Pandas read_csv: low_memory and dtype options

When using pd.read_csv('somefile.csv'), you may encounter a DtypeWarning indicating that columns have mixed types. Specifying the dtype option can prevent this error and improve performance.

Understanding the low_memory Option

The deprecated low_memory option does not actually affect behavior. However, it is related to the dtype option because guessing dtypes for each column can be memory-intensive.

Guarding Against Data Mismatches

If the last line in your file contains unexpected data, specifying dtypes can cause the loading process to fail. For example, if a column specified as integer contains a string value like "foobar", loading will break.

Specifying dtypes

To avoid such errors, explicitly specify dtypes when reading the CSV file. Using the dtype option assigns the correct data type to each column, allowing for efficient parsing and reducing memory consumption.

Available dtypes

Pandas supports various dtypes, including:

• Numpy types: float, int, bool, timedelta64[ns], datetime64[ns]

• Pandas extensions:

  • datetime64[ns, ] (time zone aware timestamp)
  • category (enum)
  • period[] (time period)
  • Sparse (data with holes)
  • Interval (indexing)
  • nullable integers (Int8, Int16, Int32, Int64, UInt8, UInt16, UInt32, UInt64)
  • string (for string operations)
  • boolean (nullable bool)

Gotchas

• Setting dtype=object silences the warning but does not enhance memory efficiency.
• Setting dtype=unicode has no effect since numpy represents unicode as object.
• Converters can be used to handle unexpected data, but they are inefficient due to Pandas' single-process nature.

The above is the detailed content of How to Optimize Pandas `read_csv` with `dtype` and `low_memory` Options?. For more information, please follow other related articles on the PHP Chinese website!