How to make multiline graph with matplotlib subplots and pandas?

Question

I\'m fairly new at coding (completely self taught), and have started using it at at my job as a research assistant in a cancer lab. I need some help setting up a few line gr

Answer 1

I wrote a subplot function that may give you a hand. I modified the data a tad to help illustrate the plotting functionality.

gene,yaxis,xaxis,pt #,gene #
ASXL1-3,34,1,3,1
ASXL1-3,3,98,3,1
IDH1-3,24,1,3,11
IDH1-3,7,98,3,11
RUNX1-3,38,1,3,21
RUNX1-3,2,98,3,21
U2AF1-3,33,1,3,26
U2AF1-3,0,98,3,26
ASXL1-3,39,1,4,1
ASXL1-3,8,62,4,1
ASXL1-3,0,119,4,1
IDH1-3,27,1,4,11
IDH1-3,12,62,4,11
IDH1-3,1,119,4,11
RUNX1-3,42,1,4,21
RUNX1-3,3,62,4,21
RUNX1-3,1,119,4,21
U2AF1-3,16,1,4,26
U2AF1-3,1,62,4,26
U2AF1-3,0,119,4,26

This is the subplotting function...with some extra bells and whistles :)

def plotByGroup(df, group, xCol, yCol, title = "", xLabel = "", yLabel = "", lineColors = ["red", "orange", "yellow", "green", "blue", "purple"], lineWidth = 2, lineOpacity = 0.7, plotStyle = 'ggplot', showLegend = False):
    """
    Plot multiple lines from a Pandas Data Frame for each group using DataFrame.groupby() and MatPlotLib PyPlot.
    @params
        df          - Required  - Data Frame    - Pandas Data Frame
        group       - Required  - String        - Column name to group on           
        xCol        - Required  - String        - Column name for X axis data
        yCol        - Required  - String        - Column name for y axis data
        title       - Optional  - String        - Plot Title
        xLabel      - Optional  - String        - X axis label
        yLabel      - Optional  - String        - Y axis label
        lineColors  - Optional  - List          - Colors to plot multiple lines
        lineWidth   - Optional  - Integer       - Width of lines to plot
        lineOpacity - Optional  - Float         - Alpha of lines to plot
        plotStyle   - Optional  - String        - MatPlotLib plot style
        showLegend  - Optional  - Boolean       - Show legend
    @return
        MatPlotLib Plot Object

    """
    # Import MatPlotLib Plotting Function & Set Style
    from matplotlib import pyplot as plt
    matplotlib.style.use(plotStyle)
    figure = plt.figure()                   # Initialize Figure
    grouped = df.groupby(group)             # Set Group
    i = 0                                   # Set iteration to determine line color indexing
    for idx, grp in grouped:
        colorIndex = i % len(lineColors)    # Define line color index
        lineLabel = grp[group].values[0]    # Get a group label from first position
        xValues = grp[xCol]                 # Get x vector
        yValues = grp[yCol]                 # Get y vector
        plt.subplot(1,1,1)                  # Initialize subplot and plot (on next line)
        plt.plot(xValues, yValues, label = lineLabel, color = lineColors[colorIndex], lw = lineWidth, alpha = lineOpacity)
        # Plot legend
        if showLegend:
            plt.legend()
        i += 1
    # Set title & Labels
    axis = figure.add_subplot(1,1,1)
    axis.set_title(title)
    axis.set_xlabel(xLabel)
    axis.set_ylabel(yLabel)
    # Return plot for saving, showing, etc.
    return plt

And to use it...

import pandas

# Load the Data into Pandas
df = pandas.read_csv('data.csv')    

#
# Plotting - by Patient
#

# Create Patient Grouping
patientGroup = df.groupby('pt #')

# Iterate Over Groups
for idx, patientDF in patientGroup:
    # Let's give them specific titles
    plotTitle = "Gene Frequency over Time by Gene (Patient %s)" % str(patientDf['pt #'].values[0])
    # Call the subplot function
    plot = plotByGroup(patientDf, 'gene', 'xaxis', 'yaxis', title = plotTitle, xLabel = "Days", yLabel = "Gene Frequency")
    # Add Vertical Lines at Assay Timepoints
    timepoints = set(patientDf.xaxis.values)
    [plot.axvline(x = timepoint, linewidth = 1, linestyle = "dashed", color='gray', alpha = 0.4) for timepoint in timepoints]
    # Let's see it
    plot.show()

And of course, we can do the same by gene.

#
# Plotting - by Gene
#

# Create Gene Grouping
geneGroup   = df.groupby('gene')

# Generate Plots for Groups
for idx, geneDF in geneGroup:
    plotTitle = "%s Gene Frequency over Time by Patient" % str(geneDf['gene'].values[0])
    plot = plotByGroup(geneDf, 'pt #', 'xaxis', 'yaxis', title = plotTitle, xLab = "Days", yLab = "Frequency")
    plot.show()

If this isn't what you're looking for, provide a clarification and I'll take another crack at it.