Correlation analysis using seaborn : TypeError: 'float' object cannot be interpreted as an integer
Correlation analysis using seaborn : TypeError: 'float' object cannot be interpreted as an integer
我有一个数据框 V 以下形式：
ECON1 ECON2 ECON3 FOOD1 FOOD2 FOOD3 ENV1 \
28 0.310071 0.096913 0.228500 0.234986 0.260894 0.267858 0.489309
28 0.353609 0.045075 0.222571 0.222803 0.248388 0.330560 0.060107
28 0.280600 0.170201 0.232027 0.226792 0.233379 0.316765 0.114550
28 0.299062 0.127866 0.198080 0.189948 0.222982 0.327082 0.052881
28 0.346291 0.645534 0.371397 0.389068 0.380557 0.386004 0.186583
ENV2 HEA1 HEA2 HEA3 PERS1 PERS2 PERS3 \
28 0.206320 0.252537 0.266968 0.248452 0.184450 0.093345 0.173952
28 -0.206570 0.263673 0.126182 0.265908 0.134481 0.191341 0.113324
28 0.237818 0.257337 0.102037 0.214423 0.159002 0.321451 0.165960
28 0.345857 0.272412 0.069192 0.251301 0.130606 0.132732 0.174925
28 0.372713 0.382155 0.373531 0.468293 0.364305 0.299510 0.350822
COM1 COM2 POL1 POL2
28 0.781430 0.487822 0.361886 0.233124
28 0.083918 0.005381 0.266604 0.237078
28 0.395897 0.257888 0.330607 0.229079
28 0.000000 0.000000 0.307907 0.238908
28 0.188402 0.101147 0.410619 0.385933
我正在尝试进行如下相关性分析：
import seaborn as sb
corr = V.corr()
ax = sb.heatmap(
corr,
vmin=-1, vmax=1, center=0,
cmap=sb.diverging_palette(20, 220, n=200),
square=True
)
ax.set_xticklabels(
ax.get_xticklabels(),
rotation=45,
horizontalalignment='right'
);
我得到如下错误：
TypeError: 'float' object cannot be interpreted as an integer
不确定如何解释此错误。有人可以对此发表评论吗？
有关相关矩阵的其他信息
print(corr)
ECON1 ECON2 ECON3 FOOD1 FOOD2 FOOD3 ENV1 \
ECON1 1.000000 0.341774 0.498779 0.522529 0.588714 0.503961 -0.074569
ECON2 0.341774 1.000000 0.964742 0.951770 0.932538 0.785570 0.003878
ECON3 0.498779 0.964742 1.000000 0.998125 0.983827 0.734790 0.104750
FOOD1 0.522529 0.951770 0.998125 1.000000 0.991068 0.703027 0.153686
FOOD2 0.588714 0.932538 0.983827 0.991068 1.000000 0.683437 0.192360
FOOD3 0.503961 0.785570 0.734790 0.703027 0.683437 1.000000 -0.580854
ENV1 -0.074569 0.003878 0.104750 0.153686 0.192360 -0.580854 1.000000
ENV2 -0.475257 0.564601 0.369804 0.349462 0.328092 0.202113 0.183099
HEA1 0.518089 0.971623 0.948098 0.938049 0.941533 0.857258 -0.087292
HEA2 0.492331 0.760587 0.854620 0.882823 0.909930 0.320175 0.579590
HEA3 0.637159 0.933654 0.949112 0.949667 0.970425 0.798348 0.014684
PERS1 0.435383 0.965742 0.986036 0.988420 0.983967 0.656593 0.222572
PERS2 0.006943 0.584600 0.574856 0.535135 0.424989 0.647732 -0.423388
PERS3 0.278137 0.981872 0.931251 0.923784 0.918289 0.687020 0.134177
COM1 -0.323141 -0.151890 -0.047608 -0.009682 -0.018765 -0.702210 0.913371
COM2 -0.405013 -0.141136 -0.060505 -0.026537 -0.041302 -0.702745 0.897211
POL1 0.024764 0.819855 0.797155 0.804438 0.792430 0.291577 0.526974
POL2 0.534160 0.974790 0.970808 0.965523 0.969961 0.813307 -0.000965
