混淆矩阵显示一些空单元格
confusion matrix presents some empty cells
我正在使用此代码来调查我的黄金列与数据中其余列之间的相关性，但我不明白为什么我的混淆矩阵中有一些空单元格是空白的，如下所示
# Run this program on your local python
# interpreter, provided you have installed
# the required libraries.
# Importing the required packages
import numpy as np
import pandas as pd
from sklearn.metrics import confusion_matrix
from sklearn.cross_validation import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
from sklearn.ensemble import ExtraTreesClassifier
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import chi2
# Function importing Dataset
column_count =0
def importdata():
balance_data = pd.read_csv( 'data1.txt', sep= ',', header = None)
row_count, column_count = balance_data.shape
# Printing the dataswet shape
print ("Dataset Length: ", len(balance_data))
print ("Dataset Shape: ", balance_data.shape)
print("Number of columns ", column_count)
# Printing the dataset obseravtions
print ("Dataset: ",balance_data.head())
return balance_data, column_count
def columns(balance_data):
row_count, column_count = balance_data.shape
return column_count
#Univariate selection
def selection(column_count):
data = pd.read_csv("data1extended.txt")
X = data.iloc[:,1:column_count] #independent columns
y = data.iloc[:,0] #target column i.e price range
#apply SelectKBest class to extract top 10 best features
bestfeatures = SelectKBest(score_func=chi2, k=5)
fit = bestfeatures.fit(X,y)
dfscores = pd.DataFrame(fit.scores_)
dfcolumns = pd.DataFrame(X.columns)
#concat two dataframes for better visualization
featureScores = pd.concat([dfcolumns,dfscores],axis=1)
featureScores.columns = ['Specs','Score'] #naming the dataframe columns
print(featureScores.nlargest(5,'Score')) #print 10 best features
return X,y,data
#Feature importance
def feature(X,y):
model = ExtraTreesClassifier()
model.fit(X,y)
print(model.feature_importances_) #use inbuilt class feature_importances of tree based classifiers
#plot graph of feature importances for better visualization
feat_importances = pd.Series(model.feature_importances_, index=X.columns)
feat_importances.nlargest(5).plot(kind='barh')
plt.show()
#Correlation Matrix
def correlation(data, column_count):
corrmat = data.corr()
top_corr_features = corrmat.index
plt.figure(figsize=(column_count,column_count))
#plot heat map
g=sns.heatmap(data[top_corr_features].corr(),annot=True,cmap="RdYlGn")
# Driver code
def main():
# Building Phase
data,column_count = importdata()
X,y,dataheaders=selection(column_count)
feature(X,y)
correlation(dataheaders,column_count)
# Calling main function
if __name__=="__main__":
main()
指向我的数据输入文件的链接：
data1.txt: https://drive.google.com/open?id=1qHAkFBboCKoVH4pKIPHoVmeyb3uVCUAk
data1Extended.txt: https://drive.google.com/open?id=1C8GZ2wiqFUW3WuYxyc0G3axgkM1Uwsb6
我检查了您的代码，这些是 data[top_corr_features].corr() 代码
输出中缺失的值
点赞
callersAtLeast1T CalleesAtLeast1T callersAllT calleesAllT CallersAtLeast1N CalleesAtLeast1N CallersAllN CalleesAllN InterfacesAtLeast1T ImplememntationsAtleast1T ... ParametersatLeast1T FieldMethodsAtLeast1T ReturnTypeAtLeast1T ParametersAtLeast1N FieldMethodsAtLeast1N ReturnTypeN ParametersAllT FieldMethodsAllT ParametersAllN FieldMethodsAllN
callersAtLeast1T 1.000000 0.345678 0.652674 0.296284 -0.543014 -0.247331 -0.462966 -0.186227 NaN NaN ... 0.063052 0.128554 0.128554 -0.066818 -0.132167 -0.132167 0.052226 0.128035 -0.056377 -0.130406
CalleesAtLeast1T 0.345678 1.000000 0.288627 0.545091 -0.312117 0.015580 -0.222012 -0.200932 NaN NaN ... 0.041103 0.100681 0.100681 -0.055963 -0.109964 -0.109964 0.013238 0.090864 -0.060733 -0.111723
