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Lesson 2

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Create Data - We begin by creating our own data set for analysis. This prevents the end user reading this tutorial from having to download any files to replicate the results below. We will export this data set to a text file so that you can get some experience pulling data from a text file.
Get Data - We will learn how to read in the text file containing the baby names. The data consist of baby names born in the year 1880.
Prepare Data - Here we will simply take a look at the data and make sure it is clean. By clean I mean we will take a look inside the contents of the text file and look for any anomalities. These can include missing data, inconsistencies in the data, or any other data that seems out of place. If any are found we will then have to make decisions on what to do with these records.
Analyze Data - We will simply find the most popular name in a specific year.
Present Data - Through tabular data and a graph, clearly show the end user what is the most popular name in a specific year.

NOTE:
Make sure you have looked through all previous lessons as the knowledge learned in previous lessons will be needed for this exercise.

Numpy will be used to help generate the sample data set. Importing the libraries is the first step we will take in the lesson.

In [1]:
# Import all libraries needed for the tutorial
import pandas as pd
from numpy import random
import matplotlib.pyplot as plt
import sys #only needed to determine Python version number
import matplotlib #only needed to determine Matplotlib version number

# Enable inline plotting
%matplotlib inline
In [2]:
print('Python version ' + sys.version)
print('Pandas version ' + pd.__version__)
print('Matplotlib version ' + matplotlib.__version__)
Python version 3.7.4 (default, Aug  9 2019, 18:34:13) [MSC v.1915 64 bit (AMD64)]
Pandas version 1.0.5
Matplotlib version 3.2.2

Create Data

The data set will consist of 1,000 baby names and the number of births recorded for that year (1880). We will also add plenty of duplicates so you will see the same baby name more than once. You can think of the multiple entries per name simply being different hospitals around the country reporting the number of births per baby name. So if two hospitals reported the baby name "Bob", the data will have two values for the name Bob. We will start by creating the random set of baby names.

In [3]:
# The inital set of baby names
names = ['Bob','Jessica','Mary','John','Mel']

To make a random list of 1,000 baby names using the five above we will do the following:

  • Generate a random number between 0 and 4

To do this we will be using the functions seed, randint, len, range, and zip.

In [4]:
# This will ensure the random samples below can be reproduced. 
# This means the random samples will always be identical.

random.seed?
Docstring:
seed(self, seed=None)

Reseed a legacy MT19937 BitGenerator

Notes
-----
This is a convenience, legacy function.

The best practice is to **not** reseed a BitGenerator, rather to
recreate a new one. This method is here for legacy reasons.
This example demonstrates best practice.

>>> from numpy.random import MT19937
>>> from numpy.random import RandomState, SeedSequence
>>> rs = RandomState(MT19937(SeedSequence(123456789)))
# Later, you want to restart the stream
>>> rs = RandomState(MT19937(SeedSequence(987654321)))
Type:      builtin_function_or_method
In [5]:
random.randint?
Docstring:
randint(low, high=None, size=None, dtype=int)

Return random integers from `low` (inclusive) to `high` (exclusive).

Return random integers from the "discrete uniform" distribution of
the specified dtype in the "half-open" interval [`low`, `high`). If
`high` is None (the default), then results are from [0, `low`).

.. note::
    New code should use the ``integers`` method of a ``default_rng()``
    instance instead; see `random-quick-start`.

Parameters
----------
low : int or array-like of ints
    Lowest (signed) integers to be drawn from the distribution (unless
    ``high=None``, in which case this parameter is one above the
    *highest* such integer).
high : int or array-like of ints, optional
    If provided, one above the largest (signed) integer to be drawn
    from the distribution (see above for behavior if ``high=None``).
    If array-like, must contain integer values
size : int or tuple of ints, optional
    Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
    ``m * n * k`` samples are drawn.  Default is None, in which case a
    single value is returned.
dtype : dtype, optional
    Desired dtype of the result. Byteorder must be native.
    The default value is int.

    .. versionadded:: 1.11.0

Returns
-------
out : int or ndarray of ints
    `size`-shaped array of random integers from the appropriate
    distribution, or a single such random int if `size` not provided.