ENV2 HEA1 HEA2 HEA3 PERS1 PERS2 PERS3 \
ECON1 -0.475257 0.518089 0.492331 0.637159 0.435383 0.006943 0.278137
ECON2 0.564601 0.971623 0.760587 0.933654 0.965742 0.584600 0.981872
ECON3 0.369804 0.948098 0.854620 0.949112 0.986036 0.574856 0.931251
FOOD1 0.349462 0.938049 0.882823 0.949667 0.988420 0.535135 0.923784
FOOD2 0.328092 0.941533 0.909930 0.970425 0.983967 0.424989 0.918289
FOOD3 0.202113 0.857258 0.320175 0.798348 0.656593 0.647732 0.687020
ENV1 0.183099 -0.087292 0.579590 0.014684 0.222572 -0.423388 0.134177
ENV2 1.000000 0.440689 0.309514 0.335610 0.473824 0.161769 0.664619
HEA1 0.440689 1.000000 0.738058 0.982913 0.938014 0.492595 0.945905
HEA2 0.309514 0.738058 1.000000 0.811820 0.901376 0.157842 0.801182
HEA3 0.335610 0.982913 0.811820 1.000000 0.942849 0.387558 0.915053
PERS1 0.473824 0.938014 0.901376 0.942849 1.000000 0.472608 0.963744
PERS2 0.161769 0.492595 0.157842 0.387558 0.472608 1.000000 0.447905
PERS3 0.664619 0.945905 0.801182 0.915053 0.963744 0.447905 1.000000
COM1 0.106662 -0.304509 0.362041 -0.239231 0.040733 -0.226292 -0.058492
COM2 0.185397 -0.307438 0.332330 -0.257903 0.034155 -0.208413 -0.041872
POL1 0.727061 0.707109 0.856765 0.695222 0.875352 0.297894 0.887691
POL2 0.424788 0.995079 0.798982 0.989886 0.965114 0.480057 0.953595
COM1 COM2 POL1 POL2
ECON1 -0.323141 -0.405013 0.024764 0.534160
ECON2 -0.151890 -0.141136 0.819855 0.974790
ECON3 -0.047608 -0.060505 0.797155 0.970808
FOOD1 -0.009682 -0.026537 0.804438 0.965523
FOOD2 -0.018765 -0.041302 0.792430 0.969961
FOOD3 -0.702210 -0.702745 0.291577 0.813307
ENV1 0.913371 0.897211 0.526974 -0.000965
ENV2 0.106662 0.185397 0.727061 0.424788
HEA1 -0.304509 -0.307438 0.707109 0.995079
HEA2 0.362041 0.332330 0.856765 0.798982
HEA3 -0.239231 -0.257903 0.695222 0.989886
PERS1 0.040733 0.034155 0.875352 0.965114
PERS2 -0.226292 -0.208413 0.297894 0.480057
PERS3 -0.058492 -0.041872 0.887691 0.953595
COM1 1.000000 0.995152 0.397261 -0.217775
COM2 0.995152 1.000000 0.419501 -0.225367
POL1 0.397261 0.419501 1.000000 0.748706
POL2 -0.217775 -0.225367 0.748706 1.000000
您可能想调查这个问题：https://github.com/mwaskom/seaborn/issues/1907
问题似乎来自cmap=sns.diverging_palette(20, 220, n=200),
您可能需要更新您的 seaborn 安装
对我来说效果很好。
import seaborn as sns # sns version '0.11.0'
corr = df_P5p5.corr()
ax = sns.heatmap(
corr,
vmin=-1, vmax=1, center=0,
cmap=sns.diverging_palette(20, 220, n=200),
square=True
)
ax.set_xticklabels(
ax.get_xticklabels(),
rotation=45,
horizontalalignment='right'
);
数据框：
absh_0 absh_p absh_m absh_0_1 absh_p_1 absh_m_1 \
599866 0.000177 0.000004 0.021056 0.000035 0.000013 1.484300
427635 0.000309 0.000006 0.017254 0.000074 0.000021 1.881500
1833852 0.000032 0.000005 0.016329 0.000006 0.000065 0.244173
690821 0.000040 0.000035 0.030917 0.000064 0.000101 1.063950
1098632 0.000327 0.000002 0.017160 0.000056 0.000016 0.005837
... ... ... ... ... ... ...
