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")

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]
Out[9]: 
                     id_0 name_0       x_0       y_0
timestamp                                           
2000-01-01 00:00:00   977  Alice -0.821225  0.906222
2000-01-01 00:01:00  1018    Bob -0.219182  0.350855
2000-01-01 00:02:00   927  Alice  0.660908 -0.798511
2000-01-01 00:03:00   997    Bob -0.852458  0.735260
2000-01-01 00:04:00   965    Bob  0.717283  0.393391
...                   ...    ...       ...       ...
2000-12-30 23:56:00  1037    Bob -0.814321  0.612836
2000-12-30 23:57:00   980    Bob  0.232195 -0.618828
2000-12-30 23:58:00   965  Alice -0.231131  0.026310
2000-12-30 23:59:00   984  Alice  0.942819  0.853128
2000-12-31 00:00:00  1003  Alice  0.201125 -0.136655

[525601 rows x 4 columns]

Option 2 only loads the columns we request.

In [10]: pd.read_parquet("timeseries_wide.parquet", columns=columns)
Out[10]: 
                     id_0 name_0       x_0       y_0
timestamp                                           
2000-01-01 00:00:00   977  Alice -0.821225  0.906222
2000-01-01 00:01:00  1018    Bob -0.219182  0.350855
2000-01-01 00:02:00   927  Alice  0.660908 -0.798511
2000-01-01 00:03:00   997    Bob -0.852458  0.735260
2000-01-01 00:04:00   965    Bob  0.717283  0.393391
...                   ...    ...       ...       ...
2000-12-30 23:56:00  1037    Bob -0.814321  0.612836
2000-12-30 23:57:00   980    Bob  0.232195 -0.618828
2000-12-30 23:58:00   965  Alice -0.231131  0.026310
2000-12-30 23:59:00   984  Alice  0.942819  0.853128
2000-12-31 00:00:00  1003  Alice  0.201125 -0.136655

[525601 rows x 4 columns]

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")

In [13]: ts = pd.read_parquet("timeseries.parquet")

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     65176434
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     1051387
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     1051387
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}")
0.20

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")
   ....: 
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 708 ms, sys: 21 ms, total: 729 ms
Wall time: 524 ms
Out[32]: 
name
Alice      1994645
Bob        1993692
Charlie    1994875
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.