mirror of
https://github.com/Estom/notes.git
synced 2026-02-07 20:44:38 +08:00
1407 lines
39 KiB
Markdown
1407 lines
39 KiB
Markdown
---
|
||
meta:
|
||
- name: keywords
|
||
content: 快速入门pandas
|
||
- name: description
|
||
content: 本节是帮助 Pandas 新手快速上手的简介。烹饪指南里介绍了更多实用案例。本节以下列方式导入 Pandas 与 NumPy：
|
||
---
|
||
|
||
# 十分钟入门 Pandas
|
||
|
||
本节是帮助 Pandas 新手快速上手的简介。[烹饪指南](/docs/user_guide/cookbook.html)里介绍了更多实用案例。
|
||
|
||
本节以下列方式导入 Pandas 与 NumPy：
|
||
|
||
``` python
|
||
In [1]: import numpy as np
|
||
|
||
In [2]: import pandas as pd
|
||
```
|
||
|
||
## 生成对象
|
||
|
||
详见[数据结构简介](/docs/getting_started/dsintro.html#dsintro)文档。
|
||
|
||
用值列表生成 [Series](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series) 时，Pandas 默认自动生成整数索引：
|
||
|
||
``` python
|
||
In [3]: s = pd.Series([1, 3, 5, np.nan, 6, 8])
|
||
|
||
In [4]: s
|
||
Out[4]:
|
||
0 1.0
|
||
1 3.0
|
||
2 5.0
|
||
3 NaN
|
||
4 6.0
|
||
5 8.0
|
||
dtype: float64
|
||
```
|
||
|
||
用含日期时间索引与标签的 NumPy 数组生成 [DataFrame](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame)：
|
||
|
||
``` python
|
||
In [5]: dates = pd.date_range('20130101', periods=6)
|
||
|
||
In [6]: dates
|
||
Out[6]:
|
||
DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',
|
||
'2013-01-05', '2013-01-06'],
|
||
dtype='datetime64[ns]', freq='D')
|
||
|
||
In [7]: df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list('ABCD'))
|
||
|
||
In [8]: df
|
||
Out[8]:
|
||
A B C D
|
||
2013-01-01 0.469112 -0.282863 -1.509059 -1.135632
|
||
2013-01-02 1.212112 -0.173215 0.119209 -1.044236
|
||
2013-01-03 -0.861849 -2.104569 -0.494929 1.071804
|
||
2013-01-04 0.721555 -0.706771 -1.039575 0.271860
|
||
2013-01-05 -0.424972 0.567020 0.276232 -1.087401
|
||
2013-01-06 -0.673690 0.113648 -1.478427 0.524988
|
||
```
|
||
|
||
用 Series 字典对象生成 DataFrame:
|
||
|
||
``` python
|
||
In [9]: 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'})
|
||
...:
|
||
|
||
In [10]: df2
|
||
Out[10]:
|
||
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 的列有不同[数据类型](https://pandas.pydata.org/pandas-docs/stable/getting_started/basics.html#basics-dtypes)。
|
||
|
||
``` python
|
||
In [11]: df2.dtypes
|
||
Out[11]:
|
||
A float64
|
||
B datetime64[ns]
|
||
C float32
|
||
D int32
|
||
E category
|
||
F object
|
||
dtype: object
|
||
```
|
||
|
||
IPython支持 tab 键自动补全列名与公共属性。下面是部分可自动补全的属性：
|
||
|
||
``` python
|
||
In [12]: df2.<TAB> # noqa: E225, E999
|
||
df2.A df2.bool
|
||
df2.abs df2.boxplot
|
||
df2.add df2.C
|
||
df2.add_prefix df2.clip
|
||
df2.add_suffix df2.clip_lower
|
||
df2.align df2.clip_upper
|
||
df2.all df2.columns
|
||
df2.any df2.combine
|
||
df2.append df2.combine_first
|
||
df2.apply df2.compound
|
||
df2.applymap df2.consolidate
|
||
df2.D
|
||
```
|
||
|
||
列 A、B、C、D 和 E 都可以自动补全；为简洁起见，此处只显示了部分属性。
|
||
|
||
## 查看数据
|
||
|
||
详见[基础用法](https://pandas.pydata.org/pandas-docs/stable/getting_started/basics.html#basics)文档。
|
||
|
||
下列代码说明如何查看 DataFrame 头部和尾部数据：
|
||
|
||
``` python
|
||
In [13]: df.head()
|
||
Out[13]:
|
||
A B C D
|
||
2013-01-01 0.469112 -0.282863 -1.509059 -1.135632
|
||
2013-01-02 1.212112 -0.173215 0.119209 -1.044236
|
||
2013-01-03 -0.861849 -2.104569 -0.494929 1.071804
|
||
2013-01-04 0.721555 -0.706771 -1.039575 0.271860
|
||
2013-01-05 -0.424972 0.567020 0.276232 -1.087401
|
||
|
||
In [14]: df.tail(3)
|
||
Out[14]:
|
||
A B C D
|
||
2013-01-04 0.721555 -0.706771 -1.039575 0.271860
|
||
2013-01-05 -0.424972 0.567020 0.276232 -1.087401
|
||
2013-01-06 -0.673690 0.113648 -1.478427 0.524988
|
||
```
|
||
|
||
显示索引与列名：
|
||
|
||
``` python
|
||
In [15]: df.index
|
||
Out[15]:
|
||
DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',
|
||
'2013-01-05', '2013-01-06'],
|
||
dtype='datetime64[ns]', freq='D')
|
||
|
||
In [16]: df.columns
|
||
Out[16]: Index(['A', 'B', 'C', 'D'], dtype='object')
|
||
```
|
||
|
||
[DataFrame.to_numpy()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_numpy.html#pandas.DataFrame.to_numpy) 输出底层数据的 NumPy 对象。注意，[DataFrame](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame) 的列由多种数据类型组成时，该操作耗费系统资源较大，这也是 Pandas 和 NumPy 的本质区别：**NumPy 数组只有一种数据类型，DataFrame 每列的数据类型各不相同**。调用 [DataFrame.to_numpy()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_numpy.html#pandas.DataFrame.to_numpy) 时，Pandas 查找支持 DataFrame 里所有数据类型的 NumPy 数据类型。还有一种数据类型是 `object`，可以把 DataFrame 列里的值强制转换为 Python 对象。
|
||
|
||
下面的 `df` 这个 [DataFrame](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame) 里的值都是浮点数，[DataFrame.to_numpy()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_numpy.html#pandas.DataFrame.to_numpy) 的操作会很快，而且不复制数据。
|
||
|
||
``` python
|
||
In [17]: df.to_numpy()
|
||
Out[17]:
|
||
array([[ 0.4691, -0.2829, -1.5091, -1.1356],
|
||
[ 1.2121, -0.1732, 0.1192, -1.0442],
|
||
[-0.8618, -2.1046, -0.4949, 1.0718],
|
||
[ 0.7216, -0.7068, -1.0396, 0.2719],
|
||
