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27
4
我正在尝试复制 this plotly tutorial在具有与示例中给出的数据集匹配的数据集的 Jupyter Notebook 上，我只需要更改一列的名称。这里的问题是离线命令
py.icreate_animations(figure)
在离线笔记本模式下变成
icreate_animations(figure)
无法识别。此外，如果我只是尝试 绘图(图)命令，我用于静态图，引发了一个很长的错误:
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-27-b81a66612f63> in <module>()
38 figure['layout']['sliders'] = [sliders_dict]
39
---> 40 iplot(figure)
//anaconda/lib/python2.7/site-packages/plotly/offline/offline.pyc in iplot(figure_or_data, show_link, link_text, validate, image, filename, image_width, image_height, config)
330 config.setdefault('linkText', link_text)
331
--> 332 figure = tools.return_figure_from_figure_or_data(figure_or_data, validate)
333
334 # Though it can add quite a bit to the display-bundle size, we include
//anaconda/lib/python2.7/site-packages/plotly/tools.pyc in return_figure_from_figure_or_data(figure_or_data, validate_figure)
1383
1384 try:
-> 1385 graph_objs.Figure(figure)
1386 except exceptions.PlotlyError as err:
1387 raise exceptions.PlotlyError("Invalid 'figure_or_data' argument. "
//anaconda/lib/python2.7/site-packages/plotly/graph_objs/graph_objs.pyc in __init__(self, *args, **kwargs)
1159
1160 def __init__(self, *args, **kwargs):
-> 1161 super(Figure, self).__init__(*args, **kwargs)
1162 if 'data' not in self:
1163 self.data = Data(_parent=self, _parent_key='data')
//anaconda/lib/python2.7/site-packages/plotly/graph_objs/graph_objs.pyc in __init__(self, *args, **kwargs)
375 d = {key: val for key, val in dict(*args, **kwargs).items()}
376 for key, val in d.items():
--> 377 self.__setitem__(key, val, _raise=_raise)
378
379 def __dir__(self):
//anaconda/lib/python2.7/site-packages/plotly/graph_objs/graph_objs.pyc in __setitem__(self, key, value, _raise)
430
431 if self._get_attribute_role(key) == 'object':
--> 432 value = self._value_to_graph_object(key, value, _raise=_raise)
433 if not isinstance(value, (PlotlyDict, PlotlyList)):
434 return
//anaconda/lib/python2.7/site-packages/plotly/graph_objs/graph_objs.pyc in _value_to_graph_object(self, key, value, _raise)
541 # this can be `None` when `_raise == False`
542 return GraphObjectFactory.create(key, value, _raise=_raise,
--> 543 _parent=self, _parent_key=key)
544
545 def help(self, attribute=None, return_help=False):
//anaconda/lib/python2.7/site-packages/plotly/graph_objs/graph_objs.pyc in create(object_name, *args, **kwargs)
791 class_name = graph_reference.OBJECT_NAME_TO_CLASS_NAME.get(object_name)
792 if class_name in ['Figure', 'Data', 'Frames']:
--> 793 return globals()[class_name](*args, **kwargs)
794 else:
795 kwargs['_name'] = object_name
//anaconda/lib/python2.7/site-packages/plotly/graph_objs/graph_objs.pyc in __init__(self, *args, **kwargs)
156
157 for index, value in enumerate(list(*args)):
--> 158 value = self._value_to_graph_object(index, value, _raise=_raise)
159
160 if isinstance(value, PlotlyBase):
//anaconda/lib/python2.7/site-packages/plotly/graph_objs/graph_objs.pyc in _value_to_graph_object(self, index, value, _raise)
1291 return value
1292 return super(Frames, self)._value_to_graph_object(index, value,
-> 1293 _raise=_raise)
1294
1295 def to_string(self, level=0, indent=4, eol='\n',
//anaconda/lib/python2.7/site-packages/plotly/graph_objs/graph_objs.pyc in _value_to_graph_object(self, index, value, _raise)
221 return GraphObjectFactory.create(item, _raise=_raise,
222 _parent=self,
--> 223 _parent_key=index, **value)
