In this Notebook we cover
Pandas methods for:
In this notebook, we will work with the Fifa World Cup data set hosted on Kaggle.
Download the data in our websire or in Kaggle. Then:
# import modules
import pandas as pd
import numpy as np
Pandas¶In our class on file management, we saw how to use connection managament tools in Python (open(), close(), with()) to load data stored locally into our Python environments. That process usually involved accessing a locally stored data row by row, and import the data a nested container (list or dictionary).
Today, we will see the use of high-level functions from Pandas that facilitate the process of loading data into our Python environment. We will focus on data input and output using pandas, though there are numerous tools in other libraries to help with reading and writing data in various formats.
pandas methods¶pandas contains a variety of methods for reading in various data types.
| Format Type | Data Description | Reader | Writer | Note |
|---|---|---|---|---|
| text | CSV | read_csv |
to_csv |
|
| text | JSON | read_json |
to_json |
|
| text | HTML | read_html |
to_html |
|
| text | Local clipboard | read_clipboard |
to_clipboard |
|
| binary | MS Excel | read_excel |
to_excel |
need the xlwt module |
| binary | HDF5 Format | read_hdf |
to_hdf |
|
| binary | Feather Format | read_feather |
to_feather |
|
| binary | Parquet Format | read_parquet |
to_parquet |
|
| binary | Msgpack | read_msgpack |
to_msgpack |
|
| binary | Stata | read_stata |
to_stata |
|
| binary | SAS | read_sas |
||
| binary | Python Pickle Format | read_pickle |
to_pickle |
|
| SQL | SQL | read_sql |
to_sql |
|
| SQL | Google Big Query | read_gbq |
to_gbq |
Read more about all the input/output methods here.
pandas¶As you can see, the purposes of each function is intuitive. For example:
pandas.read_csv(): to open flat files¶# read a csv
d = pd.read_csv("WorldCupMatches.csv")
d.head()
| Year | Datetime | Stage | Stadium | City | Home Team Name | Home Team Goals | Away Team Goals | Away Team Name | Win conditions | Attendance | Half-time Home Goals | Half-time Away Goals | Referee | Assistant 1 | Assistant 2 | RoundID | MatchID | Home Team Initials | Away Team Initials | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1930 | 13 Jul 1930 - 15:00 | Group 1 | Pocitos | Montevideo | France | 4 | 1 | Mexico | 4444.0 | 3 | 0 | LOMBARDI Domingo (URU) | CRISTOPHE Henry (BEL) | REGO Gilberto (BRA) | 201 | 1096 | FRA | MEX | |
| 1 | 1930 | 13 Jul 1930 - 15:00 | Group 4 | Parque Central | Montevideo | USA | 3 | 0 | Belgium | 18346.0 | 2 | 0 | MACIAS Jose (ARG) | MATEUCCI Francisco (URU) | WARNKEN Alberto (CHI) | 201 | 1090 | USA | BEL | |
| 2 | 1930 | 14 Jul 1930 - 12:45 | Group 2 | Parque Central | Montevideo | Yugoslavia | 2 | 1 | Brazil | 24059.0 | 2 | 0 | TEJADA Anibal (URU) | VALLARINO Ricardo (URU) | BALWAY Thomas (FRA) | 201 | 1093 | YUG | BRA | |
| 3 | 1930 | 14 Jul 1930 - 14:50 | Group 3 | Pocitos | Montevideo | Romania | 3 | 1 | Peru | 2549.0 | 1 | 0 | WARNKEN Alberto (CHI) | LANGENUS Jean (BEL) | MATEUCCI Francisco (URU) | 201 | 1098 | ROU | PER | |
| 4 | 1930 | 15 Jul 1930 - 16:00 | Group 1 | Parque Central | Montevideo | Argentina | 1 | 0 | France | 23409.0 | 0 | 0 | REGO Gilberto (BRA) | SAUCEDO Ulises (BOL) | RADULESCU Constantin (ROU) | 201 | 1085 | ARG | FRA |
pandas loading functions are highly customizable. For example, check the documentation of pandas.read_csv()
# asking for help
help(pd.read_csv)
Help on function read_csv in module pandas.io.parsers.readers:
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'
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
pandas¶All the same methods provided to load, also exists for converting and writing (locally) Pandas Dataframes.
For example:
# export as stata file
d.to_stata("worldcupmatches.dta", version=118)
/var/folders/jy/10_nyhkn3nv_rrbnd8f_fr940000gp/T/ipykernel_50502/3669814853.py:2: InvalidColumnName:
Not all pandas column names were valid Stata variable names.