[-0.425 , 0.567 , 0.2762, -1.0874],
|
||
[-0.6737, 0.1136, -1.4784, 0.525 ]])
|
||
```
|
||
|
||
`df2` 这个 [DataFrame](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame) 包含了多种类型，[DataFrame.to_numpy()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_numpy.html#pandas.DataFrame.to_numpy) 操作就会耗费较多资源。
|
||
|
||
``` python
|
||
In [18]: df2.to_numpy()
|
||
Out[18]:
|
||
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)
|
||
```
|
||
|
||
::: tip 提醒
|
||
|
||
[DataFrame.to_numpy()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_numpy.html#pandas.DataFrame.to_numpy) 的输出不包含行索引和列标签。
|
||
|
||
:::
|
||
|
||
[describe()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.describe.html#pandas.DataFrame.describe) 可以快速查看数据的统计摘要：
|
||
|
||
``` python
|
||
In [19]: df.describe()
|
||
Out[19]:
|
||
A B C D
|
||
count 6.000000 6.000000 6.000000 6.000000
|
||
mean 0.073711 -0.431125 -0.687758 -0.233103
|
||
std 0.843157 0.922818 0.779887 0.973118
|
||
min -0.861849 -2.104569 -1.509059 -1.135632
|
||
25% -0.611510 -0.600794 -1.368714 -1.076610
|
||
50% 0.022070 -0.228039 -0.767252 -0.386188
|
||
75% 0.658444 0.041933 -0.034326 0.461706
|
||
max 1.212112 0.567020 0.276232 1.071804
|
||
```
|
||
|
||
转置数据：
|
||
|
||
``` python
|
||
In [20]: df.T
|
||
Out[20]:
|
||
2013-01-01 2013-01-02 2013-01-03 2013-01-04 2013-01-05 2013-01-06
|
||
A 0.469112 1.212112 -0.861849 0.721555 -0.424972 -0.673690
|
||
B -0.282863 -0.173215 -2.104569 -0.706771 0.567020 0.113648
|
||
C -1.509059 0.119209 -0.494929 -1.039575 0.276232 -1.478427
|
||
D -1.135632 -1.044236 1.071804 0.271860 -1.087401 0.524988
|
||
```
|
||
|
||
按轴排序：
|
||
|
||
``` python
|
||
In [21]: df.sort_index(axis=1, ascending=False)
|
||
Out[21]:
|
||
D C B A
|
||
2013-01-01 -1.135632 -1.509059 -0.282863 0.469112
|
||
2013-01-02 -1.044236 0.119209 -0.173215 1.212112
|
||
2013-01-03 1.071804 -0.494929 -2.104569 -0.861849
|
||
2013-01-04 0.271860 -1.039575 -0.706771 0.721555
|
||
2013-01-05 -1.087401 0.276232 0.567020 -0.424972
|
||
2013-01-06 0.524988 -1.478427 0.113648 -0.673690
|
||
```
|
||
|
||
按值排序：
|
||
|
||
``` python
|
||
In [22]: df.sort_values(by='B')
|
||
Out[22]:
|
||
A B C D
|
||
2013-01-03 -0.861849 -2.104569 -0.494929 1.071804
|
||
2013-01-04 0.721555 -0.706771 -1.039575 0.271860
|
||
2013-01-01 0.469112 -0.282863 -1.509059 -1.135632
|
||
2013-01-02 1.212112 -0.173215 0.119209 -1.044236
|
||
2013-01-06 -0.673690 0.113648 -1.478427 0.524988
|
||
2013-01-05 -0.424972 0.567020 0.276232 -1.087401
|
||
```
|
||
|
||
## 选择
|
||
|
||
::: tip 提醒
|
||
|
||
选择、设置标准 Python / Numpy 的表达式已经非常直观，交互也很方便，但对于生产代码，我们还是推荐优化过的 Pandas 数据访问方法：`.at`、`.iat`、`.loc` 和 `.iloc`。
|
||
|
||
:::
|
||
|
||
详见[索引与选择数据](https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#indexing)、[多层索引与高级索引](https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html#advanced)文档。
|
||
|
||
### 获取数据
|
||
|
||
选择单列，产生 `Series`，与 `df.A` 等效：
|
||
|
||
``` python
|
||
In [23]: df['A']
|
||
Out[23]:
|
||
2013-01-01 0.469112
|
||
2013-01-02 1.212112
|
||
2013-01-03 -0.861849
|
||
2013-01-04 0.721555
|
||
2013-01-05 -0.424972
|
||
2013-01-06 -0.673690
|
||
Freq: D, Name: A, dtype: float64
|
||
```
|
||
|
||
用 [ ] 切片行：
|
||
|
||
``` python
|
||
In [24]: df[0:3]
|
||
Out[24]:
|
||
A B C D
|
||
2013-01-01 0.469112 -0.282863 -1.509059 -1.135632
|
||
2013-01-02 1.212112 -0.173215 0.119209 -1.044236
|
||
2013-01-03 -0.861849 -2.104569 -0.494929 1.071804
|
||
|
||
In [25]: df['20130102':'20130104']
|
||
Out[25]:
|
||
A B C D
|
||
2013-01-02 1.212112 -0.173215 0.119209 -1.044236
|
||
2013-01-03 -0.861849 -2.104569 -0.494929 1.071804
|
||
2013-01-04 0.721555 -0.706771 -1.039575 0.271860
|
||
```
|
||
|
||
### 按标签选择
|
||
|
||
详见[按标签选择](https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#indexing-label)。
|
||
|
||
用标签提取一行数据：
|
||
|
||
``` python
|
||
In [26]: df.loc[dates[0]]
|
||
Out[26]:
|
||
A 0.469112
|
||
B -0.282863
|
||
C -1.509059
|
||
D -1.135632
|
||
Name: 2013-01-01 00:00:00, dtype: float64
|
||
```
|
||
|
||
用标签选择多列数据：
|
||
|
||
``` python
|
||
In [27]: df.loc[:, ['A', 'B']]
|
||
Out[27]:
|
||
A B
|
||
2013-01-01 0.469112 -0.282863
|
||
2013-01-02 1.212112 -0.173215
|
||
2013-01-03 -0.861849 -2.104569
|
||
2013-01-04 0.721555 -0.706771
|
||
2013-01-05 -0.424972 0.567020
|
||
2013-01-06 -0.673690 0.113648
|
||
```
|
||
|
||
用标签切片，包含行与列结束点：
|
||
|
||
``` python
|
||
In [28]: df.loc['20130102':'20130104', ['A', 'B']]
|
||
Out[28]:
|
||
A B
|
||
2013-01-02 1.212112 -0.173215
|
||
2013-01-03 -0.861849 -2.104569
|
||
2013-01-04 0.721555 -0.706771
|
||
```
|
||
|
||
返回对象降维：
|
||
|
||
``` python
|
||
In [29]: df.loc['20130102', ['A', 'B']]
|
||
Out[29]:
|
||
A 1.212112
|
||
B -0.173215
|
||
Name: 2013-01-02 00:00:00, dtype: float64
|
||
```
|
||
|
||
提取标量值：
|
||
|
||
``` python
|
||
In [30]: df.loc[dates[0], 'A']
|
||
Out[30]: 0.46911229990718628
