Reading complicated CSV file

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Ali Almakhmari
Ali Almakhmari 2022-5-22
回答： Star Strider 2022-5-22
Hey guys. So I attached the CSV am having trouble reading. I tried using fileread and I got a weird format that ended up horrible, and I tried csvread which did not work as well. I need the first two rows of the CSV file to be in two seperate variables. And then the columns of the CSV file (every column under each header on row three of the file) should also be each in a seperate variable. I am expecting about 359 variables. I hope someone can help me. I would really appreciate it.

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采纳的回答

Star Strider
Star Strider 2022-5-22
Ran in:
Try something like this —
C1 = readcell('https://www.mathworks.com/matlabcentral/answers/uploaded_files/1007390/random.csv');
FirstTwoRows =[NaN(2,7), reshape([C1{1:2,8:end}], 2, [])]
FirstTwoRows = 2×357
NaN NaN NaN NaN NaN NaN NaN 137.5500 148.1300 158.7100 169.2900 179.8700 190.4500 201.0300 211.6100 222.1900 232.7700 243.3500 253.9300 264.5100 275.0900 285.6700 296.2500 306.8300 317.4100 327.9900 338.5700 349.1500 359.7300 370.3100 NaN NaN NaN NaN NaN NaN NaN 0 1.0000 2.0000 3.0000 4.0000 5.0000 6.0000 7.0000 8.0000 9.0000 10.0000 11.0000 12.0000 13.0000 14.0000 15.0000 16.0000 17.0000 18.0000 19.0000 20.0000 21.0000 22.0000
T1 = readtable('https://www.mathworks.com/matlabcentral/answers/uploaded_files/1007390/random.csv', 'HeaderLines',2, 'VariableNamingRule','preserve')
T1 = 462×357 table
high low lb t a d kk cal_rad[1] cal_rad[2] cal_rad[3] cal_rad[4] cal_rad[5] cal_rad[6] cal_rad[7] cal_rad[8] cal_rad[9] cal_rad[10] cal_rad[11] cal_rad[12] cal_rad[13] cal_rad[14] cal_rad[15] cal_rad[16] cal_rad[17] cal_rad[18] cal_rad[19] cal_rad[20] cal_rad[21] cal_rad[22] cal_rad[23] cal_rad[24] cal_rad[25] cal_rad[26] cal_rad[27] cal_rad[28] cal_rad[29] cal_rad[30] cal_rad[31] cal_rad[32] cal_rad[33] cal_rad[34] cal_rad[35] cal_rad[36] cal_rad[37] cal_rad[38] cal_rad[39] cal_rad[40] cal_rad[41] cal_rad[42] cal_rad[43] cal_rad[44] cal_rad[45] cal_rad[46] cal_rad[47] cal_rad[48] cal_rad[49] cal_rad[50] cal_rad[51] cal_rad[52] cal_rad[53] cal_rad[54] cal_rad[55] cal_rad[56] cal_rad[57] cal_rad[58] cal_rad[59] cal_rad[60] cal_rad[61] cal_rad[62] cal_rad[63] cal_rad[64] cal_rad[65] cal_rad[66] cal_rad[67] cal_rad[68] cal_rad[69] cal_rad[70] cal_rad[71] cal_rad[72] cal_rad[73] cal_rad[74] cal_rad[75] cal_rad[76] cal_rad[77] cal_rad[78] cal_rad[79] cal_rad[80] cal_rad[81] cal_rad[82] cal_rad[83] cal_rad[84] cal_rad[85] cal_rad[86] cal_rad[87] cal_rad[88] cal_rad[89] cal_rad[90] cal_rad[91] cal_rad[92] cal_rad[93] cal_rad[94] cal_rad[95] cal_rad[96] cal_rad[97] cal_rad[98] cal_rad[99] cal_rad[100] cal_rad