617761 0.000341 0.000007 0.009808 0.000061 0.000011 0.491265
204467 0.000437 0.000021 0.012242 0.000019 0.000059 1.515220
932372 0.000036 0.000022 0.035300 0.000028 0.000073 1.089760
1584400 0.000023 0.000005 0.014236 0.000001 0.000059 1.662190
139578 0.000212 0.000005 0.015321 0.000009 0.000087 0.741250
absh_p_2 absh_m_2; CQe_2322 CLQ1_2223 P_5p_LQ5_LHCb
599866 2.283040e-07 1.085160e-05 -0.162193 0.670895 -0.570265
427635 2.789950e-08 1.895490e-05 -0.204701 0.742776 -0.577415
1833852 3.995840e-06 7.339210e-06 0.658051 0.984142 -0.560692
690821 1.061680e-05 2.352440e-05 -0.566117 0.734917 -0.622448
1098632 1.566270e-06 2.679160e-05 -0.376097 0.649306 -0.585776
... ... ... ... ... ...
617761 2.077150e-06 3.115670e-05 0.079413 0.568284 -0.494631
204467 1.397560e-06 1.893320e-05 0.104801 0.618369 -0.565908
932372 1.335230e-05 6.610640e-08 0.225752 1.086700 -0.622773
1584400 1.609910e-05 3.062290e-06 -0.110411 0.635001 -0.560502
139578 9.748530e-06 7.800600e-06 -0.122956 0.679417 -0.628816
[100000 rows x 11 columns]
相关矩阵
absh_0 absh_p absh_m absh_0_1 absh_p_1 absh_m_1 \
absh_0 1.000000 0.034578 0.097363 0.095424 -0.091923 0.195620
absh_p 0.034578 1.000000 0.403523 -0.001839 0.047511 0.136713
absh_m 0.097363 0.403523 1.000000 -0.004896 -0.005719 0.340453
absh_0_1 0.095424 -0.001839 -0.004896 1.000000 0.049618 -0.001182
absh_p_1 -0.091923 0.047511 -0.005719 0.049618 1.000000 -0.069683
absh_m_1 0.195620 0.136713 0.340453 -0.001182 -0.069683 1.000000
absh_p_2 -0.050823 0.021416 -0.000820 0.180376 0.496774 -0.023613
absh_m_2; 0.167619 -0.019075 -0.059183 0.246883 -0.032227 -0.219272
CQe_2322 -0.103104 -0.071771 -0.153265 -0.106895 0.028045 -0.412171
CLQ1_2223 -0.125011 -0.036544 -0.068685 -0.011385 0.030520 -0.253707
P_5p_LQ5_LHCb 0.001508 -0.006292 -0.020996 0.163136 0.122048 -0.121965
absh_p_2 absh_m_2; CQe_2322 CLQ1_2223 P_5p_LQ5_LHCb
absh_0 -0.050823 0.167619 -0.103104 -0.125011 0.001508
absh_p 0.021416 -0.019075 -0.071771 -0.036544 -0.006292
absh_m -0.000820 -0.059183 -0.153265 -0.068685 -0.020996
absh_0_1 0.180376 0.246883 -0.106895 -0.011385 0.163136
absh_p_1 0.496774 -0.032227 0.028045 0.030520 0.122048
absh_m_1 -0.023613 -0.219272 -0.412171 -0.253707 -0.121965
absh_p_2 1.000000 -0.099868 -0.020970 0.023178 0.274502
absh_m_2; -0.099868 1.000000 -0.194679 -0.109361 0.122653
CQe_2322 -0.020970 -0.194679 1.000000 0.651148 0.006585
CLQ1_2223 0.023178 -0.109361 0.651148 1.000000 -0.003935
P_5p_LQ5_LHCb 0.274502 0.122653 0.006585 -0.003935 1.000000
剧情：
我有一个数据框 V 以下形式：
ECON1 ECON2 ECON3 FOOD1 FOOD2 FOOD3 ENV1 \
28 0.310071 0.096913 0.228500 0.234986 0.260894 0.267858 0.489309
28 0.353609 0.045075 0.222571 0.222803 0.248388 0.330560 0.060107