224 except exceptions.PlotlyGraphObjectError:
225 if i == len(items) and _raise:
//anaconda/lib/python2.7/site-packages/plotly/graph_objs/graph_objs.pyc in create(object_name, *args, **kwargs)
797 return PlotlyList(*args, **kwargs)
798 else:
--> 799 return PlotlyDict(*args, **kwargs)
800
801
//anaconda/lib/python2.7/site-packages/plotly/graph_objs/graph_objs.pyc in __init__(self, *args, **kwargs)
375 d = {key: val for key, val in dict(*args, **kwargs).items()}
376 for key, val in d.items():
--> 377 self.__setitem__(key, val, _raise=_raise)
378
379 def __dir__(self):
//anaconda/lib/python2.7/site-packages/plotly/graph_objs/graph_objs.pyc in __setitem__(self, key, value, _raise)
430
431 if self._get_attribute_role(key) == 'object':
--> 432 value = self._value_to_graph_object(key, value, _raise=_raise)
433 if not isinstance(value, (PlotlyDict, PlotlyList)):
434 return
//anaconda/lib/python2.7/site-packages/plotly/graph_objs/graph_objs.pyc in _value_to_graph_object(self, key, value, _raise)
541 # this can be `None` when `_raise == False`
542 return GraphObjectFactory.create(key, value, _raise=_raise,
--> 543 _parent=self, _parent_key=key)
544
545 def help(self, attribute=None, return_help=False):
//anaconda/lib/python2.7/site-packages/plotly/graph_objs/graph_objs.pyc in create(object_name, *args, **kwargs)
791 class_name = graph_reference.OBJECT_NAME_TO_CLASS_NAME.get(object_name)
792 if class_name in ['Figure', 'Data', 'Frames']:
--> 793 return globals()[class_name](*args, **kwargs)
794 else:
795 kwargs['_name'] = object_name
//anaconda/lib/python2.7/site-packages/plotly/graph_objs/graph_objs.pyc in __init__(self, *args, **kwargs)
156
157 for index, value in enumerate(list(*args)):
--> 158 value = self._value_to_graph_object(index, value, _raise=_raise)
159
160 if isinstance(value, PlotlyBase):
//anaconda/lib/python2.7/site-packages/plotly/graph_objs/graph_objs.pyc in _value_to_graph_object(self, index, value, _raise)
1059 return GraphObjectFactory.create(item, _raise=_raise,
1060 _parent=self,
-> 1061 _parent_key=index, **value)
1062
1063 def get_data(self, flatten=False):
//anaconda/lib/python2.7/site-packages/plotly/graph_objs/graph_objs.pyc in create(object_name, *args, **kwargs)
797 return PlotlyList(*args, **kwargs)
798 else:
--> 799 return PlotlyDict(*args, **kwargs)
800
801
//anaconda/lib/python2.7/site-packages/plotly/graph_objs/graph_objs.pyc in __init__(self, *args, **kwargs)
375 d = {key: val for key, val in dict(*args, **kwargs).items()}
376 for key, val in d.items():
--> 377 self.__setitem__(key, val, _raise=_raise)
378
379 def __dir__(self):
//anaconda/lib/python2.7/site-packages/plotly/graph_objs/graph_objs.pyc in __setitem__(self, key, value, _raise)
400 if key.endswith('src'):
401 if key in self._get_valid_attributes():
--> 402 value = graph_objs_tools.assign_id_to_src(key, value)
403 return super(PlotlyDict, self).__setitem__(key, value)
404
//anaconda/lib/python2.7/site-packages/plotly/graph_objs/graph_objs_tools.pyc in assign_id_to_src(src_name, src_value)
254 if src_id == '':
255 err = exceptions.COLUMN_NOT_YET_UPLOADED_MESSAGE
--> 256 err.format(column_name=src_value.name, reference=src_name)
257 raise exceptions.InputError(err)
258 return src_id
AttributeError: 'str' object has no attribute 'name'
那么，有谁知道如何在离线模式下制作该 plotly 吗？
编辑:我上传整个代码:
import pandas as pd
import numpy as np
from __future__ import division
import plotly.plotly as py
