import pandas as pd
df = pd.read_csv('./dataset/output.csv')
df
| Key | ef100 | ef111 | ef_sub_100 | ef_sub_111 | index | mass | radius | melting | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | Si | -1.115722 | -0.701245 | -0.569736 | -0.225225 | 14 | 28.085000 | 111 | 1687.00 |
| 1 | Sb | -1.522549 | -0.543873 | 0.870210 | 1.027179 | 51 | 121.760000 | 133 | 904.10 |
| 2 | Bi | -1.352188 | -0.177808 | 1.968962 | 1.815817 | 83 | 208.980400 | 143 | 544.67 |
| 3 | Pb | -1.160016 | -0.019294 | 1.593083 | 1.631911 | 82 | 207.200000 | 154 | 600.80 |
| 4 | In | -1.033518 | -0.200844 | 0.416942 | 0.519860 | 49 | 114.818000 | 156 | 429.91 |
| 5 | Ti | 0.554584 | 0.775988 | 0.004879 | 0.303309 | 22 | 47.867000 | 176 | 1930.00 |
| 6 | Ag | -0.527524 | 0.042239 | 0.157998 | 0.028100 | 47 | 107.868200 | 165 | 1234.00 |
| 7 | Ni | 0.177254 | 0.330622 | -0.122791 | -0.103603 | 28 | 58.693400 | 149 | 1726.00 |
| 8 | Zn | -0.667779 | 0.135557 | -0.359509 | -0.212413 | 30 | 65.380000 | 142 | 692.88 |
| 9 | Tl | -0.892003 | 0.070464 | 1.355970 | 1.374303 | 81 | 204.380000 | 156 | 577.00 |
| 10 | Ga | -1.282309 | -0.644779 | -0.719299 | -0.521194 | 31 | 69.723000 | 136 | 302.91 |
| 11 | Ir | 0.624956 | 0.319982 | 0.162133 | 0.390349 | 77 | 192.217000 | 180 | 2716.00 |
| 12 | Sn | -1.283931 | -0.485359 | 0.464447 | 0.686663 | 50 | 118.710000 | 145 | 505.21 |
| 13 | Ge | -1.151102 | -0.592351 | -0.159282 | 0.146471 | 32 | 72.630000 | 125 | 1211.40 |
| 14 | Mg | -0.843692 | -0.179419 | -0.319915 | -0.124790 | 12 | 24.305000 | 145 | 923.00 |
| 15 | Au | -0.870214 | -0.358127 | -0.124026 | -0.328712 | 79 | 196.966569 | 174 | 1337.73 |
| 16 | Al | -1.117972 | -0.600215 | -1.012658 | -0.729136 | 13 | 26.981538 | 118 | 933.40 |
| 17 | Pd | -0.718155 | -0.438415 | -0.581004 | -0.657751 | 46 | 106.420000 | 169 | 1825.00 |
| 18 | Pt | -0.650905 | -0.565435 | -0.507245 | -0.628432 | 78 | 195.084000 | 177 | 2045.00 |
| 19 | Cd | -0.557417 | 0.130099 | 0.719414 | 0.780941 | 48 | 112.414000 | 161 | 594.33 |
| 20 | Rh | -0.032545 | -0.152974 | -0.363279 | -0.197038 | 45 | 102.905500 | 173 | 2239.00 |
print(df.head())
print(df.tail())
Key ef100 ef111 ef_sub_100 ef_sub_111 index mass radius \
0 Si -1.115722 -0.701245 -0.569736 -0.225225 14 28.0850 111
1 Sb -1.522549 -0.543873 0.870210 1.027179 51 121.7600 133
2 Bi -1.352188 -0.177808 1.968962 1.815817 83 208.9804 143
3 Pb -1.160016 -0.019294 1.593083 1.631911 82 207.2000 154
4 In -1.033518 -0.200844 0.416942 0.519860 49 114.8180 156
melting
0 1687.00
1 904.10
2 544.67
3 600.80
4 429.91
Key ef100 ef111 ef_sub_100 ef_sub_111 index mass radius \
16 Al -1.117972 -0.600215 -1.012658 -0.729136 13 26.981538 118
17 Pd -0.718155 -0.438415 -0.581004 -0.657751 46 106.420000 169
18 Pt -0.650905 -0.565435 -0.507245 -0.628432 78 195.084000 177
19 Cd -0.557417 0.130099 0.719414 0.780941 48 112.414000 161
20 Rh -0.032545 -0.152974 -0.363279 -0.197038 45 102.905500 173
melting
16 933.40
17 1825.00
18 2045.00
19 594.33
20 2239.00
print(df.info())
<class 'pandas.core.frame.DataFrame'> RangeIndex: 21 entries, 0 to 20 Data columns (total 9 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Key 21 non-null object 1 ef100 21 non-null float64 2 ef111 21 non-null float64 3 ef_sub_100 21 non-null float64 4 ef_sub_111 21 non-null float64 5 index 21 non-null int64 6 mass 21 non-null float64 7 radius 21 non-null int64 8 melting 21 non-null float64 dtypes: float64(6), int64(2), object(1) memory usage: 1.6+ KB None
print(df.describe())
ef100 ef111 ef_sub_100 ef_sub_111 index mass \
count 21.000000 21.000000 21.000000 21.000000 21.000000 21.000000
mean -0.734416 -0.183580 0.136919 0.236981 47.523810 113.494696
std 0.604809 0.391552 0.789211 0.749763 24.307651 64.116072
min -1.522549 -0.701245 -1.012658 -0.729136 12.000000 24.305000
25% -1.151102 -0.543873 -0.363279 -0.225225 30.000000 65.380000
50% -0.870214 -0.179419 -0.122791 0.028100 47.000000 107.868200
75% -0.557417 0.070464 0.464447 0.686663 77.000000 192.217000
max 0.624956 0.775988 1.968962 1.815817 83.000000 208.980400
radius melting
count 21.000000 21.000000
mean 151.809524 1188.540000
std 19.943969 687.866522
min 111.000000 302.910000
25% 142.000000 594.330000
50% 154.000000 933.400000
75% 169.000000 1726.000000
max 180.000000 2716.000000
print(df.columns)
