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The Quick and Easy Way to Analyze Numpy Arrays

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The quickest and easiest way to analyze NumPy arrays is by using the numpy.array() method. This method allows you to quickly and easily analyze the values contained in a numpy array. This method can also be used to find the sum, mean, standard deviation, max, min, and other useful analysis of the value contained within a numpy array. Sum You can find the sum of Numpy arrays using the np.sum() function.  For example:  import numpy as np  a = np.array([1,2,3,4,5])  b = np.array([6,7,8,9,10])  result = np.sum([a,b])  print(result)  # Output will be 55 Mean You can find the mean of a Numpy array using the np.mean() function. This function takes in an array as an argument and returns the mean of all the values in the array.  For example, the mean of a Numpy array of [1,2,3,4,5] would be  result = np.mean([1,2,3,4,5])  print(result)  #Output: 3.0 Standard Deviation To find the standard deviation of a Numpy array, you can use the NumPy std() function. This function takes in an array as a par
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Here is a Quick Way to Know Current Working Directory in R

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If R is not finding the file you are trying to read then it may be looking in the wrong folder/directory. If you are using the graphical interface you can change the working directory from the file menu.

List of Files and Current Working Directory

Related: JOBS in R Language

If you are not sure what files are in the current working directory you can use the dir() command to list the files and the getwd() command to determine the current working directory:
> dir()
[1] "fixedWidth.dat" "simple.csv"     "trees91.csv"    "trees91.wk1"[5] "w1.dat"
> getwd()
[1] "/home/black/write/class/stat/stat383-13F/dat"

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