98 lines
3.1 KiB
Markdown
98 lines
3.1 KiB
Markdown
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# 从熊猫数据框架创建加权图
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> 原文：<https://www.askpython.com/python/examples/creating-weighted-graph-dataframe>
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嘿伙计们！在本教程中，我们将了解如何从熊猫数据框构建我们自己的加权图。
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* * *
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## 从熊猫数据帧中创建一个加权图
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任何 python 程序的第一项任务都是将必要的模块/库导入代码。
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```py
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import pandas as pd
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import numpy as np
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import networkx as nx
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import matplotlib.pyplot as plt
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```
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下一个任务是创建一个数据框，在后面的部分中需要为其绘制图表。同样可以在[熊猫](https://www.askpython.com/python-modules/pandas/python-pandas-module-tutorial)和 [numpy 模块](https://www.askpython.com/python-modules/numpy/python-numpy-module)的帮助下获得。
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首先，我们创建一个随机种子，它将有助于在一个特定的范围内生成一些随机整数，这些整数将在后面的部分中作为边的权重。接下来，使用`DataFrame`函数创建数据框，并将图表数据传递给该函数。
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```py
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r = np.random.RandomState(seed=5)
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weights = r.random_integers(1, 5, size=(5,))
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df = pd.DataFrame({'from':['A','B','C','D','E'],'to':['D','E','A','D','C'],'weight':weights})
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df.head()
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```
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Dataframe 2 Graph Data
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接下来，我们将分别借助于`draw_networkx_nodes`、`draw_networkx_edges`和`draw_networkx_labels`函数，通过分别绘制节点、边和标签来尝试可视化权重增加图。
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### 可视化节点
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```py
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fig, ax = plt.subplots()
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pos = nx.spring_layout(G)
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plt.title("Plotting Nodes")
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nx.draw_networkx_nodes(G, pos, ax = ax)
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```
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Dataframe 2 Graph Only Nodes
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### 可视化边缘
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```py
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fig, ax = plt.subplots()
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pos = nx.spring_layout(G)
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plt.title("Plotting Edges")
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nx.draw_networkx_edges(G, pos, width=durations, ax=ax)
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```
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Dataframe 2 Graph Only Edges
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### 可视化完整的图表
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```py
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fig, ax = plt.subplots()
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pos = nx.spring_layout(G)
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plt.title("Plotting Complete Graph")
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nx.draw_networkx_nodes(G, pos, ax = ax)
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nx.draw_networkx_edges(G, pos, width=durations, ax=ax)
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_ = nx.draw_networkx_labels(G, pos, labels, ax=ax)
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```
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Dataframe 2 Graph Complete Graph
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* * *
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## 结论
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恭喜你！您刚刚学习了如何在 [NetworkX 库](https://www.askpython.com/python-modules/networkx-package)中使用 pandas 数据框构建图表。希望你喜欢它！😇
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喜欢这个教程吗？我建议你看看下面提到的教程:
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1. [NetworkX 包——Python 图形库](https://www.askpython.com/python-modules/networkx-package)
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2. [计算未加权图中节点间的距离](https://www.askpython.com/python/examples/distance-between-nodes-unweighted-graph)
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3. [Python 中的图形操作【附简单例子】](https://www.askpython.com/python/examples/graph-operations)
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4. [用 Python 实现图形](https://www.askpython.com/python/examples/graph-in-python)
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感谢您抽出时间！希望你学到了新的东西！！😄
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