title: Python Data Frame how to find the local maximum in a 2D array
body: <p>I have a Data Frame of two columns namely x,y. I want to find the local maximums in x,y plot as shown in figure 1 of attached plot. I followed this way: converted each column of data frame into two separate matrix arrays. Step 1: My code first identifies index positions of local maximums in Y. Step 2: value of x corresponding to the those index positions will be identified. That's it. As a result, i could found two local maximums only. But, there are there three local maximums. My method fail to identify it. My question: is there a way I can identify the local maximum directly from 2D array ? </p>
  
  <p>My present code: </p>
  
  <pre><code>x = my_dataframe.iloc[:,0].values # conversion of Data frame column into an array
  y = my_dataframe.iloc[:,2].values # conversion of Data frame column into an array        
  
  # Step 1: for local maximum in y list
  local_y_index = argrelextrema(y, np.greater)
  print("Index position of local maximum in y = ",local_y_index[0])
  
  # Step 2: Below code is for identifying the value of x at local maximum
  local_x = x[local_mpp_index[0]]
  print("value of x corresponding to local maximum in y = ",local_x)
  </code></pre>
  
  <p>The output is: </p>
  
  <pre><code>Index position of local maximum in y =  [105 197]
  value of x corresponding to local maximum in y =  [149.21 281.06]
  </code></pre>
  
  <p>My question: As shown in Figure 1, my above approach has identified two local peaks only. But there are three peaks. Is there a better approach to identify the local maximum directly from 2D array of x and y? </p>
  
  <p><a href="https://i.stack.imgur.com/ZbzaQ.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/ZbzaQ.png" alt="enter image description here"></a></p>
  