The following replacements have been made:
Home Team Name -> Home_Team_Name
Home Team Goals -> Home_Team_Goals
Away Team Goals -> Away_Team_Goals
Away Team Name -> Away_Team_Name
Win conditions -> Win_conditions
Half-time Home Goals -> Half_time_Home_Goals
Half-time Away Goals -> Half_time_Away_Goals
Assistant 1 -> Assistant_1
Assistant 2 -> Assistant_2
Home Team Initials -> Home_Team_Initials
Away Team Initials -> Away_Team_Initials
If this is not what you expect, please make sure you have Stata-compliant
column names in your DataFrame (strings only, max 32 characters, only
alphanumerics and underscores, no Stata reserved words)
d.to_stata("worldcupmatches.dta", version=118)
# load back again
d_stata = pd.read_stata("worldcupmatches.dta")
# see the data
d_stata.head()
| index | Year | Datetime | Stage | Stadium | City | Home_Team_Name | Home_Team_Goals | Away_Team_Goals | Away_Team_Name | ... | Attendance | Half_time_Home_Goals | Half_time_Away_Goals | Referee | Assistant_1 | Assistant_2 | RoundID | MatchID | Home_Team_Initials | Away_Team_Initials | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 1930 | 13 Jul 1930 - 15:00 | Group 1 | Pocitos | Montevideo | France | 4 | 1 | Mexico | ... | 4444.0 | 3 | 0 | LOMBARDI Domingo (URU) | CRISTOPHE Henry (BEL) | REGO Gilberto (BRA) | 201 | 1096 | FRA | MEX |
| 1 | 1 | 1930 | 13 Jul 1930 - 15:00 | Group 4 | Parque Central | Montevideo | USA | 3 | 0 | Belgium | ... | 18346.0 | 2 | 0 | MACIAS Jose (ARG) | MATEUCCI Francisco (URU) | WARNKEN Alberto (CHI) | 201 | 1090 | USA | BEL |
| 2 | 2 | 1930 | 14 Jul 1930 - 12:45 | Group 2 | Parque Central | Montevideo | Yugoslavia | 2 | 1 | Brazil | ... | 24059.0 | 2 | 0 | TEJADA Anibal (URU) | VALLARINO Ricardo (URU) | BALWAY Thomas (FRA) | 201 | 1093 | YUG | BRA |
| 3 | 3 | 1930 | 14 Jul 1930 - 14:50 | Group 3 | Pocitos | Montevideo | Romania | 3 | 1 | Peru | ... | 2549.0 | 1 | 0 | WARNKEN Alberto (CHI) | LANGENUS Jean (BEL) | MATEUCCI Francisco (URU) | 201 | 1098 | ROU | PER |
| 4 | 4 | 1930 | 15 Jul 1930 - 16:00 | Group 1 | Parque Central | Montevideo | Argentina | 1 | 0 | France | ... | 23409.0 | 0 | 0 | REGO Gilberto (BRA) | SAUCEDO Ulises (BOL) | RADULESCU Constantin (ROU) | 201 | 1085 | ARG | FRA |
5 rows × 21 columns
# to csv
d_stata.to_csv("wordlcupmatches_.csv")
Explore the arguments of pd.read_csv() methods. Open the WorldCupMatches.csv with the following options:
help(pd.read_csv)
Help on function read_csv in module pandas.io.parsers.readers:
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'
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
# my answer
pd.read_csv("WorldCupMatches.csv",
sep = ",", # Separator in the data
index_col="Year", # Set a variable to the index
usecols = ["Year", "Stage", "Stadium"], # Only request specific columns
nrows = 10, # only read in n-rows of the data
na_values = "nan",
skiprows = np.arange(1, 50),
parse_dates=True, # Parse all date features as datatime
low_memory=True) # read the file in chunks for lower memory use (useful on large data)
JSON (short for JavaScript Object Notation) has become one of the most used data formats in Data Science. The main reason is that JSONs are the primary way data gets transfered by HTTP request between web browsers and other applications. So we will see a lot of JSON data when querying APIs.
Let's see an example of:
# load the csv
# notice this is a different dataset
d_wc = pd.read_csv("WorldCups.csv")
d_wc
| Year | Country | Winner | Runners-Up | Third | Fourth | GoalsScored | QualifiedTeams | MatchesPlayed | Attendance | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1930 | Uruguay | Uruguay | Argentina | USA | Yugoslavia | 70 | 13 | 18 | 590.549 |
| 1 | 1934 | Italy | Italy | Czechoslovakia | Germany | Austria | 70 | 16 | 17 | 363.000 |
| 2 | 1938 | France | Italy | Hungary | Brazil | Sweden | 84 | 15 | 18 | 375.700 |
| 3 | 1950 | Brazil | Uruguay | Brazil | Sweden | Spain | 88 | 13 | 22 | 1.045.246 |
| 4 | 1954 | Switzerland | Germany FR | Hungary | Austria | Uruguay | 140 | 16 | 26 | 768.607 |
| 5 | 1958 | Sweden | Brazil | Sweden | France | Germany FR | 126 | 16 | 35 | 819.810 |
| 6 | 1962 | Chile | Brazil | Czechoslovakia | Chile | Yugoslavia | 89 | 16 | 32 | 893.172 |
| 7 | 1966 | England | England | Germany FR | Portugal | Soviet Union | 89 | 16 | 32 | 1.563.135 |
| 8 | 1970 | Mexico | Brazil | Italy | Germany FR | Uruguay | 95 | 16 | 32 | 1.603.975 |
| 9 | 1974 | Germany | Germany FR | Netherlands | Poland | Brazil | 97 | 16 | 38 | 1.865.753 |
| 10 | 1978 | Argentina | Argentina | Netherlands | Brazil | Italy | 102 | 16 | 38 | 1.545.791 |
| 11 | 1982 | Spain | Italy | Germany FR | Poland | France | 146 | 24 | 52 | 2.109.723 |
| 12 | 1986 | Mexico | Argentina | Germany FR | France | Belgium | 132 | 24 | 52 | 2.394.031 |
| 13 | 1990 | Italy | Germany FR | Argentina | Italy | England | 115 | 24 | 52 | 2.516.215 |
| 14 | 1994 | USA | Brazil | Italy | Sweden | Bulgaria | 141 | 24 | 52 | 3.587.538 |
| 15 | 1998 | France | France | Brazil | Croatia | Netherlands | 171 | 32 | 64 | 2.785.100 |
| 16 | 2002 | Korea/Japan | Brazil | Germany | Turkey | Korea Republic | 161 | 32 | 64 | 2.705.197 |
| 17 | 2006 | Germany | Italy | France | Germany | Portugal | 147 | 32 | 64 | 3.359.439 |
| 18 | 2010 | South Africa | Spain | Netherlands | Germany | Uruguay | 145 | 32 | 64 | 3.178.856 |
| 19 | 2014 | Brazil | Germany | Argentina | Netherlands | Brazil | 171 | 32 | 64 | 3.386.810 |
