文章大纲
- 环境搭建
- python 及jupyter 环境
- conda 虚环境
- About
- Example Usage
- Load a datafile/loadfile combination.
- 样例程序
- Shortcut to loadfiles (meta data)
- 参考文献
翻译： season
美国的一部分医疗数据是通过HIPPA 脱密后在 https://www.hcup-us.ahrq.gov/ 网站上对研究者开放进行探索的。但是由于她给出的数据格式为asc 的不常见格式，我们需要转化成csv 后才能正常使用spark 等大数据分析组件进行分析。
还好2015年，有人用python 写了一个调用SAS 解析hcup 数据的开源库，那么今天我们就一起来探索一下，如何用python 对hcup 的asc 数据进行解析并使用。
环境搭建
python 及jupyter 环境
# 设置环境变量
export
PATH
=
"/root/anaconda2/bin/:
$PATH
"
source
~/.bashrc
jupyter notebook --no-browser --port 8888 --ip
=
0.0.0.0 --allow-root
jupyter notebook --generate-config
在~/home 或者c盘usrs administrators 下找到文件夹 .jupyter 修改jupyter_application_config.py 文件。
# c.NotebookApp.notebook_dir = '' 去掉注释
conda 虚环境
conda create -n iz_pyhcup --copy -y -q python
=
2.7 ipykernel pandas numpy
source
activate iz_pyhcup
echo
"y"
|
pip
install
PyHCUP
echo
"y"
|
pip
install
sqlalchemy
source
deactivate
About
PyHCUP is a Python library for parsing and importing data obtained from the United States Healthcare Cost and Utilization Program (http://hcup-us.ahrq.gov).
Data from HCUP come as a text file, with each column a specific width. However, the widths of these columns, and their names, are elsewhere. HCUP provide this meta data as either SAS or SPSS data loading programs.
PyHCUP is built to extract meta data from the SAS loading programs, then use that meta data to parse the actual data in the fixed-width text files. You’ll still need to acquire the actual data through HCUP.
A more verbose set of instructions is available in a series of posts on the author’s blog at
http://bielism.blogspot.com/2013/12/hcup-and-python-pt-i-background.html.
Example Usage
Load a datafile/loadfile combination.
import
pyhcup
# specify where your data and loadfiles live
datafile
=
'D:\\Users\\hcup\\sid\\NY_SID_2009_CORE.asc'
loadfile
=
'D:\\Users\\hcup\\sid\\sasload\\NY_SID_2009_CORE.sas'
# pull basic meta from SAS loadfile
meta_df
=
pyhcup
.
meta_from_sas
(
loadfile
)
# use meta knowledge to parse datafile into a pandas DataFrame
df
=
pyhcup
.
read
(
datafile
,
meta_df
)
# that's it. use df from here.
Deal with very large files that cannot be held in memory in two ways.
- To import a subset of rows, such as for preliminary work or troubleshooting, specify nrows to read and/or skiprows to skip using sas.df_from_sas().
# optionally specify nrows and/or skiprows to handle larger files
df
=
pyhcup
.
read
(
datafile
,
meta_df
,
nrows
=
500000
,
skiprows
=
1000000
)
- To iterate through chunks of rows, such as for importing into a database, first use the metadata to build lists of column names and widths. Next, pass a chunksize to the read() function above to create a generator yielding manageable-sized chunks.
chunk_size
=
500000
reader
=
pyhcup
.
read
(
datafile
,
meta_df
,
chunksize
=
chunk_size
)
for
df
in
reader
:
# do your business
# such as replacing sentinel values (below)
# or inserting into a database with another Python library
Whether you are pulling in all records or just a chunk of records, you can also replace all those pesky missing/invalid data placeholders from HCUP (this is less useful for generically parsing missing values for non-HCUP files).
::
# fyi, this bulldozes through all values in all columns with no per-column control
replaced = pyhcup.replace_sentinels(df)
样例程序
上文提供了两种加载大数据文件的办法(原始文件一般非常大，一次性加载到pandas 中肯定会报错)，一种是迭代，一种是直接定位到某些行，进行子数据集的分析，下面给出一段样例分析代码，将hcup 数据集中的asc 文件转化成标准csv
#### save NY_SASD_2016_CORE.asc
filename
=
"NY_SASD_2016_CORE.asc"
data_path
=
filename
load_path
=
'NY_SASD_2016_CORE.sas'
#build a pandas DataFrame object from meta data
meta_df
=
pyhcup
.
sas
.
meta_from_sas
(
load_path
)
chunk_size
=
500000
reader
=
pyhcup
.
read
(
data_path
,
meta_df
,
chunksize
=
chunk_size
)
index
=
1
for
df
in
reader
:
if
index
==
1
:
#首先读一次，去掉前两行，生成文件
index
=
index
+
1
df
[
2
:
]
.