callersAllT 0.652674 0.288627 1.000000 0.266658 -0.565592 -0.173942 -0.302166 -0.141829 NaN NaN ... 0.065166 0.113121 0.113121 -0.053850 -0.109912 -0.109912 0.081990 0.110453 -0.048177 -0.108930
calleesAllT 0.296284 0.545091 0.266658 1.000000 -0.229375 -0.177132 -0.165415 -0.109526 NaN NaN ... 0.078756 0.105691 0.105691 -0.027136 -0.075527 -0.075527 0.044648 0.107448 -0.033105 -0.074852
CallersAtLeast1N -0.543014 -0.312117 -0.565592 -0.229375 1.000000 0.094479 0.534247 0.099672 NaN NaN ... -0.008624 -0.099353 -0.099353 0.095209 0.073561 0.073561 -0.008887 -0.099207 0.085180 0.070968
CalleesAtLeast1N -0.247331 0.015580 -0.173942 -0.177132 0.094479 1.000000 0.133004 0.618332 NaN NaN ... -0.034831 -0.043234 -0.043234 0.014988 0.010699 0.010699 -0.050421 -0.048834 -0.023962 0.003937
CallersAllN -0.462966 -0.222012 -0.302166 -0.165415 0.534247 0.133004 1.000000 0.157877 NaN NaN ... 0.026512 -0.059774 -0.059774 0.091514 0.106280 0.106280 0.048346 -0.061887 0.127316 0.101202
CalleesAllN -0.186227 -0.200932 -0.141829 -0.109526 0.099672 0.618332 0.157877 1.000000 NaN NaN ... -0.004726 -0.022656 -0.022656 0.080276 0.080003 0.080003 -0.015513 -0.020702 0.044586 0.083339
InterfacesAtLeast1T NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
ImplememntationsAtleast1T NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
childrenAtLeast1T 0.037713 0.045836 0.057783 0.084090 -0.032681 -0.014895 -0.017460 -0.009210 NaN NaN ... -0.002740 -0.004719 -0.004719 -0.003112 -0.006351 -0.006351 -0.002465 -0.004677 -0.002784 -0.006294
parentsAtLeast1T 0.037713 0.045836 0.057783 0.084090 -0.032681 -0.014895 -0.017460 -0.009210 NaN NaN ... -0.002740 -0.004719 -0.004719 -0.003112 -0.006351 -0.006351 -0.002465 -0.004677 -0.002784 -0.006294
InterfacesAtLeast1N NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
ImplementationsAtLeast1N NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
childrenAtLeast1N -0.023797 -0.019580 -0.015532 -0.010673 0.027461 0.060254 0.051402 0.097445 NaN NaN ... -0.007258 -0.012497 -0.012497 -0.008241 -0.016820 -0.016820 -0.006528 -0.012387 -0.007373 -0.016670
parentsAtLeast1N -0.023797 -0.019580 -0.015532 -0.010673 0.027461 0.060254 0.051402 0.097445 NaN NaN ... -0.007258 -0.012497 -0.012497 -0.008241 -0.016820 -0.016820 -0.006528 -0.012387 -0.007373 -0.016670
InterfacesAllT NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
ImplementationsAllT NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
childrenAllT 0.037713 0.045836 0.057783 0.084090 -0.032681 -0.014895 -0.017460 -0.009210 NaN NaN ... -0.002740 -0.004719 -0.004719 -0.003112 -0.006351 -0.006351 -0.002465 -0.004677 -0.002784 -0.006294
parentsAllT 0.037713 0.045836 0.057783 0.084090 -0.032681 -0.014895 -0.017460 -0.009210 NaN NaN ... -0.002740 -0.004719 -0.004719 -0.003112 -0.006351 -0.006351 -0.002465 -0.004677 -0.002784 -0.006294
InterfacesAllN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
ImplementationsAllN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
childrenAllN -0.023797 -0.019580 -0.015532 -0.010673 0.027461 0.060254 0.051402 0.097445 NaN NaN ... -0.007258 -0.012497 -0.012497 -0.008241 -0.016820 -0.016820 -0.006528 -0.012387 -0.007373 -0.016670
ParentsAllN -0.023797 -0.019580 -0.015532 -0.010673 0.027461 0.060254 0.051402 0.097445 NaN NaN ... -0.007258 -0.012497 -0.012497 -0.008241 -0.016820 -0.016820 -0.006528 -0.012387 -0.007373 -0.016670
ParametersatLeast1T 0.063052 0.041103 0.065166 0.078756 -0.008624 -0.034831 0.026512 -0.004726 NaN NaN ... 1.000000 0.397018 0.397018 0.003145 -0.013637 -0.013637 0.899521 0.400748 -0.022512 -0.012910
