抱歉标题有点不清楚。言语使我无法简洁地描述这个问题。希望我下面的描述可以帮助澄清。欢迎对标题进行任何澄清编辑。
我正在尝试从 Pandas 数据帧创建 networkx 流程图。数据框记录订单如何流经多家公司。数据框中的大多数行都是连接的,并且连接表现在多列中。样本数据如下:
df = pd.DataFrame({'Company': ['A', 'A', 'B', 'B', 'B', 'C', 'C'],
'event_type':['new', 'route', 'receive', 'execute', 'route', 'receive', 'execute'],
'event_id': ['110', '120', '200', '210', '220', '300', '310'],
'prior_event_id': [np.nan, '110', np.nan, '120', '210', np.nan, '300'],
'route_id': [np.nan, 'foo', 'foo', np.nan, 'bar', 'bar', np.nan]}
)
数据框如下所示: Company event_type event_id prior_event_id route_id
0 A new 110 NaN NaN
1 A route 120 110 foo
2 B receive 200 NaN foo
3 B execute 210 120 NaN
4 B route 220 210 bar
5 C receive 300 NaN bar
6 C execute 310 300 NaN
订单经过 3 个公司:A、B、C。在每个公司内,后面的事件可以通过 event_id
链接到其源事件。 - prior_event_id
一对。但是这种方法不适用于属于不同公司的记录。例如,第 1 行和第 2 行将仅通过一列匹配 route_id
.因此,我试图重新创建的链接机制是分层的,因为我将只使用列 route_id
如果 event_id
匹配- prior_event_id
列对不会产生任何结果。下图可能有助于说明链接机制:
我的解决方案很笨拙:
# Make every event unique so as to not confound the linking
df['event_sub'] = df.groupby(df.event_type).cumcount()+1
df['event'] = df.event_type + ' ' + df.event_sub.astype(str)
# Find the match based on first matching criterion
replace_dict_event = dict(df[['event_id', 'event']].values)
df['source'] = df['prior_event_id'].apply(lambda x: replace_dict_event.get(x) if replace_dict_event.get(x) else np.nan )
df['target'] = df['event_id'].apply(lambda x: replace_dict_event.get(x) if replace_dict_event.get(x) else np.nan )
# From last step, find the match based on second matching criterion for the unmatched rows
replace_dict_rtd = dict(df[df.event_type == 'route'][['route_id', 'event']].values)
df.loc[df.event_type == 'receive', 'source'] = df[df.event_type == 'receive']['route_id'].apply(lambda x: replace_dict_rtd.get(x))
df
我基本上使用了 apply
两次以逐步获得匹配。我想知道是否有更干净、更 Pythonic 的方法来做到这一点。我的结果如下所示:
我由此创建的 networkx 图:
最佳答案
您有两种不同类型的链接:a) 通过匹配 prior_event_id
定义的链接和 event_id
, 和 b) 由 route_id
定义的链接.使用两组不同的命令提取两种不同类型的关系 是 pythonic(或只是简单的良好编码实践)。
话虽如此,由于您正在处理表格数据,因此最好使用合并(特别是内部连接)来提取链接——而不是使用带有应用的字典查找。表格数据的数据库针对此类查询进行了优化,而对于大型数据集,您的查找速度会慢得多。
#!/usr/bin/env python
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
if __name__ == '__main__':
df = pd.DataFrame({'Company': ['A', 'A', 'B', 'B', 'B', 'C', 'C'],
'event_type':['new', 'route', 'receive', 'execute', 'route', 'receive', 'execute'],
'event_id': ['110', '120', '200', '210', '220', '300', '310'],
'prior_event_id': [np.nan, '110', np.nan, '120', '210', np.nan, '300'],
'route_id': [np.nan, 'foo', 'foo', np.nan, 'bar', 'bar', np.nan]}
)
# --------------------------------------------------------------------------------
# a) links established by matching event_id with prior_event_id
df2 = pd.merge(df, df, left_on='event_id', right_on='prior_event_id', how='inner')
# Company_x event_type_x event_id_x prior_event_id_x route_id_x Company_y event_type_y event_id_y prior_event_id_y route_id_y
# 0 A new 110 NaN NaN A route 120 110 foo
# 1 A route 120 110 foo B execute 210 120 NaN
# 2 B execute 210 120 NaN B route 220 210 bar
# 3 C receive 300 NaN bar C execute 310 300 NaN
# --------------------------------------------------------------------------------
# b) links established by matching route_id
# remove events without route ids
valid = df['route_id'].notna()
df3 = df['valid']
# Company event_type event_id prior_event_id route_id
# 1 A route 120 110 foo
# 2 B receive 200 NaN foo
# 4 B route 220 210 bar
# 5 C receive 300 NaN bar
# join on route_id
df4 = pd.merge(df3, df3, on='route_id', how='inner')
# Company_x event_type_x event_id_x prior_event_id_x route_id Company_y event_type_y event_id_y prior_event_id_y
# 0 A route 120 110 foo A route 120 110
# 1 A route 120 110 foo B receive 200 NaN
# 2 B receive 200 NaN foo A route 120 110
# 3 B receive 200 NaN foo B receive 200 NaN
# 4 B route 220 210 bar B route 220 210
# 5 B route 220 210 bar C receive 300 NaN
# 6 C receive 300 NaN bar B route 220 210
# 7 C receive 300 NaN bar C receive 300 NaN
# remove cases where a company was matched to itself
valid = df4['Company_x'] != df4['Company_y']
df5 = df4[valid]
# Company_x event_type_x event_id_x prior_event_id_x route_id Company_y event_type_y event_id_y prior_event_id_y
# 1 A route 120 110 foo B receive 200 NaN
# 2 B receive 200 NaN foo A route 120 110
# 5 B route 220 210 bar C receive 300 NaN
# 6 C receive 300 NaN bar B route 220 210
关于python - Pandas:在多列中查找具有匹配值的行的 Pythonic 方法(分层条件),我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/67495536/