Plotly：如何使用下拉菜單按年、月和日對數據進行子集化？

Question

我正在嘗試 plot 三個圖表（日、月、年），並讓用戶可以通過下拉菜單選擇他們想要查看的圖表。 當我為（日，月）執行此操作時，它工作得很好（月份顯示為默認圖表），但是當我添加（年）時，（日，月）不顯示（在這種情況下，我想要年作為默認圖表）。

這是工作代碼：

 # Plot Day temp_day = pd.DataFrame(df.day.value_counts()) temp_day.reset_index(inplace=True) temp_day.columns = ['day', 'tweet_count'] temp_day.sort_values(by=['day'], inplace=True) temp_day.reset_index(inplace=True, drop=True) trace_day = go.Scatter( x=temp_day.day.values, y=temp_day.tweet_count.values, text = [f"{humanize.naturaldate(day)}: {count} tweets" for day,count in zip(temp_day.day.values,temp_day.tweet_count.values)], hoverinfo='text', mode='lines', line = { 'color': my_color, 'width': 1.2 }, visible=False, name="Day" ) # Plot Month temp_month = pd.DataFrame(df.YYYYMM.value_counts()) temp_month.reset_index(inplace=True) temp_month.columns = ['YYYYMM', 'tweet_count'] temp_month['YYYYMM'] = temp_month['YYYYMM'].dt.strftime('%Y-%m') temp_month.sort_values(by=['YYYYMM'], inplace=True) temp_month.reset_index(inplace=True, drop=True) trace_month = go.Scatter( x=temp_month.YYYYMM.values, y=temp_month.tweet_count.values, mode='lines', line = { 'color': my_color, 'width': 1.2 }, visible=True, name="Month" ) # Menus updatemenus = list([ dict( active=0, buttons=list([ dict(label = 'Month', method = 'update', args = [{'visible': [True, False]}, {'title': 'Number of Tweets per Month'}]), dict(label = 'Day', method = 'update', args = [{'visible': [False, True]}, {'title': 'Number of Tweets per Day'}]), ]), ) ]) # Layout layout = go.Layout(title="Number of Tweets -- Pick a scale", updatemenus=updatemenus, ) fig = go.Figure(data=[trace_month, trace_day], layout=layout) iplot(fig)

這是不起作用的代碼，我不知道為什么：

 # Plot Day temp_day = pd.DataFrame(df.day.value_counts()) temp_day.reset_index(inplace=True) temp_day.columns = ['day', 'tweet_count'] temp_day.sort_values(by=['day'], inplace=True) temp_day.reset_index(inplace=True, drop=True) trace_day = go.Scatter( x=temp_day.day.values, y=temp_day.tweet_count.values, text = [f"{humanize.naturaldate(day)}: {count} tweets" for day,count in zip(temp_day.day.values,temp_day.tweet_count.values)], hoverinfo='text', mode='lines', line = { 'color': my_color, 'width': 1.2 }, visible=False, name="Day" ) # Plot Month temp_month = pd.DataFrame(df.YYYYMM.value_counts()) temp_month.reset_index(inplace=True) temp_month.columns = ['YYYYMM', 'tweet_count'] temp_month['YYYYMM'] = temp_month['YYYYMM'].dt.strftime('%Y-%m') temp_month.sort_values(by=['YYYYMM'], inplace=True) temp_month.reset_index(inplace=True, drop=True) trace_month = go.Scatter( x=temp_month.YYYYMM.values, y=temp_month.tweet_count.values, mode='lines', line = { 'color': my_color, 'width': 1.2 }, visible=False, name="Month" ) # Plot year temp_year = pd.DataFrame(df.year.value_counts()) temp_year.reset_index(inplace=True) temp_year.columns = ['year', 'tweet_count'] temp_year.sort_values(by=['year'], inplace=True) temp_year.reset_index(inplace=True, drop=True) trace_year = go.Scatter( x=temp_year.year.values, y=temp_year.tweet_count.values, text = [f"Year {year}: {count:,.0f} tweets" for year,count in zip(temp_year.year.values,temp_year.tweet_count.values)], hoverinfo='text', mode='lines+markers', line = { 'color': my_color, 'width': 1.2 }, visible=True, name="Year" ) # Menus updatemenus = list([ dict( active=0, buttons=list([ dict(label = 'Year', method = 'update', args = [{'visible': [True, False, False]}, {'title': 'Number of Tweets per Month'}]), dict(label = 'Month', method = 'update', args = [{'visible': [False, True, False]}, {'title': 'Number of Tweets per Month'}]), dict(label = 'Day', method = 'update', args = [{'visible': [False, False, True]}, {'title': 'Number of Tweets per Day'}]), ]), ) ]) # Layout layout = go.Layout(title="Number of Tweets -- Pick a scale", updatemenus=updatemenus, ) fig = go.Figure(data=[trace_year, trace_month, trace_day], layout=layout) iplot(fig)

