英文:
List and Compare data types between two dataframes
问题
我有两个数据框，其中列的计数和标题应该匹配。但列的数据类型可能不同。
例如，我有两个数据框 - df1 和 df2。DF1如下所示：
- Geometry列具有几何数据类型
- A列是整数
- B列是字符串
在DF2中，A列是字符串，但应该是整数（就像在DF1中一样）。
我尝试过根据数据类型获取列的计数，并成功使用 df1.dtypes.value_counts()。
我还尝试过使用 groupby 根据数据类型列出所有列名，但必须删除df中的几何列，因为它会引发类型错误。我设法在创建一个新的数据框后，删除了几何列后获得了列表。
我现在想比较这两个数据框及其列的数据类型，并列出不匹配的列。我还尝试使用 equals 方法，但结果是 FALSE。
英文:
I have two data frames where the count and headers of the columns are supposed to match. But the column data type may be different. <br />
I want to be able to list out the columns as per the data type and then compare between the two, again giving me a list of column headers whose data types are not matching.
For example, I have two data frames - df1 and df2. DF1 is like below where
- Geometry has the geometry data type
- A is an integer
- B is string
| Geometry | A | B |
|---|---|---|
| 123456 | 1 | x |
| 78.900 | 2 | b |
And in DF2, A is a string, whereas it should be an integer (like in DF1)
I have tried getting the count of the columns based on data types, and was able to get so using df1.dtypes.value_counts()<br /><br /> I have also tried groupby to list all the column names based on the data type using <br />g = df1.columns.to_series().groupby(df1.dtypes).groups <br /> But in order to use the groupby, I have to delete the geometry column from the df as it is throwing a TypeError for this. I managed to get the list after creating a new df where I dropped the geometry column. <br /> I want to now compare the two dataframes and their columns' data types, and list the same. <br />
I also tried using equals like df1.equals(df2) which provided FALSE.
答案1
得分: 0
假设两个数据框具有相同数量的列和相同的列名，这将为您提供数据类型不同的列名列表：
dt = (df1.dtypes.sort_index() == df2.dtypes.sort_index())
dt.loc[dt == False].index.to_list()
英文:
Assuming the two data frames have the same number of columns and same column names, this will give you the list of column names for which the dtypes are different:
dt = (df1.dtypes.sort_index() == df2.dtypes.sort_index())
dt.loc[dt == False].index.to_list()
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