Welcome to mendeleev’s documentation

This package provides an API for accessing various properties of elements from the periodic table of elements.

Getting started

Overview

This package provides an API for accessing various properties of elements from the periodic table of elements.

The repository is hosted on github.

Contributing

All contributions are welcome!

If you would like to suggest an improvement or report a bug or data inconsistency please consider creating an issue on github. I would be especially grateful for references to possible data updates and sources and recommendations of new data.

Citing

If you use mendeleev in a scientific publication, please consider citing the software as

L. M. Mentel, mendeleev - A Python resource for properties of chemical elements, ions and isotopes. , 2014– . Available at: https://github.com/lmmentel/mendeleev.

Here’s the reference in the BibLaTeX format

 @software{mendeleev2014,
    author = {Mentel, Łukasz},
    title = {{mendeleev} -- A Python resource for properties of chemical elements, ions and isotopes},
    url = {https://github.com/lmmentel/mendeleev},
    version = {0.15.0},
    date = {2014--},
}

or the older BibTeX format

@misc{mendeleev2014,
   auhor = {Mentel, Łukasz},
   title = {mendeleev} -- A Python resource for properties of chemical elements, ions and isotopes, ver. 0.15.0},
   howpublished = {\url{https://github.com/lmmentel/mendeleev}},
   year  = {2014--},
}

Related projects

periodictable

This package provides a periodic table of the elements with support for mass, density and xray/neutron scattering information.

periodic

Periodic is an open source simple python API/command line script for the periodic table.

Funding

This project is supported by the RCN (The Research Council of Norway) project number 239193.

Installation

The package can be installed using pip

pip install mendeleev

You can also install the most recent version from the repository:

pip install git+https://github.com/lmmentel/mendeleev.git

If you use conda you can install the package from my anaconda channel by

conda install -c lmmentel mendeleev=0.15.0

Tutorials

Quick start

This simple tutorial will illustrate the basic capabilities of the package.

Table of Contents

• Basic interactive usage

  • Getting single elements

  • Getting-list-of-elements

• Extended attributes

  • Oxidation states

  • Ionization energies

  • Isotopes

  • Ionic radii

  • Electronic configuration

• Useful functions for calculating properties

• Electronegativity

• CLI utility

Basic interactive usage

Getting single elements

The simplest way of accessing the elements is importing them directly from mendeleev by symbols

from mendeleev import Si, Fe, O
print("Si's name: ", Si.name)
print("Fe's atomic number:", Fe.atomic_number)
print("O's atomic weight: ", O.atomic_weight)

Si's name:  Silicon
Fe's atomic number: 26
O's atomic weight:  15.999

An alternative interface to the data is through the element function that returns a single Element object or a list of Element object depending on the arguments.

The function can be imported directly from the mendeleev package

from mendeleev import element

The element method accepts unique identifiers: atomic number, atomic symbol or element’s name in English. To retrieve the entries on Silicon by symbol type

si = element('Si')

si

Element(
        abundance_crust=282000.0,
        abundance_sea=2.2,
        annotation='',
        atomic_number=14,
        atomic_radius=110.0,
        atomic_radius_rahm=231.99999999999997,
        atomic_volume=12.1,
        atomic_weight=28.085,
        atomic_weight_uncertainty=None,
        block='p',
        c6=305.0,
        c6_gb=308.0,
        cas='7440-21-3',
        covalent_radius_bragg=117.0,
        covalent_radius_cordero=111.00000000000001,
        covalent_radius_pyykko=115.99999999999999,
        covalent_radius_pyykko_double=107.0,
        covalent_radius_pyykko_triple=102.0,
        cpk_color='#daa520',
        density=2.3296,
        description="Metalloid element belonging to group 14 of the periodic table. It is the second most abundant element in the Earth's crust, making up 25.7% of it by weight. Chemically less reactive than carbon. First identified by Lavoisier in 1787 and first isolated in 1823 by Berzelius.",
        dipole_polarizability=37.3,
        dipole_polarizability_unc=0.7,
        discoverers='Jöns Berzelius',
        discovery_location='Sweden',
        discovery_year=1824,
        ec=<ElectronicConfiguration(conf="1s2 2s2 2p6 3s2 3p2")>,
        econf='[Ne] 3s2 3p2',
        electron_affinity=1.3895211,
        en_allen=11.33,
        en_ghosh=0.178503,
        en_pauling=1.9,
        evaporation_heat=383.0,
        fusion_heat=50.6,
        gas_basicity=814.1,
        geochemical_class='major',
        glawe_number=85,
        goldschmidt_class='litophile',
        group=<Group(symbol=IVA, name=Carbon group)>,
        group_id=14,
        heat_of_formation=450.0,
        ionic_radii=[IonicRadius(
        atomic_number=14,
        charge=4,
        coordination='IV',
        crystal_radius=40.0,
        econf='2p6',
        id=379,
        ionic_radius=26.0,
        most_reliable=True,
        origin='',
        spin='',
), IonicRadius(
        atomic_number=14,
        charge=4,
        coordination='VI',
        crystal_radius=54.0,
        econf='2p6',
        id=380,
        ionic_radius=40.0,
        most_reliable=True,
        origin='from r^3 vs V plots, ',
        spin='',
)],
        is_monoisotopic=None,
        is_radioactive=False,
        isotopes=[<Isotope(Z=14, A=22, mass=22.0361(5), abundance=None)>, <Isotope(Z=14, A=23, mass=23.0257(5), abundance=None)>, <Isotope(Z=14, A=24, mass=24.01154(2), abundance=None)>, <Isotope(Z=14, A=25, mass=25.00411(1), abundance=None)>, <Isotope(Z=14, A=26, mass=25.9923338(1), abundance=None)>, <Isotope(Z=14, A=27, mass=26.9867047(1), abundance=None)>, <Isotope(Z=14, A=28, mass=27.9769265344(6), abundance=92.254(4))>, <Isotope(Z=14, A=29, mass=28.9764946643(6), abundance=4.67(2))>, <Isotope(Z=14, A=30, mass=29.97377014(2), abundance=3.074(2))>, <Isotope(Z=14, A=31, mass=30.97536320(5), abundance=None)>, <Isotope(Z=14, A=32, mass=31.9741515(3), abundance=None)>, <Isotope(Z=14, A=33, mass=32.9779770(8), abundance=None)>, <Isotope(Z=14, A=34, mass=33.9785380(9), abundance=None)>, <Isotope(Z=14, A=35, mass=34.98455(4), abundance=None)>, <Isotope(Z=14, A=36, mass=35.98665(8), abundance=None)>, <Isotope(Z=14, A=37, mass=36.9929(1), abundance=None)>, <Isotope(Z=14, A=38, mass=37.9955(1), abundance=None)>, <Isotope(Z=14, A=39, mass=39.0025(1), abundance=None)>, <Isotope(Z=14, A=40, mass=40.0061(1), abundance=None)>, <Isotope(Z=14, A=41, mass=41.0142(3), abundance=None)>, <Isotope(Z=14, A=42, mass=42.0181(3), abundance=None)>, <Isotope(Z=14, A=43, mass=43.0261(4), abundance=None)>, <Isotope(Z=14, A=44, mass=44.0315(5), abundance=None)>, <Isotope(Z=14, A=45, mass=45.0398(6), abundance=None)>],
        jmol_color='#f0c8a0',
        lattice_constant=5.43,
        lattice_structure='DIA',
        mendeleev_number=88,
        metallic_radius=117.0,
        metallic_radius_c12=138.0,
        molar_heat_capacity=19.99,
        molcas_gv_color='#f0c8a0',
        name='Silicon',
        name_origin='Latin: silex, silicus, (flint).',
        period=3,
        pettifor_number=85,
        phase_transitions=[14 Tm=1687.15 Tb=3538.15],
        proton_affinity=837.0,
        screening_constants=[<ScreeningConstant(Z=  14, n=  1, s=s, screening=    0.4255)>, <ScreeningConstant(Z=  14, n=  2, s=p, screening=    4.0550)>, <ScreeningConstant(Z=  14, n=  2, s=s, screening=    4.9800)>, <ScreeningConstant(Z=  14, n=  3, s=p, screening=    9.7148)>, <ScreeningConstant(Z=  14, n=  3, s=s, screening=    9.0968)>],
        sources='Makes up major portion of clay, granite, quartz (SiO2), and sand. Commercial production depends on a reaction between sand (SiO2) and carbon at a temperature of around 2200 °C.',
        specific_heat_capacity=0.712,
        symbol='Si',
        thermal_conductivity=149.0,
        uses='Used in glass as silicon dioxide (SiO2). Silicon carbide (SiC) is one of the hardest substances known and used in polishing. Also the crystalline form is used in semiconductors.',
        vdw_radius=210.0,
        vdw_radius_alvarez=219.0,
        vdw_radius_batsanov=210.0,
        vdw_radius_bondi=210.0,
        vdw_radius_dreiding=426.99999999999994,
        vdw_radius_mm3=229.0,
        vdw_radius_rt=None,
        vdw_radius_truhlar=None,
        vdw_radius_uff=429.5,
)

