I remember experimenting with doing regressions in Python using R-style formulae a long time ago, and I remember it being a bit complicated. Luckily it’s become really easy now – and I’ll show you just how easy.

我记得很久以前就尝试过使用R样式公式在Python中进行回归的实验，并且我记得它有点复杂。 幸运的是，现在这变得非常容易–我将向您展示这是多么容易。

Before running this you will need to install the pandas, statsmodels and patsy packages. If you’re using conda you should be able to do this by running the following from the terminal:

运行此之前，您需要安装pandas ， statsmodels和patsy包。 如果您使用的是conda，则应该可以通过从终端运行以下命令来执行此操作：

conda install statsmodels patsy

conda install statsmodels patsy

(and then say yes when it asks you to confirm it)

（然后在要求您确认时说“是”）

Before we can do any regression, we need some data – so lets read some data on cars:

在进行任何回归之前，我们需要一些数据-因此让我们阅读一些汽车数据：

You may have noticed from the code above that you can just give a URL to the read_csv function and it will download it and open it – handy!

您可能已经从上面的代码中注意到，您可以给read_csv函数提供一个URL，它将下载并打开它-方便！

Anyway, here is the data:

无论如何，这是数据：

Before we do our regression it might be a good idea to look at simple correlations between columns. We can get the correlations between each pair of columns using the corr() method:

在进行回归之前，最好先查看各列之间的简单相关性。 我们可以使用corr()方法获得每对列之间的相关性：

Now we can do some regression using R-style formulae. In this case we’re trying to predict MPG based on the year that the car was released:

现在，我们可以使用R样式公式进行一些回归。 在这种情况下，我们试图根据汽车发布的年份来预测MPG：

The ‘formula’ that we used above is the same as R uses: on the left is the dependent variable, on the right is the independent variable. The ols method is nice and easy, we just give it the formula, and then the DataFrame to use to get the data from (in this case, it’s called df). We then call fit() to actually do the regression.

我们上面使用的“公式”与R使用的相同：左侧是因变量，右侧是自变量。 ols方法很好用，很简单，我们只给它一个公式，然后给它使用DataFrame来获取数据（在这种情况下，它称为df ）。 然后，我们调用fit()进行回归。

We can easily get a summary of the results here – including all sorts of crazy statistical measures!

我们可以在这里轻松获得结果摘要-包括各种疯狂的统计指标！

We can do a more complex model easily too. First lets list the columns of the data to remind us what variables we have:

我们也可以轻松地创建更复杂的模型。 首先让我们列出数据列以提醒我们我们拥有哪些变量：

We can now add in more variables – doing multiple regression:

现在，我们可以添加更多变量–进行多元回归：

We can see that bringing in some extra variables has increased the $R^2$ value from ~0.3 to ~0.8 – although we can see that the P value for the Horsepower is very high. If we remove Horsepower from the regression then it barely changes the results:

我们可以看到引入一些额外的变量将$ R ^ 2 $的值从〜0.3增加到了〜0.8-尽管我们可以看到Horsepower的P值非常高。 如果我们从回归中删除“ Horsepower ，那么它几乎不会改变结果：

We can also see if introducing categorical variables helps with the regression. In this case, we only have one categorical variable, called Origin. Patsy automatically treats strings as categorical variables, so we don’t have to do anything special – but if needed we could wrap the variable name in C() to force it to be a categorical variable.

我们还可以看到引入分类变量是否有助于回归。 在这种情况下，我们只有一个分类变量，称为Origin 。 Patsy自动将字符串视为分类变量，因此我们不必做任何特殊的事情-但如果需要，我们可以将变量名称包装在C()以强制它成为分类变量。

You can see here that Patsy has automatically created extra variables for Origin: in this case, European and Japanese, with the ‘default’ being American. You can configure how this is done very easily – see .

您可以在此处看到Patsy为Origin自动创建了额外的变量：在这种情况下，是欧洲和日语，“默认”是美国。 您可以非常轻松地配置此操作的方式–参见 。

Just for reference, you can easily get any of the statistical outputs as attributes on the results object:

仅供参考，您可以轻松地将任何统计输出作为results对象上的属性：

You can also really easily use the model to predict based on values you’ve got:

您还可以真正轻松地使用模型根据您获得的值进行预测：