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清空筛选条件

Remove Rows that are entirely NaN

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Tiffany
Tiffany 2022-12-23
编辑： Walter Roberson 2022-12-24
Hi,
I'm used to machine learning in Python, and I'm trying to get used to data cleaning to prepare a dataset for that in Matlab. I'm using the inflation dataset attached from the World Bank.
In short, I am trying to drop all rows that are completely null, because I need to be able to impute those NaNs, and eventually be able to attach my predictions to the same rows in the original dataset.
My process so far is to read the csv in as a table. drop all the text columns except for the country, reserve the orignal dataset for joining it back later, then normalize the data between 0 an 1 and then impute the nulls.
I've tried the following, but, I keep getting Incorrect number or types of inputs or outputs for function 'isnan'. error, and I'm not sure what I'm doing wrong.
%drop rows that are entirely NaN
%testing
N = table2cell(N);
N(cellfun(@(cell) any(isnan(cell(:))),N))={''};
empties = cellfun('isempty',N);
N(empties) = {NaN};
N(all(isnan(N),2),:) = [];
indices = find(N(:,2)==0);
N(indices,:) = [];
%testing
% N = table2array(N);
% out = sum(N,2);
Original code, minus the removing rows that are NaNs
%read in inflation dataset from worldbank.org
N = readtable('inflation.csv','NumHeaderLines',5);
%drop cols 2-4. All text data.
N(:,[2,3,4]) = [];
%reserve the text data for joining later.
n = N;
n(:,1) = [];
%normalize the dataset for neural network
n = normalize(n, 'range');
%impute nulls with nearest neighbor method
%n = table2array(n);
%n(n=='NaN') = nan;
n = knnimpute(n);

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采纳的回答

Voss
Voss 2022-12-23
Ran in:
C = readcell('Inflation.csv')
C = 269×66 cell array
{'Data Source' } {'World Development Indicators'} {1×1 missing } {1×1 missing } {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing } {1×1 missing } {1×1 missing } {1×1 missing } {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {'Last Updated Date' } {[22-Dec-2022 ]} {1×1 missing } {1×1 missing } {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing } {1×1 missing } {1×1 missing } {1×1 missing } {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {'Country Name' } {'Country Code' } {'Indicator Name' } {'Indicator Code'} {[ 1960]} {[ 1961]} {[ 1962]} {[ 1963]} {[ 1964]} {[ 1965]} {[ 1966]} {[ 1967]} {[ 1968]} {[ 1969]} {[ 1970]} {[ 1971]} {[ 1972]} {[ 1973]} {[ 1974]} {[ 1975]} {[ 1976]} {[ 1977]} {[ 1978]} {[ 1979]} {[ 1980]} {[ 1981]} {[ 1982]} {[ 1983]} {[ 