Based on the initial examination of the uploaded CSV file `dataset_malwares.csv`, I have identified several potential issues related to specific columns. Here are the issues:

### Issue 1: Non-standard File Naming Convention
- **Issue:** The `Name` column includes non-standard file naming which might be related to the hash values of the malware samples, without clear indication of malware type or association.
- **Evidence:** The first few entries in the `Name` column, for example, `VirusShare_a878ba26000edaac5c98eff4432723b3`.
- **Description:** The `Name` column potentially serves as the identifier for each malware sample. However, the naming convention used combines a generic prefix (`VirusShare_`) with what appears to be a hash value. Without additional contextual information or documentation, it's challenging to determine the malware type, family, or any categorization criteria from the name alone. This could complicate analysis or categorization tasks, especially for those not already familiar with the `VirusShare` naming conventions.

### Issue 2: Extensive Use of Numeric Columns Without Descriptions
- **Issue:** The dataset contains numerous columns with numeric values, such as `e_magic`, `e_cblp`, `DirectoryEntryImportSize`, etc., without accompanying descriptions or metadata.
- **Evidence:** Columns like `e_cblp`, `e_cparhdr`, `DirectoryEntryExport`, `ImageDirectoryEntryExport`, and many others lack clear descriptions.
- **Description:** With a significant number of columns presenting numeric data without clear definitions or context, users of the dataset might struggle to grasp the significance or interpret the data meaningfully. Ideally, the dataset should be accompanied by a metadata file or documentation that describes each column, especially for those not familiar with PE file format specifics or the domain context of malware analysis.

These issues directly relate to the hint provided, focusing on specific column-related challenges in the CSV file, which could affect data usability, interpretation, and overall analysis quality.