Are you encountering issues while reading parquet files using pandas and pyarrow, specifically with time values larger than 24 hours? You’re not alone! This article will delve into the root cause of the problem and provide a step-by-step guide to resolve it.
What is Parquet and Why is it Important?
Parquet is a columnar storage format that is widely used for storing and processing large datasets. It’s particularly useful for big data and data analytics applications due to its high compression ratio, efficient data storage, and fast query performance.
Parquet files are often used in conjunction with pandas and pyarrow to read and manipulate data. Pyarrow is a cross-language development platform for in-memory data processing, and pandas is a popular Python library for data manipulation and analysis.
The Problem: Time Values Larger than 24 Hours
When reading parquet files using pandas and pyarrow, you might encounter an issue with time values exceeding 24 hours. This is because pyarrow, by default, expects time values to be in the range of 00:00:00 to 23:59:59. Any values larger than this range will result in incorrect or missing data.
For example, consider a parquet file containing time values in seconds, where some values exceed 86,400 (which is equivalent to 24 hours). When you try to read this file using pandas and pyarrow, you might get an error or incorrect data.
Reproducing the Issue
To demonstrate the issue, let’s create a sample parquet file with time values larger than 24 hours.
import pandas as pd
import pyarrow as pa
import pyarrow.parquet as pq
# Create a sample dataframe with time values
data = {'time': [86400, 90000, 93600, 97200, 100800]}
df = pd.DataFrame(data)
# Write the dataframe to a parquet file
table = pa.Table.from_pandas(df)
pq.write_table(table, 'example.parquet')
Now, try to read the parquet file using pandas and pyarrow:
import pandas as pd
import pyarrow.parquet as pq
# Read the parquet file
table = pq.read_table('example.parquet')
df = table.to_pandas()
print(df)
You’ll notice that the time values are either missing or incorrect.
Solution: Using the `use_legacy_datetime` Argument
The solution to this issue lies in using the `use_legacy_datetime` argument when creating the pyarrow table. This argument tells pyarrow to use the legacy datetime format, which supports time values larger than 24 hours.
Let’s modify the previous example to use the `use_legacy_datetime` argument:
import pandas as pd
import pyarrow as pa
import pyarrow.parquet as pq
# Create a sample dataframe with time values
data = {'time': [86400, 90000, 93600, 97200, 100800]}
df = pd.DataFrame(data)
# Write the dataframe to a parquet file
table = pa.Table.from_pandas(df, use_legacy_datetime=True)
pq.write_table(table, 'example.parquet')
Now, read the parquet file using pandas and pyarrow:
import pandas as pd
import pyarrow.parquet as pq
# Read the parquet file
table = pq.read_table('example.parquet')
df = table.to_pandas()
print(df)
This time, you should see the correct time values in the dataframe.
Understanding the `use_legacy_datetime` Argument
The `use_legacy_datetime` argument is a boolean flag that controls how datetime and timedelta values are processed when creating a pyarrow table. When set to `True`, pyarrow uses the legacy datetime format, which supports:
- Time values larger than 24 hours
- Datetime values with nanoseconds or higher precision
- Leap seconds
However, using the legacy datetime format can lead to compatibility issues with older pyarrow versions or other libraries that don’t support this format. Therefore, it’s essential to carefully consider the implications before using this argument.
Additional Tips and Considerations
When working with parquet files and pandas, keep the following tips and considerations in mind:
- Use the correct datetime format**: When creating a pyarrow table, make sure to use the correct datetime format for your data. This can help avoid issues with time values larger than 24 hours.
- Validate your data**: Always validate your data before writing it to a parquet file. This can help catch errors or inconsistencies that might lead to issues during reading.
- Use the latest pyarrow version**: Ensure you’re using the latest version of pyarrow, as it may include bug fixes or improvements related to datetime and timedelta processing.
- Consider using other libraries**: If you’re encountering issues with pyarrow, consider using other libraries like Apache Arrow or fastparquet, which may offer better support for datetime and timedelta values.
| Library | Support for time values > 24 hours |
|---|---|
| Pyarrow | Yes, with use_legacy_datetime=True |
| Apache Arrow | Yes, by default |
| Fastparquet | Yes, by default |
Conclusion
In conclusion, reading parquet files using pandas and pyarrow can be a challenging task, especially when dealing with time values larger than 24 hours. By using the `use_legacy_datetime` argument and following the additional tips and considerations, you can overcome this issue and successfully read and manipulate parquet files.
Remember to always validate your data, use the correct datetime format, and consider alternative libraries when necessary. With practice and patience, you’ll become proficient in working with parquet files and pandas.
Happy coding!
Frequently Asked Question
Get answers to your most pressing questions about reading parquet files using pandas and pyarrow!
Why does reading a parquet file using pandas and pyarrow fail when the time values exceed 24 hours?
This issue arises because the default timestamp type in parquet is INT96, which represents timestamps in microseconds since the Unix epoch (January 1, 1970). However, this type can only store timestamps up to 24 hours. To overcome this limitation, you can use the pyarrow.parquet.ParquetFile.read_table() function and specify the timestamp类型 as Timestamp(‘us’, ‘posix’) to store timestamps with higher precision.
What is the purpose of the ‘posix’ argument in Timestamp(‘us’, ‘posix’)?
The ‘posix’ argument specifies that the timestamp values should be interpreted as seconds since the Unix epoch (January 1, 1970). This is necessary to ensure that timestamps are correctly converted between the parquet format and pandas timestamps.
Can I use pandas.read_parquet() function to read parquet files with Time values larger than 24 hours?
No, the pandas.read_parquet() function does not support reading parquet files with Time values larger than 24 hours. You need to use the pyarrow.parquet.ParquetFile.read_table() function and specify the timestamp type as Timestamp(‘us’, ‘posix’) to overcome this limitation.
How do I specify the timestamp type when reading a parquet file using pyarrow?
You can specify the timestamp type by passing a pyarrow.schema.Schema object to the pyarrow.parquet.ParquetFile.read_table() function. The schema should include a field with type Timestamp(‘us’, ‘posix’) to store timestamps with higher precision.
Why is it essential to use the correct timestamp type when reading parquet files?
Using the correct timestamp type is crucial to ensure that timestamps are correctly interpreted and converted between the parquet format and pandas timestamps. Incorrect timestamp types can lead to errors, data loss, or incorrect analysis results.
