Python 数据的累加与统计的示例代码

> import pandas

> # Read a CSV file, skipping last line
> rats = pandas.read_csv('rats.csv', skip_footer=1)
> rats
<class 'pandas.core.frame.DataFrame'>
Int64Index: 74055 entries, 0 to 74054
Data columns:
Creation Date 74055 non-null values
Status 74055 non-null values
Completion Date 72154 non-null values
Service Request Number 74055 non-null values
Type of Service Request 74055 non-null values
Number of Premises Baited 65804 non-null values
Number of Premises with Garbage 65600 non-null values
Number of Premises with Rats 65752 non-null values
Current Activity 66041 non-null values
Most Recent Action 66023 non-null values
Street Address 74055 non-null values
ZIP Code 73584 non-null values
X Coordinate 74043 non-null values
Y Coordinate 74043 non-null values
Ward 74044 non-null values
Police District 74044 non-null values
Community Area 74044 non-null values
Latitude 74043 non-null values
Longitude 74043 non-null values
Location 74043 non-null values
dtypes: float64(11), object(9)

> # 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
>