1984]} {[ 1985]} {[ 1986]} {[ 1987]} {[ 1988]} {[ 1989]} {[ 1990]} {[ 1991]} {[ 1992]} {[ 1993]} {[ 1994]} {[ 1995]} {[ 1996]} {[ 1997]} {[ 1998]} {[ 1999]} {[ 2000]} {[ 2001]} {[ 2002]} {[ 2003]} {[ 2004]} {[ 2005]} {[ 2006]} {[ 2007]} {[ 2008]} {[ 2009]} {[ 2010]} {[ 2011]} {[ 2012]} {[ 2013]} {[ 2014]} {[ 2015]} {[ 2016]} {[ 2017]} {[ 2018]} {[ 2019]} {[ 2020]} {[ 2021]} {'Aruba' } {'ABW' } {'Inflation, consumer prices (annual %)'} {'FP.CPI.TOTL.ZG'} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {[ 4.0323]} {[ 1.0740]} {[ 3.6430]} {[ 3.1219]} {[ 3.9916]} {[ 5.8367]} {[ 5.5556]} {[ 3.8734]} {[ 5.2156]} {[ 6.3111]} {[ 3.3614]} {[ 3.2253]} {[ 2.9999]} {[ 1.8695]} {[ 2.2804]} {[ 4.0440]} {[ 2.8836]} {[ 3.3152]} {[ 3.6564]} {[ 2.5291]} {[ 3.3978]} {[ 3.6080]} {[ 5.3926]} {[ 8.9560]} {[ -2.1354]} {[ 2.0781]} {[ 4.3163]} {[ 0.6275]} {[ -2.3721]} {[ 0.4214]} {[ 0.4748]} {[ -0.9312]} {[ -1.0283]} {[ 3.6260]} {[ 4.2575]} {1×1 missing} {1×1 missing} {'Africa Eastern and Southern'} {'AFE' } {'Inflation, consumer prices (annual %)'} {'FP.CPI.TOTL.ZG'} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {[ 19.5984]} {[ 15.2241]} {[ 11.2165]} {[ 14.2381]} {[ 12.5269]} {[ 15.0698]} {[ 15.0665]} {[ 14.4616]} {[ 12.1399]} {[ 11.5675]} {[ 10.9839]} {[ 13.0066]} {[ 13.8920]} {[ 12.5634]} {[ 12.5223]} {[ 12.5582]} {[ 12.4579]} {[ 17.6781]} {[ 16.1676]} {[ 13.1357]} {[ 14.8528]} {[ 12.2886]} {[ 9.7066]} {[ 10.2496]} {[ 7.4953]} {[ 7.8199]} {[ 8.6015]} {[ 5.8404]} {[ 8.7638]} {[ 7.4497]} {[ 5.0234]} {[ 8.5580]} {[ 8.8982]} {[ 8.4508]} {[ 12.5666]} {[ 8.9542]} {[ 5.5375]} {[ 8.9712]} {[ 9.1587]} {[ 5.7510]} {[ 5.3703]} {[ 5.2502]} {[ 6.5714]} {[ 6.3993]} {[ 4.7208]} {[ 4.1202]} {[ 6.3630]} {[ 6.0793]} {'Afghanistan' } {'AFG' } {'Inflation, consumer prices (annual %)'} {'FP.CPI.TOTL.ZG'} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing } {1×1 missing } {1×1 missing } {1×1 missing } {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {[ 12.6863]} {[ 6.7846]} {[ 8.6806]} {[ 26.4187]} {[ -6.8112]} {[ 2.1785]} {[ 11.8042]} {[ 6.4412]} {[ 7.3858]} {[ 4.6740]} {[ -0.6617]} {[ 4.3839]} {[ 4.9760]} {[ 0.6261]} {[ 2.3024]} {1×1 missing} {1×1 missing} {'Africa Western and Central' } {'AFW' } {'Inflation, consumer prices (annual %)'} {'FP.CPI.TOTL.ZG'} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {[ 8.7992]} {[ 12.0598]} {[ 10.6719]} {[ 11.2500]} {[ 7.3549]} {[ 5.9510]} {[ 0.2488]} {[ 2.5237]} {[ 0.8693]} {[ 1.0574]} {[ 1.7419]} {[ -0.0630]} {[ 0.5535]} {[ 31.8410]} {[ 10.5633]} {[ 4.9142]} {[ 3.9971]} {[ 4.4711]} {[ 0.3723]} {[ 2.5308]} {[ 4.3615]} {[ 3.1887]} {[ 1.7609]} {[ 0.6943]} {[ 5.6316]} {[ 