See Also
--------
random_integers : similar to `randint`, only for the closed
    interval [`low`, `high`], and 1 is the lowest value if `high` is
    omitted.
Generator.integers: which should be used for new code.

Examples
--------
>>> np.random.randint(2, size=10)
array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0]) # random
>>> np.random.randint(1, size=10)
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])

Generate a 2 x 4 array of ints between 0 and 4, inclusive:

>>> np.random.randint(5, size=(2, 4))
array([[4, 0, 2, 1], # random
       [3, 2, 2, 0]])

Generate a 1 x 3 array with 3 different upper bounds

>>> np.random.randint(1, [3, 5, 10])
array([2, 2, 9]) # random

Generate a 1 by 3 array with 3 different lower bounds

>>> np.random.randint([1, 5, 7], 10)
array([9, 8, 7]) # random

Generate a 2 by 4 array using broadcasting with dtype of uint8

>>> np.random.randint([1, 3, 5, 7], [[10], [20]], dtype=np.uint8)
array([[ 8,  6,  9,  7], # random
       [ 1, 16,  9, 12]], dtype=uint8)
Type:      builtin_function_or_method
In [6]:
len?
Signature: len(obj, /)
Docstring: Return the number of items in a container.
Type:      builtin_function_or_method
In [7]:
range?
Init signature: range(self, /, *args, **kwargs)
Docstring:     
range(stop) -> range object
range(start, stop[, step]) -> range object

Return an object that produces a sequence of integers from start (inclusive)
to stop (exclusive) by step.  range(i, j) produces i, i+1, i+2, ..., j-1.
start defaults to 0, and stop is omitted!  range(4) produces 0, 1, 2, 3.
These are exactly the valid indices for a list of 4 elements.
When step is given, it specifies the increment (or decrement).
Type:           type
Subclasses:     
In [8]:
zip?
Init signature: zip(self, /, *args, **kwargs)
Docstring:     
zip(iter1 [,iter2 [...]]) --> zip object

Return a zip object whose .__next__() method returns a tuple where
the i-th element comes from the i-th iterable argument.  The .__next__()
method continues until the shortest iterable in the argument sequence
is exhausted and then it raises StopIteration.
Type:           type
Subclasses:     

seed(500) - Create seed

randint(low=0,high=len(names)) - Generate a random integer between zero and the length of the list "names".

names[n] - Select the name where its index is equal to n.

for i in range(n) - Loop until i is equal to n, i.e. 1,2,3,....n.

random_names = Select a random name from the name list and do this n times.

In [9]:
random.seed(500)
random_names = [names[random.randint(low=0,high=len(names))] for i in range(1000)]

# Print first 10 records
random_names[:10]
Out[9]:
['Mary',
 'Jessica',
 'Jessica',
 'Bob',
 'Jessica',
 'Jessica',
 'Jessica',
 'Mary',
 'Mary',
 'Mary']

Generate a random numbers between 0 and 1000

In [10]:
# The number of births per name for the year 1880
births = [random.randint(low=0,high=1000) for i in range(1000)]
births[:10]
Out[10]:
[968, 155, 77, 578, 973, 124, 155, 403, 199, 191]

Merge the names and the births data set using the zip function.

In [11]:
BabyDataSet = list(zip(random_names,births))
BabyDataSet[:10]
Out[11]:
[('Mary', 968),
 ('Jessica', 155),
 ('Jessica', 77),
 ('Bob', 578),
 ('Jessica', 973),
 ('Jessica', 124),
 ('Jessica', 155),
 ('Mary', 403),
 ('Mary', 199),
 ('Mary', 191)]

We are basically done creating the data set. We now will use the pandas library to export this data set into a csv file.

df will be a DataFrame object. You can think of this object holding the contents of the BabyDataSet in a format similar to a sql table or an excel spreadsheet. Lets take a look below at the contents inside df.

