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使用布尔值选择数据

发布于 2025-01-16 22:00:53 字数 6820 浏览 0 评论 0原文

我有这些数据：

Time    Filename    60Ni    61Ni    62Ni    63Cu    64Ni    65Cu    66Zn
0   input/25.03.2022/220310001_Blk.TXT  0.004213561117649   0.0004941140553758  0.0008264054505464  0.0031620886176824  0.0027940864441916  0.0015828267166701  0.0016676299002331
0   input/25.03.2022/220310002_s01.TXT  10.450854313373563  0.4648053120821714  1.515178832411766   6.799761742353439   0.4018541350960731  3.1670226126909258  -0.0004149001091036
0   input/25.03.2022/220310003_Blk.TXT  0.0046728020068258  0.0005113642575452  0.0008742292275807  0.0038990775356069  0.0016948795023684  0.0018952833449778  0.0010220745559005
0   input/25.03.2022/220310004_NBS1.TXT 10.198064727783205  0.4535825699567795  1.4786694407463077  6.644256343841553   0.3992856484651566  3.094865655899048   0.0037580380868166
0   input/25.03.2022/220310004_NBS1.TXT 10.079309902191165  0.4483201849460602  1.46148056268692    6.564267292022705   0.3941892045736312  3.0576720571517946  0.0034487369190901
0   input/25.03.2022/220310004_NBS1.TXT 10.055493621826171  0.4472497546672821  1.4580038189888 6.549974250793457   0.3931813925504684  3.050980033874512   0.0033985046250745
0   input/25.03.2022/220310005_Blk.TXT  0.0054564320637534  0.000552704881799   0.0009742037393152  0.0051360657749076  0.0016950297751463  0.0024249535597239  0.0009928302722983
0   input/25.03.2022/220310006_s02.TXT  10.01531049013138   0.4454658553004265  1.4522675812244414  6.514146220684052   0.386061905324459   3.0344271540641783  0.000107468456008
0   input/25.03.2022/220310007_Blk.TXT  0.0058440430865933  0.0005767503850317  0.0010166236781515  0.005754723213613   0.0016812159873855  0.0026916580740362  0.0009854609476557
0   input/25.03.2022/220310008_IC11.TXT 9.994083042144776   0.4445306330919266  1.4492901706695556  7.470176630020141   0.3894518518447876  3.4796351766586304  0.0025492239557206
0   input/25.03.2022/220310008_IC11.TXT 9.96211524963379    0.4431046521663666  1.4446095180511476  7.446916389465332   0.3881216913461685  3.4687003660202027  0.0025008138502016
0   input/25.03.2022/220310008_IC11.TXT 9.986034145355225   0.4441730308532715  1.4480724453926086  7.463679447174072   0.3892696779966354  3.4765151739120483  0.0026400250289589
0   input/25.03.2022/220310009_Blk.TXT  0.0063325553511579  0.0006097427569329  0.0010847105528227  0.0069349051453173  0.001787266593116   0.0031935206692044  0.0010376764985267
0   input/25.03.2022/220310010_IC21.TXT 9.907929096221924   0.4407092106342316  1.436798827648163   6.06647294998169    0.381802784204483   2.8266239309310914  -1.9165552830600065e-05
0   input/25.03.2022/220310010_IC21.TXT 9.898069801330566   0.440289745926857   1.4353946447372437  6.061149816513062   0.381249166727066   2.824158968925476   -0.000127439211501
0   input/25.03.2022/220310010_IC21.TXT 9.873182182312013   0.4391724157333374  1.4317991995811463  6.046364660263062   0.3803336149454117  2.8172996759414675  -0.0001105861682299
0   input/25.03.2022/220310011_Blk.TXT  0.006776000233367   0.0006151825053772  0.0011437231053908  0.0076008092767248  0.0019233425652297  0.0034851033162946  0.0010968211592019
0   input/25.03.2022/220310012_s03.TXT  9.919624531269074   0.4412683457136154  1.4386062547564509  6.453964179754257   0.3823180440813303  3.006967249512672   -5.925155056729638e-06
0   input/25.03.2022/220310013_Blk.TXT  0.0070609439785281  0.000630383901686   0.0011728596175089  0.0080440094384054  0.002036696835421   0.0036809448773662  0.0011725965615672
0   input/25.03.2022/220310014_NBS2.TXT 9.981303691864014   0.4439967930316925  1.4475916290283204  6.529758939743042   0.3842234253883362  3.0423321390151976  -0.0003124714840669
0   input/25.03.2022/220310014_NBS2.TXT 9.914944686889648   0.4410672289133072  1.43802725315094    6.483507022857666   0.3816052615642548  3.020912132263184   -0.0003612653422169
0   input/25.03.2022/220310014_NBS2.TXT 9.886322116851806   0.4397846907377243  1.433876838684082   6.466586313247681   0.3804377472400665  3.012995510101318   -0.0004034811019664
0   input/25.03.2022/220310015_Blk.TXT  0.0073987463644395  0.0006374313224417  0.00121240914256    0.0086917337340613  0.0023169121006503  0.0039539939956739  0.0013138331084822
0   input/25.03.2022/220310016_IC12.TXT 9.85557996749878    0.4384415912628174  1.429577150344849   7.381738519668579   0.3795971584320068  3.4393920421600344  -0.0002216772679821
0   input/25.03.2022/220310016_IC12.TXT 9.82486707687378    0.4370819771289825  1.4251339054107666  7.356578054428101   0.3785045564174652  3.4277337741851808  -0.0001708243161556
0   input/25.03.2022/220310016_IC12.TXT 9.80790719985962    0.4363300800323486  1.4227154779434203  7.344034671783447   0.3781744015216827  3.421962966918945   4.260804889781866e-06
0   input/25.03.2022/220310017_Blk.TXT  0.0075777747668325  0.0006548739698094  0.0012614171913204  0.0094458545868595  0.0020529380068182  0.0042742350138723  0.0011683187098242
0   input/25.03.2022/220310018_s04.TXT  9.812697958946227   0.4365527924150228  1.4234635725617408  6.387677395343781   0.3783831790089607  2.9768310248851777  -2.183340569104075e-05
0   input/25.03.2022/220310019_Blk.TXT  0.0078119638841599  0.0006677620811387  0.001280953696308   0.0097443414541582  0.0025180794376259  0.0044003704718003  0.0014272088340173