# let's first see what a json looks like. It is a dictionary!
d_wc.to_json()
'{"Year":{"0":1930,"1":1934,"2":1938,"3":1950,"4":1954,"5":1958,"6":1962,"7":1966,"8":1970,"9":1974,"10":1978,"11":1982,"12":1986,"13":1990,"14":1994,"15":1998,"16":2002,"17":2006,"18":2010,"19":2014},"Country":{"0":"Uruguay","1":"Italy","2":"France","3":"Brazil","4":"Switzerland","5":"Sweden","6":"Chile","7":"England","8":"Mexico","9":"Germany","10":"Argentina","11":"Spain","12":"Mexico","13":"Italy","14":"USA","15":"France","16":"Korea\\/Japan","17":"Germany","18":"South Africa","19":"Brazil"},"Winner":{"0":"Uruguay","1":"Italy","2":"Italy","3":"Uruguay","4":"Germany FR","5":"Brazil","6":"Brazil","7":"England","8":"Brazil","9":"Germany FR","10":"Argentina","11":"Italy","12":"Argentina","13":"Germany FR","14":"Brazil","15":"France","16":"Brazil","17":"Italy","18":"Spain","19":"Germany"},"Runners-Up":{"0":"Argentina","1":"Czechoslovakia","2":"Hungary","3":"Brazil","4":"Hungary","5":"Sweden","6":"Czechoslovakia","7":"Germany FR","8":"Italy","9":"Netherlands","10":"Netherlands","11":"Germany FR","12":"Germany FR","13":"Argentina","14":"Italy","15":"Brazil","16":"Germany","17":"France","18":"Netherlands","19":"Argentina"},"Third":{"0":"USA","1":"Germany","2":"Brazil","3":"Sweden","4":"Austria","5":"France","6":"Chile","7":"Portugal","8":"Germany FR","9":"Poland","10":"Brazil","11":"Poland","12":"France","13":"Italy","14":"Sweden","15":"Croatia","16":"Turkey","17":"Germany","18":"Germany","19":"Netherlands"},"Fourth":{"0":"Yugoslavia","1":"Austria","2":"Sweden","3":"Spain","4":"Uruguay","5":"Germany FR","6":"Yugoslavia","7":"Soviet Union","8":"Uruguay","9":"Brazil","10":"Italy","11":"France","12":"Belgium","13":"England","14":"Bulgaria","15":"Netherlands","16":"Korea Republic","17":"Portugal","18":"Uruguay","19":"Brazil"},"GoalsScored":{"0":70,"1":70,"2":84,"3":88,"4":140,"5":126,"6":89,"7":89,"8":95,"9":97,"10":102,"11":146,"12":132,"13":115,"14":141,"15":171,"16":161,"17":147,"18":145,"19":171},"QualifiedTeams":{"0":13,"1":16,"2":15,"3":13,"4":16,"5":16,"6":16,"7":16,"8":16,"9":16,"10":16,"11":24,"12":24,"13":24,"14":24,"15":32,"16":32,"17":32,"18":32,"19":32},"MatchesPlayed":{"0":18,"1":17,"2":18,"3":22,"4":26,"5":35,"6":32,"7":32,"8":32,"9":38,"10":38,"11":52,"12":52,"13":52,"14":52,"15":64,"16":64,"17":64,"18":64,"19":64},"Attendance":{"0":"590.549","1":"363.000","2":"375.700","3":"1.045.246","4":"768.607","5":"819.810","6":"893.172","7":"1.563.135","8":"1.603.975","9":"1.865.753","10":"1.545.791","11":"2.109.723","12":"2.394.031","13":"2.516.215","14":"3.587.538","15":"2.785.100","16":"2.705.197","17":"3.359.439","18":"3.178.856","19":"3.386.810"}}'
# you can also save a json by record (row-wise as we learned)
d_wc.to_json(orient="records")
'[{"Year":1930,"Country":"Uruguay","Winner":"Uruguay","Runners-Up":"Argentina","Third":"USA","Fourth":"Yugoslavia","GoalsScored":70,"QualifiedTeams":13,"MatchesPlayed":18,"Attendance":"590.549"},{"Year":1934,"Country":"Italy","Winner":"Italy","Runners-Up":"Czechoslovakia","Third":"Germany","Fourth":"Austria","GoalsScored":70,"QualifiedTeams":16,"MatchesPlayed":17,"Attendance":"363.000"},{"Year":1938,"Country":"France","Winner":"Italy","Runners-Up":"Hungary","Third":"Brazil","Fourth":"Sweden","GoalsScored":84,"QualifiedTeams":15,"MatchesPlayed":18,"Attendance":"375.700"},{"Year":1950,"Country":"Brazil","Winner":"Uruguay","Runners-Up":"Brazil","Third":"Sweden","Fourth":"Spain","GoalsScored":88,"QualifiedTeams":13,"MatchesPlayed":22,"Attendance":"1.045.246"},{"Year":1954,"Country":"Switzerland","Winner":"Germany