Index(['Key', 'ef100', 'ef111', 'ef_sub_100', 'ef_sub_111', 'index', 'mass',
'radius', 'melting'],
dtype='object')
print(df.index)
RangeIndex(start=0, stop=21, step=1)
print(df['ef100'])
0 -1.115722 1 -1.522549 2 -1.352188 3 -1.160016 4 -1.033518 5 0.554584 6 -0.527524 7 0.177254 8 -0.667779 9 -0.892003 10 -1.282309 11 0.624956 12 -1.283931 13 -1.151102 14 -0.843692 15 -0.870214 16 -1.117972 17 -0.718155 18 -0.650905 19 -0.557417 20 -0.032545 Name: ef100, dtype: float64
df['index'] = [str(x) for x in range(len(df))]
print(df)
Key ef100 ef111 ef_sub_100 ef_sub_111 index mass radius \
0 Si -1.115722 -0.701245 -0.569736 -0.225225 0 28.085000 111
1 Sb -1.522549 -0.543873 0.870210 1.027179 1 121.760000 133
2 Bi -1.352188 -0.177808 1.968962 1.815817 2 208.980400 143
3 Pb -1.160016 -0.019294 1.593083 1.631911 3 207.200000 154
4 In -1.033518 -0.200844 0.416942 0.519860 4 114.818000 156
5 Ti 0.554584 0.775988 0.004879 0.303309 5 47.867000 176
6 Ag -0.527524 0.042239 0.157998 0.028100 6 107.868200 165
7 Ni 0.177254 0.330622 -0.122791 -0.103603 7 58.693400 149
8 Zn -0.667779 0.135557 -0.359509 -0.212413 8 65.380000 142
9 Tl -0.892003 0.070464 1.355970 1.374303 9 204.380000 156
10 Ga -1.282309 -0.644779 -0.719299 -0.521194 10 69.723000 136
11 Ir 0.624956 0.319982 0.162133 0.390349 11 192.217000 180
12 Sn -1.283931 -0.485359 0.464447 0.686663 12 118.710000 145
13 Ge -1.151102 -0.592351 -0.159282 0.146471 13 72.630000 125
14 Mg -0.843692 -0.179419 -0.319915 -0.124790 14 24.305000 145
15 Au -0.870214 -0.358127 -0.124026 -0.328712 15 196.966569 174
16 Al -1.117972 -0.600215 -1.012658 -0.729136 16 26.981538 118
17 Pd -0.718155 -0.438415 -0.581004 -0.657751 17 106.420000 169
18 Pt -0.650905 -0.565435 -0.507245 -0.628432 18 195.084000 177
19 Cd -0.557417 0.130099 0.719414 0.780941 19 112.414000 161
20 Rh -0.032545 -0.152974 -0.363279 -0.197038 20 102.905500 173
melting
0 1687.00
1 904.10
2 544.67
3 600.80
4 429.91
5 1930.00
6 1234.00
7 1726.00
8 692.88
9 577.00
10 302.91
11 2716.00
12 505.21
13 1211.40
14 923.00
15 1337.73
16 933.40
17 1825.00
18 2045.00
19 594.33
20 2239.00
filtered_df = df[df['ef100'] > 0]
filtered_df
# print(filtered_df)
| Key | ef100 | ef111 | ef_sub_100 | ef_sub_111 | index | mass | radius | melting | |
|---|---|---|---|---|---|---|---|---|---|
| 5 | Ti | 0.554584 | 0.775988 | 0.004879 | 0.303309 | 5 | 47.8670 | 176 | 1930.0 |
| 7 | Ni | 0.177254 | 0.330622 | -0.122791 | -0.103603 | 7 | 58.6934 | 149 | 1726.0 |
| 11 | Ir | 0.624956 | 0.319982 | 0.162133 | 0.390349 | 11 | 192.2170 | 180 | 2716.0 |
df.loc[df['ef100'] > 0, 'ef100'] = 0
print(df)
Key ef100 ef111 ef_sub_100 ef_sub_111 index mass radius \
0 Si -1.115722 -0.701245 -0.569736 -0.225225 0 28.085000 111
1 Sb -1.522549 -0.543873 0.870210 1.027179 1 121.760000 133
2 Bi -1.352188 -0.177808 1.968962 1.815817 2 208.980400 143
3 Pb -1.160016 -0.019294 1.593083 1.631911 3 207.200000 154
4 In -1.033518 -0.200844 0.416942 0.519860 4 114.818000 156
5 Ti 0.000000 0.775988 0.004879 0.303309 5 47.867000 176
6 Ag -0.527524 0.042239 0.157998 0.028100 6 107.868200 165
7 Ni 0.000000 0.330622 -0.122791 -0.103603 7 58.693400 149
8 Zn -0.667779 0.135557 -0.359509 -0.212413 8 65.380000 142
9 Tl -0.892003 0.070464 1.355970 1.374303 9 204.380000 156
10 Ga -1.282309 -0.644779 -0.719299 -0.521194 10 69.723000 136
11 Ir 0.000000 0.319982 0.162133 0.390349 11 192.217000 180
12 Sn -1.283931 -0.485359 0.464447 0.686663 12 118.710000 145
13 Ge -1.151102 -0.592351 -0.159282 0.146471 13 72.630000 125
14 Mg -0.843692 -0.179419 -0.319915 -0.124790 14 24.305000 145
15 Au -0.870214 -0.358127 -0.124026 -0.328712 15 196.966569 174
16 Al -1.117972 -0.600215 -1.012658 -0.729136 16 26.981538 118
17 Pd -0.718155 -0.438415 -0.581004 -0.657751 17 106.420000 169
18 Pt -0.650905 -0.565435 -0.507245 -0.628432 18 195.084000 177
19 Cd -0.557417 0.130099 0.719414 0.780941 19 112.414000 161
20 Rh -0.032545 -0.152974 -0.363279 -0.197038 20 102.905500 173
melting
0 1687.00
1 904.10
2 544.67
3 600.80
4 429.91
5 1930.00
6 1234.00
7 1726.00
8 692.88
9 577.00
10 302.91
11 2716.00
12 505.21
13 1211.40
14 923.00
15 1337.73
16 933.40
17 1825.00
18 2045.00
19 594.33
20 2239.00
df = df[df['ef100'] != 0]
print(df)
Key ef100 ef111 ef_sub_100 ef_sub_111 index mass radius \
0 Si -1.115722 -0.701245 -0.569736 -0.225225 0 28.085000 111