[101] cal_rad[102] cal_rad[103] cal_rad[104] cal_rad[105] cal_rad[106] cal_rad[107] cal_rad[108] cal_rad[109] cal_rad[110] cal_rad[111] cal_rad[112] cal_rad[113] cal_rad[114] cal_rad[115] cal_rad[116] cal_rad[117] cal_rad[118] cal_rad[119] cal_rad[120] cal_rad[121] cal_rad[122] cal_rad[123] cal_rad[124] cal_rad[125] cal_rad[126] cal_rad[127] cal_rad[128] cal_rad[129] cal_rad[130] cal_rad[131] cal_rad[132] cal_rad[133] cal_rad[134] cal_rad[135] cal_rad[136] cal_rad[137] cal_rad[138] cal_rad[139] cal_rad[140] cal_rad[141] cal_rad[142] cal_rad[143] cal_rad[144] cal_rad[145] cal_rad[146] cal_rad[147] cal_rad[148] cal_rad[149] cal_rad[150] cal_rad[151] cal_rad[152] cal_rad[153] cal_rad[154] cal_rad[155] cal_rad[156] cal_rad[157] cal_rad[158] cal_rad[159] cal_rad[160] cal_rad[161] cal_rad[162] cal_rad[163] cal_rad[164] cal_rad[165] cal_rad[166] cal_rad[167] cal_rad[168] cal_rad[169] cal_rad[170] cal_rad[171] cal_rad[172] cal_rad[173] cal_rad[174] cal_rad[175] cal_rad[176] cal_rad[177] cal_rad[178] cal_rad[179] cal_rad[180] cal_rad[181] cal_rad[182] cal_rad[183] cal_rad[184] cal_rad[185] cal_rad[186] cal_rad[187] cal_rad[188] cal_rad[189] cal_rad[190] cal_rad[191] cal_rad[192] cal_rad[193] cal_rad[194] cal_rad[195] cal_rad[196] cal_rad[197] cal_rad[198] cal_rad[199] cal_rad[200] cal_rad[201] cal_rad[202] cal_rad[203] cal_rad[204] cal_rad[205] cal_rad[206] cal_rad[207] cal_rad[208] cal_rad[209] cal_rad[210] cal_rad[211] cal_rad[212] cal_rad[213] cal_rad[214] cal_rad[215] cal_rad[216] cal_rad[217] cal_rad[218] cal_rad[219] cal_rad[220] cal_rad[221] cal_rad[222] cal_rad[223] cal_rad[224] cal_rad[225] cal_rad[226] cal_rad[227] cal_rad[228] cal_rad[229] cal_rad[230] cal_rad[231] cal_rad[232] cal_rad[233] cal_rad[234] cal_rad[235] cal_rad[236] cal_rad[237] cal_rad[238] cal_rad[239] cal_rad[240] cal_rad[241] cal_rad[242] cal_rad[243] cal_rad[244] cal_rad[245] cal_rad[246] cal_rad[247] cal_rad[248] cal_rad[249] cal_rad[250] cal_rad[251] cal_rad[252] cal_rad[253] cal_rad[254] cal_rad[255] cal_rad[256] cal_rad[257] cal_rad[258] cal_rad[259] cal_rad[260] cal_rad[261] cal_rad[262] cal_rad[263] cal_rad[264] cal_rad[265] cal_rad[266] cal_rad[267] cal_rad[268] cal_rad[269] cal_rad[270] cal_rad[271] cal_rad[272] cal_rad[273] cal_rad[274] cal_rad[275] cal_rad[276] cal_rad[277] cal_rad[278] cal_rad[279] cal_rad[280] cal_rad[281] cal_rad[282] cal_rad[283] cal_rad[284] cal_rad[285] cal_rad[286] cal_rad[287] cal_rad[288] cal_rad[289] cal_rad[290] cal_rad[291] cal_rad[292] cal_rad[293] cal_rad[294] cal_rad[295] cal_rad[296] cal_rad[297] cal_rad[298] cal_rad[299] cal_rad[300] cal_rad[301] cal_rad[302] cal_rad[303] cal_rad[304] cal_rad[305] cal_rad[306] cal_rad[307] cal_rad[308] cal_rad[309] cal_rad[310] cal_rad[311] cal_rad[312] cal_rad[313] cal_rad[314] cal_rad[315] cal_rad[316] cal_rad[317] cal_rad[318] cal_rad[319] cal_rad[320] cal_rad[321] cal_rad[322] cal_rad[323] cal_rad[324] cal_rad[325] cal_rad[326] cal_rad[327] cal_rad[328] cal_rad[329] cal_rad[330] cal_rad[331] cal_rad[332] cal_rad[333] cal_rad[334] cal_rad[335] cal_rad[336] cal_rad[337] cal_rad[338] cal_rad[339] cal_rad[340] cal_rad[341] cal_rad[342] cal_rad[343] cal_rad[344] cal_rad[345] cal_rad[346] cal_rad[347] cal_rad[348] cal_rad[349] cal_radSampleCount _______ _________ ______ ______ ______ _ __________ __________ __________ __________ __________ __________ __________ __________ __________ __________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ ___________ 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7.503 2.6347 19.627 5 6.6685e+08 -6.76e-08 3.25e-08 -4.35e-08 -2.77e-07 4.58e-07 8.35e-08 4.72e-07 7.34e-07 4.42e-07 1.07e-06 8.74e-07 1.3e-06 1.44e-06 1.66e-06 1.84e-06 1.94e-06 2.25e-06 2.22e-06 2.5e-06 2.6e-06 2.74e-06 2.9e-06 2.95e-06 3.16e-06 3.12e-06 3.29e-06 3.38e-06 3.4e-06 3.51e-06 3.5e-06 3.59e-06 3.57e-06 3.67e-06 3.67e-06 3.64e-06 3.74e-06 3.67e-06 3.69e-06 3.66e-06 3.63e-06 3.62e-06 3.59e-06 3.57e-06 3.5e-06 3.51e-06 3.39e-06 3.36e-06 3.32e-06 3.24e-06 3.2e-06 3.1e-06 3.11e-06 2.99e-06 2.99e-06 3.05e-06 3.13e-06 3.33e-06 3.19e-06 3.2e-06 2.96e-06 2.16e-06 1.71e-06 1.5e-06 1.48e-06 1.67e-06 2.18e-06 2.67e-06 2.41e-06 2.25e-06 2.23e-06 1.91e-06 1.72e-06 1.56e-06 1.5e-06 1.47e-06 1.35e-06 1.28e-06 1.22e-06 1.16e-06 1.1e-06 1.09e-06 1.05e-06 1.01e-06 9.75e-07 9.13e-07 9.04e-07 8.35e-07 8.31e-07 8.25e-07 7.34e-07 7.48e-07 7.07e-07 6.7e-07 6.44e-07 6.11e-07 5.95e-07 5.45e-07 5.45e-07 4.97e-07 4.87e-07 4.95e-07 4.48e-07 4.53e-07 4.1e-07 3.47e-07 3.68e-07 3.78e-07 3.13e-07 2.89e-07 2.82e-07 2.73e-07 2.71e-07 2.21e-07 2.16e-07 1.75e-07 1.6e-07 1.74e-07 1.22e-07 1.42e-07 1.11e-07 5.73e-08 8.48e-08 6.44e-08 7.41e-09 1.78e-08 3.16e-08 -9.23e-09 -1.88e-08 -5.07e-08 -4.97e-08 -3.35e-08 -5.7e-08 -4.14e-08 -9.44e-08 -9.83e-08 -5.62e-08 -1.27e-07 -1.54e-07 -1.29e-07 -1.7e-07 -1.44e-07 -1.19e-07 -2.32e-07 -2.16e-07 -2.37e-07 -3.19e-07 -2.99e-07 -3.68e-07 -3.76e-07 -4.1e-07 -4.03e-07 -4.96e-07 -6.49e-07 -6.17e-07 -7.99e-07 -8.25e-07 -9.97e-07 -9.35e-07 -7.27e-07 -1.11e-06 -9.24e-07 -1.01e-06 -8.02e-07 -6.19e-07 -1.09e-06 -3.02e-07 -1.98e-08 -6.93e-08 1.91e-07 4.18e-07 4.41e-07 2.22e-08 3.29e-07 6.44e-07 4.22e-07 5.85e-08 6.78e-08 9.4e-08 1.8e-07 4.11e-07 2.78e-07 3.54e-07 2.7e-07 1.74e-07 9.21e-08 5.65e-08 3.1e-07 1.95e-07 4.48e-09 2.43e-07 1.07e-08 9.57e-08 7.62e-08 8.29e-08 1.19e-07 1.16e-07 1.99e-07 1.61e-07 1.41e-07 1.61e-07 7.99e-08 1.82e-07 1.9e-07 1.09e-07 1.2e-07 3.39e-08 1.6e-07 1.48e-07 2.26e-08 1.54e-07 1.71e-07 1.4e-07 1.4e-07 7.81e-08 1.11e-07 7.87e-08 9.75e-08 1.09e-07 