将Pandas数据框转换为包含ID和权重的元组列表 [英] Convert Pandas data frame to a list of tuples containing IDs and a weight

问题描述

我有一个数据帧(称为df)，其当前格式如下:

I have a data frame (called df) which is currently formatted like so:

    1      2     3
1   1      0.26  0.02
2   0.26   1     0.61
3   0.02   0.61  1

这些ID由一个值连接，我想以某种方式提取所有唯一的ID值，以便以更有效的方式将它们添加到networkx上的图形中.

The IDs are connected by a value and I would like to somehow extract all unique ID values in order to have a more efficient way to add them to my graph on networkx.

输出应如下所示:

ed_list = [(1,2,{'weight': 0.26}),(1,3,{'weight': 0.02}),(2,3,{'weight':0.61})]

目前，我使用以下方法:

At the moment I use the following method:

# Create matrix 
new_ = df.values
A_d = np.matrix(new_)
G = nx.from_numpy_matrix(A_d) 

我想知道从df创建元组列表是否可以更容易/更有效，我可以用它来连接节点，然后在其中添加如下边:

I'm wondering if it would be easier/more efficient to create a List of tuples from my df that I could use to connect my nodes, where I could then add edges like so:

G.add_edges_from(ed_list)

我在上一个版本的问题中犯了一个错误-列名和行名只是整数

I have made a mistake in the previous version of my question - the column and row names are just integers

推荐答案

可以尝试:

# this s is what you are looking for
s = df.where(df.index.values > df.columns.values[:,None]).stack().reset_index(name='weight')

# we can use dataframe directly
G = nx.from_pandas_edgelist(s,source='level_0',target='level_1', edge_attr='weight')

或更简单:

G = nx.from_pandas_adjacency(df)

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