4.4159]} {[ 3.6074]} {[ 8.4530]} {[ 3.2824]} {[ 1.7848]} {[ 4.0187]} {[ 4.5784]} {[ 2.4392]} {[ 1.7581]} {[ 2.1303]} {[ 1.4946]} {[ 1.7646]} {[ 1.7840]} {[ 1.7586]} {[ 2.4376]} {[ 3.8379]} {'Angola' } {'AGO' } {'Inflation, consumer prices (annual %)'} {'FP.CPI.TOTL.ZG'} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {[ 83.7838]} {[ 299.5098]} {[1.3785e+03]} {[ 949.7925]} {[2.6665e+03]} {[4.1451e+03]} {[ 219.1767]} {[ 107.2848]} {[ 248.1959]} {[ 324.9969]} {[ 152.5610]} {[ 108.8974]} {[ 98.2241]} {[ 43.5421]} {[ 22.9535]} {[ 13.3052]} {[ 12.2515]} {[ 12.4758]} {[ 13.7303]} {[ 14.4697]} {[ 13.4825]} {[ 10.2779]} {[ 8.7778]} {[ 7.2804]} {[ 9.3538]} {[ 30.6990]} {[ 29.8426]} {[ 19.6306]} {[ 17.0797]} {[ 22.2716]} {[ 25.7543]} {'Albania' } {'ALB' } {'Inflation, consumer prices (annual %)'} {'FP.CPI.TOTL.ZG'} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {[ 226.0054]} {[ 85.0048]} {[ 22.5651]} {[ 7.7932]} {[ 12.7255]} {[ 33.1803]} {[ 20.6429]} {[ 0.3894]} {[ 0.0500]} {[ 3.1076]} {[ 7.7705]} {[ 0.4840]} {[ 2.2800]} {[ 2.3666]} {[ 2.3707]} {[ 2.9327]} {[ 3.3209]} {[ 2.2669]} {[ 3.6260]} {[ 3.4291]} {[ 2.0316]} {[ 1.9376]} {[ 1.6259]} {[ 3.5012]} {[ -0.3673]} {[ 2.0606]} {[ 2.0281]} {[ 1.4111]} {[ 1.6209]} {[ 2.0415]} {'Andorra' } {'AND' } {'Inflation, consumer prices (annual %)'} {'FP.CPI.TOTL.ZG'} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing } {1×1 missing } {1×1 missing } {1×1 missing } {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {'Arab World' } {'ARB' } {'Inflation, consumer prices (annual %)'} {'FP.CPI.TOTL.ZG'} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {[ 8.2667]} {[ 15.5283]} {[ 9.6697]} {[ 10.3174]} {[ 11.9893]} {[ 9.7160]} {1×1 missing} {[ 9.6261]} {[ 10.7585]} {[ 8.3313]} {[ 5.4937]} {[ 8.1164]} {[ 7.2544]} {[ 6.7989]} {[ 4.2224]} {[ 5.9115]} {[ 7.7409]} {[ 8.4515]} {[ 9.0000]} {[ 9.3598]} {[ 9.3703]} {[ 5.1126]} {[ 6.5438]} {[ 4.6813]} {[ 3.6012]} {[ 3.4173]} {[ 2.6694]} {[ 1.8538]} {[ 1.7722]} {[ 1.8330]} {[ 2.7126]} {[ 3.6323]} {[ 3.4937]} {[ 3.5450]} {[ 4.7439]} {[ 11.2707]} {[ 2.9209]} {[ 3.9111]} {[ 4.7532]} {[ 4.6118]} {[ 3.2381]} {[ 2.7735]} {[ 1.8141]} {[ 2.0688]} {[ 1.9668]} {[ 2.4581]} {[ 1.0918]} {[ 1.7774]} {[ 3.4236]} {'United Arab Emirates' } {'ARE' } {'Inflation, consumer prices (annual %)'} {'FP.CPI.TOTL.ZG'} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing } {1×1 missing } {1×1 missing } {1×1 missing } {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {[ 12.2504]} {[ 1.5618]} {[ 0.8780]} {[ 0.8773]} {[ 0.6623]} {[ 1.1011]} {[ 2.3463]} {[ 4.0700]} {[ 1.6175]} {[ 1.9668]} {[ 3.0686]} {[ -1.9311]} {[ -2.0794]} {1×1 