Concept: What is the distribution of IMLS grants, and what do these grants do?
Your second dataset will be the IMLS discretionary grants database. These grants include information about the topic of the grant, the institution that received it, its duration, and a few other pieces of data. You may also find useful and interesting data (especially related to zip codes and economic factors) at FRED.
Your visualization should be interactive. There are several frameworks you can use to do this. The three that we have discussed in detail in class have been using the IPython widgets interface to build small, exploratory visualizations, the Bokeh framework and Plotly. You may not use Tableau to build this visualization. If there is another framework you would like to use, please ask.
A few of the concepts that you may explore:
What types of grants are supported by IMLS?
How are these grants distributed across the United States? Are they particularly clustered in certain states?
Is there a relationship between the economic factors in a zip code and the grants from IMLS?
Generate from this data either a notebook that can be executed, a python script that can be executed, or a plotly dashboard that can be accessed. If you utilize plotly, at lease some of the plotly setup must be in a python script.
In [1]:
#import packages
%matplotlib inline
import matplotlib.pyplot as plt
import csv
import numpy as np
import pandas as pd
import collections
import plotly
import plotly.plotly as py
import plotly.graph_objs as go
from plotly.graph_objs import *
plotly.tools.set_credentials_file(username='alexbear', api_key='L6m9DmfDjqrksfHtUH5j')
#plotly.tools.set_credentials_file(username='jlwang233', api_key='hBFucEx4GrL7V9xxFhx0')
#plotly.tools.set_credentials_file(username='lis590dv', api_key='0jCaIttf2QaVJ3lRZZlK')
#plotly.tools.set_credentials_file(username='jxljiang221', api_key='RgXxTYxCam4vBca8DHDq')
#plotly.tools.set_credentials_file(username='jxljiang221', api_key='LOONcFbgZszSxdiuB3Kn')
In [16]:
#Q2-1 BY YINGJUN GUAN: What types of grants are supported by IMLS?
plt.rcParams["figure.figsize"] = (15, 8)
df2 = pd.read_csv("DiscGrants96to13.csv",encoding='iso-8859-1')
cc=collections.Counter(df2["ProgramType"])
vv=[]
kk=[]
for v,k in sorted(cc.items(), key=lambda i: i[1], reverse=True):
vv.append(v)
kk.append(k)
objects = vv
y_pos = np.arange(len(objects))
performance = kk
plt.bar(y_pos, performance, align='center', alpha=0.5)
plt.xticks(y_pos, objects)
plt.ylabel('Number of grants')
plt.xlabel('Type of programs')
plt.title('Program types vs their counts')
plt.show()
In [3]:
sum(performance)
Out[3]:
In [4]:
performance
Out[4]:
Description:
We make histogram to study the distribution of program types by counts of program types.
Findings:
In [5]:
df2.head()
Out[5]:
In [43]:
df3=df2[(df2["Longitude"]>-95)&(df2["Latitude"]<24)]
#df2=df2[(((df2["Longitude"]>-95)&(df2["Latitude"]<24))==False)]
df2=df2[((df2["InstState"]=="GU")|(df2["InstState"]=="PW")|(df2["InstState"]=="VI")|(df2["InstState"]=="PR")|(df2["InstState"]=="MP")|(df2["InstState"]=="FM")|(df2["InstState"]=="MH")|(df2["InstState"]=="AS")|(df2["InstState"]==""))==False]
In [7]:
df2.describe
Out[7]:
In [8]:
#Q2-2a BY XINYU ZHANG How are these grants distributed across the United States? Are they particularly clustered in certain states?
