I have the below dataframe that includes file number field and departements
where each file number is assign to one or more departments and the number (1-0-99) represent the status of the file like
- 1: mean the task is done
- 0: the task is pending
- 99: the department has no access to the file
df=pd.DataFrame({'file':[1205,2897,1171,1322,4312,2211,1242,52,443,111],
'finance':[1,99,0,0,1,99,1,0,1,99],
'IT':[1,99,0,1,99,1,0,0,99,0],
'marketing':[1,1,0,99,1,99,1,1,0,1]})
file finance IT marketing
0 1205 1 1 1
1 2897 99 99 1
2 1171 0 0 0
3 1322 0 1 99
4 4312 1 99 1
5 2211 99 1 99
6 1242 1 0 1
7 52 0 0 1
8 443 1 99 0
9 111 99 0 1
what i am asking is it possible to visualize the network of these files and departments using network graph??
where the expected result is :
- node 1 file number 1205 is connected to the three department and all the required tasks are done
- node 2 file number 2897 is connected to marketing dept
- node 3 file number 1171 is connected to the three department but the tasks still pending.
>Solution :
IIUC, you can use:
import pandas as pd
import networkx as nx
import matplotlib.pyplot as plt
# df = pd.DataFrame(...)
COLORS = {0: 'red', 1: 'green'}
edges = df.melt('file', var_name='department', value_name='status').query('status != 99')
G = nx.from_pandas_edgelist(edges, source='department', target='file', edge_attr=['status'])
pos = nx.shell_layout(G, [edges['department'].unique(), edges['file'].unique()])
node_colors = ['lightcoral' if node in edges['department'].unique() else 'yellow' for node in G.nodes]
edge_colors = [COLORS[G.edges[edge]['status']] for edge in G.edges]
nx.draw_networkx(G, pos=pos, with_labels=True, node_color=node_colors, edge_color=edge_colors)
plt.show()
>>> edges
file department status
0 1205 finance 1
2 1171 finance 0
3 1322 finance 0
4 4312 finance 1
6 1242 finance 1
7 52 finance 0
8 443 finance 1
10 1205 IT 1
12 1171 IT 0
13 1322 IT 1
15 2211 IT 1
16 1242 IT 0
17 52 IT 0
19 111 IT 0
20 1205 marketing 1
21 2897 marketing 1
22 1171 marketing 0
24 4312 marketing 1
26 1242 marketing 1
27 52 marketing 1
28 443 marketing 0
29 111 marketing 1