dataprofiler.data_readers.data_utils module¶

Contains functions for data readers.

dataprofiler.data_readers.data_utils.data_generator(data_list: List[str]) → Generator[str, None, None]¶

Take a list and return a generator on the list.

Parameters:: data_list (list) – list of strings
Returns:: item from the list
Return type:: generator

dataprofiler.data_readers.data_utils.generator_on_file(file_object: StringIO | BytesIO) → Generator[str | bytes, None, None]¶

Take a file and return a generator that returns lines.

Parameters:: file_path (path) – path to the file
Returns:: Line from file
Return type:: generator

dataprofiler.data_readers.data_utils.convert_int_to_string(x: int) → str¶

Convert the given input to string.

In particular, it is int, it converts it ensuring there is no . or 00. In addition, if the input is np.nan, the output will be ‘nan’ which is what we need to handle data properly.

Parameters:: x (Union[int, float, str, numpy.nan])
Returns:
Return type:: str

Convert data to string representation if it is a unicode string.

Parameters:

data (JSONType) – input data
ignore_dicts (boolean) – if set, ignore the dictionary type processing

Returns:

string representation of data

Return type:

str

Take list of json objects and return dataframe representing json list.

Parameters:

json_lines (list(JSONType)) – list of json objects
selected_columns (list(str)) – a list of keys to be processed
read_in_string (bool) – if True, all the values in dataframe will be converted to string

Returns:

dataframe converted from json list and list of dtypes for each column

Return type:

tuple(pd.DataFrame, pd.Series(dtypes))

dataprofiler.data_readers.data_utils.read_json_df(data_generator: Generator, selected_columns: List[str] | None = None, read_in_string: bool = False) → Tuple[DataFrame, Series]¶

Return an iterator that returns a chunk of data as dataframe in each call.

The source of input to this function is either a file or a list of JSON structured strings. If the file path is given as input, the file is expected to have one JSON structures in each line. The lines that are not valid json will be ignored. Therefore, a file with pretty printed JSON objects will not be considered valid JSON. If the input is a data list, it is expected to be a list of strings where each string is a valid JSON object. if the individual object is not valid JSON, it will be ignored.

NOTE: both data_list and file_path cannot be passed at the same time.

Parameters:

data_generator (generator) – The generator you want to read.
selected_columns (list(str)) – a list of keys to be processed
read_in_string (bool) – if True, all the values in dataframe will be converted to string

Returns:

returns an iterator that returns a chunk of file as dataframe in each call as well as original dtypes of the dataframe columns.

Return type:

tuple(pd.DataFrame, pd.Series(dtypes))

Return the lines of a json.

NOTE: both data_list and file_path cannot be passed at the same time.

Parameters:

data_generator (generator) – The generator you want to read.
selected_columns (list(str)) – a list of keys to be processed
read_in_string (bool) – if True, all the values in dataframe will be converted to string

Returns:

returns the lines of a json file

Return type:

list(dict)

dataprofiler.data_readers.data_utils.reservoir(file: TextIOWrapper, sample_nrows: int) → list¶

Implement the mathematical logic of Reservoir sampling.

Parameters:

file (TextIOWrapper) – wrapper of the opened csv file
sample_nrows (int) – number of rows to sample

Raises:

ValueError()

Returns:

sampled values

Return type:

list

dataprofiler.data_readers.data_utils.rsample(file_path: TextIOWrapper, sample_nrows: int, args: dict) → StringIO¶

Implement Reservoir Sampling to sample n rows out of a total of M rows.

Parameters:

file_path (TextIOWrapper) – path of the csv file to be read in
sample_nrows (int) – number of rows being sampled
args (dict) – options to read the csv file

Read a CSV file in chunks and return dataframe in form of iterator.

Parameters:

file_path (str) – path to the CSV file.
delimiter (str) – character used to separate csv values.
header (int) – the header row in the csv file.
selected_columns (list(str)) – a list of columns to be processed
read_in_string (bool) – if True, all the values in dataframe will be converted to string

Returns:

Iterator

Return type:

pd.DataFrame

dataprofiler.data_readers.data_utils.convert_unicode_col_to_utf8(input_df: DataFrame) → DataFrame¶

Convert all unicode columns in input dataframe to utf-8.

Parameters:: input_df (pd.DataFrame) – input dataframe
Returns:: corrected dataframe
Return type:: pd.DataFrame

dataprofiler.data_readers.data_utils.sample_parquet(file_path: str, sample_nrows: int, selected_columns: List[str] | None = None, read_in_string: bool = False) → Tuple[DataFrame, Series]¶

Read parquet file, sample specified number of rows from it and return a data frame.

Parameters:

file_path (str) – path to the Parquet file.
sample_nrows (int) – number of rows being sampled
selected_columns (list) – columns need to be read
read_in_string (bool) – return as string type

Returns:

Return type:

Iterator(pd.DataFrame)

dataprofiler.data_readers.data_utils.read_parquet_df(file_path: str, sample_nrows: int | None = None, selected_columns: List[str] | None = None, read_in_string: bool = False) → Tuple[DataFrame, Series]¶

Return an iterator that returns one row group each time.

