Pandas基本介绍

1、pandas主要的两个数据结构：Series和DataFrame

Series的字符串表现形式为：索引在左边，值在右边。由于我们没有为数据指定索引。于是会自动创建一个0到N-1（N为长度）的整数型索引。

>>> import pandas as pd
>>> import numpy as np
>>> s = pd.Series([1,3,6,np.nan,44,1])
>>> print(s)
0     1.0
1     3.0
2     6.0
3     NaN
4    44.0
5     1.0
dtype: float64

 DataFrame是一个表格型的数据结构，它包含有一组有序的列，每列可以是不同的值类型（数值，字符串，布尔值等）。DataFrame既有行索引也有列索引， 它可以被看做由Series组成的大字典。

>>> dates = pd.date_range('20160101',periods=6)
>>> df = pd.DataFrame(np.random.randn(6,4),index=dates,columns=['a','b','c','d'])
>>> print(df)
                   a         b         c         d
2016-01-01  1.306762  1.506943  0.682025 -0.054329
2016-01-02  2.626875  0.086998  0.307123 -0.498728
2016-01-03 -0.941697  0.206144  1.719719  1.084614
2016-01-04 -0.610912 -1.120358 -0.635338  1.145777
2016-01-05 -0.150501  0.768586 -0.158341  0.704960
2016-01-06 -0.759211  0.271800  0.768166 -0.293015

2、DataFrame的一些简单运用

>>> print(df['b'])
2016-01-01    1.506943
2016-01-02    0.086998
2016-01-03    0.206144
2016-01-04   -1.120358
2016-01-05    0.768586
2016-01-06    0.271800
Freq: D, Name: b, dtype: float64

>>> df1 = pd.DataFrame(np.arange(12).reshape((3,4)))#创建一组没有给定行标签和列标签的数据 df1:
>>> print(df1)
   0  1   2   3
0  0  1   2   3
1  4  5   6   7
2  8  9  10  11

>>> 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'})#另一种生成df的方法
>>>print(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

>>> print(df2.dtypes)#查看数据中的类型
A           float64
B    datetime64[ns]
C           float32
D             int32
E          category
F            object
dtype: object

>>> print(df2.index) #查看对列的序号
Int64Index([0, 1, 2, 3], dtype='int64')

>>> print(df2.columns)#查看每种数据的名称
Index(['A', 'B', 'C', 'D', 'E', 'F'], dtype='object')

>>> print(df2.values)#查看所有df2的值
[[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']]

>>> df2.describe()#数据的总结
         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

>>> print(df2.T)#翻转数据，transpose
                     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

>>> print(df2.sort_index(axis=1, ascending=False))#对数据的index进行排序并输出
     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

>>> print(df2.sort_values(by='B'))#对数据值排序输出
     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