[英]How to update dataframe based on dependent value in pandas?
I have to update a dataframe based on a dependency value.我必须根据依赖值更新数据帧。 How can this be done?如何才能做到这一点?
For example, input dataframe df
:例如,输入数据帧df
:
id dependency
10
20 30
30 40
40
50 10
60 20
Here we have: 20 -> 30
and 30 -> 40
.这里我们有: 20 -> 30
和30 -> 40
。 So the final result will be 20 -> 40
and 30 -> 40
.所以最终结果将是20 -> 40
和30 -> 40
。
In the same way, 60 -> 20 -> 30 -> 40
so final result will be 60 -> 40
.以同样的方式, 60 -> 20 -> 30 -> 40
所以最终结果将是60 -> 40
。
Final result:最后结果:
id dependency final_dependency
10
20 30 40
30 40 40
40
50 10 10
60 20 40
You can use networkx
to do this.您可以使用networkx
来执行此操作。 First, create a graph with the nodes that have a dependency:首先,创建一个具有依赖关系的节点的图:
df_edges = df.dropna(subset=['dependency'])
G = nx.from_pandas_edgelist(df_edges, create_using=nx.DiGraph, source='dependency', target='id')
Now, we can find the root ancestor for each node and add that as a new column:现在,我们可以找到每个节点的根祖先并将其添加为一个新列:
def find_root(G, node):
ancestors = list(nx.ancestors(G, node))
if len(ancestors) > 0:
root = find_root(G, ancestors[0])
else:
root = node
return root
df['final_dependency'] = df['id'].apply(lambda x: find_root(G, x))
df['final_dependency'] = np.where(df['final_dependency'] == df['id'], np.nan, df['final_dependency'])
Resulting dataframe:结果数据框:
id dependency final_dependency
0 10 NaN NaN
1 20 30.0 40.0
2 30 40.0 40.0
3 40 NaN NaN
4 50 10.0 10.0
5 60 20.0 40.0
One way is to create a custom function:一种方法是创建自定义函数:
s = df[df["dependency"].notnull()].set_index("id")["dependency"].to_dict()
def func(val):
if not s.get(val):
return None
while s.get(val):
val = s.get(val)
return val
df["final"] = df["id"].apply(func)
print (df)
id dependency final
0 10 NaN NaN
1 20 30.0 40.0
2 30 40.0 40.0
3 40 NaN NaN
4 50 10.0 10.0
5 60 20.0 40.0
You already have a few answers.你已经有了一些答案。 iterrows() is a bit expensive solution but wanted you to have this as well. iterrows() 是一个有点昂贵的解决方案,但希望你也有这个。
import pandas as pd
raw_data = {'id': [i for i in range (10,61,10)],
'dep':[None,30,40,None,10,20]}
df = pd.DataFrame(raw_data)
df['final_dep'] = df.dep
for i,r in df.iterrows():
if pd.notnull(r.dep):
x = df.loc[df['id'] == r.dep, 'dep'].values[0]
if pd.notnull(x):
df.iloc[i,df.columns.get_loc('final_dep')] = x
else:
df.iloc[i,df.columns.get_loc('final_dep')] = r.dep
print (df)
Output of this will be:输出将是:
id dep final_dep
0 10 NaN NaN
1 20 30.0 40
2 30 40.0 40
3 40 NaN NaN
4 50 10.0 10
5 60 20.0 30
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