i am trying to read a csv file and my code is as follows
param=csvRead("C:\Users\USER\Dropbox\VOA-BK code\assets\Iris.csv",",","%i",'double',[],[],[1 2 3 4]); //reads number of clusters and features
data=csvRead("C:\Users\USER\Dropbox\VOA-BK code\assets\Iris.csv",",","%f",'double',[],[],[3 1 19 4]); //reads the values
numft=param(1,1);//save number of features
numcl=param(2,1);//save number of clusters
data_pts=0;
data_pts = max(size(data, "r"));//checks how many number of rows
disp(data(numft-3:data_pts,:));//print all data points (I added -3 otherwise it displays only 15 rows)
disp(numft);//print features
disp(data_pts);//print features
disp(param);
endfunction
below is the values that i am trying to read
features,4,,
clusters,3,,
5.1,3.5,1.4,0.2
4.9,3,1.4,0.2
4.7,3.2,1.3,0.2
4.6,3.1,1.5,0.2
5,3.6,1.4,0.2
7,3.2,4.7,1.4
6.4,3.2,4.5,1.5
6.9,3.1,4.9,1.5
5.5,2.3,4,1.3
6.5,2.8,4.6,1.5
5.7,2.8,4.5,1.3
6.3,3.3,6,2.5
5.8,2.7,5.1,1.9
7.1,3,5.9,2.1
6.3,2.9,5.6,1.8
6.5,3,5.8,2.2
7.6,3,6.6,2.1
I do not know why the code only displays 15 rows instead of 17. The only time it displays the correct matrix is when i put -3 in numft but with that, the number of columns would be 1. I am so confused. Is there a better way to read the values?
In the csvRead call in the first line of your script the boundaries of the region to read is incorrect, it should be corrected like this:
param=csvRead("C:\Users\USER\Dropbox\VOA-BK code\assets\Iris.csv",",","%i",'double',[],[],[1 2 2 2]);
Related
I have a .csv File that has hundreds of thousands of lines. The information was collected in order by the user.
For example, one user's inputs may range 20-400 rows, and the corresponding target is a single row where the users first input row started.
inputs | Targets
0, 7
1
2
3
4
So one set of targets per x amount of input rows.
Some of my columns contain '-' I feel like this will mess up my model when trying to train, considering it isn't a float or int what I should do?
Also, Should I shuffle my data if it is chunked like this?
I have a data in the following form:
product/productId B000EVS4TY
1 product/title Arrowhead Mills Cookie Mix, Chocolate Chip, 1...
2 product/price unknown
3 review/userId A2SRVDDDOQ8QJL
4 review/profileName MJ23447
5 review/helpfulness 2/4
6 review/score 4.0
7 review/time 1206576000
8 review/summary Delicious cookie mix
9 review/text I thought it was funny that I bought this pro...
10 product/productId B0000DF3IX
11 product/title Paprika Hungarian Sweet
12 product/price unknown
13 review/userId A244MHL2UN2EYL
14 review/profileName P. J. Whiting "book cook"
15 review/helpfulness 0/0
16 review/score 5.0
17 review/time 1127088000
I want to convert it to a dataframe such that the entries in the 1st column
product/productId
product/title
product/price
review/userId
review/profileName
review/helpfulness
review/score
review/time
review/summary
review/text
are the column headers with the values arranged corresponding to each header in the table.
I still had a tiny doubt about your file, but since both my suggestions are quite similar, I will try to address both the scenarios you might have.
In case your file doesn't actually have the line numbers inside of it, this should do it:
filepath = "./untitled.txt" # you need to change this to your file path
column_separator="\s{3,}" # we'll use a regex, I explain some caveats of this below...
# engine='python' surpresses a warning by pandas
# header=None is that so all lines are considered 'data'
df = pd.read_csv(filepath, sep=column_separator, engine="python", header=None)
df = df.set_index(0) # this takes column '0' and uses it as the dataframe index
df = df.T # this makes the data look like you were asking (goes from multiple rows+1column to multiple columns+1 row)
df = df.reset_index(drop=True) # this is just so the first row starts at index '0' instead of '1'
# you could just do the last 3 lines with:
# df = df.set_index(0).T.reset_index(drop=True)
If you do have line numbers, then we just need to do some little adjustments
filepath = "./untitled1.txt"
column_separator="\s{3,}"
df = pd.read_csv(filepath, sep=column_separator, engine="python", header=None, index_col=0)
df.set_index(1).T.reset_index(drop=True) #I did all the 3 steps in 1 line, for brevity
In this last case, I would advise you change it in order to have line numbers in all of them (in the example you provided, the numbering starts at the second line, this might be an option about how you handle headers when exporting the data in whatever tool you might be using
Regarding the regex, the caveat is that "\s{3,}" looks for any block of 3 consecutive whitespaces or more to determine the column separator. The problem here is that we'll depend a bit on the data to find the columns. For instance, if in any of the values just so happens to appear 3 consecutive spaces, pandas will raise an exception, since the line will have one more column than the others. One solution to this could be increasing it to any other 'appropriate' number, but then we still depend on the data (for instance, with more than 3, in your example, "review/text" would have enough spaces for the two columns to be identified)
edit after realising what you meant by "stacked"
Whatever "line-number scenario" you have, you'll need to make sure you always have the same number of columns for all registers and reshape the continuous dataframe with something similar to this:
number_of_columns = 10 # you'll need to make sure all "registers" do have the same number of columns otherwise this will break
new_shape = (-1,number_of_columns) # this tuple will mean "whatever number of lines", by 10 columns
final_df = pd.DataFrame(data = df.values.reshape(new_shape)
,columns=df.columns.tolist()[:-10])
Again, take notice of making sure that all lines have the same number of columns (for instance, a file with just the data you provided, assuming 10 columns, wouldn't work). Also, this solution assumes all columns will have the same name.
