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3. Pandas使用

3. Pandas使用

作者: archmagus | 来源:发表于2020-06-29 16:12 被阅读0次

DataFrame的结构

import pandas as pd
import numpy as np


# 创建一个符合正态分布的10个股票的5天的涨跌幅数据
stock_change=np.random.normal(0,1,(10,5))


stock_change


array([[ 1.76548834, -2.1033278 , -0.44765295,  1.6133684 ,  0.56440317],
       [-0.28406163,  0.33190268,  0.15697138, -0.15352828,  0.39168077],
       [ 0.97804543,  0.70247765,  0.48852276, -0.84483313, -0.4901491 ],
       [ 0.32663842, -1.18491493, -1.85617695, -0.08334347, -0.88467526],
       [-0.0606477 , -0.3282171 ,  0.13469079, -0.33644424,  0.49229211],
       [-0.08971546,  0.29502656,  0.58225254, -1.15526343,  0.1121633 ],
       [ 0.08384949,  0.63896248,  0.03189999, -0.17538923, -0.06095104],
       [ 0.0892971 , -1.54426915,  0.22247612, -0.31827644,  1.4569718 ],
       [-1.31604909, -1.09219638, -0.64118638, -0.55174267,  2.54943429],
       [-0.75827099,  0.19581938, -0.11856612,  0.44903057, -0.07891904]])


# 创建dataframe
stock_num=["股票{}".format(i)for i in range(10)]  #构造行标
date=pd.date_range(start="20200629", periods=5,freq="B")


stock_change=pd.DataFrame(stock_change, index=stock_num,columns=date)
stock_change


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2020-06-29 00:00:00 2020-06-30 00:00:00 2020-07-01 00:00:00 2020-07-02 00:00:00 2020-07-03 00:00:00
股票0 1.765488 -2.103328 -0.447653 1.613368 0.564403
股票1 -0.284062 0.331903 0.156971 -0.153528 0.391681
股票2 0.978045 0.702478 0.488523 -0.844833 -0.490149
股票3 0.326638 -1.184915 -1.856177 -0.083343 -0.884675
股票4 -0.060648 -0.328217 0.134691 -0.336444 0.492292
股票5 -0.089715 0.295027 0.582253 -1.155263 0.112163
股票6 0.083849 0.638962 0.031900 -0.175389 -0.060951
股票7 0.089297 -1.544269 0.222476 -0.318276 1.456972
股票8 -1.316049 -1.092196 -0.641186 -0.551743 2.549434
股票9 -0.758271 0.195819 -0.118566 0.449031 -0.078919 
stock_change.shape


(10, 5)


stock_change.index


Index(['股票0', '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'], dtype='object')


stock_change.columns


DatetimeIndex(['2020-06-29', '2020-06-30', '2020-07-01', '2020-07-02',
               '2020-07-03'],
              dtype='datetime64[ns]', freq='B')


stock_change.values


array([[ 1.76548834, -2.1033278 , -0.44765295,  1.6133684 ,  0.56440317],
       [-0.28406163,  0.33190268,  0.15697138, -0.15352828,  0.39168077],
       [ 0.97804543,  0.70247765,  0.48852276, -0.84483313, -0.4901491 ],
       [ 0.32663842, -1.18491493, -1.85617695, -0.08334347, -0.88467526],
       [-0.0606477 , -0.3282171 ,  0.13469079, -0.33644424,  0.49229211],
       [-0.08971546,  0.29502656,  0.58225254, -1.15526343,  0.1121633 ],
       [ 0.08384949,  0.63896248,  0.03189999, -0.17538923, -0.06095104],
       [ 0.0892971 , -1.54426915,  0.22247612, -0.31827644,  1.4569718 ],
       [-1.31604909, -1.09219638, -0.64118638, -0.55174267,  2.54943429],
       [-0.75827099,  0.19581938, -0.11856612,  0.44903057, -0.07891904]])


