可以访问Series的索引Series 或DataFrame的列 作为属性

    1. import pandas as pd
    2. import numpy as np
    4. sa = pd.Series([1, 2, 3], index=list('abc'))
    5. print(sa)
    6. print(sa.b)   # 2，Series 数据用行索引访问

    a 1
    b 2
    c 3
    dtype: int64

    2

    1. dates = pd.date_range('1/1/2000', periods=8)
    2. df = pd.DataFrame(np.random.randn(8, 4),index=dates, columns=['A', 'B', 'C', 'D'])
    3. print(df)
    4. dfa = df.copy()   # 复制一个dateframe
    5. print(dfa.A)      # 输出列索引为A的数据

    df 输出整个dateframe中的数据
    A B C D
    2000-01-01 -1.164180 -0.074449 0.024508 0.258941
    2000-01-02 0.432623 0.609803 2.032749 1.067229
    2000-01-03 -1.527508 -0.586559 1.134569 1.394826
    2000-01-04 0.549750 1.733102 -0.309395 1.095839
    2000-01-05 1.469302 -0.803328 0.039403 0.556420
    2000-01-06 0.470629 -0.940261 0.907722 1.765288
    2000-01-07 1.937365 0.639491 -1.219057 1.858613
    2000-01-08 0.780139 1.296072 -0.793080 0.484197

    dfa.A 输出列索引为A的数据
    2000-01-01 -1.164180
    2000-01-02 0.432623
    2000-01-03 -1.527508
    2000-01-04 0.549750
    2000-01-05 1.469302
    2000-01-06 0.470629
    2000-01-07 1.937365
    2000-01-08 0.780139
    Freq: D, Name: A, dtype: float64

    1. sa.a = 5   # 修改sa.a的值为5
    2. print(sa)

    a 5
    b 2
    c 3
    dtype: int64

    1. dfa.A = list(range(len(dfa.index)))  # 用产生的数列替换dateframe中的A列的数据
    2. print(dfa)

    产生0-7的整数代替dfa.a的索引
    A B C D
    2000-01-01 0 -0.074449 0.024508 0.258941
    2000-01-02 1 0.609803 2.032749 1.067229
    2000-01-03 2 -0.586559 1.134569 1.394826
    2000-01-04 3 1.733102 -0.309395 1.095839
    2000-01-05 4 -0.803328 0.039403 0.556420
    2000-01-06 5 -0.940261 0.907722 1.765288
    2000-01-07 6 0.639491 -1.219057 1.858613
    2000-01-08 7 1.296072 -0.793080 0.484197

    1. dfa['E'] = list(range(len(dfa.index)))  # 创建一个新列 E
    2. print(dfa)
    1.                 A           B            C             D          E<br />2000-01-01  0 -0.074449  0.024508  0.258941   0<br />2000-01-02  1  0.609803  2.032749  1.067229   1<br />2000-01-03  2 -0.586559  1.134569  1.394826   2<br />2000-01-04  3  1.733102 -0.309395  1.095839   3<br />2000-01-05  4 -0.803328  0.039403  0.556420   4<br />2000-01-06  5 -0.940261  0.907722  1.765288   5<br />2000-01-07  6  0.639491 -1.219057  1.858613   6<br />2000-01-08  7  1.296072 -0.793080  0.484197   7
    x = pd.DataFrame({'x': [1, 2, 3], 'y': [3, 4, 5]})
print(x)
x.iloc[1] = {'x': 9, 'y': 99} # 修改x中行索引号为1的数据
print(x)

    x 的数据
    x y
    0 1 3
    1 2 4
    2 3 5
    修改后的数据
    x y
    0 1 3
    1 9 99
    2 3 5