Wie lies ich eine .xlsx-Datei mit der Pandas-Bibliothek in iPython?

Question

Ich mag eine XLSX-Datei mit der Pandas Bibliothek von Python und Port die Daten an eine postgreSQL Tabelle lesen.

Alles, was ich bis jetzt tun könnte ist:

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

Jetzt weiß ich, dass der Schritt erfolgreich ausgeführt wurde, aber ich möchte wissen, wie ich die Datei Excel analysieren kann, die so gelesen wurde dass ich verstehen kann, wie die Daten im Excel den Daten in den variablen Daten zugeordnet werden.
Ich habe gelernt, dass Daten ein Dataframe-Objekt sind, wenn ich nicht falsch liege. Also, wie analysiere ich dieses Datenobjekt, um jede Zeile Zeile für Zeile zu extrahieren.

Answer 1

Ich schaffe in der Regel ein Wörterbuch ein DataFrame für jedes Blatt enthält:

xl_file = pd.ExcelFile(file_name) 

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

---

Update: In Pandas Version 0.20.0+ (edit: vielleicht 0.19.2 auch) Sie werden dieses Verhalten bekommen mehr sauber von sheetname=None zu read_excel vorbei:

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

Answer 2

Datenrahmen des read_excel Methode ist wie read_csv Methode:.

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.