.232659	-2.881999	-1.077620	-1.378445	-0.135817	2.060139	1.189861
股票10	0.607444	0.523483	0.764183	-0.733856	0.548573	0.273106	-0.264642	0.773705

# 查看DataFrame形状,类似于2d array
stock_df.shape

(10, 8)

# 取行索引
stock_df.index

Index(['股票1', '股票2', '股票3', '股票4', '股票5', '股票6', '股票7', '股票8', '股票9', '股票10'], dtype='object')

# 取列索引
stock_df.columns

DatetimeIndex(['2020-03-30', '2020-03-31', '2020-04-01', '2020-04-02',
               '2020-04-03', '2020-04-04', '2020-04-05', '2020-04-06'],
              dtype='datetime64[ns]', freq='D')

# 取ndarray的值
stock_df.values

array([[ 0.74057955,  0.78604657, -0.15264135,  0.05680483,  0.09388135,
         0.7313751 , -1.52338443,  1.71156505],
       [ 0.42204925,  0.62541715, -1.41583042, -0.27434654,  0.98587136,
        -0.55797884,  0.31026482, -0.47964535],
       [ 0.99741102, -0.94397298, -0.40782973, -1.33631227, -0.0124836 ,
         1.1873408 , -0.25430393, -0.74264106],
       [ 0.34156662, -0.40621262,  0.82861416,  0.1272128 ,  1.04101412,
         0.79061324, -0.60325544,  1.29954581],
       [-1.23289547,  0.83789748,  1.19276989,  0.45092868, -1.7418129 ,
        -0.65362211, -0.17752493,  1.87679286],
       [-0.4268705 ,  1.14017572,  0.18261009, -0.28947877,  0.82489897,
         0.11566058, -0.53191371, -0.96065812],
       [ 0.92792797,  0.26086313,  0.08316582, -0.94533007, -0.77956139,
         0.23006703, -0.81971461, -1.36742474],
       [ 0.82241768,  0.54201367, -0.19331564,  0.50576697, -0.42545839,
        -0.24247517, -0.03526651, -0.02268451],
       [ 1.67480093, -1.23265948, -2.88199942, -1.07761987, -1.37844497,
        -0.13581683,  2.06013919,  1.18986057],
       [ 0.60744357,  0.52348326,  0.76418263, -0.73385554,  0.54857341,
         0.27310645, -0.26464179,  0.77370496]])

# 取转置
stock_df.T

	股票1	股票2	股票3	股票4	股票5	股票6	股票7	股票8	股票9	股票10
2020-03-30	0.740580	0.422049	0.997411	0.341567	-1.232895	-0.426871	0.927928	0.822418	1.674801	0.607444
2020-03-31	0.786047	0.625417	-0.943973	-0.406213	0.837897	1.140176	0.260863	0.542014	-1.232659	0.523483
2020-04-01	-0.152641	-1.415830	-0.407830	0.828614	1.192770	0.182610	0.083166	-0.193316	-2.881999	0.764183
2020-04-02	0.056805	-0.274347	-1.336312	0.127213	0.450929	-0.289479	-0.945330	0.505767	-1.077620	-0.733856
2020-04-03	0.093881	0.985871	-0.012484	1.041014	-1.741813	0.824899	-0.779561	-0.425458	-1.378445	0.548573
2020-04-04	0.731375	-0.557979	1.187341	0.790613	-0.653622	0.115661	0.230067	-0.242475	-0.135817	0.273106
2020-04-05	-1.523384	0.310265	-0.254304	-0.603255	-0.177525	-0.531914	-0.819715	-0.035267	2.060139	-0.264642
2020-04-06	1.711565	-0.479645	-0.742641	1.299546	1.876793	-0.960658	-1.367425	-0.022685	1.189861	0.773705

# 查看头部几行数据, 默认5行
stock_df.head(5)
# 查看倒数几行数据
stock_df.tail()

	2020-03-30	2020-03-31	2020-04-01	2020-04-02	2020-04-03	2020-04-04	2020-04-05	2020-04-06
股票6	-0.426871	1.140176	0.182610	-0.289479	0.824899	0.115661	-0.531914	-0.960658
股票7	0.927928	0.260863	0.083166	-0.945330	-0.779561	0.230067	-0.819715	-1.367425
股票8	0.822418	0.542014	-0.193316	0.505767	-0.425458	-0.242475	-0.035267	-0.022685
股票9	1.674801	-1.232659	-2.881999	-1.077620	-1.378445	-0.135817	2.060139	1.189861
股票10	0.607444	0.523483	0.764183	-0.733856	0.548573	0.273106	-0.264642	0.773705

1.1.1 设置索引

# 只能通过对整个index 重新赋值, 整行或者整列
data_index = [['股票__{}'.format(i+1) for i in range(stock_df.shape[0])]]
stock_df.index = data_index

stock_df

	2020-03-30	2020-03-31	2020-04-01	2020-04-02	2020-04-03	2020-04-04	2020-04-05	2020-04-06
股票__1	0.740580	0.786047	-0.152641	0.056805	0.093881	0.731375	-1.523384	1.711565
股票__2	0.422049	0.625417	-1.415830	-0.274347	0.985871	-0.557979	0.310265	-0.479645
股票__3	0.997411	-0.943973	-0.407830	-1.336312	-0.012484	1.187341	-0.254304	-0.742641
股票__4	0.341567	-0.406213	0.828614	0.127213	1.041014	0.790613	-0.603255	1.299546
股票__5	-1.232895	0.837897	1.192770	0.450929	-1.741813	-0.653622	-0.177525	1.876793
股票__6	-0.426871	1.140176	0.182610	-0.289479	0.824899	0.115661	-0.531914	-0.960658
股票__7	0.927928	0.260863	0.083166	-0.945330	-0.779561	0.230067	-0.819715	-1.367425
股票__8	0.822418	0.542014	-0.193316	0.505767	-0.425458	-0.242475	-0.035267	-0.022685
股票__9	1.674801	-1.232659	-2.881999	-1.077620	-1.378445	-0.135817	2.060139	1.189861
股票__10	0.607444	0.523483	0.764183	-0.733856	0.548573	0.273106	-0.264642	0.773705

# stock_df.index[3] ='hahha'
# stock_df

1.1.2 重设索引

# reset_index在原来基础上新增一列索引
# drop=False(默认) - 不丢弃原来索引
# drop=True - 丢掉原来索引 index
stock_df.reset_index()

	level_0	2020-03-30 00:00:00	2020-03-31 00:00:00	2020-04-01 00:00:00	2020-04-02 00:00:00	2020-04-03 00:00:00	2020-04-04 00:00:00	2020-04-05 00:00:00	2020-04-06 00:00:00
0	股票__1	0.740580	0.786047	-0.152641	0.056805	0.093881	0.731375	-1.523384	1.711565
1	股票__2	0.422049	0.625417	-1.415830	-0.274347	0.985871	-0.557979	0.310265	-0.479645
2	股票__3	0.997411	-0.943973	-0.407830	-1.336312	-0.012484	1.187341	-0.254304	-0.742641
3	股票__4	0.341567	-0.406213	0.828614	0.127213	1.041014	0.790613	-0.603255	1.299546
4	股票__5	-1.232895	0.837897	1.192770	0.450929	-1.741813	-0.653622	-0.177525	1.876793
5	股票__6	-0.426871	1.140176	0.182610	-0.289479	0.824899	0.115661	-0.531914	-0.960658
6	股票__7	0.927928	0.260863	0.083166	-0.945330	-0.779561	0.230067	-0.819715	-1.367425
7	股票__8	0.822418	0.542014	-0.193316	0.505767	-0.425458	-0.242475	-0.035267	-0.022685
8	股票__9	1.674801	-1.232659	-2.881999	-1.077620	-1.378445	-0.135817	2.060139	1.189861
9	股票__10	0.607444	0.523483	0.764183	-0.733856	0.548573	0.273106	-0.264642	0.773705

stock_df.reset_index(drop=True)