  <pre><code>x = [1.0330e-01, 1.0380e-01, 1.0430e-01, 1.0680e-01, 1.1932e-01, 1.8192e-01,
   3.6365e-01, 5.4539e-01, 7.9191e-01, 1.0384e+00, 1.3626e+00, 1.6869e+00,
   1.7438e+00, 2.0286e+00, 2.4825e+00, 2.9363e+00, 3.4787e+00, 4.0212e+00,
   4.7129e+00, 5.2137e+00, 6.0460e+00, 6.9486e+00, 7.8511e+00, 8.6835e+00,
   1.0092e+01, 1.0418e+01, 1.2153e+01, 1.3888e+01, 1.5623e+01, 1.7358e+01,
   1.9093e+01, 2.0828e+01, 2.2563e+01, 2.4298e+01, 2.6033e+01, 2.7768e+01,
   2.9503e+01, 3.1237e+01, 3.2972e+01, 3.4707e+01, 3.6442e+01, 3.8177e+01,
   3.9912e+01, 4.1647e+01, 4.3382e+01, 4.5117e+01, 4.6852e+01, 4.8587e+01,
   5.0322e+01, 5.2056e+01, 5.3791e+01, 5.5526e+01, 5.7261e+01, 5.8996e+01,
   6.0731e+01, 6.2466e+01, 6.4201e+01, 6.5936e+01, 6.7671e+01, 6.9406e+01,
   7.1141e+01, 7.2875e+01, 7.4610e+01, 7.6345e+01, 7.8080e+01, 7.9815e+01,
   8.1550e+01, 8.3285e+01, 8.5020e+01, 8.6755e+01, 8.8490e+01, 9.0225e+01,
   9.1960e+01, 9.3694e+01, 9.5429e+01, 9.7164e+01, 9.8899e+01, 1.0063e+02,
   1.0237e+02, 1.0410e+02, 1.0584e+02, 1.0757e+02, 1.0931e+02, 1.1104e+02,
   1.1278e+02, 1.1451e+02, 1.1625e+02, 1.1798e+02, 1.1972e+02, 1.2145e+02,
   1.2319e+02, 1.2492e+02, 1.2666e+02, 1.2839e+02, 1.3013e+02, 1.3186e+02,
   1.3360e+02, 1.3533e+02, 1.3707e+02, 1.3880e+02, 1.4054e+02, 1.4227e+02,
   1.4401e+02, 1.4574e+02, 1.4748e+02, 1.4921e+02, 1.5095e+02, 1.5268e+02,
   1.5442e+02, 1.5615e+02, 1.5684e+02, 1.5753e+02, 1.5789e+02, 1.5861e+02,
   1.5934e+02, 1.5962e+02, 1.6056e+02, 1.6136e+02, 1.6256e+02, 1.6309e+02,
   1.6482e+02, 1.6656e+02, 1.6829e+02, 1.7003e+02, 1.7176e+02, 1.7350e+02,
   1.7523e+02, 1.7697e+02, 1.7870e+02, 1.8044e+02, 1.8217e+02, 1.8391e+02,
   1.8564e+02, 1.8738e+02, 1.8911e+02, 1.9085e+02, 1.9258e+02, 1.9432e+02,
   1.9605e+02, 1.9779e+02, 1.9952e+02, 2.0126e+02, 2.0299e+02, 2.0473e+02,
   2.0646e+02, 2.0820e+02, 2.0993e+02, 2.1167e+02, 2.1340e+02, 2.1514e+02,
   2.1687e+02, 2.1861e+02, 2.1927e+02, 2.1993e+02, 2.2034e+02, 2.2103e+02,
   2.2172e+02, 2.2208e+02, 2.2296e+02, 2.2381e+02, 2.2493e+02, 2.2555e+02,
   2.2700e+02, 2.2728e+02, 2.2871e+02, 2.2902e+02, 2.3057e+02, 2.3075e+02,
   2.3164e+02, 2.3249e+02, 2.3422e+02, 2.3596e+02, 2.3769e+02, 2.3943e+02,
   2.4116e+02, 2.4290e+02, 2.4463e+02, 2.4637e+02, 2.4810e+02, 2.4984e+02,
   2.5157e+02, 2.5331e+02, 2.5504e+02, 2.5678e+02, 2.5851e+02, 2.6025e+02,
   2.6198e+02, 2.6371e+02, 2.6545e+02, 2.6718e+02, 2.6892e+02, 2.7065e+02,
   2.7239e+02, 2.7412e+02, 2.7586e+02, 2.7759e+02, 2.7933e+02, 2.8106e+02,
   2.8280e+02, 2.8453e+02, 2.8627e+02, 2.8800e+02, 2.8974e+02, 2.9147e+02,
   2.9321e+02, 2.9494e+02, 2.9668e+02, 2.9841e+02, 3.0015e+02, 3.0188e+02,
   3.0362e+02, 3.0535e+02, 3.0709e+02, 3.0882e+02, 3.1056e+02, 3.1229e+02,
   3.1403e+02, 3.1576e+02, 3.1749e+02, 3.1923e+02, 3.2096e+02, 3.2270e+02,
   3.2443e+02, 3.2617e+02, 3.2790e+02, 3.2964e+02, 3.3137e+02, 3.3311e+02,
   3.3484e+02, 3.3658e+02, 3.4686e+02, 3.4686e+02, 3.4686e+02, 3.4686e+02,
   3.4686e+02, 3.4686e+02, 3.4686e+02, 3.4686e+02, 3.4687e+02]
  