yearly=[]
state=[]
for year,group in df2.groupby('FiscalYear'):
print (year)
df_2=group.groupby('InstState')
# state=df4['InstState'].unique()
state.append(df_2.groups)
total=df_2['AwardTotal'].sum()
print(total)
yearly.append(total)
#print(yearly[0])
In [29]:
scl = [[0.0, 'rgb(242,240,247)'],[0.2, 'rgb(218,218,235)'],[0.4, 'rgb(188,189,220)'],[0.6, 'rgb(158,154,200)'],[0.8, 'rgb(117,107,177)'],[1.0, 'rgb(84,39,143)']]
df5=df2.groupby('FiscalYear').get_group(1996)
df4=df5.groupby('InstState')
#df5=df4.get_group
state=[]
amount=[]
for name,group in df4:
state.append(str(name))
amount.append(str(group['AwardTotal'].sum()))
# state=df4['InstState'].unique()
# state.append(df4.groups)
location = pd.DataFrame(state,columns = ['state'])
total= pd.DataFrame(amount,columns = ['amount'])
# yearly.append(total)
# for i in range(3):
data = [ dict(
type='choropleth',
colorscale = scl,
# autocolorscale = False,
# locations=df['InstState'].unique(),
# locations=state[i],
# z = yearly[i],
locations = location['state'],
z = total['amount'],
# df4['text'] = 'State: ' + df4.groups+ '<br>Total Amount: '+ z + ' dollar',
text = 'State: ' + location['state'] + '<br>Total Amount: '+ total['amount'] + ' dollar',
locationmode = 'USA-states',
marker = dict(
line = dict (
color = 'rgb(255,255,255)',
width = 2
) ),
colorbar = dict(
title = "Millions USD")
) ]
layout = dict(
#title = str(1996+i)+'US Administrative Discretionary Grants<br>(Hover for breakdown)',
title = '1996 US Administrative Discretionary Grants<br>(Hover for breakdown)',
geo = dict(
scope='usa',
projection=dict( type='albers usa' ),
showlakes = True,
lakecolor = 'rgb(255, 255, 255)'),
)
fig = dict( data=data, layout=layout )
py.plotly.iplot( fig)
#break
#py.iplot(fig)
Out[29]:
In [30]:
scl = [[0.0, 'rgb(242,240,247)'],[0.2, 'rgb(218,218,235)'],[0.4, 'rgb(188,189,220)'],[0.6, 'rgb(158,154,200)'],[0.8, 'rgb(117,107,177)'],[1.0, 'rgb(84,39,143)']]
df5=df2.groupby('FiscalYear').get_group(2013)
df4=df5.groupby('InstState')
#df5=df4.get_group
state=[]
amount=[]
for name,group in df4:
state.append(str(name))
amount.append(str(group['AwardTotal'].sum()))
# state=df4['InstState'].unique()
# state.append(df4.groups)
location = pd.DataFrame(state,columns = ['state'])
total= pd.DataFrame(amount,columns = ['amount'])
# yearly.append(total)
# for i in range(3):
data = [ dict(
type='choropleth',
colorscale = scl,
# autocolorscale = False,
# locations=df['InstState'].unique(),
# locations=state[i],
# z = yearly[i],
locations = location['state'],
z = total['amount'],
# df4['text'] = 'State: ' + df4.groups+ '<br>Total Amount: '+ z + ' dollar',
text = 'State: ' + location['state'] + '<br>Total Amount: '+ total['amount'] + ' dollar',
locationmode = 'USA-states',
marker = dict(
line = dict (
color = 'rgb(255,255,255)',
width = 2
) ),
colorbar = dict(
title = "Millions USD")
) ]
layout = dict(
#title = str(1996+i)+'US Administrative Discretionary Grants<br>(Hover for breakdown)',
title = '2013 US Administrative Discretionary Grants<br>(Hover for breakdown)',
geo = dict(
scope='usa',
projection=dict( type='albers usa' ),
showlakes = True,
lakecolor = 'rgb(255, 255, 255)'),
)
fig = dict( data=data, layout=layout )
py.plotly.iplot( fig)
#break
#py.iplot(fig)
Out[30]:
Description:
We make heatmap on 1996 and 2013 total grant amount by 51 states and also the DC region. For the convenience of plotting, region with the abbreviation 'GU','PW','VI','MP','FM','MH''AS','PR' are omitted. The state with deeper color was entitled with larger amount.
Findings:
Classified by the total amount granted to a state, we can see that there are some states that are more likely to be granted more grants although there might be variations through years. For instance, NYC, California, Texas, Illinois, Washington, etc.
Variations through years can be either the variation in amount or distribution. For instance, the maximum grants increased from 1.92 million up to 7.56 million. Another example is that California is the third at the granted amount in 1996 but was only amoung the middle in 2013.
There are also states who wasn't granted during these seven years. For instance, DE in the year 2010 and 2013, NJ in the year 2009 and 2012.
