* 请帮忙，这非常重要：为什么无法通过在 for 循环内使用 HeatMap 来获取 Pandas 数据框列的箱线图？
我正在尝试在迭代期间在 for 循环内部的 pandas 数据框中创建列的子图，因为我为每个周期绘制结果每 480 个值在一个窗口中并排显示属于 A、B、C 的所有 3 个子图。我只找到一个答案here https://stackoverflow.com/questions/27446455/pandas-subplots-in-a-loop恐怕不是我的情况！ @euri10 通过使用回答flat https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.flat.html#numpy.ndarray.flat.
我的脚本如下：
# Import and call the needed libraries
import numpy as np
import pandas as pd
import os
import seaborn as sns
import matplotlib.pyplot as plt
'''
Take a list and create the formatted matrix
'''
def mkdf(ListOf480Numbers):
normalMatrix = np.array_split(ListOf480Numbers,8) #Take a list and create 8 array (Sections)
fixMatrix = []
for i in range(8):
lines = np.array_split(normalMatrix[i],6) #Split each section in lines (each line contains 10 cells from 0-9)
newMatrix = [0,0,0,0,0,0] #Empty array to contain reordered lines
for j in (1,3,5):
newMatrix[j] = lines[j] #lines 1,3,5 remain equal
for j in (0,2,4):
newMatrix[j] = lines[j][::-1] #lines 2,4,6 are inverted
fixMatrix.append(newMatrix) #After last update of format of table inverted (bottom-up zig-zag)
return fixMatrix
'''
Print the matrix with the required format
'''
def print_df(fixMatrix):
values = []
for i in range(6):
values.append([*fixMatrix[4][i], *fixMatrix[7][i]]) #lines form section 6 and 7 are side by side
for i in range(6):
values.append([*fixMatrix[5][i], *fixMatrix[6][i]]) #lines form section 4 and 5 are side by side
for i in range(6):
values.append([*fixMatrix[1][i], *fixMatrix[2][i]]) #lines form section 2 and 3 are side by side
for i in range(6):
values.append([*fixMatrix[0][i], *fixMatrix[3][i]]) #lines form section 0 and 1 are side by side
df = pd.DataFrame(values)
return (df)
'''
Normalizing Formula
'''
def normalize(value, min_value, max_value, min_norm, max_norm):
new_value = ((max_norm - min_norm)*((value - min_value)/(max_value - min_value))) + min_norm
return new_value
'''
Split data in three different lists A, B and C
'''
dft = pd.read_csv('D:\me4.TXT', header=None)
id_set = dft[dft.index % 4 == 0].astype('int').values
A = dft[dft.index % 4 == 1].values
B = dft[dft.index % 4 == 2].values
C = dft[dft.index % 4 == 3].values
data = {'A': A[:,0], 'B': B[:,0], 'C': C[:,0]}
#df contains all the data
df = pd.DataFrame(data, columns=['A','B','C'], index = id_set[:,0])
'''
Data generation phase
'''
#next iteration create all plots, change the number of cycles
cycles = int(len(df)/480)
print(cycles)
for i in df:
try:
os.mkdir(i)
except:
pass
min_val = df[i].min()
min_nor = -1
max_val = df[i].max()
max_nor = 1
for cycle in range(1): #iterate thriugh all cycles range(1) by ====> range(int(len(df)/480))
count = '{:04}'.format(cycle)
j = cycle * 480
ordered_data = mkdf(df.iloc[j:j+480][i])
csv = print_df(ordered_data)
#Print .csv files contains matrix of each parameters by name of cycles respectively
csv.to_csv(f'{i}/{i}{count}.csv', header=None, index=None)
if 'C' in i:
