如何从Python中的邻接矩阵创建边缘列表数据帧？
我有一个pandas数据帧（将if视为网络中节点的加权邻接矩阵），形式为，df如何从Python中的邻接矩阵创建边缘列表数据帧？,python,pandas,numpy,dataframe,Python,Pandas,Numpy,Dataframe,我有一个pandas数据帧（将if视为网络中节点的加权邻接矩阵），形式为，df A B C D A 0 0.5 0.5 0 B 1 0 0 0 C 0.8 0 0 0.2 D 0 0 1 0 Source Target Weight 0 A B 0.5 1 A C 0.5 2 A
A B C D
A 0 0.5 0.5 0
B 1 0 0 0
C 0.8 0 0 0.2
D 0 0 1 0
Source Target Weight
0 A B 0.5
1 A C 0.5
2 A D 0
3 B A 1
4 B C 0
5 B D 0
6 C A 0.8
7 C B 0
8 C D 0.2
9 D A 0
10 D B 0
11 D C 1
A B C D
A 0 0.5 0.5 0
B 1 0 0 0
C 0.8 0 0 0.2
D 0 0 1 0
Source Target Weight
0 A B 0.5
1 A C 0.5
2 A D 0
3 B A 1
4 B C 0
5 B D 0
6 C A 0.8
7 C B 0
8 C D 0.2
9 D A 0
10 D B 0
11 D C 1
创建此项的最有效方法是什么？使用
重命名轴+重置索引+熔化：
df.rename_axis('Source')\
.reset_index()\
.melt('Source', value_name='Weight', var_name='Target')\
.query('Source != Target')\
.reset_index(drop=True)
Source Target Weight
0 B A 1.0
1 C A 0.8
2 D A 0.0
3 A B 0.5
4 C B 0.0
5 D B 0.0
6 A C 0.5
7 B C 0.0
8 D C 1.0
9 A D 0.0
10 B D 0.0
11 C D 0.2
melt
是从0.20
开始作为DataFrame
对象的函数引入的，对于较旧的版本，您需要使用pd.melt
：
v = df.rename_axis('Source').reset_index()
df = pd.melt(
v,
id_vars='Source',
value_name='Weight',
var_name='Target'
).query('Source != Target')\
.reset_index(drop=True)
计时
x = np.random.randn(1000, 1000)
x[[np.arange(len(x))] * 2] = 0
df = pd.DataFrame(x)
将对角线标记为nan
，然后我们stack
df.values[[np.arange(len(df))]*2] = np.nan
df
Out[172]:
A B C D
A NaN 0.5 0.5 0.0
B 1.0 NaN 0.0 0.0
C 0.8 0.0 NaN 0.2
D 0.0 0.0 1.0 NaN
df.stack().reset_index()
Out[173]:
level_0 level_1 0
0 A B 0.5
1 A C 0.5
2 A D 0.0
3 B A 1.0
4 B C 0.0
5 B D 0.0
6 C A 0.8
7 C B 0.0
8 C D 0.2
9 D A 0.0
10 D B 0.0
11 D C 1.0
使用NumPy工具的两种方法-
方法#1
def edgelist(df):
a = df.values
c = df.columns
n = len(c)
c_ar = np.array(c)
out = np.empty((n, n, 2), dtype=c_ar.dtype)
out[...,0] = c_ar[:,None]
out[...,1] = c_ar
mask = ~np.eye(n,dtype=bool)
df_out = pd.DataFrame(out[mask], columns=[['Source','Target']])
df_out['Weight'] = a[mask]
return df_out
样本运行-
In [155]: df
Out[155]:
A B C D
A 0.0 0.5 0.5 0.0
B 1.0 0.0 0.0 0.0
C 0.8 0.0 0.0 0.2
D 0.0 0.0 1.0 0.0
In [156]: edgelist(df)
Out[156]:
Source Target Weight
0 A B 0.5
1 A C 0.5
2 A D 0.0
3 B A 1.0
4 B C 0.0
5 B D 0.0
6 C A 0.8
7 C B 0.0
8 C D 0.2
9 D A 0.0
10 D B 0.0
11 D C 1.0
方法#2
# https://stackoverflow.com/a/46736275/ @Divakar
def skip_diag_strided(A):
m = A.shape[0]
strided = np.lib.stride_tricks.as_strided
s0,s1 = A.strides
return strided(A.ravel()[1:], shape=(m-1,m), strides=(s0+s1,s1))
# https://stackoverflow.com/a/48234170/ @Divakar
def combinations_without_repeat(a):
n = len(a)
out = np.empty((n,n-1,2),dtype=a.dtype)
out[:,:,0] = np.broadcast_to(a[:,None], (n, n-1))
out.shape = (n-1,n,2)
out[:,:,1] = onecold(a)
out.shape = (-1,2)
return out
cols = df.columns.values.astype('S1')
df_out = pd.DataFrame(combinations_without_repeat(cols))
df_out['Weight'] = skip_diag_strided(df.values.copy()).ravel()
运行时测试
使用：
使用：
@cᴏʟᴅsᴘᴇᴇᴅ 我通过Tai的问题和你的回答得到了这个：-），这个很好。增加了时间安排。这也更快。您可以使用np.fill\u diagonal（df.values，np.nan）
进行对角线设置。是否可以更改“0级”、“1级”、“0级”？例如，对于“From”、“to”、“Weight”？@LarryCai，您可以重命名~df.stack（）.reset_index（）.rename（columns={'level_0'：'From'…）
In [704]: x = np.random.randn(1000, 1000)
...: x[[np.arange(len(x))] * 2] = 0
...:
...: df = pd.DataFrame(x)
# @cᴏʟᴅsᴘᴇᴇᴅ's soln
In [705]: %%timeit
...: df.index.name = 'Source'
...: df.reset_index()\
...: .melt('Source', value_name='Weight', var_name='Target')\
...: .query('Source != Target')\
...: .reset_index(drop=True)
10 loops, best of 3: 67.4 ms per loop
# @Wen's soln
In [706]: %%timeit
...: df.values[[np.arange(len(df))]*2] = np.nan
...: df.stack().reset_index()
100 loops, best of 3: 19.6 ms per loop
# Proposed in this post - Approach #1
In [707]: %timeit edgelist(df)
10 loops, best of 3: 24.8 ms per loop
# Proposed in this post - Approach #2
In [708]: %%timeit
...: cols = df.columns.values.astype('S1')
...: df_out = pd.DataFrame(combinations_without_repeat(cols))
...: df_out['Weight'] = skip_diag_strided(df.values.copy()).ravel()
100 loops, best of 3: 17.4 ms per loop
import networkx as nx
In [246]: G = nx.from_pandas_adjacency(df, create_using=nx.MultiDiGraph())
In [247]: G.edges(data=True)
Out[247]: OutMultiEdgeDataView([('A', 'B', {'weight': 0.5}), ('A', 'C', {'weight': 0.5}), ('B', 'A', {'weight': 1.0}), ('C', 'A', {'weight': 0.8}), ('C', 'D', {
'weight': 0.2}), ('D', 'C', {'weight': 1.0})])
In [248]: nx.to_pandas_edgelist(G)
Out[248]:
source target weight
0 A B 0.5
1 A C 0.5
2 B A 1.0
3 C A 0.8
4 C D 0.2
5 D C 1.0