FR","Runners-Up":"Hungary","Third":"Austria","Fourth":"Uruguay","GoalsScored":140,"QualifiedTeams":16,"MatchesPlayed":26,"Attendance":"768.607"},{"Year":1958,"Country":"Sweden","Winner":"Brazil","Runners-Up":"Sweden","Third":"France","Fourth":"Germany FR","GoalsScored":126,"QualifiedTeams":16,"MatchesPlayed":35,"Attendance":"819.810"},{"Year":1962,"Country":"Chile","Winner":"Brazil","Runners-Up":"Czechoslovakia","Third":"Chile","Fourth":"Yugoslavia","GoalsScored":89,"QualifiedTeams":16,"MatchesPlayed":32,"Attendance":"893.172"},{"Year":1966,"Country":"England","Winner":"England","Runners-Up":"Germany FR","Third":"Portugal","Fourth":"Soviet Union","GoalsScored":89,"QualifiedTeams":16,"MatchesPlayed":32,"Attendance":"1.563.135"},{"Year":1970,"Country":"Mexico","Winner":"Brazil","Runners-Up":"Italy","Third":"Germany FR","Fourth":"Uruguay","GoalsScored":95,"QualifiedTeams":16,"MatchesPlayed":32,"Attendance":"1.603.975"},{"Year":1974,"Country":"Germany","Winner":"Germany FR","Runners-Up":"Netherlands","Third":"Poland","Fourth":"Brazil","GoalsScored":97,"QualifiedTeams":16,"MatchesPlayed":38,"Attendance":"1.865.753"},{"Year":1978,"Country":"Argentina","Winner":"Argentina","Runners-Up":"Netherlands","Third":"Brazil","Fourth":"Italy","GoalsScored":102,"QualifiedTeams":16,"MatchesPlayed":38,"Attendance":"1.545.791"},{"Year":1982,"Country":"Spain","Winner":"Italy","Runners-Up":"Germany FR","Third":"Poland","Fourth":"France","GoalsScored":146,"QualifiedTeams":24,"MatchesPlayed":52,"Attendance":"2.109.723"},{"Year":1986,"Country":"Mexico","Winner":"Argentina","Runners-Up":"Germany FR","Third":"France","Fourth":"Belgium","GoalsScored":132,"QualifiedTeams":24,"MatchesPlayed":52,"Attendance":"2.394.031"},{"Year":1990,"Country":"Italy","Winner":"Germany FR","Runners-Up":"Argentina","Third":"Italy","Fourth":"England","GoalsScored":115,"QualifiedTeams":24,"MatchesPlayed":52,"Attendance":"2.516.215"},{"Year":1994,"Country":"USA","Winner":"Brazil","Runners-Up":"Italy","Third":"Sweden","Fourth":"Bulgaria","GoalsScored":141,"QualifiedTeams":24,"MatchesPlayed":52,"Attendance":"3.587.538"},{"Year":1998,"Country":"France","Winner":"France","Runners-Up":"Brazil","Third":"Croatia","Fourth":"Netherlands","GoalsScored":171,"QualifiedTeams":32,"MatchesPlayed":64,"Attendance":"2.785.100"},{"Year":2002,"Country":"Korea\\/Japan","Winner":"Brazil","Runners-Up":"Germany","Third":"Turkey","Fourth":"Korea Republic","GoalsScored":161,"QualifiedTeams":32,"MatchesPlayed":64,"Attendance":"2.705.197"},{"Year":2006,"Country":"Germany","Winner":"Italy","Runners-Up":"France","Third":"Germany","Fourth":"Portugal","GoalsScored":147,"QualifiedTeams":32,"MatchesPlayed":64,"Attendance":"3.359.439"},{"Year":2010,"Country":"South Africa","Winner":"Spain","Runners-Up":"Netherlands","Third":"Germany","Fourth":"Uruguay","GoalsScored":145,"QualifiedTeams":32,"MatchesPlayed":64,"Attendance":"3.178.856"},{"Year":2014,"Country":"Brazil","Winner":"Germany","Runners-Up":"Argentina","Third":"Netherlands","Fourth":"Brazil","GoalsScored":171,"QualifiedTeams":32,"MatchesPlayed":64,"Attendance":"3.386.810"}]'
# see dictionary here
d_wc.to_dict(orient='records')
[{'Year': 1930,
'Country': 'Uruguay',
'Winner': 'Uruguay',
'Runners-Up': 'Argentina',
'Third': 'USA',
'Fourth': 'Yugoslavia',
'GoalsScored': 70,
'QualifiedTeams': 13,
'MatchesPlayed': 18,
'Attendance': '590.549'},
{'Year': 1934,
'Country': 'Italy',
'Winner': 'Italy',
'Runners-Up': 'Czechoslovakia',
'Third': 'Germany',
'Fourth': 'Austria',
'GoalsScored': 70,
'QualifiedTeams': 16,