min_nor = -40
max_nor = 150
#Applying normalization for C between [-40,+150]
new_value3 = normalize(df['C'].iloc[j:j+480][i].values, min_val, max_val, -40, 150)
n_cbar_kws = {"ticks":[-40,150,-20,0,25,50,75,100,125]}
df3 = print_df(mkdf(new_value3))
else:
#Applying normalizayion for A,B between [-1,+1]
new_value1 = normalize(df['A'].iloc[j:j+480][i].values, min_val, max_val, -1, 1)
new_value2 = normalize(df['B'].iloc[j:j+480][i].values, min_val, max_val, -1, 1)
n_cbar_kws = {"ticks":[-1.0,-0.75,-0.50,-0.25,0.00,0.25,0.50,0.75,1.0]}
df1 = print_df(mkdf(new_value1))
df2 = print_df(mkdf(new_value2))
#Plotting parameters by using HeatMap
plt.figure()
sns.heatmap(df, vmin=min_nor, vmax=max_nor, cmap ='coolwarm', cbar_kws=n_cbar_kws)
plt.title(i, fontsize=12, color='black', loc='left', style='italic')
plt.axis('off')
#Print .PNG images contains HeatMap plots of each parameters by name of cycles respectively
plt.savefig(f'{i}/{i}{count}.png')
#plotting all columns ['A','B','C'] in-one-window side by side
fig, axes = plt.subplots(nrows=1, ncols=3 , figsize=(20,10))
plt.subplot(131)
sns.heatmap(df1, vmin=-1, vmax=1, cmap ="coolwarm", linewidths=.75 , linecolor='black', cbar=True , cbar_kws={"ticks":[-1.0,-0.75,-0.5,-0.25,0.00,0.25,0.5,0.75,1.0]})
fig.axes[-1].set_ylabel('[MPa]', size=20) #cbar_kws={'label': 'Celsius'}
plt.title('A', fontsize=12, color='black', loc='left', style='italic')
plt.axis('off')
plt.subplot(132)
sns.heatmap(df2, vmin=-1, vmax=1, cmap ="coolwarm", cbar=True , cbar_kws={"ticks":[-1.0,-0.75,-0.5,-0.25,0.00,0.25,0.5,0.75,1.0]})
fig.axes[-1].set_ylabel('[Mpa]', size=20) #cbar_kws={'label': 'Celsius'}
#sns.despine(left=True)
plt.title('B', fontsize=12, color='black', loc='left', style='italic')
plt.axis('off')
plt.subplot(133)
sns.heatmap(df3, vmin=-40, vmax=150, cmap ="coolwarm" , cbar=True , cbar_kws={"ticks":[-40,150,-20,0,25,50,75,100,125]})
fig.axes[-1].set_ylabel('[°C]', size=20) #cbar_kws={'label': 'Celsius'}
#sns.despine(left=True)
plt.title('C', fontsize=12, color='black', loc='left', style='italic')
plt.axis('off')
plt.suptitle(f'Analysis of data in cycle Nr.: {count}', color='yellow', backgroundcolor='black', fontsize=48, fontweight='bold')
plt.subplots_adjust(top=0.7, bottom=0.3, left=0.05, right=0.95, hspace=0.2, wspace=0.2)
#plt.subplot_tool()
plt.savefig(f'{i}/{i}{i}{count}.png')
plt.show()
到目前为止，我无法获得正确的输出，因为在每个周期中，它以不同的时间间隔打印每个图 3 次，例如。它打印'A'离开然后再次打印'A'以的名义'B' and 'C'在一个窗口的中间和右侧。再次打印'B'3次而不是1次，放在中间，最后打印'C'3次而不是一次，放在右侧，放在中间和左侧！
Target是捕获所有 3 列 A、B 和 C 的子图单一窗口 for 每个周期（每 480 个值乘 480 个值）在主 for 循环中！
第一个周期：0000 -----> A,B,C 的子图 ----> 将其存储为 0000.png
第二个循环：0001 -----> A、B、C 的子图 ----> 将其存储为 0001.png
...
Problem是使用df在 for 循环内部，它传递 A 或 B 或 C 的值3 times虽然它应该传递属于每一列的值once我分别提供了一张不成功输出的图片here https://i.stack.imgur.com/4gF44.jpg这样你就可以清楚地看到问题出在哪里
我想要的输出如下：
我还提供了 3 个周期的数据集示例文本文件：dataset https://drive.google.com/file/d/1dQD_CARJ00-olkK_h7Sl8Q2lORX-d6QF/view?usp=sharing
