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fetcha-0.0.2
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مشاهده بیشتر
تبلیغات ما
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مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
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مشاهده بیشترتوضیحات
Talk to SSB using Python.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | fetcha-0.0.2 |
| نام | fetcha |
| نسخه کتابخانه | 0.0.2 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Feda Curic |
| ایمیل نویسنده | feda.curic@gmail.com |
| آدرس صفحه اصلی | https://github.com/dafeda/fetcha |
| آدرس اینترنتی | https://pypi.org/project/fetcha/ |
| مجوز | - |
# fetcha
Talk to SSB using Python.
```python
import fetcha as fetcha
import logging
# Turn off INFO-warnings
logging.getLogger().setLevel(logging.WARNING)
```
## Installation
```python
# >> pip install git+https://github.com/dafeda/fetcha.git --upgrade
```
```python
# Instantiate object with specific table_id that refers to a SSB-table.
# 10945 refers to Monetary aggregates M1, M2 and M3:
# https://www.ssb.no/en/statbank/table/10945
ssb_10945 = fetcha.SSB("10945", language="en")
```
```python
# Number of rows in table.
ssb_10945.nrows_tot()
```
1422
```python
# Number of rows per period.
ssb_10945.nrows_period()
```
9
```python
# Get all available periods
periods = ssb_10945.periods()
periods[-7:]
```
['2020M08', '2020M09', '2020M10', '2020M11', '2020M12', '2021M01', '2021M02']
```python
# Fetch latest period.
# Returns a pandas dataframe with its index set with verify_integrity set to True.
# If the dataframe is lacking an index, it means that the index columns do not make up a unique combination.
df_latest = ssb_10945.fetch()
df_latest.head()
```
<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th></th>
<th>value</th>
</tr>
<tr>
<th>contents</th>
<th>month</th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>Monetary aggregate M1. Stocks (NOK million)</th>
<th>2021M02</th>
<td>2526071.0</td>
</tr>
<tr>
<th>Monetary aggregate M2. Stocks (NOK million)</th>
<th>2021M02</th>
<td>2695383.0</td>
</tr>
<tr>
<th>Monetary aggregate M3. Stocks (NOK million)</th>
<th>2021M02</th>
<td>2697783.0</td>
</tr>
<tr>
<th>Monetary aggregate M1. Transactions last 12 months (NOK million)</th>
<th>2021M02</th>
<td>359029.0</td>
</tr>
<tr>
<th>Monetary aggregate M2. Transactions last 12 months (NOK million)</th>
<th>2021M02</th>
<td>343687.0</td>
</tr>
</tbody>
</table>
</div>
```python
# Fetch list of periods
df_periods = ssb_10945.fetch(["2019M12", "2020M01", "2020M02"])
df_periods.head()
```
<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th></th>
<th>value</th>
</tr>
<tr>
<th>contents</th>
<th>month</th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th rowspan="3" valign="top">Monetary aggregate M1. Stocks (NOK million)</th>
<th>2019M12</th>
<td>2159770.0</td>
</tr>
<tr>
<th>2020M01</th>
<td>2182450.0</td>
</tr>
<tr>
<th>2020M02</th>
<td>2175681.0</td>
</tr>
<tr>
<th rowspan="2" valign="top">Monetary aggregate M2. Stocks (NOK million)</th>
<th>2019M12</th>
<td>2345545.0</td>
</tr>
<tr>
<th>2020M01</th>
<td>2364841.0</td>
</tr>
</tbody>
</table>
</div>
```python
# Fetch whole year of data
df_year = ssb_10945.fetch("2020")
df_year.head()
```
<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th></th>
<th>value</th>
</tr>
<tr>
<th>contents</th>
<th>month</th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th rowspan="5" valign="top">Monetary aggregate M1. Stocks (NOK million)</th>
<th>2020M01</th>
<td>2182450.0</td>
</tr>
<tr>
<th>2020M02</th>
<td>2175681.0</td>
</tr>
<tr>
<th>2020M03</th>
<td>2300443.0</td>
</tr>
<tr>
<th>2020M04</th>
<td>2340381.0</td>
</tr>
<tr>
<th>2020M05</th>
<td>2374607.0</td>
</tr>
</tbody>
</table>
</div>
```python
# Fetch multiple years
df_years = ssb_10945.fetch(["2019", "2020"])
df_year.head()
```
<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th></th>
<th>value</th>
</tr>
<tr>
<th>contents</th>
<th>month</th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th rowspan="5" valign="top">Monetary aggregate M1. Stocks (NOK million)</th>
