Hierarchical Clustering¶

empire_vader.jpg

Data for demo

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1. Load data¶

In [1]:
import pandas as pd

star_wars = pd.read_csv("star_wars_planets_v5.csv", encoding = "cp1252", index_col = False)
star_wars
Out[1]:
Name Region Sector System Inhabitants Capital_City Population Record_High_Temp Record_Low_Temp Gravity Key_Element Rotation Revolution Rating Rating_Nom Resources Resources_Nom
0 Aargonar Unknown Unknown Unknown Unknown Unknown 3857305 36 -5 8.35 6 59.89 2112 7 Yay 8 Awesome
1 Abafar Outer Rim Territories Sprizen sector Unknown Unknown Unknown 2331308 33 -27 11.40 28 49.20 1818 2 Yucks 9 Awesome
2 Abednedo Colonies Unknown Unknown Abednedo Unknown 5358387 28 -5 9.55 30 127.76 1210 3 Yucks 5 Blah
3 Absanz Unknown Unknown Unknown Unknown Unknown 7229751 38 -6 10.80 57 145.98 1431 9 Yay 3 Blah
4 Affadar Unknown Unknown Unknown T'Laeem Unknown 4307513 37 -31 9.75 22 58.51 1060 9 Yay 7 Awesome
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
678 Zhanox Outer Rim Territories Anoat sector Zhanox system Ugnaughts Unknown 8396480 17 -23 10.81 10 104.19 2172 4 Meh 6 Awesome
679 Zoh Unknown Unknown Unknown Unknown Unknown 1339277 29 -12 8.10 50 20.88 145 2 Yucks 2 Blah
680 Zolan Mid Rim Unknown Unknown Clawdites Unknown 5880473 20 -18 8.60 84 197.40 939 5 Meh 8 Awesome
681 Zygerria Outer Rim Territories Unknown Unknown Zygerrians Unknown 3984810 24 -20 11.25 86 224.72 2327 4 Meh 4 Blah
682 Zyzar Unknown Unknown Unknown Unknown Unknown 1464241 23 -26 8.27 83 76.29 2438 3 Yucks 8 Awesome

683 rows × 17 columns

In [2]:
print(star_wars.columns)

Index(['Name', 'Region', 'Sector', 'System', 'Inhabitants', 'Capital_City',
       'Population', 'Record_High_Temp', 'Record_Low_Temp', 'Gravity',
       'Key_Element', 'Rotation', 'Revolution', 'Rating', 'Rating_Nom',
       'Resources', 'Resources_Nom'],
      dtype='object')
In [3]:
star_wars.count()
Out[3]:
Name                683
Region              683
Sector              683
System              683
Inhabitants         683
Capital_City        683
Population          683
Record_High_Temp    683
Record_Low_Temp     683
Gravity             683
Key_Element         683
Rotation            683
Revolution          683
Rating              683
Rating_Nom          683
Resources           683
Resources_Nom       683
dtype: int64

Set row names as the planet names.

In [4]:
star_wars.set_index("Name", inplace = True)
star_wars.head()
Out[4]:
Region Sector System Inhabitants Capital_City Population Record_High_Temp Record_Low_Temp Gravity Key_Element Rotation Revolution Rating Rating_Nom Resources Resources_Nom
Name
Aargonar Unknown Unknown Unknown Unknown Unknown 3857305 36 -5 8.35 6 59.89 2112 7 Yay 8 Awesome
Abafar Outer Rim Territories Sprizen sector Unknown Unknown Unknown 2331308 33 -27 11.40 28 49.20 1818 2 Yucks 9 Awesome
Abednedo Colonies Unknown Unknown Abednedo Unknown 5358387 28 -5 9.55 30 127.76 1210 3 Yucks 5 Blah
Absanz Unknown Unknown Unknown Unknown Unknown 7229751 38 -6 10.80 57 145.98 1431 9 Yay 3 Blah
Affadar Unknown Unknown Unknown T'Laeem Unknown 4307513 37 -31 9.75 22 58.51 1060 9 Yay 7 Awesome

2. Filter variables for clustering¶

In [5]:
star_wars_subset = star_wars.iloc[:, [5, 8, 10]]
star_wars_subset.head()
Out[5]:
Population Gravity Rotation
Name
Aargonar 3857305 8.35 59.89
Abafar 2331308 11.40 49.20
Abednedo 5358387 9.55 127.76
Absanz 7229751 10.80 145.98
Affadar 4307513 9.75 58.51

3. Normalise data¶

In [6]:
star_wars_subset.dtypes
Out[6]:
Population      int64
Gravity       float64
Rotation      float64
dtype: object
In [7]:
from sklearn.preprocessing import StandardScaler
In [8]:
star_wars_subset_norm = star_wars_subset.copy()
In [9]:
scaler = StandardScaler()
cols_to_norm = ["Population", "Gravity", "Rotation"]
In [10]:
star_wars_subset_norm[cols_to_norm] = scaler.fit_transform(star_wars_subset_norm[cols_to_norm])
star_wars_subset_norm.head()
Out[10]:
Population Gravity Rotation
Name
Aargonar -0.628387 -0.824666 -0.950861
Abafar -1.204137 1.285340 -1.101338
Abednedo -0.062038 0.005500 0.004508
Absanz 0.644017 0.870257 0.260981
Affadar -0.458526 0.143861 -0.970286

4. Compute normalised distances¶

In [11]:
from sklearn.metrics import pairwise
star_wars_subset_norm_m = pairwise.pairwise_distances(star_wars_subset_norm, metric = "euclidean")
In [12]:
star_wars_subset_norm_m
Out[12]:
array([[0.        , 2.19231782, 1.38659962, ..., 2.08790333, 3.06769107,
        0.93357671],
       [2.19231782, 0.        , 2.04090123, ..., 3.14599093, 2.55035778,
        2.22287584],
       [1.38659962, 2.04090123, 0.        , ..., 1.19653278, 1.87471034,
        1.86218003],
       ...,
       [2.08790333, 3.14599093, 1.19653278, ..., 0.        , 2.00508533,
        2.39473319],
       [3.06769107, 2.55035778, 1.87471034, ..., 2.00508533, 0.        ,
        3.0854444 ],
       [0.93357671, 2.22287584, 1.86218003, ..., 2.39473319, 3.0854444 ,
        0.        ]])

