Introduction to Pandas#
pandas is a column-oriented data analysis API. It’s a great tool for handling and analyzing input data, and many ML frameworks support pandas data structures as inputs. Although a comprehensive introduction to the pandas API would span many pages, the core concepts are fairly straightforward, and we’ll present them below. For a more complete reference, the pandas docs site contains extensive documentation and many tutorials.
Inspiration and some of the parts came from: Python Data Science GitHub repository, MIT License and Introduction to Pandas by Google, Apache 2.0
If running this from Google Colab, uncomment the cell below and run it. Otherwise, just skip it.
#!pip install watermark
Learning Objectives:#
Gain an introduction to the DataFrame and Series data structures of the pandas library
Import CSV data into a pandas DataFrame
Access and manipulate data within a DataFrame and Series
Export DataFrame to CSV
Basic Concepts#
The following line imports the pandas API and prints the API version:
import pandas as pd
pd.__version__
'2.0.3'
The primary data structures in pandas are implemented as two classes:
DataFrame, which you can imagine as a relational data table, with rows and named columns.Series, which is a single column. ADataFramecontains one or moreSeriesand a name for eachSeries.
The data frame is a commonly used abstraction for data manipulation. Similar implementations exist in Spark and R.
pandas.Series#
One way to create a Series is to construct a Series object. For example:
pd.Series(['San Francisco', 'San Jose', 'Sacramento'])
0 San Francisco
1 San Jose
2 Sacramento
dtype: object
pandas.DataFrame#
DataFrame objects can be created by passing a dict mapping string column names to their respective Series. If the Series don’t match in length, missing values are filled with special NA/NaN values. Example:
city_names = pd.Series(['San Francisco', 'San Jose', 'Sacramento'])
population = pd.Series([852469, 1015785, 485199])
cities_dataframe = pd.DataFrame({ 'City name': city_names, 'Population': population })
cities_dataframe
| City name | Population | |
|---|---|---|
| 0 | San Francisco | 852469 |
| 1 | San Jose | 1015785 |
| 2 | Sacramento | 485199 |
Reading a DataFrame from a file#
But most of the time, you load an entire file into a DataFrame. The following example loads a file with California housing data. Run the following cell to load the data and create feature definitions:
california_housing_dataframe = pd.read_csv("https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv", sep=",")
california_housing_dataframe.head()
#california_housing_dataframe.tail()
| longitude | latitude | housing_median_age | total_rooms | total_bedrooms | population | households | median_income | median_house_value | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | -114.31 | 34.19 | 15.0 | 5612.0 | 1283.0 | 1015.0 | 472.0 | 1.4936 | 66900.0 |
| 1 | -114.47 | 34.40 | 19.0 | 7650.0 | 1901.0 | 1129.0 | 463.0 | 1.8200 | 80100.0 |
| 2 | -114.56 | 33.69 | 17.0 | 720.0 | 174.0 | 333.0 | 117.0 | 1.6509 | 85700.0 |
| 3 | -114.57 | 33.64 | 14.0 | 1501.0 | 337.0 | 515.0 | 226.0 | 3.1917 | 73400.0 |
| 4 | -114.57 | 33.57 | 20.0 | 1454.0 | 326.0 | 624.0 | 262.0 | 1.9250 | 65500.0 |
If you need to take a peak to documentation, there is always fast way to use ? after function.
pd.read_csv?
Signature:
pd.read_csv(
filepath_or_buffer: 'FilePath | ReadCsvBuffer[bytes] | ReadCsvBuffer[str]',
*,
sep: 'str | None | lib.NoDefault' = <no_default>,
delimiter: 'str | None | lib.NoDefault' = None,
header: "int | Sequence[int] | None | Literal['infer']" = 'infer',
names: 'Sequence[Hashable] | None | lib.NoDefault' = <no_default>,
index_col: 'IndexLabel | Literal[False] | None' = None,
usecols=None,
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: 'bool | Sequence[Hashable] | None' = None,
infer_datetime_format: 'bool | lib.NoDefault' = <no_default>,
keep_date_col: 'bool' = False,
date_parser=<no_default>,
date_format: 'str | None' = 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,
on_bad_lines: 'str' = 'error',
delim_whitespace: 'bool' = False,
low_memory=True,
memory_map: 'bool' = False,
float_precision: "Literal['high', 'legacy'] | None" = None,
storage_options: 'StorageOptions' = None,
dtype_backend: 'DtypeBackend | lib.NoDefault' = <no_default>,
) -> 'DataFrame | TextFileReader'
Docstring:
Read a comma-separated values (csv) file into DataFrame.
Also supports optionally iterating or breaking of the file
into chunks.
Additional help can be found in the online docs for
`IO Tools <https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html>`_.
Parameters
----------
filepath_or_buffer : str, path object or file-like object
Any valid string path is acceptable. The string could be a URL. Valid
URL schemes include http, ftp, s3, gs, and file. For file URLs, a host is
expected. A local file could be: file://localhost/path/to/table.csv.
