notebook.community

Edit and run



In [55]:

    
import plotly
import plotly.plotly as py
from plotly.graph_objs import *
import pandas as pd
import math



In [7]:

    
plotly.tools.set_credentials_file(username='lis590dv', api_key='0jCaIttf2QaVJ3lRZZlK')



In [41]:

    
df2 = pd.read_csv('t3sample.csv',encoding='iso-8859-1')
df2.head()









    Out[41]:






  
    
      
      names
      dates
      latitude
      longitude
      categorical
      quant1
      quant2
      quant3
    
  
  
    
      0
      b'Rawad'
      1453008863
      -19.988651
      -41.435565
      Category01
      1.933239
      6.052785
      6105.123120
    
    
      1
      b'Eurma'
      1471778579
      -8.593256
      27.742604
      Category00
      1.520733
      -11.065076
      143.902725
    
    
      2
      b'Taleia'
      1473613446
      -67.463164
      -109.366796
      Category04
      1.863466
      11.437340
      2019.370225
    
    
      3
      b'Niley'
      1481194251
      -7.537010
      -32.218540
      Category03
      0.745431
      19.415543
      3225.920647
    
    
      4
      b'Acey'
      1454402794
      -17.116866
      2.949914
      Category05
      1.031100
      17.714492
      4976.099324



In [ ]:

    
df2



In [20]:

    
def WhaleTeam(dataset):
    df=pd.read_csv(dataset,encoding='iso-8859-1')
    colnames=df2.columns.values
    nameslen=len(colnames)
    nameslen=nameslen-5
    
    subplot(2,2,1)
    data = Data([
    Scattermapbox(
        lat=df['stop_lat'],
        lon=df['stop_lon'],
        mode='markers',
        marker=Marker(
            size=9,
            color='#cc99ff'
        ),
        text=df['stop_name'],
    )
    ])
    layout = Layout(
        autosize=True,
        hovermode='closest',
        mapbox=dict(
            accesstoken=mapbox_access_token,
            bearing=0,
            center=dict(
                lat=40.11,
                lon=-88.23
            ),
            pitch=0,
            zoom=10
        ),
    )
    
    fig = dict(data=data, layout=layout)
    py.iplot(fig, filename='Multiple Mapbox')



In [21]:

    
WhaleTeam('t3sample.csv')









    



       names       dates   latitude   longitude categorical    quant1  \
0   b'Rawad'  1453008863 -19.988651  -41.435565  Category01  1.933239   
1   b'Eurma'  1471778579  -8.593256   27.742604  Category00  1.520733   
2  b'Taleia'  1473613446 -67.463164 -109.366796  Category04  1.863466   
3   b'Niley'  1481194251  -7.537010  -32.218540  Category03  0.745431   
4    b'Acey'  1454402794 -17.116866    2.949914  Category05  1.031100   

      quant2       quant3  
0   6.052785  6105.123120  
1 -11.065076   143.902725  
2  11.437340  2019.370225  
3  19.415543  3225.920647  
4  17.714492  4976.099324



In [46]:

    
colnames=df2.columns.values



In [47]:

    
colnames









    Out[47]:





array(['names', 'dates', 'latitude', 'longitude', 'categorical', 'quant1',
       'quant2', 'quant3'], dtype=object)



In [82]:

    
nameslen=len(colnames)



In [83]:

    
nameslen=nameslen-5



In [84]:

    
nameslen









    Out[84]:





3



In [85]:

    
sqrt=math.sqrt(nameslen)



In [86]:

    
sqrt









    Out[86]:





1.7320508075688772



In [87]:

    
wd=math.trunc(sqrt)



In [88]:

    
lg=wd+1



In [89]:

    
lg









    Out[89]:





2



In [90]:

    
wd









    Out[90]:





1



In [91]:

    
if lg*wd<nameslen:
    wd=wd+1



In [94]:

    
wd









    Out[94]:





2



In [106]:

    
df2[colnames[4]]









    Out[106]:





0       Category01
1       Category00
2       Category04
3       Category03
4       Category05
5       Category00
6       Category04
7       Category04
8       Category07
9       Category09
10      Category01
11      Category01
12      Category05
13      Category08
14      Category09
15      Category02
16      Category09
17      Category00
18      Category02
19      Category09
20      Category09
21      Category05
22      Category09
23      Category08
24      Category09
25      Category07
26      Category00
27      Category06
28      Category02
29      Category07
           ...    
9970    Category01
9971    Category02
9972    Category02
9973    Category08
9974    Category03
9975    Category03
9976    Category05
9977    Category03
9978    Category07
9979    Category02
9980    Category03
9981    Category02
9982    Category03
9983    Category05
9984    Category04
9985    Category05
9986    Category09
9987    Category00
9988    Category03
9989    Category00
9990    Category03
9991    Category06
9992    Category01
9993    Category02
9994    Category05
9995    Category05
9996    Category06
9997    Category00
9998    Category06
9999    Category09
Name: categorical, dtype: object



In [185]:

    
import plotly.plotly as py
from plotly.graph_objs import *
import pandas as pd

# read in volcano database data
#df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/volcano_db.csv')
df = pd.read_csv('t3sample.csv',encoding='iso-8859-1')
# frequency of Country
#freq = df
#freq = freq.Country.value_counts().reset_index().rename(columns={'index': 'x'})
freq=df[colnames[4]].value_counts().reset_index().rename(columns={'index': 'x'})
# plot(1) top 10 countries by total volcanoes
freqcol=freq.columns.values
locations = Bar(x=freq['x'][0:10],y=freq[freqcol[1]][0:10], marker=dict(color='#CF1020'))

