用pandas和多索引读取python中的excel文件

Question

我是python新手，所以请原谅这个基本问题。我的.xlsx文件如下所示

Unnamend:1    A     Unnamend:2    B
2015-01-01    10    2015-01-01    10
2015-01-02    20    2015-01-01    20
2015-01-03    30    NaT           NaN

当我使用pandas.read_excel(...)在Python中阅读它时pandas自动使用第一列作为时间索引。

有没有一句话告诉熊猫注意，每隔一列就是一个时间索引，属于它旁边的时间序列？

所需的输出将如下所示：

date          A     B
2015-01-01    10    10
2015-01-02    20    20
2015-01-03    30    NaN

Answer 1

为了解析相邻columns的块并对齐它们各自的datetime索引，您可以执行以下操作：

从df开始

Int64Index: 3 entries, 0 to 2
Data columns (total 4 columns):
Unnamed: 0    3 non-null datetime64[ns]
A             3 non-null int64
Unnamed: 1    2 non-null datetime64[ns]
B             2 non-null float64
dtypes: datetime64[ns](2), float64(1), int64(1)

您可以在index上迭代包含2列和merge的块，如下所示：

def chunks(l, n):
    """ Yield successive n-sized chunks from l."""
    for i in range(0, len(l), n):
        yield l[i:i + n]

merged = df.loc[:, list(df)[:2]].set_index(list(df)[0])
for cols in chunks(list(df)[2:], 2):
    merged = merged.merge(df.loc[:, cols].set_index(cols[0]).dropna(), left_index=True, right_index=True, how='outer')

要获得以下信息：

             A   B
2015-01-01  10  10
2015-01-01  10  20
2015-01-02  20 NaN
2015-01-03  30 NaN

不幸的是，pd.concat不能工作，因为它不能处理重复的index条目，否则可以使用list comprehension

pd.concat([df.loc[:, cols].set_index(cols[0]) for cols in chunks(list(df), 2)], axis=1)

Answer 2

在使用pandas显示数据之后，我使用xlrd导入数据

import xlrd
import pandas as pd
workbook = xlrd.open_workbook(xls_name)
workbook = xlrd.open_workbook(xls_name, encoding_override="cp1252")
worksheet = workbook.sheet_by_index(0)
first_row = [] # The row where we stock the name of the column
for col in range(worksheet.ncols):
    first_row.append( worksheet.cell_value(0,col) )
data =[]
for row in range(10, worksheet.nrows):
    elm = {}
    for col in range(worksheet.ncols):
          elm[first_row[col]]=worksheet.cell_value(row,col)
    data.append(elm)

first_column=second_column=third_column=[]
for elm in data :
    first_column.append(elm(first_row[0]))
    second_column.append(elm(first_row[1]))
    third_column.append(elm(first_row[2]))

dict1={}
dict1[first_row[0]]=first_column
dict1[first_row[1]]=second_column
dict1[first_row[2]]=third_column
res=pd.DataFrame(dict1, columns=['column1', 'column2', 'column3'])
print res