stock_change.T


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股票0 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
2020-06-29 1.765488 -0.284062 0.978045 0.326638 -0.060648 -0.089715 0.083849 0.089297 -1.316049 -0.758271
2020-06-30 -2.103328 0.331903 0.702478 -1.184915 -0.328217 0.295027 0.638962 -1.544269 -1.092196 0.195819
2020-07-01 -0.447653 0.156971 0.488523 -1.856177 0.134691 0.582253 0.031900 0.222476 -0.641186 -0.118566
2020-07-02 1.613368 -0.153528 -0.844833 -0.083343 -0.336444 -1.155263 -0.175389 -0.318276 -0.551743 0.449031
2020-07-03 0.564403 0.391681 -0.490149 -0.884675 0.492292 0.112163 -0.060951 1.456972 2.549434 -0.078919 
stock_change.head(7)


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2020-06-29 00:00:00 2020-06-30 00:00:00 2020-07-01 00:00:00 2020-07-02 00:00:00 2020-07-03 00:00:00
股票0 1.765488 -2.103328 -0.447653 1.613368 0.564403
股票1 -0.284062 0.331903 0.156971 -0.153528 0.391681
股票2 0.978045 0.702478 0.488523 -0.844833 -0.490149
股票3 0.326638 -1.184915 -1.856177 -0.083343 -0.884675
股票4 -0.060648 -0.328217 0.134691 -0.336444 0.492292
股票5 -0.089715 0.295027 0.582253 -1.155263 0.112163
股票6 0.083849 0.638962 0.031900 -0.175389 -0.060951 
# 索引值修改
new_index=["股票00{}".format(i)for i in range(10)] 
stock_change.index=new_index


stock_change


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2020-06-29 00:00:00 2020-06-30 00:00:00 2020-07-01 00:00:00 2020-07-02 00:00:00 2020-07-03 00:00:00
股票000 1.765488 -2.103328 -0.447653 1.613368 0.564403
股票001 -0.284062 0.331903 0.156971 -0.153528 0.391681
股票002 0.978045 0.702478 0.488523 -0.844833 -0.490149
股票003 0.326638 -1.184915 -1.856177 -0.083343 -0.884675
股票004 -0.060648 -0.328217 0.134691 -0.336444 0.492292
股票005 -0.089715 0.295027 0.582253 -1.155263 0.112163
股票006 0.083849 0.638962 0.031900 -0.175389 -0.060951
股票007 0.089297 -1.544269 0.222476 -0.318276 1.456972
股票008 -1.316049 -1.092196 -0.641186 -0.551743 2.549434
股票009 -0.758271 0.195819 -0.118566 0.449031 -0.078919 
stock_change.reset_index(drop=True)


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2020-06-29 00:00:00 2020-06-30 00:00:00 2020-07-01 00:00:00 2020-07-02 00:00:00 2020-07-03 00:00:00
0 1.765488 -2.103328 -0.447653 1.613368 0.564403
1 -0.284062 0.331903 0.156971 -0.153528 0.391681
2 0.978045 0.702478 0.488523 -0.844833 -0.490149
3 0.326638 -1.184915 -1.856177 -0.083343 -0.884675
4 -0.060648 -0.328217 0.134691 -0.336444 0.492292
5 -0.089715 0.295027 0.582253 -1.155263 0.112163
6 0.083849 0.638962 0.031900 -0.175389 -0.060951
7 0.089297 -1.544269 0.222476 -0.318276 1.456972
8 -1.316049 -1.092196 -0.641186 -0.551743 2.549434
9 -0.758271 0.195819 -0.118566 0.449031 -0.078919 
stock_change


