#!/usr/bin/env python
# coding: utf-8

# # Bulk data access
# 
# This tutorial explains how to retrieve full tables from the database into [pandas DataFrames](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html).
# 
# ## 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`

# In[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

# In[3]:


ptable = fetch_table("elements")


# Now we can use [pandas'](http://pandas.pydata.org) capabilities to work with the data. 

# In[4]:


ptable.info()


# For clarity let's take only a subset of columns 

# In[5]:


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


# In[6]:


ptable[cols].head()


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

# In[7]:


ptable[cols].describe()


# ## Isotopes table
# 
# Let try and retrieve another table, namely ``isotopes``

# In[8]:


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


# In[9]:


isotopes.info()


# ### Merge the elements table with the isotopes
# 
# We can now perform SQL-like merge operation on two ``DataFrame``s and produce an [outer](http://pandas.pydata.org/pandas-docs/stable/merging.html#database-style-dataframe-joining-merging) join 

# In[10]:


import pandas as pd


# In[11]:


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


# now we have the following columns in the ``merged`` ``DataFrame``

# In[12]:


merged.info()


# In[13]:


merged.head()


# To display all the isotopes of Silicon

# In[14]:


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


# ## 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.

# In[2]:


from mendeleev.fetch import fetch_ionic_radii


# In[3]:


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


# ## 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.

# In[17]:


from mendeleev.fetch import fetch_ionization_energies


# In[25]:


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


# In[24]:


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


# ## 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.

# In[26]:


from mendeleev.fetch import fetch_electronegativities


# In[27]:


ens = fetch_electronegativities()
ens.head(10)