

In [9]:

    
%matplotlib inline
import pandas as pd

Figure 3 csv data generation

Figure data consolidation for Figure 3, which shows patterns of nestedness in beta diversity

Figure 3a: phyla occupancy plot, all samples



In [3]:

    
# read in nestedness output for all samples

fig3a = pd.read_csv('../../../data/nestedness/nest_phylum_allsamples.csv')

fig3a.head()









    Out[3]:







  
    
      
      SAMPLE_RANK
      OBSERVATION_RANK
      SAMPLE_ID
      OBSERVATION_ID
      empo_3
      METADATA_NUMERIC_CODE
    
  
  
    
      0
      1171
      68
      1747.DZF.6252012.A.log
      k__Bacteria;p__Fusobacteria
      Plant corpus
      1
    
    
      1
      1171
      82
      1747.DZF.6252012.A.log
      k__Bacteria;p__Cyanobacteria
      Plant corpus
      1
    
    
      2
      1171
      71
      1747.DZF.6252012.A.log
      k__Bacteria;p__Armatimonadetes
      Plant corpus
      1
    
    
      3
      1171
      78
      1747.DZF.6252012.A.log
      k__Bacteria;p__Planctomycetes
      Plant corpus
      1
    
    
      4
      1171
      81
      1747.DZF.6252012.A.log
      k__Bacteria;p__Firmicutes
      Plant corpus
      1

Fig 3b: phyla occupancy plot, individual Empo_2 categories



In [4]:

    
fig3b_animal = pd.read_csv('../../../data/nestedness/nest_phylum_Animal.csv')
fig3b_plant = pd.read_csv('../../../data/nestedness/nest_phylum_Plant.csv')
fig3b_saline = pd.read_csv('../../../data/nestedness/nest_phylum_Saline.csv')
fig3b_nonsaline = pd.read_csv('../../../data/nestedness/nest_phylum_Non-saline.csv')

Fig 3c: NODF model results



In [37]:

    
fig3c = pd.read_csv('../../../data/nestedness/nestedness_null_model_results_2017-09-19.csv')

fig3c.head()









    Out[37]:







  
    
      
      EMPO_SUBSET
      TAXONOMIC_LEVEL
      NODF_NULL_MEAN
      NODF_NULL_STDEV
      NODF_OBSERVED
    
  
  
    
      0
      All
      class
      0.254916
      0.006882
      0.732128
    
    
      1
      All
      family
      0.187935
      0.005303
      0.434909
    
    
      2
      All
      genus
      0.072319
      0.004046
      0.256224
    
    
      3
      All
      order
      0.225096
      0.005760
      0.582832
    
    
      4
      All
      otu
      0.010253
      0.000911
      0.039439

Write to Excel notebook



In [40]:

    
fig3 = pd.ExcelWriter('Figure3_data.xlsx')

fig3a.to_excel(fig3,'Fig-3a')
fig3b_animal.to_excel(fig3,'Fig-3b_animal')
fig3b_plant.to_excel(fig3,'Fig-3b_plant')
fig3b_saline.to_excel(fig3,'Fig-3b_saline')
fig3b_nonsaline.to_excel(fig3,'Fig-3b_nonsaline')
fig3c.to_excel(fig3,'Fig-3c')

fig3.save()



In [ ]:

	SAMPLE_RANK	OBSERVATION_RANK	SAMPLE_ID	OBSERVATION_ID	empo_3	METADATA_NUMERIC_CODE
0	1171	68	1747.DZF.6252012.A.log	k__Bacteria;p__Fusobacteria	Plant corpus	1
1	1171	82	1747.DZF.6252012.A.log	k__Bacteria;p__Cyanobacteria	Plant corpus	1
2	1171	71	1747.DZF.6252012.A.log	k__Bacteria;p__Armatimonadetes	Plant corpus	1
3	1171	78	1747.DZF.6252012.A.log	k__Bacteria;p__Planctomycetes	Plant corpus	1
4	1171	81	1747.DZF.6252012.A.log	k__Bacteria;p__Firmicutes	Plant corpus	1

	EMPO_SUBSET	TAXONOMIC_LEVEL	NODF_NULL_MEAN	NODF_NULL_STDEV	NODF_OBSERVED
0	All	class	0.254916	0.006882	0.732128
1	All	family	0.187935	0.005303	0.434909
2	All	genus	0.072319	0.004046	0.256224
3	All	order	0.225096	0.005760	0.582832
4	All	otu	0.010253	0.000911	0.039439