Add some examples of extracting and manipulating the data

The pandas module is very powerful, lets give a few examples of things
that can be done.

Author: php1ic

README.md

@@ -44,3 +44,55 @@ No comparison or validation is done on common values.
 ## Additional uses
 
 If you want to do your own thing with the data, you could import this module, access `MassTable().full_data`, then sort, slice and filter the resultant dataframe to your heart's content.
+
+For example, track how the accuracy of the mass excess of 18B changes once it is experimentally measured
+```python
+>>> import pynch.mass_table as mt
+>>> df = mt.MassTable().full_data
+>>> df[(df['A'] == 18) & (df['Z'] == 5)][['Experimental', 'NubaseMassExcess', 'NubaseMassExcessError', 'NubaseRelativeError', 'DiscoveryYear']]
+           Experimental  NubaseMassExcess  NubaseMassExcessError  NubaseRelativeError  DiscoveryYear
+TableYear
+2003              False           52320.0                  800.0             0.015291           1900
+2012               True           51850.0                  170.0             0.003279           2010
+2016               True           51790.0                  200.0             0.003862           2010
+2020               True           51790.0                  200.0             0.003862           2010
+```
+Or for all of the A=100 isotopes from the 2012 table that have a mass-excess error < 10.0keV, print the A, Z, symbol and year of discovery 
+```python
+>>> import pynch.mass_table as mt
+>>> df = mt.MassTable().full_data
+>>> df.query('TableYear == 2012 and A == 100 and NubaseMassExcessError < 10.0')[['A', 'Z', 'Symbol', 'DiscoveryYear']]
+             A   Z Symbol  DiscoveryYear
+TableYear
+2012       100  40     Zr           1970
+2012       100  41     Nb           1967
+2012       100  42     Mo           1930
+2012       100  43     Tc           1952
+2012       100  44     Ru           1931
+2012       100  47     Ag           1970
+2012       100  48     Cd           1970
+```
+Or how does the NUBASE mass-excess compare with the AME value for experimentally measured isotopes from the latest table? Which are the 10 isotopes with the biggest differences?
+```python
+>>> import pynch.mass_table as mt
+>>> df = mt.MassTable().full_data
+>>> # Create a new column comparing the measured values
+>>> df['NUBASE-AME'] = df['NubaseMassExcess'] - df['AMEMassExcess']
+>>> # Extract the data for measured isotopes and from the latest table
+>>> df_comparison = df.query('TableYear == 2020 and Experimental == True')
+>>> # Sort the difference in measured data by absolute value and print the columns we are interested in
+>>> df_comparison.sort_values(by=['NUBASE-AME'], key=abs, ascending=False)[['A', 'Z', 'Symbol', 'NubaseMassExcess', 'AMEMassExcess', 'NUBASE-AME']].head(n=10)
+             A   Z Symbol  NubaseMassExcess  AMEMassExcess  NUBASE-AME
+TableYear
+2020       221  91     Pa           20370.0      20374.937      -4.937
+2020        57  23      V          -44440.0     -44435.063      -4.937
+2020       102  50     Sn          -64930.0     -64934.896       4.896
+2020       168  75     Re          -35790.0     -35794.889       4.889
+2020       209  89     Ac            8840.0       8844.887      -4.887
+2020       241  93     Np           54320.0      54315.115       4.885
+2020       121  56     Ba          -70740.0     -70744.847       4.847
+2020       122  55     Cs          -78140.0     -78144.769       4.769
+2020       206  89     Ac           13480.0      13484.754      -4.754
+2020        23   9      F            3290.0       3285.263       4.737
+```
+These are slightly contrived examples, but hopefully you get the idea. The data can be manipulated and added to as required.