1 Sb -1.522549 -0.543873 0.870210 1.027179 1 121.760000 133
2 Bi -1.352188 -0.177808 1.968962 1.815817 2 208.980400 143
3 Pb -1.160016 -0.019294 1.593083 1.631911 3 207.200000 154
4 In -1.033518 -0.200844 0.416942 0.519860 4 114.818000 156
6 Ag -0.527524 0.042239 0.157998 0.028100 6 107.868200 165
8 Zn -0.667779 0.135557 -0.359509 -0.212413 8 65.380000 142
9 Tl -0.892003 0.070464 1.355970 1.374303 9 204.380000 156
10 Ga -1.282309 -0.644779 -0.719299 -0.521194 10 69.723000 136
12 Sn -1.283931 -0.485359 0.464447 0.686663 12 118.710000 145
13 Ge -1.151102 -0.592351 -0.159282 0.146471 13 72.630000 125
14 Mg -0.843692 -0.179419 -0.319915 -0.124790 14 24.305000 145
15 Au -0.870214 -0.358127 -0.124026 -0.328712 15 196.966569 174
16 Al -1.117972 -0.600215 -1.012658 -0.729136 16 26.981538 118
17 Pd -0.718155 -0.438415 -0.581004 -0.657751 17 106.420000 169
18 Pt -0.650905 -0.565435 -0.507245 -0.628432 18 195.084000 177
19 Cd -0.557417 0.130099 0.719414 0.780941 19 112.414000 161
20 Rh -0.032545 -0.152974 -0.363279 -0.197038 20 102.905500 173
melting
0 1687.00
1 904.10
2 544.67
3 600.80
4 429.91
6 1234.00
8 692.88
9 577.00
10 302.91
12 505.21
13 1211.40
14 923.00
15 1337.73
16 933.40
17 1825.00
18 2045.00
19 594.33
20 2239.00
df['index^2'] = [x*x*x for x in range(len(df))]
print(df)
Key ef100 ef111 ef_sub_100 ef_sub_111 index mass radius \
0 Si -1.115722 -0.701245 -0.569736 -0.225225 0 28.085000 111
1 Sb -1.522549 -0.543873 0.870210 1.027179 1 121.760000 133
2 Bi -1.352188 -0.177808 1.968962 1.815817 2 208.980400 143
3 Pb -1.160016 -0.019294 1.593083 1.631911 3 207.200000 154
4 In -1.033518 -0.200844 0.416942 0.519860 4 114.818000 156
6 Ag -0.527524 0.042239 0.157998 0.028100 6 107.868200 165
8 Zn -0.667779 0.135557 -0.359509 -0.212413 8 65.380000 142
9 Tl -0.892003 0.070464 1.355970 1.374303 9 204.380000 156
10 Ga -1.282309 -0.644779 -0.719299 -0.521194 10 69.723000 136
12 Sn -1.283931 -0.485359 0.464447 0.686663 12 118.710000 145
13 Ge -1.151102 -0.592351 -0.159282 0.146471 13 72.630000 125
14 Mg -0.843692 -0.179419 -0.319915 -0.124790 14 24.305000 145
15 Au -0.870214 -0.358127 -0.124026 -0.328712 15 196.966569 174
16 Al -1.117972 -0.600215 -1.012658 -0.729136 16 26.981538 118
17 Pd -0.718155 -0.438415 -0.581004 -0.657751 17 106.420000 169
18 Pt -0.650905 -0.565435 -0.507245 -0.628432 18 195.084000 177
19 Cd -0.557417 0.130099 0.719414 0.780941 19 112.414000 161
20 Rh -0.032545 -0.152974 -0.363279 -0.197038 20 102.905500 173
melting index^2
0 1687.00 0
1 904.10 1
2 544.67 8
3 600.80 27
4 429.91 64
6 1234.00 125
8 692.88 216
9 577.00 343
10 302.91 512
12 505.21 729
13 1211.40 1000
14 923.00 1331
15 1337.73 1728
16 933.40 2197
17 1825.00 2744
18 2045.00 3375
19 594.33 4096
20 2239.00 4913
df = df.drop(['index^2', 'index', 'ef111'], axis=1)
print(df)
Key ef100 ef_sub_100 ef_sub_111 mass radius melting 0 Si -1.115722 -0.569736 -0.225225 28.085000 111 1687.00 1 Sb -1.522549 0.870210 1.027179 121.760000 133 904.10 2 Bi -1.352188 1.968962 1.815817 208.980400 143 544.67 3 Pb -1.160016 1.593083 1.631911 207.200000 154 600.80 4 In -1.033518 0.416942 0.519860 114.818000 156 429.91 6 Ag -0.527524 0.157998 0.028100 107.868200 165 1234.00 8 Zn -0.667779 -0.359509 -0.212413 65.380000 142 692.88 9 Tl -0.892003 1.355970 1.374303 204.380000 156 577.00 10 Ga -1.282309 -0.719299 -0.521194 69.723000 136 302.91 12 Sn -1.283931 0.464447 0.686663 118.710000 145 505.21 13 Ge -1.151102 -0.159282 0.146471 72.630000 125 1211.40 14 Mg -0.843692 -0.319915 -0.124790 24.305000 145 923.00 15 Au -0.870214 -0.124026 -0.328712 196.966569 174 1337.73 16 Al -1.117972 -1.012658 -0.729136 26.981538 118 933.40 17 Pd -0.718155 -0.581004 -0.657751 106.420000 169 1825.00 18 Pt -0.650905 -0.507245 -0.628432 195.084000 177 2045.00 19 Cd -0.557417 0.719414 0.780941 112.414000 161 594.33 20 Rh -0.032545 -0.363279 -0.197038 102.905500 173 2239.00
df = df.rename(columns={'mass': 'atomic mass'})
print(df)
