Bulk data access¶

This tutorial explains how to retrieve full tables from the database into pandas DataFrames.

Available tables in `mendeleev`¶

elements: The main table with element properties
groups: Metadata about periodic table groups
ionicradii: Ionic radii data
ionizationenergies: Ionization energies
isotopes: Isotopes and their properties
isotopedecaymodes: Data on isotope decay modes
oxidationstates: Oxidation states for elements
phasetransitions: Phase transition data for elements
propertymetadata: Metadata about properties from all tables
scattering_factors: Atomic scattering factors
screeningconstants: Nuclear screening constants
series: Metadata about periodic table series

All data is stored in a sqlite database that is shipped together with the package. You can interact directly with the database if you need more flexibility but for convenience mendeleev provides a few functions in the fetch module to retrieve data.

Fetching computed properties in bulk¶

Electronegativities A collection of electronegativity scales computed based on other properties.

To fetch whole tables you can use fetch_table. The function can be imported from mendeleev.fetch

[1]:

from mendeleev.fetch import fetch_table

To retrieve a table call the fetch_table with the table name as argument. Here we’ll get probably the most important table elements with basis data on each element

elements¶

[2]:

ptable = fetch_table("elements")

Now we can use pandas’ capabilities to work with the data.

[3]:

ptable.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 118 entries, 0 to 117
Data columns (total 72 columns):
 #   Column                         Non-Null Count  Dtype
---  ------                         --------------  -----
 0   annotation                     118 non-null    object
 1   atomic_number                  118 non-null    int64
 2   atomic_radius                  90 non-null     float64
 3   atomic_volume                  91 non-null     float64
 4   block                          118 non-null    object
 5   density                        118 non-null    float64
 6   description                    109 non-null    object
 7   dipole_polarizability          117 non-null    float64
 8   electron_affinity              77 non-null     float64
 9   electronic_configuration       118 non-null    object
 10  evaporation_heat               88 non-null     float64
 11  fusion_heat                    75 non-null     float64
 12  group_id                       90 non-null     float64
 13  lattice_constant               87 non-null     float64
 14  lattice_structure              91 non-null     object
 15  name                           118 non-null    object
 16  period                         118 non-null    int64
 17  series_id                      118 non-null    int64
 18  specific_heat_capacity         85 non-null     float64
 19  symbol                         118 non-null    object
 20  thermal_conductivity           66 non-null     float64
 21  vdw_radius                     103 non-null    float64
 22  covalent_radius_cordero        96 non-null     float64
 23  covalent_radius_pyykko         118 non-null    float64
 24  en_pauling                     85 non-null     float64
 25  en_allen                       71 non-null     float64
 26  jmol_color                     109 non-null    object
 27  cpk_color                      103 non-null    object
 28  proton_affinity                32 non-null     float64
 29  gas_basicity                   32 non-null     float64
 30  heat_of_formation              89 non-null     float64
 31  c6                             43 non-null     float64
 32  covalent_radius_bragg          37 non-null     float64
 33  vdw_radius_bondi               28 non-null     float64
 34  vdw_radius_truhlar             16 non-null     float64
 35  vdw_radius_rt                  9 non-null      float64
 36  vdw_radius_batsanov            65 non-null     float64
 37  vdw_radius_dreiding            21 non-null     float64
 38  vdw_radius_uff                 103 non-null    float64
 39  vdw_radius_mm3                 94 non-null     float64
 40  abundance_crust                88 non-null     float64
 41  abundance_sea                  81 non-null     float64
 42  molcas_gv_color                103 non-null    object
 43  en_ghosh                       103 non-null    float64
 44  vdw_radius_alvarez             94 non-null     float64
 45  c6_gb                          86 non-null     float64
 46  atomic_weight                  118 non-null    float64
 47  atomic_weight_uncertainty      74 non-null     float64
 48  is_monoisotopic                21 non-null     float64
 49  is_radioactive                 118 non-null    int64
 50  cas                            118 non-null    object
 51  atomic_radius_rahm             96 non-null     float64
 52  geochemical_class              76 non-null     object
 53  goldschmidt_class              118 non-null    object
 54  metallic_radius                56 non-null     float64
 55  metallic_radius_c12            63 non-null     float64
 56  covalent_radius_pyykko_double  108 non-null    float64
 57  covalent_radius_pyykko_triple  80 non-null     float64
 58  discoverers                    118 non-null    object
 59  discovery_year                 105 non-null    float64
 60  discovery_location             105 non-null    object
 61  name_origin                    118 non-null    object
 62  sources                        118 non-null    object
 63  uses                           112 non-null    object
 64  mendeleev_number               118 non-null    int64
 65  dipole_polarizability_unc      117 non-null    float64
 66  pettifor_number                103 non-null    float64
 67  glawe_number                   103 non-null    float64
 68  molar_heat_capacity            85 non-null     float64
 69  en_miedema                     73 non-null     float64
 70  miedema_molar_volume           73 non-null     float64
 71  miedema_electron_density       73 non-null     float64
dtypes: float64(49), int64(5), object(18)
memory usage: 66.5+ KB

