def remove_duplicate_words(s): from pandas import Series as sr array=s.split(' ') s=sr(array).drop_duplicates(keep='first') print(' '.join(s))
from pandas import Series as sr week1 = sr([290000, 310000], index=['토', '일']) print(week1) print('-' * 40) week2 = sr([120000, 220000], index=['일', '토']) print(week1) print('-' * 40) # 시리즈 객체의 사칙연산 # -> index가 동일한 항목끼리 연산이 수행된다. result = week1 + week2 print(result) print(type(result)) print('-' * 40)
from pandas import Series as sr items = 10, 30, 50, 70, 90 column = sr(items) print(column) print(type(column))
import pandas as pd import numpy as np from pandas import Series as sr from pandas import DataFrame as frm dict1 = { 'name': ['taukir', 'khan', 'mango', 'rahul'], 'country': ['India', 'canada', 'U.S', 'Europ'], 'salary': [251000, 256000, 14055, 18900] } df = frm(dict1) df['dep'] = ['Developer', 'engineer', 'Designer', 'BDm'] #print df #>>>>>>>>>>>>>>>>>>>Qa 2 >>>>>>>>>>>>>>>>>>>>>>>>>>>>>> s1 = sr('aa bb cc dd ee'.split()) df2 = frm(s1) #print df2 #>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> s2 = sr('aa bb cc dd ee'.split()) s2.name = 'Series' df3 = frm(s2) #print df3 #>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> nparr = np.zeros((5, 5)) #print nparr print "===============================" npDataFrame = frm(nparr) #print npDataFrame npDataFrame.columns = ["First", "Second", "Third", "Fourth", "Fifth"] #print "==============================="
import pandas as pd from pandas import Series as sr print pd.__version__ l1 = [10, 203, 55, 30, 60] s1 = sr(l1) print s1 #===First two element print s1[0] print s1[1] s1.index = list('abcde') print s1 dict1 = dict(apple=100, mango=200, orange=500, grapes=600) sr1 = sr(dict1) print sr1
import numpy as np import pandas as pd from pandas import Series as sr, DataFrame as df import matplotlib.pyplot as plt #matplotlib的一个高级接口 import seaborn as sns datas = pd.read_excel('data/data.xls') # print(data.head()) sr1 = sr(datas.view_price) sr2 = sr(datas.sales) index = np.arange(sr1.size) sr3 = sr(sr1 * sr2) data = df(data=list(zip(sr1, sr2, sr3)), index=index, columns=["price", "sales", "GMV"]) datan = df(data=list(zip(sr1, sr3)), index=index, columns=["price", "GMV"]) # print(datan) data = data.groupby("price").sum() data.reset_index() #绘图 # plt.figure(figsize=(6,12)) plt.bar(data.index, np.log(data["sales"]), color='pink') plt.show() ''' mean, cov = [4, 6], [(1.5, .7), (.7, 1)] x, y = np.random.multivariate_normal(mean, cov, 80).T sns.regplot(x=x, y=y, color="g", marker="+") '''
from pandas import Series as sr from pandas import date_range as dr items = 10, 20, 30, 40, 50 s1 = dr(start='2020/1/1', periods=5) column = sr(items, s1) print(column)