Table of Contents
- Methods for updating the figure or graph objects
- Nicer Methods for updating the figure
- General python
- Updating a graph object.
- Using and editing the full figure description
- Exercise
Methods for updating the figure or graph objects
- Using the
update()function to add or adjust figure dictionaries and their elements - viewing, printing and using the full figure dictionary
- Using pply() or pretty print to format the full figure dictionary
Nicer Methods for updating the figure
As you've probably thought to yourself, editing and customising plotly graphs can be relatively verbose and cumbersome.
Plotly is, by design, very explicit. Each plot has it's 'DNA', which outlines very clearly how the whole plot is built. But sometimes programming a graph involves a bit too much redundant, repetitive and explicit code.
Here two general methods of dealing with and editing the code for a graph are explained which may be useful in either making the coding more efficient or making the mental task of managing the graph easier.
General python
One advantage of the explicit design of plotly figures is that, being made up of dictionaries and lists, ordinary python methods for manipulating these objects work just fine. Appending to lists, using for loops and conditionals, and whatever else is possible in python can all be used effectively to intelligently edit a plotly figure object.
Updating a graph object.
Most of the attributes of a plotly graph are in dictionaries. There are exceptions - the data is always in a list or array, as are tick values. But any styling or formatting options are in dictionaries. This includes individual traces - that is, any single scatter or line with x and y data - where each one is a single dictionary.
Dictionaries have an update() function. This function takes whatever you have passed to it, and inserts it or overwrites the particular element being updated, without overwriting anything else that is already in the dictionary.
For instance
myDict = dict(name='Errol', age=30)
myDict.update(age=31) # I know, I'm sad too
myDict
returns ...
{'age': 31, 'name': 'Errol'}
Plotly update function
In plotly, this update function can be applied to any plotly attribute or graph object, including the data list which has each of the traces of a graph.
This can make your code neater when you are customising a plot. Each modification can be a small single line of code, so you can keep track of what you've done more easily.
More significantly, though, in combination with for loops (or when dealing with the data list, as we'll cover below), it can also make editing the plot faster.
Let's make a simple plot ...
x = np.linspace(0, 4, 50)
# various linear traces ... no styling
data = [
go.Scatter(x=x, y=x),
go.Scatter(x=x, y=x*2),
go.Scatter(x=x, y=x*4),
go.Scatter(x=x, y=x*8)
]
fig = go.Figure(data=data, layout=dict())
iplot(fig)
Update the layout
We haven't formatted or styled anything so far, not even the traces.
If we were to look at the figure dictionary, without the data, it would be:
{ 'data': [ { 'type': 'scatter'},
{ 'type': 'scatter'},
{ 'type': 'scatter'},
{ 'type': 'scatter'}],
'layout': { }}
Let's say we wanted the height and width of the whole graph to be the same (plotly's auto-sizing is a bit deceptive for this graph).
A single line that updates the xaxis will do the job. Note, that the code below also takes advantage of the dict.key method of accessing the elements of a dictionary. This is specific to plotly.
fig.layout.update(height=500, width=500)
Now the figure dictionary looks like:
{ 'data': [ { 'type': 'scatter'},
{ 'type': 'scatter'},
{ 'type': 'scatter'},
{ 'type': 'scatter'}],
'layout': { 'height': 500, 'width': 500}}
Update the traces
Updating the traces works a little differently, as they are dictionaries in a list (ie each trace is a dictionary, and all the traces are stored in the list data.
Let's say that we wanted all of the traces to be styled so that they had opacity=0.7, width=10 and mode='markers'.
We could just go and add those attributes to the code for every trace, or we could update all at once. There are two ways to do this.
Use a for loop
As fig.data is a list (prove it by called fig.data[0]), we can loop through each trace. For each trace, we use the update() function as we did above.
# loop through each trace in the data list, which is an element of the figure dict
for trace in fig.data:
# update each trace using the update function
trace.update(opacity=0.7, mode='markers', line=go.Line(width=10))
our figure dictionary now looks like:
{ 'data': [ { 'line': { 'width': 10},
'mode': 'markers',
'opacity': 0.7,
'type': 'scatter'},
{ 'line': { 'width': 10},
'mode': 'markers',
'opacity': 0.7,
'type': 'scatter'},
{ 'line': { 'width': 10},
'mode': 'markers',
'opacity': 0.7,
'type': 'scatter'},
{ 'line': { 'width': 10},
'mode': 'markers',
'opacity': 0.7,
'type': 'scatter'}],
'layout': { 'xaxis': { 'range': [0, 30]}}}
Use only the update function
This is the most efficient way, but also less intuitive.
