I have a correlation matrix containing 4 time points, each with multiple samples. Each sample is identified with a time point with its name. What I am trying to accomplish here is to create a directed graph using Python's (2.7) Networkx with edges connecting nodes from the last time point to the first time point (6h --> 4h --> 2h --> 1h) given that the correlation value is above a certain threshold. On top of this I would only like to draw edges to nodes only if they have an already existing edge (with exception to the last time point). This will narrow the network the further the path gets.Additionally since there are only 4 time points, I would like to create a sort of x-axis with each time point and with the respective nodes listed vertically above the x-axis tick marks. Ultimately I would like the graph to look something like a horizontal Christmas tree. The code I have written works but it was written sloppy and I'm trying my best to compact it. I have listed my code below: import networkx as nximport matplotlib.pyplot as pltfrom pandas import DataFrameDG=nx.DiGraph()corr=DataFrame.from_csv('Correlation_LPS-timecourse.txt', header=0, sep='\t')timepoint1_allCols=corr.filter(regex=r'(?i)_1h_', axis=0)timepoint2_allCols=corr.filter(regex=r'(?i)_2h_', axis=0)timepoint3_allCols=corr.filter(regex=r'(?i)_4h_', axis=0)timepoint4_allCols=corr.filter(regex=r'(?i)_6h_', axis=0)timepoint_12=timepoint1_allCols.filter(regex=r'(?i)_2h_', axis=1)timepoint_23=timepoint2_allCols.filter(regex=r'(?i)_4h_', axis=1)timepoint_34=timepoint3_allCols.filter(regex=r'(?i)_6h_', axis=1)threshold = 0.98for idx, row in timepoint_34.iterrows():    for i,entry in enumerate(row):        if entry > threshold:            DG.add_edge(row.index[i], idx, weight=entry)for idx, row in timepoint_23.iterrows():    for i,entry in enumerate(row):            if entry > threshold and DG.degree(row.index[i]):                DG.add_edge(row.index[i], idx, weight=entry)for idx, row in timepoint_12.iterrows():    for i,entry in enumerate(row):            if entry > threshold and DG.degree(row.index[i]):                DG.add_edge(row.index[i], idx, weight=entry)nx.draw(DG, pos = nx.spring_layout(DG), with_labels=True)plt.show()If you're interested in playing with the real data that I am using I have uploaded it here. 
Creating a time-course dependent, correlation-based directed graph with Networkx
python;python 2.7;graph;pandas