©2020-2024 ioDraw All rights reserved,
Privacy Policy
pandas是一个十分强大的数据处理工具，最近需要处理数据并输入到excel,简单列举它的几个用法：
1.按行写入excel:
import os import pandas as pd cur_dir = os.path.dirname(__file__) #
c创建一个DataFrame数据类型，并写入表头 res = pd.DataFrame(columns=("k1", "k2", "k3")) #添加两行数据
res.loc[1] = ["11", "22", "33"] res.loc[2] = ["111", "222", "333"] # 写入excel文件
res.to_excel(os.path.join(cur_dir, "test.xlsx"))
#读取excel内容,默认sheet_name=‘sheet1’,按需配置 df =pd.read_excel(os.path.join(cur_dir,
"test.xlsx")) print(df.head()) #获取某一单元格数据,索引从0开始，不包含表头 df.iloc[m][m]
输出为：
Unnamed: 0 k1 k2 k3 0 1 11 22 33 1 2 111 222 333
2.按列写入：
df2 =pd.DataFrame({"data2": ['aa', 'bb', 'cc', 'dd', 'ee'],"data3":['ss', 'ff',
'gg', 'hh', 'kk']}) df2.to_excel(os.path.join(cur_dir, "test1.xlsx"), sheet_name
="sheet1",startcol=0, index=False)
to_excel每次运行会覆盖上一次生成的文件
输出为：
3.交换两列
import os cur_dir = os.path.dirname(__file__) import pandas as pd df2 =pd.
DataFrame({"data2": ['aa', 'bb', 'cc', 'dd', 'ee'],"data3":['ss', 'ff', 'gg',
'hh', 'kk']}) df2.to_excel(os.path.join(cur_dir, "test1.xlsx"), sheet_name=
"sheet1",startcol=0, index=False) dfnew = df2[['data3', 'data2']] print(dfnew)
#两种方法均可 dfnew2 =pd.DataFrame(df2, columns=['data3', 'data2']) print(dfnew2)
交换后的内容为：
data3 data2 0 ss aa 1 ff bb 2 gg cc 3 hh dd 4 kk ee data3 data2 0 ss aa 1 ff
bb2 gg cc 3 hh dd 4 kk ee
4.把某一行转换为list打印
df2 =pd.DataFrame({"data2": ['aa', 'bb', 'cc', 'dd', 'ee'],"data3":['ss', 'ff',
'gg', 'hh', 'kk']}) df2.to_excel(os.path.join(cur_dir, "test1.xlsx"), sheet_name
="sheet1",startcol=0, index=False) print(df2) print("*"*10) print(df2.loc[1])
print("*"*10) print(df2.iloc[1].tolist()) print("*"*10) tmp = df2.iloc[1].
to_string() print(tmp, type(tmp))
输出为：
data2 data3 0 aa ss 1 bb ff 2 cc gg 3 dd hh 4 ee kk ********** data2 bb data3
ff Name: 1, dtype: object ********** ['bb', 'ff'] ********** data2 bb data3 ff <
class 'str'>
5.获取行索引列索引
df2 =pd.DataFrame({"data2": ['aa', 'bb', 'cc', 'dd', 'ee'], "data3": ['ss',
'ff', 'gg', 'hh', 'kk']}) df2.to_excel(os.path.join(cur_dir, "test1.xlsx"),
sheet_name="sheet1", startcol=0, index=False) print(df2.index) #获取所有表项 print(df2
.columns) for x in df2.columns: print(x)
pandas更多功能还需要继续学习~~