Numpy是列表的话，Pandas更类似于字典，可以重命名行名和列名。
创建pandas序列 会自动加上序号和dtype
import pandas as pd
import numpy as np
s = pd.Series([1,3,6,np.nan,44,1])
s
0 1.0
1 3.0
2 6.0
3 NaN
4 44.0
5 1.0
dtype: float64
创建DataFrame
- 生成默认行号和列号
df1 = pd.DataFrame(np.arange(12).reshape((3,4)))
df1
0 1 2 3
0 0 1 2 3
1 4 5 6 7
2 8 9 10 11
- 新增日期索引
dates = pd.date_range('20160101',periods = 6)
dates
DatetimeIndex(['2016-01-01', '2016-01-02', '2016-01-03', '2016-01-04',
'2016-01-05', '2016-01-06'],
dtype='datetime64[ns]', freq='D')
df = pd.DataFrame(np.random.randn(6,4),index = dates,columns = ['a','b','c','d'])
df
a b c d
2016-01-01 -1.281511 1.713843 -0.606131 -0.699298
2016-01-02 -0.690049 -0.624657 1.521370 -0.226207
2016-01-03 1.280099 0.188350 -0.481156 0.131706
2016-01-04 -0.026690 0.899729 -0.678333 -1.096834
2016-01-05 0.517648 0.291178 -0.879998 -0.823239
2016-01-06 -1.936642 -0.286916 0.362583 0.444345
- 字典形式定义每一列
df2 = pd.DataFrame({'A':1.,
'B':pd.Timestamp('20130102'),
'C':pd.Series(1,index=list(range(4)), dtype= 'float32' ),
'D':np.array([3]*4, dtype = 'int32'),
'E':pd.Categorical(["test","train","test","train"]),
'F':'foo'})
df2
A B C D E F
0 1.0 2013-01-02 1.0 3 test foo
1 1.0 2013-01-02 1.0 3 train foo
2 1.0 2013-01-02 1.0 3 test foo
3 1.0 2013-01-02 1.0 3 train foo
DataFrame的基本属性
- 打印每一列的数据形式
df2.dtypes
A float64
B datetime64[ns]
C float32
D int32
E category
F object
dtype: object
- 打印行名、列名和值
df2.index # 打印行名
Int64Index([0, 1, 2, 3], dtype='int64')
df2.columns # 打印列名
Index(['A', 'B', 'C', 'D', 'E', 'F'], dtype='object')
df2.values # 打印值
array([[1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'test', 'foo'],
[1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'train', 'foo'],
[1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'test', 'foo'],
[1.0, Timestamp('2013-01-02 00:00:00'), 1.0, 3, 'train', 'foo']],
dtype=object)
- describe 计数、均值、标准差、分位数（只运算数值型的列）
df2.describe()
A C D
count 4.0 4.0 4.0
mean 1.0 1.0 3.0
std 0.0 0.0 0.0
min 1.0 1.0 3.0
25% 1.0 1.0 3.0
50% 1.0 1.0 3.0
75% 1.0 1.0 3.0
max 1.0 1.0 3.0
- 行列转置
df2.T
0 1 2 3
A 1 1 1 1
B 2013-01-02 00:00:00 2013-01-02 00:00:00 2013-01-02 00:00:00 2013-01-02 00:00:00
C 1 1 1 1
D 3 3 3 3
E test train test train
F foo foo foo foo
- 排序
df2.sort_index(axis=1,ascending=False) # 按列倒序
F E D C B A
0 foo test 3 1.0 2013-01-02 1.0
1 foo train 3 1.0 2013-01-02 1.0
2 foo test 3 1.0 2013-01-02 1.0
3 foo train 3 1.0 2013-01-02 1.0
df2.sort_index(axis=0,ascending=False) # 按行倒序
A B C D E F
3 1.0 2013-01-02 1.0 3 train foo
2 1.0 2013-01-02 1.0 3 test foo
1 1.0 2013-01-02 1.0 3 train foo
0 1.0 2013-01-02 1.0 3 test foo
df2.sort_values(by='E') # 按值排序
A B C D E F
0 1.0 2013-01-02 1.0 3 test foo
2 1.0 2013-01-02 1.0 3 test foo
1 1.0 2013-01-02 1.0 3 train foo
3 1.0 2013-01-02 1.0 3 train foo
