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摘要： #这里讲演pandas的汇总与统计import numpy as npimport pandas as pd #我自己的私有函数库，我都加了t_+函数名的t_data={ "name":["唐浩","小王","老王","赵三","李四","王姐"], "sex":["男","女","男","女"," 阅读全文

摘要： import pandas as pdimport numpy as np# data={# "name":["唐浩","小王","老王","赵三","李四"],# "sex":["男","女","男","女","男"],# "year":[37,22,15,18,33],# "city":["成都 阅读全文

摘要： import pandas as pd data={ "name":["唐浩","小王","老王","赵三","李四"], "sex":["男","女","男","女","男"], "year":[37,22,15,18,33], "city":["成都","北京"," 阅读全文

摘要： #这里学习索引import pandas as pd #下面来重建Series和DataFrame索引项 #Series类型的索引重建,是对索引重新排序reindex,没有的值就是NaN# objs1=pd.Series([1,2,5,3,7,4,5],index=[1,2,3,4,5,6,7])# 阅读全文

摘要： #DataFrame数据结构,很像二维表格数据结构，也是python中最常用的数据结构 import pandas as pdimport numpy as np #创建DataFrame数据#先给出一个字典data，我们用字典来创建data={ "name":["唐浩","小王","老王","赵三 阅读全文

摘要： #pandas有两个基本数据结构，Series 和 DataFrame，这两种数据类型都带索引#简单了说，Series是一列数据带索引，DataFrame是像表格一样的数据结构带索引 import pandas as pd #创建Series数据,两种方式，一种系统指派索引，二种是自己创建索引# o 阅读全文

摘要： import numpy as np nparr1=np.arange(0,20).reshape(4,5) print(nparr1) #存储 np.savetxt("nparr1.csv",nparr1,fmt="%d",delimiter=",") # np.savetxt("nparr2.c 阅读全文

摘要： #运算 import numpy as np #列表相乘# ll=[1,2,3,4,5]# ll2=ll*2 # [1, 2, 3, 4, 5, 1, 2, 3, 4, 5]# print(ll2) # 列表相加# ll3=[1,2,3,4,5]# ll4=ll3+10 #报错,不能这样用# pri 阅读全文

摘要： import numpy as np #索引与列表相同# nparr1=np.arange(0,10)# print(nparr1) # [0 1 2 3 4 5 6 7 8 9]# nparr1[1]=10# nparr1[-1]=15# print(nparr1)# print(nparr1[- 阅读全文

摘要： #numpy是个外部库，要先安装，再导入，再使用import numpy as np #创建一个ndarray数组# arr=([1,2,3,4,5],[4,5,6,7,8])# nparr=np.array(arr)# print(nparr)## nparr2=np.array([1.0,2,3 阅读全文