Pandas数据规整

版权声明：本文为博主原创文章，未经博主允许不得转载。 https://blog.csdn.net/QianYanDai/article/details/78178275

Pandas数据规整

数据分析和建模方面的大量编程工作都是用在数据准备上的，有时候存放在文件或数据库中的数据并不能满足数据处理应用的要求

Pandas提供了一组高级的、灵活的、高效的核心函数和算法，它们能够轻松地将数据规整化为你需要的形式

---

合并

连接

Pandas提供了大量方法，能轻松的对Series，DataFrame和Panel执行合并操作

连接pandas对象 .concat()

import numpy as np
import pandas as pd

df = pd.DataFrame(np.random.randn(10, 4))
df.head()

	0	1	2	3
0	0.231308	1.193636	-0.033288	0.826399
1	-0.421474	-0.618510	-1.266325	-0.439435
2	-0.279457	0.578144	1.131353	-0.639720
3	-1.197750	-0.446579	0.495728	0.900704
4	-0.638926	-0.233019	-1.106248	-0.762133

pieces = [df[:2], df[3:5], df[7:]] # 这里面切片是前闭后开的
pieces

[          0         1         2         3
 0  0.231308  1.193636 -0.033288  0.826399
 1 -0.421474 -0.618510 -1.266325 -0.439435,
           0         1         2         3
 3 -1.197750 -0.446579  0.495728  0.900704
 4 -0.638926 -0.233019 -1.106248 -0.762133,
           0         1         2         3
 7 -0.265515 -0.705797  0.695531 -0.257374
 8  0.552615 -0.137180  0.859215 -0.853752
 9 -1.014105  0.392409 -1.832748  0.612679]

df2 = pd.concat(pieces)
df2

	0	1	2	3
0	0.231308	1.193636	-0.033288	0.826399
1	-0.421474	-0.618510	-1.266325	-0.439435
3	-1.197750	-0.446579	0.495728	0.900704
4	-0.638926	-0.233019	-1.106248	-0.762133
7	-0.265515	-0.705797	0.695531	-0.257374
8	0.552615	-0.137180	0.859215	-0.853752
9	-1.014105	0.392409	-1.832748	0.612679

追加 .append()

df = pd.DataFrame(np.random.randn(4, 4), columns=['A','B','C','D'])
df

	A	B	C	D
0	1.295901	-0.742636	0.873728	-0.810075
1	1.073456	0.344627	0.156597	1.460616
2	1.696282	-1.272457	1.226460	-1.944458
3	-0.473047	0.147528	-0.538231	0.125467

s = df.iloc[2]
s

A    1.696282
B   -1.272457
C    1.226460
D   -1.944458
Name: 2, dtype: float64

df.append(s, ignore_index=True)

	A	B	C	D
0	1.295901	-0.742636	0.873728	-0.810075
1	1.073456	0.344627	0.156597	1.460616
2	1.696282	-1.272457	1.226460	-1.944458
3	-0.473047	0.147528	-0.538231	0.125467
4	1.696282	-1.272457	1.226460	-1.944458

---

分组

group by():一般指以下一个或多个操作步骤

• Splitting 将数据分组
• Applying 对每个分组应用不同的function
• Combining 使用某种数据结果展示结果

df = pd.DataFrame({
    'A' : ['foo', 'bar', 'foo', 'bar','foo', 'bar', 'foo', 'foo'],
    'B' : ['one', 'one', 'two', 'three','two', 'two', 'one', 'three'],
    'C' : np.random.randn(8),
    'D' : np.random.randn(8)
    })
df

	A	B	C	D
0	foo	one	0.556699	1.543716
1	bar	one	-0.905349	-0.054870
2	foo	two	1.220397	-0.589706
3	bar	three	0.637305	-0.046351
4	foo	two	-0.150553	-0.889157
5	bar	two	-0.771132	0.196547
6	foo	one	0.008275	-0.571672
7	foo	three	0.228275	-1.164593

# 分组后sum求和:
a = df.groupby('A').sum()
a

	C	D
A
bar	-1.039176	0.095325
foo	1.863094	-1.671411

a = df.groupby('A',as_index=False).sum()
a

	A	C	D
0	bar	-1.039176	0.095325
1	foo	1.863094	-1.671411

# 对多列分组后sum:
b = df.groupby(['A','B']).sum()
b

		C	D
A	B
bar	one	-0.905349	-0.054870
	three	0.637305	-0.046351
	two	-0.771132	0.196547
foo	one	0.564975	0.972044
	three	0.228275	-1.164593
	two	1.069844	-1.478862

b = df.groupby(['A','B'],as_index=False).sum()
b

	A	B	C	D
0	bar	one	-0.905349	-0.054870
1	bar	three	0.637305	-0.046351
2	bar	two	-0.771132	0.196547
3	foo	one	0.564975	0.972044
4	foo	three	0.228275	-1.164593
5	foo	two	1.069844	-1.478862