	2020-03-30	2020-03-31	2020-04-01	2020-04-02	2020-04-03	2020-04-04	2020-04-05	2020-04-06
0	0.740580	0.786047	-0.152641	0.056805	0.093881	0.731375	-1.523384	1.711565
1	0.422049	0.625417	-1.415830	-0.274347	0.985871	-0.557979	0.310265	-0.479645
2	0.997411	-0.943973	-0.407830	-1.336312	-0.012484	1.187341	-0.254304	-0.742641
3	0.341567	-0.406213	0.828614	0.127213	1.041014	0.790613	-0.603255	1.299546
4	-1.232895	0.837897	1.192770	0.450929	-1.741813	-0.653622	-0.177525	1.876793
5	-0.426871	1.140176	0.182610	-0.289479	0.824899	0.115661	-0.531914	-0.960658
6	0.927928	0.260863	0.083166	-0.945330	-0.779561	0.230067	-0.819715	-1.367425
7	0.822418	0.542014	-0.193316	0.505767	-0.425458	-0.242475	-0.035267	-0.022685
8	1.674801	-1.232659	-2.881999	-1.077620	-1.378445	-0.135817	2.060139	1.189861
9	0.607444	0.523483	0.764183	-0.733856	0.548573	0.273106	-0.264642	0.773705

1.1.3 以某列为索引

stock_df.set_index(keys='2020-03-30', drop=False) #此处因为类型问题,都是drop 原来的index

	2020-03-30	2020-03-31	2020-04-01	2020-04-02	2020-04-03	2020-04-04	2020-04-05	2020-04-06
2020-03-30
0.740580	0.740580	0.786047	-0.152641	0.056805	0.093881	0.731375	-1.523384	1.711565
0.422049	0.422049	0.625417	-1.415830	-0.274347	0.985871	-0.557979	0.310265	-0.479645
0.997411	0.997411	-0.943973	-0.407830	-1.336312	-0.012484	1.187341	-0.254304	-0.742641
0.341567	0.341567	-0.406213	0.828614	0.127213	1.041014	0.790613	-0.603255	1.299546
-1.232895	-1.232895	0.837897	1.192770	0.450929	-1.741813	-0.653622	-0.177525	1.876793
-0.426871	-0.426871	1.140176	0.182610	-0.289479	0.824899	0.115661	-0.531914	-0.960658
0.927928	0.927928	0.260863	0.083166	-0.945330	-0.779561	0.230067	-0.819715	-1.367425
0.822418	0.822418	0.542014	-0.193316	0.505767	-0.425458	-0.242475	-0.035267	-0.022685
1.674801	1.674801	-1.232659	-2.881999	-1.077620	-1.378445	-0.135817	2.060139	1.189861
0.607444	0.607444	0.523483	0.764183	-0.733856	0.548573	0.273106	-0.264642	0.773705

# 字典方式创建DataFrame
df = pd.DataFrame({'month': [1, 4, 7, 10],
                    'year': [2012, 2014, 2013, 2014],
                    'sale':[55, 40, 84, 31]})

df

	month	year	sale
0	1	2012	55
1	4	2014	40
2	7	2013	84
3	10	2014	31

df.set_index(keys='month')

	year	sale
month
1	2012	55
4	2014	40
7	2013	84
10	2014	31

# 设置2个index, 就是MultiIndex (三维数据结构)
# df.set_index(keys=['month', 'year'])
df.set_index(['month', 'year'])

		sale
month	year
1	2012	55
4	2014	40
7	2013	84
10	2014	31

1.2 MultiIndex

df_m = df.set_index(['year', 'month'])

df_m

		sale
year	month
2012	1	55
2014	4	40
2013	7	84
2014	10	31

# index属性
# - names: levers的名称
# - levels: 每个level的列表值
df_m.index

MultiIndex([(2012,  1),
            (2014,  4),
            (2013,  7),
            (2014, 10)],
           names=['year', 'month'])

df_m.index.names

FrozenList(['year', 'month'])

df_m.index.levels

FrozenList([[2012, 2013, 2014], [1, 4, 7, 10]])

1.3 Series

# 自动生成从0开始的行索引 index
# Data must be 1-dimensional
pd.Series(np.arange(10))

0    0
1    1
2    2
3    3
4    4
5    5
6    6
7    7
8    8
9    9
dtype: int32

# 手动指定index值
pd.Series([6.7,5.6,3,10,2], index=['a', 'b', 'c', 'd', 'e'])

a     6.7
b     5.6
c     3.0
d    10.0
e     2.0
dtype: float64

# 通过字典创建
se = pd.Series({'red':100, 'blue':200, 'green': 500, 'yellow':1000})

se

red        100
blue       200
green      500
yellow    1000
dtype: int64

# 取索引
se.index

Index(['red', 'blue', 'green', 'yellow'], dtype='object')

# 取array值
se.values

array([ 100,  200,  500, 1000], dtype=int64)

pd.Series(np.random.normal(0, 1, (10)))

0   -0.975747
1    0.021589
2   -0.384579
3   -0.412900
4    0.218133
5   -0.866525
6   -0.777209
7   -1.032130
8    0.202134
9    0.295274
dtype: float64

2.基本数据操作

2.1 索引操作

# 使用pd.read_csv()读取本地数据
data = pd.read_csv('./data/stock_day.csv')

data

	open	high	close	low	volume	price_change	p_change	ma5	ma10	ma20	v_ma5	v_ma10	v_ma20	turnover
2018-02-27	23.53	25.88	24.16	23.53	95578.03	0.63	2.68	22.942	22.142	22.875	53782.64	46738.65	55576.11	2.39
2018-02-26	22.80	23.78	23.53	22.80	60985.11	0.69	3.02	22.406	21.955	22.942	40827.52	42736.34	56007.50	1.53
2018-02-23	22.88	23.37	22.82	22.71	52914.01	0.54	2.42	21.938	21.929	23.022	35119.58	41871.97	56372.85	1.32
2018-02-22	22.25	22.76	22.28	22.02	36105.01	0.36	1.64	21.446	21.909	23.137	35397.58	39904.78	60149.60	0.90
2018-02-14	21.49	21.99	21.92	21.48	23331.04	0.44	2.05	21.366	21.923	23.253	33590.21	42935.74	61716.11	0.58
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2015-03-06	13.17	14.48	14.28	13.13	179831.72	1.12	8.51	13.112	13.112	13.112	115090.18	115090.18	115090.18	6.16
2015-03-05	12.88	13.45	13.16	12.87	93180.39	0.26	2.02	12.820	12.820	12.820	98904.79	98904.79	98904.79	3.19
2015-03-04	12.80	12.92	12.90	12.61	67075.44	0.20	1.57	12.707	12.707	12.707	100812.93	100812.93	100812.93	2.30
2015-03-03	12.52	13.06	12.70	12.52	139071.61	0.18	1.44	12.610	12.610	12.610	117681.67	117681.67	117681.67	4.76
2015-03-02	12.25	12.67	12.52	12.20	96291.73	0.32	2.62	12.520	12.520	12.520	96291.73	96291.73	96291.73	3.30

643 rows × 14 columns

# 去除一些列,简化数据
data = data.drop(["ma5","ma10","ma20","v_ma5","v_ma10","v_ma20"], axis=1)  # axis=1 去除对应的列,与numpy相反

data

	open	high	close	low	volume	price_change	p_change	turnover
2018-02-27	23.53	25.88	24.16	23.53	95578.03	0.63	2.68	2.39
2018-02-26	22.80	23.78	23.53	22.80	60985.11	0.69	3.02	1.53
2018-02-23	22.88	23.37	22.82	22.71	52914.01	0.54	2.42	1.32
2018-02-22	22.25	22.76	22.28	22.02	36105.01	0.36	1.64	0.90
2018-02-14	21.49	21.99	21.92	21.48	23331.04	0.44	2.05	0.58
...	...	...	...	...	...	...	...	...
2015-03-06	13.17	14.48	14.28	13.13	179831.72	1.12	8.51	6.16
2015-03-05	12.88	13.45	13.16	12.87	93180.39	0.26	2.02	3.19
2015-03-04	12.80	12.92	12.90	12.61	67075.44	0.20	1.57	2.30
2015-03-03	12.52	13.06	12.70	12.52	139071.61	0.18	1.44	4.76
2015-03-02	12.25	12.67	12.52	12.20	96291.73	0.32	2.62	3.30

643 rows × 8 columns

2.1.1 直接使用行列索引

# 必须先列后行
data['high']['2018-02-27']

25.88

# data['2018-02-27']['high']

2.1.2 使用loc和iloc取索引

# loc取字符串, 先行后列
data.loc['2018-02-27']['high'] 

25.88

# 两种取值方式都可以
data.loc['2018-02-27','high'] 

25.88

data.loc['2018-02-27':'2018-02-22', 'open']

2018-02-27    23.53
2018-02-26    22.80
2018-02-23    22.88
2018-02-22    22.25
Name: open, dtype: float64

# data.loc['high']['2018-02-27']

# iloc取索引数字,先行后列
data.iloc[:3, 3:5]

	low	volume
2018-02-27	23.53	95578.03
2018-02-26	22.80	60985.11
2018-02-23	22.71	52914.01

2.1.3 使用ix取混合索引

# ix可以去数字和字符串, 先行后列
# 现版本中已被取消
# data.ix[0:4, ['open', 'close', 'high', 'low']]

# 先通过data.index去除索引并切片
data.loc[data.index[0:4], ['open', 'close', 'high', 'low']]

	open	close	high	low
2018-02-27	23.53	24.16	25.88	23.53
2018-02-26	22.80	23.53	23.78	22.80
2018-02-23	22.88	22.82	23.37	22.71
2018-02-22	22.25	22.28	22.76	22.02

data.index

Index(['2018-02-27', '2018-02-26', '2018-02-23', '2018-02-22', '2018-02-14',
       '2018-02-13', '2018-02-12', '2018-02-09', '2018-02-08', '2018-02-07',
       ...
       '2015-03-13', '2015-03-12', '2015-03-11', '2015-03-10', '2015-03-09',
       '2015-03-06', '2015-03-05', '2015-03-04', '2015-03-03', '2015-03-02'],
      dtype='object', length=643)

data.iloc[0:4, data.columns.get_indexer(['open', 'close', 'high', 'low'])]

	open	close	high	low
2018-02-27	23.53	24.16	25.88	23.53
2018-02-26	22.80	23.53	23.78	22.80
2018-02-23	22.88	22.82	23.37	22.71
2018-02-22	22.25	22.28	22.76	22.02

data.columns.get_indexer(['close'])

array([2], dtype=int64)

data

	open	high	close	low	volume	price_change	p_change	turnover
2018-02-27	23.53	25.88	24.16	23.53	95578.03	0.63	2.68	2.39
2018-02-26	22.80	23.78	23.53	22.80	60985.11	0.69	3.02	1.53
2018-02-23	22.88	23.37	22.82	22.71	52914.01	0.54	2.42	1.32
2018-02-22	22.25	22.76	22.28	22.02	36105.01	0.36	1.64	0.90
2018-02-14	21.49	21.99	21.92	21.48	23331.04	0.44	2.05	0.58
...	...	...	...	...	...	...	...	...
2015-03-06	13.17	14.48	14.28	13.13	179831.72	1.12	8.51	6.16
2015-03-05	12.88	13.45	13.16	12.87	93180.39	0.26	2.02	3.19
2015-03-04	12.80	12.92	12.90	12.61	67075.44	0.20	1.57	2.30
2015-03-03	12.52	13.06	12.70	12.52	139071.61	0.18	1.44	4.76
2015-03-02	12.25	12.67	12.52	12.20	96291.73	0.32	2.62	3.30

643 rows × 8 columns

2.2 赋值操作

data

	open	high	close	low	volume	price_change	p_change	turnover
2018-02-27	23.53	25.88	24.16	23.53	95578.03	0.63	2.68	2.39
2018-02-26	22.80	23.78	23.53	22.80	60985.11	0.69	3.02	1.53
2018-02-23	22.88	23.37	22.82	22.71	52914.01	0.54	2.42	1.32
2018-02-22	22.25	22.76	22.28	22.02	36105.01	0.36	1.64	0.90
2018-02-14	21.49	21.99	21.92	21.48	23331.04	0.44	2.05	0.58
...	...	...	...	...	...	...	...	...
2015-03-06	13.17	14.48	14.28	13.13	179831.72	1.12	8.51	6.16
2015-03-05	12.88	13.45	13.16	12.87	93180.39	0.26	2.02	3.19
2015-03-04	12.80	12.92	12.90	12.61	67075.44	0.20	1.57	2.30
2015-03-03	12.52	13.06	12.70	12.52	139071.61	0.18	1.44	4.76
2015-03-02	12.25	12.67	12.52	12.20	96291.73	0.32	2.62	3.30

643 rows × 8 columns

# 赋值方式1, 直接取对应的属性值
data.volume = 100

data.head()

	open	high	close	low	volume	price_change	p_change	turnover
2018-02-27	23.53	25.88	24.16	23.53	100	0.63	2.68	2.39
2018-02-26	22.80	23.78	23.53	22.80	100	0.69	3.02	1.53
2018-02-23	22.88	23.37	22.82	22.71	100	0.54	2.42	1.32
2018-02-22	22.25	22.76	22.28	22.02	100	0.36	1.64	0.90
2018-02-14	21.49	21.99	21.92	21.48	100	0.44	2.05	0.58

# 赋值方式2, 类似于取切片
data['low'] = 100

data.head()

	open	high	close	low	volume	price_change	p_change	turnover
2018-02-27	23.53	25.88	24.16	100	100	0.63	2.68	2.39
2018-02-26	22.80	23.78	23.53	100	100	0.69	3.02	1.53
2018-02-23	22.88	23.37	22.82	100	100	0.54	2.42	1.32
2018-02-22	22.25	22.76	22.28	100	100	0.36	1.64	0.90
2018-02-14	21.49	21.99	21.92	100	100	0.44	2.05	0.58

# 直接取出Series
data.open.head()

2018-02-27    23.53
2018-02-26    22.80
2018-02-23    22.88
2018-02-22    22.25
2018-02-14    21.49
Name: open, dtype: float64

# 直接取出Series
data['open'].head()

2018-02-27    23.53
2018-02-26    22.80
2018-02-23    22.88
2018-02-22    22.25
2018-02-14    21.49
Name: open, dtype: float64

2.3 排序

data.head()

	open	high	close	low	volume	price_change	p_change	turnover
2018-02-27	23.53	25.88	24.16	100	100	0.63	2.68	2.39
2018-02-26	22.80	23.78	23.53	100	100	0.69	3.02	1.53
2018-02-23	22.88	23.37	22.82	100	100	0.54	2.42	1.32
2018-02-22	22.25	22.76	22.28	100	100	0.36	1.64	0.90
2018-02-14	21.49	21.99	21.92	100	100	0.44	2.05	0.58

2.3.1 以特征值排序

# by --> 传入特征值, 可以传一个或者多个,以列表形式,排前面的作为高优先级,默认升序
data.sort_values(by='open', ascending=False)

	open	high	close	low	volume	price_change	p_change	turnover
2015-06-15	34.99	34.99	31.69	100	100	-3.52	-10.00	6.82
2015-06-12	34.69	35.98	35.21	100	100	0.82	2.38	5.47
2015-06-10	34.10	36.35	33.85	100	100	0.51	1.53	9.21
2017-11-01	33.85	34.34	33.83	100	100	-0.61	-1.77	5.81
2015-06-11	33.17	34.98	34.39	100	100	0.54	1.59	5.92
...	...	...	...	...	...	...	...	...
2015-03-05	12.88	13.45	13.16	100	100	0.26	2.02	3.19
2015-03-04	12.80	12.92	12.90	100	100	0.20	1.57	2.30
2015-03-03	12.52	13.06	12.70	100	100	0.18	1.44	4.76
2015-09-02	12.30	14.11	12.36	100	100	-1.10	-8.17	2.40
2015-03-02	12.25	12.67	12.52	100	100	0.32	2.62	3.30

643 rows × 8 columns

data.sort_values(by=['open', 'high'], ascending=True)

	open	high	close	low	volume	price_change	p_change	turnover
2015-03-02	12.25	12.67	12.52	100	100	0.32	2.62	3.30
2015-09-02	12.30	14.11	12.36	100	100	-1.10	-8.17	2.40
2015-03-03	12.52	13.06	12.70	100	100	0.18	1.44	4.76
2015-03-04	12.80	12.92	12.90	100	100	0.20	1.57	2.30
2015-03-05	12.88	13.45	13.16	100	100	0.26	2.02	3.19
...	...	...	...	...	...	...	...	...
2015-06-11	33.17	34.98	34.39	100	100	0.54	1.59	5.92
2017-11-01	33.85	34.34	33.83	100	100	-0.61	-1.77	5.81
2015-06-10	34.10	36.35	33.85	100	100	0.51	1.53	9.21
2015-06-12	34.69	35.98	35.21	100	100	0.82	2.38	5.47
2015-06-15	34.99	34.99	31.69	100	100	-3.52	-10.00	6.82