9.88e-08 8.57e-08 6.39e-08 7.2e-08 1.07e-07 1.04e-07 8.51e-08 7.61e-08 8.58e-08 6.82e-08 7.25e-08 8.41e-08 7.3e-08 6.94e-08 6.32e-08 6.08e-08 5.95e-08 5.41e-08 3.8e-08 2.32e-08 5.19e-08 7.36e-08 6.77e-08 6.53e-08 6.01e-08 3.29e-08 4.25e-08 5.02e-08 4.23e-08 3.11e-08 3.08e-08 3.88e-08 4.12e-08 3.83e-08 3.84e-08 4.09e-08 3.64e-08 2.92e-08 3.24e-08 3.4e-08 2.64e-08 2.4e-08 1.94e-08 2.03e-08 2.74e-08 2.48e-08 2.43e-08 2.65e-08 2.29e-08 2.19e-08 1.72e-08 1.91e-08 1.98e-08 1.57e-08 1.47e-08 1.29e-08 1.24e-08 1.01e-08 1.14e-08 1.15e-08 1.12e-08 1.26e-08 1.04e-08 8.41e-09 1.13e-08 1.02e-08 9.22e-09 8.45e-09 7.74e-09 7.42e-09 6.6e-09 5.92e-09 5e-09 5.61e-09 5.98e-09 4.91e-09 4.45e-09 3.29e-09 4.94e-09 5.49e-09 3.13e-09 3.7e-09 4.49e-09 3.2e-09 2.98e-09 3e-09 2.69e-09 2.25e-09 2.8e-09 3.07e-09 2.46e-09 2.06e-09 2.1e-09 2.45e-09 2.57e-09 2.14e-09 2.48e-09 1.87e-09 1.59e-09 1.53e-09 1.48e-09 1.93e-09 1.51e-09 1.5e-09 1.22e-09 1.33e-09 1.9e-09 1.41e-09 1.97e-09 1.76e-09 4.29e-10 1.02e-09 1.25e-09 8e-10 1.15e-09 1.12e-09 9.22e-10 1.06e-09 9.57e-10 7.72e-10 9.71e-10 1.03e-09 8e-10 6.17e-10 6.1e-10 6.66e-10 7.75e-10 7.89e-10 7.41e-10 5.3e-10 5.33e-10 349 -2.5515 -1.1908 8.5256 3.6622 20.45 5 6.6703e+08 -1.27e-09 -1.22e-07 1.98e-08 -3.9e-07 6.47e-07 2.36e-08 5.2e-07 4.73e-07 7.07e-07 9.2e-07 8.14e-07 1.25e-06 1.27e-06 1.55e-06 1.6e-06 1.8e-06 1.9e-06 2e-06 2.22e-06 2.24e-06 2.44e-06 2.46e-06 2.58e-06 2.65e-06 2.69e-06 2.82e-06 2.82e-06 2.88e-06 2.9e-06 2.94e-06 2.99e-06 3.03e-06 3.05e-06 3.09e-06 3.07e-06 3.06e-06 3.07e-06 3.01e-06 3.02e-06 2.99e-06 2.94e-06 2.92e-06 2.88e-06 2.83e-06 2.76e-06 2.7e-06 2.65e-06 2.6e-06 2.53e-06 2.49e-06 2.43e-06 2.41e-06 2.3e-06 2.29e-06 2.44e-06 2.59e-06 2.88e-06 2.81e-06 2.9e-06 2.84e-06 2.05e-06 1.6e-06 1.35e-06 1.34e-06 1.57e-06 2.02e-06 2.45e-06 2.08e-06 1.9e-06 1.86e-06 1.46e-06 1.21e-06 1.06e-06 9.95e-07 9.41e-07 8.36e-07 7.86e-07 7.54e-07 7e-07 6.52e-07 6.15e-07 5.74e-07 5.71e-07 5.53e-07 5.15e-07 4.72e-07 4.58e-07 4.38e-07 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5.59e-07 5.36e-07 4.28e-07 3.12e-07 1.73e-07 5.62e-08 1.69e-07 3.69e-07 3.52e-07 3.54e-07 2.79e-07 1.72e-07 8.72e-08 5.84e-08 7.96e-09 6.24e-09 -1.52e-09 -6.98e-08 7.69e-08 5.91e-08 -9.98e-09 7.61e-08 -9.11e-08 -2.27e-07 -9.25e-08 3.6e-08 1.92e-07 1.03e-07 -5.83e-08 -8.08e-08 -5.59e-08 -1.08e-07 -1.31e-07 -1.3e-07 -1.99e-07 -2.89e-07 -1.79e-07 -2.28e-07 -2.36e-07 -1.93e-07 -1.13e-07 -2.14e-07 -1.9e-07 -2.5e-07 -2.8e-07 -3.04e-07 -2.45e-07 -2.3e-07 -2.3e-07 -2.7e-07 -3.26e-07 -4.18e-07 -2.61e-07 -5.14e-07 -3.02e-07 -2.98e-07 -2.09e-07 -2.64e-07 -3.23e-07 -5.28e-07 -2.37e-07 -2.22e-07 -1.62e-07 -4.2e-07 -1.4e-07 -2.92e-07 -1.46e-07 -5.43e-08 2.46e-08 -2.2e-07 3.29e-07 4.98e-07 4.79e-08 5.35e-07 1.23e-07 9.59e-07 -6.18e-06 1.47e-06 