如下：
import pandas as pd
df = pd.DataFrame({'Country':['China','China', 'India', 'India', 'America', 'Japan', 'China', 'India'],
'Income':[10000, 10000, 5000, 5002, 40000, 50000, 8000, 5000],
'Age':[5000, 4321, 1234, 4010, 250, 250, 4500, 4321]})
構(gòu)造的數(shù)據(jù)如下：
Age Country Income 0 5000 China 10000 1 4321 China 10000 2 1234 India 5000 3 4010 India 5002 4 250 America 40000 5 250 Japan 50000 6 4500 China 8000 7 4321 India 5000
df_gb = df.groupby('Country')
for index, data in df_gb:
print(index)
print(data)
輸出
America Age Country Income 4 250 America 40000 China Age Country Income 0 5000 China 10000 1 4321 China 10000 6 4500 China 8000 India Age Country Income 2 1234 India 5000 3 4010 India 5002 7 4321 India 5000 Japan Age Country Income 5 250 Japan 50000
df_gb = df.groupby(['Country', 'Income']) for (index1, index2), data in df_gb: print((index1, index2)) print(data)
輸出
('America', 40000)
Age Country Income
4 250 America 40000
('China', 8000)
Age Country Income
6 4500 China 8000
('China', 10000)
Age Country Income
0 5000 China 10000
1 4321 China 10000
('India', 5000)
Age Country Income
2 1234 India 5000
7 4321 India 5000
('India', 5002)
Age Country Income
3 4010 India 5002
('Japan', 50000)
Age Country Income
5 250 Japan 50000
默認(rèn)情況對(duì)分組之后其他列進(jìn)行聚合
df_agg = df.groupby('Country').agg(['min', 'mean', 'max'])
print(df_agg)
輸出
Age Income min mean max min mean max Country America 250 250.000000 250 40000 40000.000000 40000 China 4321 4607.000000 5000 8000 9333.333333 10000 India 1234 3188.333333 4321 5000 5000.666667 5002 Japan 250 250.000000 250 50000 50000.000000 50000
某些情況，只需要對(duì)部分?jǐn)?shù)據(jù)進(jìn)行不同的聚合操作，可以通過(guò)字典來(lái)構(gòu)建
num_agg = {'Age':['min', 'mean', 'max']}
print(df.groupby('Country').agg(num_agg))
輸出
Age
min mean max
Country
America 250 250.000000 250
China 4321 4607.000000 5000
India 1234 3188.333333 4321
Japan 250 250.000000 250
num_agg = {'Age':['min', 'mean', 'max'], 'Income':['min', 'max']}
print(df.groupby('Country').agg(num_agg))
輸出
Age Income min mean max min max Country America 250 250.000000 250 40000 40000 China 4321 4607.000000 5000 8000 10000 India 1234 3188.333333 4321 5000 5002 Japan 250 250.000000 250 50000 50000
補(bǔ)充：pandas——很全的groupby、agg，對(duì)表格數(shù)據(jù)分組與統(tǒng)計(jì)
我這篇groupby寫(xiě)的不好。太復(fù)雜了。其實(shí)實(shí)際上經(jīng)常用的就那么幾個(gè)。舉個(gè)例子，把常用的往那一放就很容易理解和拿來(lái)用了。日后再寫(xiě)一篇。
groupby功能：分組
groupby + agg(聚集函數(shù)們)： 分組后，對(duì)各組應(yīng)用一些函數(shù)，如'sum',‘mean',‘max',‘min'…
groupby默認(rèn)縱方向上分組，axis=0
DataFrame import pandas as pd import numpy as np
df = pd.DataFrame({'key1':['a', 'a', 'b', 'b', 'a'],
'key2':['one', 'two', 'one', 'two', 'one'],
'data1':np.random.randn(5),
'data2':np.random.randn(5)})
print(df)
data1 data2 key1 key2 0 -0.410122 0.247895 a one 1 -0.627470 -0.989268 a two 2 0.179488 -0.054570 b one 3 -0.299878 -1.640494 b two 4 -0.297191 0.954447 a one
分組，并對(duì)分組進(jìn)行迭代
list(df.groupby(['key1']))#list后得到：[(group1),(group2),......]