# read in 3d volcano surface data
#df_v = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/volcano.csv')

# plot(2) 3d surface of volcano
#threed = Surface(z=df_v.values.tolist(), colorscale='Reds', showscale=False)
#logsizes=[]
#for i in range(len(df[colnames[4]])):
#    logsize=math.log10(float(df[colnames[5]][i]))
#    logsize=logsize*1.5
#    logsizes.append(logsize)

# plot(3)  scattergeo map of volcano locations
trace3 = {
  "geo": "geo3", 
  "lon": df[colnames[3]],
  "lat": df[colnames[2]],
  "hoverinfo": 'text',
  "marker": {
    "size": 5,
    "opacity": 0.8,
    "color": '#CF1020',
    "colorscale": 'Viridis'
  }, 
  "mode": "markers", 
  "type": "scattergeo"
}

data = Data([locations, trace3])

# control the subplot below using domain in 'geo', 'scene', and 'axis'
layout = {
  "plot_bgcolor": 'black',
  "paper_bgcolor": 'black',
  "titlefont": {
      "size": 20,
      "family": "Raleway"
  },
  "font": {
      "color": 'white'
  },
  "dragmode": "zoom", 
  "geo3": {
    "domain": {
      "x": [0, 0.55], 
      "y": [0, 0.9]
    }, 
    "lakecolor": "rgba(127,205,255,1)",
    "oceancolor": "rgb(6,66,115)",
    "landcolor": 'white',
    "projection": {"type": "orthographic"}, 
    "scope": "world", 
    "showlakes": True,
    "showocean": True,
    "showland": True,
    "bgcolor": 'black'
  }, 
  "margin": {
    "r": 10, 
    "t": 25, 
    "b": 40, 
    "l": 60
  }, 
  "scene": {"domain": {
      "x": [0.5, 1], 
      "y": [0, 0.55]
    },
           "xaxis": {"gridcolor": 'white'},
           "yaxis": {"gridcolor": 'white'},
           "zaxis": {"gridcolor": 'white'}
           }, 
  "showlegend": False, 
  "title": "<br>Volcano Database", 
  "xaxis": {
    "anchor": "y", 
    "domain": [0.6, 0.95]
  }, 
  "yaxis": {
    "anchor": "x", 
    "domain": [0.1, 0.9],
    "showgrid": False
  }
}

annotations = { "text": "Histrogram of %s" % colnames[5],
               "showarrow": False,
               "xref": "paper",
               "yref": "paper",
               "x": 0.85,
               "y": 0.95}

layout['annotations'] = [annotations]

fig = Figure(data=data, layout=layout)
py.iplot(fig, filename = "Mixed Subplots Volcano")









    Out[185]:



In [111]:

    
freq









    Out[111]:






  
    
      
      x
      Country
    
  
  
    
      0
      United States
      184
    
    
      1
      Russia
      169
    
    
      2
      Indonesia
      136
    
    
      3
      Japan
      111
    
    
      4
      Chile
      87
    
    
      5
      Ethiopia
      57
    
    
      6
      Papua New Guinea
      54
    
    
      7
      Philippines
      49
    
    
      8
      Mexico
      41
    
    
      9
      Iceland
      38
    
    
      10
      Antarctica
      35
    
    
      11
      Ecuador
      34
    
    
      12
      Argentina
      34
    
    
      13
      Pacific Ocean
      33
    
    
      14
      New Zealand
      30
    
    
      15
      Kenya
      22
    
    
      16
      El Salvador
      22
    
    
      17
      Guatemala
      22
    
    
      18
      Canada
      21
    
    
      19
      Tonga
      20
    
    
      20
      Nicaragua
      19
    
    
      21
      Peru
      16
    
    
      22
      Vanuatu
      14
    
    
      23
      China
      14
    
    
      24
      Italy
      14
    
    
      25
      Portugal
      14
    
    
      26
      Colombia
      14
    
    
      27
      Turkey
      13
    
    
      28
      Atlantic Ocean
      12
    
    
      29
      Bolivia
      11
    
    
      ...
      ...
      ...
    
    
      66
      Panama
      2
    
    
      67
      South Korea
      2
    
    
      68
      India
      2
    
    
      69
      Arctic Ocean
      2
    
    
      70
      Samoa
      2
    
    
      71
      French Southern & Antarctic Lands
      2
    
    
      72
      Bouvet I.
      1
    
    
      73
      Heard I. & McDonald Is.
      1
    
    
      74
      St. Kitts & Nevis
      1
    
    
      75
      Sweden
      1
    
    
      76
      Martinique
      1
    
    
      77
      United Kingdom
      1
    
    
      78
      Guadeloupe
      1
    
    
      79
      Germany
      1
    
    
      80
      Jan Mayen
      1
    
    
      81
      Reunion
      1
    
    
      82
      Australia
      1
    
    
      83
      Rwanda
      1
    
    
      84
      American Samoa
      1
    
    
      85
      Montserrat
      1
    
    
      86
      Grenada
      1
    
    
      87
      Malaysia
      1
    
    
      88
      Mali
      1
    
    
      89
      Wallis & Futuna
      1
    
    
      90
      South Africa
      1
    
    
      91
      St. Lucia
      1
    
    
      92
      Nigeria
      1
    
    
      93
      Sao Tome & Principe
      1
    
    
      94
      St. Vincent & the Grenadines
      1
    
    
      95
      Trinidad
      1
    
  

