3.1.1 认识Pandas库¶

基于Numpy的一种工具，为解决数据分析任务而创建的，纳入了大量库和一些标准的数据模型，提供了高效地操作大型数据集所需的工具
基本上你能用Excel或者Bi工具进行的数据处理，Pandas也都能实现，而且更快

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pip install pandas

Requirement already satisfied: pandas in c:\users\dengzl\.conda\envs\data_analysis\lib\site-packages (2.1.1)
Requirement already satisfied: numpy>=1.22.4 in c:\users\dengzl\.conda\envs\data_analysis\lib\site-packages (from pandas) (1.26.0)
Requirement already satisfied: python-dateutil>=2.8.2 in c:\users\dengzl\.conda\envs\data_analysis\lib\site-packages (from pandas) (2.8.2)
Requirement already satisfied: pytz>=2020.1 in c:\users\dengzl\.conda\envs\data_analysis\lib\site-packages (from pandas) (2023.3.post1)
Requirement already satisfied: tzdata>=2022.1 in c:\users\dengzl\.conda\envs\data_analysis\lib\site-packages (from pandas) (2023.3)
Requirement already satisfied: six>=1.5 in c:\users\dengzl\.conda\envs\data_analysis\lib\site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)
Note: you may need to restart the kernel to use updated packages.

3.1.2 数据结构：Series、DataFrame¶

区别

series,只是一个一维数据结构，它由index和value组成
dataframe,是一个二维结构，处理拥有index和value之外，还拥有column. 联系
dataframe由多个series组成，无论是行还是列，单独拆分出来都是一个series

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# 创建一张二维表格 ---> 判断数据结构/属性
data = {'apples':[3,2,0,1],'oranges':[0,3,7,2]}
data

Out[ ]:

{'apples': [3, 2, 0, 1], 'oranges': [0, 3, 7, 2]}

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type(data)

Out[ ]:

dict

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import pandas as pd
data = pd.DataFrame(data)
data

Out[ ]:

	apples	oranges
0	3	0
1	2	3
2	0	7
3	1	2

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type(data)

Out[ ]:

pandas.core.frame.DataFrame

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apple = data['apples']
apple

Out[ ]:

0    3
1    2
2    0
3    1
Name: apples, dtype: int64

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type(apple)

Out[ ]:

pandas.core.series.Series

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apple = pd.Series(apple, index=[3,2,1,0])
apple

Out[ ]:

3    1
2    0
1    2
0    3
Name: apples, dtype: int64

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apple.reset_index()   # 重置索引

Out[ ]:

	index	apples
0	3	1
1	2	0
2	1	2
3	0	3

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apple.reset_index(drop=True)  # 重置索引并删除原有索引

Out[ ]:

0    1
1    0
2    2
3    3
Name: apples, dtype: int64

3.1.3 Numpy v.s. Pandas¶

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# 两者的互相转换
import numpy as np
data2 = np.arange(15).reshape(3,5)
data2

Out[ ]:

array([[ 0,  1,  2,  3,  4],
       [ 5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14]])

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pd.DataFrame(data2)

Out[ ]:

	0	1	2	3	4
0	0	1	2	3	4
1	5	6	7	8	9
2	10	11	12	13	14

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data3 = pd.DataFrame(data2,columns=['a','b','c','d','e'])   # numpy转为pandans
data3

Out[ ]:

	a	b	c	d	e
0	0	1	2	3	4
1	5	6	7	8	9
2	10	11	12	13	14

In [ ]:

data3.values   # pandans转为numpy

Out[ ]:

array([[ 0,  1,  2,  3,  4],
       [ 5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14]])

In [ ]:

3.1-Pandas数据结构

3.1.1 认识Pandas库¶

3.1.2 数据结构：Series、DataFrame¶

3.1.3 Numpy v.s. Pandas¶

公告