from plotly.offline import download_plotlyjs, init_notebook_mode, iplot
from plotly.graph_objs import *
init_notebook_mode()
from plotly.grid_objs import Grid, Column
from plotly.tools import FigureFactory as FF
dataset=pd.read_csv('gdp_mod_copia.csv')
years_from_col = set(dataset['year'])
years_ints = sorted(list(years_from_col))
years = [str(year) for year in years_ints]
# make list of continents
continents = []
for continent in dataset['continent']:
if continent not in continents:
continents.append(continent)
columns = []
# make grid
for year in years:
for continent in continents:
dataset_by_year = dataset[dataset['year'] == int(year)]
dataset_by_year_and_cont = dataset_by_year[dataset_by_year['continent'] == continent]
for col_name in dataset_by_year_and_cont:
# each column name is unique
column_name = '{year}+{continent}+{header}_grid'.format(
year=year, continent=continent, header=col_name
)
a_column = Column(list(dataset_by_year_and_cont[col_name]), column_name)
columns.append(a_column)
grid = Grid(columns)
figure = {
'data': [],
'layout': {},
'frames': [],
'config': {'scrollzoom': True}
}
# fill in most of layout
figure['layout']['xaxis'] = {'range': [30, 85], 'title': 'Life Expectancy', 'gridcolor': '#FFFFFF'}
figure['layout']['yaxis'] = {'title': 'GDP per Capita', 'type': 'log', 'gridcolor': '#FFFFFF'}
figure['layout']['hovermode'] = 'closest'
figure['layout']['plot_bgcolor'] = 'rgb(223, 232, 243)'
figure['layout']['slider'] = {
'args': [
'slider.value', {
'duration': 400,
'ease': 'cubic-in-out'
}
],
'initialValue': '2007',
'plotlycommand': 'animate',
'values': years,
'visible': True
}
figure['layout']['updatemenus'] = [
{
'buttons': [
{
'args': [None, {'frame': {'duration': 500, 'redraw': False},
'fromcurrent': True, 'transition': {'duration': 300, 'easing': 'quadratic-in-out'}}],
'label': 'Play',
'method': 'animate'
},
{
'args': [[None], {'frame': {'duration': 0, 'redraw': False}, 'mode': 'immediate',
'transition': {'duration': 0}}],
'label': 'Pause',
'method': 'animate'
}
],
'direction': 'left',
'pad': {'r': 10, 't': 87},
'showactive': False,
'type': 'buttons',
'x': 0.1,
'xanchor': 'right',
'y': 0,
'yanchor': 'top'
}
]
sliders_dict = {
'active': 0,
'yanchor': 'top',
'xanchor': 'left',
'currentvalue': {
'font': {'size': 20},
'prefix': 'Year:',
'visible': True,
'xanchor': 'right'
},
'transition': {'duration': 300, 'easing': 'cubic-in-out'},
'pad': {'b': 10, 't': 50},
'len': 0.9,
'x': 0.1,
'y': 0,
'steps': []
}
custom_colors = {
'Asia': 'rgb(171, 99, 250)',
'Europe': 'rgb(230, 99, 250)',
'Africa': 'rgb(99, 110, 250)',
'Americas': 'rgb(25, 211, 243)',
#'Oceania': 'rgb(9, 255, 255)'
'Oceania': 'rgb(50, 170, 255)'
}
col_name_template = '{year}+{continent}+{header}_grid'
year = 2007
for continent in continents:
data_dict = {
'xsrc': grid.get_column_reference(col_name_template.format(
year=year, continent=continent, header='GDP_per_capita'
)),
'ysrc': grid.get_column_reference(col_name_template.format(
year=year, continent=continent, header='Life_satisfaction'
)),
'mode': 'markers',
'textsrc': grid.get_column_reference(col_name_template.format(
year=year, continent=continent, header='country'
)),
'marker': {
'sizemode': 'area',
'sizeref': 200000,
'sizesrc': grid.get_column_reference(col_name_template.format(
year=year, continent=continent, header='Total_population'
)),
'color': custom_colors[continent]
},
'name': continent
}
figure['data'].append(data_dict)