643 rows × 8 columns

# Series 排序因为只有一个特征值,所以不需要传参
data.close.sort_values()

2015-09-02    12.36
2015-03-02    12.52
2015-03-03    12.70
2015-09-07    12.77
2015-03-04    12.90
              ...  
2017-11-01    33.83
2015-06-10    33.85
2015-06-11    34.39
2017-10-31    34.44
2015-06-12    35.21
Name: close, Length: 643, dtype: float64

2.3.2 以索引排序

# DataFrame 使用sort_index 以索引排序
data.sort_index()

	open	high	close	low	volume	price_change	p_change	turnover
2015-03-02	12.25	12.67	12.52	100	100	0.32	2.62	3.30
2015-03-03	12.52	13.06	12.70	100	100	0.18	1.44	4.76
2015-03-04	12.80	12.92	12.90	100	100	0.20	1.57	2.30
2015-03-05	12.88	13.45	13.16	100	100	0.26	2.02	3.19
2015-03-06	13.17	14.48	14.28	100	100	1.12	8.51	6.16
...	...	...	...	...	...	...	...	...
2018-02-14	21.49	21.99	21.92	100	100	0.44	2.05	0.58
2018-02-22	22.25	22.76	22.28	100	100	0.36	1.64	0.90
2018-02-23	22.88	23.37	22.82	100	100	0.54	2.42	1.32
2018-02-26	22.80	23.78	23.53	100	100	0.69	3.02	1.53
2018-02-27	23.53	25.88	24.16	100	100	0.63	2.68	2.39

643 rows × 8 columns

# Series 排序
data.high.sort_index()

2015-03-02    12.67
2015-03-03    13.06
2015-03-04    12.92
2015-03-05    13.45
2015-03-06    14.48
              ...  
2018-02-14    21.99
2018-02-22    22.76
2018-02-23    23.37
2018-02-26    23.78
2018-02-27    25.88
Name: high, Length: 643, dtype: float64

3. DataFrame运算

3.1 算数运算

data.head()

	open	high	close	low	volume	price_change	p_change	turnover
2018-02-27	23.53	25.88	24.16	100	100	0.63	2.68	2.39
2018-02-26	22.80	23.78	23.53	100	100	0.69	3.02	1.53
2018-02-23	22.88	23.37	22.82	100	100	0.54	2.42	1.32
2018-02-22	22.25	22.76	22.28	100	100	0.36	1.64	0.90
2018-02-14	21.49	21.99	21.92	100	100	0.44	2.05	0.58

# 推荐使用pd.方法
data['close'].add(100).head()

2018-02-27    124.16
2018-02-26    123.53
2018-02-23    122.82
2018-02-22    122.28
2018-02-14    121.92
Name: close, dtype: float64

# 使用符号运算
(data.close + 100).head()

2018-02-27    124.16
2018-02-26    123.53
2018-02-23    122.82
2018-02-22    122.28
2018-02-14    121.92
Name: close, dtype: float64

data.close.sub(10).head()

2018-02-27    14.16
2018-02-26    13.53
2018-02-23    12.82
2018-02-22    12.28
2018-02-14    11.92
Name: close, dtype: float64

3.2 逻辑运算

3.2.1 逻辑运算符 （ <, > , |, &)

data.head()

	open	high	close	low	volume	price_change	p_change	turnover
2018-02-27	23.53	25.88	24.16	100	100	0.63	2.68	2.39
2018-02-26	22.80	23.78	23.53	100	100	0.69	3.02	1.53
2018-02-23	22.88	23.37	22.82	100	100	0.54	2.42	1.32
2018-02-22	22.25	22.76	22.28	100	100	0.36	1.64	0.90
2018-02-14	21.49	21.99	21.92	100	100	0.44	2.05	0.58

# data.open 返回数据 True, False
# data[data.open] 逻辑判断的结果作为筛选依据
data['open'] > 23

2018-02-27     True
2018-02-26    False
2018-02-23    False
2018-02-22    False
2018-02-14    False
              ...  
2015-03-06    False
2015-03-05    False
2015-03-04    False
2015-03-03    False
2015-03-02    False
Name: open, Length: 643, dtype: bool

data[data.open>23].head()

	open	high	close	low	volume	price_change	p_change	turnover
2018-02-27	23.53	25.88	24.16	100	100	0.63	2.68	2.39
2018-02-01	23.71	23.86	22.42	100	100	-1.30	-5.48	1.66
2018-01-31	23.85	23.98	23.72	100	100	-0.11	-0.46	1.23
2018-01-30	23.71	24.08	23.83	100	100	0.05	0.21	0.81
2018-01-29	24.40	24.63	23.77	100	100	-0.73	-2.98	1.64

# 利用与或 (& |)完成逻辑判断 
# 优先级问题，多加括号
data[(data.close>23) & (data.close<24)].head()

	open	high	close	low	volume	price_change	p_change	turnover
2018-02-26	22.80	23.78	23.53	100	100	0.69	3.02	1.53
2018-02-05	22.45	23.39	23.27	100	100	0.65	2.87	1.31
2018-01-31	23.85	23.98	23.72	100	100	-0.11	-0.46	1.23
2018-01-30	23.71	24.08	23.83	100	100	0.05	0.21	0.81
2018-01-29	24.40	24.63	23.77	100	100	-0.73	-2.98	1.64

3.2.2 逻辑运算函数

# query（str) 传入字符串
data.query('close>23 & close<24').head()

	open	high	close	low	volume	price_change	p_change	turnover
2018-02-26	22.80	23.78	23.53	100	100	0.69	3.02	1.53
2018-02-05	22.45	23.39	23.27	100	100	0.65	2.87	1.31
2018-01-31	23.85	23.98	23.72	100	100	-0.11	-0.46	1.23
2018-01-30	23.71	24.08	23.83	100	100	0.05	0.21	0.81
2018-01-29	24.40	24.63	23.77	100	100	-0.73	-2.98	1.64

# isin() 可以传一个值, 也可以传一个列表范围, 判断是否在某个范围内
data['open'].isin([22.80, 23.00])

2018-02-27    False
2018-02-26     True
2018-02-23    False
2018-02-22    False
2018-02-14    False
              ...  
2015-03-06    False
2015-03-05    False
2015-03-04    False
2015-03-03    False
2015-03-02    False
Name: open, Length: 643, dtype: bool

data[data['open'].isin([22.80, 23.00])]

	open	high	close	low	volume	price_change	p_change	turnover
2018-02-26	22.8	23.78	23.53	100	100	0.69	3.02	1.53
2018-02-06	22.8	23.55	22.29	100	100	-0.97	-4.17	1.39
2017-12-18	23.0	23.49	23.13	100	100	0.12	0.52	0.74
2017-07-24	22.8	23.79	23.03	100	100	-0.17	-0.73	2.59
2017-06-21	23.0	23.84	23.57	100	100	-0.51	-2.12	5.13
2016-01-04	22.8	22.84	20.69	100	100	-2.28	-9.93	1.60

3.3 统计运算

3.3.1 describe()

# describe()方法可以快速的查看DataFrame的整体属性
# 25% - 第一四分位数(Q1),样本中从小到大排列后第25%的数据
# 50% - 中位数
data.describe()

	open	high	close	low	volume	price_change	p_change	turnover
count	643.000000	643.000000	643.000000	643.0	643.0	643.000000	643.000000	643.000000
mean	21.272706	21.900513	21.336267	100.0	100.0	0.018802	0.190280	2.936190
std	3.930973	4.077578	3.942806	0.0	0.0	0.898476	4.079698	2.079375
min	12.250000	12.670000	12.360000	100.0	100.0	-3.520000	-10.030000	0.040000
25%	19.000000	19.500000	19.045000	100.0	100.0	-0.390000	-1.850000	1.360000
50%	21.440000	21.970000	21.450000	100.0	100.0	0.050000	0.260000	2.500000
75%	23.400000	24.065000	23.415000	100.0	100.0	0.455000	2.305000	3.915000
max	34.990000	36.350000	35.210000	100.0	100.0	3.030000	10.030000	12.560000

3.3.2 统计函数

# max(), min()
data.max()

open             34.99
high             36.35
close            35.21
low             100.00
volume          100.00
price_change      3.03
p_change         10.03
turnover         12.56
dtype: float64

data.std()
# data.var()

open            3.930973
high            4.077578
close           3.942806
low             0.000000
volume          0.000000
price_change    0.898476
p_change        4.079698
turnover        2.079375
dtype: float64

data.median()

open             21.44
high             21.97
close            21.45
low             100.00
volume          100.00
price_change      0.05
p_change          0.26
turnover          2.50
dtype: float64