Convert to dataframe

In [13]:
pd.DataFrame(star_wars_subset_norm_m, columns = star_wars_subset_norm.index, index = star_wars_subset_norm.index)
Out[13]:
Name Aargonar Abafar Abednedo Absanz Affadar Agamar Agaris Agoliba-Ado Agoliba-Ena Ahakista ... Yavin Ylesia Yout Zastiga Zerm Zhanox Zoh Zolan Zygerria Zyzar
Name
Aargonar 0.000000 2.192318 1.386600 2.441381 0.983502 1.588843 3.022261 2.769768 2.115258 1.675800 ... 2.743747 3.187004 1.800890 0.885455 1.894042 2.493619 1.110864e+00 2.087903 3.067691 0.933577
Abafar 2.192318 0.000000 2.040901 2.333213 1.369702 2.611816 2.952378 2.938955 2.673998 1.780505 ... 2.887964 2.353128 2.787024 1.676911 3.690110 2.449967 2.347532e+00 3.145991 2.550358 2.222876
Abednedo 1.386600 2.040901 0.000000 1.145468 1.061401 0.571585 1.930294 1.625876 1.874722 1.698208 ... 2.062093 2.287654 1.388112 1.873671 2.385721 1.477767 2.359909e+00 1.196533 1.874710 1.862180
Absanz 2.441381 2.333213 1.145468 0.000000 1.805346 1.305745 0.975832 1.760743 2.732720 1.954575 ... 1.545647 2.358930 1.583174 2.765215 2.994576 0.734757 3.395468e+00 1.760528 1.680573 2.959337
Affadar 0.983502 1.369702 1.061401 1.805346 0.000000 1.539391 2.392825 2.537351 2.275700 1.009289 ... 2.168399 2.700799 1.606698 0.966865 2.353849 1.825177 1.684555e+00 2.192597 2.562346 1.503910
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
Zhanox 2.493619 2.449967 1.477767 0.734757 1.825177 1.626056 0.572552 2.470878 3.280446 1.580210 ... 0.818793 3.015274 1.226907 2.762342 2.705203 0.000000 3.461179e+00 2.227138 2.396201 3.175334
Zoh 1.110864 2.347532 2.359909 3.395468 1.684555 2.636918 4.018084 3.531765 2.421326 2.338152 ... 3.689843 3.602974 2.851923 0.824023 2.700850 3.461179 4.214685e-08 3.037991 3.738755 0.790201
Zolan 2.087903 3.145991 1.196533 1.760528 2.192597 0.754614 2.456173 1.257334 1.807357 2.810048 ... 2.793843 2.559005 1.999466 2.810529 2.674769 2.227138 3.037991e+00 0.000000 2.005085 2.394733
Zygerria 3.067691 2.550358 1.874710 1.680573 2.562346 2.067761 2.559895 0.894938 2.038430 3.185717 ... 3.213978 0.894636 3.052754 3.305800 4.225493 2.396201 3.738755e+00 2.005085 0.000000 3.085444
Zyzar 0.933577 2.222876 1.862180 2.959337 1.503910 2.112399 3.705071 2.811248 1.638998 2.397338 ... 3.554177 2.961826 2.674539 1.067158 2.749539 3.175334 7.902011e-01 2.394733 3.085444 0.000000

683 rows × 683 columns

5. Hierarchical clustering¶

Use Ward distance.

In [14]:
from scipy.cluster.hierarchy import dendrogram, linkage, fcluster
In [15]:
star_wars_subset_norm_m_hc = linkage(star_wars_subset_norm, method = "ward")

