一、Pandas —— 基本介绍

妖狐艹你老母 2023-10-09 10:23 80阅读 0赞

import  pandas as pd
    import  numpy as np
    print("************Series*************")
    print("=========从Array 生成 Series===========")
    #数据类型 dtype: float64，行 列名字默认 0-n 的数字
    s = pd.Series([1, 3, 6, np.nan, 44, 1])
    print(s)
    #数据类型 dtype='datetime64[ns]'，生成从指定日期 20190101 开始的 6 的日期
    # DatetimeIndex(['2019-01-01', '2019-01-02', '2019-01-03', '2019-01-04',
    '2019-01-05', '2019-01-06'], dtype='datetime64[ns]', freq='D')
    dates = pd.date_range('20190101',periods= 6)
    print(dates)
    print("************DataFrame*************")
    print("==========从矩阵生成DataFrame=============")
    #index 指定行名，columns指定列名，的dataframe
    df = pd.DataFrame(np.random.randn(6, 4), index= dates, columns=['a', 'b', 'c', 'd'])
    print(df)
    #                    a         b         c         d
    # 2019-01-01  1.909770  0.182445  1.301455  0.866027
    # 2019-01-02  0.580548 -2.191395  0.668358 -0.866770
    # 2019-01-03  1.937158 -0.715154 -0.476099  0.379382
    # 2019-01-04 -0.402467  0.821941 -0.322327  0.583404
    # 2019-01-05 -0.961753  1.265157  0.917990  0.454411
    # 2019-01-06 -1.617632 -1.184158 -0.348426 -0.396559
    print("==========从Map字典生成 DataFrame=============")
    df1 = pd.DataFrame({'A': 1.,
                        'B': pd.Timestamp('20130102'),
                        'C': pd.Series(1, index=list(range(4)),dtype='float32'),
                        'D': np.array([3] * 4, dtype='int32'),
                        'E': pd.Categorical(['test', 'trait', 'test', 'train']),
                        'F': 'foo'
                        })
    print(df1)
    #      A          B    C  D      E    F
    # 0  1.0 2013-01-02  1.0  3   test  foo
    # 1  1.0 2013-01-02  1.0  3  trait  foo
    # 2  1.0 2013-01-02  1.0  3   test  foo
    # 3  1.0 2013-01-02  1.0  3  train  foo
    print(df1.dtypes)
    # A           float64
    # B    datetime64[ns]
    # C           float32
    # D             int32
    # E          category
    # F            object
    # dtype: object
    print("=======打印行名、列名、值=========")
    print(df1.index)#Int64Index([0, 1, 2, 3], dtype='int64')
    print(df1.columns)#Index(['A', 'B', 'C', 'D', 'E', 'F'], dtype='object')
    print(df1.values)
    # [[1.0 Timestamp('2013-01-02 00:00:00') 1.0 3 'test' 'foo']
    #  [1.0 Timestamp('2013-01-02 00:00:00') 1.0 3 'trait' 'foo']
    #  [1.0 Timestamp('2013-01-02 00:00:00') 1.0 3 'test' 'foo']
    #  [1.0 Timestamp('2013-01-02 00:00:00') 1.0 3 'train' 'foo']]
    print("=============DataFrame 的基本描述==================")
    print(df1.describe())#列数，均值，方差，最小值等等，会忽略非数字的值
    #          A    C    D
    # count  4.0  4.0  4.0
    # mean   1.0  1.0  3.0
    # std    0.0  0.0  0.0
    # min    1.0  1.0  3.0
    # 25%    1.0  1.0  3.0
    # 50%    1.0  1.0  3.0
    # 75%    1.0  1.0  3.0
    # max    1.0  1.0  3.0
    print("==========DataFrame转置============")
    print(df1.T)
    print("==========DataFrame排序，按照列名、行名 、 值排序============")
    #对
    print(df1.sort_index(axis=1, ascending=False))# 1 表示列,False表示倒序，从大到小
    #      F      E  D    C          B    A
    # 0  foo   test  3  1.0 2013-01-02  1.0
    # 1  foo  trait  3  1.0 2013-01-02  1.0
    # 2  foo   test  3  1.0 2013-01-02  1.0
    # 3  foo  train  3  1.0 2013-01-02  1.0
    #
    print(df1.sort_values(by= 'E'))# by 指定列名
    #      A          B    C  D      E    F
    # 0  1.0 2013-01-02  1.0  3   test  foo
    # 2  1.0 2013-01-02  1.0  3   test  foo
    # 3  1.0 2013-01-02  1.0  3  train  foo
    # 1  1.0 2013-01-02  1.0  3  trait  foo