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2020-06-29 00:00:00 2020-06-30 00:00:00 2020-07-01 00:00:00 2020-07-02 00:00:00 2020-07-03 00:00:00
股票000 1.765488 -2.103328 -0.447653 1.613368 0.564403
股票001 -0.284062 0.331903 0.156971 -0.153528 0.391681
股票002 0.978045 0.702478 0.488523 -0.844833 -0.490149
股票003 0.326638 -1.184915 -1.856177 -0.083343 -0.884675
股票004 -0.060648 -0.328217 0.134691 -0.336444 0.492292
股票005 -0.089715 0.295027 0.582253 -1.155263 0.112163
股票006 0.083849 0.638962 0.031900 -0.175389 -0.060951
股票007 0.089297 -1.544269 0.222476 -0.318276 1.456972
股票008 -1.316049 -1.092196 -0.641186 -0.551743 2.549434
股票009 -0.758271 0.195819 -0.118566 0.449031 -0.078919 
stock_change.set_index("2020-6-29 00:00:00")


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2020-06-30 00:00:00 2020-07-01 00:00:00 2020-07-02 00:00:00 2020-07-03 00:00:00
2020-6-29 00:00:00
--- --- --- --- ---
1.765488 -2.103328 -0.447653 1.613368 0.564403
-0.284062 0.331903 0.156971 -0.153528 0.391681
0.978045 0.702478 0.488523 -0.844833 -0.490149
0.326638 -1.184915 -1.856177 -0.083343 -0.884675
-0.060648 -0.328217 0.134691 -0.336444 0.492292
-0.089715 0.295027 0.582253 -1.155263 0.112163
0.083849 0.638962 0.031900 -0.175389 -0.060951
0.089297 -1.544269 0.222476 -0.318276 1.456972
-1.316049 -1.092196 -0.641186 -0.551743 2.549434
-0.758271 0.195819 -0.118566 0.449031 -0.078919 
stock_change


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2020-06-29 00:00:00 2020-06-30 00:00:00 2020-07-01 00:00:00 2020-07-02 00:00:00 2020-07-03 00:00:00
股票000 1.765488 -2.103328 -0.447653 1.613368 0.564403
股票001 -0.284062 0.331903 0.156971 -0.153528 0.391681
股票002 0.978045 0.702478 0.488523 -0.844833 -0.490149
股票003 0.326638 -1.184915 -1.856177 -0.083343 -0.884675
股票004 -0.060648 -0.328217 0.134691 -0.336444 0.492292
股票005 -0.089715 0.295027 0.582253 -1.155263 0.112163
股票006 0.083849 0.638962 0.031900 -0.175389 -0.060951
股票007 0.089297 -1.544269 0.222476 -0.318276 1.456972
股票008 -1.316049 -1.092196 -0.641186 -0.551743 2.549434
股票009 -0.758271 0.195819 -0.118566 0.449031 -0.078919 
type(stock_change.loc["股票004"])


pandas.core.series.Series


stock_change.loc["股票004"]


2020-06-29   -0.060648
2020-06-30   -0.328217
2020-07-01    0.134691
2020-07-02   -0.336444
2020-07-03    0.492292
Freq: B, Name: 股票004, dtype: float64


pd.Series(np.arange(2,20,2))


0     2
1     4
2     6
3     8
4    10
5    12
6    14
7    16
8    18
dtype: int64


sr=pd.Series({'red':100,"blue":200,"queen":500, 'yellow':1000})


sr


blue       200
queen      500
red        100
yellow    1000
dtype: int64


sr.index


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


sr.values


array([ 200,  500,  100, 1000])

pandas 操作suoyin

data=pd.read_csv("./stock_day/stock_day.csv")


data = data.drop(["ma5","ma10","ma20","v_ma5","v_ma10","v_ma20"], axis=1)  #让数据简单一些


data.head()


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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 
data["open"]["2018-02-23"]   #pandas必须先列后行


22.88


data.loc["2018-02-23"]["open"]  #使用loc可以先行后列


22.88


data.loc["2018-02-26","open"]


22.8


data.iloc[1,0]


22.8


data.loc[data.index[0:4],['open','close','high','low']]


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


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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.price_change.head()


2018-02-27    0.63
2018-02-26    0.69
2018-02-23    0.54
2018-02-22    0.36
2018-02-14    0.44
Name: price_change, dtype: float64


# data["open"]=8


data.head()


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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 
data=data.sort_values(by="p_change", ascending=False).head()
data.head()