for year in years:
frame = {'data': [], 'name': str(year)}
for continent in continents:
data_dict = {
'xsrc': grid.get_column_reference(col_name_template.format(
year=year, continent=continent, header='GDP_per_capita'
)),
'ysrc': grid.get_column_reference(col_name_template.format(
year=year, continent=continent, header='Life_satisfaction'
)),
'mode': 'markers',
'textsrc': grid.get_column_reference(col_name_template.format(
year=year, continent=continent, header='country'
)),
'marker': {
'sizemode': 'area',
'sizeref': 200000,
'sizesrc': grid.get_column_reference(col_name_template.format(
year=year, continent=continent, header='Total_population'
)),
'color': custom_colors[continent]
},
'name': continent
}
frame['data'].append(data_dict)
figure['frames'].append(frame)
slider_step = {'args': [
[year],
{'frame': {'duration': 300, 'redraw': False},
'mode': 'immediate',
'transition': {'duration': 300}}
],
'label': year,
'method': 'animate'}
sliders_dict['steps'].append(slider_step)
figure['layout']['sliders'] = [sliders_dict]
icreate_animations(figure)
和来自数据集的样本:
,country,continent,year,Country_code,Total_population,Life_satisfaction,GDP_per_capita
62,Afghanistan,Asia,2008,AFG,29839994.0,3.723589897,1298.14315888
63,Afghanistan,Asia,2009,AFG,30577756.0,4.401778221,1531.17399272
64,Afghanistan,Asia,2010,AFG,31411743.0,4.75838089,1614.25500126
65,Afghanistan,Asia,2011,AFG,32358260.0,3.83171916,1660.73985618
66,Afghanistan,Asia,2012,AFG,33397058.0,3.782937527,1839.27357928
67,Afghanistan,Asia,2013,AFG,34499915.0,3.572100401,1814.15582533
167,Albania,Europe,2007,ALB,3169665.0,4.634251595,8447.88228539
169,Albania,Europe,2009,ALB,3192723.0,5.485469818,9524.60981095
170,Albania,Europe,2010,ALB,3204284.0,5.268936634,9927.13514733
171,Albania,Europe,2011,ALB,3215988.0,5.867421627,10207.7006745
172,Albania,Europe,2012,ALB,3227373.0,5.510124207,10369.7616592
173,Albania,Europe,2013,ALB,3238316.0,4.550647736,10504.0930888
242,Algeria,Africa,2010,DZA,35468208.0,5.46356678,12870.2162376
243,Algeria,Africa,2011,DZA,35980193.0,5.317194462,12989.9549601
244,Algeria,Africa,2012,DZA,36485828.0,5.604595661,13161.566464
451,Angola,Africa,2011,AGO,19618432.0,5.589000702,5911.25433387
452,Angola,Africa,2012,AGO,20162517.0,4.360249996,5998.63860099
最佳答案
那么让我们从代码中的问题开始。
您正在使用 from plotly.grid_objs import Grid, Column 来制作图表，但这是 plotly online 的一个功能，我用一个简单的数据框替换了您的网格逻辑，基本上做同样的事情。
其次，我注意到图表没有显示数据，经过分析我终于发现您将 X-Axis 的范围设置为 [30, 85]，但是X 轴值在 1-15k 之间。
请在下面找到我更新的代码。我希望这能解决您的问题。
代码:
import pandas as pd
import numpy as np
from __future__ import division
import plotly.plotly as py
from plotly.offline import download_plotlyjs, init_notebook_mode, iplot
from plotly.graph_objs import *
init_notebook_mode()
from plotly.grid_objs import Grid, Column
from plotly.tools import FigureFactory as FF
dataset=pd.read_csv('god_mod_copia.csv')
years_from_col = set(dataset['year'])
years_ints = sorted(list(years_from_col))
years = [str(year) for year in years_ints]
# make list of continents
continents = []
for continent in dataset['continent']:
if continent not in continents:
continents.append(continent)
df = pd.DataFrame()
# make grid
for year in years:
for continent in continents:
dataset_by_year = dataset[dataset['year'] == int(year)]
dataset_by_year_and_cont = dataset_by_year[dataset_by_year['continent'] == continent]
for col_name in dataset_by_year_and_cont:
# each column name is unique
temp = '{year}+{continent}+{header}_grid'.format(