28 0.280600 0.170201 0.232027 0.226792 0.233379 0.316765 0.114550
28 0.299062 0.127866 0.198080 0.189948 0.222982 0.327082 0.052881
28 0.346291 0.645534 0.371397 0.389068 0.380557 0.386004 0.186583
ENV2 HEA1 HEA2 HEA3 PERS1 PERS2 PERS3 \
28 0.206320 0.252537 0.266968 0.248452 0.184450 0.093345 0.173952
28 -0.206570 0.263673 0.126182 0.265908 0.134481 0.191341 0.113324
28 0.237818 0.257337 0.102037 0.214423 0.159002 0.321451 0.165960
28 0.345857 0.272412 0.069192 0.251301 0.130606 0.132732 0.174925
28 0.372713 0.382155 0.373531 0.468293 0.364305 0.299510 0.350822
COM1 COM2 POL1 POL2
28 0.781430 0.487822 0.361886 0.233124
28 0.083918 0.005381 0.266604 0.237078
28 0.395897 0.257888 0.330607 0.229079
28 0.000000 0.000000 0.307907 0.238908
28 0.188402 0.101147 0.410619 0.385933
我正在尝试进行如下相关性分析：
import seaborn as sb
corr = V.corr()
ax = sb.heatmap(
corr,
vmin=-1, vmax=1, center=0,
cmap=sb.diverging_palette(20, 220, n=200),
square=True
)
ax.set_xticklabels(
ax.get_xticklabels(),
rotation=45,
horizontalalignment='right'
);
我得到如下错误：
TypeError: 'float' object cannot be interpreted as an integer
不确定如何解释此错误。有人可以对此发表评论吗？
有关相关矩阵的其他信息
print(corr)
ECON1 ECON2 ECON3 FOOD1 FOOD2 FOOD3 ENV1 \
ECON1 1.000000 0.341774 0.498779 0.522529 0.588714 0.503961 -0.074569
ECON2 0.341774 1.000000 0.964742 0.951770 0.932538 0.785570 0.003878
ECON3 0.498779 0.964742 1.000000 0.998125 0.983827 0.734790 0.104750
FOOD1 0.522529 0.951770 0.998125 1.000000 0.991068 0.703027 0.153686
FOOD2 0.588714 0.932538 0.983827 0.991068 1.000000 0.683437 0.192360
FOOD3 0.503961 0.785570 0.734790 0.703027 0.683437 1.000000 -0.580854
ENV1 -0.074569 0.003878 0.104750 0.153686 0.192360 -0.580854 1.000000
ENV2 -0.475257 0.564601 0.369804 0.349462 0.328092 0.202113 0.183099
HEA1 0.518089 0.971623 0.948098 0.938049 0.941533 0.857258 -0.087292
HEA2 0.492331 0.760587 0.854620 0.882823 0.909930 0.320175 0.579590
HEA3 0.637159 0.933654 0.949112 0.949667 0.970425 0.798348 0.014684
PERS1 0.435383 0.965742 0.986036 0.988420 0.983967 0.656593 0.222572
PERS2 0.006943 0.584600 0.574856 0.535135 0.424989 0.647732 -0.423388
PERS3 0.278137 0.981872 0.931251 0.923784 0.918289 0.687020 0.134177
COM1 -0.323141 -0.151890 -0.047608 -0.009682 -0.018765 -0.702210 0.913371
COM2 -0.405013 -0.141136 -0.060505 -0.026537 -0.041302 -0.702745 0.897211
POL1 0.024764 0.819855 0.797155 0.804438 0.792430 0.291577 0.526974
POL2 0.534160 0.974790 0.970808 0.965523 0.969961 0.813307 -0.000965
ENV2 HEA1 HEA2 HEA3 PERS1 PERS2 PERS3 \
ECON1 -0.475257 0.518089 0.492331 0.637159 0.435383 0.006943 0.278137
ECON2 0.564601 0.971623 0.760587 0.933654 0.965742 0.584600 0.981872
