Typeerror：需要float参数，而不是dataframe [英] Typeerror: float argument required, not dataframe
问题描述
这是我的完整代码，我无法确定corr_value应该做什么。我在最后一步收到错误TypeError：float argument required，not DataFrame。任何想法都会非常有用
This is my complete code and I am not able to determine what should be done for corr_value. I am getting error "TypeError: float argument required, not DataFrame" at the last step. Any idea will be highly helpful
from sklearn.datasets import load_boston
import pandas as pd
boston = load_boston()
dataset = pd.DataFrame(data=boston.data, columns=boston.feature_names)
dataset['target'] = boston.target
print(dataset.head())
X = dataset.drop('target', axis = 1 )
Y = dataset["target"]
#Now the dataframe X has just the features that influence the target
#print the correlation matrix for dataframe X. Use '.corr()' function to compute #correlation matrix
#from the correlation matrix note down the correlation value between 'CRIM' and #'PTRATIO' and assign it to variable 'corr_value'
corr_value = X.corr()
print(corr_value.head())
import statsmodels.api as sm
statsModel = sm.OLS(Y, X)
fittedModel = statsModel.fit()
print(fittedModel.summary())
OLS回归结果
= ================================================== ===========================
Dep。变量：目标R平方：0.959
型号：OLS Adj。 R平方：0.958
方法：最小二乘法F统计：891.1
日期：星期一，2018年7月2日Prob（F-statistic）：0.00
时间：22：36：03对数 - 可能性：-1523.8
编号观察：506 AIC：3074。
Df残差：493 BIC：3129。 br />
Df型号：13
协方差类型：nonrobust
=================== ================================================== =========
coef std err t P> | t | [0.025 0.975]
-------------------------------------- ----------------------------------------
CRIM - 0.0916 0.034 -2.675 0.008 -0.159 -0.024
ZN 0.0487 0.014 3.379 0.001 0.020 0.077
INDUS -0.0038 0.064 -0.059 0.953 -0.130 0.123
CHAS 2.8564 0.904 3.160 0.002 1.080 4.633
NOX -2.8808 3.359 -0.858 0.392 -9.481 3.720
RM 5.9252 0.309 19.168 0.000 5.318 6.533
年龄 - 0.0072 0.014 -0.523 0.601 -0.034 0.020
DIS -0.9680 0.196 -4.947 0.000 -1.352 -0.584
RAD 0.1704 0.067 2.554 0.011 0.039 0.302
TAX -0.0094 0.004 -2.393 0.017 -0.017 -0.002
PTRATIO -0.3924 0.110 -3.571 0.000 -0.608 -0.177
B 0.0150 0.003 5.561 0.000 0.010 0.020
LSTAT -0.4170 0.051 -8.214 0.000 -0.517 -0.317
============================ ==================================================
Omnibus：204.050 Durbin-Watson：0.999
Prob（Omnibus）：0.000 Jarque-Bera（ JB）：1372.527
歪斜：1.609 Prob（JB）：9.11e-299
峰值：10.399 Cond。 No. 8.50e + 03
==================================== ==========================================
警告：
[1]标准错误假设错误的协方差矩阵被正确指定。
[2]条件数是大，8.5e + 03。这可能表明存在强烈的多重共线性或其他数值问题。
r_squared = fitsModel.rsquared
###结束代码（约1行）
打开（output.txt，w）为text_file：
text_file.write（corr =％f \ n％corr_value）
text_file.write（rsquared =％f \ n％r_squared）
OLS Regression Results
==============================================================================
Dep. Variable: target R-squared: 0.959
Model: OLS Adj. R-squared: 0.958
Method: Least Squares F-statistic: 891.1
Date: Mon, 02 Jul 2018 Prob (F-statistic): 0.00
Time: 22:36:03 Log-Likelihood: -1523.8
No. Observations: 506 AIC: 3074.
Df Residuals: 493 BIC: 3129.
Df Model: 13
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
CRIM -0.0916 0.034 -2.675 0.008 -0.159 -0.024
ZN 0.0487 0.014 3.379 0.001 0.020 0.077
INDUS -0.0038 0.064 -0.059 0.953 -0.130 0.123
CHAS 2.8564 0.904 3.160 0.002 1.080 4.633
NOX -2.8808 3.359 -0.858 0.392 -9.481 3.720
RM 5.9252 0.309 19.168 0.000 5.318 6.533
AGE -0.0072 0.014 -0.523 0.601 -0.034 0.020
DIS -0.9680 0.196 -4.947 0.000 -1.352 -0.584
RAD 0.1704 0.067 2.554 0.011 0.039 0.302
TAX -0.0094 0.004 -2.393 0.017 -0.017 -0.002
PTRATIO -0.3924 0.110 -3.571 0.000 -0.608 -0.177
B 0.0150 0.003 5.561 0.000 0.010 0.020
LSTAT -0.4170 0.051 -8.214 0.000 -0.517 -0.317
==============================================================================
Omnibus: 204.050 Durbin-Watson: 0.999
Prob(Omnibus): 0.000 Jarque-Bera (JB): 1372.527
Skew: 1.609 Prob(JB): 9.11e-299
Kurtosis: 10.399 Cond. No. 8.50e+03
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 8.5e+03. This might indicate that there are
strong multicollinearity or other numerical problems.
r_squared = fittedModel.rsquared
###End code(approx 1 line)
with open("output.txt", "w") as text_file:
text_file.write("corr= %f\n"% corr_value)
text_file.write("rsquared= %f\n" % r_squared)
TypeError Traceback (most recent call last)
<ipython-input-11-b55e673c78db> in <module>()
3 ###End code(approx 1 line)
4 with open("output.txt", "w") as text_file:
----> 5 text_file.write("corr= %f\n" % corr_value)
6 text_file.write("rsquared= %f\n" % r_squared)
TypeError: float argument required, not DataFrame
我的尝试：
What I have tried:
text_file.write("corr= %f\n"
推荐答案
错误非常清楚：
corr_value类型为DataFrame，而％f格式要求参数的类型为float。
即使代码中的注释也表明corr（）函数计算矩阵，你必须用它来确定相关值。
我无法回答怎么做，因为我不知道函数在做什么，矩阵是如何组织的，以及成员代表什么。
The error is quite clear:
corr_valueis of typeDataFramewhile the "%f" format requires the argument to be of typefloat.
Even the comment in your code states that thecorr()function computes a matrix and you have to use that to determine the correlation value.
I can't answer how to do that because I don't know what the functions are doing, how that matrix is organised, and what the members represent.
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