  y = [4.2014e-01, 4.2237e-01, 4.2460e-01, 4.3574e-01, 4.9146e-01, 7.7004e-01,
       1.5788e+00, 2.3874e+00, 3.4842e+00, 4.5808e+00, 6.0228e+00, 7.4647e+00,
       7.7180e+00, 8.9843e+00, 1.1002e+01, 1.3020e+01, 1.5431e+01, 1.7842e+01,
       2.0916e+01, 2.3141e+01, 2.6839e+01, 3.0848e+01, 3.4856e+01, 3.8552e+01,
       4.4807e+01, 4.6254e+01, 5.3953e+01, 6.1650e+01, 6.9344e+01, 7.7035e+01,
       8.4723e+01, 9.2409e+01, 1.0009e+02, 1.0777e+02, 1.1545e+02, 1.2312e+02,
       1.3079e+02, 1.3846e+02, 1.4613e+02, 1.5379e+02, 1.6145e+02, 1.6911e+02,
       1.7677e+02, 1.8442e+02, 1.9207e+02, 1.9971e+02, 2.0735e+02, 2.1499e+02,
       2.2263e+02, 2.3027e+02, 2.3790e+02, 2.4552e+02, 2.5315e+02, 2.6077e+02,
       2.6839e+02, 2.7600e+02, 2.8361e+02, 2.9122e+02, 2.9882e+02, 3.0642e+02,
       3.1401e+02, 3.2160e+02, 3.2918e+02, 3.3676e+02, 3.4433e+02, 3.5190e+02,
       3.5946e+02, 3.6701e+02, 3.7455e+02, 3.8209e+02, 3.8961e+02, 3.9712e+02,
       4.0462e+02, 4.1211e+02, 4.1958e+02, 4.2703e+02, 4.3447e+02, 4.4188e+02,
       4.4926e+02, 4.5661e+02, 4.6393e+02, 4.7122e+02, 4.7846e+02, 4.8565e+02,
       4.9278e+02, 4.9985e+02, 5.0685e+02, 5.1376e+02, 5.2057e+02, 5.2728e+02,
       5.3386e+02, 5.4029e+02, 5.4656e+02, 5.5265e+02, 5.5852e+02, 5.6415e+02,
       5.6950e+02, 5.7453e+02, 5.7920e+02, 5.8347e+02, 5.8727e+02, 5.9056e+02,
       5.9325e+02, 5.9527e+02, 5.9654e+02, 5.9697e+02, 5.9646e+02, 5.9490e+02,
       5.9217e+02, 5.9175e+02, 5.9419e+02, 5.9665e+02, 5.9790e+02, 6.0049e+02,
       6.0309e+02, 6.0410e+02, 6.0748e+02, 6.1034e+02, 6.1467e+02, 6.1658e+02,
       6.2282e+02, 6.2905e+02, 6.3528e+02, 6.4151e+02, 6.4772e+02, 6.5393e+02,
       6.6013e+02, 6.6632e+02, 6.7251e+02, 6.7868e+02, 6.8484e+02, 6.9099e+02,
       6.9712e+02, 7.0323e+02, 7.0931e+02, 7.1536e+02, 7.2137e+02, 7.2732e+02,
       7.3320e+02, 7.3899e+02, 7.4464e+02, 7.5013e+02, 7.5540e+02, 7.6039e+02,
       7.6502e+02, 7.6922e+02, 7.7287e+02, 7.7589e+02, 7.7817e+02, 7.7962e+02,
       7.8014e+02, 7.8039e+02, 7.8250e+02, 7.8464e+02, 7.8598e+02, 7.8823e+02,
       7.9050e+02, 7.9166e+02, 7.9458e+02, 7.9739e+02, 8.0109e+02, 8.0313e+02,
       8.0793e+02, 8.0888e+02, 8.1359e+02, 8.1462e+02, 8.1978e+02, 8.2036e+02,
       8.2330e+02, 8.2610e+02, 8.3183e+02, 8.3755e+02, 8.4326e+02, 8.4897e+02,
       8.5466e+02, 8.6035e+02, 8.6602e+02, 8.7168e+02, 8.7732e+02, 8.8295e+02,
       8.8855e+02, 8.9412e+02, 8.9965e+02, 9.0513e+02, 9.1055e+02, 9.1588e+02,
       9.2110e+02, 9.2618e+02, 9.3108e+02, 9.3576e+02, 9.4015e+02, 9.4420e+02,
       9.4784e+02, 9.5100e+02, 9.5362e+02, 9.5563e+02, 9.5698e+02, 9.5761e+02,
       9.5746e+02, 9.5650e+02, 9.5468e+02, 9.5195e+02, 9.4828e+02, 9.4363e+02,
       9.3796e+02, 9.3122e+02, 9.2337e+02, 9.1437e+02, 9.0418e+02, 8.9275e+02,
       8.8004e+02, 8.6600e+02, 8.5059e+02, 8.3376e+02, 8.1546e+02, 7.9566e+02,
       7.7430e+02, 7.5134e+02, 7.2674e+02, 7.0046e+02, 6.7244e+02, 6.4266e+02,
       6.1108e+02, 5.7765e+02, 5.4234e+02, 5.0512e+02, 4.6596e+02, 4.2483e+02,
       3.8170e+02, 3.3654e+02, 6.8800e-05, 5.1500e-05, 4.8000e-05, 4.7300e-05,
       4.7200e-05, 4.7200e-05, 4.7200e-05, 4.7200e-05, 1.5520e-04]
  </code></pre>