|
||
```
|
||
|
||
快速访问标量，与上述方法等效：
|
||
|
||
``` python
|
||
In [31]: df.at[dates[0], 'A']
|
||
Out[31]: 0.46911229990718628
|
||
```
|
||
|
||
### 按位置选择
|
||
|
||
详见[按位置选择](http://Pandas.pydata.org/Pandas-docs/stable/indexing.html#indexing-integer)。
|
||
|
||
用整数位置选择：
|
||
|
||
``` python
|
||
In [32]: df.iloc[3]
|
||
Out[32]:
|
||
A 0.721555
|
||
B -0.706771
|
||
C -1.039575
|
||
D 0.271860
|
||
Name: 2013-01-04 00:00:00, dtype: float64
|
||
```
|
||
|
||
类似 NumPy / Python，用整数切片：
|
||
|
||
``` python
|
||
In [33]: df.iloc[3:5, 0:2]
|
||
Out[33]:
|
||
A B
|
||
2013-01-04 0.721555 -0.706771
|
||
2013-01-05 -0.424972 0.567020
|
||
```
|
||
|
||
类似 NumPy / Python，用整数列表按位置切片：
|
||
|
||
``` python
|
||
In [34]: df.iloc[[1, 2, 4], [0, 2]]
|
||
Out[34]:
|
||
A C
|
||
2013-01-02 1.212112 0.119209
|
||
2013-01-03 -0.861849 -0.494929
|
||
2013-01-05 -0.424972 0.276232
|
||
```
|
||
|
||
显式整行切片：
|
||
|
||
``` python
|
||
In [35]: df.iloc[1:3, :]
|
||
Out[35]:
|
||
A B C D
|
||
2013-01-02 1.212112 -0.173215 0.119209 -1.044236
|
||
2013-01-03 -0.861849 -2.104569 -0.494929 1.071804
|
||
```
|
||
|
||
显式整列切片：
|
||
|
||
``` python
|
||
In [36]: df.iloc[:, 1:3]
|
||
Out[36]:
|
||
B C
|
||
2013-01-01 -0.282863 -1.509059
|
||
2013-01-02 -0.173215 0.119209
|
||
2013-01-03 -2.104569 -0.494929
|
||
2013-01-04 -0.706771 -1.039575
|
||
2013-01-05 0.567020 0.276232
|
||
2013-01-06 0.113648 -1.478427
|
||
```
|
||
|
||
显式提取值：
|
||
|
||
``` python
|
||
In [37]: df.iloc[1, 1]
|
||
Out[37]: -0.17321464905330858
|
||
```
|
||
|
||
快速访问标量，与上述方法等效：
|
||
|
||
``` python
|
||
In [38]: df.iat[1, 1]
|
||
Out[38]: -0.17321464905330858
|
||
```
|
||
|
||
### 布尔索引
|
||
|
||
用单列的值选择数据：
|
||
|
||
``` python
|
||
In [39]: df[df.A > 0]
|
||
Out[39]:
|
||
A B C D
|
||
2013-01-01 0.469112 -0.282863 -1.509059 -1.135632
|
||
2013-01-02 1.212112 -0.173215 0.119209 -1.044236
|
||
2013-01-04 0.721555 -0.706771 -1.039575 0.271860
|
||
```
|
||
|
||
选择 DataFrame 里满足条件的值：
|
||
|
||
``` python
|
||
In [40]: df[df > 0]
|
||
Out[40]:
|
||
A B C D
|
||
2013-01-01 0.469112 NaN NaN NaN
|
||
2013-01-02 1.212112 NaN 0.119209 NaN
|
||
2013-01-03 NaN NaN NaN 1.071804
|
||
2013-01-04 0.721555 NaN NaN 0.271860
|
||
2013-01-05 NaN 0.567020 0.276232 NaN
|
||
2013-01-06 NaN 0.113648 NaN 0.524988
|
||
```
|
||
|
||
用 [isin()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.isin.html#pandas.Series.isin) 筛选：
|
||
|
||
``` python
|
||
In [41]: df2 = df.copy()
|
||
|
||
In [42]: df2['E'] = ['one', 'one', 'two', 'three', 'four', 'three']
|
||
|
||
In [43]: df2
|
||
Out[43]:
|
||
A B C D E
|
||
2013-01-01 0.469112 -0.282863 -1.509059 -1.135632 one
|
||
2013-01-02 1.212112 -0.173215 0.119209 -1.044236 one
|
||
2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 two
|
||
2013-01-04 0.721555 -0.706771 -1.039575 0.271860 three
|
||
2013-01-05 -0.424972 0.567020 0.276232 -1.087401 four
|
||
2013-01-06 -0.673690 0.113648 -1.478427 0.524988 three
|
||
|
||
In [44]: df2[df2['E'].isin(['two', 'four'])]
|
||
Out[44]:
|
||
A B C D E
|
||
2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 two
|
||
2013-01-05 -0.424972 0.567020 0.276232 -1.087401 four
|
||
```
|
||
|
||
### 赋值
|
||
|
||
用索引自动对齐新增列的数据：
|
||
|
||
``` python
|
||
In [45]: s1 = pd.Series([1, 2, 3, 4, 5, 6], index=pd.date_range('20130102', periods=6))
|
||
|
||
In [46]: s1
|
||
Out[46]:
|
||
2013-01-02 1
|
||
2013-01-03 2
|
||
2013-01-04 3
|
||
2013-01-05 4
|
||
2013-01-06 5
|
||
2013-01-07 6
|
||
Freq: D, dtype: int64
|
||
|
||
In [47]: df['F'] = s1
|
||
```
|
||
|
||
按标签赋值：
|
||
|
||
``` python
|
||
In [48]: df.at[dates[0], 'A'] = 0
|
||
```
|
||
|
||
按位置赋值：
|
||
|
||
``` python
|
||
In [49]: df.iat[0, 1] = 0
|
||
```
|
||
|
||
按 NumPy 数组赋值：
|
||
|
||
``` python
|
||
In [50]: df.loc[:, 'D'] = np.array([5] * len(df))
|
||
```
|
||
|
||
上述赋值结果：
|
||
|
||
``` python
|
||
In [51]: df
|
||
Out[51]:
|
||
A B C D F
|
||
2013-01-01 0.000000 0.000000 -1.509059 5 NaN
|
||
2013-01-02 1.212112 -0.173215 0.119209 5 1.0
|
||
2013-01-03 -0.861849 -2.104569 -0.494929 5 2.0
|
||
2013-01-04 0.721555 -0.706771 -1.039575 5 3.0
|
||
2013-01-05 -0.424972 0.567020 0.276232 5 4.0
|
||
2013-01-06 -0.673690 0.113648 -1.478427 5 5.0
|
||
```
|
||
|
||
用 `where` 条件赋值：
|
||
|
||
``` python
|
||
In [52]: df2 = df.copy()
|
||
|
||
In [53]: df2[df2 > 0] = -df2
|
||
|
||
In [54]: df2
|
||
Out[54]:
|
||
A B C D F
|
||
2013-01-01 0.000000 0.000000 -1.509059 -5 NaN
|
||
2013-01-02 -1.212112 -0.173215 -0.119209 -5 -1.0
|
||
2013-01-03 -0.861849 -2.104569 -0.494929 -5 -2.0
|
||
2013-01-04 -0.721555 -0.706771 -1.039575 -5 -3.0
|
||
2013-01-05 -0.424972 -0.567020 -0.276232 -5 -4.0
|
||
2013-01-06 -0.673690 -0.113648 -1.478427 -5 -5.0
|
||
```
|
||
|
||
## 缺失值
|
||
|
||
Pandas 主要用 `np.nan` 表示缺失数据。 计算时，默认不包含空值。详见[缺失数据](https://pandas.pydata.org/pandas-docs/stable/user_guide/missing_data.html#missing-data)。
