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Add full dataframe to README
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@AntObi
AntObi committed Aug 7, 2023
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Expand Up @@ -142,21 +142,19 @@ The `composition_featuriser` function can be used to featurise the data. The com
```python
from elementembeddings.composition import composition_featuriser

df_featurised = composition_featuriser(df, embedding="magpie", stats="mean")
df_featurised = composition_featuriser(df, embedding="magpie", stats=["mean","sum"])

df_featurised
```

| formula | mean_Number | mean_MendeleevNumber | mean_AtomicWeight | mean_MeltingT | ... | mean_SpaceGroupNumber |
|---------|-------------|----------------------|--------------------|-------------------|-----|-----------------------|
| CsPbI3 | 59.2 | 74.8 | 144.16377238 | 412.55 | ... | 129.20000000000002 |
| Fe2O3 | 15.2 | 74.19999999999999 | 31.937640000000002 | 757.2800000000001 | ... | 98.80000000000001 |
| NaCl | 14.0 | 48.0 | 29.221384640000004 | 271.235 | ... | 146.5 |
| ZnS | 23.0 | 78.5 | 48.7225 | 540.52 | ... | 132.0 |
| formula | mean_Number | mean_MendeleevNumber | mean_AtomicWeight | mean_MeltingT | mean_Column | mean_Row | mean_CovalentRadius | mean_Electronegativity | mean_NsValence | mean_NpValence | mean_NdValence | mean_NfValence | mean_NValence | mean_NsUnfilled | mean_NpUnfilled | mean_NdUnfilled | mean_NfUnfilled | mean_NUnfilled | mean_GSvolume_pa | mean_GSbandgap | mean_GSmagmom | mean_SpaceGroupNumber | sum_Number | sum_MendeleevNumber | sum_AtomicWeight | sum_MeltingT | sum_Column | sum_Row | sum_CovalentRadius | sum_Electronegativity | sum_NsValence | sum_NpValence | sum_NdValence | sum_NfValence | sum_NValence | sum_NsUnfilled | sum_NpUnfilled | sum_NdUnfilled | sum_NfUnfilled | sum_NUnfilled | sum_GSvolume_pa | sum_GSbandgap | sum_GSmagmom | sum_SpaceGroupNumber |
|---------|-------------|----------------------|--------------------|-------------------|-------------|----------|---------------------|------------------------|----------------|----------------|--------------------|--------------------|---------------|-----------------|-----------------|-----------------|-----------------|----------------|------------------|----------------|--------------------|-----------------------|------------|---------------------|-------------------|--------------|------------|---------|--------------------|-----------------------|---------------|---------------|---------------|---------------|--------------|----------------|----------------|----------------|----------------|---------------|--------------------|---------------|--------------|----------------------|
| CsPbI3 | 59.2 | 74.8 | 144.16377238 | 412.55 | 13.2 | 5.4 | 161.39999999999998 | 2.22 | 1.8 | 3.4 | 8.0 | 2.8000000000000003 | 16.0 | 0.2 | 1.4 | 0.0 | 0.0 | 1.6 | 54.584 | 0.6372 | 0.0 | 129.20000000000002 | 296.0 | 374.0 | 720.8188619 | 2062.75 | 66.0 | 27.0 | 807.0 | 11.100000000000001 | 9.0 | 17.0 | 40.0 | 14.0 | 80.0 | 1.0 | 7.0 | 0.0 | 0.0 | 8.0 | 272.92 | 3.186 | 0.0 | 646.0 |
| Fe2O3 | 15.2 | 74.19999999999999 | 31.937640000000002 | 757.2800000000001 | 12.8 | 2.8 | 92.4 | 2.7960000000000003 | 2.0 | 2.4 | 2.4000000000000004 | 0.0 | 6.8 | 0.0 | 1.2 | 1.6 | 0.0 | 2.8 | 9.755 | 0.0 | 0.8442651200000001 | 98.80000000000001 | 76.0 | 371.0 | 159.6882 | 3786.4 | 64.0 | 14.0 | 462.0 | 13.98 | 10.0 | 12.0 | 12.0 | 0.0 | 34.0 | 0.0 | 6.0 | 8.0 | 0.0 | 14.0 | 48.775000000000006 | 0.0 | 4.2213256 | 494.0 |
| NaCl | 14.0 | 48.0 | 29.221384640000004 | 271.235 | 9.0 | 3.0 | 134.0 | 2.045 | 1.5 | 2.5 | 0.0 | 0.0 | 4.0 | 0.5 | 0.5 | 0.0 | 0.0 | 1.0 | 26.87041666665 | 1.2465 | 0.0 | 146.5 | 28.0 | 96.0 | 58.44276928000001 | 542.47 | 18.0 | 6.0 | 268.0 | 4.09 | 3.0 | 5.0 | 0.0 | 0.0 | 8.0 | 1.0 | 1.0 | 0.0 | 0.0 | 2.0 | 53.7408333333 | 2.493 | 0.0 | 293.0 |
| ZnS | 23.0 | 78.5 | 48.7225 | 540.52 | 14.0 | 3.5 | 113.5 | 2.115 | 2.0 | 2.0 | 5.0 | 0.0 | 9.0 | 0.0 | 1.0 | 0.0 | 0.0 | 1.0 | 19.8734375 | 1.101 | 0.0 | 132.0 | 46.0 | 157.0 | 97.445 | 1081.04 | 28.0 | 7.0 | 227.0 | 4.23 | 4.0 | 4.0 | 10.0 | 0.0 | 18.0 | 0.0 | 2.0 | 0.0 | 0.0 | 2.0 | 39.746875 | 2.202 | 0.0 | 264.0 |

(The columns of the resulting dataframe have been truncated for clarity.)

The returned dataframe contains the mean-pooled features of the magpie representation for the four formulas.
The returned dataframe contains the mean-pooled and sum-pooled features of the magpie representation for the four formulas.

## Development notes

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