CHAPTER 1. Loomings. Call me Ishmael. Some years ago–never mind how long precisely–having little or no money in my purse, and nothing particular to interest me on shore, I thought I would sail about a little and see the watery part of the world. It is a way I have of driving off the spleen and regulating the circulation. Whenever I find myself growing grim about the mouth; whenever it is a damp, drizzly November in my soul; whenever I find myself involuntarily pausing before coffin warehouses, and bringing up the rear of every funeral I meet; and especially whenever my hypos get such an upper hand of me, that it requires a strong moral principle to prevent me from deliberately stepping into the street, and methodically knocking people’s hats off–then, I account it high time to get to sea as soon as I can. This is my substitute for pistol and ball. With a philosophical flourish Cato throws himself upon his sword; I quietly take to the ship. There is nothing surprising in this. If they but knew it, almost all men in their degree, some time or other, cherish very nearly the same feelings towards the ocean with me. False True
When a file called file is open, you can print out the first line by executing file.readline(). If you execute the same command again, the second line will print, and so on.
# Read & print the first 4 lines with open('moby_dick.txt') as file: print(file.readline()) print(file.readline()) print(file.readline()) print(file.readline())CHAPTER 1. Loomings. Call me Ishmael. Some years ago–never mind how long precisely–having little or no money in my purse, and nothing particular to interest me on
There are a number of arguments that np.loadtxt() takes that you’ll find useful: delimiter changes the delimiter that loadtxt() is expecting, for example, you can use ‘,’ and ‘\t’ for comma-delimited and tab-delimited respectively; skiprows allows you to specify how many rows (not indices) you wish to skip; usecols takes a list of the indices of the columns you wish to keep.
# Import numpy import numpy as np # Assign the filename: file file = 'digits_header.txt' # Load the data: skip the first row and you only want to import the first and third columns. data = np.loadtxt(file, delimiter='\t', skiprows=1, usecols=[0, 2]) # Print data print(data) import numpy as np import matplotlib.pyplot as plt # Assign filename: file file = 'seaslug.txt' # Import file: data data = np.loadtxt(file, delimiter='\t', dtype=str) # Print the first element of data print(data[0]) # Import data as floats and skip the first row: data_float data_float = np.loadtxt(file, delimiter='\t', dtype=float, skiprows=1) # Print the 10th element of data_float print(data_float[9]) # Plot a scatterplot of the data plt.scatter(data_float[:, 0], data_float[:, 1]) plt.xlabel('time (min.)') plt.ylabel('percentage of larvae') plt.show()np.genfromtxt(), which can handle such structures. If we pass dtype=None to it, it will figure out what types each column should be.
data = np.genfromtxt('titanic.csv', delimiter=',', names=True, dtype=None)You have just used np.genfromtxt() to import data containing mixed datatypes. There is also another function np.recfromcsv() that behaves similarly to np.genfromtxt(), except that its default dtype is None.
# Assign the filename: file file = 'titanic.csv' # Import file using np.recfromcsv: d d = np.recfromcsv(file, delimiter = ',', names=True) # Print out first three entries of d print(d[:3])[(1, 0, 3, b’male’, 22., 1, 0, b’A/5 21171’, 7.25 , b’’, b’S’) (2, 1, 1, b’female’, 38., 1, 0, b’PC 17599’, 71.2833, b’C85’, b’C’) (3, 1, 3, b’female’, 26., 0, 0, b’STON/O2. 3101282’, 7.925 , b’’, b’S’)]