year=year, continent=continent, header=col_name
)
#if dataset_by_year_and_cont[col_name].size != 0:
df = df.append({'value': list(dataset_by_year_and_cont[col_name]), 'key': temp}, ignore_index=True)
figure = {
'data': [],
'layout': {},
'frames': []
}
figure['layout']['xaxis'] = {'title': 'GDP per Capita', 'type': 'log', 'autorange': True} #was not set properly
figure['layout']['yaxis'] = {'title': 'Life Expectancy', 'autorange': True} #was not set properly
figure['layout']['hovermode'] = 'closest'
figure['layout']['showlegend'] = True
figure['layout']['sliders'] = {
'args': [
'slider.value', {
'duration': 400,
'ease': 'cubic-in-out'
}
],
'initialValue': '2007',
'plotlycommand': 'animate',
'values': years,
'visible': True
}
figure['layout']['updatemenus'] = [
{
'buttons': [
{
'args': [None, {'frame': {'duration': 500, 'redraw': False},
'fromcurrent': True, 'transition': {'duration': 300, 'easing': 'quadratic-in-out'}}],
'label': 'Play',
'method': 'animate'
},
{
'args': [[None], {'frame': {'duration': 0, 'redraw': False}, 'mode': 'immediate',
'transition': {'duration': 0}}],
'label': 'Pause',
'method': 'animate'
}
],
'direction': 'left',
'pad': {'r': 10, 't': 87},
'showactive': False,
'type': 'buttons',
'x': 0.1,
'xanchor': 'right',
'y': 0,
'yanchor': 'top'
}
]
sliders_dict = {
'active': 0,
'yanchor': 'top',
'xanchor': 'left',
'currentvalue': {
'font': {'size': 20},
'prefix': 'Year:',
'visible': True,
'xanchor': 'right'
},
'transition': {'duration': 300, 'easing': 'cubic-in-out'},
'pad': {'b': 10, 't': 50},
'len': 0.9,
'x': 0.1,
'y': 0,
'steps': []
}
custom_colors = {
'Asia': 'rgb(171, 99, 250)',
'Europe': 'rgb(230, 99, 250)',
'Africa': 'rgb(99, 110, 250)',
'Americas': 'rgb(25, 211, 243)',
#'Oceania': 'rgb(9, 255, 255)'
'Oceania': 'rgb(50, 170, 255)'
}
col_name_template = '{year}+{continent}+{header}_grid'
year = 2007
for continent in continents:
data_dict = {
'x': df.loc[df['key']==col_name_template.format(
year=year, continent=continent, header='GDP_per_capita'
), 'value'].values[0],
'y': df.loc[df['key']==col_name_template.format(
year=year, continent=continent, header='Life_satisfaction'
), 'value'].values[0],
'mode': 'markers',
'text': df.loc[df['key']==col_name_template.format(
year=year, continent=continent, header='country'
), 'value'].values[0],
'marker': {
'sizemode': 'area',
'sizeref': 200000,
'size': df.loc[df['key']==col_name_template.format(
year=year, continent=continent, header='Total_population'
), 'value'].values[0],
'color': custom_colors[continent]
},
'name': continent
}
figure['data'].append(data_dict)
for year in years:
frame = {'data': [], 'name': str(year)}
for continent in continents:
data_dict = {
'x': df.loc[df['key']==col_name_template.format(
year=year, continent=continent, header='GDP_per_capita'
), 'value'].values[0],
'y': df.loc[df['key']==col_name_template.format(
year=year, continent=continent, header='Life_satisfaction'
), 'value'].values[0],
'mode': 'markers',
'text': df.loc[df['key']==col_name_template.format(
year=year, continent=continent, header='country'
), 'value'].values[0],
'marker': {
'sizemode': 'area',
'sizeref': 200000,
'size': df.loc[df['key']==col_name_template.format(
year=year, continent=continent, header='Total_population'
), 'value'].values[0],
'color': custom_colors[continent]
},
'name': continent
}
frame['data'].append(data_dict)
figure['frames'].append(frame) #this block was indented and should not have been.
