Importing Data

Learning Objectives

1. Import tabular data with Pandas.
2. Assess DataFrame attributes and methods.
3. Import alternative data files such as SQL tables, JSON, and pickle files.

General Model for Importing Data

Memory and Size

• Unless you're using a distributed computing framework such as Spark, Python stores its data in your computer's memory. This makes it relatively accessible but can cause size limitations depending on the data you're using.
• If memory is a concern, you can always use Dask, Spark, or another distributed framework, though those solutions are somewhat more cumbersome and we will not cover them here.
• Python memory is session-specific, so quitting Python (e.g. shutting down Jupyterlab) removes the data from memory. If you need to persist data beyond the lifetime of a single session there are methods for doing so.

General Framework

A general way to conceptualize data import into and use within Python:

• Data sits in on the computer/server - this is frequently called "disk"
• Python code can be used to copy a data file from disk to the Python session's memory
• Python data then sits within Python's memory ready to be used by other Python code

Here is a visualization of this process:

Importing Tabular Data

Tabular (i.e. relational) data remains the predominant format for data storage, though it's not suitable for all types of data (for example, images or audio). Storing tabular data as a delimited text file has limitations -- it's slow to read, doesn't contain metadata, and can take up a significant amount of storage -- but it's very common.

Importing Tabular Data with Base Python

When the data you're accessing is stored as a delimited text file, you can use builtin modules to import data into a Python object.

First we import the csv module, which handles parsing of CSV files:

import csv

The csv module can be used in the following way:

1. Open a file for reading with the open() function
2. Create an iterator over the lines of the file using the readlines() method of the opened file handle
3. Pass this iterator to the csv.reader() function, which parses each row of the CSV file
4. Parse the header raw, if applicable
5. Parse each line of the file into the relevant Python object

This is easier done than said. Note that in the following I make use of the with statement; think of this as a way of ensuring that the file handle is closed without having to close it manually. This is an example of a "context manager", which you can read more about here.

with open("../data/planes.csv") as f:
    reader = csv.reader(f.readlines())
    header = next(reader)
    data = [line for line in reader]

The result is a list of lists, where the nth element of the list is a list of items (in order) from the nth row in the dataset:

data[3]

['N104UW',
 '1999.0',
 'Fixed wing multi engine',
 'AIRBUS INDUSTRIE',
 'A320-214',
 '2',
 '182',
 '',
 'Turbo-fan']

If instead we wanted a column-based approach, so that we could grab whole columns by name, we can do so with a dictionary comprehension:

data_cols = {
    header_item: [row[i] for row in data]
    for i, header_item in enumerate(header)
}

data_cols["tailnum"][:5]

['N10156', 'N102UW', 'N103US', 'N104UW', 'N10575']

This is a somewhat cumbersome method both of parsing and of dealing with the resulting dataset, however. We'll cover an easier way.

Importing Tabular Data with Pandas

Pandas is preferred because it imports the data directly into what's known as a data frame, the data structure of choice for tabular data in Python. We'll cover this in more detail later.

Pandas is usually imported in the following way:

import pandas as pd

The read_csv() function is used to import a tabular data file, in this case a CSV, into a data frame:

planes = pd.read_csv('../data/planes.csv')

Note that although the name of the function refers to CSV, this works with other kinds of delimiters. If we wanted to be more specific about the delimiter, we can do that:

planes = pd.read_csv("../data/planes.csv", delimiter=",")

You can also be specific about which row contains the header (0 means no header):

planes = pd.read_csv("../data/planes.csv", header=1)

Full documentation can be pulled up by running the method name followed by a question mark:

pd.read_csv?

