使用 Python 的线性回归（Pandas 和 Numpy）
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Linear regression using Python (Pandas and Numpy)
提问by Pragyaditya Das
I am trying to implement linear regression using python.
我正在尝试使用 python 实现线性回归。
I did the following steps:
我做了以下步骤：
import pandas as p
import numpy as n
data = p.read_csv("...path\Housing.csv", usecols=[1]) # I want the first col
data1 = p.read_csv("...path\Housing.csv", usecols=[3]) # I want the 3rd col
x = data
y = data1
Then I try to obtain the co-efficients, and use the following:
然后我尝试获得系数，并使用以下内容：
regression_coeff = n.polyfit(x,y,1)
And then I get the following error:
然后我收到以下错误：
raise TypeError("expected 1D vector for x")
TypeError: expected 1D vector for x
I am unable to get my head around this, as when I print xand y, I can very clearly see that they are both 1D vectors.
我无法理解这一点，因为当我打印x和 时y，我可以非常清楚地看到它们都是一维向量。
Can someone please help?
有人可以帮忙吗？
Dataset can be found here: DataSets
数据集可以在这里找到：DataSets
The original code is:
原代码为：
import pandas as p
import numpy as n
data = pd.read_csv('...\housing.csv', usecols = [1])
data1 = pd.read_csv('...\housing.csv', usecols = [3])
x = data
y = data1
regression = n.polyfit(x, y, 1)
回答by Mike Müller
This should work:
这应该有效：
np.polyfit(data.values.flatten(), data1.values.flatten(), 1)
datais a dataframe and its values are 2D:
data是一个数据框，它的值是二维的：
>>> data.values.shape
(546, 1)
flatten()turns it into 1D array:
flatten()把它变成一维数组：
>> data.values.flatten().shape
(546,)
which is needed for polyfit().
这是polyfit().
Simpler alternative:
更简单的选择：
df = pd.read_csv("Housing.csv")
np.polyfit(df['price'], df['bedrooms'], 1)
回答by Alessandro
Python is telling you that the data is not in the right format, in particular x must be a 1D array, in your case it is a 2D-ish panda array. You can transform your data in a numpy array and squeeze it to fix your problem.
Python 告诉您数据格式不正确，特别是 x 必须是一维数组，在您的情况下它是二维Pandas数组。您可以将数据转换为 numpy 数组并压缩它以解决您的问题。
import pandas as pd
import numpy as np
data = pd.read_csv('../Housing.csv', usecols = [1])
data1 = pd.read_csv('../Housing.csv', usecols = [3])
data = np.squeeze(np.array(data))
data1 = np.squeeze(np.array(data1))
x = data
y = data1
regression = np.polyfit(x, y, 1)
回答by Stefan
pandas.read_csv()returns a DataFrame, which has two dimensions while np.polyfitwants a 1D vectorfor both xand yfor a single fit. You can simply convert the output of read_csv()to a pd.Seriesto match the np.polyfit()input format using .squeeze():
pandas.read_csv()返回一个DataFrame，它有两个维度，而np.polyfit一个想1D vector两个x和y单个契合。您可以简单地将 的输出转换read_csv()为 apd.Series以np.polyfit()使用.squeeze()以下方法匹配输入格式：
data = pd.read_csv('../Housing.csv', usecols = [1]).squeeze()
data1 = p.read_csv("...path\Housing.csv", usecols=[3]).squeeze()