|
||
|
||
重建索引（reindex）可以更改、添加、删除指定轴的索引，并返回数据副本，即不更改原数据。
|
||
|
||
``` python
|
||
In [55]: df1 = df.reindex(index=dates[0:4], columns=list(df.columns) + ['E'])
|
||
|
||
In [56]: df1.loc[dates[0]:dates[1], 'E'] = 1
|
||
|
||
In [57]: df1
|
||
Out[57]:
|
||
A B C D F E
|
||
2013-01-01 0.000000 0.000000 -1.509059 5 NaN 1.0
|
||
2013-01-02 1.212112 -0.173215 0.119209 5 1.0 1.0
|
||
2013-01-03 -0.861849 -2.104569 -0.494929 5 2.0 NaN
|
||
2013-01-04 0.721555 -0.706771 -1.039575 5 3.0 NaN
|
||
```
|
||
|
||
删除所有含缺失值的行：
|
||
|
||
``` python
|
||
In [58]: df1.dropna(how='any')
|
||
Out[58]:
|
||
A B C D F E
|
||
2013-01-02 1.212112 -0.173215 0.119209 5 1.0 1.0
|
||
```
|
||
|
||
填充缺失值：
|
||
|
||
``` python
|
||
In [59]: df1.fillna(value=5)
|
||
Out[59]:
|
||
A B C D F E
|
||
2013-01-01 0.000000 0.000000 -1.509059 5 5.0 1.0
|
||
2013-01-02 1.212112 -0.173215 0.119209 5 1.0 1.0
|
||
2013-01-03 -0.861849 -2.104569 -0.494929 5 2.0 5.0
|
||
2013-01-04 0.721555 -0.706771 -1.039575 5 3.0 5.0
|
||
```
|
||
|
||
提取 `nan` 值的布尔掩码：
|
||
|
||
``` python
|
||
In [60]: pd.isna(df1)
|
||
Out[60]:
|
||
A B C D F E
|
||
2013-01-01 False False False False True False
|
||
2013-01-02 False False False False False False
|
||
2013-01-03 False False False False False True
|
||
2013-01-04 False False False False False True
|
||
```
|
||
|
||
## 运算
|
||
|
||
详见[二进制操作](https://pandas.pydata.org/pandas-docs/stable/getting_started/basics.html#basics-binop)。
|
||
|
||
### 统计
|
||
|
||
一般情况下，运算时**排除**缺失值。
|
||
|
||
描述性统计：
|
||
|
||
``` python
|
||
In [61]: df.mean()
|
||
Out[61]:
|
||
A -0.004474
|
||
B -0.383981
|
||
C -0.687758
|
||
D 5.000000
|
||
F 3.000000
|
||
dtype: float64
|
||
```
|
||
|
||
在另一个轴(即，行)上执行同样的操作：
|
||
|
||
``` python
|
||
In [62]: df.mean(1)
|
||
Out[62]:
|
||
2013-01-01 0.872735
|
||
2013-01-02 1.431621
|
||
2013-01-03 0.707731
|
||
2013-01-04 1.395042
|
||
2013-01-05 1.883656
|
||
2013-01-06 1.592306
|
||
Freq: D, dtype: float64
|
||
```
|
||
|
||
不同维度对象运算时，要先对齐。 此外，Pandas 自动沿指定维度广播。
|
||
|
||
``` python
|
||
In [63]: s = pd.Series([1, 3, 5, np.nan, 6, 8], index=dates).shift(2)
|
||
|
||
In [64]: s
|
||
Out[64]:
|
||
2013-01-01 NaN
|
||
2013-01-02 NaN
|
||
2013-01-03 1.0
|
||
2013-01-04 3.0
|
||
2013-01-05 5.0
|
||
2013-01-06 NaN
|
||
Freq: D, dtype: float64
|
||
|
||
In [65]: df.sub(s, axis='index')
|
||
Out[65]:
|
||
A B C D F
|
||
2013-01-01 NaN NaN NaN NaN NaN
|
||
2013-01-02 NaN NaN NaN NaN NaN
|
||
2013-01-03 -1.861849 -3.104569 -1.494929 4.0 1.0
|
||
2013-01-04 -2.278445 -3.706771 -4.039575 2.0 0.0
|
||
2013-01-05 -5.424972 -4.432980 -4.723768 0.0 -1.0
|
||
2013-01-06 NaN NaN NaN NaN NaN
|
||
```
|
||
|
||
### Apply 函数
|
||
|
||
Apply 函数处理数据：
|
||
|
||
``` python
|
||
In [66]: df.apply(np.cumsum)
|
||
Out[66]:
|
||
A B C D F
|
||
2013-01-01 0.000000 0.000000 -1.509059 5 NaN
|
||
2013-01-02 1.212112 -0.173215 -1.389850 10 1.0
|
||
2013-01-03 0.350263 -2.277784 -1.884779 15 3.0
|
||
2013-01-04 1.071818 -2.984555 -2.924354 20 6.0
|
||
2013-01-05 0.646846 -2.417535 -2.648122 25 10.0
|
||
2013-01-06 -0.026844 -2.303886 -4.126549 30 15.0
|
||
|
||
In [67]: df.apply(lambda x: x.max() - x.min())
|
||
Out[67]:
|
||
A 2.073961
|
||
B 2.671590
|
||
C 1.785291
|
||
D 0.000000
|
||
F 4.000000
|
||
dtype: float64
|
||
```
|
||
|
||
### 直方图
|
||
|
||
详见[直方图与离散化](https://pandas.pydata.org/pandas-docs/stable/getting_started/basics.html#basics-discretization)。
|
||
|
||
``` python
|
||
In [68]: s = pd.Series(np.random.randint(0, 7, size=10))
|
||
|
||
In [69]: s
|
||
Out[69]:
|
||
0 4
|
||
1 2
|
||
2 1
|
||
3 2
|
||
4 6
|
||
5 4
|
||
6 4
|
||
7 6
|
||
8 4
|
||
9 4
|
||
dtype: int64
|
||
|
||
In [70]: s.value_counts()
|
||
Out[70]:
|
||
4 5
|
||
6 2
|
||
2 2
|
||
1 1
|
||
dtype: int64
|
||
```
|
||
|
||
### 字符串方法
|
||
|
||
Series 的 `str` 属性包含一组字符串处理功能，如下列代码所示。注意，`str` 的模式匹配默认使用[正则表达式](https://docs.python.org/3/library/re.html)。详见[矢量字符串方法](https://pandas.pydata.org/pandas-docs/stable/user_guide/text.html#text-string-methods)。
|
||
|
||
``` python
|
||
In [71]: s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])
|
||
|
||
In [72]: s.str.lower()
|
||
Out[72]:
|
||
0 a
|
||
1 b
|
||
2 c
|
||
3 aaba
|
||
4 baca
|
||
5 NaN
|
||
6 caba
|
||
7 dog
|
||
8 cat
|
||
dtype: object
|
||
```
|
||
|
||
## 合并（Merge）
|
||
|
||
### 结合（Concat）
|
||
|
||
Pandas 提供了多种将 Series、DataFrame 对象组合在一起的功能，用索引与关联代数功能的多种设置逻辑可执行连接（join）与合并（merge）操作。
|
||
|
||
详见[合并](https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#merging)。
|
||
|
||
[`concat()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.concat.html#pandas.concat) 用于连接 Pandas 对象：
|
||
|
||
``` python
|
||
In [73]: df = pd.DataFrame(np.random.randn(10, 4))
|
||
|
||
In [74]: df
|
||
Out[74]:
|
||
0 1 2 3
|
||
0 -0.548702 1.467327 -1.015962 -0.483075
|
||
1 1.637550 -1.217659 -0.291519 -1.745505
|
||