1.34e-06 2.2e-06 -2.79e-06 5.48e-07 4.31e-07 -8.46e-07 9.88e-07 9.2e-07 8.3e-07 7.16e-07 8.35e-07 2.1e-07 -1.73e-07 6.16e-07 -1.95e-08 -6.68e-07 2.64e-06 2.78e-07 -2.13e-06 4.68e-07 -2.15e-06 4.31e-07 -2.63e-07 3.55e-07 3.94e-07 7.31e-07 7.89e-07 6.14e-07 1.74e-07 5.71e-07 -7.93e-06 5.82e-07 1.42e-06 7.38e-07 4.91e-07 3.37e-07 6.24e-07 2.61e-06 -1.21e-07 5.89e-08 -3.8e-06 -1.71e-05 -7.52e-07 4.03e-07 2.26e-07 1e-07 1.29e-07 3.6e-07 3.04e-07 -1.53e-07 1.38e-07 1.42e-07 1.51e-07 -1.15e-07 2.5e-06 1.29e-07 -1.09e-05 8.53e-07 9.97e-08 1.04e-08 1.11e-07 2.96e-07 1.17e-07 2.12e-07 1.56e-07 8.62e-07 7.22e-08 4.81e-08 2.27e-05 1.36e-07 2.5e-08 3.42e-09 2.27e-08 8.73e-08 3.32e-07 6.92e-08 1.92e-08 2.49e-08 4.93e-07 2.69e-08 1.01e-07 6.22e-08 -1.46e-07 5.86e-08 2.08e-07 1.52e-08 3.92e-08 6.24e-08 4.03e-08 4.74e-08 4.4e-08 9.22e-07 -1.3e-07 -4.98e-07 1.18e-07 3.6e-08 2.91e-08 2.52e-08 4.55e-08 -1.37e-09 5.97e-09 -1.6e-11 8.26e-08 -2.93e-10 1.11e-08 3.97e-09 4.79e-09 3.03e-07 2.15e-08 1.52e-08 1.56e-07 -3.39e-09 1.44e-08 3.22e-09 7.05e-09 8.47e-09 -6.52e-08 4.44e-09 -1.34e-08 7.28e-08 3.71e-09 2.65e-09 -1.08e-08 3.23e-08 1.1e-08 6.1e-09 6e-08 5.5e-07 5.49e-08 4.88e-09 -7.77e-07 -6.36e-09 -1.48e-07 2.62e-08 3.53e-09 -5.33e-09 4.31e-10 1.3e-08 1.82e-09 2.44e-09 1.57e-08 -1.81e-09 -3.14e-08 -1.51e-09 -2.11e-09 4.15e-08 2.52e-09 -2.31e-08 1.3e-10 -7.63e-09 -3.36e-09 -2.24e-09 -2.24e-09 5.1e-09 3.51e-10 2.73e-08 5.48e-09 1.34e-08 9.32e-10 -2.28e-09 1e-09 -8.13e-11 -3.43e-09 2.52e-10 5.56e-08 -1.77e-09 -2.31e-09 1.18e-09 -3.49e-09 -7.88e-10 5.56e-09 3.68e-08 -8.69e-10 -3.73e-09 -9.66e-09 4.09e-10 -1.97e-08 -3.74e-09 -1.64e-09 2.78e-09 2.69e-09 349
I named the first two lines as ‘FirstTwoRows’ and read the rest in using readtable.
.

更多回答（1 个）

Voss
Voss 2022-5-22
Ran in:
Something along these lines might be of use:
% get the numbers from the first two lines of the file;
% maybe modify this depending on what you need:
fid = fopen('random.csv');
data1 = str2double(strsplit(fgetl(fid),','));
data2 = str2double(strsplit(fgetl(fid),','));
fclose(fid);
% now read the entire file (skipping 2 header lines)
% into a table:
t = readtable('random.csv','NumHeaderLines',2);
Warning: Column headers from the file were modified to make them valid MATLAB identifiers before creating variable names for the table. The original column headers are saved in the VariableDescriptions property.
Set 'VariableNamingRule' to 'preserve' to use the original column headers as table variable names.
% or into a matrix:
data = readmatrix('random.csv','NumHeaderLines',2);
whos data* t
Name Size Bytes Class Attributes data 462x357 1319472 double data1 1x152 1216 double data2 1x152 1216 double t 462x357 1448071 table

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