[‘i’, ‘C’, 117], [‘j’, ‘C’, 128]]

index = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

columns = [‘name’, ‘class’, ‘num’]

df = pd.DataFrame(data=data, index=index, columns=columns)

print(df)

print(“=================================================”)

df1 = df.groupby(‘class’).sum() # 分组统计求和

print(df1)

---

1.2 二级分类_分组求和

---

给groupby()传入一个列表，列表中的元素为分类字段，从左到右分类级别增大。(一级分类、二级分类…)

import pandas as pd

data = [[‘a’, ‘A’, ‘1等’, 109], [‘b’, ‘B’, ‘1等’, 112], [‘c’, ‘A’, ‘1等’, 125], [‘d’, ‘B’, ‘2等’, 120],

[‘e’, ‘B’, ‘1等’, 126], [‘f’, ‘B’, ‘2等’, 133], [‘g’, ‘A’, ‘2等’, 124], [‘h’, ‘B’, ‘1等’, 134],

[‘i’, ‘A’, ‘2等’, 117], [‘j’, ‘A’, ‘2等’, 128], [‘h’, ‘A’, ‘1等’, 130], [‘i’, ‘B’, ‘2等’, 122]]

index = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]

columns = [‘name’, ‘class_1’, ‘class_2’, ‘num’]

df = pd.DataFrame(data=data, index=index, columns=columns)

print(df)

print(“=================================================”)

df1 = df.groupby([‘class_1’, ‘class_2’]).sum() # 分组统计求和

print(df1)

---

1.3 对DataFrameGroupBy对象列名索引（对指定列统计计算）

---

其中，df.groupby(‘class_1’)得到一个DataFrameGroupBy对象，对该对象可以使用列名进行索引，以对指定的列进行统计。

如：df.groupby(‘class_1’)[‘num’].sum()

import pandas as pd

data = [[‘a’, ‘A’, ‘1等’, 109], [‘b’, ‘B’, ‘1等’, 112], [‘c’, ‘A’, ‘1等’, 125], [‘d’, ‘B’, ‘2等’, 120],

[‘e’, ‘B’, ‘1等’, 126], [‘f’, ‘B’, ‘2等’, 133], [‘g’, ‘A’, ‘2等’, 124], [‘h’, ‘B’, ‘1等’, 134],

[‘i’, ‘A’, ‘2等’, 117], [‘j’, ‘A’, ‘2等’, 128], [‘h’, ‘A’, ‘1等’, 130], [‘i’, ‘B’, ‘2等’, 122]]

index = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]

columns = [‘name’, ‘class_1’, ‘class_2’, ‘num’]

df = pd.DataFrame(data=data, index=index, columns=columns)

print(df)

print(“=================================================”)

df1 = df.groupby(‘class_1’)[‘num’].sum()

print(df1)

代码运行结果同上。

---

2. 对分组数据进行迭代

===================================================================================

2.1 对一级分类的DataFrameGroupBy对象进行遍历

---

for name, group in DataFrameGroupBy_object

其中，name指分类的类名，group指该类的所有数据。

import pandas as pd

data = [[‘a’, ‘A’, ‘1等’, 109], [‘b’, ‘C’, ‘1等’, 112], [‘c’, ‘A’, ‘1等’, 125], [‘d’, ‘B’, ‘2等’, 120],

[‘e’, ‘B’, ‘1等’, 126], [‘f’, ‘B’, ‘2等’, 133], [‘g’, ‘C’, ‘2等’, 124], [‘h’, ‘A’, ‘1等’, 134],

[‘i’, ‘C’, ‘2等’, 117], [‘j’, ‘A’, ‘2等’, 128], [‘h’, ‘B’, ‘1等’, 130], [‘i’, ‘C’, ‘2等’, 122]]

index = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]

columns = [‘name’, ‘class_1’, ‘class_2’, ‘num’]

df = pd.DataFrame(data=data, index=index, columns=columns)

print(df)

print(“===============================”)

获取目标数据。

df1 = df[[‘name’, ‘class_1’, ‘num’]]

for name, group in df1.groupby(‘class_1’):

print(name)

print(“=============================”)

print(group)

print(“==================================================”)

---

2.2 对二级分类的DataFrameGroupBy对象进行遍历

---

对二级分类的DataFrameGroupBy对象进行遍历，

以for (key1, key2), group in df.groupby([‘class_1’, ‘class_2’]) 为例

不同于一级分类的是， (key1, key2)是一个由多级类别组成的元组，而group表示该多级分类类别下的数据。

import pandas as pd

data = [[‘a’, ‘A’, ‘1等’, 109], [‘b’, ‘C’, ‘1等’, 112], [‘c’, ‘A’, ‘1等’, 125], [‘d’, ‘B’, ‘2等’, 120],

[‘e’, ‘B’, ‘1等’, 126], [‘f’, ‘B’, ‘2等’, 133], [‘g’, ‘C’, ‘2等’, 124], [‘h’, ‘A’, ‘1等’, 134],

[‘i’, ‘C’, ‘2等’, 117], [‘j’, ‘A’, ‘2等’, 128], [‘h’, ‘B’, ‘1等’, 130], [‘i’, ‘C’, ‘2等’, 122]]

index = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]

columns = [‘name’, ‘class_1’, ‘class_2’, ‘num’]

df = pd.DataFrame(data=data, index=index, columns=columns)

print(df)

print(“===============================”)

for (key1, key2), group in df.groupby([‘class_1’, ‘class_2’]):

print(key1, key2)

print(“=============================”)

print(group)

print(“==================================================”)