missing} {'Argentina' } {'ARG' } {'Inflation, consumer prices (annual %)'} {'FP.CPI.TOTL.ZG'} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing } {1×1 missing } {1×1 missing } {1×1 missing } {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {'Armenia' } {'ARM' } {'Inflation, consumer prices (annual %)'} {'FP.CPI.TOTL.ZG'} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing } {[3.3738e+03]} {[ 175.9513]} {[ 18.6812]} {[ 13.9608]} {[ 8.6725]} {[ 0.6482]} {[ -0.7909]} {[ 3.1459]} {[ 1.0600]} {[ 4.7216]} {[ 6.9613]} {[ 0.6389]} {[ 2.8924]} {[ 4.4074]} {[ 8.9500]} {[ 3.4068]} {[ 8.1764]} {[ 7.6500]} {[ 2.5580]} {[ 5.7897]} {[ 2.9813]} {[ 3.7317]} {[ -1.4036]} {[ 0.9696]} {[ 2.5202]} {[ 1.4434]} {[ 1.2114]} {[ 7.1848]} {'American Samoa' } {'ASM' } {'Inflation, consumer prices (annual %)'} {'FP.CPI.TOTL.ZG'} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing } {1×1 missing } {1×1 missing } {1×1 missing } {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {'Antigua and Barbuda' } {'ATG' } {'Inflation, consumer prices (annual %)'} {'FP.CPI.TOTL.ZG'} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing } {1×1 missing } {1×1 missing } {1×1 missing } {1×1 missing} {1×1 missing} {[ 1.1213]} {[ 0.7718]} {[ 1.4028]} {[ 2.4077]} {[ 1.9935]} {[ 2.0301]} {[ 2.0988]} {[ 1.7878]} {[ 1.4161]} {[ 5.3338]} {[ -0.5502]} {[ 3.3700]} {[ 3.4567]} {[ 3.3769]} {[ 1.0595]} {[ 1.0894]} {[ 0.9690]} {[ -0.4894]} {[ 2.4325]} {[ 1.2072]} {[ 1.4314]} {[ 0.6260]} {[ 2.0630]}
N_header_lines = 3;
rows_to_delete = N_header_lines + find(all(cellfun(@ismissing,C(N_header_lines+1:end,5:end)),2));
C(rows_to_delete,:) = []
C = 243×66 cell array
{'Data Source' } {'World Development Indicators'} {1×1 missing } {1×1 missing } {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing } {1×1 missing } {1×1 missing } {1×1 missing } {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {'Last Updated Date' } {[22-Dec-2022 ]} {1×1 missing } {1×1 missing } {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing } {1×1 missing } {1×1 missing } {1×1 missing } {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {'Country Name' } {'Country Code' } {'Indicator Name' } {'Indicator Code'} {[ 1960]} {[ 1961]} {[ 1962]} {[ 1963]} {[ 1964]} {[ 1965]} {[ 1966]} {[ 1967]} {[ 1968]} {[ 1969]} {[ 1970]} {[ 1971]} {[ 1972]} {[ 1973]} {[ 1974]} {[ 1975]} {[ 1976]} {[ 1977]} {[ 1978]} {[ 1979]} {[ 1980]} {[ 1981]} {[ 1982]} {[ 1983]} {[ 1984]} {[ 1985]} {[ 1986]} {[ 1987]} {[ 1988]} {[ 1989]} {[ 1990]} {[ 1991]} {[ 1992]} {[ 1993]} {[ 1994]} {[ 1995]} {[ 1996]} {[ 1997]} {[ 1998]} {[ 1999]} {[ 2000]} {[ 2001]} {[ 2002]} {[ 2003]} {[ 