python数据分析——数据筛选实例【茅台】

import tushare as ts
import pandas as pd
from pandas import DataFrame,Series

DataFrame

- 索引:
    - df[col] df[[c1,c2]]:取列
    - df.loc[index] : 取行
    - df.loc[index,col] : 取元素
- 切片:
    - df[a:b]:切行
    - df.loc[:,a:b]:切列
- df运算:Series运算一致
- df级联:拼接

In [8]:

df = pd.read_csv('maotai.csv',index_col='date',parse_dates=['date'])
df.drop(labels='Unnamed: 0',axis=1,inplace=True)
df

Out[8]:

4174 rows × 6 columns

In [9]:

#假如我从2010年1月1日开始，每月第一个交易日买入1手股票，每年最后一个交易日卖出所有股票，到今天为止，我的收益如何？
price_last = df['open'][-1]
df = df['2010':'2019'] #剔除首尾无用的数据
#Pandas提供了resample函数用便捷的方式对时间序列进行重采样，根据时间粒度的变大或者变小分为降采样和升采样：
df_monthly = df.resample("M").first()
df_yearly = df.resample("Y").last()[:-1] #去除最后一年
cost_money = 0
hold = 0 #每年持有的股票
for year in range(2010, 2020):
    
    cost_money -= df_monthly.loc[str(year)]['open'].sum()*100
    hold += len(df_monthly[str(year)]['open']) * 100
    if year != 2019:
        cost_money += df_yearly[str(year)]['open'][0] * hold
        hold = 0 #每年持有的股票
cost_money += hold * price_last

print(cost_money)

310250.69999999984

In [7]:

Out[7]:

2221 rows × 6 columns

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import tushare as ts

使用tushare包获取某股票的历史行情数据

In [14]:

df = ts.get_k_data("600519",start="1988-01-01")
df.to_csv("600519.csv")
df

Out[14]:

4148 rows × 7 columns

In [15]:

df.head()

Out[15]:

In [20]:

df = pd.read_csv("600519.csv", index_col='date',parse_dates=['date'])[['open','close','high','low']]
df.head()

Out[20]:

输出该股票所有收盘比开盘上涨3%以上的日期

In [19]:

df[(df['close']-df['open'])/df['open']>=0.03].index

Out[19]:

DatetimeIndex(['2001-08-27', '2001-08-28', '2001-09-10', '2001-12-21',
               '2002-01-18', '2002-01-31', '2003-01-14', '2003-10-29',
               '2004-01-05', '2004-01-14',
               ...
               '2018-06-20', '2018-08-09', '2018-08-21', '2018-08-27',
               '2018-09-18', '2018-09-26', '2018-10-19', '2018-10-31',
               '2018-11-13', '2018-12-28'],
              dtype='datetime64[ns]', name='date', length=291, freq=None)

输出该股票所有开盘比前日收盘跌幅超过2%的日期

In [20]:

df[(df['open']-df['close'].shift(1))/df['close'].shift(1)<=-0.02].index

Out[20]:

DatetimeIndex(['2001-09-12', '2002-06-26', '2002-12-13', '2004-07-01',
               '2004-10-29', '2006-08-21', '2006-08-23', '2007-01-25',
               '2007-02-01', '2007-02-06', '2007-03-19', '2007-05-21',
               '2007-05-30', '2007-06-05', '2007-07-27', '2007-09-05',
               '2007-09-10', '2008-03-13', '2008-03-17', '2008-03-25',
               '2008-03-27', '2008-04-22', '2008-04-23', '2008-04-29',
               '2008-05-13', '2008-06-10', '2008-06-13', '2008-06-24',
               '2008-06-27', '2008-08-11', '2008-08-19', '2008-09-23',
               '2008-10-10', '2008-10-15', '2008-10-16', '2008-10-20',
               '2008-10-23', '2008-10-27', '2008-11-06', '2008-11-12',
               '2008-11-20', '2008-11-21', '2008-12-02', '2009-02-27',
               '2009-03-25', '2009-08-13', '2010-04-26', '2010-04-30',
               '2011-08-05', '2012-03-27', '2012-08-10', '2012-11-22',
               '2012-12-04', '2012-12-24', '2013-01-16', '2013-01-25',
               '2013-09-02', '2014-04-25', '2015-01-19', '2015-05-25',
               '2015-07-03', '2015-07-08', '2015-07-13', '2015-08-24',
               '2015-09-02', '2015-09-15', '2017-11-17', '2018-02-06',
               '2018-02-09', '2018-03-23', '2018-03-28', '2018-07-11',
               '2018-10-11', '2018-10-24', '2018-10-25', '2018-10-29',
               '2018-10-30'],
              dtype='datetime64[ns]', name='date', freq=None)

假如我从2010年1月1日开始，每月第一个交易日买入1手股票，每年最后一个交易日卖出所有股票，到今天为止，我的收益如何？

In [21]:

df['open'][-1]

Out[21]:

690.0

In [22]:

price_last = df['open'][-1]
df = df['2010-01':'2019-01'] #剔除首尾无用的数据
df

Out[22]:

2195 rows × 4 columns

In [28]:

df_monthly = df.resample("M").first()
df_monthly.head()

Out[28]:

In [29]:

df_yearly = df.resample("A").last()[:-1] #去除最后一年
df_yearly

Out[29]:

In [31]:

df_monthly.loc[str(year)]

Out[31]:

In [33]:

cost_money = 0
hold = 0 #每年持有的股票
for year in range(2010, 2020):
    
    cost_money -= df_monthly.loc[str(year)]['open'].sum()*100
    hold += len(df_monthly[str(year)]['open']) * 100
    if year != 2019:
        cost_money += df_yearly[str(year)]['open'][0] * hold
        hold = 0 #每年持有的股票
    #print(cost_money)

cost_money += hold * price_last

print(cost_money)
        
# df_monthly['2001']

289604.69999999984

In [56]:

df['2017']

Out[56]:

223 rows × 4 columns