程序运行结果如下：

(部分)

---

3. agg()函数

=================================================================================

使用groupby()函数和agg()函数 实现 分组聚合操作运算。

3.1一般写法_对目标数据使用同一聚合函数

---

以 分组求均值、求和 为例

给agg()传入一个列表

df1.groupby([‘class_1’, ‘class_2’]).agg([‘mean’, ‘sum’])

import pandas as pd

data = [[‘a’, ‘A’, ‘1等’, 109, 144], [‘b’, ‘C’, ‘1等’, 112, 132], [‘c’, ‘A’, ‘1等’, 125, 137], [‘d’, ‘B’, ‘2等’, 120, 121],

[‘e’, ‘B’, ‘1等’, 126, 136], [‘f’, ‘B’, ‘2等’, 133, 127], [‘g’, ‘C’, ‘2等’, 124, 126], [‘h’, ‘A’, ‘1等’, 134, 125],

[‘i’, ‘C’, ‘2等’, 117, 125], [‘j’, ‘A’, ‘2等’, 128, 133], [‘h’, ‘B’, ‘1等’, 130, 122], [‘i’, ‘C’, ‘2等’, 122, 111]]

index = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]

columns = [‘name’, ‘class_1’, ‘class_2’, ‘num1’, ‘num2’]

df = pd.DataFrame(data=data, index=index, columns=columns)

print(df)

print(“===============================”)

df1 = df[[‘class_1’, ‘class_2’, ‘num1’, ‘num2’]]

print(df1.groupby([‘class_1’, ‘class_2’]).agg([‘mean’, ‘sum’]))

---

3.2 对不同列使用不同聚合函数

---

给agg()方法传入一个字典

import pandas as pd

data = [[‘a’, ‘A’, ‘1等’, 109, 144], [‘b’, ‘C’, ‘1等’, 112, 132], [‘c’, ‘A’, ‘1等’, 125, 137], [‘d’, ‘B’, ‘2等’, 120, 121],

[‘e’, ‘B’, ‘1等’, 126, 136], [‘f’, ‘B’, ‘2等’, 133, 127], [‘g’, ‘C’, ‘2等’, 124, 126], [‘h’, ‘A’, ‘1等’, 134, 125],

[‘i’, ‘C’, ‘2等’, 117, 125], [‘j’, ‘A’, ‘2等’, 128, 133], [‘h’, ‘B’, ‘1等’, 130, 122], [‘i’, ‘C’, ‘2等’, 122, 111]]

index = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]

columns = [‘name’, ‘class_1’, ‘class_2’, ‘num1’, ‘num2’]

df = pd.DataFrame(data=data, index=index, columns=columns)

print(df)

print(“===============================”)

df1 = df[[‘class_1’, ‘num1’, ‘num2’]]

print(df1.groupby(‘class_1’).agg({‘num1’: [‘mean’, ‘sum’], ‘num2’: [‘sum’]}))

---

3.3 自定义函数写法

---

也可以自定义一个函数（以名为max1为例）传入agg()中。

import pandas as pd

data = [[‘a’, ‘A’, ‘1等’, 109, 144], [‘b’, ‘C’, ‘1等’, 112, 132], [‘c’, ‘A’, ‘1等’, 125, 137], [‘d’, ‘B’, ‘2等’, 120, 121],

[‘e’, ‘B’, ‘1等’, 126, 136], [‘f’, ‘B’, ‘2等’, 133, 127], [‘g’, ‘C’, ‘2等’, 124, 126], [‘h’, ‘A’, ‘1等’, 134, 125],

[‘i’, ‘C’, ‘2等’, 117, 125], [‘j’, ‘A’, ‘2等’, 128, 133], [‘h’, ‘B’, ‘1等’, 130, 122], [‘i’, ‘C’, ‘2等’, 122, 111]]

index = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]

columns = [‘name’, ‘class_1’, ‘class_2’, ‘num1’, ‘num2’]

一、Python所有方向的学习路线

Python所有方向的技术点做的整理，形成各个领域的知识点汇总，它的用处就在于，你可以按照下面的知识点去找对应的学习资源，保证自己学得较为全面。

二、Python必备开发工具

工具都帮大家整理好了，安装就可直接上手！

三、最新Python学习笔记

当我学到一定基础，有自己的理解能力的时候，会去阅读一些前辈整理的书籍或者手写的笔记资料，这些笔记详细记载了他们对一些技术点的理解，这些理解是比较独到，可以学到不一样的思路。

四、Python视频合集

观看全面零基础学习视频，看视频学习是最快捷也是最有效果的方式，跟着视频中老师的思路，从基础到深入，还是很容易入门的。

五、实战案例

纸上得来终觉浅，要学会跟着视频一起敲，要动手实操，才能将自己的所学运用到实际当中去，这时候可以搞点实战案例来学习。

六、面试宝典

简历模板

网上学习资料一大堆，但如果学到的知识不成体系，遇到问题时只是浅尝辄止，不再深入研究，那么很难做到真正的技术提升。

需要这份系统化学习资料的朋友，可以戳这里无偿获取

一个人可以走的很快，但一群人才能走的更远！不论你是正从事IT行业的老鸟或是对IT行业感兴趣的新人，都欢迎加入我们的的圈子（技术交流、学习资源、职场吐槽、大厂内推、面试辅导），让我们一起学习成长！