首页优化下列python代码：import pandas as pd import numpy as np pd.set_option('display.max_columns',None) df1=pd.read_csv('sample.csv',sep=',') print(df1[:10]) print(df1.columns) Sichuan_chongqing=df1[(df1['departure_province']=='四川')& (df1['destination_province']=='重庆')] print(Sichuan_chongqing[:10]) Sichuan_chongqing.to_csv('四川到重庆.ccv') Sichuan_chongqing.to_csv('Sichuan_chongqing.csv')

优化下列python代码：import pandas as pd import numpy as np pd.set_option('display.max_columns',None) df1=pd.read_csv('sample.csv',sep=',') print(df1[:10]) print(df1.columns) Sichuan_chongqing=df1[(df1['departure_province']=='四川')& (df1['destination_province']=='重庆')] print(Sichuan_chongqing[:10]) Sichuan_chongqing.to_csv('四川到重庆.ccv') Sichuan_chongqing.to_csv('Sichuan_chongqing.csv')

时间: 2023-05-12 22:07:45 浏览: 144

详解pandas删除缺失数据(pd.dropna()方法)

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1.创建带有缺失值的数据库： import pandas as pd import numpy as np df = pd.DataFrame(np.random.randn(5, 3), index = list('abcde'), columns = ['one', 'two', 'three']) # 随机产生5行3列的数据 df.ix[1, :-1] = np.nan # 将指定数据定义为缺失 df.ix[1:-1, 2] = np.nan print('\ndf1') # 输出df1，然后换行 print(df) 查看数据内容： 2.通常情况下删除行，使用参数a

& (df1['arrival_province']=='重庆')] Sichuan_chongqing.to_csv('Sichuan_chongqing.csv',index=False) 优化后的代码如下： import pandas as pd pd.set_option('display.max_columns', None) df1 = pd.read_csv('sample.csv', sep=',') print(df1.head(10)) print(df1.columns) Sichuan_chongqing = df1.loc[(df1['departure_province'] == '四川') & (df1['arrival_province'] == '重庆')] Sichuan_chongqing.to_csv('Sichuan_chongqing.csv', index=False)

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