In [45]:
#Q2-2b BY XIAOLIANG JIANG
df2['Text'] = df2['Institution'] + '<br>' + df2['Program'] + '<br>' + 'Program Type: ' + df2['ProgramType'] + '<br>' + 'Total Award: ' + (df2['AwardTotal']/1e3).astype(str)+ ' thousand'
#df2['text'] = df2['Institution'] + 'Total Award' + (df2['AwardTotal']/1e3).astype(str)+ ' thousand'
limits = [(0,10),(10,100),(100,200),(200,500),(500,1000),(1000,3000)]
#colors = ["#ffcccc","#ffddcc","#ffeecc","#ffffcc","#eeffcc","#ddffcc"]
colors = ["e0e0e0","#66b2ff","#66ff66","#ffff66","#ffb266","#ff6666"]
institutions = []
scale = 6000
for i in range(len(limits)):
subdf2=df2[((df2['AwardTotal']/1e3)<limits[i][1])&((df2['AwardTotal']/1e3)>limits[i][0])]
institution = dict(
type = 'scattergeo',
locationmode = 'USA-states',
lon = subdf2['Longitude'],
lat = subdf2['Latitude'],
text = subdf2['Text'],
marker = dict(
size = subdf2['AwardTotal']/scale,
color = colors[i],
line = dict(width=0.5, color='rgb(40,40,40)'),
sizemode = 'area'
),
name ='{0} - {1}'.format(limits[i][0],limits[i][1])+' thousand dollar' )
institutions.append(institution)
In [46]:
layout = dict(
title = 'Administrative Discretionary Grants<br>(Click legend to toggle traces)',
showlegend = True,
geo = dict(
scope='usa',
projection=dict( type='albers usa' ),
showland = True,
landcolor = 'rgb(217, 217, 217)',
subunitwidth=1,
countrywidth=1,
subunitcolor="rgb(255, 255, 255)",
countrycolor="rgb(255, 255, 255)"
),
updatemenus=list([
dict(
x=-0.05,
y=1,
yanchor='top',
buttons=list([
dict(
args=['visible', [True, True, True, True, True, True]],
label='All',
method='restyle'
),
dict(
args=['visible', [True, False, False, False, False, False]],
label='0-10 thousand dollar',
method='restyle'
),
dict(
args=['visible', [False, True, False, False, False, False]],
label='10-100 thousand dollar',
method='restyle'
),
dict(
args=['visible', [False, False, True, False, False, False]],
label='100-200 thousand dollar',
method='restyle'
),
dict(
args=['visible', [False, False, False, True, False, False]],
label='200-500 thousand dollar',
method='restyle'
),
dict(
args=['visible', [False, False, False, False, True, False]],
label='500-1000 thousand dollar',
method='restyle'
),
dict(
args=['visible', [False, False, False, False, False, True]],
label='1000+ thousand dollar',
method='restyle'
)
]),
)
]),
)
fig = dict( data=institutions, layout=layout )
py.iplot( fig, validate=False, filename='q2testworldmap' )
Out[46]:
Description
The Choropleth Inset Map above is classified by project sized in different locations. There are 18 missing values and we add them mannually by looking for the address on the Google Map and record each instance's longitude and latitute. With the interactive map, you can view a single project amount class by double click on the color spot in front of the tag and hide the colorful spots by single click the spot before the item tag. If you put
Summary (thousand dollars):
Conclusion:
Large amount of grants have less counts, grants are most likely to be clustered on northeast.
In [57]:
temmm=df2["ProgramType"].value_counts().reset_index().rename(columns={'index': 'x'})
In [59]:
temmm
Out[59]:
In [58]:
df2['Text'] = df2['Institution'] + '<br>' + df2['Program'] + '<br>' + 'Program Type: ' + df2['ProgramType'] + '<br>' + 'Total Award: ' + (df2['AwardTotal']/1e3).astype(str)+ ' thousand'
#df2['text'] = df2['Institution'] + 'Total Award' + (df2['AwardTotal']/1e3).astype(str)+ ' thousand'
#limits = [(0,10),(10,100),(100,200),(200,500),(500,1000),(1000,3000)]
#colors = ["#ffcccc","#ffddcc","#ffeecc","#ffffcc","#eeffcc","#ddffcc"]
colors = ["#ff6666","#ffb266","#ffff66","#b2ff66","#66ff66","#66ffb2","#66ffff","#66b2ff","#6666ff","#ff66ff","#b266ff","#ff66b2","#000000","#404040","#808080","#c0c0c0","#ffffff"]
#pt=["IL","ST","ML","RE","IC","IM","IS","MN","LE","MP","MH","LT","LI","IG","MA","IA","IG"]
pt=temmm["x"]
institutions = []
scale = 5000
for i in range(len(colors)):
subdf2=df2[df2["ProgramType"]==pt[i]]
institution = dict(
type = 'scattergeo',
locationmode = 'USA-states',
lon = subdf2['Longitude'],
lat = subdf2['Latitude'],
text = subdf2['Text'],
marker = dict(
size = subdf2['AwardTotal']/scale,
color = colors[i],
line = dict(width=0.5, color='rgb(40,40,40)'),
sizemode = 'area'
),
name =pt[i])
institutions.append(institution)
layout = dict(
title = 'Administrative Discretionary Grants<br>(Click legend to toggle traces)',
showlegend = True,
geo = dict(
scope='usa',
projection=dict( type='albers usa' ),
showland = True,
landcolor = 'rgb(217, 217, 217)',
subunitwidth=1,
countrywidth=1,
subunitcolor="rgb(255, 255, 255)",
countrycolor="rgb(255, 255, 255)"
)
)
fig = dict( data=institutions, layout=layout )
py.iplot( fig, validate=False, filename='q2testworldmap' )
Out[58]:
Description:
This interactive Choropleth Inset Map shows the geographic distribution of grant types in the United States. The size of spots on the map represents the amount of the grant. Some spots have deeper color because there are many grants at the same position.
