IIUC，您可以stack然后value_counts

out = (df.stack().value_counts()
       .to_frame('Occurences')
       .rename_axis('UserID')
       .reset_index())

print(out)

   UserID  Occurences
0       1           2
1       2           2
2       3           2

将DataFrame.melt与GroupBy.size一起使用：

df = df.melt(value_name='UserID').groupby('UserID').size().reset_index(name='Occurences')
print (df)
   UserID  Occurences
0       1           2
1       2           2
2       3           2

pd.Series.value计数方法可用于对“from”和“to”列中每个userID的示例进行计数，pd.concat可用于合并结果。最后，使用pd.DataFrame.reset索引方法从结果系列创建 Dataframe ：

import pandas as pd
data_frame = pd.DataFrame({'from': [1, 1, 2], 'to': [3, 2, 3]})

occur = pd.concat([df['from'].value_counts(), df['to'].value_counts()])
result_df = occur.reset_index()
result_df.columns = ['UserID', 'occur']
result_df = result_df.groupby(['UserID'])['occur'].sum().reset_index()

   UserID         Occur
0       1           2
1       2           2
2       3           2