# Load and Save Data with Dask DataFrames

You can create a Dask DataFrame from various data storage formats like CSV, HDF, Apache Parquet, and others. For most formats, this data can live on various storage systems including local disk, network file systems (NFS), the Hadoop Distributed File System (HDFS), Google Cloud Storage, and Amazon S3 (excepting HDF, which is only available on POSIX like file systems).

See the DataFrame overview page for more on `dask.dataframe` scope, use, and limitations and DataFrame Best Practices for more tips and solutions to common problems.

## API

The following functions provide access to convert between Dask DataFrames, file formats, and other Dask or Python collections.

File Formats:

`read_csv`(urlpath[, blocksize, ...]) | Read CSV files into a Dask.DataFrame
`read_parquet`(path[, columns, filters, ...]) | Read a Parquet file into a Dask DataFrame
`read_hdf`(pattern, key[, start, stop, ...]) | Read HDF files into a Dask DataFrame
`read_orc`(path[, engine, columns, index, ...]) | Read dataframe from ORC file(s)
`read_json`(url_path[, orient, lines, ...]) | Create a dataframe from a set of JSON files
`read_sql_table`(table_name, con, index_col[, ...]) | Read SQL database table into a DataFrame.
`read_sql_query`(sql, con, index_col[, ...]) | Read SQL query into a DataFrame.
`read_sql`(sql, con, index_col, **kwargs) | Read SQL query or database table into a DataFrame.
`read_table`(urlpath[, blocksize, ...]) | Read delimited files into a Dask.DataFrame
`read_fwf`(urlpath[, blocksize, ...]) | Read fixed-width files into a Dask.DataFrame
`from_array`(x[, chunksize, columns, meta]) | Read any sliceable array into a Dask Dataframe
`to_csv`(df, filename[, single_file, ...]) | Store Dask DataFrame to CSV files
`to_parquet`(df, path[, engine, compression, ...]) | Store Dask.dataframe to Parquet files
`to_hdf`(df, path, key[, mode, append, ...]) | Store Dask Dataframe to Hierarchical Data Format (HDF) files
`to_sql`(df, name, uri[, schema, if_exists, ...]) | Store Dask Dataframe to a SQL table

Dask Collections:

`from_delayed`(dfs[, meta, divisions, prefix, ...]) | Create Dask DataFrame from many Dask Delayed objects.
`from_dask_array`(x[, columns, index, meta]) | Create a Dask DataFrame from a Dask Array.
`from_map`(func, *iterables[, args, meta, ...]) | Create a DataFrame collection from a custom function map.
`dask.bag.core.Bag.to_dataframe`([meta, ...]) | Create Dask Dataframe from a Dask Bag.
`DataFrame.to_delayed`([optimize_graph]) | Convert into a list of `dask.delayed` objects, one per partition.
`to_records`(df) | Create Dask Array from a Dask Dataframe
`to_bag`(df[, index, format]) | Create Dask Bag from a Dask DataFrame

Pandas:

`from_pandas`() | Construct a Dask DataFrame from a Pandas DataFrame
`DataFrame.from_dict`(data, *, npartitions[, ...]) | Construct a Dask DataFrame from a Python Dictionary

Other File Formats:

- Snowflake

- Bigquery

- Delta Lake

## Creating

### Read from CSV

You can use `read_csv()` to read one or more CSV files into a Dask DataFrame. It supports loading multiple files at once using globstrings:

    >>> df = dd.read_csv('myfiles.*.csv')

You can break up a single large file with the `blocksize` parameter:

    >>> df = dd.read_csv('largefile.csv', blocksize=25e6)  # 25MB chunks

Changing the `blocksize` parameter will change the number of partitions (see the explanation on partitions). A good rule of thumb when working with Dask DataFrames is to keep your partitions under 100MB in size.

### Read from Parquet

Similarly, you can use `read_parquet()` for reading one or more Parquet files. You can read in a single Parquet file:

    >>> df = dd.read_parquet("path/to/mydata.parquet")

Or a directory of local Parquet files:

    >>> df = dd.read_parquet("path/to/my/parquet/")

For more details on working with Parquet files, including tips and best practices, see the documentation on Dask Dataframe and Parquet.

### Read from cloud storage

Dask can read data from a variety of data stores including cloud object stores. You can do this by prepending a protocol like `s3://` to paths used in common data access functions like `dd.read_csv`:

    >>> df = dd.read_csv('s3://bucket/path/to/data-*.csv')
    >>> df = dd.read_parquet('gcs://bucket/path/to/data-*.parq')

For remote systems like Amazon S3 or Google Cloud Storage, you may need to provide credentials. These are usually stored in a configuration file, but in some cases you may want to pass storage-specific options through to the storage backend. You can do this with the `storage_options` parameter:

    >>> df = dd.read_csv('s3://bucket-name/my-data-*.csv',
    ...                  storage_options={'anon': True})
    >>> df = dd.read_parquet('gs://dask-nyc-taxi/yellowtrip.parquet',
    ...                      storage_options={'token': 'anon'})

See the documentation on connecting to Amazon S3 or Google Cloud Storage.

### Mapping from a function

For cases that are not covered by the functions above, but can be captured by a simple `map` operation, `from_map()` is likely to be the most convenient means for DataFrame creation. For example, this API can be used to convert an arbitrary PyArrow `Dataset` object into a DataFrame collection by mapping fragments to DataFrame partitions:

    >>> import pyarrow.dataset as ds
    >>> dataset = ds.dataset("hive_data_path", format="orc", partitioning="hive")
    >>> fragments = dataset.get_fragments()
    >>> func = lambda frag: frag.to_table().to_pandas()
    >>> df = dd.from_map(func, fragments)

### Dask Delayed

Dask delayed is particularly useful when simple `map` operations aren’t sufficient to capture the complexity of your data layout. It lets you construct Dask DataFrames out of arbitrary Python function calls, which can be helpful to handle custom data formats or bake in particular logic around loading data. See the documentation on using dask.delayed with collections.

## Storing

### Writing files locally

You can save files locally, assuming each worker can access the same file system. The workers could be located on the same machine, or a network file system can be mounted and referenced at the same path location for every worker node. See the documentation on accessing data locally.

### Writing to remote locations

Dask can write to a variety of data stores including cloud object stores. For example, you can write a `dask.dataframe` to an Azure storage blob as:

    >>> d = {'col1': [1, 2, 3, 4], 'col2': [5, 6, 7, 8]}
    >>> df = dd.from_pandas(pd.DataFrame(data=d), npartitions=2)
    >>> dd.to_parquet(df=df,
    ...               path='abfs://CONTAINER/FILE.parquet'
    ...               storage_options={'account_name': 'ACCOUNT_NAME',
    ...                                'account_key': 'ACCOUNT_KEY'}

See the how-to guide on connecting to remote data.