Similarly to access the data by atomic number or element names type

al = element(13)
print(al.name)

Aluminum

o = element('Oxygen')
print(o.atomic_number)

8

Getting list of elements

The element method also accepts list or tuple of identifiers and then returns a list of Element objects

c, h, o = element(['C', 'Hydrogen', 8])
print(c.name, h.name, o.name)

Carbon Hydrogen Oxygen

Extended attributes

Next to simple attributes returning str, int or float, there are extended attributes

• oxistates, returns a list of oxidation states

• ionenergies, returns a dictionary of ionization energies

• isotopes, returns a list of Isotope objects

• ionic_radii returns a list of IonicRadius objects

• ec, electronic configuration object

Oxidation states

oxistates returns a list of most common oxidation states for a given element

fe = element('Fe')
print(fe.oxistates)

[2, 3]

Ionization energies

The ionenergies returns a dictionary with ionization energies in eV as values and degrees of ionization as keys

o = element('O')
o.ionenergies

{1: 13.618054,
 2: 35.12111,
 3: 54.93554,
 4: 77.4135,
 5: 113.8989,
 6: 138.1189,
 7: 739.32679,
 8: 871.40985}

Isotopes

The isotopes attribute returns a list of Isotope objects with the following attributes per isotope

• abundance

• abundance_uncertainty

• atomic_number

• discovery_year

• g_factor

• g_factor_uncertainty

• half_life

• half_life_uncertainty

• half_life_unit

• is_radioactive

• mass

• mass_number

• mass_uncertainty

• parity

• quadrupole_moment

• quadrupole_moment_uncertainty

• spin

print("{0:^4s} {1:^4s} {2:^10s} {3:8s} {4:6s} {5:5s}\n{6}".format("AN", "MN", "Mass", "Unc.", "Abu.", "Rad.", "-" * 42))
for iso in fe.isotopes:
    print('{0:4d} {1:4d} {2:10.5f} {3:8.2e} {4:} {5:}'.format(
        iso.atomic_number, iso.mass_number, iso.mass, iso.mass_uncertainty, iso.abundance, iso.is_radioactive))

 AN   MN     Mass    Unc.     Abu.   Rad.
------------------------------------------
  26   45   45.01547 3.04e-04 None True
  26   46   46.00130 3.22e-04 None True
  26   47   46.99235 5.37e-04 None True
  26   48   47.98067 9.90e-05 None True
  26   49   48.97343 2.60e-05 None True
  26   50   49.96299 9.00e-06 None True
  26   51   50.95686 1.50e-06 None True
  26   52   51.94811 1.92e-07 None True
  26   53   52.94531 1.79e-06 None True
  26   54   53.93961 3.68e-07 5.845 False
  26   55   54.93829 3.30e-07 None True
  26   56   55.93494 2.87e-07 91.754 False
  26   57   56.93539 2.87e-07 2.119 False
  26   58   57.93327 3.39e-07 0.282 False
  26   59   58.93487 3.54e-07 None True
  26   60   59.93407 3.66e-06 None True
  26   61   60.93675 2.80e-06 None True
  26   62   61.93679 3.00e-06 None True
  26   63   62.94027 4.62e-06 None True
  26   64   63.94099 5.39e-06 None True
  26   65   64.94502 5.49e-06 None True
  26   66   65.94625 4.40e-06 None True
  26   67   66.95093 4.10e-06 None True
  26   68   67.95288 2.07e-04 None True
  26   69   68.95792 2.15e-04 None True
  26   70   69.96040 3.22e-04 None True
  26   71   70.96572 4.29e-04 None True
  26   72   71.96860 5.37e-04 None True
  26   73   72.97425 5.37e-04 None True
  26   74   73.97782 5.37e-04 None True
  26   75   74.98422 6.44e-04 None True
  26   76   75.98863 6.44e-04 None True

Accessing isotopes

Similarly to element function that can be used to fetch specific isotopes by:

• atomic_number and mass_number or

• symbol and mass_number

from mendeleev import isotope

isotope("Fe", mass_number=57)

<Isotope(Z=26, A=57, mass=56.9353920(3), abundance=2.12(3))>

# tritium
isotope(1, 3)

<Isotope(Z=1, A=3, mass=3.01604928132(8), abundance=None)>

Radioactive isotopes can have multiple decay modes and that data is available as decay_modes attrobute for each Isotope

isotope("Li", 11).decay_modes

[<IsotopeDecayMode(id=40, isotope_id=39, mode='B-', intensity=100.0)>,
 <IsotopeDecayMode(id=41, isotope_id=39, mode='B-n', intensity=86.3)>,
 <IsotopeDecayMode(id=42, isotope_id=39, mode='2n', intensity=4.1)>,
 <IsotopeDecayMode(id=43, isotope_id=39, mode='3n', intensity=1.9)>,
 <IsotopeDecayMode(id=44, isotope_id=39, mode='B-A', intensity=1.7)>,
 <IsotopeDecayMode(id=45, isotope_id=39, mode='B-d', intensity=0.013)>,
 <IsotopeDecayMode(id=46, isotope_id=39, mode='B-t', intensity=0.0093)>]

Ionic radii

Another composite attribute is ionic_radii which returns a list of IonicRadius object with the following attributes

• atomic_number, atomic number of the ion

• charge, charge of the ion

• econf, electronic configuration of the ion

• coordination, coordination type of the ion

• spin, spin state of the ion (HS or LS)

• crystal_radius, crystal radius in pm

• ionic_radius, ionic radius in pm

• origin, source of the data

• most_reliable, recommended value, (see the original paper for more information)

for ir in fe.ionic_radii:
    print(ir)

charge=   2, coordination=IV   , crystal_radius=77.000, ionic_radius=63.000
charge=   2, coordination=IVSQ , crystal_radius=78.000, ionic_radius=64.000
charge=   2, coordination=VI   , crystal_radius=75.000, ionic_radius=61.000
charge=   2, coordination=VI   , crystal_radius=92.000, ionic_radius=78.000
charge=   2, coordination=VIII , crystal_radius=106.000, ionic_radius=92.000
charge=   3, coordination=IV   , crystal_radius=63.000, ionic_radius=49.000
charge=   3, coordination=V    , crystal_radius=72.000, ionic_radius=58.000
charge=   3, coordination=VI   , crystal_radius=69.000, ionic_radius=55.000
charge=   3, coordination=VI   , crystal_radius=78.500, ionic_radius=64.500
charge=   3, coordination=VIII , crystal_radius=92.000, ionic_radius=78.000
charge=   4, coordination=VI   , crystal_radius=72.500, ionic_radius=58.500
charge=   6, coordination=IV   , crystal_radius=39.000, ionic_radius=25.000

Useful functions for calculating properties

Next to stored attributes there is a number of useful functions

si = element('Si')

# get the number of valence electrons
si.nvalence()

4

# calculate softness for an ion
si.softness(charge=2)

0.058318712346158874

# calcualte hardness for an ion
si.hardness(charge=4)

60.812605

# calculate the effective nuclear charge for a subshell using Slater's rules
si.zeff(n=3, o='s')

4.149999999999999

# calculate the effective nuclear charge for a subshell using Clemneti's and Raimondi's exponents
si.zeff(n=3, o='s', method='clementi')

4.9032

Electronegativity

Currently there are 9 electronagativity scales implemented that can me accessed though the common electronegativity method, the scales are:

• allen

• allred-rochow

• cottrell-sutton

• ghosh

• gordy

• li-xue

• martynov-batsanov

• mulliken

• nagle

• pauling

• sanderson

More information can be found in the documentation.

si.electronegativity(scale='pauling')

1.9

si.electronegativity(scale='allen')

11.33

# calculate mulliken electronegativity for a neutral atom or ion
si.electronegativity(scale="mulliken", charge=1)

12.248764000000001

CLI utility

For those who work in the terminal there is a simple command line interface (CLI) for printing the information about a given element. The script name is element.py and it accepts either the symbol or name of the element as an argument and prints the data about it. For example, to print the properties of silicon type

!element.py Si

/usr/bin/sh: 1: element.py: not found

Bulk data access

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

The following tables are available from mendeleev

• elements

• ionicradii

• ionizationenergies

• oxidationstates

• groups

• series

• isotopes

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.

