寻找数据框架中特定X值处的一系列Y值

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英文:

Finding a range of Y-values at specific X-value across dataframes

问题

You can calculate the range of Y-values around x = 1.0 for each dataframe by interpolating the values at x = 1.0. Here's how you can do it in Python using pandas and interpolation:

  1. import pandas as pd
  3. # Define your dataframes (dataframe1, dataframe2, etc.)
  5. # Define the target x value
  6. target_x = 1.0
  8. # Create a list to store the interpolated Y-values for each dataframe
  9. y_ranges = []
  11. # Iterate through your dataframes
  12. for df in [dataframe1, dataframe2, dataframe3]:
  13.     # Use interpolation to find the Y-value at the target_x
  14.     interpolated_y = df['Y'].interpolate(method='linear', limit_direction='both').loc[target_x]
  15.     y_ranges.append(interpolated_y)
  17. # Calculate the range of Y-values
  18. y_range = max(y_ranges) - min(y_ranges)
  20. # Print the range of Y-values
  21. print(f"Range of Y-values around x = {target_x}: {y_range}")

This code will give you the range of Y-values around x = 1.0 across your dataframes. You can extend this code to include all 10 of your dataframes.

英文:

I am trying to find a quick way of finding a range of Y-values across a set of dataframes that I have. Here is what my dataframes look like:

  1. #Example dataframe1
  3. X      Y
  4. 0      0
  5. 0.2    27    
  6. 0.4    31
  7. 0.6    34
  8. 0.8    39
  9. 1.0    43
  10. 1.2    44
  12. #Example dataframe2
  14. X      Y
  15. 0      0
  16. 0.2    12
  17. 0.3    14
  18. 0.4    18
  19. 0.5    21
  20. 0.6    22
  21. 0.7    24
  22. 0.8    29
  23. 0.9    34
  24. 1.0    38
  25. 1.1    40
  27. #Example dataframe3
  29. X      Y
  30. 0      0
  31. 0.2    19
  32. 0.3    24
  33. 0.4    29
  34. 0.5    34
  35. 0.6    38
  36. 0.7    39
  37. 0.8    47
  38. 0.9    51
  39. 1.0    52
  40. 1.1    56
  42. X1   0.00000000  0.00000000
  43. X2   0.00644777 -3.74942549
  44. X3   0.01250601 -1.64198832
  45. X4   0.01746194 -3.09841231
  46. X5   0.02656210 -3.24436861
  47. X6   0.03360072  1.92721788
  48. X7   0.04399755 -4.38984054
  49. X8   0.05404601 -1.07421099
  50. X9   0.06154399  1.05752545
  51. X10  0.07105423 -2.64811489
  52. X11  0.08315538 -2.10245766
  53. X12  0.08925389 -1.13184408
  54. X13  0.10060635  0.51200462
  55. X14  0.10833009  4.54644990
  56. X15  0.12196157 -3.08377282
  57. X16  0.13730928 -0.27015071
  58. X17  0.14890850 -0.65513113
  59. X18  0.16087072  2.17034586
  60. X19  0.17361501 -0.93398437
  61. X20  0.18310140  0.79111051
  62. X21  0.19810085 -3.87227906
  63. X22  0.21316248  2.13154747
  64. X23  0.22423607  1.36416314
  65. X24  0.23529990  4.99793424
  66. X25  0.24695577 -1.01918599
  67. X26  0.26119599  2.93076793
  68. X27  0.27643532  2.91155218
  69. X28  0.28517213  3.57100964
  70. X29  0.29978970 -0.73012874
  71. X30  0.31870887 -2.78233188
  72. X31  0.33418021 -1.51922907
  73. X32  0.34585044  2.87420871
  74. X33  0.36071229  1.14786764
  75. X34  0.37379062  2.99504234
  76. X35  0.39324498  0.63168705
  77. X36  0.41099992 -0.07084714
  78. X37  0.42437476  1.54668167
  79. X38  0.43643925  2.81198973
  80. X39  0.44687960  4.92643076
  81. X40  0.45864752  4.11311344
  82. X41  0.48055932  4.39500995
  83. X42  0.49634662  7.15432395
  84. X43  0.50779009  8.58246880
  85. X44  0.52861303  2.82337466
  86. X45  0.54971600  2.99739656
  87. X46  0.56138206  6.78068642
  88. X47  0.57472491  4.45406406
  89. X48  0.59202033  2.46356370
  90. X49  0.60975420  3.15161790
  91. X50  0.63057494  2.34243629
  92. X51  0.64653736  2.56960396
  93. X52  0.66088217 11.82388880
  94. X53  0.68168259  6.09572345
  95. X54  0.69649595  5.09381202
  96. X55  0.71363837  4.46075825
  97. X56  0.72988760  8.62040355
  98. X57  0.75010443  6.83203133
  99. X58  0.76776582  9.18985022
  100. X59  0.78613955  7.86350629
  101. X60  0.81099886  6.03907061
  102. X61  0.82979667  9.34560816
  103. X62  0.84431553  9.42604561
  104. X63  0.86824787  7.64843057
  105. X64  0.88447672  6.16947933
  106. X65  0.89909577  9.87215457
  107. X66  0.92162514 10.98250589
  108. X67  0.94854647  5.26614443
  109. X68  0.96631038  8.94057689
  110. X69  0.98120815 14.91703428
  111. X70  1.00357985 11.66179411
  112. X71  1.02769291  8.30042153
  113. X72  1.04733002  7.64827230
  114. X73  1.06585240 10.99394148
  115. X74  1.09083056  7.14363152
  116. X75  1.11592567  5.93705238
  117. X76  1.13627303 10.89649169
  118. X77  1.15866983  8.08089986
  119. X78  1.17941856 10.63699550
  120. X79  1.19748724 12.61902305
  121. X80  1.22012913 11.83730809
  122. X81  1.24142718  9.53528978
  123. X82  1.26937973  9.20448095
  124. X83  1.29283488  6.75640546
  125. X84  1.31011736 15.04334314
  126. X85  1.33134723 12.18910484
  127. X86  1.36014366 11.80276542
  128. X87  1.38395822 14.18290101
  129. X88  1.40615380 13.99689728
  130. X89  1.42895305 12.04541339
  131. X90  1.45046842 10.61135555
  132. X91  1.47504234 11.07833983
  133. X92  1.49920797 12.37669790
  134. X93  1.52142668 14.35112239
  135. X94  1.54559219 16.42659881
  136. X95  1.57354927 13.69037310
  137. X96  1.60000896 13.51107220
  138. X97  1.62449229 13.96191530
  139. X98  1.64506662 16.74995717
  140. X99  1.67100036 14.67649138
  141. X100 1.70185161 10.71288999
  142. X101 1.72964287 14.56576519
  143. X102 1.75235510  8.66232995
  144. X103 1.77455521 18.00492491
  145. X104 1.80183053 18.36635863
  146. X105 1.83121216 12.63300200
  147. X106 1.85619974 15.88991904
  148. X107 1.88188243 13.53745701
  149. X108 1.91002214 18.05483899
  150. X109 1.93333006 18.40951264
  151. X110 1.96133912 15.26804166
  152. X111 1.98944890 15.16029048
  153. X112 2.01225924 19.62616569
  154. X113 2.03804183 19.73062246
  155. X114 2.06614304 15.57687649
  156. X115 2.09558916 19.55369508
  157. X116 2.12400699 17.90633921
  158. X117 2.14846969 21.81952636
  159. X118 2.18011498 16.62377098
  160. X119 2.20532560 18.47341439
  161. X120 2.23031592 25.90875455
  162. X121 2.26106715 16.58422332
  163. X122 2.28796983 23.44278876
  164. X123 2.31954741 19.14855256
  165. X124 2.35274339 18.75923949
  166. X125 2.37794161 20.01523111
  167. X126 2.40643740 24.42732689
  168. X127 2.43963051 21.06897103
  169. X128 2.46948838 16.13112984
  170. X129 2.49733877 24.40617463
  171. X130 2.53035021 20.97045518
  172. X131 2.56229091 18.55134741
  173. X132 2.59265327 20.27110860
  174. X133 2.61934137 21.31728543
  175. X134 2.64592481 21.69982327
  176. X135 2.67754769 28.79262127
  177. X136 2.70779777 20.45346529
  178. X137 2.73824191 24.89121411
  179. X138 2.76880598 29.23390805
  180. X139 2.80077982 23.31624195
  181. X140 2.83057237 21.14259882