Key ef100 ef_sub_100 ef_sub_111 atomic mass radius melting 0 Si -1.115722 -0.569736 -0.225225 28.085000 111 1687.00 1 Sb -1.522549 0.870210 1.027179 121.760000 133 904.10 2 Bi -1.352188 1.968962 1.815817 208.980400 143 544.67 3 Pb -1.160016 1.593083 1.631911 207.200000 154 600.80 4 In -1.033518 0.416942 0.519860 114.818000 156 429.91 6 Ag -0.527524 0.157998 0.028100 107.868200 165 1234.00 8 Zn -0.667779 -0.359509 -0.212413 65.380000 142 692.88 9 Tl -0.892003 1.355970 1.374303 204.380000 156 577.00 10 Ga -1.282309 -0.719299 -0.521194 69.723000 136 302.91 12 Sn -1.283931 0.464447 0.686663 118.710000 145 505.21 13 Ge -1.151102 -0.159282 0.146471 72.630000 125 1211.40 14 Mg -0.843692 -0.319915 -0.124790 24.305000 145 923.00 15 Au -0.870214 -0.124026 -0.328712 196.966569 174 1337.73 16 Al -1.117972 -1.012658 -0.729136 26.981538 118 933.40 17 Pd -0.718155 -0.581004 -0.657751 106.420000 169 1825.00 18 Pt -0.650905 -0.507245 -0.628432 195.084000 177 2045.00 19 Cd -0.557417 0.719414 0.780941 112.414000 161 594.33 20 Rh -0.032545 -0.363279 -0.197038 102.905500 173 2239.00
df = df.set_index('Key')
print(df)
ef100 ef_sub_100 ef_sub_111 atomic mass radius melting Key Si -1.115722 -0.569736 -0.225225 28.085000 111 1687.00 Sb -1.522549 0.870210 1.027179 121.760000 133 904.10 Bi -1.352188 1.968962 1.815817 208.980400 143 544.67 Pb -1.160016 1.593083 1.631911 207.200000 154 600.80 In -1.033518 0.416942 0.519860 114.818000 156 429.91 Ag -0.527524 0.157998 0.028100 107.868200 165 1234.00 Zn -0.667779 -0.359509 -0.212413 65.380000 142 692.88 Tl -0.892003 1.355970 1.374303 204.380000 156 577.00 Ga -1.282309 -0.719299 -0.521194 69.723000 136 302.91 Sn -1.283931 0.464447 0.686663 118.710000 145 505.21 Ge -1.151102 -0.159282 0.146471 72.630000 125 1211.40 Mg -0.843692 -0.319915 -0.124790 24.305000 145 923.00 Au -0.870214 -0.124026 -0.328712 196.966569 174 1337.73 Al -1.117972 -1.012658 -0.729136 26.981538 118 933.40 Pd -0.718155 -0.581004 -0.657751 106.420000 169 1825.00 Pt -0.650905 -0.507245 -0.628432 195.084000 177 2045.00 Cd -0.557417 0.719414 0.780941 112.414000 161 594.33 Rh -0.032545 -0.363279 -0.197038 102.905500 173 2239.00
df = df.reset_index()
print(df)
Key ef100 ef_sub_100 ef_sub_111 atomic mass radius melting 0 Si -1.115722 -0.569736 -0.225225 28.085000 111 1687.00 1 Sb -1.522549 0.870210 1.027179 121.760000 133 904.10 2 Bi -1.352188 1.968962 1.815817 208.980400 143 544.67 3 Pb -1.160016 1.593083 1.631911 207.200000 154 600.80 4 In -1.033518 0.416942 0.519860 114.818000 156 429.91 5 Ag -0.527524 0.157998 0.028100 107.868200 165 1234.00 6 Zn -0.667779 -0.359509 -0.212413 65.380000 142 692.88 7 Tl -0.892003 1.355970 1.374303 204.380000 156 577.00 8 Ga -1.282309 -0.719299 -0.521194 69.723000 136 302.91 9 Sn -1.283931 0.464447 0.686663 118.710000 145 505.21 10 Ge -1.151102 -0.159282 0.146471 72.630000 125 1211.40 11 Mg -0.843692 -0.319915 -0.124790 24.305000 145 923.00 12 Au -0.870214 -0.124026 -0.328712 196.966569 174 1337.73 13 Al -1.117972 -1.012658 -0.729136 26.981538 118 933.40 14 Pd -0.718155 -0.581004 -0.657751 106.420000 169 1825.00 15 Pt -0.650905 -0.507245 -0.628432 195.084000 177 2045.00 16 Cd -0.557417 0.719414 0.780941 112.414000 161 594.33 17 Rh -0.032545 -0.363279 -0.197038 102.905500 173 2239.00
sorted_df = df.sort_values(by='radius')
print(sorted_df)
Key ef100 ef_sub_100 ef_sub_111 atomic mass radius melting 0 Si -1.115722 -0.569736 -0.225225 28.085000 111 1687.00 13 Al -1.117972 -1.012658 -0.729136 26.981538 118 933.40 10 Ge -1.151102 -0.159282 0.146471 72.630000 125 1211.40 1 Sb -1.522549 0.870210 1.027179 121.760000 133 904.10 8 Ga -1.282309 -0.719299 -0.521194 69.723000 136 302.91 6 Zn -0.667779 -0.359509 -0.212413 65.380000 142 692.88 2 Bi -1.352188 1.968962 1.815817 208.980400 143 544.67 9 Sn -1.283931 0.464447 0.686663 118.710000 145 505.21 11 Mg -0.843692 -0.319915 -0.124790 24.305000 145 923.00 3 Pb -1.160016 1.593083 1.631911 207.200000 154 600.80 7 Tl -0.892003 1.355970 1.374303 204.380000 156 577.00 4 In -1.033518 0.416942 0.519860 114.818000 156 429.91 16 Cd -0.557417 0.719414 0.780941 112.414000 161 594.33 5 Ag -0.527524 0.157998 0.028100 107.868200 165 1234.00 14 Pd -0.718155 -0.581004 -0.657751 106.420000 169 1825.00 17 Rh -0.032545 -0.363279 -0.197038 102.905500 173 2239.00 12 Au -0.870214 -0.124026 -0.328712 196.966569 174 1337.73 15 Pt -0.650905 -0.507245 -0.628432 195.084000 177 2045.00
df.loc[['A' in strkey for strkey in df['Key']], 'Key'] = 'A+'
grouped_df = df.groupby('Key')
# print(grouped_df.mean()) # 计算分组后的平均值
print(grouped_df.get_group('A+'))
Key ef100 ef_sub_100 ef_sub_111 atomic mass radius melting 5 A+ -0.527524 0.157998 0.028100 107.868200 165 1234.00 12 A+ -0.870214 -0.124026 -0.328712 196.966569 174 1337.73 13 A+ -1.117972 -1.012658 -0.729136 26.981538 118 933.40