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

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

ptable = fetch_table('elements')

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

ptable.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 118 entries, 0 to 117
Data columns (total 70 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   boiling_point                  96 non-null     float64
 6   density                        95 non-null     float64
 7   description                    109 non-null    object
 8   dipole_polarizability          117 non-null    float64
 9   electron_affinity              77 non-null     float64
 10  electronic_configuration       118 non-null    object
 11  evaporation_heat               88 non-null     float64
 12  fusion_heat                    75 non-null     float64
 13  group_id                       90 non-null     float64
 14  lattice_constant               87 non-null     float64
 15  lattice_structure              91 non-null     object
 16  melting_point                  100 non-null    float64
 17  name                           118 non-null    object
 18  period                         118 non-null    int64
 19  series_id                      118 non-null    int64
 20  specific_heat                  81 non-null     float64
 21  symbol                         118 non-null    object
 22  thermal_conductivity           66 non-null     float64
 23  vdw_radius                     103 non-null    float64
 24  covalent_radius_cordero        96 non-null     float64
 25  covalent_radius_pyykko         118 non-null    float64
 26  en_pauling                     85 non-null     float64
 27  en_allen                       71 non-null     float64
 28  jmol_color                     109 non-null    object
 29  cpk_color                      103 non-null    object
 30  proton_affinity                32 non-null     float64
 31  gas_basicity                   32 non-null     float64
 32  heat_of_formation              89 non-null     float64
 33  c6                             43 non-null     float64
 34  covalent_radius_bragg          37 non-null     float64
 35  vdw_radius_bondi               28 non-null     float64
 36  vdw_radius_truhlar             16 non-null     float64
 37  vdw_radius_rt                  9 non-null      float64
 38  vdw_radius_batsanov            65 non-null     float64
 39  vdw_radius_dreiding            21 non-null     float64
 40  vdw_radius_uff                 103 non-null    float64
 41  vdw_radius_mm3                 94 non-null     float64
 42  abundance_crust                88 non-null     float64
 43  abundance_sea                  81 non-null     float64
 44  molcas_gv_color                103 non-null    object
 45  en_ghosh                       103 non-null    float64
 46  vdw_radius_alvarez             94 non-null     float64
 47  c6_gb                          86 non-null     float64
 48  atomic_weight                  118 non-null    float64
 49  atomic_weight_uncertainty      74 non-null     float64
 50  is_monoisotopic                21 non-null     object
 51  is_radioactive                 118 non-null    bool
 52  cas                            118 non-null    object
 53  atomic_radius_rahm             96 non-null     float64
 54  geochemical_class              76 non-null     object
 55  goldschmidt_class              118 non-null    object
 56  metallic_radius                56 non-null     float64
 57  metallic_radius_c12            63 non-null     float64
 58  covalent_radius_pyykko_double  108 non-null    float64
 59  covalent_radius_pyykko_triple  80 non-null     float64
 60  discoverers                    118 non-null    object
 61  discovery_year                 105 non-null    float64
 62  discovery_location             106 non-null    object
 63  name_origin                    118 non-null    object
 64  sources                        118 non-null    object
 65  uses                           112 non-null    object
 66  mendeleev_number               118 non-null    int64
 67  dipole_polarizability_unc      117 non-null    float64
 68  pettifor_number                103 non-null    float64
 69  glawe_number                   103 non-null    float64
dtypes: bool(1), float64(46), int64(4), object(19)
memory usage: 63.8+ KB

For clarity let’s take only a subset of columns

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

ptable[cols].head()

	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.

ptable[cols].describe()

	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

Isotopes table

Let try and retrieve another table, namely isotopes

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

isotopes.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 406 entries, 1 to 406
Data columns (total 11 columns):
 #   Column             Non-Null Count  Dtype
---  ------             --------------  -----
 0   atomic_number      406 non-null    int64
 1   mass               377 non-null    float64
 2   abundance          288 non-null    float64
 3   mass_number        406 non-null    int64
 4   mass_uncertainty   377 non-null    float64
 5   is_radioactive     406 non-null    bool
 6   half_life          121 non-null    float64
 7   half_life_unit     85 non-null     object
 8   spin               323 non-null    float64
 9   g_factor           323 non-null    float64
 10  quadrupole_moment  320 non-null    float64
dtypes: bool(1), float64(7), int64(2), object(1)
memory usage: 35.3+ KB

Merge the elements table with the isotopes

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

import pandas as pd

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

now we have the following columns in the merged DataFrame

merged.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 406 entries, 0 to 405
Data columns (total 16 columns):
 #   Column             Non-Null Count  Dtype
---  ------             --------------  -----
 0   atomic_number      406 non-null    int64
 1   symbol             406 non-null    object
 2   atomic_radius      328 non-null    float64
 3   en_pauling         313 non-null    float64
 4   block              406 non-null    object
 5   vdw_radius_mm3     350 non-null    float64
 6   mass               377 non-null    float64
 7   abundance          288 non-null    float64
 8   mass_number        406 non-null    int64
 9   mass_uncertainty   377 non-null    float64
 10  is_radioactive     406 non-null    bool
 11  half_life          121 non-null    float64
 12  half_life_unit     85 non-null     object
 13  spin               323 non-null    float64
 14  g_factor           323 non-null    float64
 15  quadrupole_moment  320 non-null    float64
dtypes: bool(1), float64(10), int64(2), object(3)
memory usage: 51.1+ KB

merged.head()

	atomic_number	symbol	atomic_radius	en_pauling	block	vdw_radius_mm3	mass	abundance	mass_number	mass_uncertainty	is_radioactive	half_life	half_life_unit	spin	g_factor	quadrupole_moment
0	1	H	25.0	2.2	s	162.0	1.007825	0.999720	1	6.000000e-10	False	NaN	None	0.5	5.585695	0.00000
1	1	H	25.0	2.2	s	162.0	2.014102	0.000280	2	8.000000e-10	False	NaN	None	1.0	0.857438	0.00286
2	1	H	25.0	2.2	s	162.0	NaN	NaN	3	NaN	True	NaN	None	0.5	5.957994	0.00000
3	2	He	120.0	NaN	s	153.0	3.016029	0.000002	3	2.000000e-08	False	NaN	None	0.5	-4.254995	0.00000
4	2	He	120.0	NaN	s	153.0	4.002603	0.999998	4	4.000000e-10	False	NaN	None	0.0	0.000000	0.00000

To display all the isotopes of Silicon

merged[merged['symbol'] == 'Si']

	atomic_number	symbol	atomic_radius	en_pauling	block	vdw_radius_mm3	mass	abundance	mass_number	mass_uncertainty	is_radioactive	half_life	half_life_unit	spin	g_factor	quadrupole_moment
28	14	Si	110.0	1.9	p	229.0	27.976927	0.92191	28	3.000000e-09	False	NaN	None	0.0	0.00000	0.0
29	14	Si	110.0	1.9	p	229.0	28.976495	0.04699	29	3.000000e-09	False	NaN	None	0.5	-1.11058	0.0
30	14	Si	110.0	1.9	p	229.0	29.973770	0.03110	30	2.000000e-08	False	NaN	None	0.0	0.00000	0.0

Ionic radii

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

from mendeleev.fetch import fetch_ionic_radii

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

	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

Ionization energies

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.

from mendeleev.fetch import fetch_ionization_energies

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

	IE2
atomic_number
1	NaN
2	54.417763
3	75.640094
4	18.211153
5	25.154830
6	24.384500
7	29.601250
8	35.121110
9	34.970810
10	40.962960

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

	IE1	IE3	IE5
atomic_number
1	13.598434	NaN	NaN
2	24.587388	NaN	NaN
3	5.391715	122.454354	NaN
4	9.322699	153.896198	NaN
5	8.298019	37.930580	340.226008
6	11.260296	47.887780	392.090500
7	14.534130	47.445300	97.890130
8	13.618054	54.935540	113.898900
9	17.422820	62.708000	114.249000
10	21.564540	63.423310	126.247000

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.

from mendeleev.fetch import fetch_electronegativities

ens = fetch_electronegativities()
ens.head(10)

	Allen	Allred-Rochow	Cottrell-Sutton	Ghosh	Gordy	Li-Xue	Martynov-Batsanov	Mulliken	Nagle	Pauling	Sanderson
atomic_number
1	13.610	0.000977	0.176777	0.263800	0.031250	{('I', ''): -3.540721753312244, ('II', ''): -2...	3.687605	6.799217	0.605388	2.20	2.187771
2	24.590	0.000803	0.192241	0.442712	0.036957	{}	6.285107	12.293694	1.130639	NaN	1.000000
3	5.392	0.000073	0.098866	0.105093	0.009774	{('IV', ''): 1.7160634314550876, ('VI', ''): 1...	2.322007	2.695857	0.182650	0.98	0.048868
4	9.323	0.000187	0.138267	0.144986	0.019118	{}	3.710381	4.661350	0.375615	1.57	0.126847
5	12.130	0.000360	0.174895	0.184886	0.030588	{}	4.877958	4.149010	0.526974	2.04	0.254627
6	15.050	0.000578	0.208167	0.224776	0.043333	{}	6.083300	5.630148	0.707393	2.55	0.427525
7	18.130	0.000774	0.234371	0.264930	0.054930	{}	7.306768	7.267065	0.877498	3.04	0.577482
8	21.360	0.001146	0.268742	0.304575	0.072222	{}	8.496136	6.809027	1.042218	3.44	0.941649
9	24.800	0.001270	0.285044	0.344443	0.081250	{}	9.701808	8.711410	1.232373	3.98	1.017681
10	28.310	0.001303	0.295488	0.384390	0.087313	{}	10.918389	NaN	1.443255	NaN	1.000000