我读了它，然后删除了我不想要的所有内容：：

import pandas as pd
df = pd.read_csv(fullname,  delimiter='\t', index_col=False)
df.drop(['Time', '60Ni', '61Ni', '62Ni', '64Ni', '66Zn'], axis=1, inplace=True)

插入比率：

df['Cu_ratio'] = df['65Cu'] / df['63Cu']

按名称检测行：

df['blank']=df['Filename'].str.contains('_Blk|_BLK')
df['standard']=df['Filename'].str.contains('_s')
df['sample']=~df['blank'] & ~df['standard']
dfSamples=df[df['sample'] == True]

每个“样本”之前和之后检测“标准”

i = 0
for sampleIndex in dfSamples.index:
  dfBefore=df.iloc[0:sampleIndex]
  dfBeforeInverse=dfBefore[::-1]
  dfStandardBefore = dfBeforeInverse[dfBeforeInverse['standard'] == True]
  indexBefore = dfStandardBefore.index[0]
  df.at[sampleIndex, 'standardBefore'] = indexBefore
  lastIndex = df.index[-1]
  dfAfter=df.iloc[sampleIndex:lastIndex]
  dfStandardAfter = dfAfter[dfAfter['standard'] == True]
  indexAfter = dfStandardAfter.index[0] 
  df.at[sampleIndex, 'standardAfter'] = indexAfter

然后我需要在到目前为止有效的

。但我正在努力的地方：我需要进行计算： 首先，我需要计算每个“样本”之前和之后的“标准”的平均值：

我尝试过

av_std = sum(dfStandardBefore['Cu_ratio'], dfStandardAfter['Cu_ratio']) / 2

，但它计算之前和之后的所有标准的总和，而不仅仅是用布尔值掩盖的标准。我需要的例如：

NBS1 的 s01 和 s02； s02和s03用于IC11和IC21，s03和s04用于NBS2和IC12，依此类推。

最后一步是对每个样本进行计算：

delta = (av_std / sample) * 1000

并将其导出到 csv 中。

我该如何解决这个问题？

原文

I have this data:

