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文章目录题目常用包读取数据数据清理画图直方图直线图盒子图热力图题目数据清理与画图常用包import numpy as npimport pandas as pdimport matplotlib.pyplot as pltimport seaborn as sns读取数据data = pd.read_feather('./house_sales.ftr')data = pd.read_csv('./house_sales.zip')data = pd.read_json('./hou

文章目录Series可视化DataFrame可视化plot()plot.scatter()'''Description: pandas plot出图Autor: 365JHWZGoDate: 2021-11-24 19:31:23LastEditors: 365JHWZGoLastEditTime: 2021-11-24 19:48:03'''Series可视化import pandas as pdimport numpy as npimport matplotlib.pyplot

merge合并文章目录merge合并创建数据依据一组key合并创造数据依据两组key合并Indicator创造数据index创造数据解决overlapping与concat类似，但主要是用于两组有key column的数据，统一索引的数据创建数据left = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'], 'A': ['A0', 'A1', 'A2', 'A3'],

pandas合并concataxis合并方向重置indexjoin合并方式append(添加数据)concatpandas在数据处理时会使用到数据合并axis合并方向df1 = pd.DataFrame(np.ones((3,4))*0,columns=['a','b','c','d'])df2 = pd.DataFrame(np.ones((3,4))*1,columns=['a','b','c','d'])df3 = pd.DataFrame(np.ones((3,4))*2,colum

pandas导入导出pandas可以读取于存储的资料格式有很多，如csv、excel、json、html与pickle等读取csvdata = pd.read_csv("./datasets/day_wise_datashrink.csv")Run:data:Date Confirmed … Deaths / 100 Recovered No. of countries0 2020/1/22 555 … 60.71

pandas处理数据丢失创建数据修改其中几个值为NaN删除NaN去除含有NaN的所有行或列替代NaN将所有NaN替换成其他值判断是否缺失数据输出缺失数据的位置创建数据hangIndex = np.array([i for i in string.ascii_letters[:5]])table = pd.DataFrame(np.arange(20).reshape(5,4),hangIndex,pd.date_range('20211010',periods=4))Run:table:202

pandas修改数据创造数据修改值操作插入一列数据插入相同的值插入不同的值使用Series插入修改单个值按照标签loc按照索引iloc修改多个值按照标签loc按照索引iloc创造数据dates = pd.date_range('20001029',periods=5)calender = pd.DataFrame(np.arange(15).reshape(5,3),dates,columns=['x','y','z'])Run:calendar:x y z2000-10-29

pandas选择数据创建数据筛选数据简单的筛选使用标签loc根据序列iloc根据判断来筛选数据创建数据dateF = pd.DataFrame( np.linspace(0,20,20,dtype=int).reshape(4,5), index=pd.date_range('2021-11-21',periods=4), columns=['Monday','Tuesday','Wednesday','Thursday','Friday'])Run:print date

Series其字符串表现形式为：索引在左边，值在右边当没有为其指定数据索引时，系统会自动创建一个[0->N-1]的索引'''Description: pandas-seriesAutor: 365JHWZGoDate: 2021-11-20 21:24:42LastEditors: 365JHWZGoLastEditTime: 2021-11-20 21:32:18'''import pandas as pdimport numpy as npimport stringi

numpy矩阵拆解其他分割vsplithsplit水平拆解split axis=0垂直拆解split axis=1不规则拆解array_split其他分割vsplitnp.vsplit(arr,indices_or_sections)横向切分Example:a = np.array([[ 1, 11, 2, 22], [ 3, 33, 4, 44], [ 5, 55, 6, 66], [ 7, 77, 8, 88]])print(np.vsplit(a, indices_o

计算函数maxnp.max(arr)arr.max()minnp.min(arr)arr.min()sumnp.sum(arr)arr.sum()prodarr.prod()累乘count_nonzeronp.count_nonzero(arr)统计非零的个数meannp.mean(arr)计算均值mediannp.median(arr)得到中位数std[standard deviation]np.std(arr)得到标准差

