• Learn Importing and exporting datasets by digital marketing companies in delhi ncr

    Importing Datasets


    We want to basically read all rows. Up until a certain point because down here we don't want to read these two again that's really going to screw things up. So we'll go until basically the skin cold and rose or you know how many rows are going to read. Well, this is going to be our number of data points. Okay. And then we'll just need one more parameter and that's going to be called use columns in here. We're going to need digital marketing companies in Delhi NCR to put in all of the names of the columns we want. So we want price open and volume with this dot we need to make sure it's character specific. So back to here. We once the price we want open and we once all with the period. Now we want this function basically to return three values to us. We wanted to return three separate arrays one with the high prices one with the low prices or sorry the final prices the opening prices and the volume. OK. So we're going to take each of these columns to store them in a respective array and then just return kind of to pull off those three arrays. So the first one because the price is going to be the first column will be reading will be the final price.

    And what we can do is use our big data variable which is going to be the results of reading just these three columns. And just again those rows. So we're going to call on data we're going to get digital marketing companies in Delhi NCR the column that is going to be price cake and case sensitive it's really important. We want to first convert to a string Cape's so that we can get rid of any comments the other. So if you remember from ham comma's here this is going to cause issues when we try to convert floats. So we're going to go hit we're going to go docstring how we once thought or actually convert it to a string. So as type them we're going to convert it. What's that period. Go dots as type in here we want to set the string that we want to replace certain characters so String dots replace the character we want to replace is going to be just the comma. And what do we want to replace it with? Well, we want to replace it with nothing. It's just blank.

    This is basically just a way to remove that comma and some reason splits the strip wasn't working. So don't try and use those that had some issues there. So now we have successfully removed that comma. We know that it's just digital marketing agency in Delhi​ which going to be digital marketing companies in Delhi NCR a string representation of an actual number so we can convert it to a type that is going to be and P DOPs float. OK. And this is basically just going to convert it to a float array. So the final price will contain the correctly for an asset price column data. OK so now because we're going to be doing a very similar thing for the other columns I'm actually just going to copy and paste it. The final price is now going to be opening prices. So let's call this the opening prices should be signed. Cannock as the final price is pluralized it. This one is going to be open now. Again as type string one or place any commas with blanks. We do want it to be a float array again. So that should be good to go. Now we'll do the final one which is just going to be volumes at this time we want data that is going to be in the call column Vol.


    Formatting Datasets

    We don't need to replace anything with a comma because if we go to the CSP we know that there are no commas in these but there are these digital marketing companies in Delhi NCR annoying today and sometimes you'll be working with stocks that will exchange volumes in the millions. So instead of a k, there might be an AM. So at this time we actually can use the split function or rather the strip function. So we're going to know even Polycom about Ignus to a string. We could actually call on it and we go and Sukkos string dot split or strip. I keep getting those two mixed up. Okay. And we are going to strip two characters from a capsule Emond capsule K.B. sure they are capsulized or else this won't work. And it's just going to remove them if they do exist in that particular and strength. And then again we're going to convert it to an end P-doc floats array and that should be good to go. We need to do is simply return these. So we're going to return.

    First off final prices than our opening prices then our volumes. And so this function is going to be digital marketing companies in Delhi NCR doing here. It's just going to be loading the data that we want and that's going to be converting it and getting rid of all the excess characters in that data that's converting them to float arrays and then returning them here. So when we call upon our function Palsson the stock name and the number of data points we get the corresponding arrays. So what we can do here. And Porchester the voice grabbed my voice is going crazy off to the illness here. So what we can do is just call upon this function lodestar data we're going to pass in the stock name which is going to be the current Let's do the current test case it's a smaller data set to work with a number of data points going to be number of test data points it's just going to be and we're just going to print out the results because this will again return us three different arrays.

    So if we were to now run this I'm just going to go up top to our Run menu going to click on this second run. We're going to specify I will start prediction model and we're going to go ahead and run this to shirred if everything goes right and it did prints how all of our race as you can see is starts at zero goes all the way down to. And as Prince first out the final price is the closing prices than the opening prices. And then the actual volume is exchanged on the corresponding days. Okay. So that's it for this section. We really wanted to do is find digital marketing companies in Delhi NCR and a way to import the data we once picked the pieces out of that big data set that we want and then convert those to the correct format and that's exactly what we're doing in there. As you can see these formats will float. So we get to go we can actually start feeding these into the model. Of course, once we build the model because