5.1 Dendrogram¶

In [16]:
dendrogram(star_wars_subset_norm_m_hc, labels = star_wars_subset_norm.index, color_threshold = 15)
Out[16]:
{'icoord': [[5.0, 5.0, 15.0, 15.0],
  [25.0, 25.0, 35.0, 35.0],
  [10.0, 10.0, 30.0, 30.0],
  [65.0, 65.0, 75.0, 75.0],
  [55.0, 55.0, 70.0, 70.0],
  [45.0, 45.0, 62.5, 62.5],
  [20.0, 20.0, 53.75, 53.75],
  [95.0, 95.0, 105.0, 105.0],
  [85.0, 85.0, 100.0, 100.0],
  [135.0, 135.0, 145.0, 145.0],
  [125.0, 125.0, 140.0, 140.0],
  [115.0, 115.0, 132.5, 132.5],
  [92.5, 92.5, 123.75, 123.75],
  [36.875, 36.875, 108.125, 108.125],
  [155.0, 155.0, 165.0, 165.0],
  [175.0, 175.0, 185.0, 185.0],
  [195.0, 195.0, 205.0, 205.0],
  [180.0, 180.0, 200.0, 200.0],
  [160.0, 160.0, 190.0, 190.0],
  [225.0, 225.0, 235.0, 235.0],
  [215.0, 215.0, 230.0, 230.0],
  [245.0, 245.0, 255.0, 255.0],
  [265.0, 265.0, 275.0, 275.0],
  [250.0, 250.0, 270.0, 270.0],
  [222.5, 222.5, 260.0, 260.0],
  [175.0, 175.0, 241.25, 241.25],
  [285.0, 285.0, 295.0, 295.0],
  [305.0, 305.0, 315.0, 315.0],
  [290.0, 290.0, 310.0, 310.0],
  [345.0, 345.0, 355.0, 355.0],
  [335.0, 335.0, 350.0, 350.0],
  [325.0, 325.0, 342.5, 342.5],
  [375.0, 375.0, 385.0, 385.0],
  [365.0, 365.0, 380.0, 380.0],
  [333.75, 333.75, 372.5, 372.5],
  [300.0, 300.0, 353.125, 353.125],
  [405.0, 405.0, 415.0, 415.0],
  [395.0, 395.0, 410.0, 410.0],
  [435.0, 435.0, 445.0, 445.0],
  [425.0, 425.0, 440.0, 440.0],
  [402.5, 402.5, 432.5, 432.5],
  [326.5625, 326.5625, 417.5, 417.5],
  [208.125, 208.125, 372.03125, 372.03125],
  [72.5, 72.5, 290.078125, 290.078125],
  [455.0, 455.0, 465.0, 465.0],
  [495.0, 495.0, 505.0, 505.0],
  [485.0, 485.0, 500.0, 500.0],
  [475.0, 475.0, 492.5, 492.5],
  [460.0, 460.0, 483.75, 483.75],
  [525.0, 525.0, 535.0, 535.0],
  [515.0, 515.0, 530.0, 530.0],
  [545.0, 545.0, 555.0, 555.0],
  [575.0, 575.0, 585.0, 585.0],
  [565.0, 565.0, 580.0, 580.0],
  [550.0, 550.0, 572.5, 572.5],
  [522.5, 522.5, 561.25, 561.25],
  [471.875, 471.875, 541.875, 541.875],
  [595.0, 595.0, 605.0, 605.0],
  [615.0, 615.0, 625.0, 625.0],
  [600.0, 600.0, 620.0, 620.0],
  [635.0, 635.0, 645.0, 645.0],
  [610.0, 610.0, 640.0, 640.0],
  [665.0, 665.0, 675.0, 675.0],
  [655.0, 655.0, 670.0, 670.0],
  [685.0, 685.0, 695.0, 695.0],
  [662.5, 662.5, 690.0, 690.0],
  [705.0, 705.0, 715.0, 715.0],
  [725.0, 725.0, 735.0, 735.0],
  [710.0, 710.0, 730.0, 730.0],
  [755.0, 755.0, 765.0, 765.0],
  [745.0, 745.0, 760.0, 760.0],
  [775.0, 775.0, 785.0, 785.0],
  [795.0, 795.0, 805.0, 805.0],
  [780.0, 780.0, 800.0, 800.0],
  [752.5, 752.5, 790.0, 790.0],
  [720.0, 720.0, 771.25, 771.25],
  [676.25, 676.25, 745.625, 745.625],
  [625.0, 625.0, 710.9375, 710.9375],
  [506.875, 506.875, 667.96875, 667.96875],
  [825.0, 825.0, 835.0, 835.0],
  [815.0, 815.0, 830.0, 830.0],
  [845.0, 845.0, 855.0, 855.0],
  [865.0, 865.0, 875.0, 875.0],
  [850.0, 850.0, 870.0, 870.0],
  [885.0, 885.0, 895.0, 895.0],
  [860.0, 860.0, 890.0, 890.0],
  [905.0, 905.0, 915.0, 915.0],
  [925.0, 925.0, 935.0, 935.0],
  [910.0, 910.0, 930.0, 930.0],
  [945.0, 945.0, 955.0, 955.0],
  [965.0, 965.0, 975.0, 975.0],
  [950.0, 950.0, 970.0, 970.0],
  [920.0, 920.0, 960.0, 960.0],
  [875.0, 875.0, 940.0, 940.0],
  [822.5, 822.5, 907.5, 907.5],
  [985.0, 985.0, 995.0, 995.0],
  [1025.0, 1025.0, 1035.0, 1035.0],
  [1015.0, 1015.0, 1030.0, 1030.0],
  [1005.0, 1005.0, 1022.5, 1022.5],
  [990.0, 990.0, 1013.75, 1013.75],
  [1045.0, 1045.0, 1055.0, 1055.0],
  [1065.0, 1065.0, 1075.0, 1075.0],
  [1050.0, 1050.0, 1070.0, 1070.0],
  [1085.0, 1085.0, 1095.0, 1095.0],
  [1125.0, 1125.0, 1135.0, 1135.0],
  [1115.0, 1115.0, 1130.0, 1130.0],
  [1105.0, 1105.0, 1122.5, 1122.5],
  [1090.0, 1090.0, 1113.75, 1113.75],
  [1060.0, 1060.0, 1101.875, 1101.875],
  [1001.875, 1001.875, 1080.9375, 1080.9375],
  [865.0, 865.0, 1041.40625, 1041.40625],
  [587.421875, 587.421875, 953.203125, 953.203125],
  [181.2890625, 181.2890625, 770.3125, 770.3125],
  [1145.0, 1145.0, 1155.0, 1155.0],
  [1165.0, 1165.0, 1175.0, 1175.0],
  [1150.0, 1150.0, 1170.0, 1170.0],
  [1185.0, 1185.0, 1195.0, 1195.0],
  [1205.0, 1205.0, 1215.0, 1215.0],
  [1190.0, 1190.0, 1210.0, 1210.0],
  [1160.0, 1160.0, 1200.0, 1200.0],
  [1225.0, 1225.0, 1235.0, 1235.0],
  [1255.0, 1255.0, 1265.0, 1265.0],
  [1245.0, 1245.0, 1260.0, 1260.0],
  [1230.0, 1230.0, 1252.5, 1252.5],
  [1275.0, 1275.0, 1285.0, 1285.0],
  [1305.0, 1305.0, 1315.0, 1315.0],
  [1295.0, 1295.0, 1310.0, 1310.0],
  [1280.0, 1280.0, 1302.5, 1302.5],
  [1241.25, 1241.25, 1291.25, 1291.25],
  [1325.0, 1325.0, 1335.0, 1335.0],
  [1345.0, 1345.0, 1355.0, 1355.0],
  [1365.0, 1365.0, 1375.0, 1375.0],