Note - If you're comfortable with loops in python, but find this a bit odd, sticking with the loops is perfectly fine and probably produces more readable code.
You can call the update function directly on fig.data. You don't need to loop through each trace within fig.data and use the update function on them individually. The update function does that for us.
Also, recall that the update function overwrites the particular element being updated, without overwriting anything else that is already in the dictionary.
So, if we were to run:
fig.data.update( dict(mode='markers') )
The update() function will:
- loop through each trace ...
- update each trace, which is a dictionary, with the dictionary we have provided.
As nothing is overwritten, the original contents of each trace remains, and everything new in the dictionary is inserted.
To style the traces how we want them ...
fig.data.update(dict(opacity=0.7, mode='markers', line=go.Line(width=10)))
The result is the same as when we ran a loop above ... every trace is updated.
For any formatting that is identical for each trace, this is the fastest way to code it.
Using and editing the full figure description
Sometimes the easiest way to edit the parameters of a graph is to print out the full figure dictionary, edit it as desired, and copy and paste it into an iplot function.
Sometimes it helps to see all the settings in a structured format.
Viewing or printing the full figure description
As a plotly graph's DNA is made up of dictionaries and lists, it can be viewed simply by called it.
We have made again a simple plot, but with some formatting and fewer data points for brevity ...
x = np.linspace(0, 4, 20)
# various linear traces ... no styling
data = [
go.Scatter(x=x, y=x, mode='markers'),
go.Scatter(x=x, y=x*2, mode='markers+lines'),
go.Scatter(x=x, y=x*4, line=go.Line(width=12)),
go.Scatter(x=x, y=x*8)
]
fig = go.Figure(data=data, layout=dict(height=450, width=550))
# call fig
fig
Calling fig returns ...
{'data': [{'mode': 'markers',
'type': 'scatter',
'x': array([ 0. , 0.21052632, 0.42105263, 0.63157895, 0.84210526,
1.05263158, 1.26315789, 1.47368421, 1.68421053, 1.89473684,
2.10526316, 2.31578947, 2.52631579, 2.73684211, 2.94736842,
3.15789474, 3.36842105, 3.57894737, 3.78947368, 4. ]),
'y': array([ 0. , 0.21052632, 0.42105263, 0.63157895, 0.84210526,
1.05263158, 1.26315789, 1.47368421, 1.68421053, 1.89473684,
2.10526316, 2.31578947, 2.52631579, 2.73684211, 2.94736842,
3.15789474, 3.36842105, 3.57894737, 3.78947368, 4. ])},
{'mode': 'markers+lines',
'type': 'scatter',
'x': array([ 0. , 0.21052632, 0.42105263, 0.63157895, 0.84210526,
1.05263158, 1.26315789, 1.47368421, 1.68421053, 1.89473684,
2.10526316, 2.31578947, 2.52631579, 2.73684211, 2.94736842,
3.15789474, 3.36842105, 3.57894737, 3.78947368, 4. ]),
'y': array([ 0. , 0.42105263, 0.84210526, 1.26315789, 1.68421053,
2.10526316, 2.52631579, 2.94736842, 3.36842105, 3.78947368,
4.21052632, 4.63157895, 5.05263158, 5.47368421, 5.89473684,
6.31578947, 6.73684211, 7.15789474, 7.57894737, 8. ])},
{'line': {'width': 12},
'type': 'scatter',
'x': array([ 0. , 0.21052632, 0.42105263, 0.63157895, 0.84210526,
1.05263158, 1.26315789, 1.47368421, 1.68421053, 1.89473684,
2.10526316, 2.31578947, 2.52631579, 2.73684211, 2.94736842,
3.15789474, 3.36842105, 3.57894737, 3.78947368, 4. ]),
'y': array([ 0. , 0.84210526, 1.68421053, 2.52631579,
3.36842105, 4.21052632, 5.05263158, 5.89473684,
6.73684211, 7.57894737, 8.42105263, 9.26315789,
10.10526316, 10.94736842, 11.78947368, 12.63157895,
13.47368421, 14.31578947, 15.15789474, 16. ])},
{'type': 'scatter',
'x': array([ 0. , 0.21052632, 0.42105263, 0.63157895, 0.84210526,
1.05263158, 1.26315789, 1.47368421, 1.68421053, 1.89473684,
2.10526316, 2.31578947, 2.52631579, 2.73684211, 2.94736842,
3.15789474, 3.36842105, 3.57894737, 3.78947368, 4. ]),
'y': array([ 0. , 1.68421053, 3.36842105, 5.05263158,
6.73684211, 8.42105263, 10.10526316, 11.78947368,
13.47368421, 15.15789474, 16.84210526, 18.52631579,
20.21052632, 21.89473684, 23.57894737, 25.26315789,
26.94736842, 28.63157895, 30.31578947, 32. ])}],
'layout': {'height': 450, 'width': 550}}
Viewing or printing the description better - pply()
When you call the figure description, fig, it has two potential problems:
- isn't in the most readable format
- It has all the data, which can get in the way of reading the formatting parameters.