'MatchesPlayed': 17,
'Attendance': '363.000'},
{'Year': 1938,
'Country': 'France',
'Winner': 'Italy',
'Runners-Up': 'Hungary',
'Third': 'Brazil',
'Fourth': 'Sweden',
'GoalsScored': 84,
'QualifiedTeams': 15,
'MatchesPlayed': 18,
'Attendance': '375.700'},
{'Year': 1950,
'Country': 'Brazil',
'Winner': 'Uruguay',
'Runners-Up': 'Brazil',
'Third': 'Sweden',
'Fourth': 'Spain',
'GoalsScored': 88,
'QualifiedTeams': 13,
'MatchesPlayed': 22,
'Attendance': '1.045.246'},
{'Year': 1954,
'Country': 'Switzerland',
'Winner': 'Germany FR',
'Runners-Up': 'Hungary',
'Third': 'Austria',
'Fourth': 'Uruguay',
'GoalsScored': 140,
'QualifiedTeams': 16,
'MatchesPlayed': 26,
'Attendance': '768.607'},
{'Year': 1958,
'Country': 'Sweden',
'Winner': 'Brazil',
'Runners-Up': 'Sweden',
'Third': 'France',
'Fourth': 'Germany FR',
'GoalsScored': 126,
'QualifiedTeams': 16,
'MatchesPlayed': 35,
'Attendance': '819.810'},
{'Year': 1962,
'Country': 'Chile',
'Winner': 'Brazil',
'Runners-Up': 'Czechoslovakia',
'Third': 'Chile',
'Fourth': 'Yugoslavia',
'GoalsScored': 89,
'QualifiedTeams': 16,
'MatchesPlayed': 32,
'Attendance': '893.172'},
{'Year': 1966,
'Country': 'England',
'Winner': 'England',
'Runners-Up': 'Germany FR',
'Third': 'Portugal',
'Fourth': 'Soviet Union',
'GoalsScored': 89,
'QualifiedTeams': 16,
'MatchesPlayed': 32,
'Attendance': '1.563.135'},
{'Year': 1970,
'Country': 'Mexico',
'Winner': 'Brazil',
'Runners-Up': 'Italy',
'Third': 'Germany FR',
'Fourth': 'Uruguay',
'GoalsScored': 95,
'QualifiedTeams': 16,
'MatchesPlayed': 32,
'Attendance': '1.603.975'},
{'Year': 1974,
'Country': 'Germany',
'Winner': 'Germany FR',
'Runners-Up': 'Netherlands',
'Third': 'Poland',
'Fourth': 'Brazil',
'GoalsScored': 97,
'QualifiedTeams': 16,
'MatchesPlayed': 38,
'Attendance': '1.865.753'},
{'Year': 1978,
'Country': 'Argentina',
'Winner': 'Argentina',
'Runners-Up': 'Netherlands',
'Third': 'Brazil',
'Fourth': 'Italy',
'GoalsScored': 102,
'QualifiedTeams': 16,
'MatchesPlayed': 38,
'Attendance': '1.545.791'},
{'Year': 1982,
'Country': 'Spain',
'Winner': 'Italy',
'Runners-Up': 'Germany FR',
'Third': 'Poland',
'Fourth': 'France',
'GoalsScored': 146,
'QualifiedTeams': 24,
'MatchesPlayed': 52,
'Attendance': '2.109.723'},
{'Year': 1986,
'Country': 'Mexico',
'Winner': 'Argentina',
'Runners-Up': 'Germany FR',
'Third': 'France',
'Fourth': 'Belgium',
'GoalsScored': 132,
'QualifiedTeams': 24,
'MatchesPlayed': 52,
'Attendance': '2.394.031'},
{'Year': 1990,
'Country': 'Italy',
'Winner': 'Germany FR',
'Runners-Up': 'Argentina',
'Third': 'Italy',
'Fourth': 'England',
'GoalsScored': 115,
'QualifiedTeams': 24,
'MatchesPlayed': 52,
'Attendance': '2.516.215'},
{'Year': 1994,
'Country': 'USA',
'Winner': 'Brazil',
'Runners-Up': 'Italy',
'Third': 'Sweden',
'Fourth': 'Bulgaria',
'GoalsScored': 141,
'QualifiedTeams': 24,
'MatchesPlayed': 52,
'Attendance': '3.587.538'},
{'Year': 1998,
'Country': 'France',
'Winner': 'France',
'Runners-Up': 'Brazil',
'Third': 'Croatia',
'Fourth': 'Netherlands',
'GoalsScored': 171,
'QualifiedTeams': 32,
'MatchesPlayed': 64,
'Attendance': '2.785.100'},
{'Year': 2002,
'Country': 'Korea/Japan',
'Winner': 'Brazil',
'Runners-Up': 'Germany',
'Third': 'Turkey',
'Fourth': 'Korea Republic',
'GoalsScored': 161,
'QualifiedTeams': 32,
'MatchesPlayed': 64,
'Attendance': '2.705.197'},
{'Year': 2006,
'Country': 'Germany',
'Winner': 'Italy',
'Runners-Up': 'France',
'Third': 'Germany',
'Fourth': 'Portugal',
'GoalsScored': 147,
'QualifiedTeams': 32,
'MatchesPlayed': 64,
'Attendance': '3.359.439'},
{'Year': 2010,
'Country': 'South Africa',
'Winner': 'Spain',