這是數據：

 # Year Scatter({ 'hoverinfo': 'text', 'line': {'color': '#ff00a7', 'width': 1.2}, 'mode': 'lines+markers', 'name': 'Year', 'text': [Year 2011: 73 tweets, Year 2012: 562 tweets, Year 2013: 1,153 tweets, Year 2014: 700 tweets, Year 2015: 2,104 tweets, Year 2016: 1,816 tweets, Year 2017: 1,691 tweets, Year 2018: 1,082 tweets, Year 2019: 914 tweets, Year 2020: 482 tweets], 'visible': False, 'x': array([2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020]), 'y': array([ 73, 562, 1153, 700, 2104, 1816, 1691, 1082, 914, 482]) }) # Month Scatter({ 'line': {'color': '#ff00a7', 'width': 1.2}, 'mode': 'lines', 'name': 'Month', 'visible': False, 'x': array(['2011-06', '2011-07', '2011-08', '2011-09', '2011-10', '2011-11', '2011-12', '2012-01', '2012-02', '2012-03', '2012-04', '2012-05', '2012-06', '2012-07', '2012-08', '2012-09', '2012-10', '2012-11', '2012-12', '2013-01', '2013-02', '2013-03', '2013-04', '2013-05', '2013-06', '2013-07', '2013-08', '2013-09', '2013-10', '2013-11', '2013-12', '2014-01', '2014-02', '2014-03', '2014-04', '2014-05', '2014-06', '2014-07', '2014-08', '2014-09', '2014-10', '2014-11', '2014-12', '2015-01', '2015-02', '2015-03', '2015-04', '2015-05', '2015-06', '2015-07', '2015-08', '2015-09', '2015-10', '2015-11', '2015-12', '2016-01', '2016-02', '2016-03', '2016-04', '2016-05', '2016-06', '2016-07', '2016-08', '2016-09', '2016-10', '2016-11', '2016-12', '2017-01', '2017-02', '2017-03', '2017-04', '2017-05', '2017-06', '2017-07', '2017-08', '2017-09', '2017-10', '2017-11', '2017-12', '2018-01', '2018-02', '2018-03', '2018-04', '2018-05', '2018-06', '2018-07', '2018-08', '2018-09', '2018-10', '2018-11', '2018-12', '2019-01', '2019-02', '2019-03', '2019-04', '2019-05', '2019-06', '2019-08', '2019-09', '2019-10', '2019-11', '2019-12', '2020-01', '2020-02', '2020-03', '2020-04', '2020-05', '2020-06'], dtype=object), 'y': array([ 1, 1, 2, 8, 4, 20, 37, 79, 16, 13, 8, 12, 2, 5, 68, 139, 57, 64, 99, 182, 63, 60, 74, 128, 59, 109, 126, 86, 77, 112, 77, 78, 44, 32, 22, 33, 46, 61, 66, 109, 81, 78, 50, 140, 151, 297, 173, 225, 69, 119, 213, 177, 134, 217, 189, 255, 149, 114, 127, 154, 116, 110, 150, 184, 179, 117, 161, 48, 115, 147, 153, 199, 174, 195, 154, 162, 114, 140, 90, 156, 81, 107, 62, 64, 49, 128, 127, 60, 89, 115, 44, 58, 86, 65, 102, 93, 82, 78, 158, 65, 50, 77, 55, 71, 70, 105, 124, 57]) }) # Day Scatter({ 'hoverinfo': 'text', 'line': {'color': '#ff00a7', 'width': 1.2}, 'mode': 'lines', 'name': 'Day', 'text': [Jun 04 2011: 1 tweets, Jul 17 2011: 1 tweets, Aug 11 2011: 1 tweets, ..., Jun 17: 4 tweets, Jun 18: 1 tweets, Jun 19: 3 tweets], 'visible': False, 'x': array([datetime.date(2011, 6, 4), datetime.date(2011, 7, 17), datetime.date(2011, 8, 11), ..., datetime.date(2020, 6, 17), datetime.date(2020, 6, 18), datetime.date(2020, 6, 19)], dtype=object), 'y': array([1, 1, 1, ..., 4, 1, 3]) })

Answer 1

由於您提供了一個不起作用的數據樣本，因此很難分辨代碼片段中哪些有效，哪些無效。 但是，我確實有一個建議可以直接回答您的問題：

我正在嘗試 plot 三個圖表（日、月、年），並讓用戶可以通過下拉菜單選擇他們想要查看的圖表

下面的代碼片段將讓您在兩個數據集之間進行選擇： year和month 。 這些是您提供的確切數據集。 當您有days的工作樣本時，可以輕松地包含該數據集。 當你准備好時，我很樂意為你做這件事。

無論如何，下面的 plot 將讓您使用下拉菜單選擇year和month 。 如果設計 wrt 線條和標記樣式不符合您的喜好，請不要擔心。 這只是作為占位符包含在代碼中，供您根據需要進行更改。