slider_step = {'args': [
[year],
{'frame': {'duration': 300, 'redraw': False},
'mode': 'immediate',
'transition': {'duration': 300}}
],
'label': year,
'method': 'animate'}
sliders_dict['steps'].append(slider_step)
figure['layout']['sliders'] = [sliders_dict]
iplot(figure, config={'scrollzoom': True})
关于python - 在 Jupyter Notebook 上离线绘制 icreate_animations，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/45780920/
27
4
0
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我在绘制/布局期间收到 3 个对象分配警告 super.onDraw(canvas); canvas.drawColor(Color.WHITE); Paint textPaint = new Pai
我有一个示例时间序列数据框: df = pd.DataFrame({'year':'1990','1991','1992','1993','1994','1995','1996',
我试图想出一种简洁的方法来绘制 R 数据框中所有列的 GridView 。问题是我的数据框中既有离散值又有数值。为简单起见，我们可以使用 R 提供的名为 iris 的示例数据集。我会使用 par(mf
我有一个由 10 列和 50 行组成的 data.frame。我使用 apply 函数逐列计算密度函数。现在我想绘制我一次计算的密度。 换句话说，而不是绘图... plot(den[[1]]) plo
我想知道我们如何才能在第一个和第二个组件之外绘制个人，如下所示: 最佳答案 这可能有效: pc.cr <- princomp(USArrests, cor = TRUE) pairs(pc.cr$lo
我是Pandas和matplotlib的新手，想绘制此DataFrame season won team matches pct_won 0 20
我正在尝试为 distplot 子图编写一个 for 循环。 我有一个包含许多不同长度列的数据框。 (不包括 NaN 值) fig = make_subplots( rows=len(asse
我想创建一个具有密度的 3d 图。 我使用函数 density 首先为特定的 x 值创建一个二维图，然后该函数创建密度并将它们放入 y 变量中。现在我有第二组 x 值并将其再次放入密度函数中，然后我得
全部， 我一直在研究全局所有 MTB 步道的索引。我是 Python 人，所以对于所有涉及的步骤，我都尝试使用 Python 模块。 我能够像这样从 OSM 立交桥 API 中获取关系: from O
我正在使用 e1071 包中的支持向量机对我的数据进行分类，并希望可视化机器实际如何进行分类。但是，在使用 plot.svm 函数时，出现无法解决的错误。 脚本: library("e1071") d
我制作了以下图表，它是使用 xts 对象创建的。 我使用的代码很简单 plot(graphTS1$CCLL, type = "l", las = 2, ylab = "(c)\nCC for I
在绘制状态图时，您如何知道哪些状态放在框中，哪些状态用于转换箭头？我注意到转换也是状态。 我正在查看 this page 上的图 1 : 最佳答案 转换不是状态。转换是将对象从一种状态移动到下一种状态
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