data = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) # Calculate the 50th percentile (median) of the data median = np.percentile(data, 50) # Calculate the 25th and 75th percentiles (quartiles) of the data q1 = np.percentile(data, 25) q3 = np.percentile(data, 75) Median:...
本书代码包中的day_range.py文件位于对应的代码中: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 from __future__ import print_function import numpy as np import matplotlib.pyplot as plt import calendar as cal data = np.load('cbk12.npy') # Multiply to get hPa values highs = .1 * ...
>>> # index of the maxima for each series >>> ind = data.argmax(axis=0) >>> ind array([2, 0, 3, 1]) >>> # times corresponding to the maxima >>> time_max = time[ind] >>> >>> data_max = data[ind, range(data.shape[1])] # => data[ind[0], 0], data[ind[1],...
importnumpy as np data= np.floor(100*np.random.random((3, 3)))print(data)print(data.max())#返回矩阵的最大值print(data.argmax())#返回矩阵的最大值的索引print(data.argmax(axis=0))#返回每列的最大值的索引 print(data.argmax(axis=1))#返回每行的最大值的索引 [[12. 73. 58.] [31...
data2数组的列数量与data数组相等,data3数组的行数量与data数组相等,这两个numpy数组虽然规格与data数组不一样,但却依然可以与data数组进行运算。 数组的切片: In [24]: data[:2] # 沿着行(axis=0)进行索引 Out[24]: array([[1, 2, 3],
# Compute the standard deviation of the array std = np.std(arr) 1.4142135623730951 numpy.var:计算数组的方差。 numpy.histogram:计算一组数据的直方图。 numpy.percentile:计算数组的第n个百分位数。它返回低于给定百分比的数据的值。 data = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) ...
train_data.shape[0] / self.batch_size print('Start training...') for idx_epoch in range(self.max_epoch): self.shuffle_data() for idx_batch in range(max_batch): batch_images = self.train_data[idx_batch*self.batch_size:(idx_batch+1)*self.batch_size, :-1] batch_labels = self....
data1 = np.random.uniform(low=-1, high=1, size=1000000) # 1、创建画布 plt.figure(figsize=(8, 6), dpi=100) # 2、绘制直方图 plt.hist(data1, 1000) # 3、显示图像 plt.show() 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. ...
dates, close=np.loadtxt('/Users/yaojianguo/workspace/BigData/七月ML/Python数据分析视频/第4周/data.csv', delimiter=',', usecols=(1,6), converters={1: datestr2num}, unpack=True) print "Dates =", dates averages = np.zeros(5)
for i in range(feature_num):data[:, i] = (data[:, i] - minimums[i]) / (maximums[i] - minimums[i])数据处理还有一种方案是运用平均数，将每个特征的取值缩放到-1~1之间。如果当前值为最大值，则处理后也为最大值，但不一定是1，如果当前为最小值，则处理后也为最小值，但不一定为-1，...