title: Bivariate CDF/CCDF Distribution Python
body: <p>I am trying to plot a bivariate <code>ccdf</code> of the dataset that has <code>x</code> and <code>y</code> values both. </p>
  
  <p>Univariate I can plot very well, below is the input and the code is for univeriate dataset. </p>
  
  <p>Input: These are only first 20 rows of the data points. Input has 1000s of rows and of which <code>col[1]</code> and <code>col[3]</code> needs to be plotted as they posses a user and keyword frequency relationship. </p>
  
  <pre><code>tweetcricscore  34 #afgvssco   51
  tweetcricscore  23 #afgvszim   46
  tweetcricscore  24 #banvsire   12
  tweetcricscore  456 #banvsned  46
  tweetcricscore  653 #canvsnk   1
  tweetcricscore  789 #cricket   178
  tweetcricscore  625 #engvswi   46
  tweetcricscore  86 #hkvssco    23
  tweetcricscore  3 #indvsban    1
  tweetcricscore  87 #sausvsvic  8
  tweetcricscore  98 #wt20       56
  </code></pre>
  
  <p>Code: univeriate dataset </p>
  
  <pre><code>import numpy as np
  import matplotlib.pyplot as plt
  from pylab import*
  import math
  from matplotlib.ticker import LogLocator
  
  data = np.genfromtxt('keyword.csv', delimiter=',', comments=None)
  
  d0=data[:,1]
  X0 = np.sort(d0)
  cdf0 = np.arange(len(X0))/float(len(X0))
  ccdf0 = 1 - cdf0
  plt.plot(X0,ccdf0, color='b', marker='.', label='Keywords')
  
  plt.legend(loc='upper right')
  plt.xlabel('Freq (x)')
  plt.ylabel('ccdf(x)')
  plt.gca().set_xscale("log")
  #plt.gca().set_yscale("log")
  plt.show()
  </code></pre>
  
  <p>I am looking for some option for bivariate data points. I referred <a href="https://stanford.edu/~mwaskom/software/seaborn/tutorial/distributions.html?highlight=bivariate#plotting-bivariate-distributions" rel="nofollow">Seaborn Bivariate Distribution</a> But I am not able to put it in proper context with my dataset. </p>
  
  <p>Any alternative suggestion within python, matplotlib, seaborn are welcome..
  Thanks in advance. </p>