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open high close low volume price_change p_change turnover
2015-08-28 15.40 16.46 16.46 15.00 117827.60 1.50 10.03 4.03
2015-05-21 27.50 28.22 28.22 26.50 121190.11 2.57 10.02 4.15
2016-12-22 18.50 20.42 20.42 18.45 150470.83 1.86 10.02 3.77
2015-08-04 16.20 17.35 17.35 15.80 94292.63 1.58 10.02 3.23
2016-07-07 18.66 18.66 18.66 18.41 48756.55 1.70 10.02 1.67 
data=data.sort_values(by=['open','high'])


data.head()


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open high close low volume price_change p_change turnover
2015-08-28 15.40 16.46 16.46 15.00 117827.60 1.50 10.03 4.03
2015-08-04 16.20 17.35 17.35 15.80 94292.63 1.58 10.02 3.23
2016-12-22 18.50 20.42 20.42 18.45 150470.83 1.86 10.02 3.77
2016-07-07 18.66 18.66 18.66 18.41 48756.55 1.70 10.02 1.67
2015-05-21 27.50 28.22 28.22 26.50 121190.11 2.57 10.02 4.15 
data.sort_index()


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open high close low volume price_change p_change turnover
2015-05-21 27.50 28.22 28.22 26.50 121190.11 2.57 10.02 4.15
2015-08-04 16.20 17.35 17.35 15.80 94292.63 1.58 10.02 3.23
2015-08-28 15.40 16.46 16.46 15.00 117827.60 1.50 10.03 4.03
2016-07-07 18.66 18.66 18.66 18.41 48756.55 1.70 10.02 1.67
2016-12-22 18.50 20.42 20.42 18.45 150470.83 1.86 10.02 3.77 
data["p_change"].sort_values(ascending=True)


2015-08-04    10.02
2016-12-22    10.02
2016-07-07    10.02
2015-05-21    10.02
2015-08-28    10.03
Name: p_change, dtype: float64


data["p_change"].sort_index(ascending=True)


2015-05-21    10.02
2015-08-04    10.02
2015-08-28    10.03
2016-07-07    10.02
2016-12-22    10.02
Name: p_change, dtype: float64


data


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open high close low volume price_change p_change turnover
2015-08-28 15.40 16.46 16.46 15.00 117827.60 1.50 10.03 4.03
2015-08-04 16.20 17.35 17.35 15.80 94292.63 1.58 10.02 3.23
2016-12-22 18.50 20.42 20.42 18.45 150470.83 1.86 10.02 3.77
2016-07-07 18.66 18.66 18.66 18.41 48756.55 1.70 10.02 1.67
2015-05-21 27.50 28.22 28.22 26.50 121190.11 2.57 10.02 4.15 
data+100


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open high close low volume price_change p_change turnover
2015-08-28 115.40 116.46 116.46 115.00 117927.60 101.50 110.03 104.03
2015-08-04 116.20 117.35 117.35 115.80 94392.63 101.58 110.02 103.23
2016-12-22 118.50 120.42 120.42 118.45 150570.83 101.86 110.02 103.77
2016-07-07 118.66 118.66 118.66 118.41 48856.55 101.70 110.02 101.67
2015-05-21 127.50 128.22 128.22 126.50 121290.11 102.57 110.02 104.15 
data/100


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open high close low volume price_change p_change turnover
2015-08-28 0.1540 0.1646 0.1646 0.1500 1178.2760 0.0150 0.1003 0.0403
2015-08-04 0.1620 0.1735 0.1735 0.1580 942.9263 0.0158 0.1002 0.0323
2016-12-22 0.1850 0.2042 0.2042 0.1845 1504.7083 0.0186 0.1002 0.0377
2016-07-07 0.1866 0.1866 0.1866 0.1841 487.5655 0.0170 0.1002 0.0167
2015-05-21 0.2750 0.2822 0.2822 0.2650 1211.9011 0.0257 0.1002 0.0415 
data.add(100)