'Runners-Up': 'Netherlands',
'Third': 'Germany',
'Fourth': 'Uruguay',
'GoalsScored': 145,
'QualifiedTeams': 32,
'MatchesPlayed': 64,
'Attendance': '3.178.856'},
{'Year': 2014,
'Country': 'Brazil',
'Winner': 'Germany',
'Runners-Up': 'Argentina',
'Third': 'Netherlands',
'Fourth': 'Brazil',
'GoalsScored': 171,
'QualifiedTeams': 32,
'MatchesPlayed': 64,
'Attendance': '3.386.810'}]
# save and look in the file
d_wc.to_json("worldcup.json", orient="records")
# load
d = pd.read_json("worldcup.json")
# see
d.head()
| Year | Country | Winner | Runners-Up | Third | Fourth | GoalsScored | QualifiedTeams | MatchesPlayed | Attendance | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1930 | Uruguay | Uruguay | Argentina | USA | Yugoslavia | 70 | 13 | 18 | 590.549 |
| 1 | 1934 | Italy | Italy | Czechoslovakia | Germany | Austria | 70 | 16 | 17 | 363.000 |
| 2 | 1938 | France | Italy | Hungary | Brazil | Sweden | 84 | 15 | 18 | 375.700 |
| 3 | 1950 | Brazil | Uruguay | Brazil | Sweden | Spain | 88 | 13 | 22 | 1.045.246 |
| 4 | 1954 | Switzerland | Germany FR | Hungary | Austria | Uruguay | 140 | 16 | 26 | 768.607 |
Pandas also provides methods to convert your data frame in native Python Data structures. Those can be useful tool for accessing your dataframe in different format, for example, as a dictionary or a list.
# to a dictionary
d_wc.to_dict()
{'Year': {0: 1930,
1: 1934,
2: 1938,
3: 1950,
4: 1954,
5: 1958,
6: 1962,
7: 1966,
8: 1970,
9: 1974,
10: 1978,
11: 1982,
12: 1986,
13: 1990,
14: 1994,
15: 1998,
16: 2002,
17: 2006,
18: 2010,
19: 2014},
'Country': {0: 'Uruguay',
1: 'Italy',
2: 'France',
3: 'Brazil',
4: 'Switzerland',
5: 'Sweden',
6: 'Chile',
7: 'England',
8: 'Mexico',
9: 'Germany',
10: 'Argentina',
11: 'Spain',
12: 'Mexico',
13: 'Italy',
14: 'USA',
15: 'France',
16: 'Korea/Japan',
17: 'Germany',
18: 'South Africa',
19: 'Brazil'},
'Winner': {0: 'Uruguay',
1: 'Italy',
2: 'Italy',
3: 'Uruguay',
4: 'Germany FR',
5: 'Brazil',
6: 'Brazil',
7: 'England',
8: 'Brazil',
9: 'Germany FR',
10: 'Argentina',
11: 'Italy',
12: 'Argentina',
13: 'Germany FR',
14: 'Brazil',
15: 'France',
16: 'Brazil',
17: 'Italy',
18: 'Spain',
19: 'Germany'},
'Runners-Up': {0: 'Argentina',
1: 'Czechoslovakia',
2: 'Hungary',
3: 'Brazil',
4: 'Hungary',
5: 'Sweden',
6: 'Czechoslovakia',
7: 'Germany FR',
8: 'Italy',
9: 'Netherlands',
10: 'Netherlands',
11: 'Germany FR',
12: 'Germany FR',
13: 'Argentina',
14: 'Italy',
15: 'Brazil',
16: 'Germany',
17: 'France',
18: 'Netherlands',
19: 'Argentina'},
'Third': {0: 'USA',
1: 'Germany',
2: 'Brazil',
3: 'Sweden',
4: 'Austria',
5: 'France',
6: 'Chile',
7: 'Portugal',
8: 'Germany FR',
9: 'Poland',
10: 'Brazil',
11: 'Poland',
12: 'France',
13: 'Italy',
14: 'Sweden',
15: 'Croatia',
16: 'Turkey',
17: 'Germany',
18: 'Germany',
19: 'Netherlands'},
'Fourth': {0: 'Yugoslavia',
1: 'Austria',
2: 'Sweden',
3: 'Spain',
4: 'Uruguay',
5: 'Germany FR',
6: 'Yugoslavia',
7: 'Soviet Union',
8: 'Uruguay',
9: 'Brazil',
10: 'Italy',
11: 'France',
12: 'Belgium',
13: 'England',
14: 'Bulgaria',
15: 'Netherlands',
16: 'Korea Republic',
17: 'Portugal',
18: 'Uruguay',
19: 'Brazil'},
'GoalsScored': {0: 70,
1: 70,
2: 84,
3: 88,
4: 140,
5: 126,
6: 89,
7: 89,
8: 95,
9: 97,
10: 102,
11: 146,
12: 132,
13: 115,
14: 141,
15: 171,
16: 161,
17: 147,
18: 145,
19: 171},
'QualifiedTeams': {0: 13,
1: 16,
2: 15,
3: 13,
4: 16,
5: 16,
6: 16,
7: 16,
8: 16,
9: 16,
10: 16,
11: 24,
12: 24,
13: 24,
14: 24,
15: 32,
16: 32,
17: 32,
18: 32,
19: 32},
'MatchesPlayed': {0: 18,
1: 17,
2: 18,
3: 22,
4: 26,
5: 35,
6: 32,
7: 32,
8: 32,
9: 38,
10: 38,