96 rows × 2 columns



In [115]:

    
#freq = freq.Country.value_counts().reset_index().rename(columns={'index': 'x'})

df2freq=df2[colnames[4]].value_counts().reset_index().rename(columns={'index': 'x'})



In [117]:

    
df2freq









    Out[117]:






  
    
      
      x
      categorical
    
  
  
    
      0
      Category04
      1077
    
    
      1
      Category05
      1038
    
    
      2
      Category03
      1033
    
    
      3
      Category02
      1024
    
    
      4
      Category07
      1016
    
    
      5
      Category06
      998
    
    
      6
      Category01
      972
    
    
      7
      Category08
      955
    
    
      8
      Category00
      954
    
    
      9
      Category09
      933



In [126]:

    
freqcol=df2freq.columns.values
df2freq[freqcol[1]][0:10]









    Out[126]:





0    1077
1    1038
2    1033
3    1024
4    1016
5     998
6     972
7     955
8     954
9     933
Name: categorical, dtype: int64



In [124]:

    
freqcol=df2freq.columns.values



In [125]:

    
freqcol[1]









    Out[125]:





'categorical'



In [168]:

    
import plotly.plotly as py
from plotly.graph_objs import *
import pandas as pd

# read in volcano database data
#df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/volcano_db.csv')
df = pd.read_csv('DiscGrants96to13-2017_04_10_19_27_08.csv',encoding='iso-8859-1',sep='\t')
# frequency of Country
#freq = df
#freq = freq.Country.value_counts().reset_index().rename(columns={'index': 'x'})
freq=df['InstState'].value_counts().reset_index().rename(columns={'index': 'x'})
# plot(1) top 10 countries by total volcanoes
freqcol=freq.columns.values
locations = Bar(x=freq['x'][0:10],y=freq[freqcol[1]][0:10], marker=dict(color='#CF1020'))

# read in 3d volcano surface data
df_v = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/volcano.csv')

# plot(2) 3d surface of volcano
threed = Surface(z=df_v.values.tolist(), colorscale='Reds', showscale=False)
#threed = Bar(x=freq['x'][0:10],y=freq[freqcol[1]][0:10], marker=dict(color='#CF1020'))


# plot(3)  scattergeo map of volcano locations
trace3 = {
  "geo": "geo3", 
  "lon": df['Longitude'],
  "lat": df['Latitude'],
  "hoverinfo": 'text',
  "marker": {
    "symbol":"circle",
    "size": logsizes,
    "opacity": 0.05,
    "color": '#FF9999',
    "colorscale": 'Viridis'
    #"line": {
    #    "width":0,
    #},
  }, 
  "mode": "markers", 
  "type": "scattergeo"
}

data = Data([locations, trace3])

# control the subplot below using domain in 'geo', 'scene', and 'axis'
layout = {
  "plot_bgcolor": 'black',
  "paper_bgcolor": 'black',
  "titlefont": {
      "size": 20,
      "family": "Raleway"
  },
  "font": {
      "color": 'white'
  },
  "dragmode": "zoom", 
  "geo3": {
    "domain": {
      "x": [0, 0.55], 
      "y": [0, 0.9]
    }, 
    "lakecolor": "rgba(127,205,255,1)",
    "oceancolor": "rgb(6,66,115)",
    "landcolor": 'white',
    "projection": {"type": "orthographic"}, 
    "scope": "world", 
    "showlakes": True,
    "showocean": True,
    "showland": True,
    "bgcolor": 'black'
  }, 
  "margin": {
    "r": 10, 
    "t": 25, 
    "b": 40, 
    "l": 60
  }, 
  "scene": {"domain": {
      "x": [0.5, 1], 
      "y": [0, 0.55]
    },
           "xaxis": {"gridcolor": 'white'},
           "yaxis": {"gridcolor": 'white'},
           "zaxis": {"gridcolor": 'white'}
           }, 
  "showlegend": False, 
  "title": "<br>Volcano Database", 
  "xaxis": {
    "anchor": "y", 
    "domain": [0.6, 0.95]
  }, 
  "yaxis": {
    "anchor": "x", 
    "domain": [0, 0.95],
    "showgrid": False
  }
}

annotations = { "text": "Source: NOAA",
               "showarrow": False,
               "xref": "paper",
               "yref": "paper",
               "x": 0,
               "y": 0}

layout['annotations'] = [annotations]

fig = Figure(data=data, layout=layout)
py.iplot(fig, filename = "Mixed Subplots Volcano")









    Out[168]:



In [151]:

    
logsizes=[]
for i in range(len(df['AwardTotal'])):
    logsize=math.log10(df['AwardTotal'][i])
    logsize=logsize*1.5
    logsizes.append(logsize)



In [141]:

    
logsizes









    Out[141]:





