Python's DataFrame implements excel merged cells_python
This article mainly introduces the DataFrame in python to implement excel merging cells in detail. It has a certain reference value. Interested friends can refer to it.
I often encounter the need to merge cells at work. The data is output to excel, and some of the cells need to be merged. For example, in the table below, the corresponding cells in columns B and C need to be merged based on the value of column A
2. Define a my_mergewr_excel method. The parameters are: the path to output excel, the key_cols list used to determine whether it needs to be merged, and the list used to indicate which columns of cells need to be merged.
3. Add MY_DataFrame Encapsulated as a My_Module module for reuse.
2 , if it is judged that CN is greater than 1, the group needs to be merged, otherwise the group (row) does not need to be merged (CN=1 means that the data row of this group is unique and does not need to be merged)
3. Corresponding to the group that needs to be merged, judge the current column Whether it is in the given parameter [Merge Column], if so, use merge to write excel cells, otherwise, just write excel cells normally.
4. In the column that needs to be merged, if RN=1, call merge_range and write CN cells at once. If RN>1, skip the cell because RN=1 At that time, the cell has been merged and written. If erge_range is called repeatedly, an error will be reported when opening the excel document.
# -*- coding: utf-8 -*- """ Created on 20170301 @author: ARK-Z """ import xlsxwriter import pandas as pd class My_DataFrame(pd.DataFrame): def __init__(self, data=None, index=None, columns=None, dtype=None, copy=False): pd.DataFrame.__init__(self, data, index, columns, dtype, copy) def my_mergewr_excel(self,path,key_cols=[],merge_cols=[]): # sheet_name='Sheet1', na_rep='', float_format=None, columns=None, header=True, index=True, index_label=None, startrow=0, startcol=0, engine=None, merge_cells=True, encoding=None, inf_rep='inf', verbose=True): self_copy=My_DataFrame(self,copy=True) line_cn=self_copy.index.size cols=list(self_copy.columns.values) if all([v in cols for i,v in enumerate(key_cols)])==False: #校验key_cols中各元素 是否都包含与对象的列 print("key_cols is not completely include object's columns") return False if all([v in cols for i,v in enumerate(merge_cols)])==False: #校验merge_cols中各元素 是否都包含与对象的列 print("merge_cols is not completely include object's columns") return False wb2007 = xlsxwriter.Workbook(path) worksheet2007 = wb2007.add_worksheet() format_top = wb2007.add_format({'border':1,'bold':True,'text_wrap':True}) format_other = wb2007.add_format({'border':1,'valign':'vcenter'}) for i,value in enumerate(cols): #写表头 #print(value) worksheet2007.write(0,i,value,format_top) #merge_cols=['B','A','C'] #key_cols=['A','B'] if key_cols ==[]: #如果key_cols 参数不传值,则无需合并 self_copy['RN']=1 self_copy['CN']=1 else: self_copy['RN']=self_copy.groupby(key_cols,as_index=False).rank(method='first').ix[:,0] #以key_cols作为是否合并的依据 self_copy['CN']=self_copy.groupby(key_cols,as_index=False).rank(method='max').ix[:,0] #print(self) for i in range(line_cn): if self_copy.ix[i,'CN']>1: #print('该行有需要合并的单元格') for j,col in enumerate(cols): #print(self_copy.ix[i,col]) if col in (merge_cols): #哪些列需要合并 if self_copy.ix[i,'RN']==1: #合并写第一个单元格,下一个第一个将不再写 worksheet2007.merge_range(i+1,j,i+int(self_copy.ix[i,'CN']),j, self_copy.ix[i,col],format_other) ##合并单元格,根据LINE_SET[7]判断需要合并几个 #worksheet2007.write(i+1,j,df.ix[i,col]) else: pass #worksheet2007.write(i+1,j,df.ix[i,j]) else: worksheet2007.write(i+1,j,self_copy.ix[i,col],format_other) #print(',') else: #print('该行无需要合并的单元格') for j,col in enumerate(cols): #print(df.ix[i,col]) worksheet2007.write(i+1,j,self_copy.ix[i,col],format_other) wb2007.close() self_copy.drop('CN', axis=1) self_copy.drop('RN', axis=1)
import My_Module DF=My_DataFrame({'A':[1,2,2,2,3,3],'B':[1,1,1,1,1,1],'C':[1,1,1,1,1,1],'D':[1,1,1,1,1,1]}) DF Out[120]: A B C D 0 1 1 1 1 1 2 1 1 1 2 2 1 1 1 3 2 1 1 1 4 3 1 1 1 5 3 1 1 1 DF.my_mergewr_excel('000_2.xlsx',['A'],['B','C'])
DF.my_mergewr_excel('000_2.xlsx',['A'],['A','B'])
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