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# Scaling to large datasets

pandas provides data structures for in-memory analytics, which makes using pandas to analyze datasets that are larger than memory datasets somewhat tricky. Even datasets that are a sizable fraction of memory become unwieldy, as some pandas operations need to make intermediate copies.

This document provides a few recommendations for scaling your analysis to larger datasets. It’s a complement to Enhancing performance, which focuses on speeding up analysis for datasets that fit in memory.

## Load less data

Suppose our raw dataset on disk has many columns.

    In [1]: import pandas as pd

    In [2]: import numpy as np

    In [3]: def make_timeseries(start="2000-01-01", end="2000-12-31", freq="1D", seed=None):
       ...:     index = pd.date_range(start=start, end=end, freq=freq, name="timestamp")
       ...:     n = len(index)
       ...:     state = np.random.RandomState(seed)
       ...:     columns = {
       ...:         "name": state.choice(["Alice", "Bob", "Charlie"], size=n),
       ...:         "id": state.poisson(1000, size=n),
       ...:         "x": state.rand(n) * 2 - 1,
       ...:         "y": state.rand(n) * 2 - 1,
       ...:     }
       ...:     df = pd.DataFrame(columns, index=index, columns=sorted(columns))
       ...:     if df.index[-1] == end:
       ...:         df = df.iloc[:-1]
       ...:     return df
       ...:

    In [4]: timeseries = [
       ...:     make_timeseries(freq="1min", seed=i).rename(columns=lambda x: f"{x}_{i}")
       ...:     for i in range(10)
       ...: ]
       ...:

    In [5]: ts_wide = pd.concat(timeseries, axis=1)

    In [6]: ts_wide.head()
    Out[6]:
                         id_0 name_0       x_0  ...   name_9       x_9       y_9
    timestamp                                   ...
    2000-01-01 00:00:00   977  Alice -0.821225  ...  Charlie -0.957208 -0.757508
    2000-01-01 00:01:00  1018    Bob -0.219182  ...    Alice -0.414445 -0.100298
    2000-01-01 00:02:00   927  Alice  0.660908  ...  Charlie -0.325838  0.581859
    2000-01-01 00:03:00   997    Bob -0.852458  ...      Bob  0.992033 -0.686692
    2000-01-01 00:04:00   965    Bob  0.717283  ...  Charlie -0.924556 -0.184161

    [5 rows x 40 columns]

    In [7]: ts_wide.to_parquet("timeseries_wide.parquet")
    ---------------------------------------------------------------------------
    ImportError                               Traceback (most recent call last)
    Cell In[7], line 1
    ----> 1 ts_wide.to_parquet("timeseries_wide.parquet")

    File /usr/lib/python3/dist-packages/pandas/util/_decorators.py:333, in deprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper(*args, **kwargs)
        327 if len(args) > num_allow_args:
        328     warnings.warn(
        329         msg.format(arguments=_format_argument_list(allow_args)),
        330         FutureWarning,
        331         stacklevel=find_stack_level(),
        332     )
    --> 333 return func(*args, **kwargs)

    File /usr/lib/python3/dist-packages/pandas/core/frame.py:3113, in DataFrame.to_parquet(self, path, engine, compression, index, partition_cols, storage_options, **kwargs)
       3032 """
       3033 Write a DataFrame to the binary parquet format.
       3034
       (...)
       3109 >>> content = f.read()
       3110 """
       3111 from pandas.io.parquet import to_parquet
    -> 3113 return to_parquet(
       3114     self,
       3115     path,
       3116     engine,
       3117     compression=compression,
       3118     index=index,
       3119     partition_cols=partition_cols,
       3120     storage_options=storage_options,
       3121     **kwargs,
       3122 )

    File /usr/lib/python3/dist-packages/pandas/io/parquet.py:476, in to_parquet(df, path, engine, compression, index, storage_options, partition_cols, filesystem, **kwargs)
        474 if isinstance(partition_cols, str):
        475     partition_cols = [partition_cols]
    --> 476 impl = get_engine(engine)
        478 path_or_buf: FilePath | WriteBuffer[bytes] = io.BytesIO() if path is None else path
        480 impl.write(
        481     df,
        482     path_or_buf,
       (...)
        488     **kwargs,
        489 )

