错误：ValueError：仅当我尝试使用statsmodels进行散点图时，才必须将布尔值传递给DataFrame

Question

I'm new to Python and the relative question I read didn't make much sense to me. I have the following issue. I want to use Python to do multiple regression and I am trying statsmodels. In this case I want to do a scatter plot.

Sample of my data:

ID  order  V1     V2    E1  E2  E3   M
103  1    ECA    TEXT    7   3   5   7
105  1    ECA    TEXT    3   7   4   5
107  1    ECA    TEXT    7   7   7   4
109  1    ECA    TEXT    6   6   6   3

I want to do a multiple regression with E1-E3 as my IVs and the mean score of M as my DV.

This is how I loaded my data.

myRegressionData = pd.read_csv('C:/Users/user/Desktop/Folder 1/Python/Regression data file.csv')

These are my x and y:

X_sk = myRegressionData[[col for col in myRegressionData.columns if col[:8] == 'E']]

Y = myRegressionData[['M{}'.format(ii) for ii in range(1, 19)]]
y = np.mean(Y, axis=1)

and this the code where I get the error:

myRegressionData.plot(kind='scatter',x = X_sk, y=np.mean(Y, axis=1))

returns

ValueError : Must pass DataFrame with boolean values only

myRegressionData.info() 

returns

RangeIndex: 90 entries, 0 to 89 Columns: 146 entries, IDOpenEndedResponse to EngagingAA dtypes: float64(10), int64(134), object(2) memory usage: 102.7+ KB

Answer 1

In the following:

myRegressionData.plot(kind='scatter',x = X_sk, y=np.mean(Y, axis=1))

x and y expect column names , or indecies. X_sk and np.mean(Y, axis=1) is data. Supply the column names or use your plotter directly.

---

Example where we use matplotlib :

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

myRegressionData = pd.DataFrame([
    {'a0': 4, 'a1': 3, 'b0': 2, 'b1': 1}, 
    {'a0': 3, 'a1': 1, 'b0': 4, 'b1': 1}, 
    {'a0': 1, 'a1': 2, 'b0': 3, 'b1': 1}
])

X_sk = myRegressionData[[col for col in myRegressionData.columns if col[:1] == 'b']]
Y = myRegressionData[['a{}'.format(ii) for ii in range(0,2)]]
plt.scatter(X_sk['b0'], np.mean(Y, axis=1))

The example should be a simplified version of what you're doing.

---

If you insist on using the pandas DataFrame plotter you can do something like this:

y = pd.DataFrame(np.mean(Y, axis=1), columns=['y'])
df = pd.concat([X_sk, y], axis=1)
df.plot(kind='scatter', x='b0', y='y')

---

Having many X values, but only one y value and differentiate using colors:

X_sk = myRegressionData[[col for col in myRegressionData.columns if col[:1] == 'b']]
Y = myRegressionData[['a{}'.format(ii) for ii in range(0,2)]]
y = pd.DataFrame(np.mean(Y, axis=1))
yy = pd.concat([y, y])
plt.scatter(X_sk, yy, c=['b', 'r'])

---

Final alternative using scatter_matrix :

y = pd.DataFrame(np.mean(Y, axis=1), columns=['y'])
df = pd.concat([X_sk, y], axis=1)
scatter_matrix(df, alpha=0.2, figsize=(6, 6))