2004]} {[ 2005]} {[ 2006]} {[ 2007]} {[ 2008]} {[ 2009]} {[ 2010]} {[ 2011]} {[ 2012]} {[ 2013]} {[ 2014]} {[ 2015]} {[ 2016]} {[ 2017]} {[ 2018]} {[ 2019]} {[ 2020]} {[ 2021]} {'Aruba' } {'ABW' } {'Inflation, consumer prices (annual %)'} {'FP.CPI.TOTL.ZG'} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {[ 4.0323]} {[ 1.0740]} {[ 3.6430]} {[ 3.1219]} {[ 3.9916]} {[ 5.8367]} {[ 5.5556]} {[ 3.8734]} {[ 5.2156]} {[ 6.3111]} {[ 3.3614]} {[ 3.2253]} {[ 2.9999]} {[ 1.8695]} {[ 2.2804]} {[ 4.0440]} {[ 2.8836]} {[ 3.3152]} {[ 3.6564]} {[ 2.5291]} {[ 3.3978]} {[ 3.6080]} {[ 5.3926]} {[ 8.9560]} {[ -2.1354]} {[ 2.0781]} {[ 4.3163]} {[ 0.6275]} {[ -2.3721]} {[ 0.4214]} {[ 0.4748]} {[ -0.9312]} {[ -1.0283]} {[ 3.6260]} {[ 4.2575]} {1×1 missing} {1×1 missing} {'Africa Eastern and Southern'} {'AFE' } {'Inflation, consumer prices (annual %)'} {'FP.CPI.TOTL.ZG'} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {[ 19.5984]} {[ 15.2241]} {[ 11.2165]} {[ 14.2381]} {[ 12.5269]} {[ 15.0698]} {[ 15.0665]} {[ 14.4616]} {[ 12.1399]} {[ 11.5675]} {[ 10.9839]} {[ 13.0066]} {[ 13.8920]} {[ 12.5634]} {[ 12.5223]} {[ 12.5582]} {[ 12.4579]} {[ 17.6781]} {[ 16.1676]} {[ 13.1357]} {[ 14.8528]} {[ 12.2886]} {[ 9.7066]} {[ 10.2496]} {[ 7.4953]} {[ 7.8199]} {[ 8.6015]} {[ 5.8404]} {[ 8.7638]} {[ 7.4497]} {[ 5.0234]} {[ 8.5580]} {[ 8.8982]} {[ 8.4508]} {[ 12.5666]} {[ 8.9542]} {[ 5.5375]} {[ 8.9712]} {[ 9.1587]} {[ 5.7510]} {[ 5.3703]} {[ 5.2502]} {[ 6.5714]} {[ 6.3993]} {[ 4.7208]} {[ 4.1202]} {[ 6.3630]} {[ 6.0793]} {'Afghanistan' } {'AFG' } {'Inflation, consumer prices (annual %)'} {'FP.CPI.TOTL.ZG'} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing } {1×1 missing } {1×1 missing } {1×1 missing } {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {[ 12.6863]} {[ 6.7846]} {[ 8.6806]} {[ 26.4187]} {[ -6.8112]} {[ 2.1785]} {[ 11.8042]} {[ 6.4412]} {[ 7.3858]} {[ 4.6740]} {[ -0.6617]} {[ 4.3839]} {[ 4.9760]} {[ 0.6261]} {[ 2.3024]} {1×1 missing} {1×1 missing} {'Africa Western and Central' } {'AFW' } {'Inflation, consumer prices (annual %)'} {'FP.CPI.TOTL.ZG'} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {[ 8.7992]} {[ 12.0598]} {[ 10.6719]} {[ 11.2500]} {[ 7.3549]} {[ 5.9510]} {[ 0.2488]} {[ 2.5237]} {[ 0.8693]} {[ 1.0574]} {[ 1.7419]} {[ -0.0630]} {[ 0.5535]} {[ 31.8410]} {[ 10.5633]} {[ 4.9142]} {[ 3.9971]} {[ 4.4711]} {[ 0.3723]} {[ 2.5308]} {[ 4.3615]} {[ 3.1887]} {[ 1.7609]} {[ 0.6943]} {[ 5.6316]} {[ 4.4159]} {[ 3.6074]} {[ 8.4530]} {[ 3.2824]} {[ 1.7848]} {[ 4.0187]} {[ 4.5784]} {[ 2.4392]} {[ 1.7581]} {[ 2.1303]} {[ 1.4946]} {[ 1.7646]} {[ 1.7840]} {[ 1.7586]} {[ 2.4376]} {[ 