cmdxly的博客

目录 1.基本命令 1.1 基本运算 1.2 判断与循环 1.3 函数 2. 数据结构 2.1 列表/元组 2.2 字典 2.3集合 2.3 函数式编程 1.基本命令 1.1 基本运算 #直接赋值，无需定义类型 a = 2 #多重赋值 a,b,c = 2,3,4 #字符串操作,直接赋值，无需定义类型 s = 'I like pyhton' s + 'hhhh' print(s) #out<< I like pyhton hhhh #将s以空格分隔，可以得到列表 s.split(' ') #o

目录 2.Advance categorical plots in seaborn 2.1Categorical scatterplots 2.2 Categorical distribution plots 2.3 Categorical estimate plots 3.Density plots 4.Pair plots 2.Advance categorical plots in seaborn 2.1Categorical scatterplots: stripplot() (with kin

目录 1.Createing basic plots 2.Advance Categorical plots in Seaborn 1.Createing basic plots # importing required libraries import seaborn as sns sns.set() sns.set(style = "darkgrid") import numpy as np import pandas as pd # importing matplotlib import ma

目录 3.load dataset 4.chart 3.load dataset # read the dataset data_BM = pd.read_csv("bigmart_data.csv") #drop the null data data_BM = data_BM.dropna(how = 'any') # view the top results #data_BM.head() 4.chart 4.1 Line chart price_by_item = data_BM.groupby

目录 1.Setting up 2.Matplotlib basics 1.Setting up #import required libraries import numpy as np import pandas as pd #import matplotlib import matplotlib.pyplot as plt #display plots in the notebook itself %matplotlib inline 2.Matplotlib basics # set pl

#目录 5.Aggregating data 5.Aggregating data #import required dictionary import pandas as pd import numpy as np #read the dataset data_BM = pd.read_csv('bigmart_data.csv') #drop the null values data_BM = data_BM.dropna(how = "any") #reset index after dropp

目录 3.Merging dataframes-merge() 4.Apply function 3.Merging dataframes-merge() # create dummy data df_a = pd.DataFrame({ 'subject_id': ['1', '2', '3', '4', '5'], 'first_name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung'], 'last_nam

目录 1.Sorting dataframes 2.Merging dataframes-concat() 1.Sorting dataframes import pandas as pd import numpy as np #read the dataset data_BM = pd.read_csv("bigmart_data.csv") #drop the null values data_BM = data_BM.dropna(how = "any") #view the results d

目录 7.文件输入输出 8.练习题 7.文件输入输出 7.1 读取csv文件作为数组 arr = np.loadtxt('path\\name.txt',delimiter = ',') print(arr) #out<< [[ 1. 2. 3. 4. 4. 5.] [ 1. 2. 3. 4. 5. 6.] [ 1. 2. 34. 5. 5. 5.]] 7.2 数组文件读写 arr = np.arange(50).reshape(2,5,5) print(a

目录 5.逻辑运算 6.数组高级操作 5.逻辑运算 import numpy as np x_arr = np.array([1.1, 1.2, 1.3, 1.4, 1.5]) y_arr = np.array([2.1, 2.2, 2.3, 2.4, 2.5]) cond =np.array([True,False,True,True,False]) #满足条件(condition)，输出x，不满足输出y print(np.where(cond,x_arr,y_arr)) #out<< [

目录 3.数学运算 4.broadcasting 3.数学运算 3.1逐个元素的运算： 可以进行基本的加减乘除，这里只列举两种 3.1.1逐个元素求和 x = np.array([[1,2],[3,4]], dtype=np.float64) y = np.array([[5,6],[7,8]], dtype=np.float64) print(x) print(y) #out<< [[1. 2.] [3. 4.]] [[5. 6.] [7. 8.]] #直接对应相加 x + y #o

数据分析-numpy相关操作目录1.数组简介和数组的构造2. 数组的取值和赋值3.数学运算4.broadcasting5.逻辑运算6.数组高级操作7.文件输入输出8.练习题1.数组简介和数组的构造Arrays下面介绍一些常用函数，方便用来创建数组（矩阵）2.数组取值和赋值 目录 1.数组简介和数组的构造 2. 数组的取值和赋值 3.数学运算 4.broadcasting 5.逻辑运算 6.数组高级操作 7.文件输入输出 8.练习题 1.数组简介和数组的构造 #导入numpy模块 import numpy