如何在for循环内的一个窗口中对Pandas数据框中的列进行子图绘制 [英] How can make subplots of columns in Pandas dataframe in one window inside of for-loop

问题描述

*请帮助它，这一点非常重要:为什么不能通过在for循环内使用HeatMap来获取Pandas数据框的柱状图的子图?

在迭代过程中，我尝试在for循环内的pandas数据帧中创建列的子图，因为我绘制每个每个480个值的每个周期的结果，以获取所有3个子图都属于A， B，C在一个窗口中并排.我只在此处找到一个答案，恐怕不是我的情况！ @ euri10通过使用 flat来回答.

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，由于无法在每个周期内输出正确的输出，因此无法获得适当的输出，例如，每个周期以不同的间隔打印3次.它会在左边和右边分别显示'A'和'C'，然后在中间和右边一窗口中分别显示'A'和'A'.再次，它打印'B' 3次而不是一次，放在中间，最后它打印'C' 3次而不是一次，放在右边，它放在中间和左边！

目标是要捕获所有3列A，B和B的子图.在一个窗口中的C中，每个主循环中的每个周期(每480个值乘以480个值)！

第一个循环:A，B，C的0000 ----->子图---->将其存储为0000.png

第二个循环:A，B，C的0001 ----->子图---->将其存储为0001.png ...

问题是for循环中 df 的用法，它会在3遍传递A或B或C值 ，而应通过它的值分别分别属于一次的每一列.我在此处提供了失败的图片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:解决方案

因此，在查看了您的代码和您的要求之后，我想我知道问题出在哪里. 您的for循环顺序错误.您希望每个周期都有一个新图形，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整个循环，然后您的内部循环遍历i，而循环的缩进和顺序使您尝试绘制i(i='A','B','C'，cycle=1)中的所有'A','B','C'子图已经在您通过i(i='A'，cycle=1)的第一个循环中，而不是在您通过第一个循环的第一个循环之后. /p>

这也是为什么您遇到未定义df3的问题(如您在此答案的评论中所述). df3的定义是在if块中检查'C' in i，在您的第一个循环中，不满足此条件，因此未定义df3，但是您仍在尝试绘制它！

再次使用NaN/inf值，您遇到了与另一个问题相同的问题.

重新设置for循环和缩进并清除NaN/inf值将获得以下代码:

#...
#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，分别是三个单独的数字以及您提供的数据:

图1 ，图3

通常来说，您的代码非常混乱.我明白了，如果您是编程的新手，并且只想分析数据，那么您可以做任何有效的事情，无论它是否漂亮都可以.

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方式.

我建议您再次遇到类似问题时，在所有循环中写出一些伪代码"，并尝试考虑每个循环中要完成的工作.

* Please help it's very important: Why is not possible to get subplots of cloumns of Pandas dataframe by using HeatMap inside of for-loop?

I am trying to create subplots of columns in pandas dataframe inside of for-loop during iterations since I plot result for every cycle that is for each 480 values to get all 3 subplots belong to A, B, C side by side in one window. I've found only one answer here which I'm afraid is not my case! @euri10 answered by using flat.

My scripts are following:

# 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()

So far I couldn't get proper output due to in each cycle it prints plot each of them 3 times in different intervals eg. it prints 'A' left then again it prints 'A' under the name of 'B' and 'C' in middle and right in-one-window. Again it prints 'B' 3-times instead once and put it middle and in the end it prints 'C' 3-times instead of once and put in right side it put in middle and left!

Target is to catch subplots of all 3 columns A,B & C in one-window for each cycle (every 480-values by 480-values) in main for-loop!

1st cycle : 0000 -----> subplots of A,B,C ----> Store it as 0000.png

2nd cycle : 0001 -----> subplots of A,B,C ----> Store it as 0001.png ...

Problem is usage of df inside of for-loop and it passes values of A or B or C 3 times while it should pass it values belong to each column once respectively I provide a picture of unsuccessful output here so that you could see exactly where the problem is clearly

my desired output is below:

I also provide sample text file of dataset for 3 cycles: dataset

解决方案

So after looking at your code and and your requirements I think I know what the problem is. Your for loops are in the wrong order. You want a new figure for each cycle, containing each 'A', 'B' and 'C' as subplots.

This means your outer loop should go over the cycles and then your inner loop over i, whereas your indentation and order of the loops makes you trying to plot all 'A','B','C'subplots already on your first loop through i (i='A', cycle=1) and not after your first loop through the first cycle, with all i (i='A','B','C', cycle=1).

This is also why you get the problem (as mentioned in your comment on this answer ) of not defining df3. The definition of df3 ist in an if block checking if 'C' in i, on your first loop through, this condition is not met and therefore df3 is not defined, but you are still trying to plot it!

Also you got the same problem as in your other question with the NaN/inf values again.

Rearraning the for loops and the indentation and cleaning up the NaN/inf values gets you the following code:

#...
#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()

This gets you the following three images as three seperate figures with the data you provided:

Figure 1, Figure 2, Figure 3

Generally speaking, your code is quite messy. I get it, if you're new to programming and just want to analyse your data, you do whatever works, doesn't matter if it is pretty.

However, I think that the messy code means you cant properly look at the underlying logic of your script, which is how you got this problem.

I would recommend if you get a problem like that again to write out some 'pseudo code' with all of the loops and try to think about what you are trying to accomplish in each loop.

这篇关于如何在for循环内的一个窗口中对Pandas数据框中的列进行子图绘制的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！