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数据透视表¶ In [1]: import pandas as pd excelample=pd.DataFrame({'Month':["January","January","January","January", "February", "February","February","Febru 阅读全文
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In [1]: import pandas as pd 此网站可以查找具体的设置属性等:¶ https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.set_option.html#pandas.set_option 1-1显 阅读全文
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In [1]: import pandas as pd In [6]: left =pd.DataFrame({ 'A':['A0','A1','A2','A3'], 'B':['B0','B1','B2','B3'], 'key':['K0','K1','K2','K3'],}) right =p 阅读全文
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对象的增删改查¶ In [1]: import pandas as pd series结构的增删改查¶ In [2]: data =[10,11,12] index=['a','b','c'] s=pd.Series(data=data,index=index) s Out[2]: a 10 b 1 阅读全文
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Pandas章节应用的数据可以在以下链接下载: https://files.cnblogs.com/files/AI-robort/Titanic_Data-master.zip Array数值计算¶ In [2]: import numpy as np tang_array=np.array([[ 阅读全文
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Pandas章节应用的数据可以在以下链接下载: https://files.cnblogs.com/files/AI-robort/Titanic_Data-master.zip In [1]: import pandas as pd df=pd.DataFrame({'key':['A','B', 阅读全文
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Pandas章节应用的数据可以在以下链接下载: https://files.cnblogs.com/files/AI-robort/Titanic_Data-master.zip In [4]: import pandas as pd df=pd.read_csv('./Titanic_Data-m 阅读全文
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Pandas章节应用的数据可以在以下链接下载: https://files.cnblogs.com/files/AI-robort/Titanic_Data-master.zip Pandas:数据分析处理库¶ In [1]: import pandas as pd In [4]: df=pd.re 阅读全文
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判断结构¶ In [3]: tang=100 if tang>200: print('OK') print('test')##有缩进就不在就不在if条件结构中 test In [6]: tang=100 if tang >200: print('200') elif tang<100: print( 阅读全文
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In [1]: tang=1000 yudi=tang In [2]: id(tang) Out[2]: 2395452676784 In [3]: id(yudi) Out[3]: 2395452676784 In [4]: tang is yudi Out[4]: True In [5]: yu 阅读全文
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(一)利用递归方法求5! In [2]: def tang(j): sum_value=0 if j==0: sum_value=1 else: sum_value=j*tang(j-1) return sum_value for i in range(10): print('%d!=%d' % ( 阅读全文
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In [1]: import time In [2]: print(time.time()) 1564400310.6156976 In [3]: print(time.localtime(time.time())) time.struct_time(tm_year=2019, tm_mon=7, 阅读全文
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(三) 输入三个整数x,y,z,请把这三个数由小到大输出 In [7]: my_list=[] for i in range(3): x=int(input('input: ')) my_list.append(x) my_list.sort(reverse=False)#reverse是反转 my 阅读全文
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更常用的zeros ones¶ In [2]: import numpy as np np.zeros(3) Out[2]: array([0., 0., 0.]) In [3]: np.zeros((3,3)) Out[3]: array([[0., 0., 0.], [0., 0., 0.], 阅读全文
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In [1]: import numpy as np np.array([1,2,3]) Out[1]: array([1, 2, 3]) In [2]: np.arange(10) Out[2]: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) In [3]: np.a 阅读全文
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In [1]: import numpy as np tang_arrary=np.arange(10) tang_arrary Out[1]: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) In [2]: tang_arrary.shape#形状 Out[2]: (1 阅读全文
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In [2]: import numpy as np tang_array = np.array([[1.5,1.3,7.5], [5.6,7.8,1.2]]) tang_array Out[2]: array([[1.5, 1.3, 7.5], [5.6, 7.8, 1.2]]) In [3]: 阅读全文
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In [2]: import numpy as np tang_array=np.array([[1,2,3],[4,5,6]]) tang_array Out[2]: array([[1, 2, 3], [4, 5, 6]]) In [3]: np.sum(tang_array) Out[3]: 阅读全文
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In [1]: import numpy as np 对于ndarra结构来说,里面所有的元素必须 是同一类型的如果不是的话,会自动的向下进行转换 In [2]: tang_list=[1,2,3,4,5] tang_array=np.array(tang_list) tang_array Out[ 阅读全文
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In [1]: import numpy as np In [2]: array=[1,2,3,4,5] array+1#没定义成numpy.ndarray类型是不能直接操作的 TypeError Traceback (most recent call last) <ipython-input-2- 阅读全文