web-dev-qa-db-ja.com

iPythonのpandasライブラリを使用して.xlsxファイルを読み取る方法

Pandasのpython Libraryを使用して.xlsxファイルを読み取り、データをpostgreSQLテーブルに移植します。

今までできることは次のとおりです。

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

これでステップが正常に実行されたことがわかりましたが、Excelのデータが変数データのデータにマップされる方法を理解できるように、読み取ったExcelファイルを解析する方法を知りたいです。
間違っていなければ、データはDataframeオブジェクトであることを学びました。したがって、このデータフレームオブジェクトを解析して行ごとに各行を抽出するにはどうすればよいですか?.

71

通常、すべてのシートにDataFrameを含む辞書を作成します。

xl_file = pd.ExcelFile(file_name)

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

更新:pandasバージョン0.21.0+では、 sheet_name=Noneread_Excel に渡すことで、この動作をよりきれいに取得できます。

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

0.20以前では、これはsheet_nameではなくsheetnameでした(これは現在、上記を支持して廃止されています):

dfs = pd.read_Excel(file_name, sheetname=None)
116
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
13
Hafizur Rahman

DataFrameのread_Excelメソッドはread_csvメソッドに似ています:

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

スプレッドシートのファイル名をfileに割り当てます

スプレッドシートを読み込む

シート名を印刷する

Df1という名前でシートをDataFrameにロードします

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

関数read_Excel()を使用して開いたファイルでopen()を使用する場合は、エンコードエラーを回避するために、開いている関数にrbを追加してください。

1
Patrick Mutuku

シート名を使用する代わりに、Excelファイルを知らない、または開くことができない場合、ubuntu(私の場合、Python 3.6.7、ubuntu 18.04)をチェックインするために、パラメーターを使用しますindex_col(最初のシートのindex_col = 0)

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