Signature:
pd.read_csv(
    filepath_or_buffer: 'FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str]',
    *,
    sep: 'str | None | lib.NoDefault' = <no_default>,
    delimiter: 'str | None | lib.NoDefault' = None,
    header: "int | Sequence[int] | None | Literal['infer']" = 'infer',
    names: 'Sequence[Hashable] | None | lib.NoDefault' = <no_default>,
    index_col: 'IndexLabel | Literal[False] | None' = None,
    usecols=None,
    squeeze: 'bool | None' = None,
    prefix: 'str | lib.NoDefault' = <no_default>,
    mangle_dupe_cols: 'bool' = True,
    dtype: 'DtypeArg | None' = None,
    engine: 'CSVEngine | None' = None,
    converters=None,
    true_values=None,
    false_values=None,
    skipinitialspace: 'bool' = False,
    skiprows=None,
    skipfooter: 'int' = 0,
    nrows: 'int | None' = None,
    na_values=None,
    keep_default_na: 'bool' = True,
    na_filter: 'bool' = True,
    verbose: 'bool' = False,
    skip_blank_lines: 'bool' = True,
    parse_dates=None,
    infer_datetime_format: 'bool' = False,
    keep_date_col: 'bool' = False,
    date_parser=None,
    dayfirst: 'bool' = False,
    cache_dates: 'bool' = True,
    iterator: 'bool' = False,
    chunksize: 'int | None' = None,
    compression: 'CompressionOptions' = 'infer',
    thousands: 'str | None' = None,
    decimal: 'str' = '.',
    lineterminator: 'str | None' = None,
    quotechar: 'str' = '"',
    quoting: 'int' = 0,
    doublequote: 'bool' = True,
    escapechar: 'str | None' = None,
    comment: 'str | None' = None,
    encoding: 'str | None' = None,
    encoding_errors: 'str | None' = 'strict',
    dialect: 'str | csv.Dialect | None' = None,
    error_bad_lines: 'bool | None' = None,
    warn_bad_lines: 'bool | None' = None,
    on_bad_lines=None,
    delim_whitespace: 'bool' = False,
    low_memory=True,
    memory_map: 'bool' = False,
    float_precision: "Literal['high', 'legacy'] | None" = None,
    storage_options: 'StorageOptions' = None,
) -> 'DataFrame | TextFileReader'
Docstring:
Read a comma-separated values (csv) file into DataFrame.

Also supports optionally iterating or breaking of the file
into chunks.

Additional help can be found in the online docs for
`IO Tools <https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html>`_.

Parameters
----------
filepath_or_buffer : str, path object or file-like object
    Any valid string path is acceptable. The string could be a URL. Valid
    URL schemes include http, ftp, s3, gs, and file. For file URLs, a host is
    expected. A local file could be: file://localhost/path/to/table.csv.

    If you want to pass in a path object, pandas accepts any ``os.PathLike``.

    By file-like object, we refer to objects with a ``read()`` method, such as
    a file handle (e.g. via builtin ``open`` function) or ``StringIO``.
sep : str, default ','
    Delimiter to use. If sep is None, the C engine cannot automatically detect
    the separator, but the Python parsing engine can, meaning the latter will
    be used and automatically detect the separator by Python's builtin sniffer
    tool, ``csv.Sniffer``. In addition, separators longer than 1 character and
    different from ``'\s+'`` will be interpreted as regular expressions and
    will also force the use of the Python parsing engine. Note that regex
    delimiters are prone to ignoring quoted data. Regex example: ``'\r\t'``.
delimiter : str, default ``None``
    Alias for sep.
header : int, list of int, None, default 'infer'
    Row number(s) to use as the column names, and the start of the
    data.  Default behavior is to infer the column names: if no names
    are passed the behavior is identical to ``header=0`` and column
    names are inferred from the first line of the file, if column
    names are passed explicitly then the behavior is identical to
    ``header=None``. Explicitly pass ``header=0`` to be able to
    replace existing names. The header can be a list of integers that
    specify row locations for a multi-index on the columns
    e.g. [0,1,3]. Intervening rows that are not specified will be
    skipped (e.g. 2 in this example is skipped). Note that this
    parameter ignores commented lines and empty lines if
    ``skip_blank_lines=True``, so ``header=0`` denotes the first line of
    data rather than the first line of the file.
names : array-like, optional
    List of column names to use. If the file contains a header row,
    then you should explicitly pass ``header=0`` to override the column names.
    Duplicates in this list are not allowed.
index_col : int, str, sequence of int / str, or False, optional, default ``None``
  Column(s) to use as the row labels of the ``DataFrame``, either given as
  string name or column index. If a sequence of int / str is given, a
  MultiIndex is used.

