外部合并df1和df2在一起，外部合并df3到df1和df2，列变得没有排序
外部合并df1和df2在一起，外部合并df3到df1和df2，列变得没有排序
提问于 2022-09-20 22:24:41
我有3种不同的数据，每个数据都包含不同的日期(df1、df2和df3(df3包含从2014-01-01开始的每个日期的列表)。我想要完成的是将所有3个合并在一起，并将所有匹配的日期排在同一行上，以及任何日期不匹配的行，以显示“NaN”或类似的内容。在包含日期的列上，将df1外部合并到df2 (我们将调用df1 & df2合并的dataframe df4)有点问题。我的结果如下：
VIPR (Y-M-D) Viriciti (Y-M-D)
0 NaN 2021-07-07
1 NaN 2021-07-08
2 NaN 2021-07-09
3 NaN 2021-07-10
4 NaN 2021-07-11
5 NaN 2021-07-13
6 NaN 2021-07-14
7 NaN 2021-07-15
8 NaN 2021-07-16
9 NaN 2021-07-18
10 NaN 2021-07-20
11 NaN 2021-07-21
12 NaN 2021-07-22
13 NaN 2021-07-31
14 NaN 2021-08-02
15 NaN 2021-08-03
16 NaN 2021-08-10
17 NaN 2021-08-11
18 NaN 2021-08-12
19 NaN 2021-08-16
20 NaN 2021-08-17
21 NaN 2021-08-18
22 NaN 2021-08-19
23 NaN 2021-08-21
24 NaN 2021-08-24
25 NaN 2021-08-25
26 NaN 2021-08-26
27 NaN 2021-08-27
28 NaN 2021-08-28
29 NaN 2021-08-29
30 NaN 2021-08-31
31 NaN 2021-09-01
32 NaN 2021-09-02
33 NaN 2021-09-03
34 NaN 2021-09-04
35 NaN 2021-09-05
36 NaN 2021-09-10
37 NaN 2021-09-12
38 NaN 2021-09-14
39 NaN 2021-09-15
40 NaN 2021-09-16
41 NaN 2021-09-17
42 NaN 2021-09-18
43 NaN 2021-09-21
44 NaN 2021-09-22
45 NaN 2021-09-23
46 NaN 2021-09-24
47 NaN 2021-09-25
48 NaN 2021-09-28
49 NaN 2021-09-29
50 NaN 2021-09-30
51 NaN 2021-10-01
52 NaN 2021-10-02
53 NaN 2021-10-05
54 NaN 2021-10-12
55 NaN 2021-10-13
56 NaN 2021-10-14
57 NaN 2021-10-15
58 NaN 2021-10-16
59 NaN 2021-10-17
60 NaN 2021-10-18
61 NaN 2021-10-20
62 NaN 2021-10-21
63 NaN 2021-10-23
64 NaN 2021-10-27
65 NaN 2021-10-28
66 NaN 2021-10-29
67 NaN 2021-10-30
68 NaN 2021-11-01
69 NaN 2021-11-02
70 NaN 2021-11-03
71 NaN 2021-11-04
72 NaN 2021-11-09
73 NaN 2021-11-11
74 NaN 2021-11-12
75 NaN 2021-11-16
76 NaN 2021-11-19
77 NaN 2021-11-20
78 NaN 2021-11-23
79 NaN 2021-11-24
80 NaN 2021-11-25
81 NaN 2021-12-02
82 NaN 2021-12-03
83 NaN 2021-12-10
84 NaN 2021-12-11
85 NaN 2021-12-14
86 NaN 2021-12-15
87 NaN 2021-12-16
88 NaN 2021-12-17
89 NaN 2021-12-18
90 NaN 2022-01-04
91 NaN 2022-01-05
92 NaN 2022-01-06
93 NaN 2022-01-07
94 NaN 2022-01-08
95 NaN 2022-01-10
96 NaN 2022-01-11
97 NaN 2022-01-18
98 NaN 2022-01-19
99 NaN 2022-01-20
100 NaN 2022-01-21
101 NaN 2022-01-22
102 NaN 2022-01-25
103 NaN 2022-01-26
104 NaN 2022-01-27
105 NaN 2022-01-28
106 NaN 2022-01-29
107 NaN 2022-02-01
108 NaN 2022-02-02
109 NaN 2022-02-03
110 NaN 2022-02-04
111 NaN 2022-02-05
112 NaN 2022-02-08
113 NaN 2022-02-09
114 NaN 2022-02-11
115 NaN 2022-02-12
116 NaN 2022-02-15
117 NaN 2022-02-16
118 NaN 2022-02-17
119 NaN 2022-02-23
120 NaN 2022-02-24
