0
点赞
收藏
分享

微信扫一扫

如何选择数据

舟海君 2022-11-10 阅读 61
多列数据工作原理MySQL数据库yyds干货盘点

1. Series

1.1 索引

Series 对象索引的工作原理和 ndarray 对象索引非常类似，不同的一点是，在对 Series 对象进行索引时，我们不但可以使用整数还可以使用 Series 对象本身的索引，举几个例子。单个元素：

import pandas as pd

my_series = pd.Series([4, -7, 6, -5, 3, 2], index=["a", "b", "c", "d", "e", "f"])

print(my_series[3])
print(my_series['d'])

在上面的代码中，​​my_series[3]​​ 和 ​​my_series['d']​​ 访问的是同一个元素，一个使用的是整数 3，一个使用的是 Series 对象本身的索引 d。

多个元素：

import pandas as pd

my_series = pd.Series([4, -7, 6, -5, 3, 2], index=["a", "b", "c", "d", "e", "f"])

print(my_series[[1, 3, 4]])
print(my_series[['b', 'd', 'e']])

切片：

import pandas as pd

my_series = pd.Series([4, -7, 6, -5, 3, 2], index=["a", "b", "c", "d", "e", "f"])

print(my_series[0:5])
print(my_series['a':'e'])

使用整数进行切片和使用 Series 对象的索引进行切片不同的地方是，使用整数切片不包含最末的那个元素，而使用对象的索引进行切片时是包含最末的那个元素的。

布尔索引：

import pandas as pd

my_series = pd.Series([4, -7, 6, -5, 3, 2], index=["a", "b", "c", "d", "e", "f"])

print(my_series[my_series > my_series.median()])

上面代码的输出结果是大于平均值的元素。

1.2 像字典一样选择数据

import pandas as pd
import numpy as np

my_series = pd.Series([4, -7, 6, -5, 3, 2], index=["a", "b", "c", "d", "e", "f"])

print(my_series['b'])
print(my_series.get('b', np.NaN))

判断 key 是否在 Series 里：

import pandas as pd
import numpy as np

my_series = pd.Series([4, -7, 6, -5, 3, 2], index=["a", "b", "c", "d", "e", "f"])

print('e' in my_series)
print('g' in my_series)

1.3 loc 或 iloc

loc 和 iloc 的区别是，loc 使用的是轴标签，iloc 使用的是整数。

单个元素：

import pandas as pd
import numpy as np

my_series = pd.Series([4, -7, 6, -5, 3, 2], index=["a", "b", "c", "d", "e", "f"])

print(my_series.iloc[1])
print(my_series.loc['b'])

多个元素：

import pandas as pd
import numpy as np

my_series = pd.Series([4, -7, 6, -5, 3, 2], index=["a", "b", "c", "d", "e", "f"])

print(my_series.iloc[[1, 3]])
print(my_series.loc[['b', 'd']])

切片：

import pandas as pd
import numpy as np

my_series = pd.Series([4, -7, 6, -5, 3, 2], index=["a", "b", "c", "d", "e", "f"])

print(my_series.iloc[1:5])
print(my_series.loc['b': 'e'])

使用整数进行切片和使用 Series 对象的索引进行切片不同的地方是，使用整数切片不包含最末的那个元素，而使用对象的索引进行切片时是包含最末的那个元素的。

2. DataFrame

2.1 索引

使用索引的方法来访问 DataFrame 时，得到的是一列或多列。单列的访问：

import pandas as pd

d = {
    "Open": pd.Series([136, 137, 140, 143, 141, 142], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
    "High": pd.Series([137, 140, 143, 144, 144, 145], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
    "Low": pd.Series([135, 137, 140, 142, 140, 142], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
    "Close": pd.Series([137, 139, 142, 144, 143, 145], index = ['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09'])
}

df = pd.DataFrame(d)
print(df['Open'])

上面的单列访问我们还可以使用另外一种方法，例如：

import pandas as pd

d = {
    "Open": pd.Series([136, 137, 140, 143, 141, 142], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
    "High": pd.Series([137, 140, 143, 144, 144, 145], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
    "Low": pd.Series([135, 137, 140, 142, 140, 142], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
    "Close": pd.Series([137, 139, 142, 144, 143, 145], index = ['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09'])
}

df = pd.DataFrame(d)

print(df.Open)

上面代码采用的方法是类似于属性的访问方法。多列的访问：

import pandas as pd

d = {
    "Open": pd.Series([136, 137, 140, 143, 141, 142], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
    "High": pd.Series([137, 140, 143, 144, 144, 145], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
    "Low": pd.Series([135, 137, 140, 142, 140, 142], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
    "Close": pd.Series([137, 139, 142, 144, 143, 145], index = ['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09'])
}

df = pd.DataFrame(d)
print(df[['Open', 'Close']])