  Note: ``index_col=False`` can be used to force pandas to *not* use the first
  column as the index, e.g. when you have a malformed file with delimiters at
  the end of each line.
usecols : list-like or callable, optional
    Return a subset of the columns. If list-like, all elements must either
    be positional (i.e. integer indices into the document columns) or strings
    that correspond to column names provided either by the user in `names` or
    inferred from the document header row(s). If ``names`` are given, the document
    header row(s) are not taken into account. For example, a valid list-like
    `usecols` parameter would be ``[0, 1, 2]`` or ``['foo', 'bar', 'baz']``.
    Element order is ignored, so ``usecols=[0, 1]`` is the same as ``[1, 0]``.
    To instantiate a DataFrame from ``data`` with element order preserved use
    ``pd.read_csv(data, usecols=['foo', 'bar'])[['foo', 'bar']]`` for columns
    in ``['foo', 'bar']`` order or
    ``pd.read_csv(data, usecols=['foo', 'bar'])[['bar', 'foo']]``
    for ``['bar', 'foo']`` order.

    If callable, the callable function will be evaluated against the column
    names, returning names where the callable function evaluates to True. An
    example of a valid callable argument would be ``lambda x: x.upper() in
    ['AAA', 'BBB', 'DDD']``. Using this parameter results in much faster
    parsing time and lower memory usage.
squeeze : bool, default False
    If the parsed data only contains one column then return a Series.

    .. deprecated:: 1.4.0
        Append ``.squeeze("columns")`` to the call to ``read_csv`` to squeeze
        the data.
prefix : str, optional
    Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ...

    .. deprecated:: 1.4.0
       Use a list comprehension on the DataFrame's columns after calling ``read_csv``.
mangle_dupe_cols : bool, default True
    Duplicate columns will be specified as 'X', 'X.1', ...'X.N', rather than
    'X'...'X'. Passing in False will cause data to be overwritten if there
    are duplicate names in the columns.

    .. deprecated:: 1.5.0
        Not implemented, and a new argument to specify the pattern for the
        names of duplicated columns will be added instead
dtype : Type name or dict of column -> type, optional
    Data type for data or columns. E.g. {'a': np.float64, 'b': np.int32,
    'c': 'Int64'}
    Use `str` or `object` together with suitable `na_values` settings
    to preserve and not interpret dtype.
    If converters are specified, they will be applied INSTEAD
    of dtype conversion.

    .. versionadded:: 1.5.0

        Support for defaultdict was added. Specify a defaultdict as input where
        the default determines the dtype of the columns which are not explicitly
        listed.
engine : {'c', 'python', 'pyarrow'}, optional
    Parser engine to use. The C and pyarrow engines are faster, while the python engine
    is currently more feature-complete. Multithreading is currently only supported by
    the pyarrow engine.

    .. versionadded:: 1.4.0

        The "pyarrow" engine was added as an *experimental* engine, and some features
        are unsupported, or may not work correctly, with this engine.
converters : dict, optional
    Dict of functions for converting values in certain columns. Keys can either
    be integers or column labels.
true_values : list, optional
    Values to consider as True.
false_values : list, optional
    Values to consider as False.
skipinitialspace : bool, default False
    Skip spaces after delimiter.
skiprows : list-like, int or callable, optional
    Line numbers to skip (0-indexed) or number of lines to skip (int)
    at the start of the file.

    If callable, the callable function will be evaluated against the row
    indices, returning True if the row should be skipped and False otherwise.
    An example of a valid callable argument would be ``lambda x: x in [0, 2]``.
skipfooter : int, default 0
    Number of lines at bottom of file to skip (Unsupported with engine='c').
nrows : int, optional
    Number of rows of file to read. Useful for reading pieces of large files.
na_values : scalar, str, list-like, or dict, optional
    Additional strings to recognize as NA/NaN. If dict passed, specific
    per-column NA values.  By default the following values are interpreted as
    NaN: '', '#N/A', '#N/A N/A', '#NA', '-1.#IND', '-1.#QNAN', '-NaN', '-nan',
    '1.#IND', '1.#QNAN', '<NA>', 'N/A', 'NA', 'NULL', 'NaN', 'n/a',
    'nan', 'null'.
keep_default_na : bool, default True
    Whether or not to include the default NaN values when parsing the data.
    Depending on whether `na_values` is passed in, the behavior is as follows:

    * If `keep_default_na` is True, and `na_values` are specified, `na_values`
      is appended to the default NaN values used for parsing.
    * If `keep_default_na` is True, and `na_values` are not specified, only
      the default NaN values are used for parsing.
    * If `keep_default_na` is False, and `na_values` are specified, only
      the NaN values specified `na_values` are used for parsing.
    * If `keep_default_na` is False, and `na_values` are not specified, no
      strings will be parsed as NaN.

