python-pandas的基本用法06

来源：互联网发布：小白软件库编辑：程序博客网时间：2024/06/06 07:41
pandas的基本用法06-算术和数据对齐

    # -*- coding: utf-8 -*-     import numpy as np    from pandas import Series, DataFrame    import dataframeDemo    from matplotlib.pyplot import axis    s1 = Series([-1,3,4], index=['a','b','c'])    s2 = Series([0,9,2,5], index=['a','b','c','d'])    print s1+s2    # a    -1    # b    12    # c     6    # d   NaN    # dtype: float64    print 'DataFrame加法'    df1 = DataFrame(np.arange(9).reshape(3,3),              index=list('abc'),              columns=list('123')              )    df2 = DataFrame(np.arange(9).reshape(3,3),              index=list('abd'),              columns=list('023')              )    print df1    print df2    print df1+df2    #    1  2  3    # a  0  1  2    # b  3  4  5    # c  6  7  8    #    +    #    0  2  3    # a  0  1  2    # b  3  4  5    # d  6  7  8    #    =    #     0   1   2   3    # a NaN NaN   2   4    # b NaN NaN   8  10    # c NaN NaN NaN NaN    # d NaN NaN NaN NaN    print '数据填充'    df1 = DataFrame(np.arange(12.).reshape(3,4))    df2 = DataFrame(np.arange(20.).reshape(4,5))    print df1    #    0  1   2   3    # 0  0  1   2   3    # 1  4  5   6   7    # 2  8  9  10  11    print df2    #     0   1   2   3   4    # 0   0   1   2   3   4    # 1   5   6   7   8   9    # 2  10  11  12  13  14    # 3  15  16  17  18  19    print df1.add(df2)    #     0   1   2   3   4    # 0   0   2   4   6 NaN    # 1   9  11  13  15 NaN    # 2  18  20  22  24 NaN    # 3 NaN NaN NaN NaN NaN    print df1.add(df2, fill_value=0)    #     0   1   2   3   4    # 0   0   2   4   6   4    # 1   9  11  13  15   9    # 2  18  20  22  24  14    # 3  15  16  17  18  19    print df1.reindex(columns=df2.columns, fill_value=0)    #    0  1   2   3  4    # 0  0  1   2   3  0    # 1  4  5   6   7  0    # 2  8  9  10  11  0    print 'DataFrame与Series之间的操作'    dataframe = DataFrame(np.arange(12.).reshape(3,4))    series = dataframe.ix[0]    print dataframe    #    0  1   2   3    # 0  0  1   2   3    # 1  4  5   6   7    # 2  8  9  10  11    print series    # 0    0    # 1    1    # 2    2    # 3    3    print dataframe-series    #    0  1  2  3    # 0  0  0  0  0    # 1  4  4  4  4    # 2  8  8  8  8    series = dataframe[0]    print dataframe.sub(series, axis=0)# 按列减    #    0  1  2  3    # 0  0  1  2  3    # 1  0  1  2  3    # 2  0  1  2  3
阅读全文
0 0