List Highest Correlation Pairs from a Large Correlation Matrix in Pandas?

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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 4460x4460, so can't do it visually.


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

Calculation and Visualization of Correlation Matrix with Pandas , First unstack and then order the DataFrame: import pandas as pd. import numpy as np. shape = (50, 4460). 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


@HYRY's answer is perfect. Just building on that answer by adding a bit more logic to avoid duplicate and self correlations and proper sorting:

import pandas as pd
d = {'x1': [1, 4, 4, 5, 6], 
     'x2': [0, 0, 8, 2, 4], 
     'x3': [2, 8, 8, 10, 12], 
     'x4': [-1, -4, -4, -4, -5]}
df = pd.DataFrame(data = d)
print("Data Frame")
print(df)
print()

print("Correlation Matrix")
print(df.corr())
print()

def get_redundant_pairs(df):
    '''Get diagonal and lower triangular pairs of correlation matrix'''
    pairs_to_drop = set()
    cols = df.columns
    for i in range(0, df.shape[1]):
        for j in range(0, i+1):
            pairs_to_drop.add((cols[i], cols[j]))
    return pairs_to_drop

def get_top_abs_correlations(df, n=5):
    au_corr = df.corr().abs().unstack()
    labels_to_drop = get_redundant_pairs(df)
    au_corr = au_corr.drop(labels=labels_to_drop).sort_values(ascending=False)
    return au_corr[0:n]

print("Top Absolute Correlations")
print(get_top_abs_correlations(df, 3))

That gives the following output:

Data Frame
   x1  x2  x3  x4
0   1   0   2  -1
1   4   0   8  -4
2   4   8   8  -4
3   5   2  10  -4
4   6   4  12  -5

Correlation Matrix
          x1        x2        x3        x4
x1  1.000000  0.399298  1.000000 -0.969248
x2  0.399298  1.000000  0.399298 -0.472866
x3  1.000000  0.399298  1.000000 -0.969248
x4 -0.969248 -0.472866 -0.969248  1.000000

Top Absolute Correlations
x1  x3    1.000000
x3  x4    0.969248
x1  x4    0.969248
dtype: float64

Better Heatmaps and Correlation Matrix Plots in Python, I suggest some sort of play on the following: Using the UCI Abalone data for this example import matplotlib import numpy as np import matplotlib.pyplot as plt  Using the correlation coefficient you can find out how these two variables are related and to what degree. Please note that this is only a part of the whole dataset. To calculate the correlation coefficient, selecting columns, and then applying the .corr() method. We can compute the correlation pairwise between more than 2 columns.


Few lines solution without redundant pairs of variables:

corr_matrix = df.corr().abs()

#the matrix is symmetric so we need to extract upper triangle matrix without diagonal (k = 1)
sol = (corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))
                 .stack()
                 .sort_values(ascending=False))
#first element of sol series is the pair with the bigest correlation

python, Let's start by making a correlation matrix heatmap for the data set. What's the strongest and what's the weakest correlated pair (except the main counts makes it effortless to determine which group is the largest/smallest. Use list comprehensions instead pandas apply and map methods, so we can  Cluster a Correlation Matrix (in python) Below is a function to rearrange variables in a correlation matrix (either pandas. randn ( 2 , 100 ) fig , [ ax1 , ax2 ] = plt. Correlation matrix can be also reordered according to the degree of association between variables.


Combining some features of @HYRY and @arun's answers, you can print the top correlations for dataframe df in a single line using:

df.corr().unstack().sort_values().drop_duplicates()

Note: the one downside is if you have 1.0 correlations that are not one variable to itself, the drop_duplicates() addition would remove them

Correlation(s) in Python, you can use dataframe.values numpy array of data , use numpy functions such argsort() correlated pairs. but if want in pandas, can unstack  List Highest Correlation Pairs from a Large Correlation Matrix in Pandas? 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.


Use the code below to view the correlations in the descending order.

# See the correlations in descending order

corr = df.corr() # df is the pandas dataframe
c1 = corr.abs().unstack()
c1.sort_values(ascending = False)

NumPy, SciPy, and Pandas: Correlation With Python – Real Python, To install Pingouin, you need to have Python 3 installed on your computer. The correlation coefficient (sometimes referred to as Pearson's correlation In other words, we expect that the taller someone is, the larger his/her weight is, and vice between each pairs of columns in our dataframe (= pairwise correlation). 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


Plot correlation matrix using pandas, You'll use SciPy, NumPy, and Pandas correlation methods to calculate three Each of these x-y pairs represents a single observation. The maximum value r = 1 corresponds to the case when there's a perfect You'll obtain the correlation matrix again, but this one will be larger than previous ones:. Display Correlation and pvalues as a list and erase which doesn't meet certain features 1 Sort for top matrix correlations and remove reverse duplicates without apply


What is a Correlation Matrix?, How do you find the top correlations in a correlation matrix with Pandas? Compute pairwise correlation of columns, excluding NA/null values. Parameters method {‘pearson’, ‘kendall’, ‘spearman’} or callable. Method of correlation: pearson : standard correlation coefficient. kendall : Kendall Tau correlation coefficient. spearman : Spearman rank correlation. callable: callable with input two 1d ndarrays


Python, Sort correlation matrix python. List Highest Correlation Pairs from a Large Correlation Matrix in , You can use DataFrame.values to get an numpy array of the  Now you can use NumPy, SciPy, and Pandas correlation functions and methods to effectively calculate these (and other) statistics, even when you work with large datasets. You also know how to visualize data, regression lines, and correlation matrices with Matplotlib plots and heatmaps.