快速入门pandas扩展库
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2022-03-06 10:29:50
快速入门pandas扩展库导入 Pandas 与 NumPy：In [1]: import numpy as npIn [2]: import pandas as pd2.生成对象用值列表生成 Series 时，Pandas 默认自动生成整数索引In [3]: s = pd.Series([1, 3, 5, np.nan, 6, 8])In [4]: sOut[4]:0 1.01 3.02 5.03 NaN4 6.05 8.0dtype: float64用含日期时间索...
快速入门pandas扩展库
- 导入 Pandas 与 NumPy：
In [1]: import numpy as np
In [2]: import pandas as pd
2.生成对象
用值列表生成 Series 时，Pandas 默认自动生成整数索引
In [3]: s = pd.Series([1, 3, 5, np.nan, 6, 8])
In [4]: s
Out[4]:
0 1.0
1 3.0
2 5.0
3 NaN
4 6.0
5 8.0
dtype: float64
用含日期时间索引与标签的 NumPy 数组生成 DataFrame：
In [5]: dates = pd.date_range('20130101', periods=6)
In [6]: dates
Out[6]:
DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',
'2013-01-05', '2013-01-06'],
dtype='datetime64[ns]', freq='D')
In [7]: df = pd.DataFrame(np.random.randn(6, 4), index=dates,
columns=list('ABCD'))
In [8]: df
Out[8]:
A B C D
2013-01-01 0.469112 -0.282863 -1.509059 -1.135632
2013-01-02 1.212112 -0.173215 0.119209 -1.044236
2013-01-03 -0.861849 -2.104569 -0.494929 1.071804
2013-01-04 0.721555 -0.706771 -1.039575 0.271860
2013-01-05 -0.424972 0.567020 0.276232 -1.087401
2013-01-06 -0.673690 0.113648 -1.478427 0.524988
用 Series 字典对象生成 DataFrame:
In [9]: df2 = 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", "train", "test",
"train"]),
...: 'F': 'foo'})
...:
In [10]: df2
Out[10]:
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 train foo
2 1.0 2013-01-02 1.0 3 test foo
3 1.0 2013-01-02 1.0 3 train foo
DataFrame 的列有不同数据类型：
In [11]: df2.dtypes
Out[11]:
A float64
B datetime64[ns]
C float32
D int32
E category
F object
dtype: object
3.查看数据
下列代码说明如何查看 DataFrame 头部和尾部数据：
In [13]: df.head()
Out[13]:
A B C D
2013-01-01 0.469112 -0.282863 -1.509059 -1.135632
2013-01-02 1.212112 -0.173215 0.119209 -1.044236
2013-01-03 -0.861849 -2.104569 -0.494929 1.071804
2013-01-04 0.721555 -0.706771 -1.039575 0.271860
2013-01-05 -0.424972 0.567020 0.276232 -1.087401
In [14]: df.tail(3)
Out[14]:
A B C D
2013-01-04 0.721555 -0.706771 -1.039575 0.271860
2013-01-05 -0.424972 0.567020 0.276232 -1.087401
2013-01-06 -0.673690 0.113648 -1.478427 0.524988
显示索引与列名：
In [15]: df.index
Out[15]:
DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',
'2013-01-05', '2013-01-06'],
dtype='datetime64[ns]', freq='D')
In [16]: df.columns
Out[16]: Index(['A', 'B', 'C', 'D'], dtype='object')
DataFrame.to_numpy() 输出底层数据的 NumPy 对象。注意，DataFrame 的列由多种数据类型组成时，该操作
耗费系统资源较大，这也是 Pandas 和 NumPy 的本质区别：NumPy 数组只有一种数据类型，DataFrame 每列
的数据类型各不相同。调用 DataFrame.to_numpy() 时，Pandas 查找支持 DataFrame 里所有数据类型的
NumPy 数据类型。还有一种数据类型是 object，可以把 DataFrame 列里的值强制转换为 Python 对象。
下面的 df 这个 DataFrame 里的值都是浮点数，DataFrame.to_numpy() 的操作会很快，而且不复制数据。
In [17]: df.to_numpy()