If you want to pass in a path object, pandas accepts any ``os.PathLike``.
By file-like object, we refer to objects with a ``read()`` method, such as
a file handle (e.g. via builtin ``open`` function) or ``StringIO``.
sep : str, default ','
Delimiter to use. If sep is None, the C engine cannot automatically detect
the separator, but the Python parsing engine can, meaning the latter will
be used and automatically detect the separator by Python's builtin sniffer
tool, ``csv.Sniffer``. In addition, separators longer than 1 character and
different from ``'\s+'`` will be interpreted as regular expressions and
will also force the use of the Python parsing engine. Note that regex
delimiters are prone to ignoring quoted data. Regex example: ``'\r\t'``.
delimiter : str, default ``None``
Alias for sep.
header : int, list of int, None, default 'infer'
Row number(s) to use as the column names, and the start of the
data. Default behavior is to infer the column names: if no names
are passed the behavior is identical to ``header=0`` and column
names are inferred from the first line of the file, if column
names are passed explicitly then the behavior is identical to
``header=None``. Explicitly pass ``header=0`` to be able to
replace existing names. The header can be a list of integers that
specify row locations for a multi-index on the columns
e.g. [0,1,3]. Intervening rows that are not specified will be
skipped (e.g. 2 in this example is skipped). Note that this
parameter ignores commented lines and empty lines if
``skip_blank_lines=True``, so ``header=0`` denotes the first line of
data rather than the first line of the file.
names : array-like, optional
List of column names to use. If the file contains a header row,
then you should explicitly pass ``header=0`` to override the column names.
Duplicates in this list are not allowed.
index_col : int, str, sequence of int / str, or False, optional, default ``None``
Column(s) to use as the row labels of the ``DataFrame``, either given as
string name or column index. If a sequence of int / str is given, a
MultiIndex is used.
Note: ``index_col=False`` can be used to force pandas to *not* use the first
column as the index, e.g. when you have a malformed file with delimiters at
the end of each line.
usecols : list-like or callable, optional
Return a subset of the columns. If list-like, all elements must either
be positional (i.e. integer indices into the document columns) or strings
that correspond to column names provided either by the user in `names` or
inferred from the document header row(s). If ``names`` are given, the document
header row(s) are not taken into account. For example, a valid list-like
`usecols` parameter would be ``[0, 1, 2]`` or ``['foo', 'bar', 'baz']``.
Element order is ignored, so ``usecols=[0, 1]`` is the same as ``[1, 0]``.
To instantiate a DataFrame from ``data`` with element order preserved use
``pd.read_csv(data, usecols=['foo', 'bar'])[['foo', 'bar']]`` for columns
in ``['foo', 'bar']`` order or
``pd.read_csv(data, usecols=['foo', 'bar'])[['bar', 'foo']]``
for ``['bar', 'foo']`` order.
If callable, the callable function will be evaluated against the column
names, returning names where the callable function evaluates to True. An
example of a valid callable argument would be ``lambda x: x.upper() in
['AAA', 'BBB', 'DDD']``. Using this parameter results in much faster
parsing time and lower memory usage.
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 in addition to case-insensitive variants of "True".
false_values : list, optional
Values to consider as False in addition to case-insensitive variants of "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', 'None',
'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``.
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.
.. deprecated:: 2.0.0
A strict version of this argument is now the default, passing it has no effect.
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.
.. deprecated:: 2.0.0
Use ``date_format`` instead, or read in as ``object`` and then apply
:func:`to_datetime` as-needed.
date_format : str or dict of column -> format, default ``None``
If used in conjunction with ``parse_dates``, will parse dates according to this
format. For anything more complex,
please read in as ``object`` and then apply :func:`to_datetime` as-needed.
.. versionadded:: 2.0.0
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.
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, default "utf-8"
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.
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
dtype_backend : {"numpy_nullable", "pyarrow"}, defaults to NumPy backed DataFrames
Which dtype_backend to use, e.g. whether a DataFrame should have NumPy
arrays, nullable dtypes are used for all dtypes that have a nullable
implementation when "numpy_nullable" is set, pyarrow is used for all
dtypes if "pyarrow" is set.
The dtype_backends are still experimential.
.. versionadded:: 2.0
Returns
-------
DataFrame or TextFileReader
A comma-separated values (csv) file is returned as two-dimensional
data structure with labeled axes.
See Also
--------
DataFrame.to_csv : Write DataFrame to a comma-separated values (csv) file.
read_csv : Read a comma-separated values (csv) file into DataFrame.
read_fwf : Read a table of fixed-width formatted lines into DataFrame.