Electronic configuration

ec attribute is an object from the ElectronicConfiguration class that has additional method for manipulating the configuration. Internally the configuration is represented as a OrderedDict from the collections module where tuples (n, s) (n is the principal quantum number and s is the subshell label) are used as keys and shell occupations are the values

from mendeleev import Si

Si.ec.conf

OrderedDict([((1, 's'), 2),
             ((2, 's'), 2),
             ((2, 'p'), 6),
             ((3, 's'), 2),
             ((3, 'p'), 2)])

the occupation of different subshells can be access supplying a proper key

Si.ec.conf[(1, 's')]

2

to calculate the number of electrons per shell type

Si.ec.electrons_per_shell()

{'K': 2, 'L': 8, 'M': 4}

get the largest value of the pricipal quantum number

Si.ec.max_n()

3

Get the largest value of azimutal quantum number for a given value of principal quantum number

Si.ec.max_l(n=3)

'p'

Find the large noble gas-like core configuration

Si.ec.get_largest_core()

('Ne', <ElectronicConfiguration(conf="1s2 2s2 2p6")>)

Get the total number of electrons

Si.ec.ne()

14

Last subshell

Si.ec.last_subshell()

((3, 'p'), 2)

Get unpaired electrons

Si.ec.unpaired_electrons()

2

Remove electrons by ionizing returns a new configuration with an electron removed

ionized = Si.ec.ionize()
print(ionized)

1s2 2s2 2p6 3s2 3p1

We can check that it actually has less electrons:

ionized.ne()

13

Spin occupations by subshell

Si.ec.spin_occupations()

OrderedDict([((1, 's'), {'pairs': 1, 'alpha': 1, 'beta': 1, 'unpaired': 0}),
             ((2, 's'), {'pairs': 1, 'alpha': 1, 'beta': 1, 'unpaired': 0}),
             ((2, 'p'), {'pairs': 3, 'alpha': 3, 'beta': 3, 'unpaired': 0}),
             ((3, 's'), {'pairs': 1, 'alpha': 1, 'beta': 1, 'unpaired': 0}),
             ((3, 'p'), {'pairs': 0, 'alpha': 2, 'beta': 0, 'unpaired': 2})])

Calculate the spin only magnetic moment

Si.ec.spin_only_magnetic_moment()

2.8284271247461903

Calculate the screening constant using Slater’s rules for 2s orbital

Si.ec.slater_screening(n=2, o='s')

4.1499999999999995

Standalone use

You can use the ElectronicConfiguration as a standalone class and use all of the methods shown above.

from mendeleev.econf import ElectronicConfiguration

ec = ElectronicConfiguration("1s2 2s2 2p6 3s1")

Get the valence only configuration

ec.get_valence()

<ElectronicConfiguration(conf="3s1")>

Ions

You can use the Ion class to work with ions instead of elements. Ions can be created from elements and charge information.

from mendeleev.ion import Ion

fe_2 = Ion("Fe", 2)

You can access variety of properties of the ion

fe_2.charge

2

fe_2.electrons

24

fe_2.Z

26

fe_2.name

'Iron 2+ ion'

you can also print the unicode ion symbol

fe_2.unicode_ion_symbol()

'Fe²⁺'

Ionic radii for this ion are available under radius attribute

fe_2.radius

[IonicRadius(
        atomic_number=26,
        charge=2,
        coordination='IV',
        crystal_radius=77.0,
        econf='3d6',
        id=149,
        ionic_radius=63.0,
        most_reliable=False,
        origin='',
        spin='HS',
 ),
 IonicRadius(
        atomic_number=26,
        charge=2,
        coordination='IVSQ',
        crystal_radius=78.0,
        econf='3d6',
        id=150,
        ionic_radius=64.0,
        most_reliable=False,
        origin='',
        spin='HS',
 ),
 IonicRadius(
        atomic_number=26,
        charge=2,
        coordination='VI',
        crystal_radius=75.0,
        econf='3d6',
        id=151,
        ionic_radius=61.0,
        most_reliable=False,
        origin='estimated, ',
        spin='LS',
 ),
 IonicRadius(
        atomic_number=26,
        charge=2,
        coordination='VI',
        crystal_radius=92.0,
        econf='3d6',
        id=152,
        ionic_radius=78.0,
        most_reliable=True,
        origin='from r^3 vs V plots, ',
        spin='HS',
 ),
 IonicRadius(
        atomic_number=26,
        charge=2,
        coordination='VIII',
        crystal_radius=106.0,
        econf='3d6',
        id=153,
        ionic_radius=92.0,
        most_reliable=False,
        origin='calculated, ',
        spin='HS',
 )]

Appropriate value of ionization energy and electron affinity are available under ie and ea attributes

fe_2.ie

30.651

fe_2.ea

16.1992

compute ionic potential

fe_2.ionic_potential()

0.02564102564102564

Visualizing custom periodic tables

In this tutorial you’ll how to use mendeleev to create customized visualizations of the periodic table.

The most convenient method to use for this is periodic_table function from mendeleev.vis module.

from mendeleev.vis import periodic_table

Make sure you you have optional vis dependencies installed when installing mendeleev. If you are using pip install with

pip install mendeleev[vis]

To see the default visualization of the periodic table simply call the imported function

periodic_table()

mendeleev stores also two color schemes for atoms that are frequently used for visualizing molecular structures. One set is stored in the cpk_color column and refers to CPK coloring, another is stored in jmol_color column and is used by the Jmol program, finally there is also coloring scheme from MOLCAS GV program store in the molcas_gv_color attribute. They can be displayed either by hovering of the element to display a tooltip or used directly to color the element cells.

periodic_table(colorby='jmol_color', title="JMol Colors")

periodic_table(colorby='cpk_color', title='CPK Colors')

periodic_table(colorby='molcas_gv_color', title='MOLCAS GV Colors')

Visualizing properties

Any of the properties in mendeleev can now be visualized and color coded. This means that the value of selected attribute will be visible on each element and also it is possible to use the attribute to color code the background of each element.

Let’s first use the covalent_radius_pyykko and display the values with the default color coding by series

periodic_table(attribute='covalent_radius_pyykko', title="Covalent Radii of Pyykko")

Now let’s use the same attribute but in addition color code by the actual values, by adding colorby='attribute' argument

periodic_table(attribute='covalent_radius_pyykko', colorby='attribute', title="Covalent Radii of Pyykko")

The color map can aslo be csutomized using the cmap argument to any of the standard colormaps available in matplotlib

periodic_table(attribute='covalent_radius_pyykko', colorby='attribute',
              cmap='spring', title="Covalent Radii of Pyykko")

Let also see one of the more modern colormaps: viridis, plasma, inferno and magma.

periodic_table(attribute='covalent_radius_pyykko', colorby='attribute',
              cmap='inferno', title="Covalent Radii of Pyykko")

Lets try a different property: atomic_volume

periodic_table(attribute='atomic_volume', colorby='attribute', title='Atomic Volume')

periodic_table(attribute='en_pauling', colorby='attribute',
              title="Pauling's Electronegativity", cmap='viridis')

Wide 32-column version

The periodic_table function can also present the periodic table in the so-called wide format with the f-block between the s- and d-blocks resulting in 32 columns.

periodic_table(height=600, width=1500, wide_layout=True)

Advanced visualization tutorial

Next to the high level plotting function mendeleev.vis.periodic_table, mendeleev offers two lower level functions that give you more control over the result. There are two plotting backends supported:

1. Plotly (default)

2. Bokeh

Note

Depending on your environment being the classic jupyter notebook or jupyterlab you might have to do additional configuration steps, so if you’re not getting expected results see plotly of bokeh documentation.

Accessing lower level plotting functions

There are two plotting functions, one for each of the backends:

• periodic_table_plotly in mendeleev.vis.plotly

• periodic_table_bokeh in mendeleev.vis.bokeh

that you can use to customize the visualizations even further.