for name, group in grouped_df:
print(f"Group: {name}")
print(group)
print("\n")
Group: A+ Key ef100 ef_sub_100 ef_sub_111 atomic mass radius melting 5 A+ -0.527524 0.157998 0.028100 107.868200 165 1234.00 12 A+ -0.870214 -0.124026 -0.328712 196.966569 174 1337.73 13 A+ -1.117972 -1.012658 -0.729136 26.981538 118 933.40 Group: Bi Key ef100 ef_sub_100 ef_sub_111 atomic mass radius melting 2 Bi -1.352188 1.968962 1.815817 208.9804 143 544.67 Group: Cd Key ef100 ef_sub_100 ef_sub_111 atomic mass radius melting 16 Cd -0.557417 0.719414 0.780941 112.414 161 594.33 Group: Ga Key ef100 ef_sub_100 ef_sub_111 atomic mass radius melting 8 Ga -1.282309 -0.719299 -0.521194 69.723 136 302.91 Group: Ge Key ef100 ef_sub_100 ef_sub_111 atomic mass radius melting 10 Ge -1.151102 -0.159282 0.146471 72.63 125 1211.4 Group: In Key ef100 ef_sub_100 ef_sub_111 atomic mass radius melting 4 In -1.033518 0.416942 0.51986 114.818 156 429.91 Group: Mg Key ef100 ef_sub_100 ef_sub_111 atomic mass radius melting 11 Mg -0.843692 -0.319915 -0.12479 24.305 145 923.0 Group: Pb Key ef100 ef_sub_100 ef_sub_111 atomic mass radius melting 3 Pb -1.160016 1.593083 1.631911 207.2 154 600.8 Group: Pd Key ef100 ef_sub_100 ef_sub_111 atomic mass radius melting 14 Pd -0.718155 -0.581004 -0.657751 106.42 169 1825.0 Group: Pt Key ef100 ef_sub_100 ef_sub_111 atomic mass radius melting 15 Pt -0.650905 -0.507245 -0.628432 195.084 177 2045.0 Group: Rh Key ef100 ef_sub_100 ef_sub_111 atomic mass radius melting 17 Rh -0.032545 -0.363279 -0.197038 102.9055 173 2239.0 Group: Sb Key ef100 ef_sub_100 ef_sub_111 atomic mass radius melting 1 Sb -1.522549 0.87021 1.027179 121.76 133 904.1 Group: Si Key ef100 ef_sub_100 ef_sub_111 atomic mass radius melting 0 Si -1.115722 -0.569736 -0.225225 28.085 111 1687.0 Group: Sn Key ef100 ef_sub_100 ef_sub_111 atomic mass radius melting 9 Sn -1.283931 0.464447 0.686663 118.71 145 505.21 Group: Tl Key ef100 ef_sub_100 ef_sub_111 atomic mass radius melting 7 Tl -0.892003 1.35597 1.374303 204.38 156 577.0 Group: Zn Key ef100 ef_sub_100 ef_sub_111 atomic mass radius melting 6 Zn -0.667779 -0.359509 -0.212413 65.38 142 692.88
data2 = {'Key': ['A', 'B', 'C', 'D'], 'ef100': [9, 9, 9, 9], 'ef_sub_100': [4, 4, 4, 4]}
df2 = pd.DataFrame(data2)
merged_df = pd.concat([df, df2])
print(merged_df)
Key ef100 ef_sub_100 ef_sub_111 atomic mass radius melting 0 Si -1.115722 -0.569736 -0.225225 28.085000 111.0 1687.00 1 Sb -1.522549 0.870210 1.027179 121.760000 133.0 904.10 2 Bi -1.352188 1.968962 1.815817 208.980400 143.0 544.67 3 Pb -1.160016 1.593083 1.631911 207.200000 154.0 600.80 4 In -1.033518 0.416942 0.519860 114.818000 156.0 429.91 5 A+ -0.527524 0.157998 0.028100 107.868200 165.0 1234.00 6 Zn -0.667779 -0.359509 -0.212413 65.380000 142.0 692.88 7 Tl -0.892003 1.355970 1.374303 204.380000 156.0 577.00 8 Ga -1.282309 -0.719299 -0.521194 69.723000 136.0 302.91 9 Sn -1.283931 0.464447 0.686663 118.710000 145.0 505.21 10 Ge -1.151102 -0.159282 0.146471 72.630000 125.0 1211.40 11 Mg -0.843692 -0.319915 -0.124790 24.305000 145.0 923.00 12 A+ -0.870214 -0.124026 -0.328712 196.966569 174.0 1337.73 13 A+ -1.117972 -1.012658 -0.729136 26.981538 118.0 933.40 14 Pd -0.718155 -0.581004 -0.657751 106.420000 169.0 1825.00 15 Pt -0.650905 -0.507245 -0.628432 195.084000 177.0 2045.00 16 Cd -0.557417 0.719414 0.780941 112.414000 161.0 594.33 17 Rh -0.032545 -0.363279 -0.197038 102.905500 173.0 2239.00 0 A 9.000000 4.000000 NaN NaN NaN NaN 1 B 9.000000 4.000000 NaN NaN NaN NaN 2 C 9.000000 4.000000 NaN NaN NaN NaN 3 D 9.000000 4.000000 NaN NaN NaN NaN
# 插入新行，需要全部补全数值
df.loc[1.5] = ['B', 25, 1, 1, 1, 1, 1] # 序号是1.5
print(df)
Key ef100 ef_sub_100 ef_sub_111 atomic mass radius melting 0.0 Si -1.115722 -0.569736 -0.225225 28.085000 111 1687.00 1.0 Sb -1.522549 0.870210 1.027179 121.760000 133 904.10 2.0 Bi -1.352188 1.968962 1.815817 208.980400 143 544.67 3.0 Pb -1.160016 1.593083 1.631911 207.200000 154 600.80 4.0 In -1.033518 0.416942 0.519860 114.818000 156 429.91 5.0 A+ -0.527524 0.157998 0.028100 107.868200 165 1234.00 6.0 Zn -0.667779 -0.359509 -0.212413 65.380000 142 692.88 7.0 Tl -0.892003 1.355970 1.374303 204.380000 156 577.00 8.0 Ga -1.282309 -0.719299 -0.521194 69.723000 136 302.91 9.0 Sn -1.283931 0.464447 0.686663 118.710000 145 505.21 10.0 Ge -1.151102 -0.159282 0.146471 72.630000 125 1211.40 11.0 Mg -0.843692 -0.319915 -0.124790 24.305000 145 923.00 12.0 A+ -0.870214 -0.124026 -0.328712 196.966569 174 1337.73 13.0 A+ -1.117972 -1.012658 -0.729136 26.981538 118 933.40 14.0 Pd -0.718155 -0.581004 -0.657751 106.420000 169 1825.00 15.0 Pt -0.650905 -0.507245 -0.628432 195.084000 177 2045.00 16.0 Cd -0.557417 0.719414 0.780941 112.414000 161 594.33 17.0 Rh -0.032545 -0.363279 -0.197038 102.905500 173 2239.00 1.5 B 25.000000 1.000000 1.000000 1.000000 1 1.00
