Work with Baldwin - Duplicate

import pandas as pd import numpy as np !pip install openpyxl

Collecting openpyxl
  Downloading openpyxl-3.0.9-py2.py3-none-any.whl (242 kB)
     |████████████████████████████████| 242 kB 33.6 MB/s 
Collecting et-xmlfile
  Downloading et_xmlfile-1.1.0-py3-none-any.whl (4.7 kB)
Installing collected packages: et-xmlfile, openpyxl
Successfully installed et-xmlfile-1.1.0 openpyxl-3.0.9

arr = np.random.randint(low = 10, high = 100, size = (20, 10)) df = pd.DataFrame(data = arr)

df.shape

print(pd.ExcelWriter.__doc__)


    Class for writing DataFrame objects into excel sheets.

    Default is to use xlwt for xls, openpyxl for xlsx, odf for ods.
    See DataFrame.to_excel for typical usage.

    The writer should be used as a context manager. Otherwise, call `close()` to save
    and close any opened file handles.

    Parameters
    ----------
    path : str or typing.BinaryIO
        Path to xls or xlsx or ods file.
    engine : str (optional)
        Engine to use for writing. If None, defaults to
        ``io.excel.<extension>.writer``.  NOTE: can only be passed as a keyword
        argument.

        .. deprecated:: 1.2.0

            As the `xlwt <https://pypi.org/project/xlwt/>`__ package is no longer
            maintained, the ``xlwt`` engine will be removed in a future
            version of pandas.

    date_format : str, default None
        Format string for dates written into Excel files (e.g. 'YYYY-MM-DD').
    datetime_format : str, default None
        Format string for datetime objects written into Excel files.
        (e.g. 'YYYY-MM-DD HH:MM:SS').
    mode : {'w', 'a'}, default 'w'
        File mode to use (write or append). Append does not work with fsspec URLs.

        .. versionadded:: 0.24.0
    storage_options : dict, optional
        Extra options that make sense for a particular storage connection, e.g.
        host, port, username, password, etc., if using a URL that will
        be parsed by ``fsspec``, e.g., starting "s3://", "gcs://".

        .. versionadded:: 1.2.0

    Attributes
    ----------
    None

    Methods
    -------
    None

    Notes
    -----
    None of the methods and properties are considered public.

    For compatibility with CSV writers, ExcelWriter serializes lists
    and dicts to strings before writing.

    Examples
    --------
    Default usage:

    >>> with ExcelWriter('path_to_file.xlsx') as writer:
    ...     df.to_excel(writer)

    To write to separate sheets in a single file:

    >>> with ExcelWriter('path_to_file.xlsx') as writer:
    ...     df1.to_excel(writer, sheet_name='Sheet1')
    ...     df2.to_excel(writer, sheet_name='Sheet2')

    You can set the date format or datetime format:

    >>> with ExcelWriter('path_to_file.xlsx',
    ...                   date_format='YYYY-MM-DD',
    ...                   datetime_format='YYYY-MM-DD HH:MM:SS') as writer:
    ...     df.to_excel(writer)

    You can also append to an existing Excel file:

    >>> with ExcelWriter('path_to_file.xlsx', mode='a') as writer:
    ...     df.to_excel(writer, sheet_name='Sheet3')

    You can store Excel file in RAM:

    >>> import io
    >>> buffer = io.BytesIO()
    >>> with pd.ExcelWriter(buffer) as writer:
    ...     df.to_excel(writer)

    You can pack Excel file into zip archive:

    >>> import zipfile
    >>> with zipfile.ZipFile('path_to_file.zip', 'w') as zf:
    ...     with zf.open('filename.xlsx', 'w') as buffer:
    ...         with pd.ExcelWriter(buffer) as writer:
    ...             df.to_excel(writer)

with pd.ExcelWriter('myexcel.xlsx') as writer: df.to_excel(writer)

print(pd.DataFrame.__doc__)


    Two-dimensional, size-mutable, potentially heterogeneous tabular data.

    Data structure also contains labeled axes (rows and columns).
    Arithmetic operations align on both row and column labels. Can be
    thought of as a dict-like container for Series objects. The primary
    pandas data structure.

    Parameters
    ----------
    data : ndarray (structured or homogeneous), Iterable, dict, or DataFrame
        Dict can contain Series, arrays, constants, dataclass or list-like objects. If
        data is a dict, column order follows insertion-order.

        .. versionchanged:: 0.25.0
           If data is a list of dicts, column order follows insertion-order.

    index : Index or array-like
        Index to use for resulting frame. Will default to RangeIndex if
        no indexing information part of input data and no index provided.
    columns : Index or array-like
        Column labels to use for resulting frame. Will default to
        RangeIndex (0, 1, 2, ..., n) if no column labels are provided.
    dtype : dtype, default None
        Data type to force. Only a single dtype is allowed. If None, infer.
    copy : bool, default False
        Copy data from inputs. Only affects DataFrame / 2d ndarray input.

    See Also
    --------
    DataFrame.from_records : Constructor from tuples, also record arrays.
    DataFrame.from_dict : From dicts of Series, arrays, or dicts.
    read_csv : Read a comma-separated values (csv) file into DataFrame.
    read_table : Read general delimited file into DataFrame.
    read_clipboard : Read text from clipboard into DataFrame.

    Examples
    --------
    Constructing DataFrame from a dictionary.

    >>> d = {'col1': [1, 2], 'col2': [3, 4]}
    >>> df = pd.DataFrame(data=d)
    >>> df
       col1  col2
    0     1     3
    1     2     4

    Notice that the inferred dtype is int64.

    >>> df.dtypes
    col1    int64
    col2    int64
    dtype: object

    To enforce a single dtype:

    >>> df = pd.DataFrame(data=d, dtype=np.int8)
    >>> df.dtypes
    col1    int8
    col2    int8
    dtype: object

    Constructing DataFrame from numpy ndarray:

    >>> df2 = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),
    ...                    columns=['a', 'b', 'c'])
    >>> df2
       a  b  c
    0  1  2  3
    1  4  5  6
    2  7  8  9

    Constructing DataFrame from dataclass:

    >>> from dataclasses import make_dataclass
    >>> Point = make_dataclass("Point", [("x", int), ("y", int)])
    >>> pd.DataFrame([Point(0, 0), Point(0, 3), Point(2, 3)])
        x  y
    0  0  0
    1  0  3
    2  2  3

classes = np.tile(['good', 'bad', 'neutral'], 100)[:20] classes = classes[np.random.randint(low = 0, high = 20, size = 20)]

df['Classes'] = classes

df.head()

df['Classes'], _ = pd.factorize(df['Classes'])

df.head()

df_2 = df.copy()[:15]

df.shape

df_2.shape

### Concatenation of dataframes pd.concat(objs =[df, df_2.sort_index(ascending = False)], axis = 1)

angles = np.array([90, 180, 270, 360]) angles = np.deg2rad(angles)

vector = np.random.randint(low = 10, high = 100, size = (10))

vsum = np.sqrt(np.sum(vector**2))

vsum

vsum*vsum/np.dot(vector, vector)

np.cos(angles)

### Merge dataframes pd.merge(left = df, right = df_2, how = 'outer')

all(['', 'dsfbujdfbvklfnbvk'])

any(df)

try_df = pd.concat(objs =[df, df_2.sort_index(ascending = False)], axis = 1)

all(try_df)

pd.isna(try_df)

try_df[pd.isna(try_df)]

try_df.fillna(value = 'Missing', inplace = True)

try_df

all(try_df)

pd.isna(try_df).sum()