Input/Output¶

See more in File IO, such as S3 and HDFS configuration requirements.

pd.read_csv¶

pandas.read_csv
- example usage and more system specific instructions
- filepath_or_buffer should be a string and is required. It could be pointing to a single CSV file, or a directory containing multiple partitioned CSV files (must have csv file extension inside directory).
- Arguments sep, delimiter, header, names, index_col, usecols, dtype, nrows, skiprows, chunksize, parse_dates, and low_memory are supported.
- Argument anon of storage_options is supported for S3 filepaths.
- Either names and dtype arguments should be provided to enable type inference, or filepath_or_buffer should be inferrable as a constant string. This is required so bodo can infer the types at compile time, see compile time constants
- names, usecols, parse_dates should be constant lists.
- dtype should be a constant dictionary of strings and types.
- skiprows must be an integer or list of integers and if it is not a constant, names must be provided to enable type inference.
- chunksize is supported for uncompressed files only.
- low_memory internally process file in chunks while parsing. In Bodo this is set to False by default.
- When set to True, Bodo parses file in chunks but like Pandas the entire file is read into a single DataFrame regardless.
- If you want to load data in chunks, use the chunksize argument.
- When a CSV file is read in parallel (distributed mode) and each process reads only a portion of the file, reading columns that contain line breaks is not supported.
- _bodo_read_as_dict is a Bodo specific argument which forces the specified string columns to be read with dictionary-encoding. Dictionary-encoding stores data in memory in an efficient manner and is most effective when the column has many repeated values. Read more about dictionary-encoded layout here.
  
  For example:
```
@bodo.jit()
def impl(f):
  df = pd.read_csv(f, _bodo_read_as_dict=["A", "B", "C"])
  return df
```

pd.read_excel¶

pandas.read_excel
- output dataframe cannot be parallelized automatically yet.
- only arguments io, sheet_name, header, names, comment, dtype, skiprows, parse_dates are supported.
- io should be a string and is required.
- Either names and dtype arguments should be provided to enable type inference, or io should be inferrable as a constant string. This is required so bodo can infer the types at compile time, see compile time constants
- sheet_name, header, comment, and skiprows should be constant if provided.
- names and parse_dates should be constant lists if provided.
- dtype should be a constant dictionary of strings and types if provided.

pd.read_sql¶

pandas.read_sql
- example usage and more system specific instructions
- Argument sql is supported but only as a string form. SQLalchemy Selectable is not supported. There is no restriction on the form of the sql request.
- Argument con is supported but only as a string form. SQLalchemy connectable is not supported.
- Argument index_col is supported.
- Arguments chunksize, column, coerce_float, params are not supported.

pd.read_sql_table¶

pandas.read_sql_table
- This API only supports reading Iceberg tables at the moment.
- See Iceberg Section for example usage and more system specific instructions.
- Argument table_name is supported and must be the name of an Iceberg Table.
- Argument con is supported but only as a string form in a URL format. SQLalchemy connectable is not supported. It should be the absolute path to a Iceberg warehouse. If using a Hadoop-based directory catalog, it should start with the URL scheme iceberg://. If using a Thrift Hive catalog, it should start with the URL scheme iceberg+thrift://
- Argument schema is supported and currently required for Iceberg tables. It must be the name of the database schema. For Iceberg Tables, this is the directory name in the warehouse (specified by con) where your table exists.
- Arguments index_col, coerce_float, parse_dates, columns and chunksize are not supported.
- _bodo_detect_dict_cols is a Bodo specific argument which allows for users to manually enable or disable the compile-time detection process for loading string columns using dictionary encoding. Dictionary-encoding stores data in memory in an efficient manner and is most effective when the column has many repeated values. By default, this argument is true and Bodo will use a heuristic to determine if dictionary encoding a column would be benefitial. Disabling detection is useful if compilation is taking too long or is unable to access the source dataset. Note that columns specified in _bodo_read_as_dict (see below) will still be dictionary-encoded even if _bodo_detect_dict_cols is set to False. Read more about dictionary-encoded layout here.
  
  For example:
```
@bodo.jit()
def impl(conn_str):
  df = pd.read_sql_table("table_name", conn_str, schema="", _bodo_detect_dict_cols=False)
  return df
```
  - _bodo_read_as_dict is a Bodo specific argument which forces the specified string columns to be read with dictionary-encoding. Bodo automatically loads string columns using dictionary encoding when it determines it would be beneficial based on a heuristic. Dictionary-encoding stores data in memory in an efficient manner and is most effective when the column has many repeated values. Read more about dictionary-encoded layout here.
  
  For example:
```
@bodo.jit()
def impl(f):
  df = pd.read_sql_table("table_name", conn_str, schema="", _bodo_read_as_dict=["A", "B", "C"])
  return df
```

pd.read_parquet¶

pandas.read_parquet
- example usage and more system specific instructions
- Arguments path and columns are supported. columns should be a constant list of strings if provided. path can be a string or list. If string, must be a path to a file or a directory, or a glob string. If a list, must contain paths to parquet files (not directories) or glob strings.
- Argument anon of storage_options is supported for S3 filepaths.
- If path can be inferred as a constant (e.g. it is a function argument), Bodo finds the schema from file at compilation time. Otherwise, schema should be provided using the numba syntax.
  
  For example:
```
@bodo.jit(locals={'df':{'A': bodo.float64[:],
                        'B': bodo.string_array_type}})
def impl(f):
  df = pd.read_parquet(f)
  return df
```
- _bodo_input_file_name_col is a Bodo specific argument. When specified, a column with this name is added to the dataframe consisting of the name of the file the row was read from. This is similar to SparkSQL's input_file_name function.
  
  For example:
```
@bodo.jit()
def impl(f):
  df = pd.read_parquet(f, _bodo_input_file_name_col="fname")
  return df
```
- _bodo_read_as_dict is a Bodo specific argument which forces the specified string columns to be read with dictionary-encoding. Bodo automatically loads string columns using dictionary encoding when it determines it would be beneficial based on a heuristic. Dictionary-encoding stores data in memory in an efficient manner and is most effective when the column has many repeated values. Read more about dictionary-encoded layout here.
  
  For example:
```
@bodo.jit()
def impl(f):
  df = pd.read_parquet(f, _bodo_read_as_dict=["A", "B", "C"])
  return df
```

`pd.read_json`¶

pandas.read_json
- Example usage and more system specific instructions
- Only supports reading JSON Lines text file format (pd.read_json(filepath_or_buffer, orient='records', lines=True)) and regular multi-line JSON file(pd.read_json(filepath_or_buffer, orient='records', lines=False)).
- Argument filepath_or_buffer is supported: it can point to a single JSON file, or a directory containing multiple partitioned JSON files. When reading a directory, the JSON files inside the directory must be JSON Lines text file format with json file extension.
- Argument orient = 'records' is used as default, instead of Pandas' default 'columns' for dataframes. 'records' is the only supported value for orient.
- Argument typ is supported. 'frame' is the only supported value for typ.
- filepath_or_buffer must be inferrable as a constant string. This is required so bodo can infer the types at compile time, see compile time constants.
- Arguments convert_dates, precise_float, lines are supported.
- Argument anon of storage_options is supported for S3 filepaths.

Input/Output¶

pd.read_csv¶

pd.read_excel¶

pd.read_sql¶

pd.read_sql_table¶

pd.read_parquet¶

pd.read_json¶

`pd.read_json`¶