An alternative is filter out the data and use a standard python library called pprint which stands for pretty printer.
Below, a function is defined that uses pprint for us.
- All we have to do is pass in our figure dictionary.
- There's also the option of printing our data too (set the key word arg
data=True). - it's called
pplyfor pprint + plotly = pply.
Please copy the function code, use it and change it as you see fit
import pprint
# initialises a pretty printer
pp = pprint.PrettyPrinter(indent=4)
# We can now print with pp.pprint()
# use copy to make sure we don't break the original figure dictionary
import copy
# the function we'll use to print figure dictionaries
def pply(plyfig, data=False):
# the data option allows us to control whether the data is printed or not
if not data:
po = copy.deepcopy(plyfig)
po['data'] = [{i:trace[i] for i in trace if i not in ['x', 'y', 'z']} for trace in po['data']]
pp.pprint(po)
else:
pp.pprint(plyfig)
Without pply()
fig
returns ...
{'data': [{'mode': 'markers',
'type': 'scatter',
'x': array([ 0. , 0.21052632, 0.42105263, 0.63157895, 0.84210526,
1.05263158, 1.26315789, 1.47368421, 1.68421053, 1.89473684,
2.10526316, 2.31578947, 2.52631579, 2.73684211, 2.94736842,
3.15789474, 3.36842105, 3.57894737, 3.78947368, 4. ]),
'y': array([ 0. , 0.21052632, 0.42105263, 0.63157895, 0.84210526,
1.05263158, 1.26315789, 1.47368421, 1.68421053, 1.89473684,
2.10526316, 2.31578947, 2.52631579, 2.73684211, 2.94736842,
3.15789474, 3.36842105, 3.57894737, 3.78947368, 4. ])},
{'mode': 'markers+lines',
'type': 'scatter',
'x': array([ 0. , 0.21052632, 0.42105263, 0.63157895, 0.84210526,
1.05263158, 1.26315789, 1.47368421, 1.68421053, 1.89473684,
2.10526316, 2.31578947, 2.52631579, 2.73684211, 2.94736842,
3.15789474, 3.36842105, 3.57894737, 3.78947368, 4. ]),
'y': array([ 0. , 0.42105263, 0.84210526, 1.26315789, 1.68421053,
2.10526316, 2.52631579, 2.94736842, 3.36842105, 3.78947368,
4.21052632, 4.63157895, 5.05263158, 5.47368421, 5.89473684,
6.31578947, 6.73684211, 7.15789474, 7.57894737, 8. ])},
{'line': {'width': 12},
'type': 'scatter',
'x': array([ 0. , 0.21052632, 0.42105263, 0.63157895, 0.84210526,
1.05263158, 1.26315789, 1.47368421, 1.68421053, 1.89473684,
2.10526316, 2.31578947, 2.52631579, 2.73684211, 2.94736842,
3.15789474, 3.36842105, 3.57894737, 3.78947368, 4. ]),
'y': array([ 0. , 0.84210526, 1.68421053, 2.52631579,
3.36842105, 4.21052632, 5.05263158, 5.89473684,
6.73684211, 7.57894737, 8.42105263, 9.26315789,
10.10526316, 10.94736842, 11.78947368, 12.63157895,
13.47368421, 14.31578947, 15.15789474, 16. ])},
{'type': 'scatter',
'x': array([ 0. , 0.21052632, 0.42105263, 0.63157895, 0.84210526,
1.05263158, 1.26315789, 1.47368421, 1.68421053, 1.89473684,
2.10526316, 2.31578947, 2.52631579, 2.73684211, 2.94736842,
3.15789474, 3.36842105, 3.57894737, 3.78947368, 4. ]),
'y': array([ 0. , 1.68421053, 3.36842105, 5.05263158,
6.73684211, 8.42105263, 10.10526316, 11.78947368,
13.47368421, 15.15789474, 16.84210526, 18.52631579,
20.21052632, 21.89473684, 23.57894737, 25.26315789,
26.94736842, 28.63157895, 30.31578947, 32. ])}],
'layout': {'height': 450, 'width': 550}}
with pply
pply(fig)
returns...