Summary:
Conclusion:
Most grant types has a certain amount range and tends to be more clustered on CA and northeast.
In [36]:
#Q2-3 BY JIALU WANG
fn1 = "amt.csv"
with open(fn1, "r") as f:
reader1 = csv.reader(f)
header1 = next(reader1)
data1 = {}
for column1 in header1:
data1[column1] = []
for row1 in reader1:
for column1, value1 in zip(header1, row1):
data1[column1].append(value1)
fn2 = "GeoFRED_Total_Gross_Domestic_Product_by_State_Millions_of_Dollars.csv"
with open(fn2, "r") as f:
reader2 = csv.reader(f)
header2 = next(reader2)
data2 = {}
for column2 in header2:
data2[column2] = []
for row2 in reader2:
for column2, value2 in zip(header2, row2):
data2[column2].append(value2)
In [37]:
plt.rcParams["figure.figsize"] = (40, 20)
states =data2['InstState']
AWT1997=data1['1997']
GDP1997=data2['1997']
AWT1998=data1['1998']
GDP1998=data2['1998']
AWT1999=data1['1999']
GDP1999=data2['1999']
AWT2000=data1['2000']
GDP2000=data2['2000']
AWT2001=data1['2001']
GDP2001=data2['2001']
AWT2002=data1['2002']
GDP2002=data2['2002']
AWT2003=data1['2003']
GDP2003=data2['2003']
AWT2004=data1['2004']
GDP2004=data2['2004']
AWT2005=data1['2005']
GDP2005=data2['2005']
AWT2006=data1['2006']
GDP2006=data2['2006']
AWT2007=data1['2007']
GDP2007=data2['2007']
AWT2008=data1['2008']
GDP2008=data2['2008']
AWT2009=data1['2009']
GDP2009=data2['2009']
AWT2010=data1['2010']
GDP2010=data2['2010']
AWT2011=data1['2011']
GDP2011=data2['2011']
AWT2012=data1['2012']
GDP2012=data2['2012']
AWT2013=data1['2013']
GDP2013=data2['2013']
trace11 = go.Scatter(
x = states,
y = AWT1997,
mode = 'lines+markers',
name = 'Awarded Amount'
)
trace12 = go.Scatter(
x = states,
y = GDP1997,
mode = 'lines+markers',
name = 'GDP'
)
trace21 = go.Scatter(
x = states,
y = AWT1998,
mode = 'lines+markers',
name = 'Awarded Amount'
)
trace22 = go.Scatter(
x = states,
y = GDP1998,
mode = 'lines+markers',
name = 'GDP'
)
trace31 = go.Scatter(
x = states,
y = AWT1999,
mode = 'lines+markers',
name = 'Awarded Amount'
)
trace32 = go.Scatter(
x = states,
y = GDP1999,
mode = 'lines+markers',
name = 'GDP'
)
trace41 = go.Scatter(
x = states,
y = AWT2000,
mode = 'lines+markers',
name = 'Awarded Amount'
)
trace42 = go.Scatter(
x = states,
y = GDP2000,
mode = 'lines+markers',
name = 'GDP'
)
trace51 = go.Scatter(
x = states,
y = AWT2001,
mode = 'lines+markers',
name = 'Awarded Amount'
)
trace52 = go.Scatter(
x = states,
y = GDP2001,
mode = 'lines+markers',
name = 'GDP'
)
trace61 = go.Scatter(
x = states,
y = AWT2002,
mode = 'lines+markers',
name = 'Awarded Amount'
)
trace62 = go.Scatter(
x = states,
y = GDP2002,
mode = 'lines+markers',
name = 'GDP'
)
trace71 = go.Scatter(
x = states,
y = AWT2003,
mode = 'lines+markers',
name = 'Awarded Amount'
)
trace72 = go.Scatter(
x = states,
y = GDP2003,
mode = 'lines+markers',
name = 'GDP'
)
trace81 = go.Scatter(
x = states,
y = AWT2004,
mode = 'lines+markers',
name = 'Awarded Amount'
)
trace82 = go.Scatter(
x = states,
y = GDP2004,
mode = 'lines+markers',
name = 'GDP'
)
trace91 = go.Scatter(
x = states,
y = AWT2005,
mode = 'lines+markers',
name = 'Awarded Amount'
)
trace92 = go.Scatter(
x = states,
y = GDP2005,
mode = 'lines+markers',
name = 'GDP'
)
trace101 = go.Scatter(
x = states,
y = AWT2006,
mode = 'lines+markers',
name = 'Awarded Amount'
)
trace102 = go.Scatter(
x = states,
y = GDP2006,
mode = 'lines+markers',
name = 'GDP'
)
trace111 = go.Scatter(
x = states,
y = AWT2007,
mode = 'lines+markers',
name = 'Awarded Amount'
)
trace112 = go.Scatter(
x = states,
y = GDP2007,
mode = 'lines+markers',
name = 'GDP'
)
trace121 = go.Scatter(
x = states,
y = AWT2008,
mode = 'lines+markers',