    File /usr/lib/python3/dist-packages/pandas/io/parquet.py:67, in get_engine(engine)
         64         except ImportError as err:
         65             error_msgs += "\n - " + str(err)
    ---> 67     raise ImportError(
         68         "Unable to find a usable engine; "
         69         "tried using: 'pyarrow', 'fastparquet'.\n"
         70         "A suitable version of "
         71         "pyarrow or fastparquet is required for parquet "
         72         "support.\n"
         73         "Trying to import the above resulted in these errors:"
         74         f"{error_msgs}"
         75     )
         77 if engine == "pyarrow":
         78     return PyArrowImpl()

    ImportError: Unable to find a usable engine; tried using: 'pyarrow', 'fastparquet'.
    A suitable version of pyarrow or fastparquet is required for parquet support.
    Trying to import the above resulted in these errors:
     - Missing optional dependency 'pyarrow'. pyarrow is required for parquet support. Use pip or conda to install pyarrow.
     - Missing optional dependency 'fastparquet'. fastparquet is required for parquet support. Use pip or conda to install fastparquet.

To load the columns we want, we have two options. Option 1 loads in all the data and then filters to what we need.

    In [8]: columns = ["id_0", "name_0", "x_0", "y_0"]

    In [9]: pd.read_parquet("timeseries_wide.parquet")[columns]
    ---------------------------------------------------------------------------
    ImportError                               Traceback (most recent call last)
    Cell In[9], line 1
    ----> 1 pd.read_parquet("timeseries_wide.parquet")[columns]

    File /usr/lib/python3/dist-packages/pandas/io/parquet.py:651, in read_parquet(path, engine, columns, storage_options, use_nullable_dtypes, dtype_backend, filesystem, filters, **kwargs)
        498 @doc(storage_options=_shared_docs["storage_options"])
        499 def read_parquet(
        500     path: FilePath | ReadBuffer[bytes],
       (...)
        508     **kwargs,
        509 ) -> DataFrame:
        510     """
        511     Load a parquet object from the file path, returning a DataFrame.
        512
       (...)
        648     1    4    9
        649     """
    --> 651     impl = get_engine(engine)
        653     if use_nullable_dtypes is not lib.no_default:
        654         msg = (
        655             "The argument 'use_nullable_dtypes' is deprecated and will be removed "
        656             "in a future version."
        657         )

    File /usr/lib/python3/dist-packages/pandas/io/parquet.py:67, in get_engine(engine)
         64         except ImportError as err:
         65             error_msgs += "\n - " + str(err)
    ---> 67     raise ImportError(
         68         "Unable to find a usable engine; "
         69         "tried using: 'pyarrow', 'fastparquet'.\n"
         70         "A suitable version of "
         71         "pyarrow or fastparquet is required for parquet "
         72         "support.\n"
         73         "Trying to import the above resulted in these errors:"
         74         f"{error_msgs}"
         75     )
         77 if engine == "pyarrow":
         78     return PyArrowImpl()

    ImportError: Unable to find a usable engine; tried using: 'pyarrow', 'fastparquet'.
    A suitable version of pyarrow or fastparquet is required for parquet support.
    Trying to import the above resulted in these errors:
     - Missing optional dependency 'pyarrow'. pyarrow is required for parquet support. Use pip or conda to install pyarrow.
     - Missing optional dependency 'fastparquet'. fastparquet is required for parquet support. Use pip or conda to install fastparquet.

Option 2 only loads the columns we request.