[3.530199698203082,
 3.780317312140151,
 3.780317312140151,
 3.8027737252919755,
 3.780317312140151,
 3.780317312140151,
 3.780317312140151,
 3.8027737252919755,
 3.791690649020118,
 3.5415792439465807,
 3.780317312140151,
 3.7686381012476144,
 3.7686381012476144,
 3.780317312140151,
 3.780317312140151,
 3.780317312140151,
 3.530199698203082,
 3.530199698203082,
 3.791690649020118,
 3.791690649020118,
 3.791690649020118,
 3.780317312140151,
 3.5185139398778875,
 3.780317312140151,
 3.780317312140151,
 3.7686381012476144,
 3.5185139398778875,
 3.8027737252919755,
 3.7686381012476144,
 3.780317312140151,
 3.780317312140151,
 3.780317312140151,
 3.780317312140151,
 3.780317312140151,
 3.8027737252919755,
 3.8027737252919755,
 3.780317312140151,
 3.7686381012476144,
 3.780317312140151,
 3.791690649020118,
 3.530199698203082,
 3.780317312140151,
 3.5415792439465807,
 3.780317312140151,
 3.780317312140151,
 3.780317312140151,
 3.7686381012476144,
 3.780317312140151,
 3.780317312140151,
 3.780317312140151,
 3.780317312140151,
 3.780317312140151,
 3.7686381012476144,
 3.780317312140151,
 3.530199698203082,
 3.780317312140151,
 3.530199698203082,
 3.780317312140151,
 3.780317312140151,
 3.780317312140151,
 3.791690649020118,
 3.8027737252919755,
 3.530199698203082,
 3.530199698203082,
 3.7686381012476144,
 3.7686381012476144,
 3.7686381012476144,
 3.780317312140151,
 3.791690649020118,
 3.780317312140151,
 3.780317312140151,
 3.5415792439465807,
 3.7686381012476144,
 3.530199698203082,
 3.780317312140151,
 3.780317312140151,
 3.780317312140151,
 3.791690649020118,
 3.5415792439465807,
 3.8027737252919755,
 3.8027737252919755,
 3.780317312140151,
 3.7686381012476144,
 3.780317312140151,
 3.5185139398778875,
 3.8027737252919755,
 3.8027737252919755,
 3.530199698203082,
 3.780317312140151,
 3.791690649020118,
 3.7686381012476144,
 3.780317312140151,
 3.530199698203082,
 3.791690649020118,
 3.5185139398778875,
 3.5185139398778875,
 3.780317312140151,
 3.5415792439465807,
 3.780317312140151,
 3.791690649020118,
 3.8027737252919755,
 3.780317312140151,
 3.780317312140151,
 3.780317312140151,
 3.780317312140151,
 3.780317312140151,
 3.780317312140151,
 3.7686381012476144,
 3.780317312140151,
 3.530199698203082,
 3.791690649020118,
 3.5185139398778875,
 3.5185139398778875,
 3.530199698203082,
 3.7686381012476144,
 3.780317312140151,
 3.5415792439465807,
 3.530199698203082,
 3.530199698203082,
 3.780317312140151,
 3.530199698203082,
 3.8027737252919755,
 3.780317312140151,
 3.791690649020118,
 3.780317312140151,
 3.780317312140151,
 3.791690649020118,
 3.780317312140151,
 3.780317312140151,
 3.5514499979728753,
 3.791690649020118,
 3.791690649020118,
 3.7686381012476144,
 3.780317312140151,
 3.780317312140151,
 3.791690649020118,
 3.791690649020118,
 3.791690649020118,
 3.791690649020118,
 3.780317312140151,
 3.780317312140151,
 3.780317312140151,
 3.7686381012476144,
 3.791690649020118,
 3.780317312140151,
 3.530199698203082,
 3.7686381012476144,
 3.780317312140151,
 3.780317312140151,
 3.780317312140151,
 3.780317312140151,
 3.8027737252919755,
 3.8041394323353503,
 3.5717088318086874,
 3.561101383649056,
 3.8041394323353503,
 3.561101383649056,
 3.8041394323353503,
 3.8041394323353503,
 3.5717088318086874,
 3.814913181275074,
 3.561101383649056,
 3.5717088318086874,
 3.814913181275074,
 3.8041394323353503,
 3.8254261177678233,
 3.79309160017658,
 3.561101383649056,
 3.8041394323353503,
 3.5717088318086874,
 3.814913181275074,
 3.5717088318086874,
 3.79309160017658,
 3.8041394323353503,
 3.79309160017658,
 3.8041394323353503,
 3.79309160017658,
 3.814913181275074,
 3.582063362911709,
 3.8041394323353503,
 3.8041394323353503,
 3.79309160017658,
 3.79309160017658,
 3.8041394323353503,
 3.79309160017658,
 3.5717088318086874,
 3.8041394323353503,
 3.814913181275074,
 3.5717088318086874,
 3.8254261177678233,
 3.8041394323353503,
 3.79309160017658,
 3.5717088318086874,
 3.5717088318086874,
 3.8041394323353503,
 3.8041394323353503,
 3.814913181275074,
 3.8041394323353503,
 3.8041394323353503,
 3.8041394323353503,
 3.5717088318086874,
 3.79309160017658,
 3.8041394323353503,
 3.582063362911709,
 3.5717088318086874,
 3.582063362911709,
 3.5717088318086874,
 3.8254261177678233,
 3.8041394323353503,
 3.5717088318086874,
 3.8041394323353503,
 3.8041394323353503,
 3.5717088318086874,
 3.8041394323353503,
 3.8041394323353503,
 3.8041394323353503,
 3.5717088318086874,
 3.561101383649056,
 3.79309160017658,
 3.8041394323353503,
 3.561101383649056,
 3.8041394323353503,
 3.8041394323353503,
 3.79309160017658,
 3.8041394323353503,
 3.5717088318086874,
 3.8041394323353503,
 3.5717088318086874,
 3.5717088318086874,
 3.8254261177678233,
 3.8254261177678233,
 3.8254261177678233,
 3.814913181275074,
 3.561101383649056,
 3.5717088318086874,
 3.814913181275074,
 3.8041394323353503,