For clarity let’s take only a subset of columns

[4]:

cols = [
    "atomic_number",
    "symbol",
    "atomic_radius",
    "en_pauling",
    "block",
    "vdw_radius_mm3",
]

[5]:

ptable[cols].head()

[5]:

	atomic_number	symbol	atomic_radius	en_pauling	block	vdw_radius_mm3
0	1	H	25.0	2.20	s	162.0
1	2	He	120.0	NaN	s	153.0
2	3	Li	145.0	0.98	s	255.0
3	4	Be	105.0	1.57	s	223.0
4	5	B	85.0	2.04	p	215.0

It is quite easy now to get descriptive statistics on the data.

[6]:

ptable[cols].describe()

[6]:

	atomic_number	atomic_radius	en_pauling	vdw_radius_mm3
count	118.000000	90.000000	85.000000	94.000000
mean	59.500000	149.844444	1.748588	248.468085
std	34.207699	40.079110	0.634442	36.017828
min	1.000000	25.000000	0.700000	153.000000
25%	30.250000	135.000000	1.240000	229.000000
50%	59.500000	145.000000	1.700000	244.000000
75%	88.750000	178.750000	2.160000	269.250000
max	118.000000	260.000000	3.980000	364.000000

groups¶

Periodic table group metadata.

[7]:

groups = fetch_table("groups")
groups

[7]:

	group_id	symbol	name
0	1	IA	Alkali metals
1	2	IIA	Alkaline earths
2	3	IIIB
3	4	IVB
4	5	VB
5	6	VIB
6	7	VIIB
7	8	VIIIB
8	9	VIIIB
9	10	VIIIB
10	11	IB	Coinage metals
11	12	IIB
12	13	IIIA	Boron group
13	14	IVA	Carbon group
14	15	VA	Pnictogens
15	16	VIA	Chalcogens
16	17	VIIA	Halogens
17	18	VIIIA	Noble gases

ionicradii¶

The function to fetch ionic radii is called fetch_ionic_radii and can either fetch ionic or crystal radii depending on the radius argument.

[8]:

from mendeleev.fetch import fetch_ionic_radii

[9]:

irs = fetch_ionic_radii(radius="ionic_radius")
irs.head(10)