    In [10]: pd.read_parquet("timeseries_wide.parquet", columns=columns)
    ---------------------------------------------------------------------------
    ImportError                               Traceback (most recent call last)
    Cell In[10], line 1
    ----> 1 pd.read_parquet("timeseries_wide.parquet", columns=columns)

    File /usr/lib/python3/dist-packages/pandas/io/parquet.py:651, in read_parquet(path, engine, columns, storage_options, use_nullable_dtypes, dtype_backend, filesystem, filters, **kwargs)
        498 @doc(storage_options=_shared_docs["storage_options"])
        499 def read_parquet(
        500     path: FilePath | ReadBuffer[bytes],
       (...)
        508     **kwargs,
        509 ) -> DataFrame:
        510     """
        511     Load a parquet object from the file path, returning a DataFrame.
        512
       (...)
        648     1    4    9
        649     """
    --> 651     impl = get_engine(engine)
        653     if use_nullable_dtypes is not lib.no_default:
        654         msg = (
        655             "The argument 'use_nullable_dtypes' is deprecated and will be removed "
        656             "in a future version."
        657         )

    File /usr/lib/python3/dist-packages/pandas/io/parquet.py:67, in get_engine(engine)
         64         except ImportError as err:
         65             error_msgs += "\n - " + str(err)
    ---> 67     raise ImportError(
         68         "Unable to find a usable engine; "
         69         "tried using: 'pyarrow', 'fastparquet'.\n"
         70         "A suitable version of "
         71         "pyarrow or fastparquet is required for parquet "
         72         "support.\n"
         73         "Trying to import the above resulted in these errors:"
         74         f"{error_msgs}"
         75     )
         77 if engine == "pyarrow":
         78     return PyArrowImpl()

    ImportError: Unable to find a usable engine; tried using: 'pyarrow', 'fastparquet'.
    A suitable version of pyarrow or fastparquet is required for parquet support.
    Trying to import the above resulted in these errors:
     - Missing optional dependency 'pyarrow'. pyarrow is required for parquet support. Use pip or conda to install pyarrow.
     - Missing optional dependency 'fastparquet'. fastparquet is required for parquet support. Use pip or conda to install fastparquet.

If we were to measure the memory usage of the two calls, we’d see that specifying `columns` uses about 1/10th the memory in this case.

With `pandas.read_csv()`, you can specify `usecols` to limit the columns read into memory. Not all file formats that can be read by pandas provide an option to read a subset of columns.

## Use efficient datatypes

The default pandas data types are not the most memory efficient. This is especially true for text data columns with relatively few unique values (commonly referred to as “low-cardinality” data). By using more efficient data types, you can store larger datasets in memory.

    In [11]: ts = make_timeseries(freq="30s", seed=0)

    In [12]: ts.to_parquet("timeseries.parquet")
    ---------------------------------------------------------------------------
    ImportError                               Traceback (most recent call last)
    Cell In[12], line 1
    ----> 1 ts.to_parquet("timeseries.parquet")

    File /usr/lib/python3/dist-packages/pandas/util/_decorators.py:333, in deprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper(*args, **kwargs)
        327 if len(args) > num_allow_args:
        328     warnings.warn(
        329         msg.format(arguments=_format_argument_list(allow_args)),
        330         FutureWarning,
        331         stacklevel=find_stack_level(),
        332     )
    --> 333 return func(*args, **kwargs)

    File /usr/lib/python3/dist-packages/pandas/core/frame.py:3113, in DataFrame.to_parquet(self, path, engine, compression, index, partition_cols, storage_options, **kwargs)
       3032 """
       3033 Write a DataFrame to the binary parquet format.
       3034
       (...)
       3109 >>> content = f.read()
       3110 """
       3111 from pandas.io.parquet import to_parquet
    -> 3113 return to_parquet(
       3114     self,
       3115     path,
       3116     engine,
       3117     compression=compression,
       3118     index=index,
       3119     partition_cols=partition_cols,
       3120     storage_options=storage_options,
       3121     **kwargs,
       3122 )

    File /usr/lib/python3/dist-packages/pandas/io/parquet.py:476, in to_parquet(df, path, engine, compression, index, storage_options, partition_cols, filesystem, **kwargs)
        474 if isinstance(partition_cols, str):
        475     partition_cols = [partition_cols]
    --> 476 impl = get_engine(engine)
        478 path_or_buf: FilePath | WriteBuffer[bytes] = io.BytesIO() if path is None else path
        480 impl.write(
        481     df,
        482     path_or_buf,
       (...)
        488     **kwargs,
        489 )