11: 52,
12: 52,
13: 52,
14: 52,
15: 64,
16: 64,
17: 64,
18: 64,
19: 64},
'Attendance': {0: '590.549',
1: '363.000',
2: '375.700',
3: '1.045.246',
4: '768.607',
5: '819.810',
6: '893.172',
7: '1.563.135',
8: '1.603.975',
9: '1.865.753',
10: '1.545.791',
11: '2.109.723',
12: '2.394.031',
13: '2.516.215',
14: '3.587.538',
15: '2.785.100',
16: '2.705.197',
17: '3.359.439',
18: '3.178.856',
19: '3.386.810'}}
# to a numpy array
d_wc.values[0]
array([1930, 'Uruguay', 'Uruguay', 'Argentina', 'USA', 'Yugoslavia', 70,
13, 18, '590.549'], dtype=object)
# To a nested list (which is a method from numpy)
d_wc.values[0].tolist()
[1930, 'Uruguay', 'Uruguay', 'Argentina', 'USA', 'Yugoslavia', 70, 13, 18, '590.549']
You just loaded your first dataset in Python. Let's see some useful tools to preview you data.
pandas.head() : print first n rows¶d_wc.head()
| Year | Country | Winner | Runners-Up | Third | Fourth | GoalsScored | QualifiedTeams | MatchesPlayed | Attendance | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1930 | Uruguay | Uruguay | Argentina | USA | Yugoslavia | 70 | 13 | 18 | 590.549 |
| 1 | 1934 | Italy | Italy | Czechoslovakia | Germany | Austria | 70 | 16 | 17 | 363.000 |
| 2 | 1938 | France | Italy | Hungary | Brazil | Sweden | 84 | 15 | 18 | 375.700 |
| 3 | 1950 | Brazil | Uruguay | Brazil | Sweden | Spain | 88 | 13 | 22 | 1.045.246 |
| 4 | 1954 | Switzerland | Germany FR | Hungary | Austria | Uruguay | 140 | 16 | 26 | 768.607 |
pandas.tail() : print last n rows¶d_wc.tail(10)
| Year | Country | Winner | Runners-Up | Third | Fourth | GoalsScored | QualifiedTeams | MatchesPlayed | Attendance | |
|---|---|---|---|---|---|---|---|---|---|---|
| 10 | 1978 | Argentina | Argentina | Netherlands | Brazil | Italy | 102 | 16 | 38 | 1.545.791 |
| 11 | 1982 | Spain | Italy | Germany FR | Poland | France | 146 | 24 | 52 | 2.109.723 |
| 12 | 1986 | Mexico | Argentina | Germany FR | France | Belgium | 132 | 24 | 52 | 2.394.031 |
| 13 | 1990 | Italy | Germany FR | Argentina | Italy | England | 115 | 24 | 52 | 2.516.215 |
| 14 | 1994 | USA | Brazil | Italy | Sweden | Bulgaria | 141 | 24 | 52 | 3.587.538 |
| 15 | 1998 | France | France | Brazil | Croatia | Netherlands | 171 | 32 | 64 | 2.785.100 |
| 16 | 2002 | Korea/Japan | Brazil | Germany | Turkey | Korea Republic | 161 | 32 | 64 | 2.705.197 |
| 17 | 2006 | Germany | Italy | France | Germany | Portugal | 147 | 32 | 64 | 3.359.439 |
| 18 | 2010 | South Africa | Spain | Netherlands | Germany | Uruguay | 145 | 32 | 64 | 3.178.856 |
| 19 | 2014 | Brazil | Germany | Argentina | Netherlands | Brazil | 171 | 32 | 64 | 3.386.810 |
pandas.sample() : get a sample¶d_wc.sample(5)
| Year | Country | Winner | Runners-Up | Third | Fourth | GoalsScored | QualifiedTeams | MatchesPlayed | Attendance | |
|---|---|---|---|---|---|---|---|---|---|---|
| 13 | 1990 | Italy | Germany FR | Argentina | Italy | England | 115 | 24 | 52 | 2.516.215 |
| 16 | 2002 | Korea/Japan | Brazil | Germany | Turkey | Korea Republic | 161 | 32 | 64 | 2.705.197 |
| 17 | 2006 | Germany | Italy | France | Germany | Portugal | 147 | 32 | 64 | 3.359.439 |
| 3 | 1950 | Brazil | Uruguay | Brazil | Sweden | Spain | 88 | 13 | 22 | 1.045.246 |
| 10 | 1978 | Argentina | Argentina | Netherlands | Brazil | Italy | 102 | 16 | 38 | 1.545.791 |