Time    Filename    60Ni    61Ni    62Ni    63Cu    64Ni    65Cu    66Zn
0   input/25.03.2022/220310001_Blk.TXT  0.004213561117649   0.0004941140553758  0.0008264054505464  0.0031620886176824  0.0027940864441916  0.0015828267166701  0.0016676299002331
0   input/25.03.2022/220310002_s01.TXT  10.450854313373563  0.4648053120821714  1.515178832411766   6.799761742353439   0.4018541350960731  3.1670226126909258  -0.0004149001091036
0   input/25.03.2022/220310003_Blk.TXT  0.0046728020068258  0.0005113642575452  0.0008742292275807  0.0038990775356069  0.0016948795023684  0.0018952833449778  0.0010220745559005
0   input/25.03.2022/220310004_NBS1.TXT 10.198064727783205  0.4535825699567795  1.4786694407463077  6.644256343841553   0.3992856484651566  3.094865655899048   0.0037580380868166
0   input/25.03.2022/220310004_NBS1.TXT 10.079309902191165  0.4483201849460602  1.46148056268692    6.564267292022705   0.3941892045736312  3.0576720571517946  0.0034487369190901
0   input/25.03.2022/220310004_NBS1.TXT 10.055493621826171  0.4472497546672821  1.4580038189888 6.549974250793457   0.3931813925504684  3.050980033874512   0.0033985046250745
0   input/25.03.2022/220310005_Blk.TXT  0.0054564320637534  0.000552704881799   0.0009742037393152  0.0051360657749076  0.0016950297751463  0.0024249535597239  0.0009928302722983
0   input/25.03.2022/220310006_s02.TXT  10.01531049013138   0.4454658553004265  1.4522675812244414  6.514146220684052   0.386061905324459   3.0344271540641783  0.000107468456008
0   input/25.03.2022/220310007_Blk.TXT  0.0058440430865933  0.0005767503850317  0.0010166236781515  0.005754723213613   0.0016812159873855  0.0026916580740362  0.0009854609476557
0   input/25.03.2022/220310008_IC11.TXT 9.994083042144776   0.4445306330919266  1.4492901706695556  7.470176630020141   0.3894518518447876  3.4796351766586304  0.0025492239557206
0   input/25.03.2022/220310008_IC11.TXT 9.96211524963379    0.4431046521663666  1.4446095180511476  7.446916389465332   0.3881216913461685  3.4687003660202027  0.0025008138502016
0   input/25.03.2022/220310008_IC11.TXT 9.986034145355225   0.4441730308532715  1.4480724453926086  7.463679447174072   0.3892696779966354  3.4765151739120483  0.0026400250289589
0   input/25.03.2022/220310009_Blk.TXT  0.0063325553511579  0.0006097427569329  0.0010847105528227  0.0069349051453173  0.001787266593116   0.0031935206692044  0.0010376764985267
0   input/25.03.2022/220310010_IC21.TXT 9.907929096221924   0.4407092106342316  1.436798827648163   6.06647294998169    0.381802784204483   2.8266239309310914  -1.9165552830600065e-05
0   input/25.03.2022/220310010_IC21.TXT 9.898069801330566   0.440289745926857   1.4353946447372437  6.061149816513062   0.381249166727066   2.824158968925476   -0.000127439211501
0   input/25.03.2022/220310010_IC21.TXT 9.873182182312013   0.4391724157333374  1.4317991995811463  6.046364660263062   0.3803336149454117  2.8172996759414675  -0.0001105861682299
0   input/25.03.2022/220310011_Blk.TXT  0.006776000233367   0.0006151825053772  0.0011437231053908  0.0076008092767248  0.0019233425652297  0.0034851033162946  0.0010968211592019
0   input/25.03.2022/220310012_s03.TXT  9.919624531269074   0.4412683457136154  1.4386062547564509  6.453964179754257   0.3823180440813303  3.006967249512672   -5.925155056729638e-06
0   input/25.03.2022/220310013_Blk.TXT  0.0070609439785281  0.000630383901686   0.0011728596175089  0.0080440094384054  0.002036696835421   0.0036809448773662  0.0011725965615672
0   input/25.03.2022/220310014_NBS2.TXT 9.981303691864014   0.4439967930316925  1.4475916290283204  6.529758939743042   0.3842234253883362  3.0423321390151976  -0.0003124714840669
0   input/25.03.2022/220310014_NBS2.TXT 9.914944686889648   0.4410672289133072  1.43802725315094    6.483507022857666   0.3816052615642548  3.020912132263184   -0.0003612653422169
0   input/25.03.2022/220310014_NBS2.TXT 9.886322116851806   0.4397846907377243  1.433876838684082   6.466586313247681   0.3804377472400665  3.012995510101318   -0.0004034811019664
0   input/25.03.2022/220310015_Blk.TXT  0.0073987463644395  0.0006374313224417  0.00121240914256    0.0086917337340613  0.0023169121006503  0.0039539939956739  0.0013138331084822
0   input/25.03.2022/220310016_IC12.TXT 9.85557996749878    0.4384415912628174  1.429577150344849   7.381738519668579   0.3795971584320068  3.4393920421600344  -0.0002216772679821
0   input/25.03.2022/220310016_IC12.TXT 9.82486707687378    0.4370819771289825  1.4251339054107666  7.356578054428101   0.3785045564174652  3.4277337741851808  -0.0001708243161556
0   input/25.03.2022/220310016_IC12.TXT 9.80790719985962    0.4363300800323486  1.4227154779434203  7.344034671783447   0.3781744015216827  3.421962966918945   4.260804889781866e-06
0   input/25.03.2022/220310017_Blk.TXT  0.0075777747668325  0.0006548739698094  0.0012614171913204  0.0094458545868595  0.0020529380068182  0.0042742350138723  0.0011683187098242
0   input/25.03.2022/220310018_s04.TXT  9.812697958946227   0.4365527924150228  1.4234635725617408  6.387677395343781   0.3783831790089607  2.9768310248851777  -2.183340569104075e-05
0   input/25.03.2022/220310019_Blk.TXT  0.0078119638841599  0.0006677620811387  0.001280953696308   0.0097443414541582  0.0025180794376259  0.0044003704718003  0.0014272088340173