FieldMethodsAtLeast1T 0.128554 0.100681 0.113121 0.105691 -0.099353 -0.043234 -0.059774 -0.022656 NaN NaN ... 0.397018 1.000000 1.000000 -0.026187 -0.088435 -0.088435 0.404635 0.991187 -0.038763 -0.087645
ReturnTypeAtLeast1T 0.128554 0.100681 0.113121 0.105691 -0.099353 -0.043234 -0.059774 -0.022656 NaN NaN ... 0.397018 1.000000 1.000000 -0.026187 -0.088435 -0.088435 0.404635 0.991187 -0.038763 -0.087645
ParametersAtLeast1N -0.066818 -0.055963 -0.053850 -0.027136 0.095209 0.014988 0.091514 0.080276 NaN NaN ... 0.003145 -0.026187 -0.026187 1.000000 0.302727 0.302727 -0.022634 -0.025671 0.894660 0.305813
FieldMethodsAtLeast1N -0.132167 -0.109964 -0.109912 -0.075527 0.073561 0.010699 0.106280 0.080003 NaN NaN ... -0.013637 -0.088435 -0.088435 0.302727 1.000000 1.000000 -0.021141 -0.087655 0.319416 0.991067
ReturnTypeN -0.132167 -0.109964 -0.109912 -0.075527 0.073561 0.010699 0.106280 0.080003 NaN NaN ... -0.013637 -0.088435 -0.088435 0.302727 1.000000 1.000000 -0.021141 -0.087655 0.319416 0.991067
ParametersAllT 0.052226 0.013238 0.081990 0.044648 -0.008887 -0.050421 0.048346 -0.015513 NaN NaN ... 0.899521 0.404635 0.404635 -0.022634 -0.021141 -0.021141 1.000000 0.408361 -0.020250 -0.020551
FieldMethodsAllT 0.128035 0.090864 0.110453 0.107448 -0.099207 -0.048834 -0.061887 -0.020702 NaN NaN ... 0.400748 0.991187 0.991187 -0.025671 -0.087655 -0.087655 0.408361 1.000000 -0.038422 -0.086872
ParametersAllN -0.056377 -0.060733 -0.048177 -0.033105 0.085180 -0.023962 0.127316 0.044586 NaN NaN ... -0.022512 -0.038763 -0.038763 0.894660 0.319416 0.319416 -0.020250 -0.038422 1.000000 0.322522
FieldMethodsAllN -0.130406 -0.111723 -0.108930 -0.074852 0.070968 0.003937 0.101202 0.083339 NaN NaN ... -0.012910 -0.087645 -0.087645 0.305813 0.991067 0.991067 -0.020551 -0.086872 0.322522 1.000000
34 rows × 34 columns
我正在使用此代码来调查我的黄金列与数据中其余列之间的相关性，但我不明白为什么我的混淆矩阵中有一些空单元格是空白的，如下所示
# Run this program on your local python
# interpreter, provided you have installed
# the required libraries.
# Importing the required packages
import numpy as np
import pandas as pd
from sklearn.metrics import confusion_matrix
from sklearn.cross_validation import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
from sklearn.ensemble import ExtraTreesClassifier
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import chi2
# Function importing Dataset
column_count =0
def importdata():
balance_data = pd.read_csv( 'data1.txt', sep= ',', header = None)
row_count, column_count = balance_data.shape
# Printing the dataswet shape
print ("Dataset Length: ", len(balance_data))
print ("Dataset Shape: ", balance_data.shape)
print("Number of columns ", column_count)
# Printing the dataset obseravtions
print ("Dataset: ",balance_data.head())
return balance_data, column_count
def columns(balance_data):
row_count, column_count = balance_data.shape
return column_count
#Univariate selection
def selection(column_count):
data = pd.read_csv("data1extended.txt")
X = data.iloc[:,1:column_count] #independent columns
y = data.iloc[:,0] #target column i.e price range
#apply SelectKBest class to extract top 10 best features
bestfeatures = SelectKBest(score_func=chi2, k=5)
fit = bestfeatures.fit(X,y)
dfscores = pd.DataFrame(fit.scores_)
dfcolumns = pd.DataFrame(X.columns)
#concat two dataframes for better visualization
featureScores = pd.concat([dfcolumns,dfscores],axis=1)
featureScores.columns = ['Specs','Score'] #naming the dataframe columns
print(featureScores.nlargest(5,'Score')) #print 10 best features
return X,y,data
#Feature importance
def feature(X,y):
model = ExtraTreesClassifier()