布尔索引：

import pandas as pd

d = {
    "Open": pd.Series([136, 137, 140, 143, 141, 142], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
    "High": pd.Series([137, 140, 143, 144, 144, 145], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
    "Low": pd.Series([135, 137, 140, 142, 140, 142], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
    "Close": pd.Series([137, 139, 142, 144, 143, 145], index = ['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09'])
}

df = pd.DataFrame(d)
print(df[df['Open'] > 140])

上面代码中，Open 列的值大于 140 的为后面三行，所以最后得到整个 DataFrame 的后面三行。使用布尔索引，我们还可以得到整个 DataFrame 中大于 140 的元素，例如：

import pandas as pd

d = {
    "Open": pd.Series([136, 137, 140, 143, 141, 142], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
    "High": pd.Series([137, 140, 143, 144, 144, 145], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
    "Low": pd.Series([135, 137, 140, 142, 140, 142], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
    "Close": pd.Series([137, 139, 142, 144, 143, 145], index = ['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09'])
}

df = pd.DataFrame(d)
print(df[df > 140])

上面的代码得到了整个 DataFrame 中值大于 140 的元素，不大于 140 的元素用 NaN 填充。

2.2 切片

import pandas as pd

d = {
    "Open": pd.Series([136, 137, 140, 143, 141, 142], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
    "High": pd.Series([137, 140, 143, 144, 144, 145], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
    "Low": pd.Series([135, 137, 140, 142, 140, 142], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
    "Close": pd.Series([137, 139, 142, 144, 143, 145], index = ['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09'])
}

df = pd.DataFrame(d)

print(df[:3])
print(df[::2])
print(df[::-1])
print(df[1:2])

上面的切片都是针对行来的。

2.3 loc 或 iloc

2.3.1 单行或单列的访问

import pandas as pd

d = {
    "Open": pd.Series([136, 137, 140, 143, 141, 142], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
    "High": pd.Series([137, 140, 143, 144, 144, 145], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
    "Low": pd.Series([135, 137, 140, 142, 140, 142], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
    "Close": pd.Series([137, 139, 142, 144, 143, 145], index = ['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09'])
}

df = pd.DataFrame(d)
print(df.iloc[0])
print(df.loc['2021-07-01'])
print(df.iloc[:, 0])
print(df.loc[:, 'Open'])

2.3.2 多行或多列的访问

import pandas as pd

d = {
    "Open": pd.Series([136, 137, 140, 143, 141, 142], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
    "High": pd.Series([137, 140, 143, 144, 144, 145], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
    "Low": pd.Series([135, 137, 140, 142, 140, 142], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
    "Close": pd.Series([137, 139, 142, 144, 143, 145], index = ['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09'])
}

df = pd.DataFrame(d)
print(df.iloc[[0, 1, 2]])
print(df.iloc[:, [0, 1, 2]])
print(df.loc[:, ['Open', 'High', 'Low']])

2.3.3 位于行列交叉的元素

import pandas as pd

d = {
    "Open": pd.Series([136, 137, 140, 143, 141, 142], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
    "High": pd.Series([137, 140, 143, 144, 144, 145], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
    "Low": pd.Series([135, 137, 140, 142, 140, 142], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
    "Close": pd.Series([137, 139, 142, 144, 143, 145], index = ['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09'])
}

df = pd.DataFrame(d)

print(df.loc[['2021-07-01', '2021-07-02'], ['Open', 'High']])
print(df.iloc[[0, 1], [0, 1]])

切片

import pandas as pd

d = {
    "Open": pd.Series([136, 137, 140, 143, 141, 142], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
    "High": pd.Series([137, 140, 143, 144, 144, 145], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
    "Low": pd.Series([135, 137, 140, 142, 140, 142], index=['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09']),
    "Close": pd.Series([137, 139, 142, 144, 143, 145], index = ['2021-07-01', '2021-07-02', '2021-07-06', '2021-07-07', '2021-07-08', '2021-07-09'])
}

df = pd.DataFrame(d)

print(df.loc['2021-07-01':'2021-07-07', 'Open': 'Low'])
print(df.iloc[0:4, 0:3])

举报

相关推荐

Java和大数据如何选择？

蓝桥杯c语言c++数据结构算法

如何选择数据防泄露软件

安全

如何选择数据可视化图表？

数据可视化DataEase图表类型IT业界

企业如何有效防止数据泄露？如何选择数据防泄漏工具？

数据信息安全数据安全安全技术网络/安全

如何选择CDN

负载均衡数据dns服务器服务器

如何选择网线

网线屏蔽双绞线非屏蔽双绞线运维

如何选择 Offer？

ITiOS编程语言Python后端开发

如何选择ESB

云计算javaSOA企业服务数据

Mysql、MongoDB？如何选择合适的数据库

日记本读书

RDB和AOF如何选择？Redis的数据恢复

Redis持久化数据集Java后端开发yyds干货盘点
0 条评论