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

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.