numpy

问题 I am dealing with hourly data in which the date is stored in 4 different arrays, one for day, month, year, and hour. I am running a for-loop to rather store these dates as strings with this format: '01/01/1946 0' My code looks something like this: import numpy as np from datetime import datetime as dt import matplotlib.dates as mdates for nn in range(nnn): y1 = int(yr[nn]) m1 = int(mon[nn]) d1 = int(day[nn]) h1 = int(hr[nn]) #In the the last string we are specifying the format datenow = dt

问题 Closed . This question needs details or clarity. It is not currently accepting answers. Want to improve this question? Add details and clarify the problem by editing this post. Closed 5 days ago . Improve this question I have a 2D matrix of p-values. I want to draw a histogram of the pdf of this 2D matrix. How can I do that? 回答1: Just so you know: Seaborn's distplot does all of this. import seaborn as sns, numpy as np sns.set_palette("cividis"); np.random.seed(0) x = np.random.randn(100) ax =

问题 I used this code almost exactly, just changing the line: f = gzip.open("../data/mnist.pkl.gz", 'rb') training_data, validation_data, test_data = cPickle.load(f) to these lines: import pickle as cPickle f = gzip.open("mnist.pkl.gz", 'rb') u = cPickle._Unpickler(f) u.encoding='latin1' training_data, validation_data, test_data = u.load() to account for pickling issues.The original mnist.pkl.gz was downloaded from his repo (available here), or the code to generate the .pkl.gz is here. The output

问题 After searching a lot I couldn't find a simple way to extract data from .h5 and pass it to a data.Frame by Numpy or Pandas in order to save in .txt or .csv file. import h5py import numpy as np import pandas as pd filename = 'D:\data.h5' f = h5py.File(filename, 'r') # List all groups print("Keys: %s" % f.keys()) a_group_key = list(f.keys())[0] # Get the data data = list(f[a_group_key]) pd.DataFrame(data).to_csv("hi.csv") Keys: <KeysViewHDF5 ['dd48']> When I print data I see following results:

问题 Use numpy.where to get all (R, G,B) in a numpy.array with a definite value of R, G and B The problem is i'm not sure i can use numpy.where to get what i want : i tried the following code : L = numpy.array([[1,2,3],[1,1,1],[1,1,1]]) print(numpy.where(L==(1,1,1))) (array([0, 1, 1, 1, 2, 2, 2], dtype=int64), array([0, 0, 1, 2, 0, 1, 2], dtype=int64)) and i understand it's returning me the coordinates of every element == 1 but i would like it to return the index in L of the element equal to (1,1

问题 I have the code shown below, but get the following error: ValueError: could not convert string to float: BRAF Provided that this is a sample of my data ( | is just a separator I added here for demonstration, you can imagine each value in a separate cell in a CSV file): c.401C>T | skin | 23:141905805-141905805 | 9947 | BRAF Could the strings be the issue? How can I read and pass strings in this case? from keras.models import Sequential from keras.layers import Dense from keras.models import

问题 I used How do I check which version of NumPy I'm using? to learn how to get the version of numpy. However, when I run conda list | grep numpy , I get: numpy 1.15.2 py36ha559c80_0 numpy-base 1.15.2 py36h8128ebf_0 numpydoc 0.8.0 py36_0 However, when I run version from IPython shell, I get: import numpy as np np.__version__ Out: '1.13.3' np.version.version Out: '1.13.3' np.version.full_version Out: '1.13.3' Why are the two versions different? Which one should I trust? Thanks for any help. Please

问题 Following method calculates a gaussian kernel: import numpy as np def gaussian_kernel(X, X2, sigma): """ Calculate the Gaussian kernel matrix k_ij = exp(-||x_i - x_j||^2 / (2 * sigma^2)) :param X: array-like, shape=(n_samples_1, n_features), feature-matrix :param X2: array-like, shape=(n_samples_2, n_features), feature-matrix :param sigma: scalar, bandwidth parameter :return: array-like, shape=(n_samples_1, n_samples_2), kernel matrix """ norm = np.square(np.linalg.norm(X[None,:,:] - X2[:

问题 I am new to python and using numpy to read a csv into an array .So I used two methods: Approach 1 train = np.asarray(np.genfromtxt(open("/Users/mac/train.csv","rb"),delimiter=",")) Approach 2 with open('/Users/mac/train.csv') as csvfile: rows = csv.reader(csvfile) for row in rows: newrow = np.array(row).astype(np.int) train.append(newrow) I am not sure what is the difference between these two approaches? What is recommended to use? I am not concerned which is faster since my data size is

问题 I have a numpy array import numpy as np arr = np.arange(20).reshape(2,10) arr[1,:] = 0 arr[1,2] = arr[1,5] = arr[1,7] = 1 print(arr) >>>[[0 1 2 3 4 5 6 7 8 9] >>> [0 0 1 0 0 1 0 1 0 0]] I want to extract overlapping arrays, starting at a 1 and ending behind the next 1 . Expected output: [[0 1 2 3] [0 0 1 0]] [[2 3 4 5 6] [1 0 0 1 0]] [[5 6 7 8] [1 0 1 0]] [[7 8 9] [1 0 0]] At the moment, I have an index-based for-loop that feels awkward in a numpy context and also has to treat the first and