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本篇內(nèi)容介紹了“怎么使用Python中Pandas的索引對(duì)齊方法”的有關(guān)知識(shí),在實(shí)際案例的操作過(guò)程中,不少人都會(huì)遇到這樣的困境,接下來(lái)就讓小編帶領(lǐng)大家學(xué)習(xí)一下如何處理這些情況吧!希望大家仔細(xì)閱讀,能夠?qū)W有所成!
一.索引對(duì)象支持集合運(yùn)算:聯(lián)合、交叉、求差、對(duì)稱差
Demo1:
import pandas as pd import numpy as np college = pd.read_csv('data/college.csv') columns = college.columns c1 = columns[:4] c2 = columns[2:5] print(c1.union(c2)) print(c1 | c2)
Demo2:
import pandas as pd import numpy as np college = pd.read_csv('data/college.csv') columns = college.columns c1 = columns[:4] c2 = columns[2:5] print("c1 : ",c1) print("c2 : ",c2) print(c1.symmetric_difference(c2)) print(c1 ^ c2)
二.用copy()產(chǎn)生新的數(shù)據(jù)
A is B:表明二者指向的同一個(gè)對(duì)象。這意味著,如果修改一個(gè),另一個(gè)也會(huì)去改變。
Demo1:
import pandas as pd import numpy as np employee = pd.read_csv('data/employee.csv', index_col='RACE') salary1 = employee['BASE_SALARY'] salary2 = employee['BASE_SALARY'] print(salary1 is salary2) salary1 = employee['BASE_SALARY'].copy() salary2 = employee['BASE_SALARY'].copy() print(salary1 is salary2)
三.不等索引(索引的difference方法)
Demo1:
用difference,找到哪些索引標(biāo)簽在baseball_14中,卻不在baseball_15、baseball_16中
import pandas as pd import numpy as np baseball_14 = pd.read_csv('data/baseball14.csv', index_col='playerID') baseball_15 = pd.read_csv('data/baseball15.csv', index_col='playerID') baseball_16 = pd.read_csv('data/baseball16.csv', index_col='playerID') print(baseball_14.index.difference(baseball_15.index)) print(baseball_14.index.difference(baseball_16.index))
四.使用fill_value避免在算術(shù)運(yùn)算時(shí)產(chǎn)生缺失值
Demo1:
import pandas as pd import numpy as np baseball_14 = pd.read_csv('data/baseball14.csv', index_col='playerID') baseball_15 = pd.read_csv('data/baseball15.csv', index_col='playerID') #H列:每名球員的擊球數(shù) hits_14 = baseball_14['H'] hits_15 = baseball_15['H'] print(hits_14.head()) print(hits_15.head()) print(hits_14.head() + hits_15.head())
下面四條數(shù)據(jù)是有記錄的,但是因?yàn)椴煌瑫r(shí)存在14,15兩張表中,所以相加會(huì)產(chǎn)生NaN,需要用fill_value
Demo2:
import pandas as pd import numpy as np baseball_14 = pd.read_csv('data/baseball14.csv', index_col='playerID') baseball_15 = pd.read_csv('data/baseball15.csv', index_col='playerID') baseball_16 = pd.read_csv('data/baseball16.csv', index_col='playerID') #H列:每名球員的擊球數(shù) hits_14 = baseball_14['H'] hits_15 = baseball_15['H'] hits_16 = baseball_16['H'] print(hits_14.head().add(hits_15.head(),fill_value=0))
*如果一個(gè)元素在兩個(gè)Series都是缺失值,即便使用了fill_value,相加的結(jié)果也仍是缺失值
五.從不同的DataFrame追加列
Demo:
import pandas as pd import numpy as np employee = pd.read_csv('data/employee.csv') d1 = employee[['DEPARTMENT', 'BASE_SALARY']] print("排序前:") print(d1.head()) # 在每個(gè)部門內(nèi),對(duì)BASE_SALARY進(jìn)行排序 d2 = d1.sort_values(['DEPARTMENT', 'BASE_SALARY'],ascending = [True,False]) print("排序后:") print(d2.head()) #用drop_duplicates方法保留每個(gè)部門的第一行 d3 = d2.drop_duplicates(subset = 'DEPARTMENT') print('去重后:') print(d3.head()) #使用DEPARTMENT作為行索引 d3 = d3.set_index('DEPARTMENT') employee = employee.set_index('DEPARTMENT') #向employee的DataFrame新增一列 #新增時(shí),對(duì)應(yīng)缺項(xiàng)的為缺失值 #存儲(chǔ)每個(gè)Department的最高工資 employee['MAX_SALARY'] = d3['BASE_SALARY'] pd.options.display.max_columns = 3 print('合并后:') print(employee.head()) #用query查看是否有BASE_SALARY大于MAX_DEPT_SALARY的 #輸出應(yīng)該為0 print('query結(jié)果:') print(employee.query('BASE_SALARY > MAX_SALARY'))
employee[‘MAX_SALARY’] = d3[‘BASE_SALARY’]
這行語(yǔ)句能執(zhí)行成功的條件是:d3中不含有重復(fù)索引,即執(zhí)行過(guò)drop_duplicates
運(yùn)行結(jié)果:
“怎么使用Python中Pandas的索引對(duì)齊方法”的內(nèi)容就介紹到這里了,感謝大家的閱讀。如果想了解更多行業(yè)相關(guān)的知識(shí)可以關(guān)注億速云網(wǎng)站,小編將為大家輸出更多高質(zhì)量的實(shí)用文章!
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