My job as a researcher involves a lot of plotting from sequencing experiments, so I automated the plotting of whatever gene I wanted to display.

# Import necessary modules to read and plot
    import pandas as pd
    import matplotlib.pyplot as plt
    import seaborn as sns
    import numpy as np
    from google.colab import files
    
    # Open and reading the contents of genes_to_plot.txt and appending them to an iterable list
    with open("genes_to_plot.txt") as file:
        gene_list = [gene.strip() for gene in file.readlines()]
        gene_list_2 = [gene.capitalize() for gene in gene_list]
        print (f"The following genes will be plotted:\n{gene_list_2}")
    
    # Reading the .csv files and assigning them to a pandas DataFrame class
    df_duo = pd.read_csv("ApK_LsdK_DuovsApcK_Duo_deg.csv", sep = ";")
    df_ile = pd.read_csv("ApK_LsdK_IlevsApcK_Ile_deg.csv", sep = ";")
    
    for gene in gene_list_2:
    
      # Extracting full row from each gene and assigning it to a variable
      gene_row_duo = df_duo[df_duo.gene_name == gene]
      gene_row_ile = df_ile[df_ile.gene_name == gene]
    
      # Extract individual TPMs from row and add them to a list
      try:
        duo_single_ko_TPMs = []
        for n in range (1,7):
          duo_single_ko_TPMs.append(int(gene_row_duo[f"ApcK_D{n}_count"]))
      except TypeError:
        print(f"{gene} was not found on the .csv file, make sure you're using the right gene notation")
    
      try:
        duo_double_ko_TPMs = []
        for n in range (1,8):
          duo_double_ko_TPMs.append(int(gene_row_duo[f"ApcKLsd1K_D{n}_count"]))
      except TypeError:
        pass
    
      try:  
        ile_single_ko_TPMs = []
        for n in range (1,7):
          ile_single_ko_TPMs.append(int(gene_row_ile[f"ApcK_I{n}_count"]))
      except TypeError:
        pass
    
      try:
        ile_double_ko_TPMs = []
        for n in range (1,8):
          ile_double_ko_TPMs.append(int(gene_row_ile[f"ApcKLsd1K_I{n}_count"]))
      except TypeError:
        pass
    
    
      # Append an extra NaN value to the shorter lists (to avoid pandas error)
      duo_single_ko_TPMs.append(np.nan)
      ile_single_ko_TPMs.append(np.nan)
    
      # Get adjusted p-values of Duo and Ile comparisons and store them into a variable
      try:
        p_val_d = float(gene_row_duo["padj"])
        logfc_d = round(float(gene_row_duo["log2FoldChange"]), 2)
        p_val_i = float(gene_row_ile["padj"])
        logfc_i = round(float(gene_row_ile["log2FoldChange"]), 2)
      except TypeError:
        pass
    
      # Create a f-string containing all stat info that will be displayed as the x axis label
      try:
        stats = f"Duo ApcKO vs Duo ApcKO Lsd1 KO ---- p: {p_val_d} ---- logfc: {logfc_d} \nIle ApcKO vs Ile ApcKO Lsd1 KO ---- p: {p_val_i} ---- logfc: {logfc_i}"
      except NameError:
        pass
    
      # Define and create the DataFrames to plot 
      dataset = {
          "Duo ApcKO":duo_single_ko_TPMs,
          "Duo ApcKO Lsd1KO":duo_double_ko_TPMs,
          "Ile ApcKO":ile_single_ko_TPMs,
          "Ile ApcKO Lsd1KO":ile_double_ko_TPMs
      }
      try:
        df = pd.DataFrame(dataset)
    
        # Plot dataset with Seaborn
        plt.figure(figsize=(6,6), dpi=120)
        sns.swarmplot(data=df.melt(), x="variable", y="value", palette=['#00BFC4','#F8766D','#C77CFF','#7CAE00'], size = 8, hue = "variable")
        plt.legend().set_title(None)
        plt.title(label=gene, fontsize=20)
        plt.yticks(fontsize=14)
        plt.xticks(fontsize=14, rotation=45, horizontalalignment='right')
    
        # Plot the mean line
        sns.boxplot(showmeans=True,
                    meanline=True,
                    meanprops={'color': 'grey', 'ls': '-', 'lw': 2},
                    medianprops={'visible': False},
                    whiskerprops={'visible': False},
                    zorder=10,
                    x="variable",
                    y="value",
                    data=df.melt(),
                    showfliers=False,
                    showbox=False,
                    showcaps=False)
    
        # Reset the axis title after using boxplot to plot the mean line
        plt.ylabel('TPMs', fontsize=14)
        plt.xlabel(stats, color="grey", fontsize=10, loc="center")
    
    
        # Fill the NaN values in the DataFrame to find the max value using to_numpy().max()
        df.fillna(0, inplace=True)
    
        # Change the y axis limits in order to leave space to plot the p-value on top
        ax1 = plt.gca()
        y_top_limit = (df.to_numpy().max()) + (df.to_numpy().max())*0.1
        ax1.set_ylim([0, y_top_limit])
    
        # Hide top and right spines from the frame
        ax1.spines["right"].set_visible(False)
        ax1.spines["top"].set_visible(False)
    
        # Save the result as a vector image to the default content folder
        plt.savefig(f"{gene}_TPMs.svg", format = 'svg', dpi=300, bbox_inches='tight')
        plt.savefig(f"{gene}_TPMs.jpg", format = 'jpeg', dpi=300, bbox_inches='tight')
    
        # Next line automatically downloads a copy of the .svg plot
        files.download(f"{gene}_TPMs.svg")
        files.download(f"{gene}_TPMs.jpg")
    
        # Display the result
        plt.show()
      except ValueError:
        print(f"{gene} was not found on the .csv file and could not be plotted")

Easy

ddddddd