name = 'Awarded Amount'
)
trace122 = go.Scatter(
x = states,
y = GDP2008,
mode = 'lines+markers',
name = 'GDP'
)
trace131 = go.Scatter(
x = states,
y = AWT2009,
mode = 'lines+markers',
name = 'Awarded Amount'
)
trace132 = go.Scatter(
x = states,
y = GDP2009,
mode = 'lines+markers',
name = 'GDP'
)
trace141 = go.Scatter(
x = states,
y = AWT2010,
mode = 'lines+markers',
name = 'Awarded Amount'
)
trace142 = go.Scatter(
x = states,
y = GDP2010,
mode = 'lines+markers',
name = 'GDP'
)
trace151 = go.Scatter(
x = states,
y = AWT2011,
mode = 'lines+markers',
name = 'Awarded Amount'
)
trace152 = go.Scatter(
x = states,
y = GDP2011,
mode = 'lines+markers',
name = 'GDP'
)
trace161 = go.Scatter(
x = states,
y = AWT2012,
mode = 'lines+markers',
name = 'Awarded Amount'
)
trace162 = go.Scatter(
x = states,
y = GDP2012,
mode = 'lines+markers',
name = 'GDP'
)
trace171 = go.Scatter(
x = states,
y = AWT2013,
mode = 'lines+markers',
name = 'Awarded Amount'
)
trace172 = go.Scatter(
x = states,
y = GDP2013,
mode = 'lines+markers',
name = 'GDP'
)
mydata = [trace11,trace12,trace21,trace22,trace31,trace32,trace41,trace42,
trace51,trace52,trace61,trace62,trace71,trace72,trace81,trace82,
trace91,trace92,trace101,trace102,trace111,trace112,trace121,trace122,
trace131,trace132,trace141,trace142,trace151,trace152,trace161,trace162,
trace171,trace172]
layout = Layout(
title='Total State Amount vs GDP by State Per Year',
updatemenus=list([
dict(
x=-0.05,
y=1,
yanchor='top',
buttons=list([
dict(
args=['visible', [True,True, False, False,False, False, False, False,
False, False, False, False,False, False, False, False,
False, False, False, False,False, False, False, False,
False, False, False, False,False, False, False, False,
False, False]],
label='1997',
method='restyle'
),
dict(
args=['visible', [False, False, True,True,False, False, False, False,
False, False, False, False,False, False, False, False,
False, False, False, False,False, False, False, False,
False, False, False, False,False, False, False, False,
False, False]],
label='1998',
method='restyle'
),
dict(
args=['visible', [False, False, False, False, True,True,False, False,
False, False, False, False,False, False, False, False,
False, False, False, False,False, False, False, False,
False, False, False, False,False, False, False, False,
False, False]],
label='1999',
method='restyle'
),
dict(
args=['visible', [False, False, False, False, False, False,True,True,
False, False, False, False,False, False, False, False,
False, False, False, False,False, False, False, False,
False, False, False, False,False, False, False, False,
False, False]],
label='2000',
method='restyle'
),
dict(
args=['visible', [False, False, False, False, False, False,False, False,
True,True,False, False,False, False, False, False,
False, False, False, False,False, False, False, False,
False, False, False, False,False, False, False, False,
False, False]],
label='2001',
method='restyle'
),
dict(
args=['visible', [False, False, False, False, False, False,False, False,
False, False,True,True,False, False, False, False,
False, False, False, False,False, False, False, False,
False, False, False, False,False, False, False, False,
False, False]],
label='2002',
method='restyle'
),
dict(
args=['visible', [False, False, False, False, False, False,False, False,
False, False,False, False, True,True,False, False,
False, False, False, False,False, False, False, False,
False, False, False, False,False, False, False, False,
False, False]],
label='2003',
method='restyle'
),
dict(
args=['visible', [False, False, False, False, False, False,False, False,
False, False,False, False, False, False,True,True,