 3.8254261177678233,
 3.814913181275074,
 3.814913181275074,
 3.5921767573958667,
 3.8041394323353503,
 3.8041394323353503,
 3.8041394323353503,
 3.8254261177678233,
 3.79309160017658,
 3.5717088318086874,
 3.8041394323353503,
 3.8041394323353503,
 3.8041394323353503,
 3.8041394323353503,
 3.582063362911709,
 3.8041394323353503,
 3.8041394323353503,
 3.8254261177678233,
 3.814913181275074,
 3.8041394323353503,
 3.814913181275074,
 3.8254261177678233,
 3.8041394323353503,
 3.8254261177678233,
 3.5717088318086874,
 3.561101383649056,
 3.5717088318086874,
 3.814913181275074,
 3.8041394323353503,
 3.5717088318086874,
 3.582063362911709,
 3.561101383649056,
 3.814913181275074,
 3.814913181275074,
 3.814913181275074,
 3.79309160017658,
 3.8041394323353503,
 3.79309160017658,
 3.8254261177678233,
 3.8041394323353503,
 3.8041394323353503,
 3.5717088318086874,
 3.8041394323353503,
 3.582063362911709,
 3.8041394323353503,
 3.8041394323353503,
 3.8041394323353503,
 3.79309160017658,
 3.814913181275074,
 3.8254261177678233,
 3.582063362911709,
 3.582063362911709,
 3.5717088318086874,
 3.79309160017658,
 3.8041394323353503,
 3.8041394323353503,
 3.582063362911709,
 3.8041394323353503,
 3.79309160017658,
 3.8041394323353503,
 3.5717088318086874,
 3.5717088318086874,
 3.8041394323353503,
 3.79309160017658,
 3.8041394323353503,
 3.8041394323353503,
 3.79309160017658,
 3.79309160017658,
 3.561101383649056,
 3.561101383649056,
 3.814913181275074,
 3.8041394323353503,
 3.8041394323353503,
 3.5921767573958667,
 3.8041394323353503,
 3.5717088318086874,
 3.8041394323353503,
 3.8041394323353503,
 3.8041394323353503,
 3.8041394323353503,
 3.8041394323353503,
 3.8041394323353503,
 3.8041394323353503,
 3.8041394323353503,
 3.5717088318086874,
 3.5717088318086874,
 3.8041394323353503,
 3.561101383649056,
 3.8041394323353503,
 3.79309160017658,
 3.8254261177678233,
 3.814913181275074,
 3.582063362911709,
 3.561101383649056,
 3.8041394323353503,
 3.5717088318086874,
 3.8041394323353503,
 3.814913181275074,
 3.8254261177678233,
 3.8041394323353503,
 3.8041394323353503,
 3.5717088318086874,
 3.8041394323353503,
 3.814913181275074,
 3.8041394323353503,
 3.814913181275074,
 3.561101383649056,
 3.582063362911709,
 3.79309160017658,
 3.8041394323353503,
 3.8254261177678233,
 3.8041394323353503,
 3.8041394323353503,
 3.8041394323353503,
 3.8041394323353503,
 3.8041394323353503,
 3.582063362911709,
 3.5717088318086874,
 3.8041394323353503,
 3.8041394323353503,
 3.8254261177678233,
 3.5717088318086874,
 3.79309160017658,
 3.8041394323353503,
 3.79309160017658,
 3.8041394323353503,
 3.5717088318086874,
 3.8041394323353503,
 3.8041394323353503,
 3.5717088318086874,
 3.5717088318086874,
 3.5921767573958667,
 3.8041394323353503,
 3.8254261177678233,
 3.814913181275074,
 3.8041394323353503,
 3.8254261177678233,
 3.5717088318086874,
 3.8041394323353503,
 3.79309160017658,
 3.8254261177678233,
 3.8041394323353503,
 3.814913181275074,
 3.5717088318086874,
 3.5717088318086874,
 3.8254261177678233,
 3.79309160017658,
 3.8041394323353503,
 3.8041394323353503,
 3.8041394323353503,
 3.8041394323353503,
 3.8041394323353503,
 3.8041394323353503,
 3.814913181275074,
 3.814913181275074,
 3.8041394323353503,
 3.5717088318086874,
 3.5717088318086874,
 3.8041394323353503,
 3.561101383649056,
 3.814913181275074,
 3.5717088318086874,
 3.5717088318086874,
 3.79309160017658,
 3.582063362911709,
 3.814913181275074,
 3.8041394323353503,
 3.814913181275074,
 3.8041394323353503,
 3.814913181275074,
 3.814913181275074,
 3.561101383649056,
 3.8041394323353503,
 3.561101383649056,
 3.8041394323353503,
 3.79309160017658,
 3.79309160017658,
 3.814913181275074,
 3.814913181275074,
 3.8041394323353503,
 3.8041394323353503,
 3.8254261177678233,
 3.79309160017658,
 3.582063362911709,
 3.8254261177678233,
 3.561101383649056,
 3.8041394323353503,
 3.8041394323353503,
 3.79309160017658,
 3.5717088318086874,
 3.814913181275074,
 3.79309160017658,
 3.79309160017658,
 3.814913181275074,
 3.5717088318086874,
 3.8041394323353503,
 3.561101383649056,
 3.8041394323353503,
 3.814913181275074,
 3.561101383649056,
 3.8041394323353503,
 3.582063362911709,
 3.814913181275074,
 3.814913181275074,
 3.8790958795000727,
 3.89707700320942,
 3.888179493918325,
 3.8790958795000727,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.888179493918325,
 3.888179493918325,
 3.888179493918325,
 3.89707700320942,
 3.888179493918325,
 3.8790958795000727,
 3.89707700320942,
 3.663700925389648,
 3.8790958795000727,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.888179493918325,
 3.888179493918325,
 3.655138434811382,
 3.655138434811382,
 3.888179493918325,
 3.663700925389648,
 3.89707700320942,
 3.888179493918325,
 3.888179493918325,
 3.89707700320942,
 3.888179493918325,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.8790958795000727,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.663700925389648,