pandas.info() : Prints information about a DataFrame¶d_wc.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 20 entries, 0 to 19 Data columns (total 10 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Year 20 non-null int64 1 Country 20 non-null object 2 Winner 20 non-null object 3 Runners-Up 20 non-null object 4 Third 20 non-null object 5 Fourth 20 non-null object 6 GoalsScored 20 non-null int64 7 QualifiedTeams 20 non-null int64 8 MatchesPlayed 20 non-null int64 9 Attendance 20 non-null object dtypes: int64(4), object(6) memory usage: 1.7+ KB
pandas.dtypes : Atttributed to see data types¶d_wc.dtypes
Year int64 Country object Winner object Runners-Up object Third object Fourth object GoalsScored int64 QualifiedTeams int64 MatchesPlayed int64 Attendance object dtype: object
pandas.describe() : Summarize all numeric the columns¶d_wc.describe()
| Year | GoalsScored | QualifiedTeams | MatchesPlayed | |
|---|---|---|---|---|
| count | 20.000000 | 20.000000 | 20.000000 | 20.000000 |
| mean | 1974.800000 | 118.950000 | 21.250000 | 41.800000 |
| std | 25.582889 | 32.972836 | 7.268352 | 17.218717 |
| min | 1930.000000 | 70.000000 | 13.000000 | 17.000000 |
| 25% | 1957.000000 | 89.000000 | 16.000000 | 30.500000 |
| 50% | 1976.000000 | 120.500000 | 16.000000 | 38.000000 |
| 75% | 1995.000000 | 145.250000 | 26.000000 | 55.000000 |
| max | 2014.000000 | 171.000000 | 32.000000 | 64.000000 |
pandas.describe() : Summarize a particular column¶d_wc["Third"].describe()
count 20 unique 14 top Germany freq 3 Name: Third, dtype: object
Using the "WorldCups.csv" data, answer the following:
# Add your response here
d = pd.read_csv("WorldCups.csv")
# last world cup game is always the final. Germany vs Argentina
d.tail(1)
# host
d.Country.describe()
# winner unique
d.Winner.describe()
# range
d.Year.describe()
| Year | Country | Winner | Runners-Up | Third | Fourth | GoalsScored | QualifiedTeams | MatchesPlayed | Attendance | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1930 | Uruguay | Uruguay | Argentina | USA | Yugoslavia | 70 | 13 | 18 | 590.549 |
| 1 | 1934 | Italy | Italy | Czechoslovakia | Germany | Austria | 70 | 16 | 17 | 363.000 |
| 2 | 1938 | France | Italy | Hungary | Brazil | Sweden | 84 | 15 | 18 | 375.700 |
| 3 | 1950 | Brazil | Uruguay | Brazil | Sweden | Spain | 88 | 13 | 22 | 1.045.246 |
| 4 | 1954 | Switzerland | Germany FR | Hungary | Austria | Uruguay | 140 | 16 | 26 | 768.607 |
| 5 | 1958 | Sweden | Brazil | Sweden | France | Germany FR | 126 | 16 | 35 | 819.810 |
| 6 | 1962 | Chile | Brazil | Czechoslovakia | Chile | Yugoslavia | 89 | 16 | 32 | 893.172 |
| 7 | 1966 | England | England | Germany FR | Portugal | Soviet Union | 89 | 16 | 32 | 1.563.135 |
| 8 | 1970 | Mexico | Brazil | Italy | Germany FR | Uruguay | 95 | 16 | 32 | 1.603.975 |
| 9 | 1974 | Germany | Germany FR | Netherlands | Poland | Brazil | 97 | 16 | 38 | 1.865.753 |
| 10 | 1978 | Argentina | Argentina | Netherlands | Brazil | Italy | 102 | 16 | 38 | 1.545.791 |
| 11 | 1982 | Spain | Italy | Germany FR | Poland | France | 146 | 24 | 52 | 2.109.723 |
| 12 | 1986 | Mexico | Argentina | Germany FR | France | Belgium | 132 | 24 | 52 | 2.394.031 |
| 13 | 1990 | Italy | Germany FR | Argentina | Italy | England | 115 | 24 | 52 | 2.516.215 |
| 14 | 1994 | USA | Brazil | Italy | Sweden | Bulgaria | 141 | 24 | 52 | 3.587.538 |
| 15 | 1998 | France | France | Brazil | Croatia | Netherlands | 171 | 32 | 64 | 2.785.100 |
| 16 | 2002 | Korea/Japan | Brazil | Germany | Turkey | Korea Republic | 161 | 32 | 64 | 2.705.197 |
| 17 | 2006 | Germany | Italy | France | Germany | Portugal | 147 | 32 | 64 | 3.359.439 |
| 18 | 2010 | South Africa | Spain | Netherlands | Germany | Uruguay | 145 | 32 | 64 | 3.178.856 |
| 19 | 2014 | Brazil | Germany | Argentina | Netherlands | Brazil | 171 | 32 | 64 | 3.386.810 |