Come leggere un file .xlsx usando la libreria pandas in iPython?

Voglio leggere un file .xlsx usando la libreria Pandas di python e trasferire i dati su una tabella postgreSQL.

Tutto quello che ho potuto fare fino ad ora è:

import pandas as pd
data = pd.ExcelFile("*File Name*")

Ora so che il passaggio è stato eseguito con successo, ma voglio sapere come posso analizzare il file excel che è stato letto in modo da poter capire come i dati in Excel si mappano ai dati nei dati variabili.
Ho imparato che i dati sono un oggetto Dataframe se non sbaglio. Quindi, come analizzo questo oggetto dataframe per estrarre ogni riga riga per riga.

Di solito creo un dizionario contenente un DataFrameper ogni foglio:

xl_file = pd.ExcelFile(file_name)

dfs = {sheet_name: xl_file.parse(sheet_name) 
          for sheet_name in xl_file.sheet_names}

Aggiornamento: nella versione 0.21.0+ di Panda otterrai questo comportamento in modo più pulito passando sheet_name=Nonea read_excel:

dfs = pd.read_excel(file_name, sheet_name=None)

In 0.20 e precedenti, questo era sheetnameinvece di sheet_name(questo è ora deprecato a favore di quanto sopra):

dfs = pd.read_excel(file_name, sheetname=None)

from pandas import read_excel
# find your sheet name at the bottom left of your excel file and assign 
# it to my_sheet 
my_sheet = 'Sheet1' # change it to your sheet name
file_name = 'products_and_categories.xlsx' # change it to the name of your excel file
df = read_excel(file_name, sheet_name = my_sheet)
print(df.head()) # shows headers with top 5 rows

Il read_excelmetodo di DataFrame è come il read_csvmetodo:

dfs = pd.read_excel(xlsx_file, sheetname="sheet1")


Help on function read_excel in module pandas.io.excel:

read_excel(io, sheetname=0, header=0, skiprows=None, skip_footer=0, index_col=None, names=None, parse_cols=None, parse_dates=False, date_parser=None, na_values=None, thousands=None, convert_float=True, has_index_names=None, converters=None, true_values=None, false_values=None, engine=None, squeeze=False, **kwds)
    Read an Excel table into a pandas DataFrame

    Parameters
    ----------
    io : string, path object (pathlib.Path or py._path.local.LocalPath),
        file-like object, pandas ExcelFile, or xlrd workbook.
        The string could be a URL. Valid URL schemes include http, ftp, s3,
        and file. For file URLs, a host is expected. For instance, a local
        file could be file://localhost/path/to/workbook.xlsx
    sheetname : string, int, mixed list of strings/ints, or None, default 0

        Strings are used for sheet names, Integers are used in zero-indexed
        sheet positions.

        Lists of strings/integers are used to request multiple sheets.

        Specify None to get all sheets.

        str|int -> DataFrame is returned.
        list|None -> Dict of DataFrames is returned, with keys representing
        sheets.

        Available Cases

        * Defaults to 0 -> 1st sheet as a DataFrame
        * 1 -> 2nd sheet as a DataFrame
        * "Sheet1" -> 1st sheet as a DataFrame
        * [0,1,"Sheet5"] -> 1st, 2nd & 5th sheet as a dictionary of DataFrames
        * None -> All sheets as a dictionary of DataFrames

    header : int, list of ints, default 0
        Row (0-indexed) to use for the column labels of the parsed
        DataFrame. If a list of integers is passed those row positions will
        be combined into a ``MultiIndex``
    skiprows : list-like
        Rows to skip at the beginning (0-indexed)
    skip_footer : int, default 0
        Rows at the end to skip (0-indexed)
    index_col : int, list of ints, default None
        Column (0-indexed) to use as the row labels of the DataFrame.
        Pass None if there is no such column.  If a list is passed,
        those columns will be combined into a ``MultiIndex``
    names : array-like, default None
        List of column names to use. If file contains no header row,
        then you should explicitly pass header=None
    converters : dict, default None
        Dict of functions for converting values in certain columns. Keys can
        either be integers or column labels, values are functions that take one
        input argument, the Excel cell content, and return the transformed
        content.
    true_values : list, default None
        Values to consider as True

        .. versionadded:: 0.19.0

    false_values : list, default None
        Values to consider as False

        .. versionadded:: 0.19.0

    parse_cols : int or list, default None
        * If None then parse all columns,
        * If int then indicates last column to be parsed
        * If list of ints then indicates list of column numbers to be parsed
        * If string then indicates comma separated list of column names and
          column ranges (e.g. "A:E" or "A,C,E:F")
    squeeze : boolean, default False
        If the parsed data only contains one column then return a Series
    na_values : scalar, str, list-like, or dict, default None
        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', 'N/A', 'NA', 'NULL', 'NaN', 'nan'.
    thousands : str, default None
        Thousands separator for parsing string columns to numeric.  Note that
        this parameter is only necessary for columns stored as TEXT in Excel,
        any numeric columns will automatically be parsed, regardless of display
        format.
    keep_default_na : bool, default True
        If na_values are specified and keep_default_na is False the default NaN
        values are overridden, otherwise they're appended to.
    verbose : boolean, default False
        Indicate number of NA values placed in non-numeric columns
    engine: string, default None
        If io is not a buffer or path, this must be set to identify io.
        Acceptable values are None or xlrd
    convert_float : boolean, default True
        convert integral floats to int (i.e., 1.0 --> 1). If False, all numeric
        data will be read in as floats: Excel stores all numbers as floats
        internally
    has_index_names : boolean, default None
        DEPRECATED: for version 0.17+ index names will be automatically
        inferred based on index_col.  To read Excel output from 0.16.2 and
        prior that had saved index names, use True.

    Returns
    -------
    parsed : DataFrame or Dict of DataFrames
        DataFrame from the passed in Excel file.  See notes in sheetname
        argument for more information on when a Dict of Dataframes is returned.

Invece di usare il nome di un foglio, nel caso in cui non si sappia o non si possa aprire il file excel per il check in ubuntu (nel mio caso, Python 3.6.7, ubuntu 18.04), utilizzo il parametro index_col (index_col = 0 per il primo foglio)

import pandas as pd
file_name = 'some_data_file.xlsx' 
df = pd.read_excel(file_name, index_col=0)
print(df.head()) # print the first 5 rows

Assegna il nome del file del foglio di lavoro a file

Carica foglio di lavoro

Stampa i nomi dei fogli

Carica un foglio in un DataFrame per nome: df1

file = 'example.xlsx'
xl = pd.ExcelFile(file)
print(xl.sheet_names)
df1 = xl.parse('Sheet1')

Se si utilizza read_excel()su un file aperto utilizzando la funzione open(), assicurarsi di aggiungere rballa funzione open per evitare errori di codifica