[9]:

	coordination	I	II	III	IIIPY	IV	IVPY	IVSQ	IX	V	VI	VII	VIII	X	XI	XII	XIV
atomic_number	charge
1	1	-38.0	-18.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	1	NaN	NaN	NaN	NaN	59.0	NaN	NaN	NaN	NaN	76.0	NaN	92.0	NaN	NaN	NaN	NaN
4	2	NaN	NaN	16.0	NaN	27.0	NaN	NaN	NaN	NaN	45.0	NaN	NaN	NaN	NaN	NaN	NaN
5	3	NaN	NaN	1.0	NaN	11.0	NaN	NaN	NaN	NaN	27.0	NaN	NaN	NaN	NaN	NaN	NaN
6	4	NaN	NaN	-8.0	NaN	15.0	NaN	NaN	NaN	NaN	16.0	NaN	NaN	NaN	NaN	NaN	NaN
7	-3	NaN	NaN	NaN	NaN	146.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	3	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16.0	NaN	NaN	NaN	NaN	NaN	NaN
	5	NaN	NaN	-10.4	NaN	NaN	NaN	NaN	NaN	NaN	13.0	NaN	NaN	NaN	NaN	NaN	NaN
8	-2	NaN	135.0	136.0	NaN	138.0	NaN	NaN	NaN	NaN	140.0	NaN	142.0	NaN	NaN	NaN	NaN
9	-1	NaN	128.5	130.0	NaN	131.0	NaN	NaN	NaN	NaN	133.0	NaN	NaN	NaN	NaN	NaN	NaN

ionizationenergies¶

To fetch ionization energies use fetch_ionization_energies that takes a degree (default is degree=1) argument that can either be a single integer or a list if integers to fetch multiple ionization energies.

[10]:

from mendeleev.fetch import fetch_ionization_energies

[11]:

ies = fetch_ionization_energies(degree=2)
ies.head(10)

[11]:

	IE2
atomic_number
1	NaN
2	54.417766
3	75.640097
4	18.211150
5	25.154830
6	24.383143
7	29.601250
8	35.121120
9	34.970810
10	40.962970

To fetch multiple ionization degress at once simply pass a list of degrees:

[12]:

ies_multiple = fetch_ionization_energies(degree=[1, 3, 5])
ies_multiple.head(10)

[12]:

	IE1	IE3	IE5
atomic_number
1	13.598435	NaN	NaN
2	24.587389	NaN	NaN
3	5.391715	122.454359	NaN
4	9.322699	153.896205	NaN
5	8.298019	37.930590	340.226022
6	11.260288	47.887780	392.090560
7	14.534130	47.445300	97.890100
8	13.618055	54.935540	113.899000
9	17.422820	62.707980	114.249000
10	21.564541	63.423300	126.247000

isotopes¶

Let try and retrieve another table, namely isotopes

[13]:

isotopes = fetch_table("isotopes", index_col="id")

[14]:

isotopes.info()

<class 'pandas.core.frame.DataFrame'>
Index: 3557 entries, 1 to 3557
Data columns (total 17 columns):
 #   Column                         Non-Null Count  Dtype
---  ------                         --------------  -----
 0   atomic_number                  3557 non-null   int64
 1   mass                           3557 non-null   float64
 2   abundance                      288 non-null    float64
 3   mass_number                    3557 non-null   int64
 4   mass_uncertainty               3557 non-null   float64
 5   is_radioactive                 3557 non-null   int64
 6   half_life                      2831 non-null   float64
 7   half_life_unit                 3217 non-null   object
 8   spin                           3372 non-null   object
 9   g_factor                       1738 non-null   float64
 10  quadrupole_moment              1801 non-null   float64
 11  parity                         3372 non-null   object
 12  discovery_year                 3339 non-null   float64
 13  g_factor_uncertainty           1738 non-null   float64
 14  abundance_uncertainty          288 non-null    float64
 15  half_life_uncertainty          2828 non-null   float64
 16  quadrupole_moment_uncertainty  1800 non-null   float64
dtypes: float64(11), int64(3), object(3)
memory usage: 500.2+ KB

Here’s a few first records from that dataframe

[15]:

isotopes.head(10)