In [12]:
df = pd.DataFrame(data = BabyDataSet, columns=['Names', 'Births'])
df[:10]
Out[12]:
Names Births
0 Mary 968
1 Jessica 155
2 Jessica 77
3 Bob 578
4 Jessica 973
5 Jessica 124
6 Jessica 155
7 Mary 403
8 Mary 199
9 Mary 191
  • Export the dataframe to a text file. We can name the file births1880.txt. The function to_csv will be used to export. The file will be saved in the same location of the notebook unless specified otherwise.
In [13]:
df.to_csv?
Signature:
df.to_csv(
    path_or_buf: Union[str, pathlib.Path, IO[~AnyStr], NoneType] = None,
    sep: str = ',',
    na_rep: str = '',
    float_format: Union[str, NoneType] = None,
    columns: Union[Sequence[Union[Hashable, NoneType]], NoneType] = None,
    header: Union[bool, List[str]] = True,
    index: bool = True,
    index_label: Union[bool, str, Sequence[Union[Hashable, NoneType]], NoneType] = None,
    mode: str = 'w',
    encoding: Union[str, NoneType] = None,
    compression: Union[str, Mapping[str, str], NoneType] = 'infer',
    quoting: Union[int, NoneType] = None,
    quotechar: str = '"',
    line_terminator: Union[str, NoneType] = None,
    chunksize: Union[int, NoneType] = None,
    date_format: Union[str, NoneType] = None,
    doublequote: bool = True,
    escapechar: Union[str, NoneType] = None,
    decimal: Union[str, NoneType] = '.',
) -> Union[str, NoneType]
Docstring:
Write object to a comma-separated values (csv) file.

.. versionchanged:: 0.24.0
    The order of arguments for Series was changed.

Parameters
----------
path_or_buf : str or file handle, default None
    File path or object, if None is provided the result is returned as
    a string.  If a file object is passed it should be opened with
    `newline=''`, disabling universal newlines.

    .. versionchanged:: 0.24.0

       Was previously named "path" for Series.

sep : str, default ','
    String of length 1. Field delimiter for the output file.
na_rep : str, default ''
    Missing data representation.
float_format : str, default None
    Format string for floating point numbers.
columns : sequence, optional
    Columns to write.
header : bool or list of str, default True
    Write out the column names. If a list of strings is given it is
    assumed to be aliases for the column names.

    .. versionchanged:: 0.24.0

       Previously defaulted to False for Series.

index : bool, default True
    Write row names (index).
index_label : str or sequence, or False, default None
    Column label for index column(s) if desired. If None is given, and
    `header` and `index` are True, then the index names are used. A
    sequence should be given if the object uses MultiIndex. If
    False do not print fields for index names. Use index_label=False
    for easier importing in R.
mode : str
    Python write mode, default 'w'.
encoding : str, optional
    A string representing the encoding to use in the output file,
    defaults to 'utf-8'.
compression : str or dict, default 'infer'
    If str, represents compression mode. If dict, value at 'method' is
    the compression mode. Compression mode may be any of the following
    possible values: {'infer', 'gzip', 'bz2', 'zip', 'xz', None}. If
    compression mode is 'infer' and `path_or_buf` is path-like, then
    detect compression mode from the following extensions: '.gz',
    '.bz2', '.zip' or '.xz'. (otherwise no compression). If dict given
    and mode is 'zip' or inferred as 'zip', other entries passed as
    additional compression options.

    .. versionchanged:: 1.0.0

       May now be a dict with key 'method' as compression mode
       and other entries as additional compression options if
       compression mode is 'zip'.

quoting : optional constant from csv module
    Defaults to csv.QUOTE_MINIMAL. If you have set a `float_format`
    then floats are converted to strings and thus csv.QUOTE_NONNUMERIC
    will treat them as non-numeric.
quotechar : str, default '\"'
    String of length 1. Character used to quote fields.
line_terminator : str, optional
    The newline character or character sequence to use in the output
    file. Defaults to `os.linesep`, which depends on the OS in which
    this method is called ('\n' for linux, '\r\n' for Windows, i.e.).

    .. versionchanged:: 0.24.0
chunksize : int or None
    Rows to write at a time.
date_format : str, default None
    Format string for datetime objects.
doublequote : bool, default True
    Control quoting of `quotechar` inside a field.
escapechar : str, default None
    String of length 1. Character used to escape `sep` and `quotechar`
    when appropriate.
decimal : str, default '.'
    Character recognized as decimal separator. E.g. use ',' for
    European data.