121 NaN 2022-02-25
122 NaN 2022-02-26
123 NaN 2022-02-27
124 NaN 2022-03-03
125 NaN 2022-03-04
126 NaN 2022-03-08
127 NaN 2022-03-10
128 NaN 2022-03-22
129 NaN 2022-03-23
130 NaN 2022-03-24
131 NaN 2022-03-30
132 NaN 2022-03-31
133 NaN 2022-04-04
134 NaN 2022-04-05
135 NaN 2022-04-06
136 NaN 2022-04-07
137 NaN 2022-04-08
138 NaN 2022-04-09
139 NaN 2022-04-13
140 NaN 2022-04-18
141 NaN 2022-04-19
142 NaN 2022-04-20
143 NaN 2022-04-21
144 NaN 2022-04-22
145 NaN 2022-04-23
146 NaN 2022-04-24
147 NaN 2022-04-25
148 NaN 2022-04-26
149 NaN 2022-04-27
150 NaN 2022-04-28
151 NaN 2022-04-29
152 NaN 2022-05-02
153 NaN 2022-05-03
154 NaN 2022-05-04
155 NaN 2022-05-05
156 NaN 2022-05-06
157 NaN 2022-05-07
158 NaN 2022-05-08
159 NaN 2022-05-10
160 NaN 2022-05-11
161 NaN 2022-05-12
162 NaN 2022-05-13
163 NaN 2022-05-14
164 NaN 2022-05-15
165 NaN 2022-05-16
166 NaN 2022-05-17
167 NaN 2022-05-19
168 NaN 2022-05-20
169 NaN 2022-05-21
170 NaN 2022-05-22
171 NaN 2022-05-23
172 NaN 2022-05-24
173 NaN 2022-05-25
174 NaN 2022-05-26
175 NaN 2022-05-27
176 NaN 2022-05-28
177 NaN 2022-05-29
178 NaN 2022-05-30
179 NaN 2022-05-31
180 NaN 2022-06-01
181 NaN 2022-06-02
182 NaN 2022-06-03
183 2022-06-04 2022-06-04
184 2022-06-05 2022-06-05
185 2022-06-06 2022-06-06
186 2022-06-07 2022-06-07
187 NaN 2022-06-08
188 NaN 2022-06-09
189 2022-06-10 2022-06-10
190 2022-06-11 2022-06-11
191 2022-06-12 2022-06-12
192 2022-06-13 2022-06-13
193 2022-06-14 2022-06-14
194 NaN 2022-06-15
195 2022-06-16 NaN
196 NaN 2022-06-17
197 2022-06-20 NaN
198 2022-06-21 2022-06-21
199 NaN 2022-06-22
200 NaN 2022-06-23
201 NaN 2022-06-24
202 NaN 2022-06-25
203 NaN 2022-06-26
204 NaN 2022-06-27
205 NaN 2022-06-28
206 NaN 2022-06-29
207 NaN 2022-06-30
208 NaN 2022-07-01
209 NaN 2022-07-02
210 NaN 2022-07-03
211 NaN 2022-07-04
212 2022-07-05 2022-07-05
213 2022-07-06 2022-07-06
214 2022-07-07 2022-07-07
215 2022-07-08 2022-07-08
216 2022-07-09 2022-07-09
217 NaN 2022-07-10
218 NaN 2022-07-16
219 NaN 2022-07-19
220 NaN 2022-07-20
221 2022-07-21 NaN
222 2022-07-22 2022-07-22
223 2022-07-22 2022-07-22
224 2022-07-23 2022-07-23
225 2022-07-24 2022-07-24
226 2022-07-25 2022-07-25
227 2022-07-26 2022-07-26
228 2022-07-27 2022-07-27
229 2022-07-28 2022-07-28
230 2022-07-29 2022-07-29
231 2022-07-30 2022-07-30
232 2022-07-31 2022-07-31
233 2022-08-01 2022-08-01
234 2022-08-02 2022-08-02
235 2022-08-03 2022-08-03
236 2022-08-04 2022-08-04
237 2022-08-05 2022-08-05
238 NaN 2022-08-06
239 NaN 2022-08-09
240 NaN 2022-08-13
241 NaN 2022-08-14
