DataFrame测试

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最新推荐文章于 2024-04-05 15:59:30 发布

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本文链接：https://blog.youkuaiyun.com/chuan403082010/article/details/79437587

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本文深入探讨了DataFrame在数据分析中的使用，包括数据导入、清洗、转换、聚合及可视化等关键步骤，旨在提升读者对DataFrame的理解和实战能力。

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ridership_df = pd.DataFrame(
    data=[[   0,    0,    2,    5,    0],
          [1478, 3877, 3674, 2328, 2539],
          [1613, 4088, 3991, 6461, 2691],
          [1560, 3392, 3826, 4787, 2613],
          [1608, 4802, 3932, 4477, 2705],
          [1576, 3933, 3909, 4979, 2685],
          [  95,  229,  255,  496,  201],
          [   2,    0,    1,   27,    0],
          [1438, 3785, 3589, 4174, 2215],
          [1342, 4043, 4009, 4665, 3033]],
    index=['05-01-11', '05-02-11', '05-03-11', '05-04-11', '05-05-11',
           '05-06-11', '05-07-11', '05-08-11', '05-09-11', '05-10-11'],
    columns=['R003', 'R004', 'R005', 'R006', 'R007']
)

print ridership_df

          R003  R004  R005  R006  R007
05-01-11     0     0     2     5     0
05-02-11  1478  3877  3674  2328  2539
05-03-11  1613  4088  3991  6461  2691
05-04-11  1560  3392  3826  4787  2613
05-05-11  1608  4802  3932  4477  2705
05-06-11  1576  3933  3909  4979  2685
05-07-11    95   229   255   496   201
05-08-11     2     0     1    27     0
05-09-11  1438  3785  3589  4174  2215
05-10-11  1342  4043  4009  4665  3033

    print ridership_df.iloc[0]
    print ridership_df.loc['05-05-11']
    print ridership_df['R003']
    print ridership_df.iloc[1, 3]

R003    0
R004    0
R005    2
R006    5
R007    0
Name: 05-01-11, dtype: int64
R003    1608
R004    4802
R005    3932
R006    4477
R007    2705
Name: 05-05-11, dtype: int64
05-01-11       0
05-02-11    1478
05-03-11    1613
05-04-11    1560
05-05-11    1608
05-06-11    1576
05-07-11      95
05-08-11       2
05-09-11    1438
05-10-11    1342
Name: R003, dtype: int64
2328

print ridership_df.iloc[1:4]

          R003  R004  R005  R006  R007
05-02-11  1478  3877  3674  2328  2539
05-03-11  1613  4088  3991  6461  2691
05-04-11  1560  3392  3826  4787  2613

    df_1 = pd.DataFrame({'A': [0, 1, 2], 'B': [3, 4, 5]})
    print df_1

    df_2 = pd.DataFrame([[0, 1, 2], [3, 4, 5]], columns=['A', 'B', 'C'])
    print df_2

    df = pd.DataFrame({'A': [0, 1, 2], 'B': [3, 4, 5]})
    print df.sum()
    print df.sum(axis=1)
    print df.values.sum()

    df = pd.DataFrame({'A': [0, 1, 2], 'B': [3, 4, 5]})
    print df.sum()
    print df.sum(axis=1)
    print df.values.sum()

A     3
B    12
dtype: int64
0    3
1    5
2    7
dtype: int64
15