有条件地循环一个数据帧中的染色体和位置到另一数据帧中的染色体和间隔

df1= pd.DataFrame({'Chr':['1', '1', '2', '2', '3','3','4'],
         'position':[50, 500, 1030, 2005 , 3575,50, 250]})
df2 = pd.DataFrame({'Chr':['1', '1', '1', '1',           
  '1','2','2','2','2','2','3','3','3','3','3'],
             'start':  
[0,100,1000,2000,3000,0,100,1000,2000,3000,0,100,1000,2000,3000],
             'end': 
 [100,1000,2000,3000,4000,100,1000,2000,3000,4000,100,1000,2000,3000,4000],
             'logr':[3, 4, 5, 6, 7,8,9,10,11,12,13,15,16,17,18],
             'seg':[0.2,0.5,0.2,0.1,0.5,0.5,0.2,0.2,0.1,0.2,0.1,0.5,0.5,0.9,0.3]})

我想有条件地循环 df1 中的“Chr”和“位置”到 df2 中的“Chr”和间隔（其中 df1 中的位置落在“开始”和“结束”之间），然后添加“logr”和“seg”列在 df1 中

我想要的输出是：

df3= pd.DataFrame({'Chr':['1', '1', '2', '2', '3','3','4'],
         'position':[50, 500, 1030, 2005 , 3575,50, 250],
           'logr':[3, 4, 10,11, 18,13, "NA"],
             'seg':[0.2,0.5,0.2,0.1,0.3,0.1,"NA"]})

先感谢您。

Use DataFrame.merge对所有组合使用外连接，然后按Series.between and boolean indexing with DataFrame.pop对于提取列和最后一个左连接以添加缺失的行：

df3 = df1.merge(df2, on='Chr', how='outer')
#between is by default inclusive (>=, <=) orwith parameter inclusive=False (>, <)
df3 = df3[df3['position'].between(df3.pop('start'), df3.pop('end'))]
#if need one inclusive and  another interval not (e.g. >, <=)
#df3 = df3[(df3['position'] > df3.pop('start')) & (df3['position'] <= df3.pop('end'))]
df3 = df1.merge(df3, how='left')
print (df3)
  Chr  position  logr  seg
0   1        50   3.0  0.2
1   1       500   4.0  0.5
2   2      1030  10.0  0.2
3   2      2005  11.0  0.1
4   3      3575  18.0  0.3
5   3        50  13.0  0.1
6   4       250   NaN  NaN

另一个解决方案：

df3 = df1.merge(df2, on='Chr', how='outer')
s = df3.pop('start')
e = df3.pop('end')
df3 = df3[df3['position'].between(s, e) | s.isna() | e.isna()]
#if different closed intervals
#df3 = df3[(df3['position'] > s) & (df3['position'] <= e) | s.isna() | e.isna()]
print (df3)
   Chr  position  logr  seg
0    1        50   3.0  0.2
6    1       500   4.0  0.5
12   2      1030  10.0  0.2
18   2      2005  11.0  0.1
24   3      3575  18.0  0.3
25   3        50  13.0  0.1
30   4       250   NaN  NaN