## Question

Asked By – Kyle Brandt

How do you find the top correlations in a correlation matrix with Pandas? There are many answers on how to do this with R (Show correlations as an ordered list, not as a large matrix or Efficient way to get highly correlated pairs from large data set in Python or R), but I am wondering how to do it with pandas? In my case the matrix is 4460×4460, so can’t do it visually.

**Now we will see solution for issue: List Highest Correlation Pairs from a Large Correlation Matrix in Pandas? **

## Answer

You can use `DataFrame.values`

to get an numpy array of the data and then use NumPy functions such as `argsort()`

to get the most correlated pairs.

But if you want to do this in pandas, you can `unstack`

and sort the DataFrame:

```
import pandas as pd
import numpy as np
shape = (50, 4460)
data = np.random.normal(size=shape)
data[:, 1000] += data[:, 2000]
df = pd.DataFrame(data)
c = df.corr().abs()
s = c.unstack()
so = s.sort_values(kind="quicksort")
print so[-4470:-4460]
```

Here is the output:

```
2192 1522 0.636198
1522 2192 0.636198
3677 2027 0.641817
2027 3677 0.641817
242 130 0.646760
130 242 0.646760
1171 2733 0.670048
2733 1171 0.670048
1000 2000 0.742340
2000 1000 0.742340
dtype: float64
```

This question is answered By – HYRY

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