# 插入新行，只指定部分列的值，其余列自动填充为 NaN
df.loc[2.5] = {'Key': 'C', 'ef100': 1, 'ef_sub_100': 1}
print(df.tail())
Key ef100 ef_sub_100 ef_sub_111 atomic mass radius melting 15.0 Pt -0.650905 -0.507245 -0.628432 195.0840 177.0 2045.00 16.0 Cd -0.557417 0.719414 0.780941 112.4140 161.0 594.33 17.0 Rh -0.032545 -0.363279 -0.197038 102.9055 173.0 2239.00 1.5 B 25.000000 1.000000 1.000000 1.0000 1.0 1.00 2.5 C 1.000000 1.000000 NaN NaN NaN NaN
df = df.drop([1, 2]) # 删除第1,2行
print(df)
Key ef100 ef_sub_100 ef_sub_111 atomic mass radius melting 0.0 Si -1.115722 -0.569736 -0.225225 28.085000 111.0 1687.00 3.0 Pb -1.160016 1.593083 1.631911 207.200000 154.0 600.80 4.0 In -1.033518 0.416942 0.519860 114.818000 156.0 429.91 5.0 A+ -0.527524 0.157998 0.028100 107.868200 165.0 1234.00 6.0 Zn -0.667779 -0.359509 -0.212413 65.380000 142.0 692.88 7.0 Tl -0.892003 1.355970 1.374303 204.380000 156.0 577.00 8.0 Ga -1.282309 -0.719299 -0.521194 69.723000 136.0 302.91 9.0 Sn -1.283931 0.464447 0.686663 118.710000 145.0 505.21 10.0 Ge -1.151102 -0.159282 0.146471 72.630000 125.0 1211.40 11.0 Mg -0.843692 -0.319915 -0.124790 24.305000 145.0 923.00 12.0 A+ -0.870214 -0.124026 -0.328712 196.966569 174.0 1337.73 13.0 A+ -1.117972 -1.012658 -0.729136 26.981538 118.0 933.40 14.0 Pd -0.718155 -0.581004 -0.657751 106.420000 169.0 1825.00 15.0 Pt -0.650905 -0.507245 -0.628432 195.084000 177.0 2045.00 16.0 Cd -0.557417 0.719414 0.780941 112.414000 161.0 594.33 17.0 Rh -0.032545 -0.363279 -0.197038 102.905500 173.0 2239.00 1.5 B 25.000000 1.000000 1.000000 1.000000 1.0 1.00 2.5 C 1.000000 1.000000 NaN NaN NaN NaN
df['melting'] = df['melting'].fillna(0)
print(df)
Key ef100 ef_sub_100 ef_sub_111 atomic mass radius melting 0.0 Si -1.115722 -0.569736 -0.225225 28.085000 111.0 1687.00 3.0 Pb -1.160016 1.593083 1.631911 207.200000 154.0 600.80 4.0 In -1.033518 0.416942 0.519860 114.818000 156.0 429.91 5.0 A+ -0.527524 0.157998 0.028100 107.868200 165.0 1234.00 6.0 Zn -0.667779 -0.359509 -0.212413 65.380000 142.0 692.88 7.0 Tl -0.892003 1.355970 1.374303 204.380000 156.0 577.00 8.0 Ga -1.282309 -0.719299 -0.521194 69.723000 136.0 302.91 9.0 Sn -1.283931 0.464447 0.686663 118.710000 145.0 505.21 10.0 Ge -1.151102 -0.159282 0.146471 72.630000 125.0 1211.40 11.0 Mg -0.843692 -0.319915 -0.124790 24.305000 145.0 923.00 12.0 A+ -0.870214 -0.124026 -0.328712 196.966569 174.0 1337.73 13.0 A+ -1.117972 -1.012658 -0.729136 26.981538 118.0 933.40 14.0 Pd -0.718155 -0.581004 -0.657751 106.420000 169.0 1825.00 15.0 Pt -0.650905 -0.507245 -0.628432 195.084000 177.0 2045.00 16.0 Cd -0.557417 0.719414 0.780941 112.414000 161.0 594.33 17.0 Rh -0.032545 -0.363279 -0.197038 102.905500 173.0 2239.00 1.5 B 25.000000 1.000000 1.000000 1.000000 1.0 1.00 2.5 C 1.000000 1.000000 NaN NaN NaN 0.00
df.loc[3.5] = {'Key': 'C', 'ef100': 1, 'ef_sub_100': 1, 'melting': 0}
print(df.tail())
Key ef100 ef_sub_100 ef_sub_111 atomic mass radius melting 16.0 Cd -0.557417 0.719414 0.780941 112.4140 161.0 594.33 17.0 Rh -0.032545 -0.363279 -0.197038 102.9055 173.0 2239.00 1.5 B 25.000000 1.000000 1.000000 1.0000 1.0 1.00 2.5 C 1.000000 1.000000 NaN NaN NaN 0.00 3.5 C 1.000000 1.000000 NaN NaN NaN 0.00
df = df.drop_duplicates()
print(df.tail())
Key ef100 ef_sub_100 ef_sub_111 atomic mass radius melting 15.0 Pt -0.650905 -0.507245 -0.628432 195.0840 177.0 2045.00 16.0 Cd -0.557417 0.719414 0.780941 112.4140 161.0 594.33 17.0 Rh -0.032545 -0.363279 -0.197038 102.9055 173.0 2239.00 1.5 B 25.000000 1.000000 1.000000 1.0000 1.0 1.00 2.5 C 1.000000 1.000000 NaN NaN NaN 0.00
NBA数据样例 https://www.cnblogs.com/Yanjy-OnlyOne/p/11195621.html
pivot_table = pd.pivot_table(df, values=['ef100','ef_sub_100'], index=[u'Key',u'melting'])
print(pivot_table)
ef100 ef_sub_100
Key melting