242 2022-08-15 NaN
243 NaN 2022-08-16
244 NaN 2022-08-17
245 2022-08-20 NaN
246 NaN 2022-08-21
247 2022-08-25 NaN
248 2022-08-26 2022-08-26
249 NaN 2022-08-27
250 2022-08-29 NaN
251 2022-08-30 2022-08-30
252 2022-08-31 2022-08-31
253 2022-09-01 2022-09-01
254 NaN 2022-09-02
255 NaN 2022-09-03
256 2022-09-08 NaN
257 2022-09-09 2022-09-09
258 NaN 2022-09-10
259 2022-09-13 NaN
260 2022-09-14 2022-09-14
261 2022-09-15 2022-09-15
262 2022-09-16 2022-09-16
263 NaN 2022-09-17
264 NaN 2022-09-21这是我正在寻找的结果！现在，当我尝试将df3外部合并到df4时，'VIPR (Y)‘列就没有排序了。似乎在“VIPR(Y-M-D)”中，在df4中“df4”旁边有“df3”的日期变得没有排序，并且正在下降，因为在df3与df4的最后一次合并中，我的代码是：
df5 = df3.merge(df4, how='outer', left_on='Date', right_on='Viriciti (Y-M-D)', sort=True)合并的结果是：
Date VIPR (Y-M-D) Viriciti (Y-M-D)
0 2014-01-01 NaN NaN
1 2014-01-02 NaN NaN
2 2014-01-03 NaN NaN
3 2014-01-04 NaN NaN
4 2014-01-05 NaN NaN
5 2014-01-06 NaN NaN
...
2744 2021-07-07 NaN 2021-07-07
...
3076 2022-06-04 2022-06-04 2022-06-04
3077 2022-06-05 2022-06-05 2022-06-05
3078 2022-06-06 2022-06-06 2022-06-06
3079 2022-06-07 2022-06-07 2022-06-07
3080 2022-06-08 NaN 2022-06-08
3081 2022-06-09 NaN 2022-06-09
3082 2022-06-10 2022-06-10 2022-06-10
3083 2022-06-11 2022-06-11 2022-06-11
3084 2022-06-12 2022-06-12 2022-06-12
3085 2022-06-13 2022-06-13 2022-06-13
3086 2022-06-14 2022-06-14 2022-06-14
3087 2022-06-15 NaN 2022-06-15
3088 2022-06-16 NaN NaN
3089 2022-06-17 NaN 2022-06-17
3090 2022-06-18 NaN NaN
3091 2022-06-19 NaN NaN
3092 2022-06-20 NaN NaN
3093 2022-06-21 2022-06-21 2022-06-21
3094 2022-06-22 NaN 2022-06-22
3095 2022-06-23 NaN 2022-06-23
3096 2022-06-24 NaN 2022-06-24
3097 2022-06-25 NaN 2022-06-25
3098 2022-06-26 NaN 2022-06-26
3099 2022-06-27 NaN 2022-06-27
3100 2022-06-28 NaN 2022-06-28
3101 2022-06-29 NaN 2022-06-29
3102 2022-06-30 NaN 2022-06-30
3103 2022-07-01 NaN 2022-07-01
3104 2022-07-02 NaN 2022-07-02
3105 2022-07-03 NaN 2022-07-03
3106 2022-07-04 NaN 2022-07-04
3107 2022-07-05 2022-07-05 2022-07-05
3108 2022-07-06 2022-07-06 2022-07-06
3109 2022-07-07 2022-07-07 2022-07-07
3110 2022-07-08 2022-07-08 2022-07-08
3111 2022-07-09 2022-07-09 2022-07-09
3112 2022-07-10 NaN 2022-07-10
3113 2022-07-11 NaN NaN
3114 2022-07-12 NaN NaN
3115 2022-07-13 NaN NaN
3116 2022-07-14 NaN NaN
3117 2022-07-15 NaN NaN
3118 2022-07-16 NaN 2022-07-16
3119 2022-07-17 NaN NaN
3120 2022-07-18 NaN NaN
3121 2022-07-19 NaN 2022-07-19
3122 2022-07-20 NaN 2022-07-20
3123 2022-07-21 NaN NaN
3124 2022-07-22 2022-07-22 2022-07-22
3125 2022-07-22 2022-07-22 2022-07-22
