learningcoder的专栏

转自： https://www.jb51.net/article/79494.htm https://blog.youkuaiyun.com/weixin_39274753/article/details/82221859

# -*- coding:utf-8 -*- import pandas as pd # 一行转多行 df = pd.DataFrame( {'Code': ['1111', '2222'], 'Color': ['a, b, c,d', 'blue, pink, yellow'], 'Size': ['1, 2, 3', '7, 6, 10']} ) id_df ...

df1 df2 join是按照行索引来关联操作,列名不能相同 df1.join(df2) #返回df1的行数，df1和df2的所有列名 值没有的话用NaN merge是按照列索引来关联操作，和关系型数据库很相近 1. df1.merge(df2,on=0) #等价于df1.0=df2.0 0 1_x 2 3 4 1_y 0 18 15 19...

# -*- coding:utf-8 -*- import pandas as pd import numpy as np # DataFrame初始化 numpy.ndarray df = pd.DataFrame(np.arange(12).reshape(3, 4), columns=["col" + str(i) for i in range(4)], index=list("ABC"...

# -*- coding:utf-8 -*- import pandas as pd import string ''' Series 对象是由两个数组组成，一个是索引(Index) 一个是值(values) 构造成键值对 ''' # Series数据初始化 列表 s1 = pd.Series([1, 2, 3], index=["a", "b", "c"]) print(type(s1))...

# -*- coding:utf-8 -*- import numpy as np t1 = np.arange(6).reshape(2, 3) t2 = np.arange(10, 16).reshape(2, 3) # 竖直拼接,vertically 相当于union all # print(np.vstack((t1, t2))) # 水平拼接 horizontally # print...

# -*- coding:utf-8 -*- import numpy as np t1 = np.arange(24).reshape(4, 6) print(t1) print("-" * 100) # 获取二维表中记录,通用方法 [行,列] # 取一行记录 print(t1[1, :]) # <class 'numpy.ndarray'> # 取连续多行记录 print...

# -*- coding:utf-8 -*- import numpy as np # 和数字计算，广播计算(加，减，乘，除) t1 = np.arange(5) print(t1) # [0 1 2 3 4 ] print(t1 + 1) # [ 1 2 3 4 5] # print(t1 / 0) # [nan inf inf inf inf] nan=not a num...

# -*- coding:utf-8 -*- import numpy as np import random # ############数值型的数组############# # 创建数组 t1 = np.array([1, 2, 3]) print(t1) # [1 2 3] print(type(t1)) # <class 'numpy.ndarray'> t2 =...