从熊猫数据框中匹配和提取值

Question

I'm trying to find matching values in a pandas dataframe. Once a match is found I want to perform some operations on the row of the dataframe.

Currently I'm using this Code:

import pandas as pd

d = {'child_id': [1,2,5,4,7,8,9,10],
 'parent_id': [3,4,1,3,11,6,12,13],
 'content': ["thon","pan","py","das","ten","sor","js","on"]}

df = pd.DataFrame(data=d)

df2 = pd.DataFrame(columns = ("content_child", "content_parent"))

for i in range(len(df)):

        for j in range(len(df)):

            if str(df['child_id'][j]) == str(df['parent_id'][i]):
                content_child = str(df["content"][i])

                content_parent = str(df["content"][j])

                s = pd.Series([content_child, content_parent], index=['content_child', 'content_parent'])
                df2 = df2.append(s, ignore_index=True)
            else:
                pass

 print(df2)

This Returns:

  content_child content_parent
0           pan            das
1            py           thon

I tried using df.loc functions, but I only succeed in getting either Content from child or Content from parent:

df.loc[df.parent_id.isin(df.child_id),['child_id','content']]

Returns:

      child_id content
1         2     pan
2         5      py

Is there an fast alternative to the loop I have written?

Answer 1

You can use just join data frames with condition if left part child_id is equal to right part parent_id .

df.set_index('parent_id').join(df.set_index('child_id'), rsuffix='_').dropna()

this code will create two data tables with ids parent_id and child_id . Then join them as usual SQL join. After all drop NaN values and get content column. Which is what you want. There are 2 content columns. one of them is parent content and second is child content.

Answer 2

For improve performance use map :

df['content_parent'] = df['parent_id'].map(df.set_index('child_id')['content'])
df = (df.rename(columns={'content':'content_child'})
        .dropna(subset=['content_parent'])[['content_child','content_parent']])
print (df)
  content_child content_parent
1           pan            das
2            py           thon

Or merge with default inner join:

df = (df.rename(columns={'child_id':'id'})
        .merge(df.rename(columns={'parent_id':'id'}), 
              on='id', 
              suffixes=('_parent','_child')))[['content_child','content_parent']]
print (df)
  content_child content_parent
0            py           thon
1           pan            das