我有以下数据框
age sex cp
0 63.0 1.0 1.0
1 67.0 1.0 4.0
2 41.0 0.0 2.0
我对每一列应用了转换过程，如下所示:
age = store_data['age']
age_bins = [0, 40, 60, 100]
age_categories = pd.cut(age, age_bins)
sex = store_data['sex']
sex_series = pd.Series(sex, dtype = "category")
sex_rename = sex_series.cat.rename_categories(['F','M'])
cp = store_data['cp']
cp_series = pd.Series(cp, dtype = "category")
cp_rename = cp_series.cat.rename_categories(["typical","atypical","non-anginal","asymptomatic"])
每个输出看起来像这样:
>>age_categories
0 (60, 100]
1 (60, 100]
2 (40, 60]
>>sex_rename
0 M
1 M
4 F
>>cp_rename
0 typical
1 asymptomatic
2 atypical
如何使用新的转换值更新原始列:age_categories、sex_rename、cp_rename？我想保留旧名字(年龄、性别、cp)作为头部
最佳答案
尝试消除额外的变量？我没有运行它，因为没有数据，但这应该直接更新你的数据框。
age_bins = [0, 40, 60, 100]
store_data['age'] = pd.cut(store_data['age'], age_bins)
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store_data['sex'] = pd.Series(store_data['sex'], dtype = "category").cat.rename_categories(['F','M'])
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store_data['cp'] = pd.Series(store_data['cp'], dtype = "category").cat.rename_categories(["typical","atypical","non-anginal","asymptomatic"])
关于python - 转换后如何更新数据框的列？，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/56030629/