使用袖扣设置多个具有移动平均值的子图，并使用图表离线

Question

我试图从我的数据框中选择4种不同的产品价格，并使用绘图袖扣绘制他们的移动平均值作为子图（2,2）。如果有人能指导我，我将不胜感激。

我试着将价格绘制如下。我遇到了袖扣技术分析，这使我能够以更清洁的方式绘制移动平均线，但是，我不太确定如何应用它。

from plotly.offline import download_plotlyjs,init_notebook_mode,plot,iplot
from plotly import tools
import plotly.graph_objs as go

trace1= go.Scatter(name=',milk', x=df.Date, y=df['milk'])
trace2= go.Scatter(name='soap', x=df.Date, y=df['soap'])
trace3= go.Scatter(name='rice', x=df.Date, y=df['rice'])
trace4= go.Scatter(name='water', x=df.Date, y=df['water'])

fig = tools.make_subplots(rows=2, cols=2, subplot_titles=('milk', 'soap',
                                                      'rice', 'water'))
fig.append_trace(trace1, 1, 1)
fig.append_trace(trace2, 1, 2)
fig.append_trace(trace3, 2, 1)
fig.append_trace(trace4, 2, 2)

fig['layout'].update(height=1000, width=1800, title='supermarket')

plot(fig, filename='supermarket.html')

如果有人可以教我如何使用绘图袖扣从数据框中使用绘图离线绘制所选列中的四个移动平均线，我将不胜感激。

Answer 1

在Jupyter Notebook中插入下面的代码部分，使用袖扣和绘图离线生成以下图表：

情节：

码：

# imports
import plotly
from plotly import tools
import cufflinks as cf
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
import pandas as pd
import numpy as np
from IPython.core.display import display, HTML
import copy
import plotly.graph_objs as go

####### PART 1 - SETUP AND SAMPLE DATA #######
# setup
display(HTML("<style>.container { width:55% !important; } .widget-select > select {background-color: gainsboro;}</style>"))
init_notebook_mode(connected=True)
np.random.seed(123)
cf.set_config_file(theme='pearl')

# Random data using cufflinks
df = cf.datagen.lines().iloc[:,0:4]
df.columns = ['StockA', 'StockB', 'StockC', 'StockD']

####### PART 2 - FUNCTION FOR MOVING AVERAGES #######

# Function for moving averages
def movingAvg(df, win, keepSource):
    """Add moving averages for all columns in a dataframe.

    Arguments: 
    df -- pandas dataframe
    win -- length of movingAvg estimation window
    keepSource -- True or False for keep or drop source data in output dataframe

    """

    df_temp = df.copy()
    # Manage existing column names
    colNames = list(df_temp.columns.values).copy()
    removeNames = colNames.copy()

    i = 0
    for col in colNames:
        # Make new names for movingAvgs
        movingAvgName = colNames[i] + '_MA' #+ str(win)   
        # Add movingAvgs
        df_temp[movingAvgName] = df[col].rolling(window=win).mean()


        i = i + 1

    # Remove estimates with insufficient window length
    df_temp = df_temp.iloc[win:]

    # Remove or keep source data
    if keepSource == False:
        df_temp = df_temp.drop(removeNames,1)
    return df_temp

# Add moving averages to df
windowLength = 10
df = movingAvg(df=df, win=windowLength, keepSource = True)

####### PART 3 -PLOTLY RULES #######
# Structure lines / traces for the plots
# trace 1
trace1 = go.Scatter(
    x=df.index,
    y=df['StockA'],
    name='StockA'
)

trace1_ma = go.Scatter(
    x=df.index,
    y=df['StockA_MA'],
    name='StockA_MA'
)

# trace 2
trace2 = go.Scatter(
    x=df.index,
    y=df['StockB'],
    name='StockB'

)
trace2_ma = go.Scatter(
    x=df.index,
    y=df['StockB_MA'],
    name='StockB_MA'
)

# trace 3
trace3 = go.Scatter(
    x=df.index,
    y=df['StockC'],
    name='StockC'
)

trace3_ma = go.Scatter(
    x=df.index,
    y=df['StockC_MA'],
    name='StockC_MA'
)

# trace 4
trace4 = go.Scatter(
    x=df.index,
    y=df['StockD'],
    name='StockD'
)

trace4_ma = go.Scatter(
    x=df.index,
    y=df['StockD_MA'],
    name='StockD_MA'
)

# Structure traces as datasets
data1 = [trace1, trace1_ma]
data2 = [trace2, trace2_ma]
data3 = [trace3, trace3_ma]
data4 = [trace4, trace4_ma]

# Build figures
fig1 = go.Figure(data=data1)
fig2 = go.Figure(data=data2)
fig3 = go.Figure(data=data3)
fig4 = go.Figure(data=data4)

# Subplots setup and layout
figs = cf.subplots([fig1, fig2, fig3, fig4],shape=(2,2))
figs['layout'].update(height=800, width=1200,
                      title='Stocks with moving averages = '+ str(windowLength))

iplot(figs)