[15]:

	atomic_number	mass	abundance	mass_number	mass_uncertainty	is_radioactive	half_life	half_life_unit	spin	g_factor	quadrupole_moment	parity	discovery_year	g_factor_uncertainty	abundance_uncertainty	half_life_uncertainty	quadrupole_moment_uncertainty
id
1	1	1.007825	99.9855	1	1.400000e-11	0	NaN	None	1/2	5.585695	0.0	+	1920.0	1.800000e-08	0.0078	NaN	0.0
2	1	2.014102	0.0145	2	1.500000e-11	0	NaN	None	1	0.857438	NaN	+	1932.0	5.000000e-09	0.0078	NaN	NaN
3	1	3.016049	NaN	3	8.000000e-11	1	12.32	year	1/2	5.957925	0.0	+	1934.0	2.800000e-08	NaN	0.02	0.0
4	2	3.016029	0.0002	3	6.000000e-11	0	NaN	None	1/2	-4.255251	0.0	+	1934.0	6.000000e-08	0.0002	NaN	0.0
5	3	3.030775	NaN	3	2.147000e-03	1	NaN	None	3/2	NaN	NaN	-	NaN	NaN	NaN	NaN	NaN
6	1	4.026432	NaN	4	1.073540e-04	1	139.00	ysec	2	NaN	NaN	-	1981.0	NaN	NaN	10.00	NaN
7	2	4.002603	99.9998	4	1.600000e-10	0	NaN	None	0	0.000000	0.0	+	1908.0	0.000000e+00	0.0002	NaN	0.0
8	3	4.027186	NaN	4	2.277330e-04	1	91.00	ysec	2	NaN	NaN	-	1965.0	NaN	NaN	9.00	NaN
9	1	5.035311	NaN	5	9.602000e-05	1	86.00	ysec	1/2	NaN	0.0	+	1987.0	NaN	NaN	6.00	0.0
10	2	5.012057	NaN	5	2.147000e-05	1	602.00	ysec	3/2	NaN	NaN	-	1937.0	NaN	NaN	22.00	NaN

Merge the elements table with the isotopes¶

We can now perform SQL-like merge operation on two DataFrames and produce an outer join

[16]:

import pandas as pd

[17]:

merged = pd.merge(ptable[cols], isotopes, how="outer", on="atomic_number")

now we have the following columns in the merged DataFrame

[18]:

merged.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3557 entries, 0 to 3556
Data columns (total 22 columns):
 #   Column                         Non-Null Count  Dtype
---  ------                         --------------  -----
 0   atomic_number                  3557 non-null   int64
 1   symbol                         3557 non-null   object
 2   atomic_radius                  3020 non-null   float64
 3   en_pauling                     2899 non-null   float64
 4   block                          3557 non-null   object
 5   vdw_radius_mm3                 3186 non-null   float64
 6   mass                           3557 non-null   float64
 7   abundance                      288 non-null    float64
 8   mass_number                    3557 non-null   int64
 9   mass_uncertainty               3557 non-null   float64
 10  is_radioactive                 3557 non-null   int64
 11  half_life                      2831 non-null   float64
 12  half_life_unit                 3217 non-null   object
 13  spin                           3372 non-null   object
 14  g_factor                       1738 non-null   float64
 15  quadrupole_moment              1801 non-null   float64
 16  parity                         3372 non-null   object
 17  discovery_year                 3339 non-null   float64
 18  g_factor_uncertainty           1738 non-null   float64
 19  abundance_uncertainty          288 non-null    float64
 20  half_life_uncertainty          2828 non-null   float64
 21  quadrupole_moment_uncertainty  1800 non-null   float64
dtypes: float64(14), int64(3), object(5)
memory usage: 611.5+ KB

[19]:

merged.head()