Both functions take the same keyword arguments as the periodic_table function but the also require a DataFrame with periodic table data. That dataframe needs to have x and y columns for each element that play the role of coordinates. You can get the default data using the create_vis_dataframe function. Let’s start with an example using the plotly backend.

from mendeleev.vis import create_vis_dataframe, periodic_table_plotly

The function has only one required argument which is the data itself.

elements = create_vis_dataframe()
periodic_table_plotly(elements)

Custom coloring scheme

To apply a custom color scheme you can assign color to all the elments in the DataFrame. This can be done by creating a custom column in the DataFrame and then using colorby argument to specify which column contains colors. Let’s try to color the elements according to the block they belong to.

import seaborn as sns
from matplotlib import colors
blockcmap = {b : colors.rgb2hex(c) for b, c in zip(['s', 'p', 'd', 'f'], sns.color_palette('deep'))}

elements['block_color'] = elements['block'].map(blockcmap)
periodic_table_plotly(elements, colorby='block_color')

Custom properties

You can also visualize custom properties using pandas’ awesome methods for manipulating data. For example let’s consider the difference of electronegativity between every element and the Oxygen atom. To calculate the values we will use Allen scale this time and call our new value ENX-ENO.

elements.loc[:, 'ENX-ENO'] =  elements.loc[elements['symbol'] == 'O', 'en_allen'].values - elements.loc[:, 'en_allen']

periodic_table_plotly(elements, attribute='ENX-ENO', colorby='attribute',
              cmap='viridis', title='Allen Electronegativity wrt. Oxygen')

As a second example let’s consider a difference between the covalent_radius_slater and covalent_radius_pyykko values

elements['cov_rad_diff'] = elements['atomic_radius'] - elements['covalent_radius_pyykko']

periodic_table_plotly(elements, attribute='cov_rad_diff', colorby='attribute',
              title='Covalent Radii Difference', cmap='viridis')

Bokeh backend

We can also use the Bokeh backed in the same way but we need to take a few extra steps to render the result in a notebook

from bokeh.plotting import show, output_notebook
from mendeleev.vis import periodic_table_bokeh

First we need to enable notebook output

output_notebook()

Loading BokehJS ...

fig = periodic_table_bokeh(elements)
show(fig)

fig = periodic_table_bokeh(elements, attribute="atomic_radius", colorby="attribute")
show(fig)

Jupyter notebooks

All tutorials are available as Jupyter notebooks on binder where you can explore the examples interactively:

• Quick start

• Bulk data access

• Electronic Configuration

• Ions

• Visualizations

• Advanced visualizations

Data

To find out how to fetch data in bulk, check out the documentation about data access.

Elements

The following data are currently available:

Name	Type	Comment	Unit	Data Source
abundance_crust	float	Abundance in the Earth’s crust	mg/kg	[23]
abundance_sea	float	Abundance in the seas	mg/L	[23]
annotation	str	Annotations regarding the data
atomic_number	int	Atomic number
atomic_radius	float	Atomic radius	pm	[53]
atomic_radius_rahm	float	Atomic radius by Rahm et al.	pm	[45, 46]
atomic_volume	float	Atomic volume	cm3/mol
atomic_weight	float	Atomic weight([1])		[35, 63]
atomic_weight_uncertainty	float	Atomic weight uncertainty([1])		[35, 63]
block	str	Block in periodic table
boiling_point	float	Boiling temperature	K	[22]
c6	float	C_6 dispersion coefficient in a.u.	a.u.	[13, 56]
c6_gb	float	C_6 dispersion coefficient in a.u. (Gould & Bučko)	a.u.	[21]
cas	str	Chemical Abstracts Serice identifier
covalent_radius_bragg	float	Covalent radius by Bragg	pm	[10]
covalent_radius_cordero	float	Covalent radius by Cerdero et al.([2])	pm	[16]
covalent_radius_pyykko	float	Single bond covalent radius by Pyykko et al.	pm	[43]
covalent_radius_pyykko_double	float	Double bond covalent radius by Pyykko et al.	pm	[42]
covalent_radius_pyykko_triple	float	Triple bond covalent radius by Pyykko et al.	pm	[44]
cpk_color	str	Element color in CPK convention	HEX	[61]
critical_pressure	float	Critical pressure	MPa	[22]
critical_temperature	float	Critical temperature	K	[22]
density	float	Density at 295K([9])	g/cm3	[23, 66]
description	str	Short description of the element
dipole_polarizability	float	Dipole polarizability	a.u.	[51]
dipole_polarizability_unc	float	Dipole polarizability uncertainty	a.u.	[51]
discoverers	str	The discoverers of the element
discovery_location	str	The location where the element was discovered
discovery_year	int	The year the element was discovered
electron_affinity	float	Electron affinity([3])	eV	[6, 23]
electrons	int	Number of electrons
electrophilicity	float	Electrophilicity index	eV	[39]
en_allen	float	Allen’s scale of electronegativity([4])	eV	[31, 32]
en_ghosh	float	Ghosh’s scale of electronegativity		[18]
en_mulliken	float	Mulliken’s scale of electronegativity	eV	[36]
en_pauling	float	Pauling’s scale of electronegativity		[23]
econf	str	Ground state electron configuration
evaporation_heat	float	Evaporation heat	kJ/mol
fusion_heat	float	Fusion heat	kJ/mol
gas_basicity	float	Gas basicity	kJ/mol	[23]
geochemical_class	str	Geochemical classification		[59]
glawe_number	int	Glawe’s number (scale)		[19]
goldschmidt_class	str	Goldschmidt classification		[59, 60]
group	int	Group in periodic table
heat_of_formation	float	Heat of formation	kJ/mol	[23]
inchi	str	International Chemical Identifier		[24]
ionenergy	tuple	Ionization energies	eV	[26]
ionic_radii	list	Ionic and crystal radii in pm([8])	pm	[29, 52]
is_monoisotopic	bool	Is the element monoisotopic
is_radioactive	bool	Is the element radioactive
isotopes	list	Isotopes
jmol_color	str	Element color in Jmol convention	HEX	[64]
lattice_constant	float	Lattice constant	Angstrom
lattice_structure	str	Lattice structure code
mass_number	int	Mass number (most abundant isotope)
melting_point	float	Melting temperature	K	[22]
mendeleev_number	int	Mendeleev’s number([5])		[41, 57]
metallic_radius	float	Single-bond metallic radius	pm	[1]
metallic_radius_c12	float	Metallic radius with 12 nearest neighbors	pm	[1]
molar_heat_capacity	float	Molar heat capacity @ 25 C, 1 bar	J/(mol K)	[23]
molcas_gv_color	str	Element color in MOCAS GV convention	HEX	[65]
name	str	Name in English
name_origin	str	Origin of the name
neutrons	int	Number of neutrons (most abundant isotope)
oxistates	list	Commonly occurring oxidation states		[67]
nist_webbook_url	str	URL for the NIST Chemistry WebBook		[38]
oxistates	list	Oxidation states
period	int	Period in periodic table
pettifor_number	float	Pettifor scale		[41]
proton_affinity	float	Proton affinity	kJ/mol	[23]
protons	int	Number of protons
sconst	float	Nuclear charge screening constants([6])		[14, 15]
series	int	Index to chemical series
sources	str	Sources of the element
specific_heat_capacity	float	Specific heat capacity @ 25 C, 1 bar	J/(g K)	[23]
symbol	str	Chemical symbol
thermal_conductivity	float	Thermal conductivity @25 C	W/(m K)
triple_point_pressure	float	Triple point pressure	kPa	[22]
triple_point_temperature	float	Triple point temperature	K	[22]
uses	str	Applications of the element
vdw_radius	float	Van der Waals radius	pm	[23]
vdw_radius_alvarez	float	Van der Waals radius according to Alvarez([7])	pm	[5, 58]
vdw_radius_batsanov	float	Van der Waals radius according to Batsanov	pm	[8]
vdw_radius_bondi	float	Van der Waals radius according to Bondi	pm	[9]
vdw_radius_dreiding	float	Van der Waals radius from the DREIDING FF	pm	[34]
vdw_radius_mm3	float	Van der Waals radius from the MM3 FF	pm	[3]
vdw_radius_rt	float	Van der Waals radius according to Rowland and Taylor	pm	[48]
vdw_radius_truhlar	float	Van der Waals radius according to Truhlar	pm	[33]
vdw_radius_uff	float	Van der Waals radius from the UFF	pm	[47]

Isotopes

Name	Type	Comment	Unit	Data Source
abundance	float	Relative Abundance		[25]
abundance_uncertainty	float	Uncertainty of relative abundance		[25]
atomic_number	int	Atomic number
decay_modes	obj	Decay modes with intensities		[25]
discovery_year	int	Year the isotope was discovered		[25]
g_factor	float	Nuclear g-factor		[55]
g_factor_uncertainty	float	Uncertainty of the nuclear g-factor		[55]
half_life	float	Half life of the isotope		[25]
half_life_uncertainty	float	Uncertainty of the half life		[25]
half_life_unit	str	Unit in which the half life is given		[25]
is_radioactive	bool	Is the isotope radioactive		[62]
mass	float	Atomic mass	Da	[62]
mass_number	int	Mass number of the isotope		[62]
mass_uncertainty	float	Uncertainty of the atomic mass	Da	[62]
parity	str	Parity, if present, it can be either + or -		[25]
quadrupole_moment	float	Nuclear electric quadrupole moment	b [100 fm2]	[54]
quadrupole_moment_uncertainty	float	Nuclear electric quadrupole moment	b [100 fm2]	[54]
spin	str	Nuclear spin quantum number		[25]

Isotope Decay Modes

Name	Type	Comment	Unit	Data Source
isotope_id	int	ID of the isotope
mode	str	ASCII symbol of the decay mode		[25]
relation	str	Uncertainty of relative abundance		[25]
intensity	float	Intensity of the decay mode	%	[25]
is_allowed_not_observed	bool	If True decay mode is energetically allowed, but not experimentally observed		[25]
is_observed_intensity_unknown	bool	If True decay mode is observed, but its intensity is not experimentally known		[25]

The different modes in the table are stores as ASCII representations for compatibility. The table below provides explanations of the symbols.