# idxmax ( index-max) 最大值的索引值
data.idxmax()
# data,idxmin()

open            2015-06-15
high            2015-06-10
close           2015-06-12
low             2018-02-27
volume          2018-02-27
price_change    2015-06-09
p_change        2015-08-28
turnover        2017-10-26
dtype: object

3.4 累计统计函数

# 常见累计统计函数为:
# cumsum - 累加
# cummax - 累计取最大值, 新的最大值替换原来的最大值
# cummin - 累计取最小值
# cumprod - 累积

data = data.sort_index()
data.p_change

2015-03-02    2.62
2015-03-03    1.44
2015-03-04    1.57
2015-03-05    2.02
2015-03-06    8.51
              ... 
2018-02-14    2.05
2018-02-22    1.64
2018-02-23    2.42
2018-02-26    3.02
2018-02-27    2.68
Name: p_change, Length: 643, dtype: float64

data.p_change.cumsum()

2015-03-02      2.62
2015-03-03      4.06
2015-03-04      5.63
2015-03-05      7.65
2015-03-06     16.16
               ...  
2018-02-14    112.59
2018-02-22    114.23
2018-02-23    116.65
2018-02-26    119.67
2018-02-27    122.35
Name: p_change, Length: 643, dtype: float64

# 利用Pandas自带的绘图功能, 需要运行2次才能出结果
data.p_change.cumsum().plot()

<matplotlib.axes._subplots.AxesSubplot at 0x1d54ecb2848>

data.p_change.cummax().plot()

<matplotlib.axes._subplots.AxesSubplot at 0x1d54ff73a88>

3.5 自定义函数

# apply(func), fun - lambda函数
data[['open']] # [[]]取出DataFrame

	open
2015-03-02	12.25
2015-03-03	12.52
2015-03-04	12.80
2015-03-05	12.88
2015-03-06	13.17
...	...
2018-02-14	21.49
2018-02-22	22.25
2018-02-23	22.88
2018-02-26	22.80
2018-02-27	23.53

643 rows × 1 columns

data[['open']].apply(lambda x: x.max()-x.min()) # 默认axis=0

open    22.74
dtype: float64

4. Pandas内置画图

# DataFrame(x, y, kind='line')
# kind: 绘图的类型, line, bar, barh, hist, pie, scatter

# DataFrame
data['open'].plot(kind='hist')

<matplotlib.axes._subplots.AxesSubplot at 0x1d54ffe6c88>

5. 文件读取与存储

5.1 csv文件

# usecols -abs 读取特定列,列表形式传入
# sep=',' 分隔

# 读取文件
data = pd.read_csv('./data/stock_day.csv', usecols=['open', 'high', 'low'], sep=',')

data

	open	high	low
2018-02-27	23.53	25.88	23.53
2018-02-26	22.80	23.78	22.80
2018-02-23	22.88	23.37	22.71
2018-02-22	22.25	22.76	22.02
2018-02-14	21.49	21.99	21.48
...	...	...	...
2015-03-06	13.17	14.48	13.13
2015-03-05	12.88	13.45	12.87
2015-03-04	12.80	12.92	12.61
2015-03-03	12.52	13.06	12.52
2015-03-02	12.25	12.67	12.20

643 rows × 3 columns

# 存储文件
# columns ：存储指定列,
# index：是否存储index
data[:10].to_csv('./data/test_write_in.csv',columns=['high', 'low'], index=False)

5.2 hdf文件

# hdf文件格式是官方推荐的格式，存储读取速度快
# 压缩方式读取速度快，节省空间
# 支持跨平台

day_eps = pd.read_hdf('./data/stock_data/day/day_close.h5')

# 需要安装tables模块才能显示
# hdf文件不能直接打开，需要导入后才能打开
day_eps

	000001.SZ	000002.SZ	000004.SZ	000005.SZ	000006.SZ	000007.SZ	000008.SZ	000009.SZ	000010.SZ	000011.SZ	...	001965.SZ	603283.SH	002920.SZ	002921.SZ	300684.SZ	002922.SZ	300735.SZ	603329.SH	603655.SH	603080.SH
0	16.30	17.71	4.58	2.88	14.60	2.62	4.96	4.66	5.37	6.02	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	17.02	19.20	4.65	3.02	15.97	2.65	4.95	4.70	5.37	6.27	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	17.02	17.28	4.56	3.06	14.37	2.63	4.82	4.47	5.37	5.96	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	16.18	16.97	4.49	2.95	13.10	2.73	4.89	4.33	5.37	5.77	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	16.95	17.19	4.55	2.99	13.18	2.77	4.97	4.42	5.37	5.92	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2673	12.96	35.99	22.84	4.37	9.85	16.66	8.47	7.52	6.20	17.88	...	12.99	23.42	47.99	32.40	22.45	28.79	23.18	24.45	14.98	26.06
2674	13.08	35.84	23.02	4.41	9.85	16.66	8.49	7.48	6.01	17.75	...	12.83	25.76	45.14	35.64	24.70	31.67	25.50	26.90	16.48	28.67
2675	13.47	35.67	22.40	4.32	9.85	16.66	8.49	7.38	5.97	17.45	...	12.20	28.34	43.21	39.20	27.17	34.84	28.05	29.59	18.13	31.54
2676	13.40	35.15	22.29	4.29	9.85	16.66	8.56	7.04	5.84	17.49	...	12.11	31.17	43.76	40.88	29.89	34.84	29.64	32.55	19.94	34.69
2677	13.55	35.55	22.20	4.37	9.85	16.66	8.67	7.06	5.99	17.76	...	11.91	34.29	41.71	39.10	32.88	34.84	27.92	31.82	21.93	38.16

2678 rows × 3562 columns

# 存储格式为 .h5
day_eps_test = day_eps.to_hdf('./data/day_eps_test.h5', key='day_eps')

pd.read_hdf('./data/day_eps_test.h5')

	000001.SZ	000002.SZ	000004.SZ	000005.SZ	000006.SZ	000007.SZ	000008.SZ	000009.SZ	000010.SZ	000011.SZ	...	001965.SZ	603283.SH	002920.SZ	002921.SZ	300684.SZ	002922.SZ	300735.SZ	603329.SH	603655.SH	603080.SH
0	16.30	17.71	4.58	2.88	14.60	2.62	4.96	4.66	5.37	6.02	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	17.02	19.20	4.65	3.02	15.97	2.65	4.95	4.70	5.37	6.27	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	17.02	17.28	4.56	3.06	14.37	2.63	4.82	4.47	5.37	5.96	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	16.18	16.97	4.49	2.95	13.10	2.73	4.89	4.33	5.37	5.77	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	16.95	17.19	4.55	2.99	13.18	2.77	4.97	4.42	5.37	5.92	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2673	12.96	35.99	22.84	4.37	9.85	16.66	8.47	7.52	6.20	17.88	...	12.99	23.42	47.99	32.40	22.45	28.79	23.18	24.45	14.98	26.06
2674	13.08	35.84	23.02	4.41	9.85	16.66	8.49	7.48	6.01	17.75	...	12.83	25.76	45.14	35.64	24.70	31.67	25.50	26.90	16.48	28.67
2675	13.47	35.67	22.40	4.32	9.85	16.66	8.49	7.38	5.97	17.45	...	12.20	28.34	43.21	39.20	27.17	34.84	28.05	29.59	18.13	31.54
2676	13.40	35.15	22.29	4.29	9.85	16.66	8.56	7.04	5.84	17.49	...	12.11	31.17	43.76	40.88	29.89	34.84	29.64	32.55	19.94	34.69
2677	13.55	35.55	22.20	4.37	9.85	16.66	8.67	7.06	5.99	17.76	...	11.91	34.29	41.71	39.10	32.88	34.84	27.92	31.82	21.93	38.16

2678 rows × 3562 columns

5.3 json文件

# oritent: 读取方式
# lines: 是否按行读取

json_read = pd.read_json("./data/Sarcasm_Headlines_Dataset.json", orient="records", lines=True)

json_read

	article_link	headline	is_sarcastic
0	https://www.huffingtonpost.com/entry/versace-b...	former versace store clerk sues over secret 'b...	0
1	https://www.huffingtonpost.com/entry/roseanne-...	the 'roseanne' revival catches up to our thorn...	0
2	https://local.theonion.com/mom-starting-to-fea...	mom starting to fear son's web series closest ...	1
3	https://politics.theonion.com/boehner-just-wan...	boehner just wants wife to listen, not come up...	1
4	https://www.huffingtonpost.com/entry/jk-rowlin...	j.k. rowling wishes snape happy birthday in th...	0
...	...	...	...
26704	https://www.huffingtonpost.com/entry/american-...	american politics in moral free-fall	0
26705	https://www.huffingtonpost.com/entry/americas-...	america's best 20 hikes	0
26706	https://www.huffingtonpost.com/entry/reparatio...	reparations and obama	0
26707	https://www.huffingtonpost.com/entry/israeli-b...	israeli ban targeting boycott supporters raise...	0
26708	https://www.huffingtonpost.com/entry/gourmet-g...	gourmet gifts for the foodie 2014	0