Returns
-------
None or str
    If path_or_buf is None, returns the resulting csv format as a
    string. Otherwise returns None.

See Also
--------
read_csv : Load a CSV file into a DataFrame.
to_excel : Write DataFrame to an Excel file.

Examples
--------
>>> df = pd.DataFrame({'name': ['Raphael', 'Donatello'],
...                    'mask': ['red', 'purple'],
...                    'weapon': ['sai', 'bo staff']})
>>> df.to_csv(index=False)
'name,mask,weapon\nRaphael,red,sai\nDonatello,purple,bo staff\n'

Create 'out.zip' containing 'out.csv'

>>> compression_opts = dict(method='zip',
...                         archive_name='out.csv')  # doctest: +SKIP
>>> df.to_csv('out.zip', index=False,
...           compression=compression_opts)  # doctest: +SKIP
File:      c:\users\17862\anaconda3\lib\site-packages\pandas\core\generic.py
Type:      method

The only parameters we will use is index and header. Setting these parameters to False will prevent the index and header names from being exported. Change the values of these parameters to get a better understanding of their use.

In [14]:
df.to_csv('births1880.txt',index=False,header=False)

Get Data

To pull in the text file, we will use the pandas function read_csv. Let us take a look at this function and what inputs it takes.

In [15]:
pd.read_csv?
Signature:
pd.read_csv(
    filepath_or_buffer: Union[str, pathlib.Path, IO[~AnyStr]],
    sep=',',
    delimiter=None,
    header='infer',
    names=None,
    index_col=None,
    usecols=None,
    squeeze=False,
    prefix=None,
    mangle_dupe_cols=True,
    dtype=None,
    engine=None,
    converters=None,
    true_values=None,
    false_values=None,
    skipinitialspace=False,
    skiprows=None,
    skipfooter=0,
    nrows=None,
    na_values=None,
    keep_default_na=True,
    na_filter=True,
    verbose=False,
    skip_blank_lines=True,
    parse_dates=False,
    infer_datetime_format=False,
    keep_date_col=False,
    date_parser=None,
    dayfirst=False,
    cache_dates=True,
    iterator=False,
    chunksize=None,
    compression='infer',
    thousands=None,
    decimal: str = '.',
    lineterminator=None,
    quotechar='"',
    quoting=0,
    doublequote=True,
    escapechar=None,
    comment=None,
    encoding=None,
    dialect=None,
    error_bad_lines=True,
    warn_bad_lines=True,
    delim_whitespace=False,
    low_memory=True,
    memory_map=False,
    float_precision=None,
)
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, 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 handler (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, 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, 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). 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.
prefix : str, optional
    Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ...
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.
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.
engine : {'c', 'python'}, optional
    Parser engine to use. The C engine is faster while the python engine is
    currently more feature-complete.
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 unparseable 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()``.
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``.
compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, 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', or '.xz' (otherwise no
    decompression). If using 'zip', the ZIP file must contain only one data
    file to be read in. Set to None for no decompression.
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>`_ .
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, default True
    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 dropped from the DataFrame that is
    returned.
warn_bad_lines : bool, default True
    If error_bad_lines is False, and warn_bad_lines is True, a warning for each
    "bad line" will be output.
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` for the ordinary converter,
    `high` for the high-precision converter, and `round_trip` for the
    round-trip converter.

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

See Also
--------
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:      c:\users\17862\anaconda3\lib\site-packages\pandas\io\parsers.py
Type:      function

Even though this functions has many parameters, we will simply pass it the location of the text file.

Location = C:\Users\TYPE_USER_NAME.xy\startups\births1880.txt

Note: Depending on where you save your notebooks, you may need to modify the location above.

In [16]:
Location = r'C:\notebooks\pandas\births1880.txt'
df = pd.read_csv(Location)

Notice the r before the string. Since the slashes are special characters, prefixing the string with a r will escape the whole string.