    Note that if `na_filter` is passed in as False, the `keep_default_na` and
    `na_values` parameters will be ignored.
na_filter : bool, default True
    Detect missing value markers (empty strings and the value of na_values). In
    data without any NAs, passing na_filter=False can improve the performance
    of reading a large file.
verbose : bool, default False
    Indicate number of NA values placed in non-numeric columns.
skip_blank_lines : bool, default True
    If True, skip over blank lines rather than interpreting as NaN values.
parse_dates : bool or list of int or names or list of lists or dict, default False
    The behavior is as follows:

    * boolean. If True -> try parsing the index.
    * list of int or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3
      each as a separate date column.
    * list of lists. e.g.  If [[1, 3]] -> combine columns 1 and 3 and parse as
      a single date column.
    * dict, e.g. {'foo' : [1, 3]} -> parse columns 1, 3 as date and call
      result 'foo'

    If a column or index cannot be represented as an array of datetimes,
    say because of an unparsable value or a mixture of timezones, the column
    or index will be returned unaltered as an object data type. For
    non-standard datetime parsing, use ``pd.to_datetime`` after
    ``pd.read_csv``. To parse an index or column with a mixture of timezones,
    specify ``date_parser`` to be a partially-applied
    :func:`pandas.to_datetime` with ``utc=True``. See
    :ref:`io.csv.mixed_timezones` for more.

    Note: A fast-path exists for iso8601-formatted dates.
infer_datetime_format : bool, default False
    If True and `parse_dates` is enabled, pandas will attempt to infer the
    format of the datetime strings in the columns, and if it can be inferred,
    switch to a faster method of parsing them. In some cases this can increase
    the parsing speed by 5-10x.
keep_date_col : bool, default False
    If True and `parse_dates` specifies combining multiple columns then
    keep the original columns.
date_parser : function, optional
    Function to use for converting a sequence of string columns to an array of
    datetime instances. The default uses ``dateutil.parser.parser`` to do the
    conversion. Pandas will try to call `date_parser` in three different ways,
    advancing to the next if an exception occurs: 1) Pass one or more arrays
    (as defined by `parse_dates`) as arguments; 2) concatenate (row-wise) the
    string values from the columns defined by `parse_dates` into a single array
    and pass that; and 3) call `date_parser` once for each row using one or
    more strings (corresponding to the columns defined by `parse_dates`) as
    arguments.
dayfirst : bool, default False
    DD/MM format dates, international and European format.
cache_dates : bool, default True
    If True, use a cache of unique, converted dates to apply the datetime
    conversion. May produce significant speed-up when parsing duplicate
    date strings, especially ones with timezone offsets.

    .. versionadded:: 0.25.0
iterator : bool, default False
    Return TextFileReader object for iteration or getting chunks with
    ``get_chunk()``.

    .. versionchanged:: 1.2

       ``TextFileReader`` is a context manager.
chunksize : int, optional
    Return TextFileReader object for iteration.
    See the `IO Tools docs
    <https://pandas.pydata.org/pandas-docs/stable/io.html#io-chunking>`_
    for more information on ``iterator`` and ``chunksize``.

    .. versionchanged:: 1.2

       ``TextFileReader`` is a context manager.
compression : str or dict, default 'infer'
    For on-the-fly decompression of on-disk data. If 'infer' and 'filepath_or_buffer' is
    path-like, then detect compression from the following extensions: '.gz',
    '.bz2', '.zip', '.xz', '.zst', '.tar', '.tar.gz', '.tar.xz' or '.tar.bz2'
    (otherwise no compression).
    If using 'zip' or 'tar', the ZIP file must contain only one data file to be read in.
    Set to ``None`` for no decompression.
    Can also be a dict with key ``'method'`` set
    to one of {``'zip'``, ``'gzip'``, ``'bz2'``, ``'zstd'``, ``'tar'``} and other
    key-value pairs are forwarded to
    ``zipfile.ZipFile``, ``gzip.GzipFile``,
    ``bz2.BZ2File``, ``zstandard.ZstdDecompressor`` or
    ``tarfile.TarFile``, respectively.
    As an example, the following could be passed for Zstandard decompression using a
    custom compression dictionary:
    ``compression={'method': 'zstd', 'dict_data': my_compression_dict}``.

        .. versionadded:: 1.5.0
            Added support for `.tar` files.

