> For the complete documentation index, see [llms.txt](https://l1nwatch.gitbook.io/python-cookbook/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://l1nwatch.gitbook.io/python-cookbook/di-6-zhang-shu-ju-bian-ma-he-chu-li/6.13-shu-ju-de-lei-jia-yu-tong-ji-cao-zuo.md).

# 6.13 数据的累加与统计操作

## 问题

你需要处理一个很大的数据集并需要计算数据总和或其他统计量。

## 解决方案

对于任何涉及到统计、时间序列以及其他相关技术的数据分析问题，都可以考虑使用 [Pandas库](http://pandas.pydata.org/) 。

为了让你先体验下，下面是一个使用Pandas来分析芝加哥城市的 [老鼠和啮齿类动物数据库](https://data.cityofchicago.org/Service-Requests/311-Service-Requests-Rodent-Baiting/97t6-zrhs) 的例子。 在我写这篇文章的时候，这个数据库是一个拥有大概74,000行数据的CSV文件。

```python
>>> import pandas

>>> # Read a CSV file, skipping last line
>>> rats = pandas.read_csv('rats.csv', skip_footer=1)
>>> # Investigate range of values for a certain field
>>> rats['Current Activity'].unique()
array([nan, Dispatch Crew, Request Sanitation Inspector], dtype=object)
>>> # Filter the data
>>> crew_dispatched = rats[rats['Current Activity'] == 'Dispatch Crew']
>>> len(crew_dispatched)
65676

>>> # Find 10 most rat-infested ZIP codes in Chicago
>>> crew_dispatched['ZIP Code'].value_counts()[:10]
60647 3837
60618 3530
60614 3284
60629 3251
60636 2801
60657 2465
60641 2238
60609 2206
60651 2152
60632 2071

>>> # Group by completion date
>>> dates = crew_dispatched.groupby('Completion Date')
<pandas.core.groupby.DataFrameGroupBy object at 0x10d0a2a10>
>>> len(dates)
472

>>> # Determine counts on each day
>>> date_counts = dates.size()
>>> date_counts[0:10]
Completion Date
01/03/2011 4
01/03/2012 125
01/04/2011 54
01/04/2012 38
01/05/2011 78
01/05/2012 100
01/06/2011 100
01/06/2012 58
01/07/2011 1
01/09/2012 12

>>> # Sort the counts
>>> date_counts.sort()
>>> date_counts[-10:]
Completion Date
10/12/2012 313
10/21/2011 314
09/20/2011 316
10/26/2011 319
02/22/2011 325
10/26/2012 333
03/17/2011 336
10/13/2011 378
10/14/2011 391
10/07/2011 457
```

## 讨论

Pandas是一个拥有很多特性的大型函数库。