ECON3 0.369804 0.948098 0.854620 0.949112 0.986036 0.574856 0.931251
FOOD1 0.349462 0.938049 0.882823 0.949667 0.988420 0.535135 0.923784
FOOD2 0.328092 0.941533 0.909930 0.970425 0.983967 0.424989 0.918289
FOOD3 0.202113 0.857258 0.320175 0.798348 0.656593 0.647732 0.687020
ENV1 0.183099 -0.087292 0.579590 0.014684 0.222572 -0.423388 0.134177
ENV2 1.000000 0.440689 0.309514 0.335610 0.473824 0.161769 0.664619
HEA1 0.440689 1.000000 0.738058 0.982913 0.938014 0.492595 0.945905
HEA2 0.309514 0.738058 1.000000 0.811820 0.901376 0.157842 0.801182
HEA3 0.335610 0.982913 0.811820 1.000000 0.942849 0.387558 0.915053
PERS1 0.473824 0.938014 0.901376 0.942849 1.000000 0.472608 0.963744
PERS2 0.161769 0.492595 0.157842 0.387558 0.472608 1.000000 0.447905
PERS3 0.664619 0.945905 0.801182 0.915053 0.963744 0.447905 1.000000
COM1 0.106662 -0.304509 0.362041 -0.239231 0.040733 -0.226292 -0.058492
COM2 0.185397 -0.307438 0.332330 -0.257903 0.034155 -0.208413 -0.041872
POL1 0.727061 0.707109 0.856765 0.695222 0.875352 0.297894 0.887691
POL2 0.424788 0.995079 0.798982 0.989886 0.965114 0.480057 0.953595
COM1 COM2 POL1 POL2
ECON1 -0.323141 -0.405013 0.024764 0.534160
ECON2 -0.151890 -0.141136 0.819855 0.974790
ECON3 -0.047608 -0.060505 0.797155 0.970808
FOOD1 -0.009682 -0.026537 0.804438 0.965523
FOOD2 -0.018765 -0.041302 0.792430 0.969961
FOOD3 -0.702210 -0.702745 0.291577 0.813307
ENV1 0.913371 0.897211 0.526974 -0.000965
ENV2 0.106662 0.185397 0.727061 0.424788
HEA1 -0.304509 -0.307438 0.707109 0.995079
HEA2 0.362041 0.332330 0.856765 0.798982
HEA3 -0.239231 -0.257903 0.695222 0.989886
PERS1 0.040733 0.034155 0.875352 0.965114
PERS2 -0.226292 -0.208413 0.297894 0.480057
PERS3 -0.058492 -0.041872 0.887691 0.953595
COM1 1.000000 0.995152 0.397261 -0.217775
COM2 0.995152 1.000000 0.419501 -0.225367
POL1 0.397261 0.419501 1.000000 0.748706
POL2 -0.217775 -0.225367 0.748706 1.000000
您可能想调查这个问题：https://github.com/mwaskom/seaborn/issues/1907
问题似乎来自cmap=sns.diverging_palette(20, 220, n=200),
您可能需要更新您的 seaborn 安装
对我来说效果很好。
import seaborn as sns # sns version '0.11.0'
corr = df_P5p5.corr()
ax = sns.heatmap(
corr,
vmin=-1, vmax=1, center=0,
cmap=sns.diverging_palette(20, 220, n=200),
square=True
)
ax.set_xticklabels(
ax.get_xticklabels(),
rotation=45,
horizontalalignment='right'
);
数据框：
absh_0 absh_p absh_m absh_0_1 absh_p_1 absh_m_1 \
599866 0.000177 0.000004 0.021056 0.000035 0.000013 1.484300
427635 0.000309 0.000006 0.017254 0.000074 0.000021 1.881500
1833852 0.000032 0.000005 0.016329 0.000006 0.000065 0.244173
690821 0.000040 0.000035 0.030917 0.000064 0.000101 1.063950
1098632 0.000327 0.000002 0.017160 0.000056 0.000016 0.005837
... ... ... ... ... ... ...