    .. versionchanged:: 1.4.0 Zstandard support.

thousands : str, optional
    Thousands separator.
decimal : str, default '.'
    Character to recognize as decimal point (e.g. use ',' for European data).
lineterminator : str (length 1), optional
    Character to break file into lines. Only valid with C parser.
quotechar : str (length 1), optional
    The character used to denote the start and end of a quoted item. Quoted
    items can include the delimiter and it will be ignored.
quoting : int or csv.QUOTE_* instance, default 0
    Control field quoting behavior per ``csv.QUOTE_*`` constants. Use one of
    QUOTE_MINIMAL (0), QUOTE_ALL (1), QUOTE_NONNUMERIC (2) or QUOTE_NONE (3).
doublequote : bool, default ``True``
   When quotechar is specified and quoting is not ``QUOTE_NONE``, indicate
   whether or not to interpret two consecutive quotechar elements INSIDE a
   field as a single ``quotechar`` element.
escapechar : str (length 1), optional
    One-character string used to escape other characters.
comment : str, optional
    Indicates remainder of line should not be parsed. If found at the beginning
    of a line, the line will be ignored altogether. This parameter must be a
    single character. Like empty lines (as long as ``skip_blank_lines=True``),
    fully commented lines are ignored by the parameter `header` but not by
    `skiprows`. For example, if ``comment='#'``, parsing
    ``#empty\na,b,c\n1,2,3`` with ``header=0`` will result in 'a,b,c' being
    treated as the header.
encoding : str, optional
    Encoding to use for UTF when reading/writing (ex. 'utf-8'). `List of Python
    standard encodings
    <https://docs.python.org/3/library/codecs.html#standard-encodings>`_ .

    .. versionchanged:: 1.2

       When ``encoding`` is ``None``, ``errors="replace"`` is passed to
       ``open()``. Otherwise, ``errors="strict"`` is passed to ``open()``.
       This behavior was previously only the case for ``engine="python"``.

    .. versionchanged:: 1.3.0

       ``encoding_errors`` is a new argument. ``encoding`` has no longer an
       influence on how encoding errors are handled.

encoding_errors : str, optional, default "strict"
    How encoding errors are treated. `List of possible values
    <https://docs.python.org/3/library/codecs.html#error-handlers>`_ .

    .. versionadded:: 1.3.0

dialect : str or csv.Dialect, optional
    If provided, this parameter will override values (default or not) for the
    following parameters: `delimiter`, `doublequote`, `escapechar`,
    `skipinitialspace`, `quotechar`, and `quoting`. If it is necessary to
    override values, a ParserWarning will be issued. See csv.Dialect
    documentation for more details.
error_bad_lines : bool, optional, default ``None``
    Lines with too many fields (e.g. a csv line with too many commas) will by
    default cause an exception to be raised, and no DataFrame will be returned.
    If False, then these "bad lines" will be dropped from the DataFrame that is
    returned.

    .. deprecated:: 1.3.0
       The ``on_bad_lines`` parameter should be used instead to specify behavior upon
       encountering a bad line instead.
warn_bad_lines : bool, optional, default ``None``
    If error_bad_lines is False, and warn_bad_lines is True, a warning for each
    "bad line" will be output.

    .. deprecated:: 1.3.0
       The ``on_bad_lines`` parameter should be used instead to specify behavior upon
       encountering a bad line instead.
on_bad_lines : {'error', 'warn', 'skip'} or callable, default 'error'
    Specifies what to do upon encountering a bad line (a line with too many fields).
    Allowed values are :

        - 'error', raise an Exception when a bad line is encountered.
        - 'warn', raise a warning when a bad line is encountered and skip that line.
        - 'skip', skip bad lines without raising or warning when they are encountered.