  [1350.0, 1350.0, 1370.0, 1370.0],
  [1330.0, 1330.0, 1360.0, 1360.0],
  [1395.0, 1395.0, 1405.0, 1405.0],
  [1385.0, 1385.0, 1400.0, 1400.0],
  [1415.0, 1415.0, 1425.0, 1425.0],
  [1455.0, 1455.0, 1465.0, 1465.0],
  [1445.0, 1445.0, 1460.0, 1460.0],
  [1435.0, 1435.0, 1452.5, 1452.5],
  [1420.0, 1420.0, 1443.75, 1443.75],
  [1392.5, 1392.5, 1431.875, 1431.875],
  [1345.0, 1345.0, 1412.1875, 1412.1875],
  [1266.25, 1266.25, 1378.59375, 1378.59375],
  [1180.0, 1180.0, 1322.421875, 1322.421875],
  [1485.0, 1485.0, 1495.0, 1495.0],
  [1475.0, 1475.0, 1490.0, 1490.0],
  [1515.0, 1515.0, 1525.0, 1525.0],
  [1505.0, 1505.0, 1520.0, 1520.0],
  [1482.5, 1482.5, 1512.5, 1512.5],
  [1535.0, 1535.0, 1545.0, 1545.0],
  [1575.0, 1575.0, 1585.0, 1585.0],
  [1565.0, 1565.0, 1580.0, 1580.0],
  [1555.0, 1555.0, 1572.5, 1572.5],
  [1540.0, 1540.0, 1563.75, 1563.75],
  [1497.5, 1497.5, 1551.875, 1551.875],
  [1595.0, 1595.0, 1605.0, 1605.0],
  [1625.0, 1625.0, 1635.0, 1635.0],
  [1615.0, 1615.0, 1630.0, 1630.0],
  [1600.0, 1600.0, 1622.5, 1622.5],
  [1645.0, 1645.0, 1655.0, 1655.0],
  [1675.0, 1675.0, 1685.0, 1685.0],
  [1665.0, 1665.0, 1680.0, 1680.0],
  [1650.0, 1650.0, 1672.5, 1672.5],
  [1611.25, 1611.25, 1661.25, 1661.25],
  [1524.6875, 1524.6875, 1636.25, 1636.25],
  [1705.0, 1705.0, 1715.0, 1715.0],
  [1695.0, 1695.0, 1710.0, 1710.0],
  [1725.0, 1725.0, 1735.0, 1735.0],
  [1745.0, 1745.0, 1755.0, 1755.0],
  [1730.0, 1730.0, 1750.0, 1750.0],
  [1702.5, 1702.5, 1740.0, 1740.0],
  [1765.0, 1765.0, 1775.0, 1775.0],
  [1795.0, 1795.0, 1805.0, 1805.0],
  [1785.0, 1785.0, 1800.0, 1800.0],
  [1770.0, 1770.0, 1792.5, 1792.5],
  [1815.0, 1815.0, 1825.0, 1825.0],
  [1835.0, 1835.0, 1845.0, 1845.0],
  [1855.0, 1855.0, 1865.0, 1865.0],
  [1840.0, 1840.0, 1860.0, 1860.0],
  [1820.0, 1820.0, 1850.0, 1850.0],
  [1781.25, 1781.25, 1835.0, 1835.0],
  [1721.25, 1721.25, 1808.125, 1808.125],
  [1580.46875, 1580.46875, 1764.6875, 1764.6875],
  [1251.2109375, 1251.2109375, 1672.578125, 1672.578125],
  [1885.0, 1885.0, 1895.0, 1895.0],
  [1875.0, 1875.0, 1890.0, 1890.0],
  [1915.0, 1915.0, 1925.0, 1925.0],
  [1905.0, 1905.0, 1920.0, 1920.0],
  [1935.0, 1935.0, 1945.0, 1945.0],
  [1955.0, 1955.0, 1965.0, 1965.0],
  [1940.0, 1940.0, 1960.0, 1960.0],
  [1912.5, 1912.5, 1950.0, 1950.0],
  [1882.5, 1882.5, 1931.25, 1931.25],
  [1975.0, 1975.0, 1985.0, 1985.0],
  [1995.0, 1995.0, 2005.0, 2005.0],
  [1980.0, 1980.0, 2000.0, 2000.0],
  [2015.0, 2015.0, 2025.0, 2025.0],
  [2045.0, 2045.0, 2055.0, 2055.0],
  [2035.0, 2035.0, 2050.0, 2050.0],
  [2020.0, 2020.0, 2042.5, 2042.5],
  [1990.0, 1990.0, 2031.25, 2031.25],
  [1906.875, 1906.875, 2010.625, 2010.625],
  [2065.0, 2065.0, 2075.0, 2075.0],
  [2095.0, 2095.0, 2105.0, 2105.0],
  [2085.0, 2085.0, 2100.0, 2100.0],
  [2070.0, 2070.0, 2092.5, 2092.5],
  [2125.0, 2125.0, 2135.0, 2135.0],
  [2115.0, 2115.0, 2130.0, 2130.0],
  [2145.0, 2145.0, 2155.0, 2155.0],
  [2122.5, 2122.5, 2150.0, 2150.0],
  [2165.0, 2165.0, 2175.0, 2175.0],
  [2185.0, 2185.0, 2195.0, 2195.0],
  [2170.0, 2170.0, 2190.0, 2190.0],
  [2136.25, 2136.25, 2180.0, 2180.0],
  [2081.25, 2081.25, 2158.125, 2158.125],
  [2215.0, 2215.0, 2225.0, 2225.0],
  [2205.0, 2205.0, 2220.0, 2220.0],
  [2245.0, 2245.0, 2255.0, 2255.0],
  [2235.0, 2235.0, 2250.0, 2250.0],
  [2212.5, 2212.5, 2242.5, 2242.5],
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 'ivl': ['Mardona III',
  'Unidentified planet (Monsua Nebula)',
  'Arieli',
  'Cholganna',
  'Gamerong',
  'Ahch-To',
  'Milvayne',
  'Seelos',
  'Kerkoidia',
  'Ningoth',
  'Ocahont',
  'Takodana',
  'Hevurion',
  'Ando Prime',
  'Bestine IV',
  'Dantooine',
  'Rattatak',
  'Hosra',
  "Lew'el",
  'Sarka',
  'Virujansi',
  'Lothal',
  'Lexrul',
  'Mikkia',
  'Atrisia',
  'Dinzo',
  'Denash',
  'Shadda-Bi-Boran',
  'Tamsye Prime',
  'Unidentified Outer Rim planet (Jenra)',
  'Dinwa Prime',
  'Gravlex Med',
  'Miser',
  'Horuz',
  'Qhulosk',
  'Stewjon',
  'Hinari',
  'Pastoria',
  'Spalex',
  'Rion',
  'Tei-Untis',
  'Unidentified green planet',
  'Tehar',
  'Mannius Eight',
  'Noult',
  'Raxus Prime',
  'Ubduria',
  'Vixnix',
  'Zolan',
  'Chad',
  'Sevarcos',
  'Orra',
  'Gentes',
  'Rajtiri',
  'Cyrkon',
  'Polyneus',
  'Bith',
  'Manpha',
  'Unidentified Snee homeworld',
  'Clerion',
  'Unidentified Outer Rim planet (Slitherette)',
  'Cardota',
  'Florn',
  'Mijos',
  'Vanqor',
  'Xirl',
  'Necropolis',
  'Nixus',
  'Gutretee',
  'Sembla',
  'Graf-World',
  'Kanzi',
  'Montross',
  'Unidentified planet (Dragon Void Run)',
  'Rinn',
  'Dagobah',
  'Unidentified ringed planet',
  'Duro',