Now, let's say that I want to determine the rate of increase at about x = 1.0, relative to the beginning of the period (i.e. 0 at the beginning in each case). We can see clearly here that the answer would be y = 43, y = 38 and y = 52 among the three cases, with 52 being the highest and 38 being the lowest (so, a range of 14). My question is would it be possible to quickly determine the value at around x = 1.0 across the 10 dataframes that I have like this without having to manually check them? The idea is to determine the range of values for y at around x = 1.0 across all dataframes. Note that it's not perfectly x = 1.0 in all dataframes - some could be something like 0.99 or 1.01.

Thank you, and I your feedback would be most appreciated!

答案1

得分: 1

  1. 你可以将所有的数据框进行`rbind`，然后进行子集筛选，使用`range`函数来获取`X`等于特定值的范围，接着使用`diff`函数计算你所需的数值。例如，在你的示例中：
  3. ```R
  4. ll <- rbind(df1, df2, df3)
  5. range(ll[ll$X == 1, "Y"])
  6. # [1] 38 52
  8. rr <- range(ll[ll$X == 1, "Y"], na.rm = TRUE)
  9. diff(rr)
  10. # [1] 14

你可以稍微扩展这个范围以涵盖1.0附近的值，创建一个最小值和最大值来腾出一些余地：

  1. minwiggle <- 0.99
  2. maxwiggle <- 1.01
  4. range(ll[ll$X <= maxwiggle & ll$X >= minwiggle, "Y"])
  5. # [1] 38 52
  7. rr <- range(ll[ll$X <= maxwiggle & ll$X >= minwiggle, "Y"], na.rm = TRUE)
  8. diff(rr)
  9. # [1] 14
  2. <details>
  3. <summary>英文:</summary>
  5. You could `rbind` all the data frames and then subset and use `range` where `X` equals, then `diff` to calculate your desired value. For instance, in your example:

ll <- rbind(df1, df2, df3)
range(ll[ll$X == 1, "Y"])

[1] 38 52

rr <- range(ll[ll$X == 1, "Y"], na.rm = TRUE)
diff(rr)

[1] 14

  1. You could expand this a little bit around the range of 1.0 by creating a min and max value for wiggle room:

minwiggle <- 0.99
maxwiggle <- 1.01

range(ll[ll$X <= maxwiggle & ll$X >= minwiggle, "Y"])

[1] 38 52

rr <- range(ll[ll$X <= maxwiggle & ll$X >= minwiggle, "Y"], na.rm = TRUE)
diff(rr)

[1] 14

  2. </details>
  6. # 答案2
  7. **得分**: 1
  9. Here is the translation of the provided code:
  11. ```text
  12. 一种方法：
  13. ```R
  14. target_val = 1 ## 要匹配的X值（尽可能接近）
  16. list(df_A, df_B, df_C) |&gt; ## 数据框列表
  17.   Map(f = \(the_df) {  
  18.     ## 找到X最接近target_val的行索引
  19.     row_index = which.min(abs(the_df$X - target_val))
  20.     the_df$Y[row_index]
  21.   }) |&gt;
  22.   Reduce(f = c) |&gt; ## 连接值
  23.   fivenum() ## 在这里放入聚合函数

对于X == 1，使用您的示例数据框的输出：

  1. [1] 38.0 40.5 43.0 47.5 52.0
  2. Please note that I've translated the code portion, as requested, and provided the code in a formatted manner.
  4. <details>
  5. <summary>英文:</summary>
  7. one approach:

target_val = 1 ## X value to match (as close as possible)

list(df_A, df_B, df_C) |> ## list of dataframes
Map(f = (the_df) {
## find row index where X is closest to the target_val
row_index = which.min(abs(the_df$X - target_val))
the_df$Y[row_index]
}) |>
Reduce(f = c) |> ## concatenate values
fivenum() ## put aggregating function here

  1. output for X == 1, using your example dataframes:

[1] 38.0 40.5 43.0 47.5 52.0

  2. </details>

huangapple
  • 本文由 发表于 2023年5月10日 20:50:42
  • 转载请务必保留本文链接：https://go.coder-hub.com/76218641.html
  • dataframe
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  • range
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