26709 rows × 3 columns

# lines 表示存储数据分行， 否则全部为一整行
json_read.to_json('./data/test.json', orient='records', lines=True)

6.高级处理

6.1 处理缺失值

# 缺失值一般使用nan(not a number)来表示
type(np.nan)

float

# 导入数据
movie = pd.read_csv('./data/IMDB-Movie-Data.csv')
movie.head()

	Rank	Title	Genre	Description	Director	Actors	Year	Runtime (Minutes)	Rating	Votes	Revenue (Millions)	Metascore
0	1	Guardians of the Galaxy	Action,Adventure,Sci-Fi	A group of intergalactic criminals are forced ...	James Gunn	Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...	2014	121	8.1	757074	333.13	76.0
1	2	Prometheus	Adventure,Mystery,Sci-Fi	Following clues to the origin of mankind, a te...	Ridley Scott	Noomi Rapace, Logan Marshall-Green, Michael Fa...	2012	124	7.0	485820	126.46	65.0
2	3	Split	Horror,Thriller	Three girls are kidnapped by a man with a diag...	M. Night Shyamalan	James McAvoy, Anya Taylor-Joy, Haley Lu Richar...	2016	117	7.3	157606	138.12	62.0
3	4	Sing	Animation,Comedy,Family	In a city of humanoid animals, a hustling thea...	Christophe Lourdelet	Matthew McConaughey,Reese Witherspoon, Seth Ma...	2016	108	7.2	60545	270.32	59.0
4	5	Suicide Squad	Action,Adventure,Fantasy	A secret government agency recruits some of th...	David Ayer	Will Smith, Jared Leto, Margot Robbie, Viola D...	2016	123	6.2	393727	325.02	40.0

# 判断缺失值是否存在
# isnull() :nan - True
# notnull()：nan - False
pd.isnull(movie)

	Rank	Title	Genre	Description	Director	Actors	Year	Runtime (Minutes)	Rating	Votes	Revenue (Millions)	Metascore
0	False	False	False	False	False	False	False	False	False	False	False	False
1	False	False	False	False	False	False	False	False	False	False	False	False
2	False	False	False	False	False	False	False	False	False	False	False	False
3	False	False	False	False	False	False	False	False	False	False	False	False
4	False	False	False	False	False	False	False	False	False	False	False	False
...	...	...	...	...	...	...	...	...	...	...	...	...
995	False	False	False	False	False	False	False	False	False	False	True	False
996	False	False	False	False	False	False	False	False	False	False	False	False
997	False	False	False	False	False	False	False	False	False	False	False	False
998	False	False	False	False	False	False	False	False	False	False	True	False
999	False	False	False	False	False	False	False	False	False	False	False	False

1000 rows × 12 columns

np.any(pd.isnull(movie))  # 其中有任何一个为True(nan值存在), 则返回True

True

np.all(pd.notnull(movie)) # 所有的元素都非nan

False

6.1.1 丢弃缺失值

# 直接丢弃含有nan的一行数据
data = movie.dropna()

np.any(pd.isnull(data))

False

6.1.2 替换缺失值 （常见：平均值或者0）

# 使用平均值替换
# inplace=True , 表示直接对原来movie值进行修改
data = movie['Revenue (Millions)'].fillna(movie['Revenue (Millions)'].mean())

# inplace默认为False, 返回了新的替换后的一个data数据, 原来的movie中仍含有nan
np.any(pd.isnull(movie['Revenue (Millions)']))

True

movie['Revenue (Millions)'].fillna(movie['Revenue (Millions)'].mean(), inplace=True)

# movie['Revenue (Millions)'] 中的nan 已经被替换
np.any(pd.isnull(movie['Revenue (Millions)']))

False

6.1.3 缺失值不是nan

# 全局取消证书验证
# 读取数据
import ssl
ssl._create_default_https_context = ssl._create_unverified_context

wis = pd.read_csv("https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data")

# 先将? 数据替换成nan
# 再对nan进行处理

wis

	1000025	5	1	1.1	1.2	2	1.3	3	1.4	1.5	2.1
0	1002945	5	4	4	5	7	10	3	2	1	2
1	1015425	3	1	1	1	2	2	3	1	1	2
2	1016277	6	8	8	1	3	4	3	7	1	2
3	1017023	4	1	1	3	2	1	3	1	1	2
4	1017122	8	10	10	8	7	10	9	7	1	4
...	...	...	...	...	...	...	...	...	...	...	...
693	776715	3	1	1	1	3	2	1	1	1	2
694	841769	2	1	1	1	2	1	1	1	1	2
695	888820	5	10	10	3	7	3	8	10	2	4
696	897471	4	8	6	4	3	4	10	6	1	4
697	897471	4	8	8	5	4	5	10	4	1	4

698 rows × 11 columns

# to_replace: 被替换的值, value:去替换的值
wis = wis.replace(to_replace='?', value=np.nan)
wis = wis.dropna()

wis

	1000025	5	1	1.1	1.2	2	1.3	3	1.4	1.5	2.1
0	1002945	5	4	4	5	7	10	3	2	1	2
1	1015425	3	1	1	1	2	2	3	1	1	2
2	1016277	6	8	8	1	3	4	3	7	1	2
3	1017023	4	1	1	3	2	1	3	1	1	2
4	1017122	8	10	10	8	7	10	9	7	1	4
...	...	...	...	...	...	...	...	...	...	...	...
693	776715	3	1	1	1	3	2	1	1	1	2
694	841769	2	1	1	1	2	1	1	1	1	2
695	888820	5	10	10	3	7	3	8	10	2	4
696	897471	4	8	6	4	3	4	10	6	1	4
697	897471	4	8	8	5	4	5	10	4	1	4

682 rows × 11 columns

6.2 数据离散化

# 数据离散化可以简化数据结构,将数据划分到若干离散的区间,可以简化数据结构, 常用于搭配one-hot编码

# 获取数据
data = pd.read_csv("./data/stock_day.csv")
data_p= data['p_change']

data_p

2018-02-27    2.68
2018-02-26    3.02
2018-02-23    2.42
2018-02-22    1.64
2018-02-14    2.05
              ... 
2015-03-06    8.51
2015-03-05    2.02
2015-03-04    1.57
2015-03-03    1.44
2015-03-02    2.62
Name: p_change, Length: 643, dtype: float64

# pd.qcut() 智能分组
# q: 分组数量
q_cut = pd.qcut(data_p, q=10)
q_cut

2018-02-27    (1.738, 2.938]
2018-02-26     (2.938, 5.27]
2018-02-23    (1.738, 2.938]
2018-02-22     (0.94, 1.738]
2018-02-14    (1.738, 2.938]
                   ...      
2015-03-06     (5.27, 10.03]
2015-03-05    (1.738, 2.938]
2015-03-04     (0.94, 1.738]
2015-03-03     (0.94, 1.738]
2015-03-02    (1.738, 2.938]
Name: p_change, Length: 643, dtype: category
Categories (10, interval[float64]): [(-10.030999999999999, -4.836] < (-4.836, -2.444] < (-2.444, -1.352] < (-1.352, -0.462] ... (0.94, 1.738] < (1.738, 2.938] < (2.938, 5.27] < (5.27, 10.03]]

# value_counts(): 每个分组区间内的数据数量
q_cut.value_counts()

(5.27, 10.03]                    65
(0.26, 0.94]                     65
(-0.462, 0.26]                   65
(-10.030999999999999, -4.836]    65
(2.938, 5.27]                    64
(1.738, 2.938]                   64
(-1.352, -0.462]                 64
(-2.444, -1.352]                 64
(-4.836, -2.444]                 64
(0.94, 1.738]                    63
Name: p_change, dtype: int64