  'Gwongdeen',
  'Tython',
  'Xala',
  'Paucris Major',
  'Lira San',
  'Mahranee',
  'Allyuen',
  'Feenix',
  'Iakar',
  'Teralov',
  'Geonosis',
  'Ilum',
  'Cantonica',
  'Unidentified planet (Han Solo)',
  'Vardos',
  'Vyndal',
  'Sterdic IV',
  'Unidentified planet (Eeth Koth)',
  "Er'Kit",
  'Fillithar',
  'Malpaz',
  'Sirpar',
  'Ganthel',
  'Orn Kios',
  'Camson',
  'Nacronis',
  'Brentaal IV',
  'Halbara',
  'Kiros',
  'Prefsbelt IV',
  'Suba',
  'Unidentified Squamatan homeworld',
  'Stygeon Prime',
  'Starkiller Base',
  'Numidian Prime',
  'Quarzite',
  'Commenor',
  'Ylesia',
  'Plexis',
  'Skorii-Lei',
  'Ogem',
  'Wobani',
  'Gesaral Beta',
  'Unidentified agricultural planet',
  'Baraan-Fa',
  'Carida',
  'Teth',
  'Atollon',
  'Draboon',
  'Coruscant',
  'Edis VII',
  'Scarif',
  'Cosia',
  'Indoumodo',
  'Ansion',
  'Ossus',
  'Alderaan',
  'Belderone',
  'Ralltiir',
  'Vrogas Vas',
  'Tureen VII',
  'Andelm IV',
  'Bracca',
  'Aria Prime',
  'Pasaana',
  'Veros',
  'Lasan',
  'Konkiv',
  'Telerath',
  "D'Qar",
  'Agoliba-Ado',
  'Bromlarch',
  'Balnab',
  'Jeyell',
  'Ord Mantell',
  'Hynestia',
  'Vendaxa',
  'Haidoral Prime',
  'Saleucami',
  'Unidentified planet (Nes Ukul)',
  'Viamarr',
  'Iktotchon',
  'Pillio',
  'Codia',
  'Daxam IV',
  'Manaan',
  'Keitum',
  'Theron',
  'Phorsa Gedd',
  'Chandrila',
  'Radhii',
  'Malachor',
  'Gamorr',
  'Lokori',
  "Mek'tradi",
  'Vorlag',
  'Dandoran',
  'Gan Moradir',
  'Galzez',
  'Oosalon',
  'Epiphany',
  'Chandel',
  'Trandosha',
  'Fest',
  'Murkhana',
  'Tammuz-an',
  'Zygerria',
  'Nar Kanji',
  'Tund',
  'Athulla',
  'Khorm',
  "Lah'mu",
  'Aleen Minor',
  'Inusagi',
  'Pheryon',
  'Gigor',
  'Oktaro',
  'Jaelen',
  'Ushruu',
  'Agamar',
  'Sedratis',
  'Kellux',
  'Mydasos',
  'Naboo',
  'Tarsunt',
  'Courtsilius',
  'Abednedo',
  'Tiferep Major',
  'Anthan Prime',
  'Boonta',
  'Giju',
  'Vassek',
  'Xagobah',
  'Batuu',
  'Malastare',
  'Refnu',
  'Czerialus',
  'Xendek',
  'Crucival',
  'Gerrenthum',
  'Moonus Mandel',
  'Unidentified planet (Havoc Outpost)',
  'Carnelion IV',
  'Eufornis Major',
  'Irudiru',
  'Kintoni',
  'Lola Sayu',
  'Unidentified ocean planet',
  'Uba',
  'Chasmeene',
  'Merj',
  'Kessel',
  'Karfeddion',
  'Sibensko',
  'Najan-Rovi',
  'Tosste',
  'Espirion',
  'Unidentified planet (Raada)',
  "I'vorcia Prime",
  'Rango Tan',
  'Megalox Beta',
  'Skuhl',
  'Gossam',
  'Retta',
  'Mathas',
  'Protobranch',
  'Kudo',
  'Oliu',
  'Kubindi',
  'Unidentified planet (Ilum system)',
  'Kethmandi',
  'Dasoor',
  'Kirtarkin',
  'Whiforla II',
  'Hoth',
  'Riflor',
  'Grizmallt',
  'Bakura',
  'Lannik',
  'Sarrish',
  'Fondor',
  'Sullust',
  'Ruusan',
  'Alpinn',
  'Orto',
  'Eriadu',
  'Jelucan',
  'Yarma',
  'Ringo Vinda',
  'Livno III',
  'Askaji',
  'Sesid',
  'Dallenor',
  'Bardotta',
  'Ord Cestus',
  'Unidentified planet (Lexim)',
  'Vandor',
  'Chalcedon',
  'Nal Hutta',
  'Halcyon',
  'NaJedha',
  'Agoliba-Ena',
  'Burnin Konn',
  'Ques',
  'Metellos',
  'Raddan',
  'Eadu',
  'Arreyel',
  'Glee Anselm',
  'Ruoss Minor',
  'Cianap',
  'Unidentified planet (Vagadarr system)',
  'Jiruus',
  'Ordo',
  'Ord Radama',
  'Odona',
  'Trogan',
  'Kashyyyk',
  'Cyphar',
  'Rendel',
  'Mygeeto',
  'Jakku',
  'Kartoosh',
  'Zyzar',
  "Nanth'ri",
  'Unidentified volcanic planet (Margo)',
  'Mokivj',
  'Aargonar',
  'Ithor',
  'Kamino',
  'Thisspias',
  'Dressel',
  'Satotai',
  'Zastiga',
  'Asusto',
  'Tynna',
  'Ruuria',
  "Bar'leth",
  'Helska',
  'Nam Chorios',
  'Ezaraa',
  'Firrerre',
  "Kril'Dor",
  'Patitite Pattuna',
  'Uyter',
  'Jeosyn',
  'Zoh',
  'Csilla',
  'Batonn',
  'Ivarujar',
  'Concord Dawn',
  'Chrona',
  'Petrusia',
  'Mexeluine',
  'Sneeve',
  'Hudalla',
  'Ione',
  'Toprawa',
  'Uchinao',
  'Hosnian Prime',
  'Cona',
  'Onderon',
  'Harloff Minor',
  'Ibaar',
  'Kintan',
  'Phatrong',
  'Asmeru',
  'Naalol',
  'Nothoiin',
  'Ord Tiddell',
  'Frong',
  "Unidentified planet (Thrawn's exile)",
  'Castell',
  'Takobo',
  'Gatalenta',
  'Ison',
  'Hays Major',
  'Sha Qarot',
  'Ivera X',
  'Vulpter',
  'Calcoraan',
  'Dowut',
  'Polis Massa',
  'Ertegas',
  'Ojom',
  'Dwartii',
  'Mataou',
  'Socorro',
  'Wrea',
  'Hays Minor',
  'Kinrah',
  'Pendarr III',
  'Touksingal',
  'Itapi Prime',
  'Mustafar',
  'Demir',
  'Yar Togna',
  'Strokill Prime',
  'Elom',
  'Ukio',
  'Jhas',
  'Thune',
  'Iego',
  'Jalindi',
  'Kooriva',
  'Iskalon',
  'Kalee',
  'Bilzen',
  'Birren',
  'Cerea',
  'Mykapo',
  'Carajam',
  'Skaradosh',
  'Vetine',
  'Zerm',
  'Bavva',
  'Taul',
  'Felucia',
  'Quermia',
  'Chalacta',
  'Chal Hudda',