2 -0.263952 0.991460 -0.919069 0.266046
|
||
3 -0.709661 1.669052 1.037882 -1.705775
|
||
4 -0.919854 -0.042379 1.247642 -0.009920
|
||
5 0.290213 0.495767 0.362949 1.548106
|
||
6 -1.131345 -0.089329 0.337863 -0.945867
|
||
7 -0.932132 1.956030 0.017587 -0.016692
|
||
8 -0.575247 0.254161 -1.143704 0.215897
|
||
9 1.193555 -0.077118 -0.408530 -0.862495
|
||
|
||
# 分解为多组
|
||
In [75]: pieces = [df[:3], df[3:7], df[7:]]
|
||
|
||
In [76]: pd.concat(pieces)
|
||
Out[76]:
|
||
0 1 2 3
|
||
0 -0.548702 1.467327 -1.015962 -0.483075
|
||
1 1.637550 -1.217659 -0.291519 -1.745505
|
||
2 -0.263952 0.991460 -0.919069 0.266046
|
||
3 -0.709661 1.669052 1.037882 -1.705775
|
||
4 -0.919854 -0.042379 1.247642 -0.009920
|
||
5 0.290213 0.495767 0.362949 1.548106
|
||
6 -1.131345 -0.089329 0.337863 -0.945867
|
||
7 -0.932132 1.956030 0.017587 -0.016692
|
||
8 -0.575247 0.254161 -1.143704 0.215897
|
||
9 1.193555 -0.077118 -0.408530 -0.862495
|
||
```
|
||
|
||
### 连接（join）
|
||
|
||
SQL 风格的合并。 详见[数据库风格连接](https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#merging-join)。
|
||
|
||
``` python
|
||
In [77]: left = pd.DataFrame({'key': ['foo', 'foo'], 'lval': [1, 2]})
|
||
|
||
In [78]: right = pd.DataFrame({'key': ['foo', 'foo'], 'rval': [4, 5]})
|
||
|
||
In [79]: left
|
||
Out[79]:
|
||
key lval
|
||
0 foo 1
|
||
1 foo 2
|
||
|
||
In [80]: right
|
||
Out[80]:
|
||
key rval
|
||
0 foo 4
|
||
1 foo 5
|
||
|
||
In [81]: pd.merge(left, right, on='key')
|
||
Out[81]:
|
||
key lval rval
|
||
0 foo 1 4
|
||
1 foo 1 5
|
||
2 foo 2 4
|
||
3 foo 2 5
|
||
```
|
||
|
||
这里还有一个例子：
|
||
|
||
``` python
|
||
In [82]: left = pd.DataFrame({'key': ['foo', 'bar'], 'lval': [1, 2]})
|
||
|
||
In [83]: right = pd.DataFrame({'key': ['foo', 'bar'], 'rval': [4, 5]})
|
||
|
||
In [84]: left
|
||
Out[84]:
|
||
key lval
|
||
0 foo 1
|
||
1 bar 2
|
||
|
||
In [85]: right
|
||
Out[85]:
|
||
key rval
|
||
0 foo 4
|
||
1 bar 5
|
||
|
||
In [86]: pd.merge(left, right, on='key')
|
||
Out[86]:
|
||
key lval rval
|
||
0 foo 1 4
|
||
1 bar 2 5
|
||
```
|
||
|
||
### 追加（Append）
|
||
|
||
为 DataFrame 追加行。详见[追加](https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#merging-concatenation)文档。
|
||
|
||
``` python
|
||
In [87]: df = pd.DataFrame(np.random.randn(8, 4), columns=['A', 'B', 'C', 'D'])
|
||
|
||
In [88]: df
|
||
Out[88]:
|
||
A B C D
|
||
0 1.346061 1.511763 1.627081 -0.990582
|
||
1 -0.441652 1.211526 0.268520 0.024580
|
||
2 -1.577585 0.396823 -0.105381 -0.532532
|
||
3 1.453749 1.208843 -0.080952 -0.264610
|
||
4 -0.727965 -0.589346 0.339969 -0.693205
|
||
5 -0.339355 0.593616 0.884345 1.591431
|
||
6 0.141809 0.220390 0.435589 0.192451
|
||
7 -0.096701 0.803351 1.715071 -0.708758
|
||
|
||
In [89]: s = df.iloc[3]
|
||
|
||
In [90]: df.append(s, ignore_index=True)
|
||
Out[90]:
|
||
A B C D
|
||
0 1.346061 1.511763 1.627081 -0.990582
|
||
1 -0.441652 1.211526 0.268520 0.024580
|
||
2 -1.577585 0.396823 -0.105381 -0.532532
|
||
3 1.453749 1.208843 -0.080952 -0.264610
|
||
4 -0.727965 -0.589346 0.339969 -0.693205
|
||
5 -0.339355 0.593616 0.884345 1.591431
|
||
6 0.141809 0.220390 0.435589 0.192451
|
||
7 -0.096701 0.803351 1.715071 -0.708758
|
||
8 1.453749 1.208843 -0.080952 -0.264610
|
||
```
|
||
|
||
## 分组（Grouping）
|
||
|
||
“group by” 指的是涵盖下列一项或多项步骤的处理流程：
|
||
|
||
* **分割**：按条件把数据分割成多组；
|
||
* **应用**：为每组单独应用函数；
|
||
* **组合**：将处理结果组合成一个数据结构。
|
||
|
||
详见[分组](https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#groupby)。
|
||
|
||
``` python
|
||
In [91]: 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)})
|
||
....:
|
||
|
||
In [92]: df
|
||
Out[92]:
|
||
A B C D
|
||
0 foo one -1.202872 -0.055224
|
||
1 bar one -1.814470 2.395985
|
||
2 foo two 1.018601 1.552825
|
||
3 bar three -0.595447 0.166599
|
||
4 foo two 1.395433 0.047609
|
||
5 bar two -0.392670 -0.136473
|
||
6 foo one 0.007207 -0.561757
|
||
7 foo three 1.928123 -1.623033
|
||
```
|
||
|
||
先分组，再用 [`sum()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sum.html#pandas.DataFrame.sum)函数计算每组的汇总数据：
|
||
|
||
``` python
|
||
In [93]: df.groupby('A').sum()
|
||
Out[93]:
|
||
C D
|
||
A
|
||
bar -2.802588 2.42611
|
||
foo 3.146492 -0.63958
|
||
```
|
||
|
||
多列分组后，生成多层索引，也可以应用 `sum` 函数：
|
||
|
||
``` python
|
||
In [94]: df.groupby(['A', 'B']).sum()
|
||
Out[94]:
|
||
C D
|
||
A B
|
||
bar one -1.814470 2.395985
|
||
three -0.595447 0.166599
|
||
two -0.392670 -0.136473
|
||
foo one -1.195665 -0.616981
|
||
three 1.928123 -1.623033
|
||
two 2.414034 1.600434
|
||
```
|
||
|
||
## 重塑（Reshaping）
|
||
|
||
详见[多层索引](https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html#advanced-hierarchical)与[重塑](https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html#reshaping-stacking)。
|
||
|
||
### 堆叠（Stack）
|
||
|
||
``` python
|
||
In [95]: tuples = list(zip(*[['bar', 'bar', 'baz', 'baz',
|
||
