Python Matplotlib - Multiple series in same data file

I think you can read_csv only once and then post processing create dataframe:

import pandas as pd
import io

temp=u""""1stSerie"    
2 23
4 12
6 12

"2ndSerie"
2 51
4 90
6 112
"""

s = pd.read_csv(io.StringIO(temp), #after testing replace io.StringIO(temp) to filename
                 sep="\s+", 
                 engine='python', #because ParserWarning
                 squeeze=True,
                 header=None) #try convert output to series

print s

"1stSerie"    NaN
2              23
4              12
6              12
"2ndSerie"    NaN
2              51
4              90
6             112
Name: 0, dtype: float64

df = s.reset_index()
#set column names
df.columns = ['idx','val']

#try convert column idx to numeric, if string get NaN
print pd.to_numeric(df['idx'], errors='coerce')
0   NaN
1     2
2     4
3     6
4   NaN
5     2
6     4
7     6
Name: idx, dtype: float64

#find NaN - which values are string
print pd.isnull(pd.to_numeric(df['idx'], errors='coerce'))
0     True
1    False
2    False
3    False
4     True
5    False
6    False
7    False
Name: idx, dtype: bool

#this values get to new column names
df.loc[pd.isnull(pd.to_numeric(df['idx'], errors='coerce')), 'names'] = df['idx']

#forward fill NaN values
df['names'] = df['names'].ffill()

#remove values, where column val in NaN
df = df[pd.notnull(df['val'])]

print df
  idx  val       names
1   2   23  "1stSerie"
2   4   12  "1stSerie"
3   6   12  "1stSerie"
5   2   51  "2ndSerie"
6   4   90  "2ndSerie"
7   6  112  "2ndSerie"

df.pivot(index='idx', columns='names', values='val').plot()

Or you can use read_csv and plot. If you need set names of Series to legend, use figure and legend:

import pandas as pd
import matplotlib.pyplot as plt
import io

temp=u""""1stSerie"    
2 23
4 12
6 12

"2ndSerie"
2 51
4 90
6 112"""

s1 = pd.read_csv(io.StringIO(temp), #after testing replace io.StringIO(temp) to filename
                 sep="\s+", 
                 engine='python', #because ParserWarning
                 nrows=3, #read only 3 rows of data
                 squeeze=True) #try convert output to series

print s1
2    23
4    12
6    12
Name: "1stSerie", dtype: int64

#after testing replace io.StringIO(temp) to filename
s2 = pd.read_csv(io.StringIO(temp), 
                 sep="\s+", 
                 header=4, #read row 4 to header - series name
                 engine='python',      
                 nrows=3,
                 squeeze=True)

print s2
2     51
4     90
6    112
Name: "2ndSerie", dtype: int64

plt.figure()
s1.plot()
ax = s2.plot()
ax.legend(['1stSerie','2ndSerie'])

Or you can read file only once and then cut Serie s to Series s1, s2 and s3 and then create DataFrame:

import pandas as pd
import matplotlib.pyplot as plt
import io

temp=u""""1stSerie"    
2 23
4 12
6 12

"2ndSerie"
2 51
4 90
6 112

"3rdSerie"
2 51
4 90
6 112
"""

s = pd.read_csv(io.StringIO(temp), #after testing replace io.StringIO(temp) to filename
                 sep="\s+", 
                 engine='python', #because ParserWarning
                 squeeze=True) #try convert output to series

print s
2              23
4              12
6              12
"2ndSerie"    NaN
2              51
4              90
6             112
"3rdSerie"    NaN
2              51
4              90
6             112
Name: "1stSerie", dtype: float64

s1 = s[:3]
print s1
2    23
4    12
6    12
Name: "1stSerie", dtype: float64

s2 = s[4:7]
s2.name='2ndSerie'
print s2
2     51
4     90
6    112
Name: 2ndSerie, dtype: float64

s3 = s[8:]
s3.name='3rdSerie'
print s3
2     51
4     90
6    112
Name: 3rdSerie, dtype: float64

print pd.DataFrame({'a': s1, 'b': s2, 'c': s3})
    a    b    c
2  23   51   51
4  12   90   90
6  12  112  112

You can step through the file using itertools.groupby. The LastHeader class below checks each line for a sentinal character. If the character is there, the headerline is updated, and itertools.groupby starts a new segment. The only place this runs into trouble with your dataset is where you have two series labeled "CRE". My workaround was to just delete the second one from the textfile, but you'll probably want to do something else.

The upshot here is that you can just injest the data in a single pass. No writing out and reading back in required.

from itertools import groupby
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

from pandas import DataFrame, Series

class LastHeader():
    """Checks for new header strings. For use with groupby"""
    def __init__(self, sentinel='#'):
        self.sentinel = sentinel
        self.lastheader = ''
        self.index=0

    def check(self, line):
        self.index += 1
        if line.startswith(self.sentinel):
            self.lastheader = line
        return self.lastheader

fname = 'dist_caribou.dat'

with open(fname, 'r') as fobj:
    lastheader = LastHeader('"')
    data = []
    for headerline, readlines in groupby(fobj, lastheader.check):
        name = headerline.strip().strip('"')
        thisdat = np.loadtxt(readlines, comments='"')
        data.append(Series(thisdat[:, 1], index=thisdat[:, 0], name=name))
data = pd.concat(data, axis=1)
data.plot().set_yscale('log')
plt.show()

Given the appropriate parameters for read_csv in pandas, this is relatively trivial to plot.

s1 = pd.read_csv('series1.txt', 
                 index_col=0, 
                 sep=" ", 
                 squeeze=True, 
                 header=0, 
                 skipinitialspace=True)
>>> s1
tSerie
2    23
4    12
6    12
Name: Unnamed: 1, dtype: int64

s2 = pd.read_csv('series2.txt', 
                 index_col=0, 
                 sep=" ", 
                 squeeze=True, 
                 header=0, 
                 skipinitialspace=True)

%matplotlib inline  # If not already enabled.
s1.plot();s2.plot()