    File /usr/lib/python3/dist-packages/pandas/io/parquet.py:67, in get_engine(engine)
         64         except ImportError as err:
         65             error_msgs += "\n - " + str(err)
    ---> 67     raise ImportError(
         68         "Unable to find a usable engine; "
         69         "tried using: 'pyarrow', 'fastparquet'.\n"
         70         "A suitable version of "
         71         "pyarrow or fastparquet is required for parquet "
         72         "support.\n"
         73         "Trying to import the above resulted in these errors:"
         74         f"{error_msgs}"
         75     )
         77 if engine == "pyarrow":
         78     return PyArrowImpl()

    ImportError: Unable to find a usable engine; tried using: 'pyarrow', 'fastparquet'.
    A suitable version of pyarrow or fastparquet is required for parquet support.
    Trying to import the above resulted in these errors:
     - Missing optional dependency 'pyarrow'. pyarrow is required for parquet support. Use pip or conda to install pyarrow.
     - Missing optional dependency 'fastparquet'. fastparquet is required for parquet support. Use pip or conda to install fastparquet.

    In [13]: ts = pd.read_parquet("timeseries.parquet")
    ---------------------------------------------------------------------------
    ImportError                               Traceback (most recent call last)
    Cell In[13], line 1
    ----> 1 ts = pd.read_parquet("timeseries.parquet")

    File /usr/lib/python3/dist-packages/pandas/io/parquet.py:651, in read_parquet(path, engine, columns, storage_options, use_nullable_dtypes, dtype_backend, filesystem, filters, **kwargs)
        498 @doc(storage_options=_shared_docs["storage_options"])
        499 def read_parquet(
        500     path: FilePath | ReadBuffer[bytes],
       (...)
        508     **kwargs,
        509 ) -> DataFrame:
        510     """
        511     Load a parquet object from the file path, returning a DataFrame.
        512
       (...)
        648     1    4    9
        649     """
    --> 651     impl = get_engine(engine)
        653     if use_nullable_dtypes is not lib.no_default:
        654         msg = (
        655             "The argument 'use_nullable_dtypes' is deprecated and will be removed "
        656             "in a future version."
        657         )

    File /usr/lib/python3/dist-packages/pandas/io/parquet.py:67, in get_engine(engine)
         64         except ImportError as err:
         65             error_msgs += "\n - " + str(err)
    ---> 67     raise ImportError(
         68         "Unable to find a usable engine; "
         69         "tried using: 'pyarrow', 'fastparquet'.\n"
         70         "A suitable version of "
         71         "pyarrow or fastparquet is required for parquet "
         72         "support.\n"
         73         "Trying to import the above resulted in these errors:"
         74         f"{error_msgs}"
         75     )
         77 if engine == "pyarrow":
         78     return PyArrowImpl()

    ImportError: Unable to find a usable engine; tried using: 'pyarrow', 'fastparquet'.
    A suitable version of pyarrow or fastparquet is required for parquet support.
    Trying to import the above resulted in these errors:
     - Missing optional dependency 'pyarrow'. pyarrow is required for parquet support. Use pip or conda to install pyarrow.
     - Missing optional dependency 'fastparquet'. fastparquet is required for parquet support. Use pip or conda to install fastparquet.

    In [14]: ts
    Out[14]:
                           id     name         x         y
    timestamp
    2000-01-01 00:00:00  1041    Alice  0.889987  0.281011
    2000-01-01 00:00:30   988      Bob -0.455299  0.488153
    2000-01-01 00:01:00  1018    Alice  0.096061  0.580473
    2000-01-01 00:01:30   992      Bob  0.142482  0.041665
    2000-01-01 00:02:00   960      Bob -0.036235  0.802159
    ...                   ...      ...       ...       ...
    2000-12-30 23:58:00  1022    Alice  0.266191  0.875579
    2000-12-30 23:58:30   974    Alice -0.009826  0.413686
    2000-12-30 23:59:00  1028  Charlie  0.307108 -0.656789
    2000-12-30 23:59:30  1002    Alice  0.202602  0.541335
    2000-12-31 00:00:00   987    Alice  0.200832  0.615972

    [1051201 rows x 4 columns]

Now, let’s inspect the data types and memory usage to see where we should focus our attention.