# pd.cut(data, bins): 自己指定分组区间
bins  = [-100, -7, -5, -3, 0, 3, 5, 7, 100]
cut = pd.cut(data_p, bins=bins)

cut

2018-02-27      (0, 3]
2018-02-26      (3, 5]
2018-02-23      (0, 3]
2018-02-22      (0, 3]
2018-02-14      (0, 3]
                ...   
2015-03-06    (7, 100]
2015-03-05      (0, 3]
2015-03-04      (0, 3]
2015-03-03      (0, 3]
2015-03-02      (0, 3]
Name: p_change, Length: 643, dtype: category
Categories (8, interval[int64]): [(-100, -7] < (-7, -5] < (-5, -3] < (-3, 0] < (0, 3] < (3, 5] < (5, 7] < (7, 100]]

cut.value_counts()

(0, 3]        215
(-3, 0]       188
(3, 5]         57
(-5, -3]       51
(7, 100]       35
(5, 7]         35
(-100, -7]     34
(-7, -5]       28
Name: p_change, dtype: int64

# get_dummies() 取独热矩阵
pd.get_dummies(q_cut)

	(-10.030999999999999, -4.836]	(-4.836, -2.444]	(-2.444, -1.352]	(-1.352, -0.462]	(-0.462, 0.26]	(0.26, 0.94]	(0.94, 1.738]	(1.738, 2.938]	(2.938, 5.27]	(5.27, 10.03]
2018-02-27	0	0	0	0	0	0	0	1	0	0
2018-02-26	0	0	0	0	0	0	0	0	1	0
2018-02-23	0	0	0	0	0	0	0	1	0	0
2018-02-22	0	0	0	0	0	0	1	0	0	0
2018-02-14	0	0	0	0	0	0	0	1	0	0
...	...	...	...	...	...	...	...	...	...	...
2015-03-06	0	0	0	0	0	0	0	0	0	1
2015-03-05	0	0	0	0	0	0	0	1	0	0
2015-03-04	0	0	0	0	0	0	1	0	0	0
2015-03-03	0	0	0	0	0	0	1	0	0	0
2015-03-02	0	0	0	0	0	0	0	1	0	0

643 rows × 10 columns

data_dummy = pd.get_dummies(q_cut)

6.3 数据拼接

6.3.1 pd.concat()

# 不指定axis 可能会造成拼接错位,产生很多nan
pd.concat([data, data_dummy], axis=1)

	open	high	close	low	volume	price_change	p_change	ma5	ma10	ma20	...	(-10.030999999999999, -4.836]	(-4.836, -2.444]	(-2.444, -1.352]	(-1.352, -0.462]	(-0.462, 0.26]	(0.26, 0.94]	(0.94, 1.738]	(1.738, 2.938]	(2.938, 5.27]	(5.27, 10.03]
2018-02-27	23.53	25.88	24.16	23.53	95578.03	0.63	2.68	22.942	22.142	22.875	...	0	0	0	0	0	0	0	1	0	0
2018-02-26	22.80	23.78	23.53	22.80	60985.11	0.69	3.02	22.406	21.955	22.942	...	0	0	0	0	0	0	0	0	1	0
2018-02-23	22.88	23.37	22.82	22.71	52914.01	0.54	2.42	21.938	21.929	23.022	...	0	0	0	0	0	0	0	1	0	0
2018-02-22	22.25	22.76	22.28	22.02	36105.01	0.36	1.64	21.446	21.909	23.137	...	0	0	0	0	0	0	1	0	0	0
2018-02-14	21.49	21.99	21.92	21.48	23331.04	0.44	2.05	21.366	21.923	23.253	...	0	0	0	0	0	0	0	1	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2015-03-06	13.17	14.48	14.28	13.13	179831.72	1.12	8.51	13.112	13.112	13.112	...	0	0	0	0	0	0	0	0	0	1
2015-03-05	12.88	13.45	13.16	12.87	93180.39	0.26	2.02	12.820	12.820	12.820	...	0	0	0	0	0	0	0	1	0	0
2015-03-04	12.80	12.92	12.90	12.61	67075.44	0.20	1.57	12.707	12.707	12.707	...	0	0	0	0	0	0	1	0	0	0
2015-03-03	12.52	13.06	12.70	12.52	139071.61	0.18	1.44	12.610	12.610	12.610	...	0	0	0	0	0	0	1	0	0	0
2015-03-02	12.25	12.67	12.52	12.20	96291.73	0.32	2.62	12.520	12.520	12.520	...	0	0	0	0	0	0	0	1	0	0

643 rows × 24 columns

6.3.2 pd.merge()

# 获取数据
left = pd.DataFrame({'key1': ['K0', 'K0', 'K1', 'K2'],
                        'key2': ['K0', 'K1', 'K0', 'K1'],
                        'A': ['A0', 'A1', 'A2', 'A3'],
                        'B': ['B0', 'B1', 'B2', 'B3']})

right = pd.DataFrame({'key1': ['K0', 'K1', 'K1', 'K2'],
                        'key2': ['K0', 'K0', 'K0', 'K0'],
                        'C': ['C0', 'C1', 'C2', 'C3'],
                        'D': ['D0', 'D1', 'D2', 'D3']})
# 默认内连接
# on: 以什么作为键来拼接
result = pd.merge(left, right, on=['key1', 'key2'])

# 内连接就是取交集
result

	key1	key2	A	B	C	D
0	K0	K0	A0	B0	C0	D0
1	K1	K0	A2	B2	C1	D1
2	K1	K0	A2	B2	C2	D2

# 外连接
pd.merge(left, right, on=['key1', 'key2'], how='outer')

	key1	key2	A	B	C	D
0	K0	K0	A0	B0	C0	D0
1	K0	K1	A1	B1	NaN	NaN
2	K1	K0	A2	B2	C1	D1
3	K1	K0	A2	B2	C2	D2
4	K2	K1	A3	B3	NaN	NaN
5	K2	K0	NaN	NaN	C3	D3

# 左连接
pd.merge(left, right, on=['key1', 'key2'], how='left')

	key1	key2	A	B	C	D
0	K0	K0	A0	B0	C0	D0
1	K0	K1	A1	B1	NaN	NaN
2	K1	K0	A2	B2	C1	D1
3	K1	K0	A2	B2	C2	D2
4	K2	K1	A3	B3	NaN	NaN

# 右连接
pd.merge(left, right, on=['key1', 'key2'], how='right')

	key1	key2	A	B	C	D
0	K0	K0	A0	B0	C0	D0
1	K1	K0	A2	B2	C1	D1
2	K1	K0	A2	B2	C2	D2
3	K2	K0	NaN	NaN	C3	D3

6.4 交叉表和透视表

data.head()

	open	high	close	low	volume	price_change	p_change	ma5	ma10	ma20	v_ma5	v_ma10	v_ma20	turnover
2018-02-27	23.53	25.88	24.16	23.53	95578.03	0.63	2.68	22.942	22.142	22.875	53782.64	46738.65	55576.11	2.39
2018-02-26	22.80	23.78	23.53	22.80	60985.11	0.69	3.02	22.406	21.955	22.942	40827.52	42736.34	56007.50	1.53
2018-02-23	22.88	23.37	22.82	22.71	52914.01	0.54	2.42	21.938	21.929	23.022	35119.58	41871.97	56372.85	1.32
2018-02-22	22.25	22.76	22.28	22.02	36105.01	0.36	1.64	21.446	21.909	23.137	35397.58	39904.78	60149.60	0.90
2018-02-14	21.49	21.99	21.92	21.48	23331.04	0.44	2.05	21.366	21.923	23.253	33590.21	42935.74	61716.11	0.58

# 将date.index 转化为datetime格式
date = pd.to_datetime(data.index).weekday
data['week'] = date

data

	open	high	close	low	volume	price_change	p_change	ma5	ma10	ma20	v_ma5	v_ma10	v_ma20	turnover	posi_neg	week
2018-02-27	23.53	25.88	24.16	23.53	95578.03	0.63	2.68	22.942	22.142	22.875	53782.64	46738.65	55576.11	2.39	1	1
2018-02-26	22.80	23.78	23.53	22.80	60985.11	0.69	3.02	22.406	21.955	22.942	40827.52	42736.34	56007.50	1.53	1	0
2018-02-23	22.88	23.37	22.82	22.71	52914.01	0.54	2.42	21.938	21.929	23.022	35119.58	41871.97	56372.85	1.32	1	4
2018-02-22	22.25	22.76	22.28	22.02	36105.01	0.36	1.64	21.446	21.909	23.137	35397.58	39904.78	60149.60	0.90	1	3
2018-02-14	21.49	21.99	21.92	21.48	23331.04	0.44	2.05	21.366	21.923	23.253	33590.21	42935.74	61716.11	0.58	1	2
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2015-03-06	13.17	14.48	14.28	13.13	179831.72	1.12	8.51	13.112	13.112	13.112	115090.18	115090.18	115090.18	6.16	1	4
2015-03-05	12.88	13.45	13.16	12.87	93180.39	0.26	2.02	12.820	12.820	12.820	98904.79	98904.79	98904.79	3.19	1	3
2015-03-04	12.80	12.92	12.90	12.61	67075.44	0.20	1.57	12.707	12.707	12.707	100812.93	100812.93	100812.93	2.30	1	2
2015-03-03	12.52	13.06	12.70	12.52	139071.61	0.18	1.44	12.610	12.610	12.610	117681.67	117681.67	117681.67	4.76	1	1
2015-03-02	12.25	12.67	12.52	12.20	96291.73	0.32	2.62	12.520	12.520	12.520	96291.73	96291.73	96291.73	3.30	1	0

