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Comment lire un fichier .xlsx en utilisant la bibliothèque de pandas dans iPython?

Je souhaite lire un fichier .xlsx à l'aide de la bibliothèque de pandas de Python et porter les données sur une table postgreSQL. 

Tout ce que je pouvais faire jusqu'à maintenant, c'est:

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

Maintenant, je sais que l'étape a été exécutée avec succès, mais je veux savoir comment analyser le fichier Excel lu afin que je puisse comprendre comment les données d'Excel correspondent aux données des données variables. 
J'ai appris que les données sont un objet Dataframe si je ne me trompe pas. Alors, comment analyser cet objet dataframe pour extraire chaque ligne ligne par ligne.

pythonpandasipythonipython-notebookdataframe
54
Sabareesh Kappagantu

Je crée généralement un dictionnaire contenant une DataFrame pour chaque feuille:

xl_file = pd.ExcelFile(file_name)

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

Mise à jour: Dans la version 0.21.0+ de pandas, vous obtiendrez ce problème plus proprement en passant sheet_name=None à read_Excel :

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

Dans les versions 0,20 et antérieure, c'était sheetname plutôt que sheet_name (c'est maintenant déconseillé en faveur de ce qui précède):

dfs = pd.read_Excel(file_name, sheetname=None)
90
Andy Hayden
from pandas import read_Excel
# find your sheet name at the bottom left of your Excel file and assign 
# it to sheet_name
my_sheet = 'Sheet1'
file_name = 'products_and_categories.xlsx' # name of your Excel file
df = read_Excel(file_name, sheet_name = my_sheet)
print(df.head()) # shows headers with top 5 rows
8
Hafizur Rahman

La méthode read_Excel de DataFrame est similaire à la méthode read_csv: 

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.
6
flowera

Affecter le nom de fichier de la feuille de calcul à file

Charger le tableur

Imprimer les noms des feuilles

Charger une feuille dans un DataFrame par son nom: df1

file = 'example.xlsx'
xl = pd.ExcelFile(file)
print(xl.sheet_names)
df1 = xl.parse('Sheet1')
1
ALI

Si vous utilisez read_Excel() sur un fichier ouvert à l'aide de la fonction open(), veillez à ajouter rb à la fonction open pour éviter les erreurs de codage. 

0
Patrick Mutuku

Au lieu d’utiliser un nom de feuille, au cas où vous ne sauriez pas ou ne pouvez pas ouvrir le fichier Excel à archiver dans Ubuntu (dans mon cas, Python 3.6.7, Ubuntu 18.04), j’utilise le paramètre index_col (index_col = 0 pour la première feuille)

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
0
Harry