Python Pandas Pivot 图像数据表

Question

我有一个使用 Pandas 数据帧，我创建了一个 3000000x3(像素 x rob 波段(的表，现在正在尝试透视它，所以我有一个 3x3000000 的表，然后对新数据运行 PCA。

我正在使用熊猫枢轴功能，但无法弄清楚我做错了什么。

import pandas as pd 
import numpy as np 
import random as rd
from sklearn.decomposition import PCA
from sklearn import preprocessing
import matplotlib.pyplot as plt
import cv2 
#read in image
img = cv2.imread('/Volumes/EXTERNAL/Stitched-Photos-for-Chris/p7_0015_20161005-949am-75m-pass-1.jpg.png',1)
row,col = img.shape[:2]
#print(row , col)
#get a unique pixel ID for each pixel
pixel = [i for i in range(0,row*col)]
data = pd.DataFrame(columns=['bBand','gBand','rBand'],index = pixel)
#populate data for each band
b,g,r = cv2.split(img)
data.loc[pixel,'bBand'] = b.flat[:]
data.loc[pixel,'gBand'] = g.flat[:]
data.loc[pixel,'rBand'] = r.flat[:]
datapivoted = data.pivot(index=['bBand','gBand','rBand'], columns=pixel, values=[data.loc[pixel,'bBand'],data.loc[pixel,'bBand'],data.loc[pixel,'bBand']])
print(data.head())
print(data.shape)

更新

使用以下代码重新创建数据帧，我认为该代码正确且更有效，但仍然无法透视

img = cv2.imread('/Volumes/EXTERNAL/Stitched-Photos-for-Chris/p7_0015_20161005-949am-75m-pass-1.jpg.png',1)
row,col = img.shape[:2]
#print(row , col)
b,g,r = cv2.split(img)
data = pd.DataFrame({'bBnad':b.flat[:],'gBnad':g.flat[:],'rBnad':r.flat[:]})

Answer 1

严重过度思考这个问题。由于我需要完全交换轴，因此使用.T转置很简单

data = pd.DataFrame({'bBnad':b.flat[:],'gBnad':g.flat[:],'rBnad':r.flat[:]})
datapivoted = data.T
print(datapivoted.head())
print(datapivoted.shape)