Parameters:

file_path (str) – path to the Parquet file.
sample_nrows (int) – number of rows being sampled
selected_columns (list) – columns need to be read
read_in_string (bool) – return as string type

Returns:

Return type:

Iterator(pd.DataFrame)

dataprofiler.data_readers.data_utils.read_text_as_list_of_strs(file_path: str, encoding: str | None = None) → List[str]¶

Return list of strings relative to the chunk size.

Each line is 1 chunk.

Parameters:: file_path (str) – path to the file
Returns:
Return type:: list(str)

dataprofiler.data_readers.data_utils.detect_file_encoding(file_path: str, buffer_size: int = 1024, max_lines: int = 20) → str¶

Determine encoding of files within initial max_lines of length buffer_size.

Parameters:

file_path (str) – path to the file
buffer_size (int) – buffer length for each line being read
max_lines (int) – number of lines to read from file of length buffer_size

Returns:

encoding type

Return type:

str

dataprofiler.data_readers.data_utils.detect_cell_type(cell: str) → str¶

Detect the cell type (int, float, etc).

Parameters:: cell (str) – String designated for data type detection

dataprofiler.data_readers.data_utils.get_delimiter_regex(delimiter: str = ',', quotechar: str = ',') → Pattern[str]¶

Build regex for delimiter checks.

Parameters:

delimiter (str) – Delimiter to be added to regex
quotechar – Quotechar to be added to regex

dataprofiler.data_readers.data_utils.find_nth_loc(string: str | None = None, search_query: str | None = None, n: int = 0, ignore_consecutive: bool = True) → Tuple[int, int]¶

Search string via search_query and return nth index in which query occurs.

If there are less than ‘n’ the last loc is returned

Parameters:

string (str) – Input string, to be searched
search_query (str) – char(s) to find nth occurrence of
n (int) – The number of occurrences to iterate through
ignore_consecutive (bool) – Ignore consecutive matches in the search query.

Return idx:

Index of the nth or last occurrence of the search_query

Rtype idx:

int

Return id_count:

Number of identifications prior to idx

Rtype id_count:

int

dataprofiler.data_readers.data_utils.load_as_str_from_file(file_path: str, file_encoding: str | None = None, max_lines: int = 10, max_bytes: int = 65536, chunk_size_bytes: int = 1024) → str¶

Load data from a csv file up to a specific line OR byte_size.

Parameters:

file_path (str) – Path to file to load data from
file_encoding (str) – File encoding
max_lines (int) – Maximum number of lines to load from file
max_bytes (int) – Maximum number of bytes to load from file
chunk_size_bytes (int) – Chunk size to load every data load

Returns:

Data as string

Return type:

str

dataprofiler.data_readers.data_utils.is_valid_url(url_as_string: Any) → typing_extensions.TypeGuard[Url]¶

Determine whether a given string is a valid URL.

Parameters:: url_as_string (str) – string to be tested if URL
Returns:: true if string is a valid URL
Return type:: boolean

dataprofiler.data_readers.data_utils.url_to_bytes(url_as_string: Url, options: Dict) → BytesIO¶

Read in URL and converts it to a byte stream.

Parameters:

url_as_string (str) – string to read as URL
options (dict) – options for the url

Returns:

BytesIO stream of data downloaded from URL

Return type:

BytesIO stream

class dataprofiler.data_readers.data_utils.S3Helper¶

Bases: object

A utility class for working with Amazon S3.

This class provides methods to check if a path is an S3 URI: and to create an S3 client.

static is_s3_uri(path: str, logger: Logger) → bool¶

Check if the given path is an S3 URI.

This function checks for common S3 URI prefixes “s3://” and “s3a://”.

Parameters:

path (str) – The path to check for an S3 URI.
logger (logging.Logger) – The logger instance for logging.

Returns:

True if the path is an S3 URI, False otherwise.

Return type:

bool

static create_s3_client(aws_access_key_id: str | None = None, aws_secret_access_key: str | None = None, aws_session_token: str | None = None, region_name: str | None = None) → client¶

Create and return an S3 client.

Parameters:

aws_access_key_id (str) – The AWS access key ID.
aws_secret_access_key (str) – The AWS secret access key.
aws_session_token (str) – The AWS session token (optional, typically used for temporary credentials).
region_name (str) – The AWS region name (default is ‘us-east-1’).

Returns:

A S3 client instance.

Return type:

boto3.client

static get_s3_uri(s3_uri: str, s3_client: client) → BytesIO¶

Download an object from an S3 URI and return its content as BytesIO.

Parameters:

s3_uri (str) – The S3 URI specifying the location of the object to download.
s3_client (boto3.client) – An initialized AWS S3 client for accessing the S3 service.

Returns:

A BytesIO object containing the content of: the downloaded S3 object.

Return type:

BytesIO