ASCII	Unicode	Description
A	\(\alpha\)	\(\alpha\) emission
p	p	proton emission
2p	2p	2-proton emission
n	n	neutron emission
2n	2n	2-neutron emission
EC	\(\epsilon\)	electron capture
e+	\(e^{+}\)	positron emission
B+	\(\beta^{+}\)	\(\beta^{+}\) decay (\(\beta^{+}=\epsilon+e^{+}\))
B-	\(\beta^{-}\)	\(\beta^{-}\) decay
2B-	2\(\beta^{-}\)	double \(\beta^{-}\) decay
2B+	2\(\beta^{+}\)	double \(\beta^{+}\) decay
B-n	\(\beta^{-}\) n	\(\beta^{-}\)-delayed neutron emission
B-2n	\(\beta^{-}\) 2n	\(\beta^{-}\)-delayed 2-neutron emission
B-3n	\(\beta^{-}\) 3n	\(\beta^{-}\)-delayed 3-neutron emission
B+p	\(\beta^{+}\) p	\(\beta^{+}\)-delayed proton emission
B+2p	\(\beta^{+}\) 2p	\(\beta^{+}\)-delayed 2-proton emission
B+3p	\(\beta^{+}\) 3p	\(\beta^{+}\)-delayed 3-proton emission
B-A	\(\beta^{-}\alpha\)	\(\beta^{-}\)-delayed \(\alpha\) emission
B+A	\(\beta^{+}\alpha\)	\(\beta^{+}\)-delayed \(\alpha\) emission
B-d	\(\beta^{-}\) d	\(\beta^{-}\)-delayed deuteron emission
B-t	\(\beta^{-}\) t	\(\beta^{-}\)-delayed triton emission
IT	IT	internal transition
SF	SF	spontaneous fission
B+SF	\(\beta^{+}\) SF	\(\beta^{+}\)-delayed fission
B-SF	\(\beta^{-}\) SF	\(\beta^{-}\)-delayed fission
24Ne	24Ne	heavy cluster emission

Data Footnotes

[1] (1,2)

Atomic Weights

Atomic weights and their uncertainties were retrieved mainly from ref. [63]. For elements whose values were given as ranges the conventional atomic weights from Table 3 in ref. [35] were taken. For radioactive elements the standard approach was adopted where the weight is taken as the mass number of the most stable isotope. The data was obtained from CIAAW page on radioactive elements. In cases where two isotopes were specified the one with the smaller standard deviation was chosen. In case of Tc and Pm relative weights of their isotopes were used, for Tc isotope 98, and for Pm isotope 145 were taken from CIAAW.

[2]

Covalent Radius by Cordero et al.

In order to have a more homogeneous data for covalent radii taken from ref. [16] the values for 3 different valences for C, also the low and high spin values for Mn, Fe Co, were respectively averaged.

[3]

Electron affinity

Electron affinities were taken from [23] for the elements for which the data was available. For He, Be, N, Ar and Xe affinities were taken from [6] where they were specified for metastable ions and therefore the values are negative.

Updates

• Electron affinity of niobium was taken from [30].

• Electron affinity of cobalt was taken from [11].

• Electron affinity of lead was taken from [12].

[4]

Allen’s configuration energies

The values of configurational energies from refs. [31] and [32] were taken as reported in eV without converting to Pauling units.

[5]

Mendeleev numbers

Mendeleev numbers were mostly taken from [57] but the range was extended to cover the whole periodic table following the prescription in the article of increasing the numbers going from top to bottom in each group and group by group from left to right in the periodic table.

[6]

Nuclear charge screening constants

The screening constants were calculated according to the following formula

\[\sigma_{n,l,m} = Z - n\cdot\zeta_{n,l,m}\]

where \(n\) is the principal quantum number, \(Z\) is the atomic number, \(\sigma_{n,l,m}\) is the screening constant, \(\zeta_{n,l,m}\) is the optimized exponent from [14, 15].

For elements Nb, Mo, Ru, Rh, Pd and Ag the exponent values corresponding to the ground state electronic configuration were taken (entries with superscript a in Table II in [15]).

For elements La, Pr, Nd and Pm two exponent were reported for 4f shell denoted 4f and 4f’ in [15]. The value corresponding to 4f were used since according to the authors these are the dominant ones.

[7]

van der Waals radii according to Alvarez

The bulk of the radii data was taken from Ref. [5], but the radii for noble gasses were update according to the values in Ref. [58].

[8]

Ionic radii for Actinoid (III) ions

Ionic radii values for 3⁺ Actinoids were with coordination number 9 were taken from [29]. In addition crystal_radius values were computed by adding 14 pm to the ionic_radius values according to [52].

[9]

Densities

Density values for solids and liquids are always in units of grams per cubic centimeter and can be assumed to refer to temperatures near room temperature unless otherwise stated. Values for gases are the calculated ideal gas densities at 25°C and 101.325 kPa.

Original values for gasses are converted from g/L to g/cm³.

For elements where several allotropes exist, the density corresponding to the most abundand are reported (for full list refer to [23]), namely:

• Antimony (gray)

• Berkelium (α form)

• Carbon (graphite)

• Phosphorus (white)

• Selenium (gray)

• Sulfur (rhombic)

• Tin (white)

For elements where experimental data is not available, theoretical estimates taken from [66] are used, namely for:

• Astatine

• Francium

• Einsteinium

• Fermium

• Mendelevium

• Nobelium

• Lawrencium

• Rutherfordium

• Dubnium

• Seaborgium

• Bohrium

• Hassium

• Meitnerium

• Darmstadtium

• Roentgenium

• Copernicium

• Nihonium

• Flerovium

• Moscovium

• Livermorium

• Tennessine

• Oganesson

Accessing data

Individual Elements

The easiest way to access individual elements is simply by importing them from the mendeleev directly using their symbols:

>>> from mendeleev import H, C, O, Og
>>> [x.name for x in [H, C, O, Og]]
['Hydrogen', 'Carbon', 'Oxygen', 'Oganesson']

An alternative method of access is through the element() function that returns either a single Element instance or a tuple of those instances depending on the input. It provides a more flexible interface since it accepts element names, atomic numbers and symbols as well as their combinations.

Fetching data in bulk

If you want a whole set of data you can retrieve one of the tables from the database as pandas DataFrame through the fetch_table(). The following tables are available:

• elements

• groups

• ionicradii

• ionizationenergies

• isotopes

• oxidationstates

• screeningconstants

• series

fetch_table(table: str, **kwargs) → pandas.DataFrame

Return a table from the database as pandas.DataFrame

Parameters:

• table – Name of the table from the database

• kwargs – A dictionary of keyword arguments to pass to the pandas.read_qsl()

Returns:

Pandas DataFrame with the contents of the table

Return type:

df (pandas.DataFrame)

Example

>>> from mendeleev.fetch import fetch_table
>>> df = fetch_table('elements')
>>> type(df)
pandas.core.frame.DataFrame

fetch_ionization_energies(degree: List[int] | int = 1) → pandas.DataFrame

Fetch a pandas.DataFrame with ionization energies for all elements indexed by atomic number.

Parameters:

degree – Degree of ionization, either as int or a list of ints. If a list is passed then the output will contain ionization energies corresponding to particalr degrees in columns.

Returns:

ionization energies, indexed by atomic number

Return type:

df (pandas.DataFrame)

fetch_ionic_radii(radius: str = 'ionic_radius') → pandas.DataFrame

Fetch a pandas DataFrame with ionic radii for all the elements.