A+ 933.40 -1.117972 -1.012658
1234.00 -0.527524 0.157998
1337.73 -0.870214 -0.124026
B 1.00 25.000000 1.000000
C 0.00 1.000000 1.000000
Cd 594.33 -0.557417 0.719414
Ga 302.91 -1.282309 -0.719299
Ge 1211.40 -1.151102 -0.159282
In 429.91 -1.033518 0.416942
Mg 923.00 -0.843692 -0.319915
Pb 600.80 -1.160016 1.593083
Pd 1825.00 -0.718155 -0.581004
Pt 2045.00 -0.650905 -0.507245
Rh 2239.00 -0.032545 -0.363279
Si 1687.00 -1.115722 -0.569736
Sn 505.21 -1.283931 0.464447
Tl 577.00 -0.892003 1.355970
Zn 692.88 -0.667779 -0.359509
transposed_df = df.T
print(transposed_df)
0.0 3.0 4.0 5.0 6.0 7.0 \
Key Si Pb In A+ Zn Tl
ef100 -1.115722 -1.160016 -1.033518 -0.527524 -0.667779 -0.892003
ef_sub_100 -0.569736 1.593083 0.416942 0.157998 -0.359509 1.35597
ef_sub_111 -0.225225 1.631911 0.51986 0.0281 -0.212413 1.374303
atomic mass 28.085 207.2 114.818 107.8682 65.38 204.38
radius 111.0 154.0 156.0 165.0 142.0 156.0
melting 1687.0 600.8 429.91 1234.0 692.88 577.0
8.0 9.0 10.0 11.0 12.0 13.0 \
Key Ga Sn Ge Mg A+ A+
ef100 -1.282309 -1.283931 -1.151102 -0.843692 -0.870214 -1.117972
ef_sub_100 -0.719299 0.464447 -0.159282 -0.319915 -0.124026 -1.012658
ef_sub_111 -0.521194 0.686663 0.146471 -0.12479 -0.328712 -0.729136
atomic mass 69.723 118.71 72.63 24.305 196.966569 26.981538
radius 136.0 145.0 125.0 145.0 174.0 118.0
melting 302.91 505.21 1211.4 923.0 1337.73 933.4
14.0 15.0 16.0 17.0 1.5 2.5
Key Pd Pt Cd Rh B C
ef100 -0.718155 -0.650905 -0.557417 -0.032545 25.0 1.0
ef_sub_100 -0.581004 -0.507245 0.719414 -0.363279 1.0 1.0
ef_sub_111 -0.657751 -0.628432 0.780941 -0.197038 1.0 NaN
atomic mass 106.42 195.084 112.414 102.9055 1.0 NaN
radius 169.0 177.0 161.0 173.0 1.0 NaN
melting 1825.0 2045.0 594.33 2239.0 1.0 0.0
df3 = pd.DataFrame({'Key': ['D'], 'melting': [300], 'ef100': [0]})
print(df3)
Key melting ef100 0 D 300 0
connected_df = pd.concat([df, df3])
print(connected_df)
Key ef100 ef_sub_100 ef_sub_111 atomic mass radius melting 0.0 Si -1.115722 -0.569736 -0.225225 28.085000 111.0 1687.00 3.0 Pb -1.160016 1.593083 1.631911 207.200000 154.0 600.80 4.0 In -1.033518 0.416942 0.519860 114.818000 156.0 429.91 5.0 A+ -0.527524 0.157998 0.028100 107.868200 165.0 1234.00 6.0 Zn -0.667779 -0.359509 -0.212413 65.380000 142.0 692.88 7.0 Tl -0.892003 1.355970 1.374303 204.380000 156.0 577.00 8.0 Ga -1.282309 -0.719299 -0.521194 69.723000 136.0 302.91 9.0 Sn -1.283931 0.464447 0.686663 118.710000 145.0 505.21 10.0 Ge -1.151102 -0.159282 0.146471 72.630000 125.0 1211.40 11.0 Mg -0.843692 -0.319915 -0.124790 24.305000 145.0 923.00 12.0 A+ -0.870214 -0.124026 -0.328712 196.966569 174.0 1337.73 13.0 A+ -1.117972 -1.012658 -0.729136 26.981538 118.0 933.40 14.0 Pd -0.718155 -0.581004 -0.657751 106.420000 169.0 1825.00 15.0 Pt -0.650905 -0.507245 -0.628432 195.084000 177.0 2045.00 16.0 Cd -0.557417 0.719414 0.780941 112.414000 161.0 594.33 17.0 Rh -0.032545 -0.363279 -0.197038 102.905500 173.0 2239.00 1.5 B 25.000000 1.000000 1.000000 1.000000 1.0 1.00 2.5 C 1.000000 1.000000 NaN NaN NaN 0.00 0.0 D 0.000000 NaN NaN NaN NaN 300.00
sliced_df = df[3:4]
print(sliced_df)
Key ef100 ef_sub_100 ef_sub_111 atomic mass radius melting 3.0 Pb -1.160016 1.593083 1.631911 207.200 154.0 600.80 4.0 In -1.033518 0.416942 0.519860 114.818 156.0 429.91
for index, row in df.iterrows():
print(row)
Key Si ef100 -1.115722 ef_sub_100 -0.569736 ef_sub_111 -0.225225 atomic mass 28.085 radius 111.0 melting 1687.0 Name: 0.0, dtype: object Key Pb ef100 -1.160016 ef_sub_100 1.593083 ef_sub_111 1.631911 atomic mass 207.2 radius 154.0 melting 600.8 Name: 3.0, dtype: object Key In ef100 -1.033518 ef_sub_100 0.416942 ef_sub_111 0.51986 atomic mass 114.818 radius 156.0 melting 429.91 Name: 4.0, dtype: object Key A+ ef100 -0.527524 ef_sub_100 0.157998 ef_sub_111 0.0281 atomic mass 107.8682 radius 165.0 melting 1234.0 Name: 5.0, dtype: object Key Zn ef100 -0.667779 ef_sub_100 -0.359509 ef_sub_111 -0.212413 atomic mass 65.38 radius 142.0 melting 692.88 Name: 6.0, dtype: object Key Tl ef100 -0.892003 ef_sub_100 1.35597 ef_sub_111 1.374303 atomic mass 204.38 radius 156.0 melting 577.0 Name: 7.0, dtype: object Key Ga ef100 -1.282309 ef_sub_100 -0.719299 ef_sub_111 -0.521194 atomic mass 69.723 radius 136.0 melting 302.91 Name: 8.0, dtype: object Key Sn ef100 -1.283931 ef_sub_100 0.464447 ef_sub_111 0.686663 atomic