Question

如下所示的两个数据帧，我想计算相关系数。

当两列都用实际值完成时，它可以正常工作。但是，如果不是，则在计算相关系数时取零作为值。

例如，Addison和Caden的权重为0。Jack和Noah没有权重。我想排除它们进行计算。

（在尝试中，似乎只考虑了相同的长度，即杰克和诺亚自动被排除在外-是吗？）

如何只包含非零值的人员进行计算？

谢谢。

import pandas as pd

Weight = {'Name': ["Abigail","Addison","Aiden","Amelia","Aria","Ava","Caden","Charlotte","Chloe","Elijah"], 
'Weight': [10, 0, 12, 20, 25, 10, 0, 18, 16, 13]}

df_wt = pd.DataFrame(Weight)

Score = {'Name': ["Abigail","Addison","Aiden","Amelia","Aria","Ava","Caden","Charlotte","Chloe","Elijah", "Jack", "Noah"], 
'Score': [360, 476, 345, 601, 604, 313, 539, 531, 507, 473, 450, 470]}

df_sc = pd.DataFrame(Score)

print df_wt.Weight.corr(df_sc.Score)

Answer 1

屏蔽并采用非零值和公共索引：

mask1 = df_wt['Weight'].ne(0)
mask2 = df_sc['Score'].ne(0)
common_index = df_wt.loc[mask1, :].index.intersection(df_sc.loc[mask2, :].index)
df_wt.loc[common_index, 'Weight'].corr(df_sc.loc[common_index, 'Score'])

如果两个数据帧均包含零，则：

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Answer 2

使用map添加新列，删除boolean indexing的0行，最后在同一DataFrame中应用您的解决方案：

df_wt['Score'] = df_wt['Name'].map(df_sc.set_index('Name')['Score'])

df_wt = df_wt[df_wt['Weight'].ne(0)]
print (df_wt)
        Name  Weight  Score
0    Abigail      10    360
2      Aiden      12    345
3     Amelia      20    601
4       Aria      25    604
5        Ava      10    313
7  Charlotte      18    531
8      Chloe      16    507
9     Elijah      13    473

print (df_wt.Weight.corr(df_wt.Score))
0.923425144491911

在数据框中查找通讯对象以进行计算

2 个答案: