文档建议反馈控制台
首页
学习
活动
专区
圈层
工具
MCP广场
文章/答案/技术大牛
发布
社区首页 >问答首页 >如何将一个柱融化成另一个在潘达斯内熔化的柱？

问如何将一个柱融化成另一个在潘达斯内熔化的柱？
EN

Stack Overflow用户
提问于 2022-09-20 15:53:13
回答 1查看 29关注 0票数 1

我有一份不同商品的销售档案。我有一列模型预测，名为outputs，哪个模型产生了这些预测，名为model。我需要将名为“FCAST_QTYand make them part of the outputs column with the string "FCAST_QTY" being put into the model column. So essentially, melting that column (FCAST_QTY”的列中当前模型的预测(即生产中的预测)转换为列output和model，以便当前的生产模型位于相同的列和正在开发的多个模型中。这将使比较/对比更加容易。我不知道该怎么做。下面的示例数据。

代码语言：javascript
复制
import pandas as pd
from pandas import Timestamp
sales_dict = {'MO_YR': {0: Timestamp('2021-01-01 00:00:00'),
  1: Timestamp('2021-02-01 00:00:00'),
  2: Timestamp('2021-03-01 00:00:00'),
  3: Timestamp('2021-04-01 00:00:00'),
  4: Timestamp('2021-05-01 00:00:00'),
  5: Timestamp('2021-06-01 00:00:00'),
  6: Timestamp('2021-07-01 00:00:00'),
  7: Timestamp('2021-08-01 00:00:00'),
  8: Timestamp('2021-09-01 00:00:00'),
  9: Timestamp('2021-10-01 00:00:00'),
  10: Timestamp('2021-11-01 00:00:00'),
  11: Timestamp('2021-12-01 00:00:00'),
  12: Timestamp('2021-01-01 00:00:00'),
  13: Timestamp('2021-02-01 00:00:00'),
  14: Timestamp('2021-03-01 00:00:00')},
 'ITEM_BASE': {0: '289461K',
  1: '289461K',
  2: '289461K',
  3: '289461K',
  4: '289461K',
  5: '289461K',
  6: '289461K',
  7: '289461K',
  8: '289461K',
  9: '289461K',
  10: '289461K',
  11: '289461K',
  12: '400520J',
  13: '400520J',
  14: '400520J'},
'eaches': {0: 2592,
  1: 3844,
  2: 759,
  3: 825,
  4: 663,
  5: 2025,
  6: 471,
  7: 1160,
  8: 5987,
  9: 679,
  10: 469,
  11: 907,
  12: 64,
  13: 48,
  14: 63},
'FCAST_QTY': {0: 2800.0,
  1: 5200.0,
  2: 550.0,
  3: 475.0,
  4: 575.0,
  5: 475.0,
  6: 650.0,
  7: 550.0,
  8: 7900.0,
  9: 1187.0,
  10: 1187.0,
  11: 1900.0,
  12: 51.0,
  13: 55.0,
  14: 59.0},
'log_eaches': {0: 7.860185057472165,
  1: 8.254268770090183,
  2: 6.63200177739563,
  3: 6.715383386334682,
  4: 6.496774990185863,
  5: 7.61332497954064,
  6: 6.154858094016418,
  7: 7.05617528410041,
  8: 8.697345730925353,
  9: 6.520621127558696,
  10: 6.15060276844628,
  11: 6.810142450115136,
  12: 4.158883083359672,
  13: 3.871201010907891,
  14: 4.143134726391533},
 'output': {0: 8.646015798513993,
  1: 8.378197900630752,
  2: 7.045235414873291,
  3: 5.117058321275769,
  4: 9.082928370640056,
  5: 5.225648643174155,
  6: 7.446383013291042,
  7: 6.307484284901181,
  8: 7.752673979530179,
  9: 9.02189934080111,
  10: 4.677594714421006,
  11: 7.218749101888444,
  12: 4.04018241973268,
  13: 3.940978322900716,
  14: 3.962359464699719},
 'model': {0: 'LR_output',
  1: 'LR_output',
  2: 'LR_output',
  3: 'LR_output',
  4: 'LR_output',
  5: 'LR_output',
  6: 'LR_output',
  7: 'LR_output',
  8: 'LR_output',
  9: 'LR_output',
  10: 'LR_output',
  11: 'LR_output',
  12: 'AR(12)',
  13: 'AR(12)',
  14: 'AR(12)'}}

df = pd.DataFrame.from_dict(sales_dict)

预期输出增加：

代码语言：javascript
复制
expected_dict = {'MO_YR': {0: Timestamp('2021-01-01 00:00:00'),
  1: Timestamp('2021-02-01 00:00:00'),
  2: Timestamp('2021-03-01 00:00:00'),
  3: Timestamp('2021-04-01 00:00:00'),
  4: Timestamp('2021-05-01 00:00:00'),
  5: Timestamp('2021-06-01 00:00:00'),
  6: Timestamp('2021-07-01 00:00:00'),
  7: Timestamp('2021-08-01 00:00:00'),
  8: Timestamp('2021-09-01 00:00:00'),
  9: Timestamp('2021-10-01 00:00:00'),
  10: Timestamp('2021-11-01 00:00:00'),
  11: Timestamp('2021-12-01 00:00:00'),
  12: Timestamp('2021-01-01 00:00:00'),
  13: Timestamp('2021-02-01 00:00:00'),
  14: Timestamp('2021-03-01 00:00:00'),
15: Timestamp('2021-01-01 00:00:00'),
  16: Timestamp('2021-02-01 00:00:00'),
  17: Timestamp('2021-03-01 00:00:00'),
  18: Timestamp('2021-04-01 00:00:00'),
  19: Timestamp('2021-05-01 00:00:00'),
  20: Timestamp('2021-06-01 00:00:00'),
  21: Timestamp('2021-07-01 00:00:00'),
  22: Timestamp('2021-08-01 00:00:00'),
  23: Timestamp('2021-09-01 00:00:00'),
  24: Timestamp('2021-10-01 00:00:00'),
  25: Timestamp('2021-11-01 00:00:00'),
  26: Timestamp('2021-12-01 00:00:00'),
  27: Timestamp('2021-01-01 00:00:00'),
  28: Timestamp('2021-02-01 00:00:00'),
  29: Timestamp('2021-03-01 00:00:00')},
 'ITEM_BASE': {0: '289461K',
  1: '289461K',
  2: '289461K',
  3: '289461K',
  4: '289461K',
  5: '289461K',
  6: '289461K',
  7: '289461K',
  8: '289461K',
  9: '289461K',
  10: '289461K',
  11: '289461K',
  12: '400520J',
  13: '400520J',
  14: '400520J',
15: '289461K',
  16: '289461K',
  17: '289461K',
  18: '289461K',
  19: '289461K',
  20: '289461K',
  21: '289461K',
  22: '289461K',
  23: '289461K',
  24: '289461K',
  25: '289461K',
  26: '289461K',
  27: '400520J',
  28: '400520J',
  29: '400520J'},
'eaches': {0: 2592,
  1: 3844,
  2: 759,
  3: 825,
  4: 663,
  5: 2025,
  6: 471,
  7: 1160,
  8: 5987,
  9: 679,
  10: 469,
  11: 907,
  12: 64,
  13: 48,
  14: 63,
15: 2592,
  16: 3844,
  17: 759,
  18: 825,
  19: 663,
  20: 2025,
  21: 471,
  22: 1160,
  23: 5987,
  24: 679,
  25: 469,
  26: 907,
  27: 64,
  28: 48,
  29: 63},
'log_eaches': {0: 7.860185057472165,
  1: 8.254268770090183,
  2: 6.63200177739563,
  3: 6.715383386334682,
  4: 6.496774990185863,
  5: 7.61332497954064,
  6: 6.154858094016418,
  7: 7.05617528410041,
  8: 8.697345730925353,
  9: 6.520621127558696,
  10: 6.15060276844628,
  11: 6.810142450115136,
  12: 4.158883083359672,
  13: 3.871201010907891,
  14: 4.143134726391533,
  15: 7.860185057472165,
  16: 8.254268770090183,
  17: 6.63200177739563,
  18: 6.715383386334682,
  19: 6.496774990185863,
  20: 7.61332497954064,
  21: 6.154858094016418,
  22: 7.05617528410041,
  23: 8.697345730925353,
  24: 6.520621127558696,
  25: 6.15060276844628,
  26: 6.810142450115136,
  27: 4.158883083359672,
  28: 3.871201010907891,
  29: 4.143134726391533,},
 'output': {0: 8.646015798513993,
  1: 8.378197900630752,
  2: 7.045235414873291,
  3: 5.117058321275769,
  4: 9.082928370640056,
  5: 5.225648643174155,
  6: 7.446383013291042,
  7: 6.307484284901181,
  8: 7.752673979530179,
  9: 9.02189934080111,
  10: 4.677594714421006,
  11: 7.218749101888444,
  12: 4.04018241973268,
  13: 3.940978322900716,
  14: 3.962359464699719,
  15: 2800.0,
  16: 5200.0,
  17: 550.0,
  18: 475.0,
  19: 575.0,
  20: 475.0,
  21: 650.0,
  22: 550.0,
  23: 7900.0,
  24: 1187.0,
  25: 1187.0,
  26: 1900.0,
  27: 51.0,
  28: 55.0,
  29: 59.0},
 'model': {0: 'LR_output',
  1: 'LR_output',
  2: 'LR_output',
  3: 'LR_output',
  4: 'LR_output',
  5: 'LR_output',
  6: 'LR_output',
  7: 'LR_output',
  8: 'LR_output',
  9: 'LR_output',
  10: 'LR_output',
  11: 'LR_output',
  12: 'AR(12)',
  13: 'AR(12)',
  14: 'AR(12)',
15:'FCAST_QTY',
16:'FCAST_QTY',
17:'FCAST_QTY',
18:'FCAST_QTY',
19:'FCAST_QTY',
20:'FCAST_QTY',
21:'FCAST_QTY',
22:'FCAST_QTY',
23:'FCAST_QTY',
24:'FCAST_QTY',
25:'FCAST_QTY',
26:'FCAST_QTY',
27:'FCAST_QTY',
28:'FCAST_QTY',
29:'FCAST_QTY'}}
 df = pd.DataFrame.from_dict(expected_dict)
pandas
pandas-melt
EN