    In [15]: ts.dtypes
    Out[15]:
    id        int64
    name     object
    x       float64
    y       float64
    dtype: object

    In [16]: ts.memory_usage(deep=True)  # memory usage in bytes
    Out[16]:
    Index     8409608
    id        8409608
    name     56766826
    x         8409608
    y         8409608
    dtype: int64

The `name` column is taking up much more memory than any other. It has just a few unique values, so it’s a good candidate for converting to a `pandas.Categorical`. With a `pandas.Categorical`, we store each unique name once and use space-efficient integers to know which specific name is used in each row.

    In [17]: ts2 = ts.copy()

    In [18]: ts2["name"] = ts2["name"].astype("category")

    In [19]: ts2.memory_usage(deep=True)
    Out[19]:
    Index    8409608
    id       8409608
    name     1051471
    x        8409608
    y        8409608
    dtype: int64

We can go a bit further and downcast the numeric columns to their smallest types using `pandas.to_numeric()`.

    In [20]: ts2["id"] = pd.to_numeric(ts2["id"], downcast="unsigned")

    In [21]: ts2[["x", "y"]] = ts2[["x", "y"]].apply(pd.to_numeric, downcast="float")

    In [22]: ts2.dtypes
    Out[22]:
    id        uint16
    name    category
    x        float32
    y        float32
    dtype: object

    In [23]: ts2.memory_usage(deep=True)
    Out[23]:
    Index    8409608
    id       2102402
    name     1051471
    x        4204804
    y        4204804
    dtype: int64

    In [24]: reduction = ts2.memory_usage(deep=True).sum() / ts.memory_usage(deep=True).sum()

    In [25]: print(f"{reduction:0.2f}")
    0.22

In all, we’ve reduced the in-memory footprint of this dataset to 1/5 of its original size.

See Categorical data for more on `pandas.Categorical` and dtypes for an overview of all of pandas’ dtypes.

## Use chunking

Some workloads can be achieved with chunking by splitting a large problem into a bunch of small problems. For example, converting an individual CSV file into a Parquet file and repeating that for each file in a directory. As long as each chunk fits in memory, you can work with datasets that are much larger than memory.

Note

Chunking works well when the operation you’re performing requires zero or minimal coordination between chunks. For more complicated workflows, you’re better off using other libraries.

Suppose we have an even larger “logical dataset” on disk that’s a directory of parquet files. Each file in the directory represents a different year of the entire dataset.

    In [26]: import pathlib

    In [27]: N = 12

    In [28]: starts = [f"20{i:>02d}-01-01" for i in range(N)]

    In [29]: ends = [f"20{i:>02d}-12-13" for i in range(N)]

    In [30]: pathlib.Path("data/timeseries").mkdir(exist_ok=True)

    In [31]: for i, (start, end) in enumerate(zip(starts, ends)):
       ....:     ts = make_timeseries(start=start, end=end, freq="1min", seed=i)
       ....:     ts.to_parquet(f"data/timeseries/ts-{i:0>2d}.parquet")
       ....:
    ---------------------------------------------------------------------------
    ImportError                               Traceback (most recent call last)
    Cell In[31], line 3
          1 for i, (start, end) in enumerate(zip(starts, ends)):
          2     ts = make_timeseries(start=start, end=end, freq="1min", seed=i)
    ----> 3     ts.to_parquet(f"data/timeseries/ts-{i:0>2d}.parquet")

    File /usr/lib/python3/dist-packages/pandas/util/_decorators.py:333, in deprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper(*args, **kwargs)
        327 if len(args) > num_allow_args:
        328     warnings.warn(
        329         msg.format(arguments=_format_argument_list(allow_args)),
        330         FutureWarning,
        331         stacklevel=find_stack_level(),
        332     )
    --> 333 return func(*args, **kwargs)