Parameters:

radius – The radius to be returned either ionic_radius or crystal_radius

Returns:

a table with atomic numbers, symbols and ionic radii for all

coordination numbers

Return type:

df (pandas.DataFrame)

Computed properties

Some properties need to be computed rather than directly retrieved from the database. Electronegativities

fetch_electronegativities(scales: List[str] = None) → pandas.DataFrame

Fetch electronegativity scales for all elements as pandas.DataFrame

Parameters:

scales – list of scale names, defaults to all available scales

Returns:

Pandas DataFrame with the contents of the table

Return type:

df (pandas.DataFrame)

Database session and engine

For those how want to interact with the database through a layer of SQLAlchemy there are methods for getting the session or the engine:

get_session(dbpath: str = None) → Session

Return the database session connection.

get_engine(dbpath: str = None) → Engine

Return the db engine

Electronegativities

Since electronegativity is useful concept rather than a physical observable, several scales of electronegativity exist and some of them are available in mendeleev. Depending on the definition of a particular scale the values are either stored directly or recomputed on demand with appropriate formulas. The following scales are stored:

• Allen

• Ghosh

• Pauling

Moreover there are electronegativity scales that can be computed from their respective definition and the atomic properties available in mendeleev:

• Allred-Rochow

• Cottrell-Sutton

• Gordy

• Li and Xue

• Martynov and Batsanov

• Mulliken

• Nagle

• Sanderson

For a short overview on electronegativity see this presentation.

All the examples shown below are for Silicon:

>>> from mendeleev import element
>>> Si = element('Si')

Allen

The electronegativity scale proposed by Allen in ref [2] is defined as:

\[\chi_{A} = \frac{\sum_{x} n_{x}\varepsilon_{x}}{\sum_{x}n_{x}}\]

where: \(\varepsilon_{x}\) is the multiplet-averaged one-electron energy of the subshell \(x\) and \(n_{x}\) is the number of electrons in subshell \(x\) and the summation runs over the valence shell.

The values that are tabulated were obtained from refs. [31] and [32].

Example:

>>> Si.en_allen
11.33
>>> Si.electronegativity('allen')
11.33

Allred and Rochow

The scale of Allred and Rochow [4] introduces the electronegativity as the electrostatic force exerted on the electron by the nuclear charge:

\[\chi_{AR} = \frac{e^{2}Z_{\text{eff}}}{r^{2}} \notag\]

where: \(Z_{\text{eff}}\) is the effective nuclear charge and \(r\) is the covalent radius.

Example:

>>> Si.electronegativity('allred-rochow')
0.00028240190249702736

Cottrell and Sutton

The scale proposed by Cottrell and Sutton [17] is derived from the equation:

\[\chi_{CS} = \sqrt{\frac{Z_{\text{eff}}}{r}}\]

where: \(Z_{\text{eff}}\) is the effective nuclear charge and \(r\) is the covalent radius.

Example:

>>> Si.electronegativity('cottrell-sutton')
0.18099342720014772

Ghosh

Ghosh [18] presented a scale of electronegativity based on the absolute radii of atoms computed as

\[\chi_{GH} = a \cdot (1/R) + b\]

where: \(R\) is the absolute atomic radius and \(a\) and \(b\) are empirical parameters.

Example:

>>> Si.en_ghosh
0.178503

Gordy

Gordy’s scale [20] is based on the potential that measures the work necessary to achieve the charge separation, according to:

\[\chi_{G} = \frac{eZ_{\text{eff}}}{r}\]

where: \(Z_{\text{eff}}\) is the effective nuclear charge and \(r\) is the covalent radius.

Example:

>>> Si.electronegativity('gordy')
0.03275862068965517

Li and Xue

Li and Xue [27, 28] proposed a scale that takes into account different valence states and coordination environment of atoms and is calculated according to the following formula:

\[\chi_{LX} = \frac{n^{*}\sqrt{I_{j}/Ry}}{r}\]

where: \(n^{*}\) is the effective principal quantum number, \(I_{j}\) is the j’th ionization energy in eV, \(Ry\) is the Rydberg constant in eV and \(r\) is either the crystal radius or ionic radius.

Example:

>>> Si.en_li_xue(charge=4)
{u'IV': 13.16033405547733, u'VI': 9.748395596649873}
>>> Si.electronegativity('li-xue', charge=4)
{u'IV': 13.16033405547733, u'VI': 9.748395596649873}

Martynov and Batsanov

Martynov and Batsanov [7] used the square root of the averaged valence ionization energy as a measure of electronegativity:

\[\chi_{MB} = \sqrt{\frac{1}{n_{v}}\sum^{n_{v}}_{k=1} I_{k}}\]

where: \(n_{v}\) is the number of valence electrons and \(I_{k}\) is the \(k\) th ionization potential.

Example:

>>> Si.en_martynov_batsanov()
5.0777041564076963
>>> Si.electronegativity(scale='martynov-batsanov')
5.0777041564076963

Mulliken

Mulliken scale [36] is defined as the arithmetic average of the ionization potential (\(IP\)) and the electron affinity (\(EA\)):

\[\chi_{M} = \frac{IP + EA}{2}\]

Example:

>>> Si.en_mulliken()
4.0758415
>>> Si.electronegativity('mulliken')
4.0758415

Nagle

Nagle [37] derived his scale from the atomic dipole polarizability:

\[\chi_{N} = \sqrt[3]{\frac{n}{\alpha}} \notag\]

Example:

>>> Si.electronegativity('nagle')
0.47505611644667534

Pauling

Pauling’s thermochemical scale was introduced in [40] as a relative scale based on electronegativity differences:

\[\chi_{A} - \chi_{B} = \sqrt{E_{d}(AB) - \frac{1}{2}\left[E_{d}(AA) + E_{d}(BB)\right] }\]

where: \(E_{d}(XY)\) is the bond dissociation energy of a diatomic \(XY\). The values available in mendeleev are taken from ref. [23].

Example:

>>> Si.en_pauling
1.9
>>> Si.electronegativity('pauling')
1.9

Sanderson

Sanderson [49, 50] established his scale of electronegativity based on the stability ratio:

\[\chi_{S} = \frac{\rho}{\rho_{\text{ng}}}\]

where: \(\rho\) is the average electron density \(\rho=\frac{Z}{4\pi r^{3}/3}\), and \(\rho_{\text{ng}}\) is the average electron density of a hypothetical noble gas atom with charge \(Z\).

Example:

>>> Si.en_sanderson()
0.3468157872145231
>>> Si.electronegativity()
0.3468157872145231

Fetching all electronegativities

If you want to fetch all the available scales for all elements you can use the fetch_electronegativities function, that collect all the values into a DataFrame.

API Reference

Here you’ll find API documentation of the mendeleev’s modules.

mendeleev.db
mendeleev.cli
mendeleev.econf	Implementation of the abstraction for the elctronic configuration object.
mendeleev.electronegativity	Electronegativity scale formulas.
mendeleev.fetch
mendeleev.mendeleev
mendeleev.models	module specifying the database models
mendeleev.ion
mendeleev.vis.periodictable
mendeleev.vis.bokeh
mendeleev.vis.plotly
mendeleev.vis.seaborn
mendeleev.vis.utils
mendeleev.utils

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mendeleev Changelog

v0.15.0 (26.12.2023)

• [FIX] Fix a few issues with README.md by @paulromano in #119

• [MNT] Remove six dependency by @paulromano in #120

• [FIX] Update abundance for 126Te isotope by @lmmentel in #123

• [MNT] add python 3.12 support and bump various package versions @lmmentel in #134

v0.14.0 (07.06.2023)

• Fix Mulliken electronegativity by @lmmentel in #116

• [FIX] Enable fetch of phase transition data by @lmmentel in #112

v0.13.1 (24.04.2023)

• Fix URL in references.bib by @paulromano in #108

• Fix import warning for declarative_base by @lmmentel in #109

• Add vis extra by @lmmentel in #110

v0.13.0 (11.04.2023)

• [MNT] Relax dependencies for sqlalchemy and pandas and drop python 3.7 by @lmmentel in #103

• Bump ipython from 7.34.0 to 8.10.0 by @dependabot in #104

• [MNT] Add API docs for vis module by @lmmentel in #105

v0.12.1 (28.11.2022)

• Add CodeQL workflow for GitHub code scanning by @lgtm-com in #89

• Fix number of valence electrons (#91) for Pd by @lmmentel in #92

• Add missing type hints by @lmmentel in #93

v0.12.0 (9.10.2022)

• Configure concurrency in github actions by @lmmentel in #82

• Fix abundancies for isotopes with one naturally occurring isotope by @lmmentel in #80

• Add IsotopeDecayMode model and data by @lmmentel in #84

• Update boiling and melting point data and add triple point and critical temperature and pressure, by @lmmentel in #88

• Include compatibility with python 3.11.

v0.11.0 (29.09.2022)

• Update data.rst by @Eben60 in #66

• Set discovery_location for Zinc to null by @lmmentel in #68

• Change “Oxidation states” to “Commonly occurring oxidation states” by @Eben60 in #69

• Add International Chemical Identifier property by @lmmentel in #76

• Update data for isotopes by @lmmentel in #74

• Update oxidation states and add method to fetch values by @lmmentel in #77

• Documentation fixes by @lmmentel in #78

v0.10.0 (17.07.2022)

• Corrected specific heat capacity values with CRC Handbook of Chemistry and Physics as the data source Issue #60

• Renamed specific_heat attribute to specific_heat_capacity PR #61 (for backwards compatibility specific_heat will still work)

• Added molar_heat_capacity property from CRC Handbook of Chemistry and Physics PR #61

• Corrected wrong units in the docs for specific_heat Issue #59

• Fixed usage of pytest.approx after api change PR #62

• Refactored format call to f-strings PR #62

• Updated locked dependencies to eliminate known vulnerabilities PR #63

• Added python 3.10 to CI workflows to increase test coverage PR #62

v0.9.0 (24.09.2021)

• Correct density data with CRC Handbook of Chemistry and Physics as the data source PR #39 that fixes issue #38.