随机数和随机操作多种随机数生成randnp.random.rand(dim1,dim2,dim3...)生成带shape[0,1)之间取值的Arrayrandomnp.random.random([dim1,dim2])与rand类似randintnp.random.randint(low,high,size)随机生成再[low,high)之间的size个数对已有数据进行随机处理choicenp.random.choice(arr,size)在一组数据中随机

numpy中如何改变数据形态arr = np.array([1,2,3])升维newaxis,arr[np.newaxis,:]==>arr[None,:]按行扩维Run:[[1,2,3]]arr[:,newaxis]==>arr[:,None]按列扩维Run:[[1],[2],[3]]unsqueezenp.unsqueeze()降维squeezenp.squeeze(arr)只能减少axis=1的维度其他reshapear

numpy中矩阵的数据选取例子arr = np.array([[1,2,3],[4,5,6],[7,8,9]])单个选取arr[1]Run:[4,5,6]arr[1,2]选取第二行中的第三个数Run:6arr[[1,2,2]]相当于[arr[1],arr[2],arr[2]]Run:[[4 5 6][7 8 9][7 8 9]]arr[[[[[1,2]]]]]相当于[[[arr[1],arr[2]]]]Run:[[[[4 5 6][7 8 9]

有关array的创建创建规则数组Arraynp.array(arr)Example:import numpy as npcars = np.array([5,10,12,6])print(cars)Run:[5,10,12,6]Linspacenp.linspace(start,end,num，endpoint)创造间隔一样的点start 起点end 终点num 需要几个点endpoint 是否算最后一个节点Example:x = n

'''Description: numpy 数据的保存Autor: 365JHWZGoDate: 2021-11-12 20:53:14LastEditors: 365JHWZGoLastEditTime: 2021-11-12 21:11:49'''import numpy as np#原数据路径path = 'pytorch/datasets/day_wise_datashrink.csv'#保存数据路径savePath = 'pytorch/datasets/day_wise

'''Description: numpy数据的保存和提取Autor: 365JHWZGoDate: 2021-11-12 18:32:54LastEditors: 365JHWZGoLastEditTime: 2021-11-12 20:25:09'''import osimport numpy as np# 超参数path = 'pytorch/datasets/day_wise_datashrink.csv'#文末提取# 打印当前目录的文件# print('pytorch

'''Description: numpy中特定行数和列数的截取【矩阵任意截！】Autor: 365JHWZGoDate: 2021-11-12 16:34:19LastEditors: 365JHWZGoLastEditTime: 2021-11-12 16:57:41'''import numpy as np#create some dataarr = np.linspace(1,30,30).reshape(5,6)#print dataprint('原始数据:\n',ar

'''Description: covid datasets显示Autor: 365JHWZGoDate: 2021-11-11 22:15:46LastEditors: 365JHWZGoLastEditTime: 2021-11-11 23:35:41'''import numpy as np# 我们需要处理的脏数据row_data = [ ["Name", "StudentID", "Age", "AttendClass", "Score"], ["小明", 201

'''Author: 365JHWZGoDescription: matplotlib--titleDate: 2021/11/5 18:09FilePath: day1105-2.py'''import matplotlib.pyplot as pltimport numpy as npx = np.arange(0, 20, 1)y1 = 2 * x + np.random.rand(20)y = 2 * xplt.scatter(x, y1,c = 'r', label='s

'''Author: 365JHWZGoDescription: numpy.flattenDate: 2021/11/4 19:34FilePath: day1104-3.py'''import numpy as npa = np.linspace(1,10,10).reshape(2,5) #arrayc = np.mat([[1,2],[2,3],[3,4]]) #matf = a.ravel()print('a:',a)b = a.flatten()print('f:

'''Author: 365JHWZGoDescription:比较Tensor.size()和Tensor.shape()中的区别Date: 2021/11/3 21:02FilePath: day1103-2.py'''#导包import torchimport numpy as npprint('numpy中size和shape的区别'.center(20,'-'))#创造numpy数据x = np.linspace(1,10,10).reshape(2,5)print(

有关numpy中array的学习'''Author: 365JHWZGoDescription: arrayDate: 2021-10-17 15:09:57FilePath: \pytorch\pytorch\day01-2.pyLastEditTime: 2021-10-17 15:11:54LastEditors: 365JHWZGo'''np.array()data = np.array([[1,2,3],[4,5,6]])print(data.shape) 运行结果