mass 118.71 radius 145.0 melting 505.21 Name: 9.0, dtype: object Key Ge ef100 -1.151102 ef_sub_100 -0.159282 ef_sub_111 0.146471 atomic mass 72.63 radius 125.0 melting 1211.4 Name: 10.0, dtype: object Key Mg ef100 -0.843692 ef_sub_100 -0.319915 ef_sub_111 -0.12479 atomic mass 24.305 radius 145.0 melting 923.0 Name: 11.0, dtype: object Key A+ ef100 -0.870214 ef_sub_100 -0.124026 ef_sub_111 -0.328712 atomic mass 196.966569 radius 174.0 melting 1337.73 Name: 12.0, dtype: object Key A+ ef100 -1.117972 ef_sub_100 -1.012658 ef_sub_111 -0.729136 atomic mass 26.981538 radius 118.0 melting 933.4 Name: 13.0, dtype: object Key Pd ef100 -0.718155 ef_sub_100 -0.581004 ef_sub_111 -0.657751 atomic mass 106.42 radius 169.0 melting 1825.0 Name: 14.0, dtype: object Key Pt ef100 -0.650905 ef_sub_100 -0.507245 ef_sub_111 -0.628432 atomic mass 195.084 radius 177.0 melting 2045.0 Name: 15.0, dtype: object Key Cd ef100 -0.557417 ef_sub_100 0.719414 ef_sub_111 0.780941 atomic mass 112.414 radius 161.0 melting 594.33 Name: 16.0, dtype: object Key Rh ef100 -0.032545 ef_sub_100 -0.363279 ef_sub_111 -0.197038 atomic mass 102.9055 radius 173.0 melting 2239.0 Name: 17.0, dtype: object Key B ef100 25.0 ef_sub_100 1.0 ef_sub_111 1.0 atomic mass 1.0 radius 1.0 melting 1.0 Name: 1.5, dtype: object Key C ef100 1.0 ef_sub_100 1.0 ef_sub_111 NaN atomic mass NaN radius NaN melting 0.0 Name: 2.5, dtype: object
filtered_df = df[(df['melting'] > 1000) & (df['ef_sub_100'] < 0)]
print(filtered_df)
Key ef100 ef_sub_100 ef_sub_111 atomic mass radius melting 0.0 Si -1.115722 -0.569736 -0.225225 28.085000 111.0 1687.00 10.0 Ge -1.151102 -0.159282 0.146471 72.630000 125.0 1211.40 12.0 A+ -0.870214 -0.124026 -0.328712 196.966569 174.0 1337.73 14.0 Pd -0.718155 -0.581004 -0.657751 106.420000 169.0 1825.00 15.0 Pt -0.650905 -0.507245 -0.628432 195.084000 177.0 2045.00 17.0 Rh -0.032545 -0.363279 -0.197038 102.905500 173.0 2239.00
df['Key'] = df['Key'].replace('A+', 'A')
print(df)
Key ef100 ef_sub_100 ef_sub_111 atomic mass radius melting 0.0 Si -1.115722 -0.569736 -0.225225 28.085000 111.0 1687.00 3.0 Pb -1.160016 1.593083 1.631911 207.200000 154.0 600.80 4.0 In -1.033518 0.416942 0.519860 114.818000 156.0 429.91 5.0 A -0.527524 0.157998 0.028100 107.868200 165.0 1234.00 6.0 Zn -0.667779 -0.359509 -0.212413 65.380000 142.0 692.88 7.0 Tl -0.892003 1.355970 1.374303 204.380000 156.0 577.00 8.0 Ga -1.282309 -0.719299 -0.521194 69.723000 136.0 302.91 9.0 Sn -1.283931 0.464447 0.686663 118.710000 145.0 505.21 10.0 Ge -1.151102 -0.159282 0.146471 72.630000 125.0 1211.40 11.0 Mg -0.843692 -0.319915 -0.124790 24.305000 145.0 923.00 12.0 A -0.870214 -0.124026 -0.328712 196.966569 174.0 1337.73 13.0 A -1.117972 -1.012658 -0.729136 26.981538 118.0 933.40 14.0 Pd -0.718155 -0.581004 -0.657751 106.420000 169.0 1825.00 15.0 Pt -0.650905 -0.507245 -0.628432 195.084000 177.0 2045.00 16.0 Cd -0.557417 0.719414 0.780941 112.414000 161.0 594.33 17.0 Rh -0.032545 -0.363279 -0.197038 102.905500 173.0 2239.00 1.5 B 25.000000 1.000000 1.000000 1.000000 1.0 1.00 2.5 C 1.000000 1.000000 NaN NaN NaN 0.00
mapping = {'A': 'A++', 'B': 'B++'}
df['Key'] = df['Key'].map(mapping).fillna(df['Key'])
print(df)
Key ef100 ef_sub_100 ef_sub_111 atomic mass radius melting 0.0 Si -1.115722 -0.569736 -0.225225 28.085000 111.0 1687.00 3.0 Pb -1.160016 1.593083 1.631911 207.200000 154.0 600.80 4.0 In -1.033518 0.416942 0.519860 114.818000 156.0 429.91 5.0 A++ -0.527524 0.157998 0.028100 107.868200 165.0 1234.00 6.0 Zn -0.667779 -0.359509 -0.212413 65.380000 142.0 692.88 7.0 Tl -0.892003 1.355970 1.374303 204.380000 156.0 577.00 8.0 Ga -1.282309 -0.719299 -0.521194 69.723000 136.0 302.91 9.0 Sn -1.283931 0.464447 0.686663 118.710000 145.0 505.21 10.0 Ge -1.151102 -0.159282 0.146471 72.630000 125.0 1211.40 11.0 Mg -0.843692 -0.319915 -0.124790 24.305000 145.0 923.00 12.0 A++ -0.870214 -0.124026 -0.328712 196.966569 174.0 1337.73 13.0 A++ -1.117972 -1.012658 -0.729136 26.981538 118.0 933.40 14.0 Pd -0.718155 -0.581004 -0.657751 106.420000 169.0 1825.00 15.0 Pt -0.650905 -0.507245 -0.628432 195.084000 177.0 2045.00 16.0 Cd -0.557417 0.719414 0.780941 112.414000 161.0 594.33 17.0 Rh -0.032545 -0.363279 -0.197038 102.905500 173.0 2239.00 1.5 B++ 25.000000 1.000000 1.000000 1.000000 1.0 1.00 2.5 C 1.000000 1.000000 NaN NaN NaN 0.00