How Can Make Subplots Of Columns In Pandas Dataframe In One Window Inside Of For-loop

* 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 column

Solution 1:

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.