散点图矩阵

Question

我想得到我所有矩阵的散点图。

我在海上找到了模块，我做了这样的事情。

import seaborn as sns

data_ = pd.read_csv('/content/drive/My Drive/Colab Notebooks/Machine Learning/iris.csv', skiprows=1, delimiter=',')
sns.pairplot(data_)

​

​

我在想怎么给它颜色，就像在这张照片里一样。

​

​

另外，我想知道是否有什么方法可以看到我的Matplotlib的情节矩阵，而不是一个一个地制作自己的情节矩阵。非常感谢!

更新：

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
   2645             try:
-> 2646                 return self._engine.get_loc(key)
   2647             except KeyError:

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

KeyError: 'variety'

During handling of the above exception, another exception occurred:

KeyError                                  Traceback (most recent call last)
4 frames
/usr/local/lib/python3.6/dist-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
   2646                 return self._engine.get_loc(key)
   2647             except KeyError:
-> 2648                 return self._engine.get_loc(self._maybe_cast_indexer(key))
   2649         indexer = self.get_indexer([key], method=method, tolerance=tolerance)
   2650         if indexer.ndim > 1 or indexer.size > 1:

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

KeyError: 'variety'

Answer 1

你可以直接做hue='species'

sns.pairplot(data_, hue='species')

输出：

​

​