删除在不断变化的列范围中具有缺失值的行我有一个数据表，我想在其中删除在可变列范围的所有列中没有值的行。
例如，对于下面的数据表：
dt =
| 样本 | 产品类别 | 第 1 列 | Col2 | 第 3 列 | Col4 | 第 5 列 |
| 10001 | 一种 | 0.0122 | 0.0223 | 0.01546 | 0.8742 | 23.12 |
| 10003 | 乙 | | | | | |
| 10004 | 一种 | 0.123 | 0.124 | 0.1240 | 0.9712 | 23.78 |
我想删除带有示例“10003”的行，因为它在 Col1 - Col5 中的条目是空的。 我知道这个帖子非常相似，但对我来说不是很有帮助，因为我要检查的列数为空通常大于 15 并且列名可以/将更改，因此手动输入此代码并调整列名
dt << Select Where(
Is Empty(:Col1) &
Is Empty(:Col2) &
is Empty(:Col3) & ... etc.
) << Delete Rows;我能够很容易地让我需要检查的所有列在列表中都是空的，因为它们是/将都与
cols = dt << Get Column Names(Continuous);
但这就是我卡住的地方，不确定如何继续。 我是否使用 for 循环遍历每一列并检查它是否为空？ 但我只想删除列范围内所有列都为空的行。 如果我使用 Python，我只会使用 pandas.DataFrame.dropna(axis=0)。 我将如何在 JMP 中执行此操作？
数据表脚本编写回复：删除在不断变化的列范围中具有缺失值的行这是处理您的问题的一种方法。其他社区成员可能有更好的方法，但这个方法似乎总能奏效。我正在使用名为血压的样本数据表。
下面的脚本的作用是生成选择行所需的 JSL 语句，如果将数据表中的所有连续列相加，并且总和是缺失值，则选择该行。所以需要构建的目标JSL是
dt << select where(ismissing(sum(as column(\!"BP 8M\!"),as column(\!"BP 8M\!"),as column(\!"BP 8M\!"),as column(\!"BP 8M\!"),as column(\!"BP 8M\!"),as column(\!"BP 8M\!"),as column(\!"BP 8M\!"),as column(\!"BP 8M\!"),as column(\!"BP 8M\!")))==1);
该脚本在字符串变量中创建此代码，然后使用
Eval( Parse( ...........);
在示例中，我在数据表中创建了 3 个新行，默认情况下这些行都有缺失值。然后脚本运行并找到这 3 行。
Names Default To Here( 1 );
dt =
// Open Data Table: Blood Pressure.jmp
// → Data Table( "Blood Pressure" )
Open( "$SAMPLE_DATA/Blood Pressure.jmp" );
dt << add rows(3);
colNames = dt << get column names( continuous, string );
theExpr = "dt << select where(ismissing(sum(as column(\!"" || colNames[1] || "\!")";
For( i = 2, i <= N Items( colNames ), i++,
theExpr = theExpr || ",as column(\!"" || colNames[1] || "\!")"
);
theExpr = theExpr || "))==1);";
Eval( Parse( theExpr ) )
;
回复：删除在不断变化的列范围中具有缺失值的行有一个内置的 JMP - 缺失数据模式。无需重新发明轮子
Names Default To Here( 1 );
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
// column names to search for missing values
names = {"CO", "SO2", "NO", "Lead"};
// you can uncomment the following line to use all 'Continuous' columns
//names = dt << Get Column Names( "Continuous" );
// turn the list into a 'Columns( a, b, c )' expression
columns = Substitute( names, {}, Expr( Columns ) );
// run the missing data pattern
Eval( Eval Expr( table = dt << Missing Data Pattern( Expr( name expr( columns ) ) ) ) );
// select rows where all values are missing
Eval( Eval Expr( table << Select Where( :Number of Columns Missing == N Items( Expr( names ) ) ) ) );
Close( table, No Save );
rows = dt << Get Selected Rows();
// if any rows are selected then they have missing values for all columns given, delete
If( N Rows( rows ) > 0, dt << Delete Rows( rows ) );回复：删除在不断变化的列范围中具有缺失值的行你好@cxu04 ,
两个都@txnelson和@ErraticAttach提供了优秀的解决方案。@ErraticAttach具有选择排除标准、1 列缺失或全部缺失以及介于两者之间的任何值的额外好处。下面，我附上了一个使用矩阵函数的替代解决方案。作为仅供参考，一些 JMP 平台分析和图表具有排除或包括缺失值以及插补的选项。
Names Default To Here( 1 );
// Open Data Table: Blood Pressure.jmp
// → Data Table( "Blood Pressure" )
dt = Open( "$SAMPLE_DATA/Blood Pressure.jmp" );
dt << add rows( 3 );
colNames = dt << get column names( continuous, string );
mymat = dt << get as matrix( colNames );
//this method counts the number of missing values in your column range for each row
//you can set the threshold number for exclusions, misslimit
// any value is missing, missLimit = 0
// all are missing, misslimit = nitems( colNames ) -1
// 20% + misslimit = Ceiling( nitems( colNames)*0.2 ) ) - 1
//
misslimit = 3;
xx = Loc( V Sum( Is Missing( Transpose( mymat ) ) ) > misslimit ) ; //get the row numbers where more than misslimit are missing.