617761 0.000341 0.000007 0.009808 0.000061 0.000011 0.491265
204467 0.000437 0.000021 0.012242 0.000019 0.000059 1.515220
932372 0.000036 0.000022 0.035300 0.000028 0.000073 1.089760
1584400 0.000023 0.000005 0.014236 0.000001 0.000059 1.662190
139578 0.000212 0.000005 0.015321 0.000009 0.000087 0.741250
absh_p_2 absh_m_2; CQe_2322 CLQ1_2223 P_5p_LQ5_LHCb
599866 2.283040e-07 1.085160e-05 -0.162193 0.670895 -0.570265
427635 2.789950e-08 1.895490e-05 -0.204701 0.742776 -0.577415
1833852 3.995840e-06 7.339210e-06 0.658051 0.984142 -0.560692
690821 1.061680e-05 2.352440e-05 -0.566117 0.734917 -0.622448
1098632 1.566270e-06 2.679160e-05 -0.376097 0.649306 -0.585776
... ... ... ... ... ...
617761 2.077150e-06 3.115670e-05 0.079413 0.568284 -0.494631
204467 1.397560e-06 1.893320e-05 0.104801 0.618369 -0.565908
932372 1.335230e-05 6.610640e-08 0.225752 1.086700 -0.622773
1584400 1.609910e-05 3.062290e-06 -0.110411 0.635001 -0.560502
139578 9.748530e-06 7.800600e-06 -0.122956 0.679417 -0.628816
[100000 rows x 11 columns]
相关矩阵
absh_0 absh_p absh_m absh_0_1 absh_p_1 absh_m_1 \
absh_0 1.000000 0.034578 0.097363 0.095424 -0.091923 0.195620
absh_p 0.034578 1.000000 0.403523 -0.001839 0.047511 0.136713
absh_m 0.097363 0.403523 1.000000 -0.004896 -0.005719 0.340453
absh_0_1 0.095424 -0.001839 -0.004896 1.000000 0.049618 -0.001182
absh_p_1 -0.091923 0.047511 -0.005719 0.049618 1.000000 -0.069683
absh_m_1 0.195620 0.136713 0.340453 -0.001182 -0.069683 1.000000
absh_p_2 -0.050823 0.021416 -0.000820 0.180376 0.496774 -0.023613
absh_m_2; 0.167619 -0.019075 -0.059183 0.246883 -0.032227 -0.219272
CQe_2322 -0.103104 -0.071771 -0.153265 -0.106895 0.028045 -0.412171
CLQ1_2223 -0.125011 -0.036544 -0.068685 -0.011385 0.030520 -0.253707
P_5p_LQ5_LHCb 0.001508 -0.006292 -0.020996 0.163136 0.122048 -0.121965
absh_p_2 absh_m_2; CQe_2322 CLQ1_2223 P_5p_LQ5_LHCb
absh_0 -0.050823 0.167619 -0.103104 -0.125011 0.001508
absh_p 0.021416 -0.019075 -0.071771 -0.036544 -0.006292
absh_m -0.000820 -0.059183 -0.153265 -0.068685 -0.020996
absh_0_1 0.180376 0.246883 -0.106895 -0.011385 0.163136
absh_p_1 0.496774 -0.032227 0.028045 0.030520 0.122048
absh_m_1 -0.023613 -0.219272 -0.412171 -0.253707 -0.121965
absh_p_2 1.000000 -0.099868 -0.020970 0.023178 0.274502
absh_m_2; -0.099868 1.000000 -0.194679 -0.109361 0.122653
CQe_2322 -0.020970 -0.194679 1.000000 0.651148 0.006585
CLQ1_2223 0.023178 -0.109361 0.651148 1.000000 -0.003935
P_5p_LQ5_LHCb 0.274502 0.122653 0.006585 -0.003935 1.000000
剧情：