....: 'foo', 'foo', 'qux', 'qux'],
|
||
....: ['one', 'two', 'one', 'two',
|
||
....: 'one', 'two', 'one', 'two']]))
|
||
....:
|
||
|
||
In [96]: index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
|
||
|
||
In [97]: df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=['A', 'B'])
|
||
|
||
In [98]: df2 = df[:4]
|
||
|
||
In [99]: df2
|
||
Out[99]:
|
||
A B
|
||
first second
|
||
bar one 0.029399 -0.542108
|
||
two 0.282696 -0.087302
|
||
baz one -1.575170 1.771208
|
||
two 0.816482 1.100230
|
||
```
|
||
|
||
[`stack()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.stack.html#pandas.DataFrame.stack)方法把 DataFrame 列压缩至一层：
|
||
|
||
``` python
|
||
In [100]: stacked = df2.stack()
|
||
|
||
In [101]: stacked
|
||
Out[101]:
|
||
first second
|
||
B -0.542108
|
||
two A 0.282696
|
||
B -0.087302
|
||
baz one A -1.575170
|
||
B 1.771208
|
||
two A 0.816482
|
||
B 1.100230
|
||
dtype: float64
|
||
```
|
||
|
||
**压缩**后的 DataFrame 或 Series 具有多层索引， [`stack()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.stack.html#pandas.DataFrame.stack) 的逆操作是 [`unstack()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.unstack.html#pandas.DataFrame.unstack)，默认为拆叠最后一层：
|
||
|
||
``` python
|
||
In [102]: stacked.unstack()
|
||
Out[102]:
|
||
A B
|
||
first second
|
||
bar one 0.029399 -0.542108
|
||
two 0.282696 -0.087302
|
||
baz one -1.575170 1.771208
|
||
two 0.816482 1.100230
|
||
|
||
In [103]: stacked.unstack(1)
|
||
Out[103]:
|
||
second one two
|
||
first
|
||
bar A 0.029399 0.282696
|
||
B -0.542108 -0.087302
|
||
baz A -1.575170 0.816482
|
||
B 1.771208 1.100230
|
||
|
||
In [104]: stacked.unstack(0)
|
||
Out[104]:
|
||
first bar baz
|
||
second
|
||
one A 0.029399 -1.575170
|
||
B -0.542108 1.771208
|
||
two A 0.282696 0.816482
|
||
B -0.087302 1.100230
|
||
```
|
||
|
||
## 数据透视表（Pivot Tables）
|
||
|
||
详见[数据透视表](https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html#reshaping-pivot)。
|
||
|
||
``` python
|
||
In [105]: df = pd.DataFrame({'A': ['one', 'one', 'two', 'three'] * 3,
|
||
.....: 'B': ['A', 'B', 'C'] * 4,
|
||
.....: 'C': ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 2,
|
||
.....: 'D': np.random.randn(12),
|
||
.....: 'E': np.random.randn(12)})
|
||
.....:
|
||
|
||
In [106]: df
|
||
Out[106]:
|
||
A B C D E
|
||
0 one A foo 1.418757 -0.179666
|
||
1 one B foo -1.879024 1.291836
|
||
2 two C foo 0.536826 -0.009614
|
||
3 three A bar 1.006160 0.392149
|
||
4 one B bar -0.029716 0.264599
|
||
5 one C bar -1.146178 -0.057409
|
||
6 two A foo 0.100900 -1.425638
|
||
7 three B foo -1.035018 1.024098
|
||
8 one C foo 0.314665 -0.106062
|
||
9 one A bar -0.773723 1.824375
|
||
10 two B bar -1.170653 0.595974
|
||
11 three C bar 0.648740 1.167115
|
||
```
|
||
|
||
用上述数据生成数据透视表非常简单：
|
||
|
||
``` python
|
||
In [107]: pd.pivot_table(df, values='D', index=['A', 'B'], columns=['C'])
|
||
Out[107]:
|
||
C bar foo
|
||
A B
|
||
one A -0.773723 1.418757
|
||
B -0.029716 -1.879024
|
||
C -1.146178 0.314665
|
||
three A 1.006160 NaN
|
||
B NaN -1.035018
|
||
C 0.648740 NaN
|
||
two A NaN 0.100900
|
||
B -1.170653 NaN
|
||
C NaN 0.536826
|
||
```
|
||
|
||
## 时间序列(TimeSeries)
|
||
|
||
Pandas 为频率转换时重采样提供了虽然简单易用，但强大高效的功能，如，将秒级的数据转换为 5 分钟为频率的数据。这种操作常见于财务应用程序，但又不仅限于此。详见[时间序列](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries)。
|
||
|
||
``` python
|
||
In [108]: rng = pd.date_range('1/1/2012', periods=100, freq='S')
|
||
|
||
In [109]: ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng)
|
||
|
||
In [110]: ts.resample('5Min').sum()
|
||
Out[110]:
|
||
2012-01-01 25083
|
||
Freq: 5T, dtype: int64
|
||
```
|
||
|
||
时区表示：
|
||
|
||
``` python
|
||
In [111]: rng = pd.date_range('3/6/2012 00:00', periods=5, freq='D')
|
||
|
||
In [112]: ts = pd.Series(np.random.randn(len(rng)), rng)
|
||
|
||
In [113]: ts
|
||
Out[113]:
|
||
2012-03-06 0.464000
|
||
2012-03-07 0.227371
|
||
2012-03-08 -0.496922
|
||
2012-03-09 0.306389
|
||
2012-03-10 -2.290613
|
||
Freq: D, dtype: float64
|
||
|
||
In [114]: ts_utc = ts.tz_localize('UTC')
|
||
|
||
In [115]: ts_utc
|
||
Out[115]:
|
||
2012-03-06 00:00:00+00:00 0.464000
|
||
2012-03-07 00:00:00+00:00 0.227371
|
||
2012-03-08 00:00:00+00:00 -0.496922
|
||
2012-03-09 00:00:00+00:00 0.306389
|
||
2012-03-10 00:00:00+00:00 -2.290613
|
||
Freq: D, dtype: float64
|
||
```
|
||
|
||
转换成其它时区：
|
||
|
||
``` python
|
||
In [116]: ts_utc.tz_convert('US/Eastern')
|
||
Out[116]:
|
||
2012-03-05 19:00:00-05:00 0.464000
|
||
2012-03-06 19:00:00-05:00 0.227371
|
||
2012-03-07 19:00:00-05:00 -0.496922
|
||
2012-03-08 19:00:00-05:00 0.306389
|
||
2012-03-09 19:00:00-05:00 -2.290613
|
||
Freq: D, dtype: float64
|
||
```
|
||
|
||
转换时间段：
|
||
|
||
``` python
|
||
In [117]: rng = pd.date_range('1/1/2012', periods=5, freq='M')
|
||
|
||
In [118]: ts = pd.Series(np.random.randn(len(rng)), index=rng)