model.fit(X,y)
print(model.feature_importances_) #use inbuilt class feature_importances of tree based classifiers
#plot graph of feature importances for better visualization
feat_importances = pd.Series(model.feature_importances_, index=X.columns)
feat_importances.nlargest(5).plot(kind='barh')
plt.show()
#Correlation Matrix
def correlation(data, column_count):
corrmat = data.corr()
top_corr_features = corrmat.index
plt.figure(figsize=(column_count,column_count))
#plot heat map
g=sns.heatmap(data[top_corr_features].corr(),annot=True,cmap="RdYlGn")
# Driver code
def main():
# Building Phase
data,column_count = importdata()
X,y,dataheaders=selection(column_count)
feature(X,y)
correlation(dataheaders,column_count)
# Calling main function
if __name__=="__main__":
main()
指向我的数据输入文件的链接：
data1.txt: https://drive.google.com/open?id=1qHAkFBboCKoVH4pKIPHoVmeyb3uVCUAk
data1Extended.txt: https://drive.google.com/open?id=1C8GZ2wiqFUW3WuYxyc0G3axgkM1Uwsb6
我检查了您的代码，这些是 data[top_corr_features].corr() 代码
点赞
callersAtLeast1T CalleesAtLeast1T callersAllT calleesAllT CallersAtLeast1N CalleesAtLeast1N CallersAllN CalleesAllN InterfacesAtLeast1T ImplememntationsAtleast1T ... ParametersatLeast1T FieldMethodsAtLeast1T ReturnTypeAtLeast1T ParametersAtLeast1N FieldMethodsAtLeast1N ReturnTypeN ParametersAllT FieldMethodsAllT ParametersAllN FieldMethodsAllN
callersAtLeast1T 1.000000 0.345678 0.652674 0.296284 -0.543014 -0.247331 -0.462966 -0.186227 NaN NaN ... 0.063052 0.128554 0.128554 -0.066818 -0.132167 -0.132167 0.052226 0.128035 -0.056377 -0.130406
CalleesAtLeast1T 0.345678 1.000000 0.288627 0.545091 -0.312117 0.015580 -0.222012 -0.200932 NaN NaN ... 0.041103 0.100681 0.100681 -0.055963 -0.109964 -0.109964 0.013238 0.090864 -0.060733 -0.111723
callersAllT 0.652674 0.288627 1.000000 0.266658 -0.565592 -0.173942 -0.302166 -0.141829 NaN NaN ... 0.065166 0.113121 0.113121 -0.053850 -0.109912 -0.109912 0.081990 0.110453 -0.048177 -0.108930
calleesAllT 0.296284 0.545091 0.266658 1.000000 -0.229375 -0.177132 -0.165415 -0.109526 NaN NaN ... 0.078756 0.105691 0.105691 -0.027136 -0.075527 -0.075527 0.044648 0.107448 -0.033105 -0.074852
CallersAtLeast1N -0.543014 -0.312117 -0.565592 -0.229375 1.000000 0.094479 0.534247 0.099672 NaN NaN ... -0.008624 -0.099353 -0.099353 0.095209 0.073561 0.073561 -0.008887 -0.099207 0.085180 0.070968
CalleesAtLeast1N -0.247331 0.015580 -0.173942 -0.177132 0.094479 1.000000 0.133004 0.618332 NaN NaN ... -0.034831 -0.043234 -0.043234 0.014988 0.010699 0.010699 -0.050421 -0.048834 -0.023962 0.003937
CallersAllN -0.462966 -0.222012 -0.302166 -0.165415 0.534247 0.133004 1.000000 0.157877 NaN NaN ... 0.026512 -0.059774 -0.059774 0.091514 0.106280 0.106280 0.048346 -0.061887 0.127316 0.101202
CalleesAllN -0.186227 -0.200932 -0.141829 -0.109526 0.099672 0.618332 0.157877 1.000000 NaN NaN ... -0.004726 -0.022656 -0.022656 0.080276 0.080003 0.080003 -0.015513 -0.020702 0.044586 0.083339
InterfacesAtLeast1T NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
ImplememntationsAtleast1T NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
childrenAtLeast1T 0.037713 0.045836 0.057783 0.084090 -0.032681 -0.014895 -0.017460 -0.009210 NaN NaN ... -0.002740 -0.004719 -0.004719 -0.003112 -0.006351 -0.006351 -0.002465 -0.004677 -0.002784 -0.006294
parentsAtLeast1T 0.037713 0.045836 0.057783 0.084090 -0.032681 -0.014895 -0.017460 -0.009210 NaN NaN ... -0.002740 -0.004719 -0.004719 -0.003112 -0.006351 -0.006351 -0.002465 -0.004677 -0.002784 -0.006294
InterfacesAtLeast1N NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