Examples
--------
>>> pd.read_csv('data.csv') # doctest: +SKIP
File: c:\users\mazo260d\mambaforge\envs\devbio-napari-clone\lib\site-packages\pandas\io\parsers\readers.py
Type: function
Selecting Columns, Rows and Creating Subsets#
We index DataFrames by columns, like this:
california_housing_dataframe['population']
0 1015.0
1 1129.0
2 333.0
3 515.0
4 624.0
...
16995 907.0
16996 1194.0
16997 1244.0
16998 1298.0
16999 806.0
Name: population, Length: 17000, dtype: float64
We can get more columns by passing their names as a list. Furthermore, we can store this “sub-dataframe” in a new variable.
sub_dataframe = california_housing_dataframe[ ['population', 'households'] ]
sub_dataframe
| population | households | |
|---|---|---|
| 0 | 1015.0 | 472.0 |
| 1 | 1129.0 | 463.0 |
| 2 | 333.0 | 117.0 |
| 3 | 515.0 | 226.0 |
| 4 | 624.0 | 262.0 |
| ... | ... | ... |
| 16995 | 907.0 | 369.0 |
| 16996 | 1194.0 | 465.0 |
| 16997 | 1244.0 | 456.0 |
| 16998 | 1298.0 | 478.0 |
| 16999 | 806.0 | 270.0 |
17000 rows × 2 columns
If we want to get a single row, the proper way of doing that is to use the .loc method:
row_with_index_2 = california_housing_dataframe.loc[2, ['population', 'households'] ]
row_with_index_2
population 333.0
households 117.0
Name: 2, dtype: float64
In addition, pandas provides an extremely rich API for advanced indexing and selection that is too extensive to be covered here.
Saving data#
A DataFrame can be saved as a .csv file with the .to_csv method.
cities_dataframe.to_csv('cities_out.csv', index=False, sep=";")
Exercise#
From the following loaded CSV file, create a table that only contains these columns:
minor_axis_length
major_axis_length
aspect_ratio
blobs_df = pd.read_csv('../../data/blobs_statistics.csv')
blobs_df
| Unnamed: 0 | area | mean_intensity | minor_axis_length | major_axis_length | eccentricity | extent | feret_diameter_max | equivalent_diameter_area | bbox-0 | bbox-1 | bbox-2 | bbox-3 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 422 | 192.379147 | 16.488550 | 34.566789 | 0.878900 | 0.586111 | 35.227830 | 23.179885 | 0 | 11 | 30 | 35 |
| 1 | 1 | 182 | 180.131868 | 11.736074 | 20.802697 | 0.825665 | 0.787879 | 21.377558 | 15.222667 | 0 | 53 | 11 | 74 |
| 2 | 2 | 661 | 205.216339 | 28.409502 | 30.208433 | 0.339934 | 0.874339 | 32.756679 | 29.010538 | 0 | 95 | 28 | 122 |
| 3 | 3 | 437 | 216.585812 | 23.143996 | 24.606130 | 0.339576 | 0.826087 | 26.925824 | 23.588253 | 0 | 144 | 23 | 167 |
| 4 | 4 | 476 | 212.302521 | 19.852882 | 31.075106 | 0.769317 | 0.863884 | 31.384710 | 24.618327 | 0 | 237 | 29 | 256 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 56 | 56 | 211 | 185.061611 | 14.522762 | 18.489138 | 0.618893 | 0.781481 | 18.973666 | 16.390654 | 232 | 39 | 250 | 54 |
| 57 | 57 | 78 | 185.230769 | 6.028638 | 17.579799 | 0.939361 | 0.722222 | 18.027756 | 9.965575 | 248 | 170 | 254 | 188 |
| 58 | 58 | 86 | 183.720930 | 5.426871 | 21.261427 | 0.966876 | 0.781818 | 22.000000 | 10.464158 | 249 | 117 | 254 | 139 |
| 59 | 59 | 51 | 190.431373 | 5.032414 | 13.742079 | 0.930534 | 0.728571 | 14.035669 | 8.058239 | 249 | 228 | 254 | 242 |
| 60 | 60 | 46 | 175.304348 | 3.803982 | 15.948714 | 0.971139 | 0.766667 | 15.033296 | 7.653040 | 250 | 67 | 254 | 82 |
61 rows × 13 columns
Watermark
from watermark import watermark
watermark(iversions=True, globals_=globals())
print(watermark())
print(watermark(packages="watermark,numpy,pandas,seaborn,pivottablejs"))
Last updated: 2023-08-24T14:24:06.278180+02:00
Python implementation: CPython
Python version : 3.9.17
IPython version : 8.14.0
Compiler : MSC v.1929 64 bit (AMD64)
OS : Windows
Release : 10
Machine : AMD64
Processor : Intel64 Family 6 Model 165 Stepping 2, GenuineIntel
CPU cores : 16
Architecture: 64bit
watermark : 2.4.3
numpy : 1.23.5
pandas : 2.0.3
seaborn : 0.12.2
pivottablejs: 0.9.0