python - 如何在 for 循环内的一个窗口中制作 Pandas 数据框中的列子图
问题描述
* 请帮助它非常重要:为什么无法通过在 for 循环中使用 HeatMap 来获取 Pandas 数据帧的子图?
我试图在迭代期间在 for 循环内的 pandas 数据框中创建列的子图,因为我为每个循环绘制结果,即每个 480 个值的结果, 以使所有 3 个子图在一个窗口中并排属于 A、B、C。我在这里只找到了一个答案,恐怕不是我的情况!@euri10 使用flat回答。
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,它会以不同的间隔打印它们中的每一个 3 次,例如。它'A'
向左打印,然后在一个窗口中以中间和右侧的'A'
名称打印。它再次打印3 次而不是一次并将其放在中间,最后它打印3 次而不是一次并放在右侧它放在中间和左侧!'B'
'C'
'B'
'C'
目标是在主 for 循环中的每个循环(每 480 个值乘以 480 个值)的一个窗口中捕获所有 3 列 A、B 和 C 的子图!
第一个循环: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 ...
问题是在 for 循环中使用df并且它传递 A 或 B 或 C 的值3 次,而它应该分别传递属于每一列的值一次我在此处提供了不成功输出的图片,以便您可以确切地看到问题很明显
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:数据集
解决方案
因此,在查看您的代码和您的要求后,我想我知道问题出在哪里。您的for
循环顺序错误。您需要为每个周期创建一个新图形,其中包含每个“A”、“B”和“C”作为子图。
这意味着您的外部循环应该遍历循环,然后您的内部循环遍历i
,而循环的缩进和顺序使您尝试'A','B','C'
通过i
( i='A'
, cycle=1
) 而不是在第一次循环之后绘制所有子图循环,所有i
( i='A','B','C'
, cycle=1
)。
这也是您遇到未定义df3的问题(如您对此答案的评论中所述)的原因。df3 ist 的定义在 if 块中检查 if 'C' in i
,在您的第一次循环中,不满足此条件,因此 df3 未定义,但您仍在尝试绘制它!
此外,您再次遇到与 NaN/inf 值相同的问题。
重新排列for
循环和缩进并清理 NaN/inf 值可以获得以下代码:
#...
#df contains all the data
df = pd.DataFrame(data, columns=['A','B','C'], index = id_set[:,0])
df = df.replace(np.inf, np.nan)
df = df.fillna(0)
'''
Data generation phase
'''
#next iteration create all plots, change the number of cycles
cycles = int(len(df)/480)
print(cycles)
for cycle in range(cycles): #iterate thriugh all cycles range(1) by ====> range(int(len(df)/480))
count = '{:04}'.format(cycle)
j = cycle * 480
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
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], 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], min_val, max_val, -1, 1)
new_value2 = normalize(df['B'].iloc[j:j+480], 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()
这将为您提供以下三个图像作为三个单独的数字以及您提供的数据:
一般来说,您的代码非常混乱。我明白了,如果你是编程新手,只想分析你的数据,你可以做任何有效的事情,不管它是否漂亮。
但是,我认为凌乱的代码意味着您无法正确查看脚本的底层逻辑,这就是您遇到此问题的原因。
如果你再次遇到这样的问题,我会建议你用所有的循环写出一些“伪代码”,并试着想想你在每个循环中要完成什么。
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