to_csv
(
'NY_SASD_2016_CORE.csv'
,
index
=
None
)
else
:
#后面不带header，追加文件
index
=
index
+
1
df
.
to_csv
(
'NY_SASD_2016_CORE.csv'
,
mode
=
'a'
,
header
=
False
,
index
=
None
)
print
(
index
)
写了两个封装的函数，对应的status 类的asc 文件进行csv 文件的导出
##################### 批量写入 ####################################
def
write_hcupAsc_to_csv
(
file_name_for_status_And_Year
)
:
filename
=
file_name_for_status_And_Year
+
".asc"
load_path
=
file_name_for_status_And_Year
+
".sas"
save_name
=
file_name_for_status_And_Year
+
".csv"
meta_df
=
pyhcup
.
sas
.
meta_from_sas
(
load_path
)
chunk_size
=
500000
reader
=
pyhcup
.
read
(
filename
,
meta_df
,
chunksize
=
chunk_size
)
index
=
1
for
df
in
reader
:
if
index
==
1
:
#首先读一次，去掉前两行，生成文件
index
=
index
+
1
df
[
2
:
]
.
to_csv
(
save_name
,
index
=
None
)
print
(
type
(
df
[
'KEY'
]
.
dtype
)
)
else
:
#后面不带header，追加文件
index
=
index
+
1
df
.
to_csv
(
save_name
,
mode
=
'a'
,
header
=
False
,
index
=
None
)
print
(
index
)
########################### 测试写入 从开头第二行开始写 nrows 行 ################################
def
write_Test_hcupAsc_to_csv
(
file_name_for_status_And_Year
,
save_name
,
nrows
)
:
filename
=
file_name_for_status_And_Year
+
".asc"
load_path
=
file_name_for_status_And_Year
+
".sas"
save_name
=
save_name
+
".csv"
meta_df
=
pyhcup
.
sas
.
meta_from_sas
(
load_path
)
df
=
pyhcup
.
read
(
filename
,
meta_df
,
nrows
=
nrows
,
skiprows
=
2
)
df
.
to_csv
(
save_name
,
index
=
None
)
还有一种读取的方法，我们没有用常用的chunksize，而是每次计算从特定位置开始读取
#第二种方式，不用chunksize
filename
=
"NY_SID_2016_CORE.asc"
load_path
=
'NY_SID_2016_CORE.sas'
save_name
=
'NY_SID_2016_CORE.csv'
#build a pandas DataFrame object from meta data
meta_df
=
pyhcup
.
sas
.
meta_from_sas
(
load_path
)
#获取文件行数
length
=
len
(
[
""
for
line
in
open
(
filename
,
"r"
)
]
)
print
(
length
)
chunk_size
=
500000
step
=
int
(
length
/
chunk_size
)
df
=
pyhcup
.
read
(
filename
,
meta_df
,
nrows
=
nrows
,
skiprows
=
2
)
df
.
to_csv
(
save_name
,
index
=
None
)
for
i
in
range
(
1
,
step
)
:
reader
=
pyhcup
.
read
(
filename
,
meta_df
,
nrows
=
chunk_size
,
skiprows
=
2
+
i
*
chunk_size
)
df
.
to_csv
(
save_name
,
mode
=
'a'
,
header
=
False
,
index
=
None
)
Shortcut to loadfiles (meta data)
The SAS loading program files provided by HCUP for the State Inpatient Database (SID), State Ambulatory Surgery Database (SASD), and State Emergency Department Database (SEDD) are bundled in this package for easy access. You can retrieve the meta data for these directly, without having to specify a loadfile path as described above.
Acquire meta in this way using the get_meta() function. You must pass a state abbreviation as the first argument and a year as the second arugment, like so.
meta_df
=
pyhcup
.
get_meta
(
'NY'
,
2009
)
By default, get_meta() acquires SID CORE data. Other meta can be acquired with the optional keyword arguments datafile (‘SID’, ‘SEDD’, or ‘SASD’) and category (‘CORE’, ‘CHGS’, ‘SEVERITY’, ‘DX_PR_GRPS’, or ‘AHAL’).
# California emergency department charges meta for 2010
ca_2010_emergency_charges_meta
=
pyhcup
.
get_meta
(
'CA'
,
2010
,
datafile
=
'SEDD'
,
category
=
'CHGS'
)
# Arizona outpatient surgery DRG records meta for 2004
az_2004_surg_groups_meta
=
pyhcup
.
get_meta
(
'AZ'
,
2004
,
datafile
=
'SASD'
,
category
=
'DX_PR_GRPS'
# etc.
参考文献
http://bielism.blogspot.com/2013/12/hcup-and-python-pt-5-nulls-and-pre.html