• Fixed plotly based visualizations not rendering at https://mendeleev.readthedocs.io.

• Added DOI number.

v0.8.0 (22.08.2021)

• Enable visualizations of periodic tables with plotly as well as bokeh backends through mendeleev.vis.plotly.periodic_table_plotly and mendeleev.vis.bokeh.periodic_table_bokeh functions.

• Add mendeleev.vis.periodic_table function for convenient periodic table plotting wrapping both plotting backends.

• Refactored the mendeleev.vis module so it can be wasily extended with plotting backends.

• Add CITATION.cff file.

v0.7.0 (20.03.2021)

• Update ionic and crytal radii for III+ actinoids.

• Refactor electronegativity calculations for easier calculation and retrieval of the different scales.

• Add fetch.py module with methods for accessing bulk data.

• Add oxides methods to Element that returns possible oxides (Issue #17).

• Add tutorials on fetching data and electronic configuration.

• tables.py is renamed to models.py.

• Switch from pipenv to poetry for development.

• Switch from travis CI to github actions and extend testing matrix to Win and MacOS.

• Documentation udpate.

v0.6.1 (03.11.2020)

• Add electrophilicity index.

• Pin sqlalchemy version to prevent further issues with old versions, see Issue #22

v0.6.0 (10.04.2020)

• Add Ion class to handle atomic ions.

• Add Github templates for bug reports, feature requests and pull requests.

• Update the values of atomic_radius_rahm according to corrigendum, (PR #13).

• Switch the default documentation theme to material with sphinx-material.

v0.5.2 (29.01.2020)

• Fix a UnicodeDecodeError from README.md while installing on windows.

• Code quality improvements based on lgtm.com

v0.5.1 (26.08.2019)

• Fix issue #3, get_table('elements') throwing an error

v0.5.0 (25.08.2019)

• Migrate the package from bitbucket to github

• Add Pettifor scale: pettifor_number attribute

• Add Glawe scale: glawe_number attribute

• Restore default printing of isotopic abundancies, fix issue #9

• Correct the oxidation states for Xe, fix issue #10

v0.4.5 (17.03.2018)

• Update dipole polarizability value to the latest recommended (2018)

• Fix issues/8/typeerror-on-some-of-the-element

v0.4.4 (10.12.2018)

• Fix issues/6/type-of-block-is-wrong

v0.4.3 (16-07-2018)

• Added mendeleev_number attribute to elements.

• Added footnotes to the data documentation.

v0.4.2 (26-12-2018)

• Fixed issue #3: encoding issue in econf.py.

v0.4.1 (03-12-2017)

• Corrected passing integers to the CLI script.

• Various documentation readability and structure improvements.

v0.4.0 (22-11-2017)

• The elements can now be directly imported from mendeleev by symbols.

• Added sphinxcontrib.bibtex extension to the docs to handle BibTeX style references to improve handling of the bibliographic entries.

• Added nbsphinx to include Jupyter Notebook tutorials in the docs.

v0.3.6 (17-09-2017)

• Added API documentation

• Corrected the sphinx configuration

• Updated the documentation

v0.3.5 (07-09-2017)

• Added a module with functions to scrape data from ciaaw.org

• Added new Element attributes, name_origin, uses and sources

• Added new Element attributes related to the discovery: discoverers, discovery_location, discovery_year

v0.3.4 (28-06-2017)

• Fixed python2.7 compatibility issue

• Added double and triple bond covalent radii from Pyykko

• Corrected minor error in the documentation

• Replaced lazy loading with eager in db queries

v0.3.3 (16-05-2017)

• Corrected the coordination of Br5+ ion in the ionic radii table

v0.3.2 (01-05-2017)

• Added metallic_radius

• Added Goldschmidt and geochemical classifications

• Corrected the docs configuration

• Added cas number attribute

• Added atomic radii by Rahm et al.

• Created a conda recipe

• Added a citation information to the readme

• Electronic configuration code was split into a separate module

v0.3.1 (25-01-2017)

• Added new properties of isotopes: spin, g_factor, quadrupole_moment

v0.3.0 (09-01-2017)

• Updates of the documentation and tutorials

• Added radioactive isotope half-lifes

v0.2.17 (08-01-2017)

• Extended the schema for isotopes with additional attributes and updated the values of abundancies, half lifes and mass uncertainties.

• Updates to the tutorials and docs.

v0.2.16 (06-01-2017)

• Corrected the radioactive attribute of Th, Pa and U elements.

v0.2.15 (02-01-2017)

• Patched the sphinx configuration.

v0.2.14 (02-01-2017)

• Patched typos in README.

v0.2.13 (01-01-2017)

• Updated atomic weight with the newest IUPAC and CIAAW recommendations.

• Added is_radioactive and is_monoisotopic attributes.

• Updated the docs.

v0.2.12 (21-12-2016)

• Got rid of the scipy dependency.

v0.2.11 (10-11-2016)

• Updated the names and symbols of elements 113, 115, 117, 118.

• Updated the docs.

v0.2.10 (18-10-2016)

• Added the C6 coefficients from Gould and Bucko.

• Added van der Waals radii from Alvarez.

v0.2.9 (16-10-2016)

• Added a scale of electronegativities by Ghosh.

v0.2.8 (29-08-2016)

• Updated the electron affinity of Pb and Co.

• Updates of the docs.

v0.2.7 (02-04-2016)

• Maintenance.

v0.2.6 (02-04-2016)

• Mainly maintenance updates to docs, sphinx conf.py, setup.py, requirements.

v0.2.5 (02-04-2016)

Features added

• Added calculation of Martynov and Batsanov scale of electronegativity in en_martynov_batsanov method in the Element class

• Added abundance_crust and abundance_sea with element abundancies in the crust and seas

• Added molcas_gv_color attribute with MOLCAS GV colors

Bugs fixed

• Restored Python 3.x compatibility

v0.2.4 (05-02-2016)

Features added

• Extended and corrected the documentation and Jupyter notebook tutorials on basic usage electronegativities, plotting and tables

Bugs fixed

• Corrected raise to return when calling en_sanderson from electronegativity

• Fixed and tested the formula for calculating the Li and Xue scale of electronegativity in en_lie-xue

v0.2.3 (27-01-2016)

Features added

• Added new vdW radii: vdw_radius_batsanov, vdw_radius_bondi, vdw_radius_dreiding, vdw_radius_mm3, vdw_radius_rt, vdw_radius_truhlar, vdw_radius_uff

• Added an option to plot the long (wide) version of the periodic table in periodic_plot

Bugs fixed

• Typos in the docstrings

v0.2.2 (29-11-2015)

Features added

• Added new covalent radii: covalent_radius_bragg, covalent_radius_slater

• Added the c6 dispersion coefficients

• Added gas_basicity, proton_affinity and heat_of_formation

• Added periodic_plot function for producing bokeh <https://bokeh.org/> based plots of the periodic table

• Added jmol_color and cpk_color with different coloring schemes for atoms

Bug fixes

• Changed the series of elements 113, 114, 115, 116 to poor metals

v0.2.1 (26-10-2015)

Features added

• Extended the list of options for calculating Mulliken electronegativities in en_mulliken

• Added electrons_per_shell method

• Added a function to calculate linear interpolation of radii required for calculation of Sandersons electronegativity

• Added hybrid attributes electrons, protons, neutrons and mass_number

Bug fixes

• Changed the type of the melting_point from str to float

v0.2.0 (22-10-2015)

Features added

• Instead of covalent_radius added covalent_radius_2008 and covalent_radius_2009

• Instead of electronegativity added en_pauling and en_mulliken

• Added a method for getting ionic radii

• Improved the method for calculating the nuclear screening constants

• Added ElectronicConfiguration class initialized as Element attribute

• Added nuclear screening constants from Clementi and Raimondi

• Added a method to calculate the absolute softness, absolute hardness and absolute electronegativity

• Added get_table method to retrieve the tables as pandas DataFrames

Bug fixes

• Added missing electronic configurations

• Converted ionic radii from Angstrom to pico meters

v0.1.0 (11-07-2015)

First tagged version with the initial structure of the package and first version of the database and the python interface

License

The MIT License (MIT)

Copyright (c) 2015 Lukasz Mentel

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Indices and tables

• Index

• Module Index

• Search Page