There is an inconsistency with dataframes that I cant explain. In the following, I'm not looking for a workaround (already found one) but an explanation of what is going on under the hood and how it explains the output.
One of my colleagues which I talked into using python and pandas, has a dataframe "data" with 12,000 rows.
"data" has a column "length" that contains numbers from 0 to 20. she wants to divided the dateframe into groups by length range: 0 to 9 in group 1, 9 to 14 in group 2, 15 and more in group 3. her solution was to add another column, "group", and fill it with the appropriate values. she wrote the following code:
data['group'] = np.nan
mask = data['length'] < 10;
data['group'][mask] = 1;
mask2 = (data['length'] > 9) & (data['phraseLength'] < 15);
data['group'][mask2] = 2;
mask3 = data['length'] > 14;
data['group'][mask3] = 3;
This code is not good, of course. the reason it is not good is because you dont know in run time whether data['group'][mask3], for example, will be a view and thus actually change the dataframe, or it will be a copy and thus the dataframe would remain unchanged. It took me quit sometime to explain it to her, since she argued correctly that she is doing an assignment, not a selection, so the operation should always return a view.
But that was not the strange part. the part the even I couldn't understand is this:
After performing this set of operation, we verified that the assignment took place in two different ways:
By typing data in the console and examining the dataframe summary. It told us we had a few thousand of null values. The number of null values was the same as the size of mask3 so we assumed the last assignment was made on a copy and not on a view.
By typing data.group.value_counts(). That returned 3 values: 1,2 and 3 (surprise) we then typed data.group.value_counts.sum() and it summed up to 12,000!
So by method 2, the group column contained no null values and all the values we wanted it to have. But by method 1 - it didnt!
Can anyone explain this?
see docs here.
You dont' want to set values this way for exactly the reason you pointed; since you don't know if its a view, you don't know that you are actually changing the data. 0.13 will raise/warn that you are attempting to do this, but easiest/best to just access like:
data.loc[mask3,'group'] = 3
which will guarantee you inplace setitem
I have a rinex file and is shown here..an image showing the first part of rinex file
http://imageshack.us/photo/my-images/593/65961409.jpg
The data (AOPR Rinex file) is downloaded from the site after entering a year and a day.
http://www.naic.edu/aisr/GPSTEC/gpstec.html
I want to open this file as a matrix in matlab for further processing..After the end of header at the 42nd line the time information is on 43 rd line. Then data starts. But time information is coming again after some rows say 64 the line, which should be discarded. Header should also be discarded. Also the last column is coming below the first column as a second row which should be transferred to the last column. Totally there are 55700 rows. Kindly help me with this.
I suspect the last column appearing on the line below it is just an artifact of how large the window of your text reader is...
For the rest, I think a trial-and-error loop is in place here:
fid = fopen('test.txt','r');
C = {};
while ~feof(fid)
% read lines with dictated format.
D = textscan(fid, '%d %d %d %d');
% this will fail on headerlines, empty lines, etc.
if isempty(D{1})
% in those cases, advance the file pointer by one line
fgetl(fid);
else
% if that's not the case, save the lines thus read
C = [C;D]; %#ok
end
end
fclose(fid);
% Post-process: concatenate all sub-arrays into one
C = arrayfun(#(ii) cat(1, C{:,ii}), 1:size(C,2), 'UniformOutput', false);
This works, at least with my test.txt:
header
random
garbage
1 2 3 4
4 5 6 7
4 6 7 8
more random garbage
2 5 6 7
5 6 7 8
8 6 3 7
I suspect the last column appearing on the line below it is just an artifact of how large >the window of your text reader is...
For the rest, I think a trial-and-error loop is in place here
Dear Rody I don't have any matlab background and just a beginner. It is actually a Rinex file..with 2780 epochs and 6 observables with 30 satellite values..Decoding it in matlab is tough. That is the problem. You can read a sample code at
http://web.ics.purdue.edu/~tdauterm/EAS591/Lab7/read_rinexo.m
But the problem is that the observables are six and there only 5 in the m-file which also is not in the correct order. I need C1 P2 L1 L2 S1 S2...but the code at the link gives L1 L2 C1 P1 P2. :( Can you just correct that..Then it will be a great help..
Below is some data:
Test Day1 Day2 Score
A 1 2 100
B 1 3 62
C 3 4 90
D 2 4 20
E 4 5 80
I am trying to take the values from column 'day' and 'day2' and use them to select the row number for the column score. For example for Test A I would like to find the sum of 100 and 62 because that is the values of the first and second rows of score. Test B I would like to find the sum of 100, 62 and 90.
Is their anyway to do this in the Compute Variable window? Found in the menu Transform-Compute Variable?
I tried the following:
Score(MEAN(VALUE(Day1), VALUE(DAY2)))
This is not the proper way to call the cell location of Score and I received an error.
Can anyone help?
Thank you!
You really have two different datasets here. One is a dataset of scores numbered 1 through 5.
The other is a dataset that includes indexes into the score dataset. So the steps would be something like this.
First take the scores dataset and transpose it so that it has one row and 5 columns (Data>Transpose)
Then match that dataset to each case in the main dataset (Data>Merge Files>Add Variables).
Next you have to resort to using syntax directly.
You would declare a vector for the scores (VECTOR)
Finally, you use COMPUTE to index into the scores.
For your real problem, I suppose that you might have batches of scores and maybe there are some gaps. The Restructure Data Wizard can help you generalize this - convert cases into variables, but let's not go there yet.
HTH,
Jon Peck