[19]:

	atomic_number	symbol	atomic_radius	en_pauling	block	vdw_radius_mm3	mass	abundance	mass_number	mass_uncertainty	...	half_life_unit	spin	g_factor	quadrupole_moment	parity	discovery_year	g_factor_uncertainty	abundance_uncertainty	half_life_uncertainty	quadrupole_moment_uncertainty
0	1	H	25.0	2.2	s	162.0	1.007825	99.9855	1	1.400000e-11	...	None	1/2	5.585695	0.0	+	1920.0	1.800000e-08	0.0078	NaN	0.0
1	1	H	25.0	2.2	s	162.0	2.014102	0.0145	2	1.500000e-11	...	None	1	0.857438	NaN	+	1932.0	5.000000e-09	0.0078	NaN	NaN
2	1	H	25.0	2.2	s	162.0	3.016049	NaN	3	8.000000e-11	...	year	1/2	5.957925	0.0	+	1934.0	2.800000e-08	NaN	0.02	0.0
3	1	H	25.0	2.2	s	162.0	4.026432	NaN	4	1.073540e-04	...	ysec	2	NaN	NaN	-	1981.0	NaN	NaN	10.00	NaN
4	1	H	25.0	2.2	s	162.0	5.035311	NaN	5	9.602000e-05	...	ysec	1/2	NaN	0.0	+	1987.0	NaN	NaN	6.00	0.0

5 rows × 22 columns

To display all the isotopes of Silicon

[20]:

merged[merged["symbol"] == "Si"]

[20]:

	atomic_number	symbol	atomic_radius	en_pauling	block	vdw_radius_mm3	mass	abundance	mass_number	mass_uncertainty	...	half_life_unit	spin	g_factor	quadrupole_moment	parity	discovery_year	g_factor_uncertainty	abundance_uncertainty	half_life_uncertainty	quadrupole_moment_uncertainty
212	14	Si	110.0	1.9	p	229.0	22.036114	NaN	22	5.370000e-04	...	msec	0	0.000000	0.000	+	1987.0	0.000000	NaN	1.1000	0.000
213	14	Si	110.0	1.9	p	229.0	23.025711	NaN	23	5.370000e-04	...	msec	3/2	NaN	NaN	+	1986.0	NaN	NaN	0.4000	NaN
214	14	Si	110.0	1.9	p	229.0	24.011535	NaN	24	2.090400e-05	...	msec	0	0.000000	0.000	+	1979.0	0.000000	NaN	2.1000	0.000
215	14	Si	110.0	1.9	p	229.0	25.004109	NaN	25	1.073500e-05	...	msec	5/2	NaN	NaN	+	1963.0	NaN	NaN	1.0000	NaN
216	14	Si	110.0	1.9	p	229.0	25.992334	NaN	26	1.150000e-07	...	sec	0	0.000000	0.000	+	1960.0	0.000000	NaN	0.0007	0.000
217	14	Si	110.0	1.9	p	229.0	26.986705	NaN	27	1.150000e-07	...	sec	5/2	0.346080	0.063	+	1939.0	0.000120	NaN	0.0140	0.014
218	14	Si	110.0	1.9	p	229.0	27.976927	92.2545	28	5.500000e-10	...	None	0	0.000000	0.000	+	1920.0	0.000000	0.0037	NaN	0.000
219	14	Si	110.0	1.9	p	229.0	28.976495	4.6720	29	6.000000e-10	...	None	1/2	-1.110104	0.000	+	1920.0	0.000006	0.0160	NaN	0.000
220	14	Si	110.0	1.9	p	229.0	29.973770	3.0735	30	2.300000e-08	...	None	0	0.000000	0.000	+	1924.0	0.000000	0.0021	NaN	0.000
221	14	Si	110.0	1.9	p	229.0	30.975363	NaN	31	4.600000e-08	...	minute	3/2	NaN	NaN	+	1934.0	NaN	NaN	0.2000	NaN
222	14	Si	110.0	1.9	p	229.0	31.974152	NaN	32	3.200000e-07	...	year	0	0.000000	0.000	+	1953.0	0.000000	NaN	7.0000	0.000
223	14	Si	110.0	1.9	p	229.0	32.977977	NaN	33	7.500000e-07	...	sec	3/2	NaN	NaN	+	1971.0	NaN	NaN	0.1800	NaN
224	14	Si	110.0	1.9	p	229.0	33.978538	NaN	34	8.600000e-07	...	sec	0	0.000000	0.000	+	1971.0	0.000000	NaN	0.2000	0.000
225	14	Si	110.0	1.9	p	229.0	34.984550	NaN	35	3.849400e-05	...	msec	7/2	0.468286	NaN	-	1971.0	0.001143	NaN	120.0000	NaN
226	14	Si	110.0	1.9	p	229.0	35.986649	NaN	36	7.707700e-05	...	msec	0	0.000000	0.000	+	1971.0	0.000000	NaN	2.0000	0.000
227	14	Si	110.0	1.9	p	229.0	36.992945	NaN	37	1.221790e-04	...	msec	5/2	NaN	NaN	-	1979.0	NaN	NaN	3.5000	NaN
228	14	Si	110.0	1.9	p	229.0	37.995523	NaN	38	1.125000e-04	...	msec	0	0.000000	0.000	+	1979.0	0.000000	NaN	8.0000	0.000
229	14	Si	110.0	1.9	p	229.0	39.002491	NaN	39	1.455000e-04	...	msec	5/2	NaN	NaN	-	1979.0	NaN	NaN	4.1000	NaN
230	14	Si	110.0	1.9	p	229.0	40.006084	NaN	40	1.309620e-04	...	msec	0	0.000000	0.000	+	1989.0	0.000000	NaN	2.6000	0.000
231	14	Si	110.0	1.9	p	229.0	41.014171	NaN	41	3.220000e-04	...	msec	7/2	NaN	NaN	-	1989.0	NaN	NaN	2.5000	NaN
232	14	Si	110.0	1.9	p	229.0	42.018078	NaN	42	3.220000e-04	...	msec	0	0.000000	0.000	+	1990.0	0.000000	NaN	3.5000	0.000
233	14	Si	110.0	1.9	p	229.0	43.026119	NaN	43	4.290000e-04	...	msec	3/2	NaN	NaN	-	2002.0	NaN	NaN	NaN	NaN
234	14	Si	110.0	1.9	p	229.0	44.031466	NaN	44	5.370000e-04	...	msec	0	0.000000	0.000	+	2007.0	0.000000	NaN	NaN	0.000
235	14	Si	110.0	1.9	p	229.0	45.039818	NaN	45	6.440000e-04	...	msec	3/2	NaN	NaN	-	NaN	NaN	NaN	NaN	NaN