{ 'data': [ { 'mode': 'markers', 'type': 'scatter'},
{ 'mode': 'markers+lines', 'type': 'scatter'},
{ 'line': { 'width': 12}, 'type': 'scatter'},
{ 'type': 'scatter'}],
'layout': { 'height': 450, 'width': 550}}
Using the full description
This full description has everything that makes up the plot, including the data. In this sense, it can be a useful means of checking what's going on with the plot.
Or, once the paraeter or attribute that is relevant to us has been identified, we can then use the update function or redo our original code accordingly.
This full description can be copied and pasted and edited. We can assign it to a new variable or simply pass it directly into an iplot function once edited.
Note on copying and pasting the full description when using numpy
You'll note that in the full figure description above the data is written not as a list but as: 'x': array([ ... ]). This is because we provided numpy arrays to the traces, not lists. This is a good thing. Numpy arrays are useful. But, if we were to copy and paste the figure description above into iplot(), we would get an error ...
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-138-adacd319273c> in <module>()
1 iplot({'data': [{'mode': 'markers',
2 'type': 'scatter',
----> 3 'x': array([ 0. , 0.21052632, 0.42105263, 0.63157895, 0.84210526,
4 1.05263158, 1.26315789, 1.47368421, 1.68421053, 1.89473684,
5 2.10526316, 2.31578947, 2.52631579, 2.73684211, 2.94736842,
NameError: name 'array' is not defined
We have imported numpy as np, but the array function is np.array(). array() is not a global function. Python is usually programmed this way to keep all the function names neat and tidy.
If we wished to copy and paste these figure descriptions, we would need to make the array() function a global variable, like so:
import numpy as np
from numpy import array
After this, plotly won't have any problems understanding 'x': array([...]).
An alternative, which some of you may already be employing, is to use pylab, which is a convenience library full of many of the useful functions for using python for research. It's purpose is to make these useful functions global.
from pylab import *
Exercise
- Copy and paste the full figure description in the collapsed section below into your own notebook or environment
- Replicate the plot by passing it into
iplot()orplot().- You may need to import array from numpy, on which see the section above.
- You may want to simply assign it to a a new variable, like
fig.
- Update the layout to have a title using the
updatefunction - Update each of the traces to have whatever style you'd like using the
updatefunction.
Use this raw figure description for the exercise.
It is a plot of life expectancy over time for a few nations. It was generated in previous chapters.
{'data': [{'name': 'UK',
'type': 'scatter',
'x': array([1900, 1901, 1902, 1903, 1904, 1905, 1906, 1907, 1908, 1909, 1910,
1911, 1912, 1913, 1914, 1915, 1916, 1917, 1918, 1919, 1920, 1921,
1922, 1923, 1924, 1925, 1926, 1927, 1928, 1929, 1930, 1931, 1932,
1933, 1934, 1935, 1936, 1937, 1938, 1939, 1940, 1941, 1942, 1943,
1944, 1945, 1946, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954,
1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965,
