title: Plotting data from multiple pandas data frames in one plot
body: <p>I am interested in plotting a time series with data from several different pandas data frames. I know how to plot a data for a single time series and I know how to do subplots, but how would I manage to plot from several different data frames in a single plot? I have my code below. Basically what I am doing is I am scanning through a folder of json files and parsing that json file into a panda so that I can plot. When I run this code it is only plotting from one of the pandas instead of the ten pandas created. I know that 10 pandas are created because I have a print statement to ensure they are all correct. </p>
  
  <pre><code>import sys, re
  import numpy as np
  import smtplib
  import matplotlib.pyplot as plt
  from random import randint
  import csv
  import pylab as pl
  import math
  import pandas as pd
  from pandas.tools.plotting import scatter_matrix
  import argparse
  import matplotlib.patches as mpatches
  import os
  import json
  
  
  
  parser = argparse.ArgumentParser()
  parser.add_argument('-file', '--f', help = 'folder where JSON files are stored')
  if len(sys.argv) == 1:
      parser.print_help()
      sys.exit(1)
  args = parser.parse_args()
  
  
  dat = {}
  i = 0
  
  direc = args.f
  directory = os.fsencode(direc)
  
  fig1 = plt.figure()
  ax1 = fig1.add_subplot(111)
  
  for files in os.listdir(direc):
      filename = os.fsdecode(files)
      if filename.endswith(".json"):
          path = '/Users/Katie/Desktop/Work/' + args.f + "/" +filename
          with open(path, 'r') as data_file:
              data = json.load(data_file)
              for r in data["commits"]:
                  dat[i] = (r["author_name"], r["num_deletions"], r["num_insertions"], r["num_lines_changed"],
                            r["num_files_changed"], r["author_date"])
                  name = "df" + str(i).zfill(2)
                  i = i + 1
                  name = pd.DataFrame.from_dict(dat, orient='index').reset_index()
                  name.columns = ["index", "author_name", "num_deletions",
                                            "num_insertions", "num_lines_changed",
                                            "num_files_changed",  "author_date"]
                  del name['index']
                  name['author_date'] = name['author_date'].astype(int)
                  name['author_date'] =  pd.to_datetime(name['author_date'], unit='s')
                  ax1.plot(name['author_date'], name['num_lines_changed'], '*',c=np.random.rand(3,))
                  print(name)
                  continue
  
      else:
          continue
  plt.xticks(rotation='35')
  plt.title('Number of Lines Changed vs. Author Date')
  plt.show()
  </code></pre>