英文:
How to generate numeric mapping for categorical columns in pandas?
问题
我想使用pandas数据框来操作分类数据，然后将它们转换为numpy数组以用于模型训练。
假设我有以下的pandas数据框：
import pandas as pddf2 = pd.DataFrame({"c1": ['a', 'b', None], "c2": ['d', 'e', 'f']})>>> df2c1 c20 a d1 b e2 None f
现在我想横向"压缩分类"，如下所示：
compressed_categories0 c1-a, c2-d # 这可以是一个字符串，例如 "c1-a, c2-d"，或数组 ["c1-a", "c2-d"]，或分类数据1 c1-b, c2-e2 c1-nan, c2-f
接下来，我想基于compressed_categories中的唯一出现加上 "nan" 列来生成一个字典/词汇表，例如：
volcab = {"c1-a": 0,"c1-b": 1,"c1-nan": 2,"c2-d": 3,"c2-e": 4,"c2-f": 5,"c2-nan": 6,}
这样，我可以进一步进行数值编码，如下所示：
compressed_categories_numeric0 [0, 3]1 [1, 4]2 [2, 5]
因此，我的最终目标是使其易于将它们转换为numpy数组，以便每一行，从而可以进一步转换为张量。
input_data = np.asarray(df['compressed_categories_numeric'].tolist())
然后我可以使用 input_data 来训练我的模型。
请问是否可以给我一个示例，如何进行这一系列的转换？提前感谢！
英文:
I want to manipulate categorical data using pandas data frame and then convert them to numpy array for model training.
Say I have the following data frame in pandas.
import pandas as pddf2 = pd.DataFrame({"c1": ['a','b',None], "c2": ['d','e','f']})>>> df2c1 c20 a d1 b e2 None f
And now I want "compress the categories" horizontally as the following:
compressed_categories0 c1-a, c2-d <--- this could be a string, ex. "c1-a, c2-d" or array ["c1-a", "c2-d"] or categorical data1 c1-b, c2-e2 c1-nan, c2-f
Next I want to generate a dictionary/vocabulary based on the unique occurrences plus "nan" columns in compressed_categories, ex:
volcab = {"c1-a": 0,"c1-b": 1,"c1-c": 2,"c1-nan": 3,"c2-d": 4,"c2-e": 5,"c2-f": 6,"c2-nan": 7,}
So I can further numerically encoding then as follows:
compressed_categories_numeric0 [0, 4]1 [1, 5]2 [3, 6]
So my ultimate goal is to make it easy to convert them to numpy array for each row and thus I can further convert it to tensor.
input_data = np.asarray(df['compressed_categories_numeric'].tolist())
then I can train my model using input_data.
Can anyone please show me an example how to make this series of conversion? Thanks in advance!
答案1
得分: 2
為了建立volcab字典和compressed_categories_numeric，您可以使用以下代碼：
df3 = df2.fillna(np.nan).astype(str).apply(lambda x: x.name + '-' + x)volcab = {k: v for v, k in enumerate(np.unique(df3))}df2['compressed_categories_numeric'] = df3.replace(volcab).agg(list, axis=1)
輸出：
>>> volcab{'c1-a': 0, 'c1-b': 1, 'c1-nan': 2, 'c2-d': 3, 'c2-e': 4, 'c2-f': 5}>>> df2c1 c2 compressed_categories_numeric0 a d [0, 3]1 b e [1, 4]2 None f [2, 5]>>> np.array(df2['compressed_categories_numeric'].tolist())array([[0, 3],[1, 4],[2, 5]])
英文:
To build volcab dictionary and compressed_categories_numeric, you can use:
df3 = df2.fillna(np.nan).astype(str).apply(lambda x: x.name + '-' + x)volcab = {k: v for v, k in enumerate(np.unique(df3))}df2['compressed_categories_numeric'] = df3.replace(volcab).agg(list, axis=1)
Output:
>>> volcab{'c1-a': 0, 'c1-b': 1, 'c1-nan': 2, 'c2-d': 3, 'c2-e': 4, 'c2-f': 5}>>> df2c1 c2 compressed_categories_numeric0 a d [0, 3]1 b e [1, 4]2 None f [2, 5]>>> np.array(df2['compressed_categories_numeric'].tolist())array([[0, 3],[1, 4],[2, 5]])
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