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open high close low volume price_change p_change turnover
2015-08-28 115.40 116.46 116.46 115.00 117927.60 101.50 110.03 104.03
2015-08-04 116.20 117.35 117.35 115.80 94392.63 101.58 110.02 103.23
2016-12-22 118.50 120.42 120.42 118.45 150570.83 101.86 110.02 103.77
2016-07-07 118.66 118.66 118.66 118.41 48856.55 101.70 110.02 101.67
2015-05-21 127.50 128.22 128.22 126.50 121290.11 102.57 110.02 104.15 
data["p_change"]>2


2015-08-28    True
2015-08-04    True
2016-12-22    True
2016-07-07    True
2015-05-21    True
Name: p_change, dtype: bool


data


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open high close low volume price_change p_change turnover
2015-08-28 15.40 16.46 16.46 15.00 117827.60 1.50 10.03 4.03
2015-08-04 16.20 17.35 17.35 15.80 94292.63 1.58 10.02 3.23
2016-12-22 18.50 20.42 20.42 18.45 150470.83 1.86 10.02 3.77
2016-07-07 18.66 18.66 18.66 18.41 48756.55 1.70 10.02 1.67
2015-05-21 27.50 28.22 28.22 26.50 121190.11 2.57 10.02 4.15 
data[data["open"]>17]


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open high close low volume price_change p_change turnover
2016-12-22 18.50 20.42 20.42 18.45 150470.83 1.86 10.02 3.77
2016-07-07 18.66 18.66 18.66 18.41 48756.55 1.70 10.02 1.67
2015-05-21 27.50 28.22 28.22 26.50 121190.11 2.57 10.02 4.15 
data.query("p_change>10&open>18").head()


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open high close low volume price_change p_change turnover
2016-12-22 18.50 20.42 20.42 18.45 150470.83 1.86 10.02 3.77
2016-07-07 18.66 18.66 18.66 18.41 48756.55 1.70 10.02 1.67
2015-05-21 27.50 28.22 28.22 26.50 121190.11 2.57 10.02 4.15 
data["price_change"].isin([1.86,2.57])


2015-08-28    False
2015-08-04    False
2016-12-22     True
2016-07-07    False
2015-05-21     True
Name: price_change, dtype: bool


data.describe()


.dataframe thead th {
    text-align: left;
}

.dataframe tbody tr th {
    vertical-align: top;
}
open high close low volume price_change p_change turnover
count 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000
mean 19.252000 20.222000 20.222000 18.832000 106507.544000 1.842000 10.022000 3.370000
std 4.824367 4.712963 4.712963 4.555203 37950.208695 0.428976 0.004472 1.014101
min 15.400000 16.460000 16.460000 15.000000 48756.550000 1.500000 10.020000 1.670000
25% 16.200000 17.350000 17.350000 15.800000 94292.630000 1.580000 10.020000 3.230000
50% 18.500000 18.660000 18.660000 18.410000 117827.600000 1.700000 10.020000 3.770000
75% 18.660000 20.420000 20.420000 18.450000 121190.110000 1.860000 10.020000 4.030000
max 27.500000 28.220000 28.220000 26.500000 150470.830000 2.570000 10.030000 4.150000 
data.idxmax()


open            2015-05-21
high            2015-05-21
close           2015-05-21
low             2015-05-21
volume          2016-12-22
price_change    2015-05-21
p_change        2015-08-28
turnover        2015-05-21
dtype: object


data=pd.read_csv("./stock_day/stock_day.csv")
data = data.drop(["ma5","ma10","ma20","v_ma5","v_ma10","v_ma20"], axis=1)  #让数据简单一些


data.head()


.dataframe thead th {
    text-align: left;
}

.dataframe tbody tr th {
    vertical-align: top;
}
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 
data["p_change"].sort_index().cumsum().head()


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
Name: p_change, dtype: float64


data["p_change"].sort_index().cumsum().plot()


<matplotlib.axes._subplots.AxesSubplot at 0x7f6c86b3c9e8>

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