False, False, False, False,False, False, False, False,
False, False, False, False,False, False, False, False,
False, False]],
label='2004',
method='restyle'
),
dict(
args=['visible', [False, False, False, False, False, False,False, False,
False, False,False, False, False, False,False, False,
True,True,False, False,False, False, False, False,
False, False, False, False,False, False, False, False,
False, False]],
label='2005',
method='restyle'
),
dict(
args=['visible', [False, False, False, False, False, False,False, False,
False, False,False, False, False, False,False, False,
False, False,True,True,False, False, False, False,
False, False, False, False,False, False, False, False,
False, False]],
label='2006',
method='restyle'
),
dict(
args=['visible', [False, False, False, False, False, False,False, False,
False, False,False, False, False, False,False, False,
False, False,False, False, True,True,False, False,
False, False, False, False,False, False, False, False,
False, False]],
label='2007',
method='restyle'
),
dict(
args=['visible', [False, False, False, False, False, False,False, False,
False, False,False, False, False, False,False, False,
False, False,False, False, False, False,True,True,
False, False, False, False,False, False, False, False,
False, False]],
label='2008',
method='restyle'
),
dict(
args=['visible', [False, False, False, False, False, False,False, False,
False, False,False, False, False, False,False, False,
False, False,False, False, False, False,False, False,
True,True,False, False,False, False, False, False,
False, False]],
label='2009',
method='restyle'
),
dict(
args=['visible', [False, False, False, False, False, False,False, False,
False, False,False, False, False, False,False, False,
False, False,False, False, False, False,False, False,
False, False,True,True,False, False, False, False,
False, False]],
label='2010',
method='restyle'
),
dict(
args=['visible', [False, False, False, False, False, False,False, False,
False, False,False, False, False, False,False, False,
False, False,False, False, False, False,False, False,
False, False,False, False, True,True,False, False,
False, False]],
label='2011',
method='restyle'
),
dict(
args=['visible', [False, False, False, False, False, False,False, False,
False, False,False, False, False, False,False, False,
False, False,False, False, False, False,False, False,
False, False,False, False, False, False,True,True,
False, False]],
label='2012',
method='restyle'
),
dict(
args=['visible', [False, False, False, False, False, False,False, False,
False, False,False, False, False, False,False, False,
False, False,False, False, False, False,False, False,
False, False,False, False, False, False,False, False,
True,True]],
label='2013',
method='restyle'
),
]),
)
]),
)
fig = Figure(data=mydata, layout=layout)
py.iplot(fig)
Out[37]:
Description： The graph is a comparison between total awarded amount by state and GDP by state, as an indicator for the economic environment.The dropdown menu is the year of the statistics.
Findings:
there are states with higher GDP that generally has higher amount of grants. For instance, CA, FL, PA, TX, NM has higher GDP compared to other states and they are also entitled with higher grant amount.
However, there are also some states such as IL and NY with low GDP but high grant amount. They might be promoted by the government to support local library and museum development.
with the steady increasement of GDP year by year, the grant amount also sees a general increasing trend.
during financial crisis, some states are granted with less or no funds.