In [17]:
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 999 entries, 0 to 998
Data columns (total 2 columns):
 #   Column  Non-Null Count  Dtype 
---  ------  --------------  ----- 
 0   Mary    999 non-null    object
 1   968     999 non-null    int64 
dtypes: int64(1), object(1)
memory usage: 15.7+ KB

Info says:

  • There are 999 records in the data set
  • There is a column named Mary with 999 values
  • There is a column named 968 with 999 values
  • Out of the two columns, one is numeric, the other is non numeric

To actually see the contents of the dataframe we can use the head() function which by default will return the first five records. You can also pass in a number n to return the top n records of the dataframe.

In [18]:
df.head()
Out[18]:
Mary 968
0 Jessica 155
1 Jessica 77
2 Bob 578
3 Jessica 973
4 Jessica 124

This brings us to our first problem of the exercise. The read_csv function treated the first record in the text file as the header names. This is obviously not correct since the text file did not provide us with header names.

To correct this we will pass the header parameter to the read_csv function and set it to None (means null in python).

In [19]:
df = pd.read_csv(Location, header=None)
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000 entries, 0 to 999
Data columns (total 2 columns):
 #   Column  Non-Null Count  Dtype 
---  ------  --------------  ----- 
 0   0       1000 non-null   object
 1   1       1000 non-null   int64 
dtypes: int64(1), object(1)
memory usage: 15.8+ KB

Info now says:

  • There are 1000 records in the data set
  • There is a column named 0 with 1000 values
  • There is a column named 1 with 1000 values
  • Out of the two columns, one is numeric, the other is non numeric

Now lets take a look at the last five records of the dataframe

In [20]:
df.tail()
Out[20]:
0 1
995 John 151
996 Jessica 511
997 John 756
998 Jessica 294
999 John 152

If we wanted to give the columns specific names, we would have to pass another paramter called names. We can also omit the header parameter.

In [21]:
df = pd.read_csv(Location, names=['Names','Births'])
df.head(5)
Out[21]:
Names Births
0 Mary 968
1 Jessica 155
2 Jessica 77
3 Bob 578
4 Jessica 973

You can think of the numbers [0,1,2,3,4,...] as the row numbers in an Excel file. In pandas these are part of the index of the dataframe. You can think of the index as the primary key of a sql table with the exception that an index is allowed to have duplicates.

[Names, Births] can be though of as column headers similar to the ones found in an Excel spreadsheet or sql database.

Delete the txt file now that we are done using it.

In [22]:
import os
os.remove(Location)

Prepare Data

The data we have consists of baby names and the number of births in the year 1880. We already know that we have 1,000 records and none of the records are missing (non-null values). We can verify the "Names" column still only has five unique names.

We can use the unique property of the dataframe to find all the unique records of the "Names" column.

In [23]:
# Method 1:
df['Names'].unique()
Out[23]:
array(['Mary', 'Jessica', 'Bob', 'John', 'Mel'], dtype=object)
In [24]:
# If you actually want to print the unique values:
for x in df['Names'].unique():
    print(x)
Mary
Jessica
Bob
John
Mel
In [25]:
# Method 2:
print(df['Names'].describe())
count     1000
unique       5
top        Bob
freq       206
Name: Names, dtype: object

Since we have multiple values per baby name, we need to aggregate this data so we only have a baby name appear once. This means the 1,000 rows will need to become 5. We can accomplish this by using the groupby function.

In [26]:
df.groupby?
Signature:
df.groupby(
    by=None,
    axis=0,
    level=None,
    as_index: bool = True,
    sort: bool = True,
    group_keys: bool = True,
    squeeze: bool = False,
    observed: bool = False,
) -> 'groupby_generic.DataFrameGroupBy'
Docstring:
Group DataFrame using a mapper or by a Series of columns.

A groupby operation involves some combination of splitting the
object, applying a function, and combining the results. This can be
used to group large amounts of data and compute operations on these
groups.