年

月

天

（待定……）

完整代碼

import plotly.graph_objects as go
import pandas as pd

df_y=pd.DataFrame({'x':[2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020],
                   'y':[  73,  562, 1153,  700, 2104, 1816, 1691, 1082,  914,  482]})

df_m=pd.DataFrame({'x':['2011-06', '2011-07', '2011-08', '2011-09', '2011-10', '2011-11',
                        '2011-12', '2012-01', '2012-02', '2012-03', '2012-04', '2012-05',
                        '2012-06', '2012-07', '2012-08', '2012-09', '2012-10', '2012-11',
                        '2012-12', '2013-01', '2013-02', '2013-03', '2013-04', '2013-05',
                        '2013-06', '2013-07', '2013-08', '2013-09', '2013-10', '2013-11',
                        '2013-12', '2014-01', '2014-02', '2014-03', '2014-04', '2014-05',
                        '2014-06', '2014-07', '2014-08', '2014-09', '2014-10', '2014-11',
                        '2014-12', '2015-01', '2015-02', '2015-03', '2015-04', '2015-05',
                        '2015-06', '2015-07', '2015-08', '2015-09', '2015-10', '2015-11',
                        '2015-12', '2016-01', '2016-02', '2016-03', '2016-04', '2016-05',
                        '2016-06', '2016-07', '2016-08', '2016-09', '2016-10', '2016-11',
                        '2016-12', '2017-01', '2017-02', '2017-03', '2017-04', '2017-05',
                        '2017-06', '2017-07', '2017-08', '2017-09', '2017-10', '2017-11',
                        '2017-12', '2018-01', '2018-02', '2018-03', '2018-04', '2018-05',
                        '2018-06', '2018-07', '2018-08', '2018-09', '2018-10', '2018-11',
                        '2018-12', '2019-01', '2019-02', '2019-03', '2019-04', '2019-05',
                        '2019-06', '2019-08', '2019-09', '2019-10', '2019-11', '2019-12',
                        '2020-01', '2020-02', '2020-03', '2020-04', '2020-05', '2020-06'],
                    'y':[  1,   1,   2,   8,   4,  20,  37,  79,  16,  13,   8,  12,   2,   5,
                         68, 139,  57,  64,  99, 182,  63,  60,  74, 128,  59, 109, 126,  86,
                         77, 112,  77,  78,  44,  32,  22,  33,  46,  61,  66, 109,  81,  78,
                         50, 140, 151, 297, 173, 225,  69, 119, 213, 177, 134, 217, 189, 255,
                        149, 114, 127, 154, 116, 110, 150, 184, 179, 117, 161,  48, 115, 147,
                        153, 199, 174, 195, 154, 162, 114, 140,  90, 156,  81, 107,  62,  64,
                         49, 128, 127,  60,  89, 115,  44,  58,  86,  65, 102,  93,  82,  78,
                        158,  65,  50,  77,  55,  71,  70, 105, 124,  57]})

# IMPROVEMENT 1
# INSERT ANOTHER DATAFRAME FOR DAYS HERE WITH THE SAME STRUCTURE AS ABOVE

# IMPROVEMENT 1
# INCLUDE THE DATAFRAME AS VALUE AND THE NAME df_d as key
# in the dict below:

dfc = {'year':df_y, 'month':df_m}

# set index
for df in dfc.keys():
    dfc[df].set_index('x', inplace=True)


# plotly start 
fig = go.Figure()
# menu setup    
updatemenu= []

# buttons for menu 1, names
buttons=[]

# plotly start 
fig = go.Figure()
# one trace for each column per dataframe: AI and RANDOM
for df in dfc.keys():
    fig.add_trace(go.Scatter(x=dfc[df].index,
                             y=dfc[df]['y'],
                             visible=True,
                             #marker=dict(size=12, line=dict(width=2)),
                             #marker_symbol = 'diamond',
                             name=df
                  )
             )


# some line settings for fun
lines = [dict(color='royalblue', width=2, dash='dot'), dict(color='firebrick', width=1, dash='dash')]
markers = [dict(size=12, line=dict(width=2)), dict(size=12, line=dict(width=2))]

# create traces for each color: 
# build argVals for buttons and create buttons
for i, df in enumerate(dfc.keys()):
    args_y = []
    args_x = []
    for col in dfc[df]:
        args_y.append(dfc[df][col].values)
        args_x.append(dfc[df].index)
    argVals = [ {'y':args_y, 'x':args_x,
                 'marker':markers[i], 'line': lines[i]}]

    buttons.append(dict(method='update',
                        label=df,
                        visible=True,
                        args=argVals))

updatemenu=[]
your_menu=dict()
updatemenu.append(your_menu)
updatemenu[0]['buttons']=buttons
updatemenu[0]['direction']='down'
updatemenu[0]['showactive']=True


fig.update_layout(showlegend=False, updatemenus=updatemenu)
fig.show()

Answer 2

事實證明，所有軸都需要采用相同的格式。 通過將 plot 年份中的年份設置為：

 x=temp_year.day.astype('datetime64[Y]').astype(str).values y=temp_year.tweet_count.values