[('a', data1 data2 key1 key2
0 -0.410122 0.247895 a one
1 -0.627470 -0.989268 a two
4 -0.297191 0.954447 a one), ('b', data1 data2 key1 key2
2 0.179488 -0.054570 b one
3 -0.299878 -1.640494 b two)]
list后得到：[(group1),(group2),…]
每個(gè)數(shù)據(jù)片(group)格式: (name,group)元組
1. 按key1（一個(gè)列）分組，其實(shí)是按key1的值
groupby對(duì)象支持迭代，產(chǎn)生一組二元元組：（分組名，數(shù)據(jù)塊）,（分組名，數(shù)據(jù)塊）…
for name,group in df.groupby(['key1']): print(name) print(group)
a data1 data2 key1 key2 0 -0.410122 0.247895 a one 1 -0.627470 -0.989268 a two 4 -0.297191 0.954447 a one b data1 data2 key1 key2 2 0.179488 -0.054570 b one 3 -0.299878 -1.640494 b two
2. 按[key1, key2]（多個(gè)列）分組
對(duì)于多重鍵，產(chǎn)生的一組二元元組：（（k1,k2），數(shù)據(jù)塊）,（（k1,k2），數(shù)據(jù)塊）…
第一個(gè)元素是由鍵值組成的元組
for name,group in df.groupby(['key1','key2']): print(name) #name=(k1,k2) print(group)
('a', 'one')
data1 data2 key1 key2
0 -0.410122 0.247895 a one
4 -0.297191 0.954447 a one
('a', 'two')
data1 data2 key1 key2
1 -0.62747 -0.989268 a two
('b', 'one')
data1 data2 key1 key2
2 0.179488 -0.05457 b one
('b', 'two')
data1 data2 key1 key2
3 -0.299878 -1.640494 b two
3. 按函數(shù)分組
4. 按字典分組
5. 按索引級(jí)別分組
6.將函數(shù)跟數(shù)組、列表、字典、Series混合使用也不是問(wèn)題，因?yàn)槿魏螙|西最終都會(huì)被轉(zhuǎn)換為數(shù)組
將這些數(shù)據(jù)片段做成字典
dict(list(df.groupby(['key1'])))#dict(list())
{'a': data1 data2 key1 key2
0 -0.410122 0.247895 a one
1 -0.627470 -0.989268 a two
4 -0.297191 0.954447 a one, 'b': data1 data2 key1 key2
2 0.179488 -0.054570 b one
3 -0.299878 -1.640494 b two}
分組后進(jìn)行一些統(tǒng)計(jì)、計(jì)算等
1. 分組后，返回一個(gè)含有分組大小的Series
按key1分組
df.groupby(['key1']).size()
key1 a 3 b 2 dtype: int64
dict(['a1','x2','e3'])
{'a': '1', 'e': '3', 'x': '2'}
按[key1,key2]分組
df.groupby(['key1','key2']).size()
key1 key2 a one 2 two 1 b one 1 two 1 dtype: int64
2. 對(duì)data1按key1進(jìn)行分組，并計(jì)算data1列的平均值
df['data1'].groupby(df['key1']).mean() #groupby沒(méi)有進(jìn)行任何的計(jì)算。它只是進(jìn)行了一個(gè)分組
key1 a -0.444928 b -0.060195 Name: data1, dtype: float64
df.groupby(['key1'])['data1'].mean()#理解：對(duì)df按key1分組，并計(jì)算分組后df['data1']的均值 #等價(jià)于：df.groupby(['key1']).data1.mean()
key1 a -0.444928 b -0.060195 Name: data1, dtype: float64
說(shuō)明：
groupby沒(méi)有進(jìn)行任何的計(jì)算。它只是進(jìn)行了一個(gè)分組。
數(shù)據(jù)（Series）根據(jù)分組鍵進(jìn)行了聚合，產(chǎn)生了一個(gè)新的Series，其索引為key1列中的唯一值。
這種索引操作所返回的對(duì)象是一個(gè)已分組的DataFrame（如果傳入的是列表或數(shù)組）或已分組的Series
df.groupby(['key1'])['data1'].size()
key1 a 3 b 2 Name: data1, dtype: int64
3.對(duì)data1按[key1,key2]進(jìn)行分組，并計(jì)算data1的平均值
df['data1'].groupby([df['key1'],df['key2']]).mean()
key1 key2 a one -0.353657 two -0.627470 b one 0.179488 two -0.299878 Name: data1, dtype: float64
df.groupby(['key1','key2'])['data1'].mean() #等價(jià)于：df.groupby(['key1','key2']).data1'.mean()
key1 key2 a one -0.353657 two -0.627470 b one 0.179488 two -0.299878 Name: data1, dtype: float64
通過(guò)兩個(gè)鍵對(duì)數(shù)據(jù)進(jìn)行了分組，得到的Series具有一個(gè)層次化索引（由唯一的鍵對(duì)組成）：
df.groupby(['key1','key2'])['data1'].mean().unstack()
| key2 | one | two |