    .. versionadded:: 1.3.0

    .. versionadded:: 1.4.0

        - callable, function with signature
          ``(bad_line: list[str]) -> list[str] | None`` that will process a single
          bad line. ``bad_line`` is a list of strings split by the ``sep``.
          If the function returns ``None``, the bad line will be ignored.
          If the function returns a new list of strings with more elements than
          expected, a ``ParserWarning`` will be emitted while dropping extra elements.
          Only supported when ``engine="python"``

delim_whitespace : bool, default False
    Specifies whether or not whitespace (e.g. ``' '`` or ``'    '``) will be
    used as the sep. Equivalent to setting ``sep='\s+'``. If this option
    is set to True, nothing should be passed in for the ``delimiter``
    parameter.
low_memory : bool, default True
    Internally process the file in chunks, resulting in lower memory use
    while parsing, but possibly mixed type inference.  To ensure no mixed
    types either set False, or specify the type with the `dtype` parameter.
    Note that the entire file is read into a single DataFrame regardless,
    use the `chunksize` or `iterator` parameter to return the data in chunks.
    (Only valid with C parser).
memory_map : bool, default False
    If a filepath is provided for `filepath_or_buffer`, map the file object
    directly onto memory and access the data directly from there. Using this
    option can improve performance because there is no longer any I/O overhead.
float_precision : str, optional
    Specifies which converter the C engine should use for floating-point
    values. The options are ``None`` or 'high' for the ordinary converter,
    'legacy' for the original lower precision pandas converter, and
    'round_trip' for the round-trip converter.

    .. versionchanged:: 1.2

storage_options : dict, optional
    Extra options that make sense for a particular storage connection, e.g.
    host, port, username, password, etc. For HTTP(S) URLs the key-value pairs
    are forwarded to ``urllib.request.Request`` as header options. For other
    URLs (e.g. starting with "s3://", and "gcs://") the key-value pairs are
    forwarded to ``fsspec.open``. Please see ``fsspec`` and ``urllib`` for more
    details, and for more examples on storage options refer `here
    <https://pandas.pydata.org/docs/user_guide/io.html?
    highlight=storage_options#reading-writing-remote-files>`_.

    .. versionadded:: 1.2

Returns
-------
DataFrame or TextParser
    A comma-separated values (csv) file is returned as two-dimensional
    data structure with labeled axes.

See Also
--------
DataFrame.to_csv : Write DataFrame to a comma-separated values (csv) file.
read_csv : Read a comma-separated values (csv) file into DataFrame.
read_fwf : Read a table of fixed-width formatted lines into DataFrame.

Examples
--------
>>> pd.read_csv('data.csv')  # doctest: +SKIP
File:      /opt/homebrew/Caskroom/miniforge/base/envs/cincinnati-insurance-training/lib/python3.10/site-packages/pandas/io/parsers/readers.py
Type:      function

Your Turn (Optional)

Load the ../data/flights.csv file into Python using pandas. Using the documentation for pd.read_csv(), run it twice, once for each possible value of the engine argument. Note any differences.

Importing Data from SQL Tables

Organizations continue to use relational databases along with SQL to interact with these data assets. Python has many tools to interact with these databases and you can even query SQL database tables using pandas' read_sql() function.

In a real-world scenario you could use a Python library specific to your database vendor, or else a library that abstracts away those details (such as SQLAlchemy). For demonstration purposes we'll use sqlite, a database that's bundled with Python.

To connect to a sqlite database, simply call its connect() function while specifying the database file's location on disk:

import sqlite3

con = sqlite3.connect("../data/chinook.db")

You could use that connection object to read from a table manually, but it's easier to use the pd.read_sql() function from pandas to read the "tracks" table directly as a data frame:

pd.read_sql("select * from tracks", con=con).head()

	TrackId	Name	AlbumId	MediaTypeId	GenreId	Composer	Milliseconds	Bytes	UnitPrice
0	1	For Those About To Rock (We Salute You)	1	1	1	Angus Young, Malcolm Young, Brian Johnson	343719	11170334	0.99
1	2	Balls to the Wall	2	2	1	None	342562	5510424	0.99
2	3	Fast As a Shark	3	2	1	F. Baltes, S. Kaufman, U. Dirkscneider & W. Ho...	230619	3990994	0.99
3	4	Restless and Wild	3	2	1	F. Baltes, R.A. Smith-Diesel, S. Kaufman, U. D...	252051	4331779	0.99
4	5	Princess of the Dawn	3	2	1	Deaffy & R.A. Smith-Diesel	375418	6290521	0.99

If you are familiar with SQL then you can even pass any conformant query directly in the pd.read_sql() call. For example, the following SQL query:

1. SELECTS the name, composer, and milliseconds columns,
2. FROM the tracks table,
3. WHERE observations in the milliseconds column are greater than 200,000 and WHERE observations in the composer column are not missing (NULL)

sql_query = (
    "select name, composer, milliseconds "
    "from tracks "
    "where milliseconds > 200000 and composer is not null"
)

long_tracks = pd.read_sql(sql_query, con=con)
long_tracks.head()