 3.888179493918325,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.8790958795000727,
 3.663700925389648,
 3.655138434811382,
 3.888179493918325,
 3.89707700320942,
 3.663700925389648,
 3.888179493918325,
 3.655138434811382,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.888179493918325,
 3.888179493918325,
 3.89707700320942,
 3.89707700320942,
 3.888179493918325,
 3.89707700320942,
 3.89707700320942,
 3.6464037262230695,
 3.888179493918325,
 3.888179493918325,
 3.888179493918325,
 3.888179493918325,
 3.888179493918325,
 3.8790958795000727,
 3.888179493918325,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.888179493918325,
 3.8790958795000727,
 3.8790958795000727,
 3.663700925389648,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.888179493918325,
 3.89707700320942,
 3.663700925389648,
 3.89707700320942,
 3.89707700320942,
 3.888179493918325,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.663700925389648,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.888179493918325,
 3.89707700320942,
 3.663700925389648,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.888179493918325,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.888179493918325,
 3.89707700320942,
 3.888179493918325,
 3.8790958795000727,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.8790958795000727,
 3.888179493918325,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.888179493918325,
 3.663700925389648,
 3.89707700320942,
 3.655138434811382,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.6464037262230695,
 3.663700925389648,
 3.89707700320942,
 3.888179493918325,
 3.888179493918325,
 3.888179493918325,
 3.89707700320942,
 3.8790958795000727,
 3.89707700320942,
 3.6464037262230695,
 3.888179493918325,
 3.888179493918325,
 3.89707700320942,
 3.89707700320942,
 3.663700925389648,
 3.89707700320942,
 3.663700925389648,
 3.888179493918325,
 3.89707700320942,
 3.888179493918325,
 3.89707700320942,
 3.663700925389648,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.663700925389648,
 3.89707700320942,
 3.89707700320942,
 3.655138434811382,
 3.89707700320942,
 3.663700925389648,
 3.89707700320942,
 3.888179493918325,
 3.89707700320942,
 3.888179493918325,
 3.663700925389648,
 3.89707700320942,
 3.655138434811382,
 3.89707700320942,
 3.655138434811382,
 3.888179493918325,
 3.888179493918325,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.663700925389648,
 3.888179493918325,
 3.888179493918325,
 3.89707700320942,
 3.888179493918325,
 3.655138434811382,
 3.89707700320942,
 3.89707700320942,
 3.8790958795000727,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.888179493918325,
 3.89707700320942,
 3.89707700320942,
 3.888179493918325,
 3.663700925389648,
 3.663700925389648,
 3.89707700320942,
 3.655138434811382,
 3.655138434811382,
 3.663700925389648,
 3.89707700320942,
 3.663700925389648,
 3.89707700320942,
 3.655138434811382,
 3.663700925389648,
 3.663700925389648,
 3.8790958795000727,
 3.663700925389648,
 3.8790958795000727,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.888179493918325,
 3.888179493918325,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.6464037262230695,
 3.89707700320942,
 3.888179493918325,
 3.89707700320942,
 3.888179493918325,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.888179493918325,
 3.89707700320942,
 3.663700925389648,
 3.663700925389648,
 3.89707700320942,
 3.663700925389648,
 3.89707700320942,
 3.6464037262230695,
 3.89707700320942,
 3.888179493918325,
 3.89707700320942,
 3.888179493918325,
 3.663700925389648,
 3.89707700320942,
 3.655138434811382,
 3.89707700320942,
 3.89707700320942,
 3.6464037262230695,
 3.888179493918325,
 3.888179493918325,
 3.655138434811382,
 3.6464037262230695,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.663700925389648,
 3.89707700320942,
 3.89707700320942,
 3.8790958795000727,
 3.888179493918325,
 3.89707700320942,
 3.89707700320942,
 3.663700925389648,
 3.89707700320942,
 3.888179493918325,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.89707700320942,
 3.655138434811382,
 3.888179493918325,
 3.89707700320942,
 3.89707700320942,
 3.655138434811382,
 3.89707700320942,
 3.89707700320942,
 3.888179493918325,
 3.89707700320942,
 3.89707700320942,
 3.655138434811382,
 3.89707700320942,
 3.89707700320942,
 3.655138434811382,
 3.888179493918325,
 3.6464037262230695,
 3.89707700320942,
 3.8790958795000727,
 3.89707700320942,
 3.6464037262230695,
 3.89707700320942,
 3.8790958795000727,
 3.663700925389648,
 3.8790958795000727,
 3.89707700320942,
 3.8790958795000727,
 3.8790958795000727,
 3.655138434811382,
 3.6464037262230695,
 3.663700925389648,
 3.655138434811382,
 3.888179493918325,
 3.663700925389648,
 3.8790958795000727,
 3.89707700320942,
 3.8790958795000727,