|---|---|---|
| key1 | ||
| a | -0.353657 | -0.627470 |
| b | 0.179488 | -0.299878 |
在上面這些示例中，分組鍵均為Series。實(shí)際上，分組鍵可以是任何長(zhǎng)度適當(dāng)?shù)臄?shù)組。非常靈活。
橫方向上
按列的數(shù)據(jù)類型（df.dtypes）來(lái)分
df共兩種數(shù)據(jù)類型：float64和object，所以會(huì)分為兩組（dtype(‘float64'）,數(shù)據(jù)片),（dtype(‘O'), 數(shù)據(jù)片）
list(df.groupby(df.dtypes, axis=1))
[(dtype('float64'), data1 data2
0 -0.410122 0.247895
1 -0.627470 -0.989268
2 0.179488 -0.054570
3 -0.299878 -1.640494
4 -0.297191 0.954447), (dtype('O'), key1 key2
0 a one
1 a two
2 b one
3 b two
4 a one)]
agg的應(yīng)用
groupby+agg 可以對(duì)groupby的結(jié)果同時(shí)應(yīng)用多個(gè)函數(shù)
SeriesGroupBy的方法agg()參數(shù)：
aggregate(self, func_or_funcs, * args, ** kwargs) func: function, string, dictionary, or list of string/functions
返回：aggregated的Series
s= pd.Series([10,20,30,40]) s
0 10 1 20 2 30 3 40 dtype: int64
for n,g in s.groupby([1,1,2,2]): print(n) print(g)
0 10 1 20 dtype: int64 2 2 30 3 40 dtype: int64
s.groupby([1,1,2,2]).min() 1 1 10 2 30 dtype: int64
#等價(jià)于這個(gè)：
s.groupby([1,1,2,2]).agg('min')
1 10 2 30 dtype: int64
s.groupby([1,1,2,2]).agg(['min','max'])#加[]，func僅接受一個(gè)參數(shù)
| min | max | |
|---|---|---|
| 1 | 10 | 20 |
| 2 | 30 | 40 |
常常這樣用：
df
| data1 | data2 | key1 | key2 | |
|---|---|---|---|---|
| 0 | -0.410122 | 0.247895 | a | one |
| 1 | -0.627470 | -0.989268 | a | two |
| 2 | 0.179488 | -0.054570 | b | one |
| 3 | -0.299878 | -1.640494 | b | two |
| 4 | -0.297191 | 0.954447 | a | one |
比較下面，可以看出agg的用處：
df.groupby(['key1'])['data1'].min()
key1 a -0.627470 b -0.299878 Name: data1, dtype: float64
df.groupby(['key1'])['data1'].agg({'min'})
| min | |
|---|---|
| key1 | |
| a | -0.627470 |
| b | -0.299878 |
#推薦用這個(gè)√
df.groupby(['key1']).agg({'data1':'min'})#對(duì)data1列，取各組的最小值，名字還是data1
| data1 | |
|---|---|
| key1 | |
| a | -0.627470 |
| b | -0.299878 |
#按key1分組后，aggregate各組data1的最小值和最大值：
df.groupby(['key1'])['data1'].agg({'min','max'})
| max | min | |
|---|---|---|
| key1 | ||
| a | -0.297191 | -0.627470 |
| b | 0.179488 | -0.299878 |
#推薦用這個(gè)√
df.groupby(['key1']).agg({'data1':['min','max']})
| data1 | ||
|---|---|---|
| min | max | |
| key1 | ||
| a | -0.627470 | -0.297191 |
| b | -0.299878 | 0.179488 |
可以對(duì)groupby的結(jié)果更正列名（不推薦用這個(gè)，哪怕在后面單獨(dú)更改列名）
# 對(duì)data1，把min更名為a，max更名為b
df.groupby(['key1'])['data1'].agg({'a':'min','b':'max'})#這里的'min' 'max'為兩個(gè)函數(shù)名
d:\python27\lib\site-packages\ipykernel_launcher.py:2: FutureWarning: using a dict on a Series for aggregation is deprecated and will be removed in a future version
| a | b | |
|---|---|---|
| key1 | ||
| a | -0.627470 | -0.297191 |
| b | -0.299878 | 0.179488 |
重要技巧： groupby之后直接.reset_index()可以得到一個(gè)沒(méi)有多級(jí)索引的DataFrame
之后可以通過(guò)df.rename({‘old_col1':‘new_col1',‘old_col2':‘new_col2',…})重命名
eg:
df1= df.groupby(['date'])['price'].agg({'sum','count'}).reset_index()
以上為個(gè)人經(jīng)驗(yàn)，希望能給大家一個(gè)參考，也希望大家多多支持腳本之家。如有錯(cuò)誤或未考慮完全的地方，望不吝賜教。
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