I read it and I drop everything I don't want::

import pandas as pd
df = pd.read_csv(fullname,  delimiter='\t', index_col=False)
df.drop(['Time', '60Ni', '61Ni', '62Ni', '64Ni', '66Zn'], axis=1, inplace=True)

inserting a ratio:

df['Cu_ratio'] = df['65Cu'] / df['63Cu']

detecting rows by name:

df['blank']=df['Filename'].str.contains('_Blk|_BLK')
df['standard']=df['Filename'].str.contains('_s')
df['sample']=~df['blank'] & ~df['standard']
dfSamples=df[df['sample'] == True]

then I need to detect the "standard" before and after each "sample"

i = 0
for sampleIndex in dfSamples.index:
  dfBefore=df.iloc[0:sampleIndex]
  dfBeforeInverse=dfBefore[::-1]
  dfStandardBefore = dfBeforeInverse[dfBeforeInverse['standard'] == True]
  indexBefore = dfStandardBefore.index[0]
  df.at[sampleIndex, 'standardBefore'] = indexBefore
  lastIndex = df.index[-1]
  dfAfter=df.iloc[sampleIndex:lastIndex]
  dfStandardAfter = dfAfter[dfAfter['standard'] == True]
  indexAfter = dfStandardAfter.index[0] 
  df.at[sampleIndex, 'standardAfter'] = indexAfter

this works so far.

But where I'm struggling: I need to make a calculation:
First, I need to calculate the average of the "standard" before and after each "sample":

I tried it with

av_std = sum(dfStandardBefore['Cu_ratio'], dfStandardAfter['Cu_ratio']) / 2

but it calculates the sum of ALL standards before and after, not only the masked with boolean. What I need it to be for example:

s01 and s02 for NBS1; s02 and s03 for IC11 and IC21, s03 and s04 for NBS2 and IC12, and so on.

The last step would be to make a calculation for each sample:

delta = (av_std / sample) * 1000

and export it into a csv.

How can I solve this?

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