    File /usr/lib/python3/dist-packages/pandas/core/frame.py:3113, in DataFrame.to_parquet(self, path, engine, compression, index, partition_cols, storage_options, **kwargs)
       3032 """
       3033 Write a DataFrame to the binary parquet format.
       3034
       (...)
       3109 >>> content = f.read()
       3110 """
       3111 from pandas.io.parquet import to_parquet
    -> 3113 return to_parquet(
       3114     self,
       3115     path,
       3116     engine,
       3117     compression=compression,
       3118     index=index,
       3119     partition_cols=partition_cols,
       3120     storage_options=storage_options,
       3121     **kwargs,
       3122 )

    File /usr/lib/python3/dist-packages/pandas/io/parquet.py:476, in to_parquet(df, path, engine, compression, index, storage_options, partition_cols, filesystem, **kwargs)
        474 if isinstance(partition_cols, str):
        475     partition_cols = [partition_cols]
    --> 476 impl = get_engine(engine)
        478 path_or_buf: FilePath | WriteBuffer[bytes] = io.BytesIO() if path is None else path
        480 impl.write(
        481     df,
        482     path_or_buf,
       (...)
        488     **kwargs,
        489 )

    File /usr/lib/python3/dist-packages/pandas/io/parquet.py:67, in get_engine(engine)
         64         except ImportError as err:
         65             error_msgs += "\n - " + str(err)
    ---> 67     raise ImportError(
         68         "Unable to find a usable engine; "
         69         "tried using: 'pyarrow', 'fastparquet'.\n"
         70         "A suitable version of "
         71         "pyarrow or fastparquet is required for parquet "
         72         "support.\n"
         73         "Trying to import the above resulted in these errors:"
         74         f"{error_msgs}"
         75     )
         77 if engine == "pyarrow":
         78     return PyArrowImpl()

    ImportError: Unable to find a usable engine; tried using: 'pyarrow', 'fastparquet'.
    A suitable version of pyarrow or fastparquet is required for parquet support.
    Trying to import the above resulted in these errors:
     - Missing optional dependency 'pyarrow'. pyarrow is required for parquet support. Use pip or conda to install pyarrow.
     - Missing optional dependency 'fastparquet'. fastparquet is required for parquet support. Use pip or conda to install fastparquet.

    data
    └── timeseries
        ├── ts-00.parquet
        ├── ts-01.parquet
        ├── ts-02.parquet
        ├── ts-03.parquet
        ├── ts-04.parquet
        ├── ts-05.parquet
        ├── ts-06.parquet
        ├── ts-07.parquet
        ├── ts-08.parquet
        ├── ts-09.parquet
        ├── ts-10.parquet
        └── ts-11.parquet

Now we’ll implement an out-of-core `pandas.Series.value_counts()`. The peak memory usage of this workflow is the single largest chunk, plus a small series storing the unique value counts up to this point. As long as each individual file fits in memory, this will work for arbitrary-sized datasets.

    In [32]: %%time
       ....: files = pathlib.Path("data/timeseries/").glob("ts*.parquet")
       ....: counts = pd.Series(dtype=int)
       ....: for path in files:
       ....:     df = pd.read_parquet(path)
       ....:     counts = counts.add(df["name"].value_counts(), fill_value=0)
       ....: counts.astype(int)
       ....:
    CPU times: user 436 us, sys: 0 ns, total: 436 us
    Wall time: 445 us
    Out[32]: Series([], dtype: int64)

Some readers, like `pandas.read_csv()`, offer parameters to control the `chunksize` when reading a single file.

Manually chunking is an OK option for workflows that don’t require too sophisticated of operations. Some operations, like `pandas.DataFrame.groupby()`, are much harder to do chunkwise. In these cases, you may be better switching to a different library that implements these out-of-core algorithms for you.

## Use Other Libraries

There are other libraries which provide similar APIs to pandas and work nicely with pandas DataFrame, and can give you the ability to scale your large dataset processing and analytics by parallel runtime, distributed memory, clustering, etc. You can find more information in the ecosystem page.