1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976,
1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987,
1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998,
1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,
2010, 2011, 2012, 2013, 2014, 2015]),
'y': array([ 46.32 , 47.91 , 49.08 , 50.43 , 49.11 , 50.86 , 50.58 ,
51.44 , 51.84 , 52.52 , 53.99 , 51.54 , 54.55 , 53.83 ,
51.99 , 48.2 , 47.73 , 45.57 , 40.43 , 54.12 , 56.6 ,
58.346, 57.122, 59.388, 58.154, 58.49 , 59.636, 59.012,
59.958, 57.674, 60.85 , 60.066, 60.582, 60.618, 61.344,
61.99 , 61.786, 61.812, 63.228, 63.614, 60.89 , 61.286,
63.902, 63.918, 64.754, 65.75 , 66.366, 66.332, 68.388,
68.074, 68.58 , 68.176, 69.472, 69.738, 70.104, 70.07 ,
70.336, 70.452, 70.628, 70.724, 70.94 , 70.686, 70.752,
70.658, 71.444, 71.43 , 71.346, 71.972, 71.598, 71.554,
71.8 , 72. , 72. , 72. , 72.3 , 72.6 , 72.9 ,
73.1 , 73. , 73.1 , 73.4 , 73.8 , 74.1 , 74.3 ,
74.6 , 74.7 , 74.9 , 75.1 , 75.3 , 75.5 , 75.7 ,
76. , 76.3 , 76.5 , 76.7 , 76.8 , 76.9 , 77.2 ,
77.4 , 77.6 , 77.8 , 78. , 78.2 , 78.5 , 78.8 ,
79.1 , 79.3 , 79.4 , 79.5 , 79.7 , 80. , 80.4 ,
80.8 , 81. , 81.2 , 81.4 ])},
{'name': 'France',
'type': 'scatter',
'x': array([1900, 1901, 1902, 1903, 1904, 1905, 1906, 1907, 1908, 1909, 1910,
1911, 1912, 1913, 1914, 1915, 1916, 1917, 1918, 1919, 1920, 1921,
1922, 1923, 1924, 1925, 1926, 1927, 1928, 1929, 1930, 1931, 1932,
1933, 1934, 1935, 1936, 1937, 1938, 1939, 1940, 1941, 1942, 1943,
1944, 1945, 1946, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954,
1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965,
1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976,
1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987,
1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998,
1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,
2010, 2011, 2012, 2013, 2014, 2015]),
'y': array([ 45.08 , 47.01 , 48.01 , 48.43 , 48.08 , 48.36 ,
47.74 , 48.28 , 49.3 , 50.01 , 51.37 , 48.17 ,
51.62 , 51.35 , 37.85 , 35.63 , 39.51 , 42.6 ,
34.34 , 47.52 , 51.6 , 52.6968, 54.9336, 54.6704,
55.2972, 54.424 , 54.0708, 55.8476, 55.4944, 54.3312,
56.938 , 57.0048, 57.3416, 57.7884, 58.4552, 58.422 ,
58.9188, 59.2856, 59.0924, 59.7492, 49.586 , 57.8128,
57.5896, 53.4864, 47.3532, 55.13 , 62.5568, 64.1636,
66.0204, 65.1172, 66.594 , 66.3308, 67.6276, 67.5644,
68.4412, 68.708 , 68.7448, 69.1816, 70.4184, 70.4552,
70.672 , 71.2588, 70.7956, 70.6524, 71.6192, 71.456 ,
71.8728, 71.8696, 71.8664, 71.6032, 72.5 , 72.6 ,
72.8 , 73.1 , 73.3 , 73.2 , 73.4 , 73.8 ,
74.1 , 74.3 , 74.5 , 74.8 , 75. , 75.2 ,
75.5 , 75.7 , 76. , 76.4 , 76.6 , 76.9 ,
77.1 , 77.3 , 77.5 , 77.7 , 77.9 , 78.1 ,
78.4 , 78.7 , 78.8 , 78.9 , 79.1 , 79.2 ,
79.4 , 79.7 , 80.1 , 80.4 , 80.7 , 80.9 ,
81. , 81. , 81.2 , 81.4 , 81.6 , 81.7 ,
81.8 , 81.9 ])},
{'name': 'Germany',
'type': 'scatter',
'x': array([1900, 1901, 1902, 1903, 1904, 1905, 1906, 1907, 1908, 1909, 1910,
1911, 1912, 1913, 1914, 1915, 1916, 1917, 1918, 1919, 1920, 1921,
1922, 1923, 1924, 1925, 1926, 1927, 1928, 1929, 1930, 1931, 1932,
1933, 1934, 1935, 1936, 1937, 1938, 1939, 1940, 1941, 1942, 1943,
1944, 1945, 1946, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954,
1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965,