3126 2022-07-23 2022-07-23 2022-07-23
3127 2022-07-24 2022-07-24 2022-07-24
3128 2022-07-25 2022-07-25 2022-07-25
3129 2022-07-26 2022-07-26 2022-07-26
3130 2022-07-27 2022-07-27 2022-07-27
3131 2022-07-28 2022-07-28 2022-07-28
3132 2022-07-29 2022-07-29 2022-07-29
3133 2022-07-30 2022-07-30 2022-07-30
3134 2022-07-31 2022-07-31 2022-07-31
3135 2022-08-01 2022-08-01 2022-08-01
3136 2022-08-02 2022-08-02 2022-08-02
3137 2022-08-03 2022-08-03 2022-08-03
3138 2022-08-04 2022-08-04 2022-08-04
3139 2022-08-05 2022-08-05 2022-08-05
3140 2022-08-06 NaN 2022-08-06
3141 2022-08-07 NaN NaN
3142 2022-08-08 NaN NaN
3143 2022-08-09 NaN 2022-08-09
3144 2022-08-10 NaN NaN
3145 2022-08-11 NaN NaN
3146 2022-08-12 NaN NaN
3147 2022-08-13 NaN 2022-08-13
3148 2022-08-14 NaN 2022-08-14
3149 2022-08-15 NaN NaN
3150 2022-08-16 NaN 2022-08-16
3151 2022-08-17 NaN 2022-08-17
3152 2022-08-18 NaN NaN
3153 2022-08-19 NaN NaN
3154 2022-08-20 NaN NaN
3155 2022-08-21 NaN 2022-08-21
3156 2022-08-22 NaN NaN
3157 2022-08-23 NaN NaN
3158 2022-08-24 NaN NaN
3159 2022-08-25 NaN NaN
3160 2022-08-26 2022-08-26 2022-08-26
3161 2022-08-27 NaN 2022-08-27
3162 2022-08-28 NaN NaN
3163 2022-08-29 NaN NaN
3164 2022-08-30 2022-08-30 2022-08-30
3165 2022-08-31 2022-08-31 2022-08-31
3166 2022-09-01 2022-09-01 2022-09-01
3167 2022-09-02 NaN 2022-09-02
3168 2022-09-03 NaN 2022-09-03
3169 2022-09-04 NaN NaN
3170 2022-09-05 NaN NaN
3171 2022-09-06 NaN NaN
3172 2022-09-07 NaN NaN
3173 2022-09-08 NaN NaN
3174 2022-09-09 2022-09-09 2022-09-09
3175 2022-09-10 NaN 2022-09-10
3176 2022-09-11 NaN NaN
3177 2022-09-12 NaN NaN
3178 2022-09-13 NaN NaN
3179 2022-09-14 2022-09-14 2022-09-14
3180 2022-09-15 2022-09-15 2022-09-15
3181 2022-09-16 2022-09-16 2022-09-16
3182 2022-09-17 NaN 2022-09-17
3183 2022-09-18 NaN NaN
3184 2022-09-19 NaN NaN
3185 2022-09-20 NaN NaN
3186 NaN NaN 2022-09-21
3187 NaN 2022-06-16 NaN
3188 NaN 2022-06-20 NaN
3189 NaN 2022-07-21 NaN
3190 NaN 2022-08-15 NaN
3191 NaN 2022-08-20 NaN
3192 NaN 2022-08-25 NaN
3193 NaN 2022-08-29 NaN
3194 NaN 2022-09-08 NaN
3195 NaN 2022-09-13 NaN如您所见，“VIPR( you )”列有一些日期在最下面不匹配。如何解决这个问题，以便在相应的行中匹配所有的日期？
回答 1
Stack Overflow用户
回答已采纳
发布于 2022-09-20 23:43:06
因为您的第三个数据包括所有日期，所以使用how="left"。此外，在第二个join中，您将加入一个列，该列具有NaNs。相反，您应该将df1和df2直接连接到df3：
merged = (
df3
.merge(df1, how="left", left_on="Date", right_on="VIPR (Y-M-D)")
.merge(df2, how="left", left_on="Date", right_on="Viriciti (Y-M-D)")
)页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/73793220
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