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Let's say I have a data file called data.txt that looks like:
TIME FX FY FZ 0 10 5 6 1 2 4 7 2 5 2 6 ... In Python run:
import numpy as np myData = np.genfromtxt("data.txt", names=True) >>> print myData["TIME"] [0, 1, 2] The names at the top of my data file will vary, so what I would like to do is find out what the names of my arrays in the data file are. I would like something like:
>>> print myData.names [TIME, F0, F1, F2] I thought about just to read in the data file and get the first line and parse it as a separate operation, but that doesn't seem very efficient or elegant.
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Columns attribute of the dataframe returns the column labels of the dataframe. You can get the column names as an array by using the . columns. values property of the dataframe.
To get the column names of DataFrame, use DataFrame. columns property. The columns property returns an object of type Index. We could access individual names using any looping technique in Python.
In the NumPy with the help of shape() function, we can find the number of rows and columns. In this function, we pass a matrix and it will return row and column number of the matrix. Return: The number of rows and columns.
The main data structure in NumPy is the ndarray, which is a shorthand name for N-dimensional array. When working with NumPy, data in an ndarray is simply referred to as an array. It is a fixed-sized array in memory that contains data of the same type, such as integers or floating point values.
Try:
myData.dtype.names This will return a tuple of the field names.
In [10]: myData.dtype.names Out[10]: ('TIME', 'FX', 'FY', 'FZ') If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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