One of the main features of pandas is being NaN friendly. To calculate correlation matrix, simply call df_counties.corr(). Below is an example to demonstrate df.corr() is NaN tolerant whereas np.corrcoef is not.

import pandas as pd
import numpy as np

# data
# ==============================
np.random.seed(0)
df = pd.DataFrame(np.random.randn(100,5), columns=list('ABCDE'))
df[df < 0] = np.nan
df

         A       B       C       D       E
0   1.7641  0.4002  0.9787  2.2409  1.8676
1      NaN  0.9501     NaN     NaN  0.4106
2   0.1440  1.4543  0.7610  0.1217  0.4439
3   0.3337  1.4941     NaN  0.3131     NaN
4      NaN  0.6536  0.8644     NaN  2.2698
5      NaN  0.0458     NaN  1.5328  1.4694
6   0.1549  0.3782     NaN     NaN     NaN
7   0.1563  1.2303  1.2024     NaN     NaN
8      NaN     NaN     NaN  1.9508     NaN
9      NaN     NaN  0.7775     NaN     NaN
..     ...     ...     ...     ...     ...
90     NaN  0.8202  0.4631  0.2791  0.3389
91  2.0210     NaN     NaN  0.1993     NaN
92     NaN     NaN     NaN  0.1813     NaN
93  2.4125     NaN     NaN     NaN  0.2515
94     NaN     NaN     NaN     NaN  1.7389
95  0.9944  1.3191     NaN  1.1286  0.4960
96  0.7714  1.0294     NaN     NaN  0.8626
97     NaN  1.5133  0.5531     NaN  0.2205
98     NaN     NaN  1.1003  1.2980  2.6962
99     NaN     NaN     NaN     NaN     NaN

[100 rows x 5 columns]

# calculations
# ================================
df.corr()

        A       B       C       D       E
A  1.0000  0.2718  0.2678  0.2822  0.1016
B  0.2718  1.0000 -0.0692  0.1736 -0.1432
C  0.2678 -0.0692  1.0000 -0.3392  0.0012
D  0.2822  0.1736 -0.3392  1.0000  0.1562
E  0.1016 -0.1432  0.0012  0.1562  1.0000


np.corrcoef(df, rowvar=False)

array([[ nan,  nan,  nan,  nan,  nan],
       [ nan,  nan,  nan,  nan,  nan],
       [ nan,  nan,  nan,  nan,  nan],
       [ nan,  nan,  nan,  nan,  nan],
       [ nan,  nan,  nan,  nan,  nan]])

List 2 is comprised of completely identical elements. Its standard deviation is therefore zero. My stats is rusty, but according to Wikipedia, the correlation coefficient is calculated by dividing by the SDs, and you can't divide by zero.

When NaNs appear in the output but are not present in the inputs Notice that all of the values in y are identical y=[-0.414; -0.414; -0.414]; If you look at the equations for corr2() or Pearson's corr() you'll notice that both have a term in the denominator that subtracts the mean of y from each y-value. When each value of y is identical, the result is a vector of 0s. When you divide by zero, you get NaN. Another way of putting it, the standard deviation of x or y cannot be 0. When you have a vector of identical values, the std is 0. The NaN, in this case, is interpretted as no correlation between the two variables. The correlation describes how much one variable changes as the other variable changes. That requires both variables to change. NaN values in the inputs spreading to the outputs For r=corr2(x,y): When there is 1 or more NaN values in the inputs, to corr2(x,y), the output will be NaN. Fill in the missing data before computing the 2D correlation coefficient. For r=corr(x): A single NaN value in position (i,j) of input matrix x will result in a full row of NaN values at row i and a full column of NaN values in column j of the output matrix r (see explanation). x = [ 6 5 1 3 NaN 9 5 3 7 9 5 5 ]; r = corr(x) 1 NaN -0.52699 NaN NaN NaN -0.52699 NaN 1 For r=corr(x,y): A single NaN value in position (i,j) of either x or y inputs will results in a column of NaN values in column j of the output matrix r. x = [ 9 5 1 1 4 4 2 6 4 2 5 9 ]; y = [ 6 5 1 3 NaN 9 5 3 7 9 5 5 ]; r = corr(x,y) 0.1623 NaN -0.92394 0.3266 NaN -0.23905 0.62312 NaN 0.32367 Ignoring NaNs in corr() inputs The rows option in corr() can be set to complete or pairwise which will ignore NaN values using different methods. 'rows','complete' removes the entire row if the row contains a NaN. In other words, it will remove row 2 from both x and y input matrices. Using the same inputs above, r = corr(x,y,'rows','complete') -0.27735 0.5 -0.94491 -0.69338 -1 0.75593 0.81224 0.14286 0.53995 r2 = corr(x,y) % for comparison 0.1623 NaN -0.92394 0.3266 NaN -0.23905 0.62312 NaN 0.32367 Notice that this changes all of the correlation values since the entire row #2 was removed from both inputs x and y. To confirm that, we can remove those rows and recompute the correlation matrix. % Remove row 2 which contains a NaN in y r3 = corr(x([1,3,4],:) ,y([1,3,4],:)); -0.27735 0.5 -0.94491 -0.69338 -1 0.75593 0.81224 0.14286 0.53995 Voila! Outputs r and r3 match. 'rows','pairwise' only removes rows only if a NaN appears in the pairing of two columns. For the same x, y inputs as above, the correlation with columns in x paired with the 2nd column in y will omit the NaN and will be based on the remaining 3 values. All other column-paired correlations will use all 4 rows of values. r = corr(x,y,'rows','pairwise') 0.1623 0.5 -0.92394 0.3266 -1 -0.23905 0.62312 0.14286 0.32367 r2 = corr(x,y) % for comparison 0.1623 NaN -0.92394 0.3266 NaN -0.23905 0.62312 NaN 0.32367 Notice that values in columns 1 and 3 haven't changed since they do not involve column #2 in y. To confirm the correlation values in column 2 of r, % Remove row 2 which contains a NaN in y r3 = corr(x([1,3,4],:) ,y([1,3,4],:)); % Replace NaN column in r2 with new r values r2(:,2) = r3(:,2) 0.1623 0.5 -0.92394 0.3266 -1 -0.23905 0.62312 0.14286 0.32367 Voila! Updated output r2 matches r.