|
||
|
||
In [119]: ts
|
||
Out[119]:
|
||
2012-01-31 -1.134623
|
||
2012-02-29 -1.561819
|
||
2012-03-31 -0.260838
|
||
2012-04-30 0.281957
|
||
2012-05-31 1.523962
|
||
Freq: M, dtype: float64
|
||
|
||
In [120]: ps = ts.to_period()
|
||
|
||
In [121]: ps
|
||
Out[121]:
|
||
2012-01 -1.134623
|
||
2012-02 -1.561819
|
||
2012-03 -0.260838
|
||
2012-04 0.281957
|
||
2012-05 1.523962
|
||
Freq: M, dtype: float64
|
||
|
||
In [122]: ps.to_timestamp()
|
||
Out[122]:
|
||
2012-01-01 -1.134623
|
||
2012-02-01 -1.561819
|
||
2012-03-01 -0.260838
|
||
2012-04-01 0.281957
|
||
2012-05-01 1.523962
|
||
Freq: MS, dtype: float64
|
||
```
|
||
|
||
Pandas 函数可以很方便地转换时间段与时间戳。下例把以 11 月为结束年份的季度频率转换为下一季度月末上午 9 点：
|
||
|
||
``` python
|
||
In [123]: prng = pd.period_range('1990Q1', '2000Q4', freq='Q-NOV')
|
||
|
||
In [124]: ts = pd.Series(np.random.randn(len(prng)), prng)
|
||
|
||
In [125]: ts.index = (prng.asfreq('M', 'e') + 1).asfreq('H', 's') + 9
|
||
|
||
In [126]: ts.head()
|
||
Out[126]:
|
||
1990-03-01 09:00 -0.902937
|
||
1990-06-01 09:00 0.068159
|
||
1990-09-01 09:00 -0.057873
|
||
1990-12-01 09:00 -0.368204
|
||
1991-03-01 09:00 -1.144073
|
||
Freq: H, dtype: float64
|
||
```
|
||
|
||
## 类别型（Categoricals）
|
||
|
||
Pandas 的 DataFrame 里可以包含类别数据。完整文档详见[类别简介](https://pandas.pydata.org/pandas-docs/stable/user_guide/categorical.html#categorical) 和 [API 文档](https://pandas.pydata.org/pandas-docs/stable/reference/arrays.html#api-arrays-categorical)。
|
||
|
||
``` python
|
||
In [127]: df = pd.DataFrame({"id": [1, 2, 3, 4, 5, 6],
|
||
.....: "raw_grade": ['a', 'b', 'b', 'a', 'a', 'e']})
|
||
.....:
|
||
```
|
||
|
||
将 `grade` 的原生数据转换为类别型数据：
|
||
|
||
``` python
|
||
In [128]: df["grade"] = df["raw_grade"].astype("category")
|
||
|
||
In [129]: df["grade"]
|
||
Out[129]:
|
||
0 a
|
||
1 b
|
||
2 b
|
||
3 a
|
||
4 a
|
||
5 e
|
||
Name: grade, dtype: category
|
||
Categories (3, object): [a, b, e]
|
||
```
|
||
|
||
用有含义的名字重命名不同类型，调用 `Series.cat.categories`。
|
||
|
||
``` python
|
||
In [130]: df["grade"].cat.categories = ["very good", "good", "very bad"]
|
||
```
|
||
|
||
重新排序各类别，并添加缺失类，`Series.cat` 的方法默认返回新 `Series`。
|
||
|
||
``` python
|
||
In [131]: df["grade"] = df["grade"].cat.set_categories(["very bad", "bad", "medium",
|
||
.....: "good", "very good"])
|
||
.....:
|
||
|
||
In [132]: df["grade"]
|
||
Out[132]:
|
||
0 very good
|
||
1 good
|
||
2 good
|
||
3 very good
|
||
4 very good
|
||
5 very bad
|
||
Name: grade, dtype: category
|
||
Categories (5, object): [very bad, bad, medium, good, very good]
|
||
```
|
||
|
||
注意，这里是按生成类别时的顺序排序，不是按词汇排序：
|
||
|
||
``` python
|
||
In [133]: df.sort_values(by="grade")
|
||
Out[133]:
|
||
id raw_grade grade
|
||
5 6 e very bad
|
||
1 2 b good
|
||
2 3 b good
|
||
0 1 a very good
|
||
3 4 a very good
|
||
4 5 a very good
|
||
```
|
||
|
||
按类列分组（groupby）时，即便某类别为空，也会显示：
|
||
|
||
``` python
|
||
In [134]: df.groupby("grade").size()
|
||
Out[134]:
|
||
grade
|
||
very bad 1
|
||
bad 0
|
||
medium 0
|
||
good 2
|
||
very good 3
|
||
dtype: int64
|
||
```
|
||
|
||
## 可视化
|
||
|
||
详见[可视化](https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html#visualization)文档。
|
||
|
||
``` python
|
||
In [135]: ts = pd.Series(np.random.randn(1000),
|
||
.....: index=pd.date_range('1/1/2000', periods=1000))
|
||
.....:
|
||
|
||
In [136]: ts = ts.cumsum()
|
||
|
||
In [137]: ts.plot()
|
||
Out[137]: <matplotlib.axes._subplots.AxesSubplot at 0x7f2b5771ac88>
|
||
```
|
||
|
||
|
||
|
||
DataFrame 的 [plot()](https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html#visualization) 方法可以快速绘制所有带标签的列：
|
||
|
||
``` python
|
||
In [138]: df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index,
|
||
.....: columns=['A', 'B', 'C', 'D'])
|
||
.....:
|
||
|
||
In [139]: df = df.cumsum()
|
||
|
||
In [140]: plt.figure()
|
||
Out[140]: <Figure size 640x480 with 0 Axes>
|
||
|
||
In [141]: df.plot()
|
||
Out[141]: <matplotlib.axes._subplots.AxesSubplot at 0x7f2b53a2d7f0>
|
||
|
||
In [142]: plt.legend(loc='best')
|
||
Out[142]: <matplotlib.legend.Legend at 0x7f2b539728d0>
|
||
```
|
||
|
||
|
||
|
||
## 数据输入 / 输出
|
||
|
||
### CSV
|
||
|
||
[写入 CSV 文件](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#io-store-in-csv)。
|
||
|
||
``` python
|
||
In [143]: df.to_csv('foo.csv')
|
||
```
|
||
|
||
读取 CSV 文件数据：
|
||
|
||
``` python
|
||
In [144]: pd.read_csv('foo.csv')
|
||
Out[144]:
|
||
Unnamed: 0 A B C D
|
||
0 2000-01-01 0.266457 -0.399641 -0.219582 1.186860
|
||
1 2000-01-02 -1.170732 -0.345873 1.653061 -0.282953
|
||
2 2000-01-03 -1.734933 0.530468 2.060811 -0.515536
|
||
3 2000-01-04 -1.555121 1.452620 0.239859 -1.156896
|
||
4 2000-01-05 0.578117 0.511371 0.103552 -2.428202
|
||
5 2000-01-06 0.478344 0.449933 -0.741620 -1.962409
|
||
6 2000-01-07 1.235339 -0.091757 -1.543861 -1.084753
|
||
.. ... ... ... ... ...