回答 1

Stack Overflow用户

回答已采纳

发布于 2022-09-20 21:52:06

使用您的逻辑创建一个新的dataframe，并将其附加到原始的dataframe中：

代码语言：javascript
复制
fcast_qty = (df
             .drop(columns = ['output', 'model'])
             .rename(columns={"FCAST_QTY":"output"})
             .assign(model="FCAST_QTY")
            )

pd.concat([df.drop(columns='FCAST_QTY'), fcast_qty], ignore_index = True)
        MO_YR ITEM_BASE  eaches  log_eaches       output      model
0  2021-01-01   289461K    2592    7.860185     8.646016  LR_output
1  2021-02-01   289461K    3844    8.254269     8.378198  LR_output
2  2021-03-01   289461K     759    6.632002     7.045235  LR_output
3  2021-04-01   289461K     825    6.715383     5.117058  LR_output
4  2021-05-01   289461K     663    6.496775     9.082928  LR_output
5  2021-06-01   289461K    2025    7.613325     5.225649  LR_output
6  2021-07-01   289461K     471    6.154858     7.446383  LR_output
7  2021-08-01   289461K    1160    7.056175     6.307484  LR_output
8  2021-09-01   289461K    5987    8.697346     7.752674  LR_output
9  2021-10-01   289461K     679    6.520621     9.021899  LR_output
10 2021-11-01   289461K     469    6.150603     4.677595  LR_output
11 2021-12-01   289461K     907    6.810142     7.218749  LR_output
12 2021-01-01   400520J      64    4.158883     4.040182     AR(12)
13 2021-02-01   400520J      48    3.871201     3.940978     AR(12)
14 2021-03-01   400520J      63    4.143135     3.962359     AR(12)
15 2021-01-01   289461K    2592    7.860185  2800.000000  FCAST_QTY
16 2021-02-01   289461K    3844    8.254269  5200.000000  FCAST_QTY
17 2021-03-01   289461K     759    6.632002   550.000000  FCAST_QTY
18 2021-04-01   289461K     825    6.715383   475.000000  FCAST_QTY
19 2021-05-01   289461K     663    6.496775   575.000000  FCAST_QTY
20 2021-06-01   289461K    2025    7.613325   475.000000  FCAST_QTY
21 2021-07-01   289461K     471    6.154858   650.000000  FCAST_QTY
22 2021-08-01   289461K    1160    7.056175   550.000000  FCAST_QTY
23 2021-09-01   289461K    5987    8.697346  7900.000000  FCAST_QTY
24 2021-10-01   289461K     679    6.520621  1187.000000  FCAST_QTY
25 2021-11-01   289461K     469    6.150603  1187.000000  FCAST_QTY
26 2021-12-01   289461K     907    6.810142  1900.000000  FCAST_QTY
27 2021-01-01   400520J      64    4.158883    51.000000  FCAST_QTY
28 2021-02-01   400520J      48    3.871201    55.000000  FCAST_QTY
29 2021-03-01   400520J      63    4.143135    59.000000  FCAST_QTY
票数 2
EN
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/73789471

复制
相关文章

相似问题

领券

腾讯云开发者

扫码关注腾讯云开发者

扫码关注腾讯云开发者

领取腾讯云代金券

热门产品

热门推荐

更多推荐

Copyright © 2013 - 2026 Tencent Cloud. All Rights Reserved. 腾讯云 版权所有 

深圳市腾讯计算机系统有限公司 ICP备案/许可证号：粤B2-20090059 粤公网安备44030502008569号

腾讯云计算（北京）有限责任公司 京ICP证150476号 |  京ICP备11018762号

问题归档专栏文章快讯文章归档关键词归档开发者手册归档开发者手册 Section 归档