python pandas 分类汇总用法_Python pandas用法最全整理
1、首先导入pandas库，一般都会用到numpy库，所以我们先导入备用：
import numpy as npimport pandas as pd
2、导入CSV或者xlsx文件：
df = pd.DataFrame(pd.read_csv(name.csv,header=1))df = pd.DataFrame(pd.read_excel(name.xlsx))
3、用pandas创建数据表：
df = pd.DataFrame({"id":[1001,1002,1003,1004,1005,1006], "date":pd.date_range(20130102, periods=6), "city":[Beijing , SH, guangzhou , Shenzhen, shanghai, BEIJING ], "age":[23,44,54,32,34,32], "category":[100-A,100-B,110-A,110-C,210-A,130-F], "price":[1200,np.nan,2133,5433,np.nan,4432]},columns =[id,date,city,category,age,price])
二、数据表信息查看
1、维度查看：
df.shape
1、首先导入pandas库，一般都会用到numpy库，所以我们先导入备用： import numpy as npimport pandas as pd 2、导入CSV或者xlsx文件： df = pd.DataFrame(pd.read_csv(name.csv,header=1))df = pd.DataFrame(pd.read_excel(name.xlsx)) 3、用pandas创建数据表： df = pd.DataFrame({"id":[1001,1002,1003,1004,1005,1006], "date":pd.date_range(20130102, periods=6), "city":[Beijing , SH, guangzhou , Shenzhen, shanghai, BEIJING ], "age":[23,44,54,32,34,32], "category":[100-A,100-B,110-A,110-C,210-A,130-F], "price":[1200,np.nan,2133,5433,np.nan,4432]},columns =[id,date,city,category,age,price]) 二、数据表信息查看 1、维度查看： df.shape