24 rows × 22 columns

isotopedecaymodes¶

Different modes are explained in the documentation

[21]:

idm = fetch_table("isotopedecaymodes")
idm

[21]:

	id	isotope_id	mode	relation	intensity	is_allowed_not_observed	is_observed_intensity_unknown
0	1	3	B-	=	100.0	NaN	NaN
1	2	5	p		NaN	1.0	NaN
2	3	6	n	=	100.0	NaN	NaN
3	4	8	p	=	100.0	NaN	NaN
4	5	9	2n	=	100.0	NaN	NaN
...	...	...	...	...	...	...	...
5778	5779	3554	A		NaN	1.0	NaN
5779	5780	3555	A	=	100.0	NaN	NaN
5780	5781	3556	A	~	100.0	NaN	NaN
5781	5782	3556	SF		NaN	1.0	NaN
5782	5783	3557	A	~	100.0	NaN	NaN

5783 rows × 7 columns

oxidationstates¶

[22]:

ox = fetch_table("oxidationstates")
ox

[22]:

	id	atomic_number	oxidation_state	category
0	1	1	1	main
1	2	1	-1	main
2	3	3	1	main
3	4	4	0	extended
4	5	4	2	main
...	...	...	...	...
596	597	118	4	extended
597	598	118	6	extended
598	599	118	-1	extended
599	600	118	1	extended
600	601	118	2	extended

601 rows × 4 columns

phasetransitions¶

For most elements a single row is present with phase transition properies, however for elements with multiple allotropes (i.e. Carbon, Phosphorus, Selenium, Sulfur, Tin) values are provided for each allotrope.

