因为人对图像信息的解析效率比文字更高,所以可视化可以使数据更为直观,便于理解,使决策变得高效,所以信息可视化就显得尤为重要。
df.plot()方法的函数说明
df['open'].plot(kind='line', figsize=[5,5], legend=True, title='code')
# 如果需要加入折线,设置参数grid=True即可
df['open'].plot(kind='line', figsize=[10,5], legend=True, title='code', grid=True)
import tushare as ts
df = ts.get_tick_data('000001', date='2018-05-21')
返回值说明:
df = df.head(200)
df['amount'].plot(kind='line', figsize=[15,3], legend=True, title='code', grid=True)
DataFrame.rolling(*window*,*min_periods = None*,*center = False*,
*win_type = None*,*on = None*,*axis = 0*,*closed = None *)[[source]]
参数说明:
df['mvg2']=df['amount'].rolling(window=2).mean()
df[['amount', 'mvg2']].plot(kind='line',figsize=[10,5])
df.ix[(df.time>='14:55:00')&(df.time<='14:57:00'),'amount'].plot(kind='bar', figsize=[10,5], legend=True, title='amount')
import tushare
# 获取大盘指数实时行情列表
df = ts.get_index()
df['diff'] = df['close'] - df['open']
df['rise'] = df['diff'] > 0 # 涨
df['fall'] = df['diff'] < 0 # 跌
# counterclock 布尔值,可选参数,默认为:None。指定指针方向,顺时针或者逆时针
# startangle浮点类型,可选参数,默认:None。如果不是None,从x轴逆时针旋转饼图的开始角度。
df[['rise', 'fall']].sum().plot(kind='pie', figsize=[5,5], counterclock=True,
startangle=90, legend=True, title='diff')