NetworkX is the most popular Python package for manipulating and analyzing graphs. Several packages offer the same basic level of graph manipulation, and study of the structure, dynamics, and functions of complex networks. Pygraphviz is a Python interface to the Graphviz graph layout and visualization package.Python language data structures for graphs, digraphs, and multigraphs. Nodes can be "anything" (e.g. text, images, XML records)Edges can hold arbitrary data (e.g. weights, time-series)Generators for classic graphs, random graphs, and synthetic networksStandard graph algorithmsNetwork structure and analysis measuresBasic graph drawingOpen source BSD licenseWell tested: more than 1500 unit testsAdditional benefits from Python: fast prototyping, easy to teach, multi-platform.

**Installing Packages**

If you've done any sort of data analysis in Python or have the Anaconda distribution, my guess is you probably have pandas and matplotlib. However, you might not have networkx.

pip install pandas pip install networkx pip install matplotlib

Next we will present simple example of network with 5 nodes. To fire up network, we have to import networkx.

import networkx as nx

import matplotlib.pyplot as plt

%matplotlib inline

**Create Graph**

Now you use the edge list and the node list to create a graph object in networkx.

```
# Create empty graph
G = nx.Graph()
```

`"G" is the name of the graph you build. Next we add nodes. There are two ways to add nodes: one by one,`

`print(type(G)) #type=global keyword/class`

`G.add_node("A")`

`G.add_node("Python")`

`##to find the first node`

`#first make a node list`

`nodelist = list(G.node)`

`print(nodelist[0]) #python starts with 0`

**Add in a list of Nodes**

```
nodelist = ['B','C','D','E']
G.add_nodes_from(nodelist)
```

**Then we add edges. Also there are two ways, either one by one**

```
G.add_edge('A','B')
G.remove_node('Python')
```

**Add in a batch**

```
edgeList = [('A','B'),('B','C'),('C','D'),('A','D')] #list of tuples
G.add_edges_from(edgeList)
```

**Finally we will visualize our work and get basic information of network. **

### nx.draw(G, with_labels =True, node_color = 'pink', node_size = 900)

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