data['open'].add(1)

2018-02-27 24.53
2018-02-26 23.80
2018-02-23 23.88
2018-02-22 23.25
2018-02-14 22.49

data["open"] > 23

2018-02-27  True
2018-02-26 False
2018-02-23 False
2018-02-22 False
2018-02-14 False

# 逻辑判断的结果可以作为筛选的依据
data[data["open"] > 23].head()

data[(data["open"] > 23) & (data["open"] < 24)].head()

# 以字符串形式
data.query("open<24 & open>23").head()

# 可以指定值进行一个判断，从而进行筛选操作
data[data["open"].isin([23.53, 23.85])]

# 计算平均值、标准差、最大值、最小值
data.describe()

# 使用统计函数：0 代表列求结果， 1 代表行求统计结果
data.max(0)

open     34.99
high     36.35
close     35.21
low     34.01
volume    501915.41
price_change   3.03
p_change    10.03
turnover    12.56
my_price_change   3.41
dtype: float64

# 方差
data.var(0)

open    1.545255e+01
high    1.662665e+01
close    1.554572e+01
low    1.437902e+01
volume    5.458124e+09
price_change  8.072595e-01
p_change   1.664394e+01
turnover   4.323800e+00
my_price_change 6.409037e-01
dtype: float64

# 标准差
data.std(0)

open     3.930973
high     4.077578
close     3.942806
low     3.791968
volume    73879.119354
price_change   0.898476
p_change    4.079698
turnover    2.079375
my_price_change  0.800565
dtype: float64

df = pd.DataFrame({'COL1' : [2,3,4,5,4,2],
     'COL2' : [0,1,2,3,4,2]})

df.median()

COL1 3.5
COL2 2.0
dtype: float64

# 求出最大值的位置
data.idxmax(axis=0)

open    2015-06-15
high    2015-06-10
close    2015-06-12
low    2015-06-12
volume    2017-10-26
price_change  2015-06-09
p_change   2015-08-28
turnover   2017-10-26
my_price_change 2015-07-10
dtype: object


# 求出最小值的位置
data.idxmin(axis=0)

open    2015-03-02
high    2015-03-02
close    2015-09-02
low    2015-03-02
volume    2016-07-06
price_change  2015-06-15
p_change   2015-09-01
turnover   2016-07-06
my_price_change 2015-06-15
dtype: object

# 排序之后，进行累计求和
data = data.sort_index()

stock_rise = data['p_change']
# plot方法集成了前面直方图、条形图、饼图、折线图
stock_rise.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
2015-03-09  16.37
2015-03-10  18.75
2015-03-11  16.36
2015-03-12  15.03
2015-03-13  17.58
2015-03-16  20.34
2015-03-17  22.42
2015-03-18  23.28
2015-03-19  23.74
2015-03-20  23.48
2015-03-23  23.74

import matplotlib.pyplot as plt
# plot显示图形
stock_rise.cumsum().plot()
# 需要调用show，才能显示出结果
plt.show()

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

open  22.74
close 22.85
dtype: float64