Python 如何首先将_组合起来,将特定列的两个数据帧索引为一个?
通过c列映射后, 如果A列有值,则插入A列的值;如果不是,则插入B列Python 如何首先将_组合起来,将特定列的两个数据帧索引为一个?,python,pandas,dataframe,path-combine,Python,Pandas,Dataframe,Path Combine,通过c列映射后, 如果A列有值,则插入A列的值;如果不是,则插入B列 data1 data2 a b c a c d a1 b1 c1 1a c1 1d b2 c2 2a c2 2d a3 c3
data1 data2
a b c a c d
a1 b1 c1 1a c1 1d
b2 c2 2a c2 2d
a3 c3 3a c3 3d
4a c4 4d
我想要的结果
result
a b c
a1 b1 c1
2a b2 c2
a3 c3
我尝试了以下方法,但不满意
->>> result = data1.merge(data2, on=['c'])
Prefixes _x and _y are created. combine_first is not applied.
->>> result = data1.combine_first(data2)
It is not mapped by column c.
如何获得好的结果?
我请求你的帮助。
谢谢我不是100%清楚您是如何索引数据帧的(
data1
和data2
),但是如果您在'c'
列上索引它们,应该可以
以下是我创建您的数据的方式:
import pandas as pd
data1 = pd.DataFrame({'a': ['a1', None, 'a3'],
'b': ['b1', 'b2', None],
'c': ['c1', 'c2', 'c3']})
data2 = pd.DataFrame({'a': ['1a', '2a', '3a', '4a'],
'c': ['c1', 'c2', 'c3', 'c4'],
'd': ['1d', '2d', '3d', '4d']})
然后我将两者的索引设置为列'c'
:
data1 = data1.set_index('c')
data2 = data2.set_index('c')
然后我首先使用combine\u
,就像您所做的那样:
data_combined = data1.combine_first(data_2)
我明白了:
a b d
c
c1 a1 b1 1d
c2 2a b2 2d
c3 a3 None 3d
c4 4a NaN 4d
不确定为什么不希望索引为'c4'
的行或列为'd'
,但删除它们很容易:
data_combined = data_combined.drop('d', axis=1)
data_combined = data_combined.loc[data_combined.index != 'c4']
然后我重新排序以获得您想要的结果:
data_combined = data_combined.reset_index()
data_combined = data_combined[['a', 'b', 'c']]
data_combined = data_combined.fillna('')
a b c
0 a1 b1 c1
1 2a b2 c2
2 a3 c3
您也可以这样尝试:
# set indexes
data1 = data1.set_index('c')
data2 = data2.set_index('c')
# join data on indexes
datax = data1.join(data2.drop('d', axis=1), rsuffix='_rr').reset_index()
# fill missing value in column a
datax['a'] = datax['a'].fillna(datax['a_rr'])
# drop unwanted columns
datax.drop('a_rr', axis=1, inplace=True)
# fill missing values with blank spaces
datax.fillna('', inplace=True)
# output
a b c
0 a1 b1 c1
1 2a b2 c2
2 a3 c3
使用@IdoS设置:
import pandas as pd
data1 = pd.DataFrame({'a': ['a1', None, 'a3'],
'b': ['b1', 'b2', None],
'c': ['c1', 'c2', 'c3']})
data2 = pd.DataFrame({'a': ['1a', '2a', '3a', '4a'],
'c': ['c1', 'c2', 'c3', 'c4'],
'd': ['1d', '2d', '3d', '4d']})
您可以使用设置索引
,先合并
,然后重新索引:
df_out = data1.set_index('c').combine_first(data2.set_index('c'))\
.reindex(data1.c)\
.reset_index()
df_out
输出:
c a b d
0 c1 a1 b1 1d
1 c2 2a b2 2d
2 c3 a3 None 3d
非常感谢非常感谢非常感谢
c a b d
0 c1 a1 b1 1d
1 c2 2a b2 2d
2 c3 a3 None 3d