  'Haruun Kal',
  'Skako Minor',
  'Yout',
  'Mirrin Prime',
  'Ovanis',
  'Tirahnn',
  'Kijimi',
  'Nakadia',
  'Basteel',
  'Raysho',
  'Uro',
  'Kaller',
  'Champala',
  'Pipada',
  'Lonera',
  'Naraka',
  'Honoghr',
  'Cato Neimoidia',
  'The Deep',
  'Antar',
  'Chaaktil',
  'Hissrich',
  'Pijal',
  'Umbara',
  'Orto Plutonia',
  'Troiken',
  'Ankhural',
  'Rina Major',
  'Galagolos V',
  'Salient I',
  'Bray',
  'Vir Aphshire',
  'Vallt',
  'Klatooine',
  'Cartao',
  'Rodia',
  'Karlinus',
  'Vashka',
  'Endor',
  'Ponemah Terminal',
  'Abafar',
  'Nag Ubdur',
  'Quell',
  'Dhandu',
  'Savareen',
  'Atterra Primo',
  'Shili',
  'Candovant',
  'Obroa-skai',
  'Eufornis Minor',
  'Falleen',
  'Kaddak',
  'Tansyl 5',
  'Uquine',
  'Nepotis',
  'Moraband',
  'Mordal',
  'Bogden',
  'Unidentified Gank homeworld',
  'Patrolia',
  'Dennogra',
  'Aleen',
  'Denon',
  'Trivar II',
  'Nordis Prime',
  'Vernid',
  'Tinnel IV',
  'Kupoh',
  'Morellia',
  'Tibrin',
  'Kowak',
  'Atterra Alpha',
  'Mendavi',
  'Mandalore',
  'Insk',
  'Oon',
  'Kadavo',
  'Luprora',
  'Crul',
  'Ammon IV',
  'Pujool',
  'Ord Pardron',
  'Wecacoe',
  'Moraga',
  'Wor Tandell',
  'Marleyvane',
  'Toola',
  'Anoat',
  'Taris',
  'Farhava Beta',
  'Corulag',
  'Pion',
  'Trasse',
  'Muunilinst',
  'Unidentified planet (Klikklak)',
  'Bothawui',
  'Byss',
  'Gorse',
  'Gand',
  'Thustra',
  "Bri'n",
  "Unidentified planet (Lupr'or)",
  'Ando',
  'Arkanis',
  'Iloh',
  'Unidentified planet (Sunshi system)',
  'Krownest',
  'Shu-Torun',
  'Akiva',
  'Imvur',
  'Quila',
  'Maridun',
  'Anelsana',
  'Unidentified planet (Zardossa Stix)',
  'Neimoidia',
  'Samovar',
  'Shantipole',
  'Affadar',
  'Irff',
  'Botajef',
  'Ikkrukk',
  "Chandar's Folly",
  'Salient II',
  'Kuat',
  'Serenno',
  'Tangenine',
  'Moorja',
  'Pamarthe',
  'Omereth',
  'Orondia',
  'Pandem Nai',
  'Ubardia',
  'Galidraan IV',
  'Watassay',
  'Unidentified planet (Cora Vessora)',
  'Narq',
  'Unidentified Outer Rim planet (Jul Jerjerrod)',
  'Makem Te',
  'Unidentified planet (Live islands)',
  'Bamayar IX',
  'Unidentified planet (Emil Graf)',
  'Rishi',
  'Utapau',
  'Dathomir',
  'Lokud 7',
  'Son-tuul',
  'Kiffu',
  'Rumitaka',
  'Mimban',
  'Pasher',
  'Ahakista',
  'Unidentified planet (Jaxxon)',
  'Etti IV',
  'Lolet',
  'Lahn',
  'Vertseth',
  'Tholoth',
  'Li-Toran',
  'Unidentified uninhabited volcanic planet',
  'Galidraan',
  'Raxus',
  'Unidentified planet (Outer Rim Territories)',
  'Parnassos',
  'Trionak',
  'Neral',
  'Unidentified planet (Freidal)',
  'Rhen Var',
  'Anantapar',
  'Lefrani',
  'Kardoa',
  'Subterrel',
  'Hosnian',
  'Vurdon Ka',
  'Coyerti',
  'Noctu',
  'Darlyn Boda',
  'Roche',
  'Thrad',
  'Galaan',
  'Ontotho',
  'Atterra Bravo',
  'Cos Secundu',
  'Pothor',
  'Egips',
  'Dorin',
  'Orish',
  'Saki',
  'GUHL-JO387O',
  'Dermos',
  'Hovun IV',
  'Crait',
  'Marca',
  'Anaxes',
  'Ryloth',
  'Najra-Va',
  'Reamma',
  'Bendeluum',
  'Amethia Prime',
  'Colla IV',
  'Aquaris',
  'Kirdo III',
  'Velusia',
  'Wadi Raffa',
  'Boothi XII',
  'Tyegin',
  'Grange',
  'Llanic',
  'Vjun',
  'Wellspring of Life',
  'Bonadan',
  'Carlac',
  'Durkteel',
  'Iridonia',
  'Ikari',
  "N'Zoth",
  'Yavin',
  'Garel',
  'Cadomai Prime',
  'Panna',
  'Sriluur',
  'Tsevuka',
  'Delphon',
  'Galpos II',
  'Mon Cala',
  'Plin Minor',
  'Berzite',
  'Oulanne',
  'Klytus V',
  'Tokmia',
  'Unidentified planet (Tholme and Ventress)',
  'Lotho Minor',
  'Castilon',
  'Kalevala',
  'Lutrillia',
  'Tatooine',
  'Allst Prime',
  'Gilvaanen',
  'Florrum',
  'Jaresh',
  'Unidentified planet (droid uprising)',
  'Devaron',
  'Taanab',
  'Lysatra',
  'Malian',
  'Klonoid',
  'Unidentified gas giant (Sentinel Base)',
  'Thabeska',
  'Lanteeb',
  'Pollillus',
  'Minfar',
  'Bespin',
  'Galidraan III',
  'Isobe',
  'Kiffex',
  'Riosa',
  'Artiod Minor',
  "Ktath'atn",
  'Vodran',
  'Kergans',
  'Toydaria',
  'Kerev Doi',
  'Hirara',
  'Phindar',
  'Sammun',
  'Espinar',
  'Wynkahthu',
  'Oba Diah',
  'Polmanar',
  'Moldour',
  'Bastatha',
  'Wayland',
  'Scipio',
  'Twon Ketee',
  'Sump',
  'Absanz',
  'Raydonia',
  'Christophsis',
  'Agaris',
  'Corellia',
  'Council',
  'Daxan Beta',
  'Nimban',
  'Harkrova I',
  'Zhanox'],
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  'C6',
  'C6',
  'C6',
  'C6',
  'C6',
  'C6',
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  'C6',
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  'C6',
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  'C6',
  'C6',
  'C6',
  'C6',
  'C6',
  'C6',
  'C6',
  'C6',
  'C6',
  'C6']}

5.2 Cluster membership¶

In [17]:
star_wars_subset_norm_m_hc_memb = fcluster(star_wars_subset_norm_m_hc , t = 6, criterion = "maxclust")
star_wars_subset_norm_m_hc_memb
Out[17]:
array([4, 5, 3, 6, 5, 3, 6, 2, 3, 6, 1, 5, 2, 5, 3, 6, 1, 3, 6, 5, 6, 6,
       2, 5, 1, 5, 5, 5, 2, 4, 3, 6, 2, 1, 5, 3, 6, 3, 4, 4, 3, 2, 1, 5,
       6, 5, 3, 2, 6, 2, 3, 4, 6, 4, 4, 3, 4, 2, 6, 6, 6, 1, 1, 4, 4, 5,
       6, 3, 6, 5, 5, 2, 5, 1, 5, 2, 3, 5, 6, 4, 1, 5, 1, 4, 1, 2, 6, 3,
       5, 4, 6, 4, 4, 4, 1, 4, 3, 4, 4, 5, 2, 2, 3, 1, 6, 4, 3, 1, 2, 6,
       2, 4, 4, 6, 5, 2, 2, 6, 6, 3, 6, 6, 3, 5, 4, 4, 1, 3, 2, 1, 3, 2,
       1, 6, 3, 6, 2, 6, 6, 4, 1, 5, 5, 6, 6, 5, 1, 1, 6, 4, 2, 4, 6, 1,
       4, 3, 2, 6, 4, 5, 2, 1, 3, 4, 6, 3, 6, 3, 5, 4, 5, 5, 1, 4, 2, 1,
       4, 1, 6, 3, 4, 6, 5, 6, 6, 5, 6, 2, 1, 2, 2, 5, 1, 6, 4, 1, 1, 3,
       2, 3, 3, 6, 3, 5, 3, 1, 6, 1, 3, 6, 1, 1, 2, 1, 3, 6, 4, 4, 4, 4,
       4, 1, 1, 6, 4, 4, 1, 6, 4, 1, 3, 6, 4, 2, 1, 4, 4, 6, 5, 2, 5, 1,
       5, 2, 5, 3, 4, 5, 6, 3, 6, 4, 4, 4, 4, 4, 4, 3, 3, 4, 4, 6, 4, 2,
       3, 4, 3, 5, 5, 4, 6, 4, 4, 1, 6, 3, 5, 4, 4, 2, 3, 6, 6, 1, 3, 3,
       3, 6, 6, 4, 4, 4, 3, 6, 1, 3, 5, 6, 6, 2, 4, 5, 4, 5, 6, 5, 3, 3,
       5, 3, 6, 3, 6, 2, 1, 6, 1, 6, 1, 3, 6, 2, 6, 3, 6, 4, 1, 6, 5, 6,
       6, 1, 5, 2, 3, 6, 1, 2, 5, 1, 1, 6, 1, 5, 5, 4, 3, 3, 2, 5, 3, 3,
       4, 1, 1, 1, 6, 6, 4, 1, 4, 6, 6, 1, 3, 5, 5, 5, 5, 5, 2, 4, 5, 3,
       4, 4, 4, 3, 1, 5, 3, 3, 6, 4, 3, 4, 4, 4, 2, 5, 1, 5, 5, 6, 6, 1,
       1, 6, 5, 4, 1, 1, 6, 6, 5, 1, 4, 2, 4, 3, 3, 5, 4, 6, 5, 2, 3, 2,
       3, 5, 3, 4, 6, 1, 5, 1, 3, 4, 2, 6, 4, 5, 5, 6, 6, 2, 6, 1, 4, 5,
       1, 4, 4, 4, 3, 6, 2, 4, 2, 5, 4, 2, 6, 4, 6, 6, 1, 5, 6, 1, 3, 5,
       1, 1, 5, 4, 3, 5, 3, 2, 1, 2, 3, 1, 6, 1, 6, 4, 6, 3, 4, 3, 6, 3,
       5, 3, 1, 1, 6, 6, 6, 5, 6, 3, 4, 3, 6, 6, 2, 5, 5, 6, 5, 3, 1, 4,
       5, 2, 6, 3, 1, 1, 5, 3, 1, 1, 4, 5, 5, 5, 3, 1, 4, 4, 2, 3, 4, 4,
       6, 1, 6, 1, 1, 1, 4, 1, 1, 6, 3, 6, 6, 4, 1, 2, 1, 5, 5, 5, 3, 6,
       4, 1, 1, 2, 1, 2, 6, 4, 2, 4, 6, 6, 4, 5, 5, 3, 5, 4, 6, 5, 4, 3,
       4, 6, 2, 5, 6, 5, 4, 4, 6, 2, 2, 6, 6, 4, 1, 3, 5, 1, 4, 4, 4, 2,
       5, 6, 1, 3, 1, 5, 1, 5, 1, 6, 1, 6, 6, 1, 3, 3, 6, 5, 3, 5, 5, 1,
       2, 6, 3, 5, 6, 4, 3, 5, 1, 1, 1, 6, 4, 5, 4, 3, 6, 4, 5, 3, 1, 1,
       5, 3, 6, 2, 5, 2, 6, 4, 2, 5, 1, 1, 6, 6, 2, 2, 4, 6, 1, 6, 5, 6,
       5, 6, 2, 5, 3, 4, 6, 3, 1, 3, 1, 4, 3, 6, 2, 4, 4, 4, 6, 4, 1, 2,
       4], dtype=int32)
In [18]:
star_wars_subset_norm_m_hc_memb = pd.Series(star_wars_subset_norm_m_hc_memb, 
                                            index = star_wars_subset_norm.index)
star_wars_subset_norm_m_hc_memb
Out[18]:
Name
Aargonar    4
Abafar      5
Abednedo    3
Absanz      6
Affadar     5
           ..
Zhanox      6
Zoh         4
Zolan       1
Zygerria    2
Zyzar       4
Length: 683, dtype: int32

5.3 Cluster quality¶

In [19]:
from sklearn.metrics import silhouette_score
hc6_silhouette = silhouette_score(star_wars_subset_norm, star_wars_subset_norm_m_hc_memb).round(2)
hc6_silhouette
Out[19]:
0.24
In [20]:
star_wars_subset_norm_m_hc_memb_df = star_wars_subset_norm_m_hc_memb.to_frame()
star_wars_subset_norm_m_hc_memb_df
Out[20]:
0
Name
Aargonar 4
Abafar 5
Abednedo 3
Absanz 6
Affadar 5
... ...
Zhanox 6
Zoh 4
Zolan 1
Zygerria 2
Zyzar 4

683 rows × 1 columns

6. Merge with original data¶

In [21]:
star_wars_w_cluster = pd.concat([star_wars, star_wars_subset_norm_m_hc_memb], axis = 1)
star_wars_w_cluster
Out[21]:
Region Sector System Inhabitants Capital_City Population Record_High_Temp Record_Low_Temp Gravity Key_Element Rotation Revolution Rating Rating_Nom Resources Resources_Nom 0
Name
Aargonar Unknown Unknown Unknown Unknown Unknown 3857305 36 -5 8.35 6 59.89 2112 7 Yay 8 Awesome 4
Abafar Outer Rim Territories Sprizen sector Unknown Unknown Unknown 2331308 33 -27 11.40 28 49.20 1818 2 Yucks 9 Awesome 5
Abednedo Colonies Unknown Unknown Abednedo Unknown 5358387 28 -5 9.55 30 127.76 1210 3 Yucks 5 Blah 3
Absanz Unknown Unknown Unknown Unknown Unknown 7229751 38 -6 10.80 57 145.98 1431 9 Yay 3 Blah 6
Affadar Unknown Unknown Unknown T'Laeem Unknown 4307513 37 -31 9.75 22 58.51 1060 9 Yay 7 Awesome 5
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
Zhanox Outer Rim Territories Anoat sector Zhanox system Ugnaughts Unknown 8396480 17 -23 10.81 10 104.19 2172 4 Meh 6 Awesome 6
Zoh Unknown Unknown Unknown Unknown Unknown 1339277 29 -12 8.10 50 20.88 145 2 Yucks 2 Blah 4
Zolan Mid Rim Unknown Unknown Clawdites Unknown 5880473 20 -18 8.60 84 197.40 939 5 Meh 8 Awesome 1
Zygerria Outer Rim Territories Unknown Unknown Zygerrians Unknown 3984810 24 -20 11.25 86 224.72 2327 4 Meh 4 Blah 2
Zyzar Unknown Unknown Unknown Unknown Unknown 1464241 23 -26 8.27 83 76.29 2438 3 Yucks 8 Awesome 4

683 rows × 17 columns

In [22]:
print(star_wars_w_cluster.columns)