3.8379]} {'Angola' } {'AGO' } {'Inflation, consumer prices (annual %)'} {'FP.CPI.TOTL.ZG'} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {[ 83.7838]} {[ 299.5098]} {[1.3785e+03]} {[ 949.7925]} {[2.6665e+03]} {[4.1451e+03]} {[ 219.1767]} {[ 107.2848]} {[ 248.1959]} {[ 324.9969]} {[ 152.5610]} {[ 108.8974]} {[ 98.2241]} {[ 43.5421]} {[ 22.9535]} {[ 13.3052]} {[ 12.2515]} {[ 12.4758]} {[ 13.7303]} {[ 14.4697]} {[ 13.4825]} {[ 10.2779]} {[ 8.7778]} {[ 7.2804]} {[ 9.3538]} {[ 30.6990]} {[ 29.8426]} {[ 19.6306]} {[ 17.0797]} {[ 22.2716]} {[ 25.7543]} {'Albania' } {'ALB' } {'Inflation, consumer prices (annual %)'} {'FP.CPI.TOTL.ZG'} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {[ 226.0054]} {[ 85.0048]} {[ 22.5651]} {[ 7.7932]} {[ 12.7255]} {[ 33.1803]} {[ 20.6429]} {[ 0.3894]} {[ 0.0500]} {[ 3.1076]} {[ 7.7705]} {[ 0.4840]} {[ 2.2800]} {[ 2.3666]} {[ 2.3707]} {[ 2.9327]} {[ 3.3209]} {[ 2.2669]} {[ 3.6260]} {[ 3.4291]} {[ 2.0316]} {[ 1.9376]} {[ 1.6259]} {[ 3.5012]} {[ -0.3673]} {[ 2.0606]} {[ 2.0281]} {[ 1.4111]} {[ 1.6209]} {[ 2.0415]} {'Arab World' } {'ARB' } {'Inflation, consumer prices (annual %)'} {'FP.CPI.TOTL.ZG'} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {[ 8.2667]} {[ 15.5283]} {[ 9.6697]} {[ 10.3174]} {[ 11.9893]} {[ 9.7160]} {1×1 missing} {[ 9.6261]} {[ 10.7585]} {[ 8.3313]} {[ 5.4937]} {[ 8.1164]} {[ 7.2544]} {[ 6.7989]} {[ 4.2224]} {[ 5.9115]} {[ 7.7409]} {[ 8.4515]} {[ 9.0000]} {[ 9.3598]} {[ 9.3703]} {[ 5.1126]} {[ 6.5438]} {[ 4.6813]} {[ 3.6012]} {[ 3.4173]} {[ 2.6694]} {[ 1.8538]} {[ 1.7722]} {[ 1.8330]} {[ 2.7126]} {[ 3.6323]} {[ 3.4937]} {[ 3.5450]} {[ 4.7439]} {[ 11.2707]} {[ 2.9209]} {[ 3.9111]} {[ 4.7532]} {[ 4.6118]} {[ 3.2381]} {[ 2.7735]} {[ 1.8141]} {[ 2.0688]} {[ 1.9668]} {[ 2.4581]} {[ 1.0918]} {[ 1.7774]} {[ 3.4236]} {'United Arab Emirates' } {'ARE' } {'Inflation, consumer prices (annual %)'} {'FP.CPI.TOTL.ZG'} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing } {1×1 missing } {1×1 missing } {1×1 missing } {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {[ 12.2504]} {[ 1.5618]} {[ 0.8780]} {[ 0.8773]} {[ 0.6623]} {[ 1.1011]} {[ 2.3463]} {[ 4.0700]} {[ 1.6175]} {[ 1.9668]} {[ 3.0686]} {[ -1.9311]} {[ -2.0794]} {1×1 missing} {'Armenia' } {'ARM' } {'Inflation, consumer prices (annual %)'} {'FP.CPI.TOTL.ZG'} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing } {[3.3738e+03]} {[ 175.9513]} {[ 18.6812]} {[ 13.9608]} {[ 8.6725]} {[ 0.6482]} {[ -0.7909]} {[ 3.1459]} {[ 1.0600]} {[ 4.7216]} {[ 6.9613]} {[ 0.6389]} {[ 2.8924]} {[ 4.4074]} {[ 8.9500]} {[ 3.4068]} {[ 8.1764]} {[ 7.6500]} {[ 2.5580]} {[ 5.7897]} {[ 2.9813]} {[ 3.7317]} {[ -1.4036]} {[ 