Parameters
----------
by : mapping, function, label, or list of labels
    Used to determine the groups for the groupby.
    If ``by`` is a function, it's called on each value of the object's
    index. If a dict or Series is passed, the Series or dict VALUES
    will be used to determine the groups (the Series' values are first
    aligned; see ``.align()`` method). If an ndarray is passed, the
    values are used as-is determine the groups. A label or list of
    labels may be passed to group by the columns in ``self``. Notice
    that a tuple is interpreted as a (single) key.
axis : {0 or 'index', 1 or 'columns'}, default 0
    Split along rows (0) or columns (1).
level : int, level name, or sequence of such, default None
    If the axis is a MultiIndex (hierarchical), group by a particular
    level or levels.
as_index : bool, default True
    For aggregated output, return object with group labels as the
    index. Only relevant for DataFrame input. as_index=False is
    effectively "SQL-style" grouped output.
sort : bool, default True
    Sort group keys. Get better performance by turning this off.
    Note this does not influence the order of observations within each
    group. Groupby preserves the order of rows within each group.
group_keys : bool, default True
    When calling apply, add group keys to index to identify pieces.
squeeze : bool, default False
    Reduce the dimensionality of the return type if possible,
    otherwise return a consistent type.
observed : bool, default False
    This only applies if any of the groupers are Categoricals.
    If True: only show observed values for categorical groupers.
    If False: show all values for categorical groupers.

    .. versionadded:: 0.23.0

Returns
-------
DataFrameGroupBy
    Returns a groupby object that contains information about the groups.

See Also
--------
resample : Convenience method for frequency conversion and resampling
    of time series.

Notes
-----
See the `user guide
<https://pandas.pydata.org/pandas-docs/stable/groupby.html>`_ for more.

Examples
--------
>>> df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',
...                               'Parrot', 'Parrot'],
...                    'Max Speed': [380., 370., 24., 26.]})
>>> df
   Animal  Max Speed
0  Falcon      380.0
1  Falcon      370.0
2  Parrot       24.0
3  Parrot       26.0
>>> df.groupby(['Animal']).mean()
        Max Speed
Animal
Falcon      375.0
Parrot       25.0

**Hierarchical Indexes**

We can groupby different levels of a hierarchical index
using the `level` parameter:

>>> arrays = [['Falcon', 'Falcon', 'Parrot', 'Parrot'],
...           ['Captive', 'Wild', 'Captive', 'Wild']]
>>> index = pd.MultiIndex.from_arrays(arrays, names=('Animal', 'Type'))
>>> df = pd.DataFrame({'Max Speed': [390., 350., 30., 20.]},
...                   index=index)
>>> df
                Max Speed
Animal Type
Falcon Captive      390.0
       Wild         350.0
Parrot Captive       30.0
       Wild          20.0
>>> df.groupby(level=0).mean()
        Max Speed
Animal
Falcon      370.0
Parrot       25.0
>>> df.groupby(level="Type").mean()
         Max Speed
Type
Captive      210.0
Wild         185.0
File:      c:\users\17862\anaconda3\lib\site-packages\pandas\core\frame.py
Type:      method
In [27]:
# Create a groupby object
name = df.groupby('Names')

# Apply the sum function to the groupby object
df = name.sum()
df
Out[27]:
Births
Names
Bob 106817
Jessica 97826
John 90705
Mary 99438
Mel 102319

Analyze Data

To find the most popular name or the baby name with the higest birth rate, we can do one of the following.

  • Sort the dataframe and select the top row
  • Use the max() attribute to find the maximum value
In [28]:
# Method 1:
Sorted = df.sort_values(['Births'], ascending=False)
Sorted.head(1)
Out[28]:
Births
Names
Bob 106817
In [29]:
# Method 2:
df['Births'].max()
Out[29]:
106817

Present Data

Here we can plot the Births column and label the graph to show the end user the highest point on the graph. In conjunction with the table, the end user has a clear picture that Bob is the most popular baby name in the data set.

In [30]:
# Create graph
df['Births'].plot.bar()

print("The most popular name")
df.sort_values(by='Births', ascending=False)
The most popular name
Out[30]:
Births
Names
Bob 106817
Mel 102319
Mary 99438
Jessica 97826
John 90705

This tutorial was created by HEDARO