Index([          'Region',           'Sector',           'System',
            'Inhabitants',     'Capital_City',       'Population',
       'Record_High_Temp',  'Record_Low_Temp',          'Gravity',
            'Key_Element',         'Rotation',       'Revolution',
                 'Rating',       'Rating_Nom',        'Resources',
          'Resources_Nom',                  0],
      dtype='object')
In [23]:
star_wars_w_cluster.columns.values[16] = "Cluster"
star_wars_w_cluster.head()
Out[23]:
Region Sector System Inhabitants Capital_City Population Record_High_Temp Record_Low_Temp Gravity Key_Element Rotation Revolution Rating Rating_Nom Resources Resources_Nom Cluster
Name
Aargonar Unknown Unknown Unknown Unknown Unknown 3857305 36 -5 8.35 6 59.89 2112 7 Yay 8 Awesome 4
Abafar Outer Rim Territories Sprizen sector Unknown Unknown Unknown 2331308 33 -27 11.40 28 49.20 1818 2 Yucks 9 Awesome 5
Abednedo Colonies Unknown Unknown Abednedo Unknown 5358387 28 -5 9.55 30 127.76 1210 3 Yucks 5 Blah 3
Absanz Unknown Unknown Unknown Unknown Unknown 7229751 38 -6 10.80 57 145.98 1431 9 Yay 3 Blah 6
Affadar Unknown Unknown Unknown T'Laeem Unknown 4307513 37 -31 9.75 22 58.51 1060 9 Yay 7 Awesome 5
In [24]:
star_wars_w_cluster.iloc[:, 16] = star_wars_w_cluster.iloc[:, 16].astype("category")
In [25]:
pd.DataFrame(star_wars_w_cluster.columns.values, columns = ["variables"])
Out[25]:
variables
0 Region
1 Sector
2 System
3 Inhabitants
4 Capital_City
5 Population
6 Record_High_Temp
7 Record_Low_Temp
8 Gravity
9 Key_Element
10 Rotation
11 Revolution
12 Rating
13 Rating_Nom
14 Resources
15 Resources_Nom
16 Cluster

6.1 Comparison 1¶

In [26]:
star_wars_w_cluster_comparison_1 = star_wars_w_cluster.iloc[:,[5, 8, 10, 16]]
star_wars_w_cluster_comparison_1
Out[26]:
Population Gravity Rotation Cluster
Name
Aargonar 3857305 8.35 59.89 4
Abafar 2331308 11.40 49.20 5
Abednedo 5358387 9.55 127.76 3
Absanz 7229751 10.80 145.98 6
Affadar 4307513 9.75 58.51 5
... ... ... ... ...
Zhanox 8396480 10.81 104.19 6
Zoh 1339277 8.10 20.88 4
Zolan 5880473 8.60 197.40 1
Zygerria 3984810 11.25 224.72 2
Zyzar 1464241 8.27 76.29 4

683 rows × 4 columns

In [27]:
import plotly.express as px

fig_1 = px.scatter_3d(star_wars_w_cluster_comparison_1, 
                    x = "Population", 
                    y = "Gravity", 
                    z = "Rotation",
                    color = "Cluster",
                    symbol = "Cluster",
                    category_orders = {"Cluster": [1, 2, 3, 4, 5, 6]})
fig_1.show()
In [28]:
star_wars_w_cluster_comparison_1_mean = star_wars_w_cluster_comparison_1.groupby(by = "Cluster").mean().round(2)
star_wars_w_cluster_comparison_1_mean
Out[28]:
Population Gravity Rotation
Cluster
1 8291225.91 8.93 200.53
2 3590969.30 10.83 212.03
3 3647330.58 8.45 176.46
4 5548260.40 8.14 64.63
5 3078082.18 10.54 79.66
6 7571941.52 10.67 85.97
In [29]:
star_wars_w_cluster_comparison_1_mean = star_wars_w_cluster_comparison_1_mean.rename(columns = 
                                                                                     {"Population": "Population_Mean",
                                                                                     "Gravity": "Gravity_Mean",
                                                                                     "Rotation": "Rotation_Mean"})
star_wars_w_cluster_comparison_1_mean
Out[29]:
Population_Mean Gravity_Mean Rotation_Mean
Cluster
1 8291225.91 8.93 200.53
2 3590969.30 10.83 212.03
3 3647330.58 8.45 176.46
4 5548260.40 8.14 64.63
5 3078082.18 10.54 79.66
6 7571941.52 10.67 85.97

6.2 Comparison 2¶

In [30]:
star_wars_w_cluster_comparison_2 = star_wars_w_cluster.iloc[:,[6, 7, 16]]
star_wars_w_cluster_comparison_2
Out[30]:
Record_High_Temp Record_Low_Temp Cluster
Name
Aargonar 36 -5 4
Abafar 33 -27 5
Abednedo 28 -5 3
Absanz 38 -6 6
Affadar 37 -31 5
... ... ... ...
Zhanox 17 -23 6
Zoh 29 -12 4
Zolan 20 -18 1
Zygerria 24 -20 2
Zyzar 23 -26 4

683 rows × 3 columns

In [31]:
import plotly.express as px

fig_2 = px.scatter_3d(star_wars_w_cluster_comparison_2,
                      x = "Record_High_Temp",
                      y = "Record_Low_Temp",
                      z = "Cluster",
                      color = "Cluster",
                      symbol = "Cluster",
                      category_orders = {"Cluster": [1, 2, 3, 4, 5, 6]})
fig_2.show()
In [32]:
star_wars_w_cluster_comparison_2_median = star_wars_w_cluster_comparison_2.groupby(by = "Cluster").median().round(2)
star_wars_w_cluster_comparison_2_median
Out[32]:
Record_High_Temp Record_Low_Temp
Cluster
1 24.5 -18.0
2 18.0 -20.0
3 20.5 -19.5
4 20.0 -20.0
5 19.0 -20.0
6 22.0 -19.0
In [33]:
star_wars_w_cluster_comparison_2_median = star_wars_w_cluster_comparison_2_median.rename(columns = 
                                                                                     {"Record_High_Temp": "Record_High_Temp_Median",
                                                                                     "Record_Low_Temp": "Record_Low_Temp_Median"})
star_wars_w_cluster_comparison_2_median
Out[33]:
Record_High_Temp_Median Record_Low_Temp_Median
Cluster
1 24.5 -18.0
2 18.0 -20.0
3 20.5 -19.5
4 20.0 -20.0
5 19.0 -20.0
6 22.0 -19.0
In [34]:
star_wars_w_cluster_comparison_2_median = star_wars_w_cluster_comparison_2_median.assign(
    Range = star_wars_w_cluster_comparison_2_median["Record_High_Temp_Median"] -
    star_wars_w_cluster_comparison_2_median["Record_Low_Temp_Median"])
star_wars_w_cluster_comparison_2_median
Out[34]:
Record_High_Temp_Median Record_Low_Temp_Median Range
Cluster
1 24.5 -18.0 42.5
2 18.0 -20.0 38.0
3 20.5 -19.5 40.0
4 20.0 -20.0 40.0
5 19.0 -20.0 39.0
6 22.0 -19.0 41.0
In [35]:
star_wars_w_cluster_comparison_2_median.sort_values(by = ["Range"], ascending = False)
Out[35]:
Record_High_Temp_Median Record_Low_Temp_Median Range
Cluster
1 24.5 -18.0 42.5
6 22.0 -19.0 41.0
3 20.5 -19.5 40.0
4 20.0 -20.0 40.0
5 19.0 -20.0 39.0
2 18.0 -20.0 38.0