0.9696]} {[ 2.5202]} {[ 1.4434]} {[ 1.2114]} {[ 7.1848]} {'Antigua and Barbuda' } {'ATG' } {'Inflation, consumer prices (annual %)'} {'FP.CPI.TOTL.ZG'} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing } {1×1 missing } {1×1 missing } {1×1 missing } {1×1 missing} {1×1 missing} {[ 1.1213]} {[ 0.7718]} {[ 1.4028]} {[ 2.4077]} {[ 1.9935]} {[ 2.0301]} {[ 2.0988]} {[ 1.7878]} {[ 1.4161]} {[ 5.3338]} {[ -0.5502]} {[ 3.3700]} {[ 3.4567]} {[ 3.3769]} {[ 1.0595]} {[ 1.0894]} {[ 0.9690]} {[ -0.4894]} {[ 2.4325]} {[ 1.2072]} {[ 1.4314]} {[ 0.6260]} {[ 2.0630]} {'Australia' } {'AUS' } {'Inflation, consumer prices (annual %)'} {'FP.CPI.TOTL.ZG'} {[ 3.7288]} {[ 2.2876]} {[ -0.3195]} {[ 0.6410]} {[ 2.8662]} {[ 3.4056]} {[ 3.2934]} {[ 3.4783]} {[ 2.5210]} {[ 3.2787]} {[ 3.4392]} {[ 6.1381]} {[ 6.0241]} {[ 9.0909]} {[ 15.4167]} {[ 15.1625]} {[ 13.3229]} {[ 12.3098]} {[ 8.0049]} {[ 9.1220]} {[ 10.1358]} {[ 9.4877]} {[ 11.3518]} {[ 10.0389]} {[ 3.9604]} {[ 6.7347]} {[ 9.0504]} {[ 8.5330]} {[ 7.2159]} {[ 7.5339]} {[ 7.3330]} {[ 3.1767]} {[ 1.0122]} {[ 1.7537]} {[ 1.9696]} {[ 4.6278]} {[ 2.6154]} {[ 0.2249]} {[ 0.8601]} {[ 1.4831]} {[ 4.4574]} {[ 4.4071]} {[ 2.9816]} {[ 2.7326]} {[ 2.3433]} {[ 2.6918]} {[ 3.5553]} {[ 2.3276]} {[ 4.3503]} {[ 1.7711]} {[ 2.9183]} {[ 3.3039]} {[ 1.7628]} {[ 2.4499]} {[ 2.4879]} {[ 1.5084]} {[ 1.2770]} {[ 1.9486]} {[ 1.9114]} {[ 1.6108]} {[ 0.8469]} {[ 2.8639]} {'Austria' } {'AUT' } {'Inflation, consumer prices (annual %)'} {'FP.CPI.TOTL.ZG'} {[ 1.9457]} {[ 3.5422]} {[ 4.3818]} {[ 2.7088]} {[ 3.8686]} {[ 4.9309]} {[ 2.0548]} {[ 3.9747]} {[ 2.7649]} {[ 3.0804]} {[ 4.3728]} {[ 4.7043]} {[ 6.3551]} {[ 7.5311]} {[ 9.5218]} {[ 8.4453]} {[ 7.3187]} {[ 5.4946]} {[ 3.5743]} {[ 3.7074]} {[ 6.3283]} {[ 6.8030]} {[ 5.4360]} {[ 3.3392]} {[ 5.6632]} {[ 3.1895]} {[ 1.7054]} {[ 1.4020]} {[ 1.9157]} {[ 2.5683]} {[ 3.2619]} {[ 3.3374]} {[ 4.0208]} {[ 3.6318]} {[ 2.9534]} {[ 2.2434]} {[ 1.8610]} {[ 1.3060]} {[ 0.9225]} {[ 0.5690]} {[ 2.3449]} {[ 2.6500]} {[ 1.8104]} {[ 1.3556]} {[ 2.0612]} {[ 2.2991]} {[ 1.4415]} {[ 2.1686]} {[ 3.2160]} {[ 0.5063]} {[ 1.8135]} {[ 3.2866]} {[ 2.4857]} {[ 2.0002]} {[ 1.6058]} {[ 0.8966]} {[ 0.8916]} {[ 2.0813]} {[ 1.9984]} {[ 1.5309]} {[ 1.3819]} {[ 2.7667]} {'Azerbaijan' } {'AZE' } {'Inflation, consumer prices (annual %)'} {'FP.CPI.TOTL.ZG'} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {1×1 missing} {[ -10.6301]} {[1.1280e+03]} {[1.6622e+03]} {[ 411.7596]} {[ 19.7948]} {[ 3.6743]} {[ -0.7727]} {[ -8.5252]} {[ 1.8050]} {[ 1.5472]} {[ 2.7712]} {[ 2.2339]} {[ 6.7089]} {[ 9.6795]} {[ 8.3289]} {[ 16.6998]} {[ 20.8491]} {[ 1.4570]} {[ 5.7269]} {[ 7.8583]} {[ 1.0662]} {[ 2.4157]} {[ 1.3734]} {[ 4.0277]} {[ 12.4434]} {[ 12.9359]} {[ 2.2685]} {[ 2.6106]} {[ 2.7598]} {[ 6.6503]}
