Pandas学习笔记（未完成~~~）

发表于 | 分类于 | 热度: ℃
字数统计: 932 字 | 阅读时长 ≈ 5 分钟 
1
2
import pandas as pd
import numpy as np

数据定义及使用

定义序列, pandas中的数据形式通常是float32或float64

1
2
>>> s = pd.Series([1,3,4,np.nan,11,99])
>>> s
0     1.0
1     3.0
2     4.0
3     NaN
4    11.0
5    99.0
dtype: float64

定义日期列表

1
2
>>> dates = pd.date_range('20180927',periods=6)
>>> dates
DatetimeIndex(['2018-09-27', '2018-09-28', '2018-09-29', '2018-09-30',
               '2018-10-01', '2018-10-02'],
              dtype='datetime64[ns]', freq='D')

DataFrame类似于numpy的array

不指定索引的DataFrame

1
2
>>> df = pd.DataFrame(np.arange(12).reshape(3, 4))
>>> df
0 1 2 3
0 0 1 2 3
1 4 5 6 7
2 8 9 10 11

DataFrame的定义

1
2
>>>df = pd.DataFrame({'A': 1., 'B': 'Foo', 'C': np.array([3] * 4)})
>>>df
A B C
0 1.0 Foo 3
1 1.0 Foo 3
2 1.0 Foo 3
3 1.0 Foo 3

行索引为dates, 列索引为[a, b, c, d]

1
2
>>> df = pd.DataFrame(np.random.randn(6,4),index=dates,columns=['a','b','c','d'])
>>> df
a b c d
2018-09-27 0.338831 1.036578 -2.573243 -2.204440
2018-09-28 -0.473799 -0.646747 0.435539 -0.758833
2018-09-29 0.305467 0.816041 -0.116044 1.197494
2018-09-30 -0.435368 -0.082337 0.624486 -1.234057
2018-10-01 -0.667478 0.233274 -1.380012 2.261031
2018-10-02 -0.654250 -0.180604 0.609015 -0.924455

查看DataFrame的数据类型

1
>>> df.dtypes
a    float64
b    float64
c    float64
d    float64
dtype: object

查看DataFrame的索引

1
>>> df.index
DatetimeIndex(['2018-09-27', '2018-09-28', '2018-09-29', '2018-09-30',
               '2018-10-01', '2018-10-02'],
              dtype='datetime64[ns]', freq='D')

查看DataFrame的列索引

1
>>> df.columns
Index(['a', 'b', 'c', 'd'], dtype='object')

查看DataFrame的值

1
>>> df.values
array([[ 0.33883139,  1.03657755, -2.5732431 , -2.20443975],
       [-0.47379902, -0.64674734,  0.43553894, -0.75883344],
       [ 0.30546684,  0.81604074, -0.11604421,  1.19749384],
       [-0.43536792, -0.08233739,  0.62448617, -1.23405699],
       [-0.66747791,  0.23327389, -1.38001185,  2.26103083],
       [-0.65425047, -0.18060444,  0.60901542, -0.92445528]])

查看DataFrame的描述

1
>>> df.describe()
a b c d
count 6.000000 6.000000 6.000000 6.000000
mean -0.264433 0.196034 -0.400043 -0.277210
std 0.463933 0.635914 1.306128 1.667218
min -0.667478 -0.646747 -2.573243 -2.204440
25% -0.609138 -0.156038 -1.064020 -1.156657
50% -0.454583 0.075468 0.159747 -0.841644
75% 0.120258 0.670349 0.565646 0.708412
max 0.338831 1.036578 0.624486 2.261031

DataFrame的转置

1
>>> df.T
2018-09-27 00:00:00 2018-09-28 00:00:00 2018-09-29 00:00:00 2018-09-30 00:00:00 2018-10-01 00:00:00 2018-10-02 00:00:00
a 0.338831 -0.473799 0.305467 -0.435368 -0.667478 -0.654250
b 1.036578 -0.646747 0.816041 -0.082337 0.233274 -0.180604
c -2.573243 0.435539 -0.116044 0.624486 -1.380012 0.609015
d -2.204440 -0.758833 1.197494 -1.234057 2.261031 -0.924455

DataFrame的index排序

1
>>> df.sort_index(axis=1)
a b c d
2018-09-27 0.338831 1.036578 -2.573243 -2.204440
2018-09-28 -0.473799 -0.646747 0.435539 -0.758833
2018-09-29 0.305467 0.816041 -0.116044 1.197494
2018-09-30 -0.435368 -0.082337 0.624486 -1.234057
2018-10-01 -0.667478 0.233274 -1.380012 2.261031
2018-10-02 -0.654250 -0.180604 0.609015 -0.924455

DataFrame的index排序, 逆序

1
>>> df.sort_index(axis=1,ascending=False)
d c b a
2018-09-27 -2.204440 -2.573243 1.036578 0.338831
2018-09-28 -0.758833 0.435539 -0.646747 -0.473799
2018-09-29 1.197494 -0.116044 0.816041 0.305467
2018-09-30 -1.234057 0.624486 -0.082337 -0.435368
2018-10-01 2.261031 -1.380012 0.233274 -0.667478
2018-10-02 -0.924455 0.609015 -0.180604 -0.654250

DataFrame按值排序

1
>>> df.sort_values(by='c')
a b c d
2018-09-27 0.338831 1.036578 -2.573243 -2.204440
2018-10-01 -0.667478 0.233274 -1.380012 2.261031
2018-09-29 0.305467 0.816041 -0.116044 1.197494
2018-09-28 -0.473799 -0.646747 0.435539 -0.758833
2018-10-02 -0.654250 -0.180604 0.609015 -0.924455
2018-09-30 -0.435368 -0.082337 0.624486 -1.234057
-------------本文结束感谢您的阅读-------------
您的支持将是我前进的动力！