|
||
993 2002-09-20 -10.628548 -9.153563 -7.883146 28.313940
|
||
994 2002-09-21 -10.390377 -8.727491 -6.399645 30.914107
|
||
995 2002-09-22 -8.985362 -8.485624 -4.669462 31.367740
|
||
996 2002-09-23 -9.558560 -8.781216 -4.499815 30.518439
|
||
997 2002-09-24 -9.902058 -9.340490 -4.386639 30.105593
|
||
998 2002-09-25 -10.216020 -9.480682 -3.933802 29.758560
|
||
999 2002-09-26 -11.856774 -10.671012 -3.216025 29.369368
|
||
|
||
[1000 rows x 5 columns]
|
||
```
|
||
|
||
### HDF5
|
||
|
||
详见 [HDFStores](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#io-hdf5) 文档。
|
||
|
||
写入 HDF5 Store：
|
||
|
||
``` python
|
||
In [145]: df.to_hdf('foo.h5', 'df')
|
||
```
|
||
|
||
读取 HDF5 Store：
|
||
|
||
``` python
|
||
In [146]: pd.read_hdf('foo.h5', 'df')
|
||
Out[146]:
|
||
A B C D
|
||
2000-01-01 0.266457 -0.399641 -0.219582 1.186860
|
||
2000-01-02 -1.170732 -0.345873 1.653061 -0.282953
|
||
2000-01-03 -1.734933 0.530468 2.060811 -0.515536
|
||
2000-01-04 -1.555121 1.452620 0.239859 -1.156896
|
||
2000-01-05 0.578117 0.511371 0.103552 -2.428202
|
||
2000-01-06 0.478344 0.449933 -0.741620 -1.962409
|
||
2000-01-07 1.235339 -0.091757 -1.543861 -1.084753
|
||
... ... ... ... ...
|
||
2002-09-20 -10.628548 -9.153563 -7.883146 28.313940
|
||
2002-09-21 -10.390377 -8.727491 -6.399645 30.914107
|
||
2002-09-22 -8.985362 -8.485624 -4.669462 31.367740
|
||
2002-09-23 -9.558560 -8.781216 -4.499815 30.518439
|
||
2002-09-24 -9.902058 -9.340490 -4.386639 30.105593
|
||
2002-09-25 -10.216020 -9.480682 -3.933802 29.758560
|
||
2002-09-26 -11.856774 -10.671012 -3.216025 29.369368
|
||
|
||
[1000 rows x 4 columns]
|
||
```
|
||
|
||
### Excel
|
||
|
||
详见 [Excel](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#io-excel) 文档。
|
||
|
||
写入 Excel 文件：
|
||
|
||
``` python
|
||
In [147]: df.to_excel('foo.xlsx', sheet_name='Sheet1')
|
||
```
|
||
|
||
读取 Excel 文件：
|
||
|
||
``` python
|
||
In [148]: pd.read_excel('foo.xlsx', 'Sheet1', index_col=None, na_values=['NA'])
|
||
Out[148]:
|
||
Unnamed: 0 A B C D
|
||
0 2000-01-01 0.266457 -0.399641 -0.219582 1.186860
|
||
1 2000-01-02 -1.170732 -0.345873 1.653061 -0.282953
|
||
2 2000-01-03 -1.734933 0.530468 2.060811 -0.515536
|
||
3 2000-01-04 -1.555121 1.452620 0.239859 -1.156896
|
||
4 2000-01-05 0.578117 0.511371 0.103552 -2.428202
|
||
5 2000-01-06 0.478344 0.449933 -0.741620 -1.962409
|
||
6 2000-01-07 1.235339 -0.091757 -1.543861 -1.084753
|
||
.. ... ... ... ... ...
|
||
993 2002-09-20 -10.628548 -9.153563 -7.883146 28.313940
|
||
994 2002-09-21 -10.390377 -8.727491 -6.399645 30.914107
|
||
995 2002-09-22 -8.985362 -8.485624 -4.669462 31.367740
|
||
996 2002-09-23 -9.558560 -8.781216 -4.499815 30.518439
|
||
997 2002-09-24 -9.902058 -9.340490 -4.386639 30.105593
|
||
998 2002-09-25 -10.216020 -9.480682 -3.933802 29.758560
|
||
999 2002-09-26 -11.856774 -10.671012 -3.216025 29.369368
|
||
|
||
[1000 rows x 5 columns]
|
||
```
|
||
|
||
## 各种坑（Gotchas）
|
||
|
||
执行某些操作，将触发异常，如:
|
||
|
||
``` python
|
||
>>> if pd.Series([False, True, False]):
|
||
... print("I was true")
|
||
Traceback
|
||
...
|
||
ValueError: The truth value of an array is ambiguous. Use a.empty, a.any() or a.all().
|
||
```
|
||
|
||
参阅[比较操作](https://pandas.pydata.org/pandas-docs/stable/getting_started/basics.html#basics-compare)文档，查看错误提示与解决方案。
|
||
|
||
详见[各种坑](https://pandas.pydata.org/Pandas-docs/stable/gotchas.html#gotchas)文档。 |