1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976,
1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987,
1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998,
1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,
2010, 2011, 2012, 2013, 2014, 2015]),
'y': array([ 43.915 , 44.222 , 44.529 , 44.836 ,
45.143 , 45.45 , 46.04166667, 46.63333333,
47.225 , 47.81666667, 48.40833333, 49. ,
49.59166667, 50.18333333, 46.17648422, 40.52556325,
38.9888193 , 40.08037644, 32.93682031, 48.32065914,
53.5 , 55.01820257, 55.62354801, 56.22889344,
56.83423887, 57.4395843 , 57.85150116, 58.26341802,
58.67533488, 59.08725174, 59.4991686 , 59.91108546,
60.32300232, 60.73491918, 61.14683604, 61.5587529 ,
61.84933643, 62.13991996, 62.43050348, 61.07442034,
60.73821096, 59.08225406, 55.08617092, 49.79008778,
37.09400464, 29.0979215 , 60.61319836, 62.21527522,
63.81735208, 65.41942894, 67.0215058 , 67.18742266,
67.51033952, 67.82125638, 68.12117324, 68.4080901 ,
68.70345102, 68.62532856, 69.36929231, 69.48021979,
69.40190727, 69.99702797, 70.16889372, 70.26131586,
70.82344196, 70.81075623, 70.92828395, 71.15404398,
70.80345367, 70.65682236, 70.9 , 71. ,
71.2 , 71.5 , 71.8 , 71.9 ,
72.3 , 72.6 , 72.7 , 72.9 ,
73.1 , 73.4 , 73.6 , 74. ,
74.4 , 74.6 , 74.8 , 75.1 ,
75.3 , 75.4 , 75.4 , 75.6 ,
75.9 , 76.2 , 76.4 , 76.6 ,
76.9 , 77.3 , 77.7 , 77.9 ,
78.1 , 78.3 , 78.5 , 78.8 ,
79.1 , 79.4 , 79.7 , 79.8 ,
80. , 80. , 80.2 , 80.3 ,
80.5 , 80.7 , 80.9 , 81.1 ])},
{'name': 'Aust',
'type': 'scatter',
'x': array([1900, 1901, 1902, 1903, 1904, 1905, 1906, 1907, 1908, 1909, 1910,
1911, 1912, 1913, 1914, 1915, 1916, 1917, 1918, 1919, 1920, 1921,
1922, 1923, 1924, 1925, 1926, 1927, 1928, 1929, 1930, 1931, 1932,
1933, 1934, 1935, 1936, 1937, 1938, 1939, 1940, 1941, 1942, 1943,
1944, 1945, 1946, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954,
1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965,
1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976,
1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987,
1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998,
1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009,
2010, 2011, 2012, 2013, 2014, 2015]),
'y': array([ 49.92647059, 50.45568627, 50.98490196, 51.51411765,
52.04333333, 52.57254902, 53.10176471, 53.63098039,
54.16019608, 54.68941176, 55.21862745, 55.74784314,
56.27705882, 56.80627451, 57.3354902 , 57.86470588,
58.39392157, 58.92313725, 54.80239165, 59.98156863,
60.51078431, 61.0438 , 62.8976 , 61.7414 ,
62.5452 , 63.239 , 62.9628 , 62.9366 ,
62.9904 , 63.1842 , 64.998 , 65.4318 ,
65.7356 , 65.5694 , 64.9332 , 65.207 ,
65.3308 , 65.8946 , 65.9384 , 65.8922 ,
66.336 , 66.2198 , 65.9636 , 66.4874 ,
68.1212 , 68.555 , 68.0788 , 68.6926 ,
68.6164 , 69.2402 , 69.134 , 68.8378 ,
69.2416 , 69.8254 , 69.9792 , 70.303 ,
70.1868 , 70.4706 , 71.0244 , 70.5982 ,
71.042 , 71.3158 , 71.0896 , 71.1534 ,
70.8172 , 71.151 , 70.9948 , 71.2786 ,
70.9124 , 71.3262 , 71. , 71.3 ,
71.7 , 72. , 72.1 , 72.5 ,
73. , 73.4 , 73.8 , 74.2 ,
74.5 , 74.8 , 75. , 75.3 ,
75.5 , 75.7 , 76. , 76.2 ,
76.4 , 76.6 , 77. , 77.4 ,
77.7 , 78. , 78.2 , 78.4 ,
78.6 , 78.9 , 79.1 , 79.3 ,
79.7 , 80.1 , 80.4 , 80.7 ,
81. , 81.2 , 81.4 , 81.5 ,
81.5 , 81.6 , 81.7 , 81.8 ,
81.8 , 81.8 , 81.8 , 81.8 ])}],
'layout': {}}