I encountered a decoding issue while trying to read the `spotify-2023.csv` file, which suggests there might be corrupted data or improper character encoding. To resolve this, I'll try reading the file with a different encoding or attempt to automatically detect the correct encoding. Let's proceed with these steps.

The CSV file has been successfully loaded using the ISO-8859-1 encoding. I'll now inspect the dataset to identify any potentially corrupted entries. This includes checking for anomalies like missing values, unrealistic or inconsistent data, and examining the data types to ensure they align with the expected format described in the data card. Let's perform these checks.

Upon inspecting the dataset `spotify-2023.csv`, I have identified several issues that may correspond to data corruption or improper entries, as hinted at:

### Issue 1: Missing Values in Key Fields
**Issue**: Missing data entries in essential fields.
**Evidence**: 
- "in_shazam_charts" field has 50 missing entries.
- "key" field has 95 missing entries.
**Description**: Fields such as "in_shazam_charts" and "key" are missing data for several records, which can impact data analysis and might suggest data corruption or incomplete data collection processes.

### Issue 2: Improper Data Type
**Issue**: Inconsistent data type in a field expected to be numeric.
**Evidence**: 
- "streams" field is read as an object type instead of a numeric type.
**Description**: The "streams" field, which should logically contain numerical data representing the total number of streams, is improperly stored as a string. This could be due to corrupted entries, like the presence of non-numeric characters, leading to incorrect data type assignment.

### Issue 3: Unexpected Data Range
**Issue**: Unusually old release years for some tracks.
**Evidence**: 
- "released_year" minimum value is 1930, which is implausible for a dataset intended to cover songs from the year 2023.
**Description**: The presence of a release year such as 1930 in a dataset meant to include songs from 2023 indicates either a data entry error or potential corruption. This discrepancy needs to be corrected for accurate dataset representation.

These issues suggest the need for a thorough review and potential cleaning of the dataset to ensure its integrity and usefulness for any intended analytical or operational purposes.