  2 个评论
Tiffany
Tiffany 2022-12-24
Thank you, this worked best for me.
Image Analyst
Image Analyst 2022-12-24
Not sure you even saw my answer, but anyway...
If this Answer solves your original question, then could you please click the "Accept this answer" link to award the answerer with "reputation points" for their efforts in helping you? They'd appreciate it. Thanks in advance. 🙂 Note: you can only accept one answer (so pick the best one) but you can click the "Vote" icon for as many Answers as you want. Voting for an answer will also award reputation points.

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更多回答（2 个）

the cyclist
the cyclist 2022-12-23
编辑：the cyclist 2022-12-23
Ran in:
For tables, there is a very handy rmmissing function:
% I am reading from the file you posted here, but you can of course read your local file
T = readtable("https://www.mathworks.com/matlabcentral/answers/uploaded_files/1241637/Inflation.csv",'NumHeaderLines',5);
size(T)
ans = 1×2
266 66
R = rmmissing(T);
size(R)
ans = 1×2
66 66
  3 个评论
显示 1更早的评论
the cyclist
the cyclist 2022-12-24
Ah, sorry, I misread what you wanted to do
the cyclist
the cyclist 2022-12-24
Ran in:
You can still use this command, if you use the Name-Value pair to specify the minimum number of missing elements needed to warrant row removal. For example,
T = readtable("https://www.mathworks.com/matlabcentral/answers/uploaded_files/1241637/Inflation.csv",'NumHeaderLines',5);
size(T)
ans = 1×2
266 66
R = rmmissing(T,'MinNumMissing',62);
size(R)
ans = 1×2
240 66

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Image Analyst
Image Analyst 2022-12-23
Try this:
filename = 'Inflation.csv'
data = readmatrix(filename)
rowsToDelete = all(isnan(data), 2) % Rows where all columns are nan.
data(rowsToDelete, :) = [] % Delete those rows.
  3 个评论
显示 1更早的评论
Tiffany
Tiffany 2022-12-24
Hi there,
Thank you for this. The readmatrix eliminated too much data that I need to retain for later, but thank you.
Image Analyst
Image Analyst 2022-12-24
The usual recommendation is to avoid cell arrays if at all possible, in favor of a table. In your case you can use a table. Here is my code adapted to read your data into a table and remove any rows with a nan in them:
filename = 'Inflation.csv'
t = readtable(filename); % Read into table.
data = table2array(t(:, 5:end));
rowsToDelete = any(isnan(data), 2) % Rows where any columns are nan.
t(rowsToDelete, :) = [] % Delete those rows.

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