If I have understood you correctly, both from this question as well as yourprevious one on the same subject, the following should be basic solutions you could customize to your needs.

Several subplots:

Note that this solution will output as many subplots as there are Spectral classes (M0, M1, ...) vertically on the same figure. If you wish tosave the plot of each Spectral class in a separate figure, the code needs some modifications.

import pandas as pdfrom pandas import DataFrame, read_csvimport numpy as npimport matplotlib.pyplot as plt# Here you put your code to read the CSV-file into a DataFrame dfplt.figure(figsize=(7,5)) # Set the size of your figure, customize for more subplotsfor i in range(len(df)):    xs = np.array(df[df.columns[0::2]])[i] # Use values from odd numbered columns as x-values    ys = np.array(df[df.columns[1::2]])[i] # Use values from even numbered columns as y-values    plt.subplot(len(df), 1, i+1)    plt.plot(xs, ys, marker='o') # Plot circle markers with a line connecting the points    for j in range(len(xs)):        plt.annotate(df.columns[0::2][j][-3:] + '"', # Annotate every plotted point with last three characters of the column-label                     xy = (xs[j],ys[j]),                     xytext = (0, 5),                     textcoords = 'offset points',                     va = 'bottom',                     ha = 'center',                     clip_on = True)    plt.title('Spectral class ' + df.index[i])    plt.xlabel('Limiting Magnitude')    plt.ylabel('Exposure Time')    plt.grid(alpha=0.4)plt.tight_layout()plt.show()

All in same Axes, grouped by rows (M0, M1, ...)

Here is another solution to get all the different Spectral classes plotted in the same Axes with a legend identifying the different classes. Theplt.yscale('log') is optional, but seeing as how the values span such a great range, it is recommended.

import pandas as pdfrom pandas import DataFrame, read_csvimport numpy as npimport matplotlib.pyplot as plt# Here you put your code to read the CSV-file into a DataFrame dffor i in range(len(df)):    xs = np.array(df[df.columns[0::2]])[i] # Use values from odd numbered columns as x-values    ys = np.array(df[df.columns[1::2]])[i] # Use values from even numbered columns as y-values    plt.plot(xs, ys, marker='o', label=df.index[i])    for j in range(len(xs)):        plt.annotate(df.columns[0::2][j][-3:] + '"', # Annotate every plotted point with last three characters of the column-label                     xy = (xs[j],ys[j]),                     xytext = (0, 6),                     textcoords = 'offset points',                     va = 'bottom',                     ha = 'center',                     rotation = 90,                     clip_on = True)plt.title('Spectral classes')plt.xlabel('Limiting Magnitude')plt.ylabel('Exposure Time')plt.grid(alpha=0.4)    plt.yscale('log')plt.legend(loc='best', title='Spectral classes')plt.show()

All in same Axes, grouped by columns (1.3", 2.0", 2.5")

A third solution is as shown below, where the data are grouped by the series (columns 1.3", 2.0", 2.5") rather than by the Spectral class (M0, M1, ...). This example is very similar to @askewchan's solution. One difference is that the y-axis here is a logarithmic axis, making the lines pretty much parallel.

import pandas as pdfrom pandas import DataFrame, read_csvimport numpy as npimport matplotlib.pyplot as plt# Here you put your code to read the CSV-file into a DataFrame dfxs = np.array(df[df.columns[0::2]]) # Use values from odd numbered columns as x-valuesys = np.array(df[df.columns[1::2]]) # Use values from even numbered columns as y-valuesfor i in range(df.shape[1]/2):     plt.plot(xs[:,i], ys[:,i], marker='o', label=df.columns[0::2][i][-3:]+'"')     for j in range(len(xs[:,i])):        plt.annotate(df.index[j], # Annotate every plotted point with its Spectral class                     xy = (xs[:,i][j],ys[:,i][j]),                     xytext = (0, -6),                     textcoords = 'offset points',                     va = 'top',                     ha = 'center',                     clip_on = True)plt.title('Spectral classes')plt.xlabel('Limiting Magnitude')plt.ylabel('Exposure Time')plt.grid(alpha=0.4)    plt.yscale('log')plt.legend(loc='best', title='Series')plt.show()

You can callpyplot.plot(time, mag) three different times in the same figure. It would be wise to give a label to them. Something like this:

import matplotlib.pyplot as plt...fig = plt.figure()ax = fig.add_subplot(111)ax.plot(df['LimMag1.3'], df['ExpTime1.3'], label="1.3")ax.plot(df['LimMag2.0'], df['ExpTime2.0'], label="2.0")ax.plot(df['LimMag2.5'], df['ExpTime2.5'], label="2.5")plt.show()

If you want to loop it, this would work:

fig = plt.figure()ax = fig.add_subplot(111)for x,y in [['LimMag1.3', 'ExpTime1.3'],['LimMag2.0', 'ExpTime2.0'], ['LimMag2.5','ExpTime2.5']]:    ax.plot(df[x], df[y], label=y)plt.show()

• csv
• matplotlib
• pandas