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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 This notebook counts the number of evolutions and reversions in a binary trait on a large phylogenetic tree.
Import the dependencies - Dendropy and Pandas
Step1:  Read data and tree.
Step2:  Iterate over the tips of the trees and annotate with data (in this case, whether the tip is viviparous or oviparous). T
Step3:  The counting loop. If we have a tip that has data, append to either the oviparous or viviparous list, as apporpriate. If the node label is annotated as having over a 50% probability of being oviparous, add to the oviparous list. If the node is likely to be viviparous, add to the viviparous list.
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Python Code:
import dendropy
import pandas as pd
Explanation: This notebook counts the number of evolutions and reversions in a binary trait on a large phylogenetic tree.
Import the dependencies - Dendropy and Pandas
End of explanation
data = pd.read_csv('../Data/PyronParityData.csv', index_col=0, header=False)
taxa = dendropy.TaxonSet()
mle = dendropy.Tree.get_from_path('../TotalOpt/annotatedTO_0param_2598364.dated', 'newick', taxon_set=taxa, preserve_underscores=True) 
Explanation: Read data and tree.
End of explanation
for idx, nd in enumerate(mle.postorder_node_iter()):
    if nd.label is None:
        lookup = '{}'.format(nd.taxon)
        nd.label = int(data.ix[lookup])
    else: 
        pass
Explanation: Iterate over the tips of the trees and annotate with data (in this case, whether the tip is viviparous or oviparous). T
End of explanation
putative_c = []
putative_co = []
total = []
childs = []
for index, node in enumerate(mle.postorder_node_iter()):
    total.append(index)
    if node.parent_node is None:
        pass
    elif .5 < float(node.label) < 1 or float(node.label) == 0:    #Is likely oviparous 
        if float(node.parent_node.label) < .05 : #List of nodes that demonstrate change away from oviparity. 
            if node.taxon is not None :
                putative_co.append([node.parent_node.label, node.taxon])
            else:
                putative_co.append(node.parent_node.label)
                for nd in node.child_nodes():
#                    print nd.taxon
                     pass
    elif 0 < float(node.label) < .95 or float(node.label) == 1: 
            if float(node.parent_node.label) > .05: 
                putative_c.append([node.parent_node.label,node.taxon])      
print len(putative_c), 'changes to viviparity' 
print len(putative_co), 'reversions to oviparity'  
Explanation: The counting loop. If we have a tip that has data, append to either the oviparous or viviparous list, as apporpriate. If the node label is annotated as having over a 50% probability of being oviparous, add to the oviparous list. If the node is likely to be viviparous, add to the viviparous list.
End of explanation
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