If I have understood you correctly, both from this question as well as your previous 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 to save the plot of each Spectral class in a separate figure, the code needs some modifications.

import pandas as pd
from pandas import DataFrame, read_csv
import numpy as np
import matplotlib.pyplot as plt

# Here you put your code to read the CSV-file into a DataFrame df

plt.figure(figsize=(7,5)) # Set the size of your figure, customize for more subplots

for 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. The plt.yscale('log') is optional, but seeing as how the values span such a great range, it is recommended.

import pandas as pd
from pandas import DataFrame, read_csv
import numpy as np
import matplotlib.pyplot as plt

# Here you put your code to read the CSV-file into a DataFrame df

for 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 pd
from pandas import DataFrame, read_csv
import numpy as np
import matplotlib.pyplot as plt

# Here you put your code to read the CSV-file into a DataFrame df

xs = np.array(df[df.columns[0::2]]) # Use values from odd numbered columns as x-values
ys = np.array(df[df.columns[1::2]]) # Use values from even numbered columns as y-values

for 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 call pyplot.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()