When using python to perform batch operations on excel or csv files, in addition to using the xlrd library or xlwt library for table operations to read and write, you can also use the pandas library for similar operations, and in some cases pandas operations are more brief and convenient.
The read_csv or read_excel method of pandas can be read. We see that there are many parameters. Use skiprows to set the number of skipped rows :
pd.read_excel(io, sheetname=0,header=0,skiprows=None,index_col=None,names=None,
arse_cols=None,date_parser=None,na_values=None,thousands=None,
convert_float=True,has_index_names=None,converters=None,dtype=None,
true_values=None,false_values=None,engine=None,squeeze=False,**kwds)
io参数：一般是文件路径+文件名，
sheet_name参数：用来设置读取的工作表名称，可以为str，int，list或None，默认0。
设置为整数用于零索引工作表位置，字符串/整数列表用于请求多个工作表。设置None获取所有工作表。
header参数：用来指定哪些行作为列名的，默认是第0行，接收的参数可以是整数，可以是有整数组成的列表
index_col参数：指定用哪些列做为行索引，可以接收整数，或者由整数组成的列表，默认是None。
这个参数的作用是指定用哪一列做为行索引。如果传给参数的是整数n，则表示指定第n列作为行索引，如果传入的是列表，则表示需要指定多列作为行索引。
skiprows参数：要跳过的行或行范围（甚至是lambda）The following is an example of operating files:
We have observed that a certain number of blank lines before the title in multiple worksheets are useless, and the number of blank lines before the title of different worksheets is not the same, which leads to the uncertainty of the row position of the row index. Can the program determine by itself? Skip the corresponding number of lines
import pandas as pd
import numpy as np
import re
dfs=pd.read_excel(r".\cz_2020.9-2021.1.xls",sheet_name=None,header=None,index_col=None,skiprows=None)
df_dst=pd.DataFrame(data=None,columns=["姓名","年级","合计"])
for sht_name,df in dfs.items():
#仅仅读取 XXXX.XX格式的工作表
if re.match(r"\d{4}\.\d{1,2}",sht_name):
# first_row = (df.count(axis=1) >= df.shape[1]).idxmax() #查找列名所在行数的一种方法，处理10,11月份没有问题，9月份出现问题
first_row = (df.count(axis=1) >= df.count(axis=1).max()).idxmax() #查找列名所在行数的另一种办法
print("工作表:%s 跳过了%d行" %(sht_name,first_row))
df.columns=df.loc[first_row] #更改当前df的列索引名称
df.rename(columns =lambda x:str(x).strip("\r\n\t ."),inplace=True) #去掉列名首位的空白字符
df=df.loc[first_row+1:]
df_dst=pd.merge(df_dst,df[["姓名","年级","合计"]],how="outer",on="姓名",suffixes=("",sht_name)) #以姓名作为公共列，对多个df数据集进行连接。
#print(df)
else:
print(sht_name,"ignore")
#删掉第一个年级列，将第一个合并列移动到最后
df_tmp=df_dst.pop("合计")
df_dst.insert(df_dst.shape[1],"合计",df_tmp)
df_dst= df_dst.drop(["年级"],axis=1)
df_dst.to_excel(r"..\wage\wage_dst.xls")
The results of the program are as follows:
The most critical code is as follows:
# first_row = (df.count(axis=1) >= df.shape[1]).idxmax() #查找列名所在行数的一种方法，处理10,11月份没有问题，9月份出现问题
first_row = (df.count(axis=1) >= df.count(axis=1).max()).idxmax() #查找列名所在行数的另一种办法
df.columns=df.loc[first_row] #更改当前df的列索引名称
df=df.loc[first_row+1:]
That is to make full use of the effect of df.count(axis=1), and use (df.count(axis=1) >= df.shape[1]).idxmax() for the worksheets in October and November can also achieve the effect ,but
(df.count(axis=1) >= df.count(axis=1).max()).idxmax() is more applicable