[23]:

pt = fetch_table("phasetransitions")
pt.sort_values(by="atomic_number").head(15)

[23]:

	id	atomic_number	boiling_point	melting_point	critical_temperature	critical_pressure	triple_point_temperature	triple_point_pressure	allotrope
39	40	1	20.271	13.990	32.938	1.28580	13.8033	7.0410	None
37	38	2	4.222	NaN	5.195	0.22746	2.1770	5.0430	None
48	49	3	1615.150	453.650	3223.150	67.00000	NaN	NaN	None
9	10	4	2741.150	1560.150	5205.150	NaN	NaN	NaN	None
11	12	5	4273.150	2350.150	NaN	NaN	NaN	NaN	None
16	17	6	4098.150	4762.150	NaN	NaN	4762.1500	10300.0000	graphite
17	18	6	NaN	4713.150	NaN	NaN	NaN	NaN	diamond
60	61	7	77.355	63.150	126.192	3.39580	63.1510	12.5200	None
63	64	8	90.188	54.360	154.581	5.04300	54.3584	0.1463	None
30	31	9	85.040	53.480	144.410	5.17240	53.4800	90.0000	None
56	57	10	27.104	24.560	44.492	2.67860	24.5561	43.3700	None
86	87	11	1156.090	370.944	2573.150	35.00000	NaN	NaN	None
50	51	12	1363.150	923.150	NaN	NaN	NaN	NaN	None
1	2	13	2792.150	933.473	6700.150	NaN	NaN	NaN	None
84	85	14	3538.150	1687.150	NaN	NaN	NaN	NaN	None

propertymetadata¶

This table holds a lot of the metadata that is used to render the data documentation page. Probably the most useful piece of information here is the unit assocaited with a given property. The units are compatible with the pint package so properties might be used direclty with units.

[24]:

metadata = fetch_table("propertymetadata")
metadata.head(10)

[24]:

	id	table_name	column_name	class_name	attribute_name	category	value_origin	description	unit	annotations	citation_keys
0	1	elements	abundance_crust	Element	abundance_crust	abundance	STORED	Abundance in the Earth's crust	mg/kg	None	haynes2014crc
1	2	elements	abundance_sea	Element	abundance_sea	abundance	STORED	Abundance in the seas	mg/L	None	haynes2014crc
2	3	elements	atomic_number	Element	atomic_number	basic properties	STORED	Atomic number	None	None	None
3	4	elements	atomic_radius	Element	atomic_radius	atomic size properties	STORED	Atomic radius	pm	None	Slater1964
4	5	elements	atomic_radius_rahm	Element	atomic_radius_rahm	atomic size properties	STORED	Atomic radius by Rahm et al.	pm	None	Rahm2016,Rahm2017
5	6	elements	atomic_volume	Element	atomic_volume	atomic size properties	STORED	Atomic volume	cm^3/mol	None	None
6	7	elements	atomic_weight	Element	atomic_weight	basic properties	STORED	Relative atomic weight	Da	Atomic weights and their uncertainties were re...	Meija2016,iupac-weights
7	8	elements	atomic_weight_uncertainty	Element	atomic_weight_uncertainty	basic properties	STORED	Atomic weight uncertainty	Da	None	Meija2016,iupac-weights
8	9	elements	block	Element	block	periodic table properties	STORED	Block in periodic table	None	None	None
9	10	elements	c6	Element	c6	electric and optical properties	STORED	C_6 dispersion coefficient	hartree/bohr^6	None	Chu2004,Tang1976

scattering_factors¶

[25]:

scattering_factors = fetch_table("scattering_factors")
scattering_factors.head(10)