dt << select rows( xx );
dt << exclude();
dt << clear select;
//look at the log output to get an idea what these functions are doing
show( mymat, VSum(IsMissing( Transpose(mymat) )), xx);
这是 show() 命令的 JMP LOG 输出的一部分。
show( mymat, VSum(IsMissing( Transpose(mymat) )), xx);
/*:
mymat =
[ 182.595915155309 173.550216520736 180.116643270986 173.797442210887 178.416197148117 181.152265967985 171.278300921568 177.570953487335 170.514896659448,
172.512940709438 180.718560564297 180.917546679043 170.201828509985 178.839136233913 176.084997418848 175.161843431476 184.884491085702 187.603078247176,
180.774421425438 188.500473854652 177.384986937421 188.441549950731 175.346240386252 181.924943930192 183.12980493925 182.794049100295 180.121927541929,
180.620679187184 176.718084823659 182.381646560932 176.337805749776 172.773758285362 183.608276048664 183.04930217403 187.31279033708 182.931206367403,
184.202187190097 180.174755903154 176.277045698247 171.91229023858 174.678173508279 175.578954629626 170.259254710046 189.822176301411 182.694806601573,
171.439595422176 181.462075626649 179.517397097984 183.021068043181 175.683661608275 174.15485890414 184.878981656321 182.387718653956 184.169322994033,
174.589230394192 180.330100756984 180.187858248718 183.498290114926 179.622232533597 172.866924129507 184.16481869131 184.653326274105 180.368840760416,
179.109260746268 172.886222586229 182.904992672503 187.881911714776 192.773275959188 169.793497264777 179.098192996471 184.653672194875 170.403578559484,
177.787439187438 192.050244302843 176.785646502378 184.924564594592 172.499108408691 179.464417655732 186.765084181627 184.384583007231 177.427685029706,
180.687489407623 187.037431530346 181.618651921524 181.868587651411 177.245563534856 176.983573711354 183.864008024749 183.43593738674 184.394933320607,
181.804016028632 185.501158177984 178.209310795898 177.186342937671 176.573252982367 177.20643071091 182.640175499696 190.972901519754 174.986928330607,
178.085353285743 183.877324525276 186.575178728884 174.027593116806 174.72327532089 176.115218469266 179.80577644674 181.46178488602 184.070139882305,
186.313144806757 179.931613365546 180.989181249718 171.059060559811 172.873089144473 181.319863949352 179.038752187802 181.459302956398 189.242086812316,
175.347853139956 180.493132509235 177.2578428812 184.517143208058 171.169474537616 181.880353653926 175.727537977595 184.604441269869 169.540004898628,
181.103289178403 174.944992509985 180.642613381863 179.143677395064 185.445956950321 183.622763290159 183.636564430071 173.249587056935 185.21824057543,
181.288352739913 177.663853371957 178.850703910826 178.211632365764 181.685844613992 178.639264502305 188.483289884327 183.125809166649 173.182049720424,
178.777832744608 174.717037207769 188.986597488081 190.546847185169 178.055520662495 176.991735583422 182.282596238225 187.951725739917 170.940897197153,
176.763152449944 186.677919707096 172.45356859494 179.666182707754 182.726891244295 177.924284755616 183.200306257339 190.139279669601 177.823031925273,
175.768608854708 178.12326354079 182.404679249467 176.614597739946 178.320419876662 177.143465976767 175.856743742269 183.549834391574 183.621024712236,
184.167672392284 171.230704316198 182.335148506991 181.334797218108 186.395858538357 183.421182544757 183.517577768589 171.907000056173 178.774574166082,
. . . . . . . . .,
. . . . . . . . .,
. . . . . . . . .];
V Sum(Is Missing(mymat`)) = [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9 9 9];
xx = [21 22 23];
回复：删除在不断变化的列范围中具有缺失值的行所有好的建议都在这里@txnelson , @ErraticAttach和@gzmorgan0 .
我是否可以提供我认为 JSL 命令的最基本用法，我认为这些用法足够健壮，可以在不同情况下完成任务。
本质上，我是手动完成的，并且只对脚本进行了最少的修改，以便在 JSL 中对其进行概括。
想知道其他人的想法。
dt = Open( "$SAMPLE_DATA/Missing Data Pattern.jmp" );
// this data set actually doesn't have any row that is completely missing.
dt << add rows(3); // now it does
// now the work begins
colNames = dt << get column names( continuous, string );
// make a missing data pattern table
missingdt = dt << Missing Data Pattern(
columns( eval (colNames) ),
Count Missing Value Codes,
Add Value Colors Property,
Output Table( "Missing Data Pattern" )
);
// the next two steps should be fast enough in almost any size of data set that i do not think it is worth writing it as a condition for the cases that no row is completely missing.
// select the row representing all missing. if no such row exists nothing will be selected.
missingdt << select where (:Number of columns missing == N items (colNames));
wait (0);
// delete the selected rows from the original table (if anything was selected)
dt << delete rows;
close (missingdt, no save);
