## Python Data Frame how to find the local maximum in a 2D array

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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 ?

My present 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)
```

The output is:

```Index position of local maximum in y =  [105 197]
value of x corresponding to local maximum in y =  [149.21 281.06]
```

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?

```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]
```

Any extremum is such that the derivative at the extremum is zero. As we do not have an analytic expression for the data, the next best thing we can do is approximate the derivative. This is essentially the same as taking the 1-step difference and looking for those values that are 'small'.

The following works well for me,

```def find_extrema(frame, tolerance=0.5):
diff = frame.diff()

extrema = diff[np.abs(diff) < tolerance]

return extrema[~np.isnan(extrema.y)]

df = pd.DataFrame(dict(y=y), index=x)

candidates = find_extrema(df)

print(candidates)
```

And I find,

```                      y
0.10380    2.230000e-03
0.10430    2.230000e-03
0.10680    1.114000e-02
0.11932    5.572000e-02
0.18192    2.785800e-01
1.74380    2.533000e-01
149.21000  4.300000e-01
156.15000 -4.200000e-01
218.61000  2.500000e-01
282.80000 -1.500000e-01
346.86000 -1.730000e-05
346.86000 -3.500000e-06
346.86000 -7.000000e-07
346.86000 -1.000000e-07
346.86000  0.000000e+00
346.86000  0.000000e+00
346.86000  0.000000e+00
346.87000  1.080000e-04
```

This will require some cleaning still (mostly on the edges), but the general idea should hopefully be clear to you.

The following plot was made with,

```tolerance = 0.75

diff = df.diff()

ax = diff[np.abs(diff) < tolerance].y.plot(
title="Derivative approximation for tolerance = {0}".format(tolerance))

ax.set_xlabel("x")
ax.set_ylabel("y[x] - y[x - 1]")

plt.show()
```

(notice the larger tolerance, so we can actually observe some lines rather than just points)

Pandas: Find maximum values & position in columns or rows of a , How do you find the max of a data frame? Return the maximum of the values for the requested axis. If you want the index of the maximum, use idxmax. This is the equivalent of the numpy.ndarray method argmax. Axis for the function to be applied on. Exclude NA/null values when computing the result. If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing

You can also use the `np.gradient` function and look where the gradient changes sign:

```z = np.gradient(y, x)
i = 0
while i < len(x)-2:
if (z[i]*z[i+2]<=0 and z[i]>0): #gradient changes sign > optima, and point previous to optima has a positive slope
print(i+1, x[i+1], y[i+1])
i = i+1
i+=1

plt.ylim(-1, 1)
plt.plot(x, z)
```

Looing at the plot, it seems the point at around 210 is not a maxima (the gradient doesnt reach zero). You can check this by replacing the `if` statement with the following `if (y[i+1]>y[i] and y[i+1]>y[i+2]):`

Python min() function, How do you find the max of an array in Python? Similarly, Python has built-in min and max functions, used to find the minimum value and maximum value of any given array: In [5]: min ( big_array ), max ( big_array )

Here comes my naive approach:

Step 1: find a list containing slopes, which is `+1` if two consecutive`y`-values are increasing, `-1` if decreasing and `0` if are the same:

```import numpy as np
slope = [np.sign(y[i]-y[i-1]) for i in range(1, len(y))]
```

Now if you print `slope`, it's gonna be just either `0,1,-1` which says about slopes between each two consecutive `y` points.

Step2: To find `minimas` and `maximas`, I wrote this code which evaluates if the slope changes or not. If it changes from `1` to `-1` the index will be saved as a `maxima`, otherwise as `minima`.

```x_prev = slope[0]
optima_dic={'minima':[], 'maxima':[]}
for i in range(1, len(slope)):
if slope[i]*x_prev==-1: #slope changed
if x_prev==1: # slope changed from 1 to -1
optima_dic['maxima'].append(i)
else: # slope changed from -1 to 1
optima_dic['minima'].append(i)
x_prev=-x_prev
```

and if you print the results:

```print(optima_dic)
```

Output:

```{'minima': [109, 237], 'maxima': [105, 197]}
```

Quick and dirty :)

Get coordinates of local maxima in 2D array above certain value , How do you find the minimum of a Numpy array? The maximum value r = 1 corresponds to the case when there’s a perfect positive linear relationship between x and y. In other words, larger x values correspond to larger y values and vice versa. The value r > 0 indicates positive correlation between x and y. The value r = 0 corresponds to the case when x and y are independent.

Find all local Maxima and Minima when x and y values are given as , It refers to the single argument function to customize the sort order. The function is applied on each of the items. Dealing with Rows and Columns in Pandas DataFrame. A Data frame is a two-dimensional data structure, i.e., data is aligned in a tabular fashion in rows and columns. We can perform basic operations on rows/columns like selecting, deleting, adding, and renaming. In this article, we are using nba.csv file.

Aggregations: Min, Max, and Everything In Between, import numpy as np import scipy import scipy.ndimage as ndimage import scipy.​ndimage.filters as filters import matplotlib.pyplot as plt fname  The lowest datatype of DataFrame is considered for the datatype of the NumPy Array. In the following example, the DataFrame consists of columns of datatype int64 and float64. When this DataFrame is converted to NumPy Array, the lowest datatype of int64 and float64, which is float64 is selected. Python Program

scipy.signal.find_peaks, The plot looks like this; I shifted the x-values so that they correspond to the returned indices in minm and maxm ): x=np.array([6,3,5,2,1,4,9,7,8]) y=np.array​([2,1,3,5,7,9,8,10,7]) sort_idx = np.argsort(x) Python uses += instead of ++ . At this point you know how to load CSV data in Python. In this lesson, you will learn how to access rows, columns, cells, and subsets of rows and columns from a pandas dataframe. Let’s open the CSV file again, but this time we will work smarter.

• Could you also post the data (or a way to generate similar data)? Furthermore, it might help if we have the body of your `argrelextrema` method.
• It takes the difference with the previous element in `y` (previous row), which does not respect the `x`-axis. You could include that also, but then the approach becomes a bit more elaborate. But yes, fundamentally, I do try to approximate `dy/dx` and find those places where it is 'small' (less than some tolerance).