np load csv

from numpy import genfromtxt
my_data = genfromtxt('my_file.csv', delimiter=',')

Here is what the above code is Doing:
1. Importing the NumPy package.
2. Creating a variable called my_data and setting it equal to the data in my_file.csv.
3. Setting the delimiter to a comma.

The delimiter is the character that separates the values in your file. In this case, the comma is the delimiter.

If your file has a different delimiter, you can change it. For example, if your file uses tabs as a delimiter, you would set delimiter=’\t’.

If you want to skip the first row of data in your file, you can add the skiprows parameter. For example, if you want to skip the first two rows of data, you would set skiprows=2.

If your file has a header, you can add the names parameter. This parameter takes a list of strings that correspond to the names of the columns in your data.

For example, if your file has a header row with the column names [‘id’, ‘name’, ‘age’], you would set names=[‘id’, ‘name’, ‘age’].

If you want to read in only a subset of the columns in your file, you can use the usecols parameter. This parameter takes a list of integers that correspond to the indices of the columns you want to read in.

For example, if you only want to read in the first and third columns, you would set usecols=[0,2].

You can also read in only a subset of the rows in your file by using the skiprows and/or nrows parameters.

The skiprows parameter takes a list of integers that correspond to the indices of the rows you want to skip. For example, if you want to skip the first five rows, you would set skiprows=[0,1,2,3,4].

The nrows parameter takes an integer that corresponds to the number of rows you want to read in from your file. For example, if you only want to read in the first ten rows, you would set nrows=10.

You can use any combination of the parameters above to read in only the data you want from your file.

Once you’ve read in your data, you can use it just like any other NumPy array.

For example, if you want to find the mean of the first column, you would do the following:

my_data[:,0].mean()

If you want to find the median of the second column, you would do the following:

np.median(my_data[:,1])

And if you want to find the standard deviation of the third column, you would do the following:

my_data[:,2].std()

You can also use NumPy to save your data to a file.

To save your data to a CSV file, you can use the savetxt() function.

The savetxt() function takes two arguments:

1. The name of the file you want to save your data to.
2. The data you want to save.

For example, if you want to save your data to a file called my_data_out.csv, you would do the following:

np.savetxt(‘my_data_out.csv’, my_data, delimiter=’,’)

The delimiter argument is optional, but it specifies the character that will be used to separate the values in your file. If you don’t specify a delimiter, the savetxt() function will use whitespace to separate the values in your file.

You can also use the savetxt() function to save your data to a tab-delimited file. To do this, you would set the delimiter argument to ‘\t’.

You can also use the savetxt() function to save your data to a file in a different format. For example, if you want to save your data to a file in the NumPy binary format, you would set the fmt argument to ‘%i’.

The fmt argument is optional, but it specifies the format of the data in your file. For example, if you want to save your data as integers, you would set fmt=’%i’.

You can also use the savetxt() function to save your data to a file in a different format. For example, if you want to save your data as floating point numbers with four decimal places, you would set fmt=’%.4f’.

You can also use the savetxt() function to save your data to a file in a different format. For example, if you want to save your data as floating point numbers in scientific notation, you would set fmt=’%e’.

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