[25]:

	id	atomic_number	energy	f1	f2
0	1	10	10.0000	-9999.0	4.795760e-16
1	2	10	10.1617	-9999.0	4.873330e-16
2	3	10	10.3261	-9999.0	4.952150e-16
3	4	10	10.4931	-9999.0	5.032250e-16
4	5	10	10.6628	-9999.0	5.113640e-16
5	6	10	10.8353	-9999.0	5.196350e-16
6	7	10	11.0106	-9999.0	5.280400e-16
7	8	10	11.1886	-9999.0	5.365800e-16
8	9	10	11.3696	-9999.0	5.452590e-16
9	10	10	11.5535	-9999.0	5.540780e-16

screeningconstants¶

[26]:

screening = fetch_table("screeningconstants")
screening.head(10)

[26]:

	id	atomic_number	n	s	screening
0	1	1	1	s	0.0000
1	2	2	1	s	0.3125
2	3	3	1	s	0.3094
3	4	3	2	s	1.7208
4	5	4	1	s	0.3152
5	6	4	2	s	2.0880
6	7	5	1	s	0.3205
7	8	5	2	p	2.5786
8	9	5	2	s	2.4238
9	10	6	1	s	0.3273

series¶

Metadata about periodic table series.

[27]:

series = fetch_table("series")
series

[27]:

	id	name	color
0	1	Nonmetals	#baa2a6
1	2	Noble gases	#bbbb88
2	3	Alkali metals	#a6cee3
3	4	Alkaline earth metals	#1f78b4
4	5	Metalloids	#33a02c
5	6	Halogens	#fdbf6f
6	7	Poor metals	#b2df8a
7	8	Transition metals	#e08e79
8	9	Lanthanides	#cab2d6
9	10	Actinides	#6a3d9a

Electronegativities¶

To fetch all data from electronegatuivity scales use fetch_electronegativities. This can take a few seconds since most of the values need to be computed.

[28]:

from mendeleev.fetch import fetch_electronegativities

[29]:

ens = fetch_electronegativities()
ens.head(10)

[29]:

	symbol	radius	Pauling	Allen	Ghosh	Miedema	zeff	Allred-Rochow	Cottrell-Sutton	Gordy	Li-Xue	Martynov-Batsanov	Mulliken	Nagle	Sanderson
atomic_number
1	H	32.0	2.20	13.610	0.263800	5.20	1.00	0.000977	0.176777	0.031250	{('I', ''): -3.5407218307179495, ('II', ''): -...	3.687606	7.176315	0.605388	2.187771
2	He	46.0	NaN	24.590	0.442712	NaN	1.70	0.000803	0.192241	0.036957	{}	6.285108	2.443695	1.130639	1.000000
3	Li	133.0	0.98	5.392	0.105093	2.85	1.30	0.000073	0.098866	0.009774	{('IV', ''): 1.7160634688527356, ('VI', ''): 1...	2.322007	3.004882	0.182650	0.048868
4	Be	102.0	1.57	9.323	0.144986	5.05	1.95	0.000187	0.138267	0.019118	{}	3.710381	3.461349	0.375615	0.126847
5	B	85.0	2.04	12.130	0.184886	5.30	2.60	0.000360	0.174895	0.030588	{}	4.877959	4.288871	0.526974	0.254627
6	C	75.0	2.55	15.050	0.224776	6.24	3.25	0.000578	0.208167	0.043333	{}	6.083271	6.261203	0.707393	0.427525
7	N	71.0	3.04	18.130	0.264930	6.86	3.90	0.000774	0.234371	0.054930	{}	7.306768	6.567065	0.877498	0.577482
8	O	63.0	3.44	21.360	0.304575	NaN	4.55	0.001146	0.268742	0.072222	{}	8.496138	7.539584	1.042218	0.941649
9	F	64.0	3.98	24.800	0.344443	NaN	5.20	0.001270	0.285044	0.081250	{}	9.701808	10.412005	1.232373	1.017681
10	Ne	67.0	NaN	28.310	0.384390	NaN	5.85	0.001303	0.295488	0.087313	{}	10.918389	10.782270	1.443255	1.000000

Version information¶

[30]:

import mendeleev

print(f"{mendeleev.__version__=}")

mendeleev.__version__='0.19.0'