	Name	Composer	Milliseconds
0	For Those About To Rock (We Salute You)	Angus Young, Malcolm Young, Brian Johnson	343719
1	Fast As a Shark	F. Baltes, S. Kaufman, U. Dirkscneider & W. Ho...	230619
2	Restless and Wild	F. Baltes, R.A. Smith-Diesel, S. Kaufman, U. D...	252051
3	Princess of the Dawn	Deaffy & R.A. Smith-Diesel	375418
4	Put The Finger On You	Angus Young, Malcolm Young, Brian Johnson	205662

Reading Other Data Formats with Pandas

The pandas package is filled with methods for reading various data formats, including:

1. pd.read_parquet() for parquet files
2. pd.read_excel() for excel files
3. pd.read_xml() for XML files
4. ... and many others ...

I encourage you to look through the package documentation here.

Many Other File Types

There are many other file types that you may encounter, most of which we can import into Python one way or another. Most tabular (2-dimensional) data sets can be imported directly with pandas. For example, this page shows a list of the many pandas.read_xxx() functions that allow you to read various data file types.

Other types of data should be treated on a case-by-case basis.

JSON Files

A common example is a JSON file. These are non-tabular structured data files that are popular as a data interchange format due to their human readability and flexibility. Here is an example JSON file:

{
    "planeId": "1xc2345g",
    "manufacturerDetails": {
        "manufacturer": "Airbus",
        "model": "A330",
        "year": 1999
    },
    "airlineDetails": {
        "currentAirline": "Southwest",
        "previousAirlines": {
            "1st": "Delta"
        },
        "lastPurchased": 2013
    },
    "numberOfFlights": 4654
}

JSON Files can be imported using the json library (from the standard library) paired with the with statement and the open() function.

import json

with open('../data/json_example.json', 'r') as f:
    imported_json = json.load(f)

We can then verify that our imported object is a dict:

type(imported_json)

dict

And we can view the data:

imported_json

{'planeId': '1xc2345g',
 'manufacturerDetails': {'manufacturer': 'Airbus',
  'model': 'A330',
  'year': 1999},
 'airlineDetails': {'currentAirline': 'Southwest',
  'previousAirlines': {'1st': 'Delta'},
  'lastPurchased': 2013},
 'numberOfFlights': 4654}

Note that you can also read JSON with pandas, though it may not work as you'd expect with nested data:

pd.read_json("../data/json_example.json")

	planeId	manufacturerDetails	airlineDetails	numberOfFlights
manufacturer	1xc2345g	Airbus	NaN	4654
model	1xc2345g	A330	NaN	4654
year	1xc2345g	1999	NaN	4654
currentAirline	1xc2345g	NaN	Southwest	4654
previousAirlines	1xc2345g	NaN	{'1st': 'Delta'}	4654
lastPurchased	1xc2345g	NaN	2013	4654

Pickle Files

So far, we've seen that tabular data files can be imported and represented as DataFrames and JSON files can be imported and represented as dicts, but what about other, more complex data?

Python's native data files are known as Pickle files:

• They generally end in the .pickle extension
• Great for saving native Python data that can't easily be represented by other file types such as:
  • pre-processed data,
  • models,
  • most (but not all) Python objects
• However, they aren't guaranteed to be compatible across different Python environments
  • Best to use them as temporary storage only

Similar to JSON files, pickle files can be imported using the pickle library paired with the with statement and the open() function:

import pickle

with open('../data/pickle_example.pickle', 'rb') as f:
    imported_pickle = pickle.load(f)

We can view this file and see it's the same data as the JSON:

imported_pickle

{'planeId': '1xc2345g',
 'manufacturerDetails': {'manufacturer': 'Airbus',
  'model': 'A330',
  'year': 1999},
 'airlineDetails': {'currentAirline': 'Southwest',
  'previousAirlines': {'1st': 'Delta'},
  'lastPurchased': 2013},
 'numberOfFlights': 4654}

Others

If you have to import data in some relatively standardized format, there is almost always an off the shelf library you can use.

1. ini: the builtin configparser module
2. yaml: the yaml package
3. toml: the toml package
4. Almost anything you can think of: hdf5, parquet, matlab, images, etc

Questions

Are there any questions before moving on?