 3.663700925389648,
 3.6464037262230695,
 3.89707700320942,
 3.89707700320942,
 3.655138434811382,
 3.888179493918325,
 3.663700925389648,
 3.655138434811382,
 3.655138434811382,
 3.89707700320942,
 3.8790958795000727,
 3.663700925389648,
 3.89707700320942,
 3.663700925389648,
 3.663700925389648,
 3.89707700320942,
 3.6464037262230695,
 3.8790958795000727,
 3.89707700320942,
 3.888179493918325,
 3.89707700320942,
 3.663700925389648,
 3.888179493918325,
 3.89707700320942,
 3.655138434811382,
 3.888179493918325,
 3.8790958795000727,
 3.8790958795000727,
 3.89707700320942,
 3.89707700320942,
 5.90687353472207,
 6.2040931098534715,
 6.2057455409426625,
 3.8135809885681917,
 3.786041210242554,
 3.513217600067939,
 3.8135809885681917,
 3.756636108245848,
 3.8135809885681917,
 3.8135809885681917,
 3.8135809885681917,
 3.756636108245848,
 3.8135809885681917,
 3.786041210242554,
 3.756636108245848,
 3.786041210242554,
 3.786041210242554,
 3.8135809885681917,
 3.756636108245848,
 3.786041210242554,
 3.8135809885681917,
 3.756636108245848,
 3.786041210242554,
 3.8135809885681917,
 3.513217600067939,
 3.786041210242554,
 3.756636108245848,
 3.513217600067939,
 3.513217600067939,
 3.756636108245848,
 3.8135809885681917,
 3.5634810853944106,
 3.786041210242554,
 3.8135809885681917,
 3.8135809885681917,
 3.8135809885681917,
 3.786041210242554,
 3.8135809885681917,
 3.756636108245848,
 3.8135809885681917,
 3.756636108245848,
 3.8135809885681917,
 3.8135809885681917,
 3.8135809885681917,
 3.786041210242554,
 3.756636108245848,
 3.5634810853944106,
 3.5634810853944106,
 3.8135809885681917,
 3.8135809885681917,
 3.756636108245848,
 3.8135809885681917,
 3.5634810853944106,
 3.8135809885681917,
 3.756636108245848,
 3.8135809885681917,
 3.756636108245848,
 3.786041210242554,
 3.5634810853944106,
 3.786041210242554,
 3.8135809885681917,
 3.786041210242554,
 3.756636108245848,
 3.786041210242554,
 3.5634810853944106,
 3.756636108245848,
 3.756636108245848,
 3.8135809885681917,
 3.786041210242554,
 3.5634810853944106,
 3.8135809885681917,
 3.513217600067939,
 3.786041210242554,
 3.8135809885681917,
 3.8135809885681917,
 3.8135809885681917,
 3.8135809885681917,
 3.8135809885681917,
 3.786041210242554,
 3.756636108245848,
 3.756636108245848,
 3.8135809885681917,
 3.5634810853944106,
 3.8135809885681917,
 3.513217600067939,
 3.756636108245848,
 3.786041210242554,
 3.786041210242554,
 3.8135809885681917,
 3.8135809885681917,
 3.8135809885681917,
 3.8135809885681917,
 3.786041210242554,
 3.5634810853944106,
 3.8135809885681917,
 3.8135809885681917,
 3.5634810853944106,
 3.8135809885681917,
 3.8135809885681917,
 3.756636108245848,
 3.786041210242554,
 3.8135809885681917,
 3.756636108245848,
 3.8135809885681917,
 3.5634810853944106,
 3.8135809885681917,
 3.756636108245848,
 3.756636108245848,
 3.8135809885681917,
 3.8135809885681917,
 3.786041210242554,
 3.8135809885681917,
 3.5634810853944106,
 3.8135809885681917,
 3.756636108245848,
 3.8135809885681917,
 3.786041210242554,
 3.786041210242554,
 3.756636108245848,
 3.8135809885681917,
 3.5634810853944106,
 3.8135809885681917,
 3.8135809885681917,
 3.8135809885681917,
 3.786041210242554,
 3.8135809885681917,
 3.8135809885681917,
 3.8135809885681917,
 3.5185139398778875,
 3.7528164311882715,
 3.780317312140151,
 3.7641761323903307,
 3.7234556720351857,
 3.7528164311882715,
 3.780317312140151,
 3.780317312140151,
 3.530199698203082,
 3.530199698203082,
 3.7528164311882715,
 3.780317312140151,
 3.530199698203082,
 3.7234556720351857,
 3.711807229041191,
 3.780317312140151,
 3.780317312140151,
 3.7234556720351857,
 3.780317312140151,
 3.7234556720351857,
 3.734799829588847,
 3.4800069429571505,
 3.7686381012476144,
 3.780317312140151,
 3.780317312140151,
 3.741151598851785,
 3.7528164311882715,
 3.741151598851785,
 3.530199698203082,
 3.780317312140151,
 3.780317312140151,
 3.5185139398778875,
 3.7234556720351857,
 3.5415792439465807,
 3.7528164311882715,
 3.5185139398778875,
 3.530199698203082,
 3.7528164311882715,
 3.5185139398778875,
 3.7234556720351857,
 3.780317312140151,
 3.780317312140151,
 3.7528164311882715,
 3.780317312140151,
 3.530199698203082,
 3.780317312140151,
 3.7686381012476144,
 3.4683473304121573,
 3.5185139398778875,
 3.780317312140151,
 3.5415792439465807,
 3.780317312140151,
 3.734799829588847,
 3.7234556720351857,
 3.530199698203082,
 3.7641761323903307,
 3.791690649020118,
 3.530199698203082,
 3.4800069429571505,
 3.780317312140151,
 3.780317312140151,
 3.734799829588847,
 3.7234556720351857,
 3.745855195173729,
 3.7686381012476144,
 3.745855195173729,
 3.530199698203082,
 3.734799829588847,
 3.780317312140151,
 3.5415792439465807,
 3.780317312140151,
 3.780317312140151,
 3.7528164311882715,
 3.7528164311882715,
 3.4913616938342726,
 3.780317312140151,
 3.7234556720351857,
 3.780317312140151,
 ...]