643 rows × 16 columns

# 把p_change 划分为0, 1两类
data['posi_neg'] = np.where(data['p_change']>0, 1, 0)

data.head()

	open	high	close	low	volume	price_change	p_change	ma5	ma10	ma20	v_ma5	v_ma10	v_ma20	turnover	posi_neg	week
2018-02-27	23.53	25.88	24.16	23.53	95578.03	0.63	2.68	22.942	22.142	22.875	53782.64	46738.65	55576.11	2.39	1	1
2018-02-26	22.80	23.78	23.53	22.80	60985.11	0.69	3.02	22.406	21.955	22.942	40827.52	42736.34	56007.50	1.53	1	0
2018-02-23	22.88	23.37	22.82	22.71	52914.01	0.54	2.42	21.938	21.929	23.022	35119.58	41871.97	56372.85	1.32	1	4
2018-02-22	22.25	22.76	22.28	22.02	36105.01	0.36	1.64	21.446	21.909	23.137	35397.58	39904.78	60149.60	0.90	1	3
2018-02-14	21.49	21.99	21.92	21.48	23331.04	0.44	2.05	21.366	21.923	23.253	33590.21	42935.74	61716.11	0.58	1	2

# 手动构造交叉表
count = pd.crosstab(data['week'], data['posi_neg'])

sum = count.sum(axis=1).astype(np.float32)
pro = count.div(sum, axis=0)

pro.plot(kind='bar', stacked=True)

<matplotlib.axes._subplots.AxesSubplot at 0x1d55bc28408>

# 自动构造交叉表
data.pivot_table(['posi_neg'], index='week')

	posi_neg
week
0	0.496000
1	0.580153
2	0.537879
3	0.507812
4	0.535433

6.5 分组和聚合

6.5.1 pd.groupby()

# 创建数据
col =pd.DataFrame({'color': ['white','red','green','red','green'], 'object': ['pen','pencil','pencil','ashtray','pen'],'price1':[5.56,4.20,1.30,0.56,2.75],'price2':[4.75,4.12,1.60,0.75,3.15]})

col

	color	object	price1	price2
0	white	pen	5.56	4.75
1	red	pencil	4.20	4.12
2	green	pencil	1.30	1.60
3	red	ashtray	0.56	0.75
4	green	pen	2.75	3.15

# DataFrame 分组, 推荐使用
# 单独的分组没有意义,进行聚合(求值)才有价值
col.groupby(['color'])['price1'].mean()

color
green    2.025
red      2.380
white    5.560
Name: price1, dtype: float64

# 如果设置as_index= False 会创建一列新索引
col.groupby(['color'], as_index= False)['price1'].mean()

	color	price1
0	green	2.025
1	red	2.380
2	white	5.560

col.price1

0    5.56
1    4.20
2    1.30
3    0.56
4    2.75
Name: price1, dtype: float64

# Series 分组
col.price1.groupby(col['color']).mean()

color
green    2.025
red      2.380
white    5.560
Name: price1, dtype: float64

6.5.2 分组实例

# 获取数据
starbucks = pd.read_csv("./data/starbucks/directory.csv")

starbucks.head()

	Brand	Store Number	Store Name	Ownership Type	Street Address	City	State/Province	Country	Postcode	Phone Number	Timezone	Longitude	Latitude
0	Starbucks	47370-257954	Meritxell, 96	Licensed	Av. Meritxell, 96	Andorra la Vella	7	AD	AD500	376818720	GMT+1:00 Europe/Andorra	1.53	42.51
1	Starbucks	22331-212325	Ajman Drive Thru	Licensed	1 Street 69, Al Jarf	Ajman	AJ	AE	NaN	NaN	GMT+04:00 Asia/Dubai	55.47	25.42
2	Starbucks	47089-256771	Dana Mall	Licensed	Sheikh Khalifa Bin Zayed St.	Ajman	AJ	AE	NaN	NaN	GMT+04:00 Asia/Dubai	55.47	25.39
3	Starbucks	22126-218024	Twofour 54	Licensed	Al Salam Street	Abu Dhabi	AZ	AE	NaN	NaN	GMT+04:00 Asia/Dubai	54.38	24.48
4	Starbucks	17127-178586	Al Ain Tower	Licensed	Khaldiya Area, Abu Dhabi Island	Abu Dhabi	AZ	AE	NaN	NaN	GMT+04:00 Asia/Dubai	54.54	24.51

# 以一个值为分组依据
starbucks.groupby(['Country']).count()

	Brand	Store Number	Store Name	Ownership Type	Street Address	City	State/Province	Postcode	Phone Number	Timezone	Longitude	Latitude
Country
AD	1	1	1	1	1	1	1	1	1	1	1	1
AE	144	144	144	144	144	144	144	24	78	144	144	144
AR	108	108	108	108	108	108	108	100	29	108	108	108
AT	18	18	18	18	18	18	18	18	17	18	18	18
AU	22	22	22	22	22	22	22	22	0	22	22	22
...	...	...	...	...	...	...	...	...	...	...	...	...
TT	3	3	3	3	3	3	3	3	0	3	3	3
TW	394	394	394	394	394	394	394	365	39	394	394	394
US	13608	13608	13608	13608	13608	13608	13608	13607	13122	13608	13608	13608
VN	25	25	25	25	25	25	25	25	23	25	25	25
ZA	3	3	3	3	3	3	3	3	2	3	3	3

73 rows × 12 columns

starbucks_count = starbucks.groupby(['Country']).count()
starbucks_count['Brand'].plot(kind='bar', figsize=(20, 8))

<matplotlib.axes._subplots.AxesSubplot at 0x1d563dbd148>

# 为了阅读方便,对数据进行排序后画图
starbucks_count.sort_values(by='Brand', ascending=False).head(20)['Brand'].plot(kind='bar', figsize=(20, 8))

<matplotlib.axes._subplots.AxesSubplot at 0x1d55ca68f48>

# 多种分组依据
starbucks.groupby(['Country', 'State/Province']).count().head(20)

		Brand	Store Number	Store Name	Ownership Type	Street Address	City	Postcode	Phone Number	Timezone	Longitude	Latitude
Country	State/Province
AD	7	1	1	1	1	1	1	1	1	1	1	1
AE	AJ	2	2	2	2	2	2	0	0	2	2	2
	AZ	48	48	48	48	48	48	7	20	48	48	48
	DU	82	82	82	82	82	82	16	50	82	82	82
	FU	2	2	2	2	2	2	1	0	2	2	2
	RK	3	3	3	3	3	3	0	3	3	3	3
	SH	6	6	6	6	6	6	0	5	6	6	6
	UQ	1	1	1	1	1	1	0	0	1	1	1
AR	B	21	21	21	21	21	21	18	5	21	21	21
	C	73	73	73	73	73	73	71	24	73	73	73
	M	5	5	5	5	5	5	2	0	5	5	5
	S	3	3	3	3	3	3	3	0	3	3	3
	X	6	6	6	6	6	6	6	0	6	6	6
AT	3	1	1	1	1	1	1	1	1	1	1	1
	5	3	3	3	3	3	3	3	3	3	3	3
	9	14	14	14	14	14	14	14	13	14	14	14
AU	NSW	9	9	9	9	9	9	9	0	9	9	9
	QLD	8	8	8	8	8	8	8	0	8	8	8
	VIC	5	5	5	5	5	5	5	0	5	5	5
AW	AW	3	3	3	3	3	3	0	3	3	3	3

来源：https://www.cnblogs.com/hp-lake/p/12622892.html