如何从使用熊猫上传的大型CSV文件中仅打印出某些行

Question

我将如何以特定顺序输出某些行（即，我想使用（rep1，rep2），（rep10，rep12），（rep12，rep16）打印行？）我的csv文件是：

 Node1  Node2   Trail  Time  color  estimate
0     rep1   rep2   rep_1  1811    red         0
1     rep2   rep4   rep_1  1811    red         0
2     rep4   rep5   rep_1  1135    red         0
3     rep5   rep7   rep_1  2000    red         0
4     rep7   rep8   rep_1   885    red         0
5     rep8  rep10   rep_1  1010    red         0
6    rep10  rep12   rep_1  1310    red         0
7    rep12  rep13   rep_1  1135    red         0
8    rep13  rep16   rep_1  1435    red         0
9    rep16  rep17   rep_1   885    red         0
10   rep17  rep19   rep_1  1435    red         0
11   rep19  rep26   rep_1  1000    red         0
12   rep26  rep27   rep_1   850    red         0
13    rep2   rep1   rep_2  1811   blue         0
14    rep1   rep4   rep_2  1811   blue         0
15    rep4   rep5   rep_2  1135   blue         0
16    rep5   rep7   rep_2  2000   blue         0
17    rep7   rep8   rep_2   885   blue         0
18    rep8  rep10   rep_2  1010   blue         0
19   rep10  rep12   rep_2  1310   blue         0
 ..     ...    ...     ...   ...    ...       ...
159   rep5   rep7  rep_26  2000  brown         0
160   rep7   rep8  rep_26   885  brown         0
161   rep8  rep10  rep_26  1010  brown         0
162  rep10  rep12  rep_26  1310  brown         0
163  rep12  rep13  rep_26  1135  brown         0
164  rep13  rep16  rep_26  1435  brown         0
165  rep16  rep17  rep_26   885  brown         0
166  rep17  rep19  rep_26  1435  brown         0
167  rep19  rep27  rep_26  1000  brown         0
168  rep27   rep1  rep_27   885   blue         0
169   rep1   rep2  rep_27  1181   blue         0
170   rep2   rep4  rep_27  1811   blue         0
171   rep4   rep5  rep_27  1135   blue         0
172   rep5   rep7  rep_27  2000   blue         0
173   rep7   rep8  rep_27   885   blue         0
174   rep8  rep10  rep_27  1010   blue         0
175  rep10  rep12  rep_27  1310   blue         0
176  rep12  rep13  rep_27  1135   blue         0
177  rep13  rep16  rep_27  1435   blue         0
178  rep16  rep17  rep_27   885   blue         0
179  rep17  rep19  rep_27  1435   blue         0
180  rep19  rep26  rep_27   850   blue         0

[181 rows x 6 columns]

这是我尝试用于输出的内容：

print(df3(odd_matching))

我也尝试过：

tcount=0
bcount=0
for cols in df3.iterrows():
tcount += 1
if cols ['Node1'] == odd_matching
bcount +=1

print(Node1, 'This', Node2, 'This', )

我的其余代码：

import itertools
import copy
import networkx as nx
import pandas as pd
import matplotlib.pyplot as plt
import csv

df3=pd.read_csv(r"U:\\user\edge_list_4.csv")
print(df3)

df4=pd.read_csv(r"U:\\user\nodes_fixed_2.csv",error_bad_lines=False)
df4.dropna() 
print(df4)

# Compute min weight matching.
# Note: max_weight_matching uses the 'weight' attribute by default as the 
attribute to maximize.
odd_matching_dupes= nx.algorithms.max_weight_matching(g_odd_complete, True)

print('Number of edges in matching: {}'.format(len(odd_matching_dupes)))

# Preview of matching with dupes
odd_matching_dupes

# Convert matching to list of deduped tuples
odd_matching = list(pd.unique([tuple(sorted([k, v])) for k, v in 
odd_matching_dupes]))

#Counts
print('Number of edges in matching (deduped): 
{}'.format(len(odd_matching)))

# Preview of deduped matching
odd_matching

g_odd_complete_min_edges = nx.Graph(odd_matching)

def add_augmenting_path_to_graph(graph, min_weight_pairs):
"""
Add the min weight matching edges to the original graph
Parameters:
    graph: NetworkX graph (original graph from trailmap)
    min_weight_pairs: list[tuples] of node pairs from min weight matching
Returns:
    augmented NetworkX graph
"""

# We need to make the augmented graph a MultiGraph so we can add parallel 
edges
graph_aug=nx.MultiGraph(graph.copy())
for pair in min_weight_pairs:
    graph_aug.add_edge(pair[0], 
                       pair[1], 
                       **{'Time': nx.dijkstra_path_length(graph, pair[0], 
pair[1]), 'Trail': 'augmented'}
                       # attr_dict={'distance': 
nx.dijkstra_path_length(graph, pair[0], pair[1]),
                       #            'trail': 'augmented'}  # deprecated 
after 1.11
                      )
return graph_aug

#Create augmented graph: add the min weight matching edges to g
g_aug=add_augmenting_path_to_graph(g, odd_matching)

奇数匹配为：

[('rep19', 'rep27'), ('rep2', 'rep5'), ('rep10', 'rep7'), ('rep1', 'rep8'), ('rep12', 'rep13'), ('rep16', 'rep17')]

The error I get is:

---

-TypeError Traceback（最近一次通话最近一次）在271272-> 273 print（“ this sample”，df3（odd_matching））

  TypeError: 'DataFrame' object is not callable

Answer 1

假设您给定的数据框为“ df”，

df1 = df[(df['Node1']=='rep1') & (df['Node2']=='rep2')]

df2 = df[(df['Node1']=='rep10') & (df['Node2']=='rep12')]

df2 = df[(df['Node1']=='rep12') & (df['Node2']=='rep16')]

合并以上数据框以获得所需的输出。

Answer 2

从odd_matching开始是很尴尬的，因为由于排序，每对的第一个元素可以是Node1或Node2 ，而该对的第二个元素可以是“其他”节点。

从odd_matching_dupes开始，因为这里的键是Node1 ，值是Node2 。 不要为重复而烦恼，稍后您将对其进行处理。

注意：

[ df3[df3.Node1 == k and df3.Node2 == v].index for k, v in odd_matching_dupes ]

为您提供了一个odd_matching_dupes列表中所有行的索引列表。

然后：

• 从此列表中删除重复的值，
• 打印在该（压缩的）列表中具有索引的行。