ImplementationsAtLeast1N NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
childrenAtLeast1N -0.023797 -0.019580 -0.015532 -0.010673 0.027461 0.060254 0.051402 0.097445 NaN NaN ... -0.007258 -0.012497 -0.012497 -0.008241 -0.016820 -0.016820 -0.006528 -0.012387 -0.007373 -0.016670
parentsAtLeast1N -0.023797 -0.019580 -0.015532 -0.010673 0.027461 0.060254 0.051402 0.097445 NaN NaN ... -0.007258 -0.012497 -0.012497 -0.008241 -0.016820 -0.016820 -0.006528 -0.012387 -0.007373 -0.016670
InterfacesAllT NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
ImplementationsAllT NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
childrenAllT 0.037713 0.045836 0.057783 0.084090 -0.032681 -0.014895 -0.017460 -0.009210 NaN NaN ... -0.002740 -0.004719 -0.004719 -0.003112 -0.006351 -0.006351 -0.002465 -0.004677 -0.002784 -0.006294
parentsAllT 0.037713 0.045836 0.057783 0.084090 -0.032681 -0.014895 -0.017460 -0.009210 NaN NaN ... -0.002740 -0.004719 -0.004719 -0.003112 -0.006351 -0.006351 -0.002465 -0.004677 -0.002784 -0.006294
InterfacesAllN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
ImplementationsAllN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
childrenAllN -0.023797 -0.019580 -0.015532 -0.010673 0.027461 0.060254 0.051402 0.097445 NaN NaN ... -0.007258 -0.012497 -0.012497 -0.008241 -0.016820 -0.016820 -0.006528 -0.012387 -0.007373 -0.016670
ParentsAllN -0.023797 -0.019580 -0.015532 -0.010673 0.027461 0.060254 0.051402 0.097445 NaN NaN ... -0.007258 -0.012497 -0.012497 -0.008241 -0.016820 -0.016820 -0.006528 -0.012387 -0.007373 -0.016670
ParametersatLeast1T 0.063052 0.041103 0.065166 0.078756 -0.008624 -0.034831 0.026512 -0.004726 NaN NaN ... 1.000000 0.397018 0.397018 0.003145 -0.013637 -0.013637 0.899521 0.400748 -0.022512 -0.012910
FieldMethodsAtLeast1T 0.128554 0.100681 0.113121 0.105691 -0.099353 -0.043234 -0.059774 -0.022656 NaN NaN ... 0.397018 1.000000 1.000000 -0.026187 -0.088435 -0.088435 0.404635 0.991187 -0.038763 -0.087645
ReturnTypeAtLeast1T 0.128554 0.100681 0.113121 0.105691 -0.099353 -0.043234 -0.059774 -0.022656 NaN NaN ... 0.397018 1.000000 1.000000 -0.026187 -0.088435 -0.088435 0.404635 0.991187 -0.038763 -0.087645
ParametersAtLeast1N -0.066818 -0.055963 -0.053850 -0.027136 0.095209 0.014988 0.091514 0.080276 NaN NaN ... 0.003145 -0.026187 -0.026187 1.000000 0.302727 0.302727 -0.022634 -0.025671 0.894660 0.305813
FieldMethodsAtLeast1N -0.132167 -0.109964 -0.109912 -0.075527 0.073561 0.010699 0.106280 0.080003 NaN NaN ... -0.013637 -0.088435 -0.088435 0.302727 1.000000 1.000000 -0.021141 -0.087655 0.319416 0.991067
ReturnTypeN -0.132167 -0.109964 -0.109912 -0.075527 0.073561 0.010699 0.106280 0.080003 NaN NaN ... -0.013637 -0.088435 -0.088435 0.302727 1.000000 1.000000 -0.021141 -0.087655 0.319416 0.991067
ParametersAllT 0.052226 0.013238 0.081990 0.044648 -0.008887 -0.050421 0.048346 -0.015513 NaN NaN ... 0.899521 0.404635 0.404635 -0.022634 -0.021141 -0.021141 1.000000 0.408361 -0.020250 -0.020551
FieldMethodsAllT 0.128035 0.090864 0.110453 0.107448 -0.099207 -0.048834 -0.061887 -0.020702 NaN NaN ... 0.400748 0.991187 0.991187 -0.025671 -0.087655 -0.087655 0.408361 1.000000 -0.038422 -0.086872
ParametersAllN -0.056377 -0.060733 -0.048177 -0.033105 0.085180 -0.023962 0.127316 0.044586 NaN NaN ... -0.022512 -0.038763 -0.038763 0.894660 0.319416 0.319416 -0.020250 -0.038422 1.000000 0.322522
FieldMethodsAllN -0.130406 -0.111723 -0.108930 -0.074852 0.070968 0.003937 0.101202 0.083339 NaN NaN ... -0.012910 -0.087645 -0.087645 0.305813 0.991067 0.991067 -0.020551 -0.086872 0.322522 1.000000
34 rows × 34 columns