In [137]:

    
df['AwardTotal']









    Out[137]:





0          3390.0
1          6030.0
2          6030.0
3          6350.0
4          6030.0
5          6030.0
6          6030.0
7          6350.0
8          6190.0
9          3480.0
10         6030.0
11         5870.0
12         5870.0
13         6030.0
14         6030.0
15         6030.0
16         3390.0
17         3390.0
18         6190.0
19         6190.0
20         6190.0
21         6030.0
22         3300.0
23         6030.0
24         6030.0
25         5870.0
26         3300.0
27         6350.0
28         5870.0
29         6030.0
           ...   
13170     57197.0
13171    131000.0
13172    823016.0
13173    139762.0
13174    328329.0
13175     59100.0
13176    149983.0
13177    129000.0
13178    249837.0
13179    150000.0
13180    499710.0
13181    148150.0
13182    149996.0
13183    212367.0
13184    239443.0
13185    222623.0
13186     98908.0
13187    166500.0
13188    135000.0
13189    505826.0
13190    150000.0
13191    883171.0
13192    646720.0
13193    141500.0
13194    150000.0
13195    140341.0
13196     52329.0
13197    673344.0
13198    150000.0
13199     64594.0
Name: AwardTotal, dtype: float64

	names	dates	latitude	longitude	categorical	quant1	quant2	quant3
0	b'Rawad'	1453008863	-19.988651	-41.435565	Category01	1.933239	6.052785	6105.123120
1	b'Eurma'	1471778579	-8.593256	27.742604	Category00	1.520733	-11.065076	143.902725
2	b'Taleia'	1473613446	-67.463164	-109.366796	Category04	1.863466	11.437340	2019.370225
3	b'Niley'	1481194251	-7.537010	-32.218540	Category03	0.745431	19.415543	3225.920647
4	b'Acey'	1454402794	-17.116866	2.949914	Category05	1.031100	17.714492	4976.099324

	x	Country
0	United States	184
1	Russia	169
2	Indonesia	136
3	Japan	111
4	Chile	87
5	Ethiopia	57
6	Papua New Guinea	54
7	Philippines	49
8	Mexico	41
9	Iceland	38
10	Antarctica	35
11	Ecuador	34
12	Argentina	34
13	Pacific Ocean	33
14	New Zealand	30
15	Kenya	22
16	El Salvador	22
17	Guatemala	22
18	Canada	21
19	Tonga	20
20	Nicaragua	19
21	Peru	16
22	Vanuatu	14
23	China	14
24	Italy	14
25	Portugal	14
26	Colombia	14
27	Turkey	13
28	Atlantic Ocean	12
29	Bolivia	11
...	...	...
66	Panama	2
67	South Korea	2
68	India	2
69	Arctic Ocean	2
70	Samoa	2
71	French Southern & Antarctic Lands	2
72	Bouvet I.	1
73	Heard I. & McDonald Is.	1
74	St. Kitts & Nevis	1
75	Sweden	1
76	Martinique	1
77	United Kingdom	1
78	Guadeloupe	1
79	Germany	1
80	Jan Mayen	1
81	Reunion	1
82	Australia	1
83	Rwanda	1
84	American Samoa	1
85	Montserrat	1
86	Grenada	1
87	Malaysia	1
88	Mali	1
89	Wallis & Futuna	1
90	South Africa	1
91	St. Lucia	1
92	Nigeria	1
93	Sao Tome & Principe	1
94	St. Vincent & the Grenadines	1
95	Trinidad	1

	x	categorical
0	Category04	1077
1	Category05	1038
2	Category03	1033
3	Category02	1024
4	Category07	1016
5	Category06	998
6	Category01	972
7	Category08	955
8	Category00	954
9	Category09	933