Out[17]:
array([[ 0.4691, -0.2829, -1.5091, -1.1356],
[ 1.2121, -0.1732, 0.1192, -1.0442],
[-0.8618, -2.1046, -0.4949, 1.0718],
[ 0.7216, -0.7068, -1.0396, 0.2719],
[-0.425 , 0.567 , 0.2762, -1.0874],
[-0.6737, 0.1136, -1.4784, 0.525 ]])
df2 这个 DataFrame 包含了多种类型，DataFrame.to_numpy() 操作就会耗费较多资源。
In [18]: df2.to_numpy()
Out[18]:
array([[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'],
[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']],
dtype=object)
DataFrame.to_numpy() 的输出不包含行索引和列标签。
describe() 可以快速查看数据的统计摘要：`
In [19]: df.describe()
Out[19]:
A B C D
count 6.000000 6.000000 6.000000 6.000000
mean 0.073711 -0.431125 -0.687758 -0.233103
std 0.843157 0.922818 0.779887 0.973118
min -0.861849 -2.104569 -1.509059 -1.135632
25% -0.611510 -0.600794 -1.368714 -1.076610
50% 0.022070 -0.228039 -0.767252 -0.386188
75% 0.658444 0.041933 -0.034326 0.461706
max 1.212112 0.567020 0.276232 1.071804
转置数据：
In [20]: df.T
Out[20]:
2013-01-01 2013-01-02 2013-01-03 2013-01-04 2013-01-05 2013-01-06
A 0.469112 1.212112 -0.861849 0.721555 -0.424972 -0.673690
B -0.282863 -0.173215 -2.104569 -0.706771 0.567020 0.113648
C -1.509059 0.119209 -0.494929 -1.039575 0.276232 -1.478427
D -1.135632 -1.044236 1.071804 0.271860 -1.087401 0.524988
按轴排序：
In [21]: df.sort_index(axis=1, ascending=False)
Out[21]:
D C B A
2013-01-01 -1.135632 -1.509059 -0.282863 0.469112
2013-01-02 -1.044236 0.119209 -0.173215 1.212112
2013-01-03 1.071804 -0.494929 -2.104569 -0.861849
2013-01-04 0.271860 -1.039575 -0.706771 0.721555
2013-01-05 -1.087401 0.276232 0.567020 -0.424972
2013-01-06 0.524988 -1.478427 0.113648 -0.673690
按值排序：
In [22]: df.sort_values(by='B')
Out[22]:
A B C D
2013-01-03 -0.861849 -2.104569 -0.494929 1.071804
2013-01-04 0.721555 -0.706771 -1.039575 0.271860
2013-01-01 0.469112 -0.282863 -1.509059 -1.135632
2013-01-02 1.212112 -0.173215 0.119209 -1.044236
2013-01-06 -0.673690 0.113648 -1.478427 0.524988
2013-01-05 -0.424972 0.567020 0.276232 -1.087401
4.选择
选择、设置标准 Python / Numpy 的表达式已经非常直观，交互也很方便，但对于生产代码，我们还是
推荐优化过的 Pandas 数据访问方法：.at、.iat、.loc 和 .iloc。
获取数据
选择单列，产生 Series，与 df.A 等效：
In [23]: df['A']
Out[23]:
2013-01-01 0.469112
2013-01-02 1.212112
2013-01-03 -0.861849
2013-01-04 0.721555
2013-01-05 -0.424972
2013-01-06 -0.673690
Freq: D, Name: A, dtype: float64
切片行：
In [24]: df[0:3]
Out[24]:
A B C D
2013-01-01 0.469112 -0.282863 -1.509059 -1.135632
2013-01-02 1.212112 -0.173215 0.119209 -1.044236
2013-01-03 -0.861849 -2.104569 -0.494929 1.071804
In [25]: df['20130102':'20130104']
Out[25]:
A B C D
2013-01-02 1.212112 -0.173215 0.119209 -1.044236
2013-01-03 -0.861849 -2.104569 -0.494929 1.071804
2013-01-04 0.721555 -0.706771 -1.039575 0.271860
本文地址：https://blog.csdn.net/weixin_46737224/article/details/109817935