<th>2020M01</th>
<td>2182450.0</td>
</tr>
<tr>
<th>2020M02</th>
<td>2175681.0</td>
</tr>
<tr>
<th>2020M03</th>
<td>2300443.0</td>
</tr>
<tr>
<th>2020M04</th>
<td>2340381.0</td>
</tr>
<tr>
<th>2020M05</th>
<td>2374607.0</td>
</tr>
</tbody>
</table>
</div>
```python
# Reset index before pivoting
df_year = df_year.reset_index().pivot(index="month", columns="contents")
df_year.head()
```
<div>
<table border="1" class="dataframe">
<thead>
<tr>
<th></th>
<th colspan="9" halign="left">value</th>
</tr>
<tr>
<th>contents</th>
<th>Monetary aggregate M1. 12-month growth (per cent</th>
<th>Monetary aggregate M1. Stocks (NOK million)</th>
<th>Monetary aggregate M1. Transactions last 12 months (NOK million)</th>
<th>Monetary aggregate M2. 12-month growth (per cent)</th>
<th>Monetary aggregate M2. Stocks (NOK million)</th>
<th>Monetary aggregate M2. Transactions last 12 months (NOK million)</th>
<th>Monetary aggregate M3. 12-month growth (per cent)</th>
<th>Monetary aggregate M3. Stocks (NOK million)</th>
<th>Monetary aggregate M3. Transactions last 12 months (NOK million)</th>
</tr>
<tr>
<th>month</th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>2020M01</th>
<td>3.1</td>
<td>2182450.0</td>
<td>66236.0</td>
<td>3.9</td>
<td>2364841.0</td>
<td>87622.0</td>
<td>3.7</td>
<td>2368402.0</td>
<td>84912.0</td>
</tr>
<tr>
<th>2020M02</th>
<td>3.2</td>
<td>2175681.0</td>
<td>66037.0</td>
<td>3.8</td>
<td>2360484.0</td>
<td>86360.0</td>
<td>3.7</td>
<td>2364033.0</td>
<td>83138.0</td>
</tr>
<tr>
<th>2020M03</th>
<td>7.0</td>
<td>2300443.0</td>
<td>148469.0</td>
<td>7.5</td>
<td>2489403.0</td>
<td>170692.0</td>
<td>7.3</td>
<td>2492801.0</td>
<td>167960.0</td>
</tr>
<tr>
<th>2020M04</th>
<td>9.8</td>
<td>2340381.0</td>
<td>205486.0</td>
<td>9.5</td>
<td>2522315.0</td>
<td>216155.0</td>
<td>9.4</td>
<td>2525731.0</td>
<td>214558.0</td>
</tr>
<tr>
<th>2020M05</th>
<td>10.9</td>
<td>2374607.0</td>
<td>232311.0</td>
<td>10.2</td>
<td>2552508.0</td>
<td>234581.0</td>
<td>10.1</td>
<td>2555817.0</td>
<td>232003.0</td>
</tr>
</tbody>
</table>
</div>
```python
ssb_10948 = fetcha.SSB("10948", language="en")
df_10948 = ssb_10948.fetch("2020")
```
```python
df_10948.head()
```
<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th></th>
<th></th>
<th>value</th>
</tr>
<tr>
<th>holding sector</th>
<th>contents</th>
<th>month</th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th rowspan="5" valign="top">Money holding sector</th>
<th rowspan="5" valign="top">Monetary aggregate M3. Stocks, seasonally adjusted (NOK million)</th>
<th>2020M01</th>
<td>2374459.0</td>
</tr>
<tr>
<th>2020M02</th>
<td>2387955.0</td>
</tr>
<tr>
<th>2020M03</th>
<td>2499994.0</td>
</tr>
<tr>
<th>2020M04</th>
<td>2543868.0</td>
</tr>
<tr>
<th>2020M05</th>
<td>2580435.0</td>
</tr>
</tbody>
</table>
</div>
```python
# Fetch and join
# Get another table so we have something to join with.
ssb_10948 = fetcha.SSB("10948", language="en")
df_10948 = ssb_10948.fetch("2020")
df_10948 = df_10948.reset_index().pivot_table(
index="month", columns="contents", aggfunc="mean"
)
df_10948.join(df_year).head()
```
<div>
<table border="1" class="dataframe">
<thead>
<tr>
<th></th>
<th colspan="12" halign="left">value</th>
</tr>
<tr>
<th>contents</th>
<th>1-month growth, seasonally adjusted (per cent)</th>
<th>Monetary aggregate M3. Stocks, seasonally adjusted (NOK million)</th>
<th>Transactions last month, seasonally adjusted (NOK million)</th>
<th>Monetary aggregate M1. 12-month growth (per cent</th>
<th>Monetary aggregate M1. Stocks (NOK million)</th>
<th>Monetary aggregate M1. Transactions last 12 months (NOK million)</th>
<th>Monetary aggregate M2. 12-month growth (per cent)</th>
<th>Monetary aggregate M2. Stocks (NOK million)</th>
<th>Monetary aggregate M2. Transactions last 12 months (NOK million)</th>
<th>Monetary aggregate M3. 12-month growth (per cent)</th>
<th>Monetary aggregate M3. Stocks (NOK million)</th>
<th>Monetary aggregate M3. Transactions last 12 months (NOK million)</th>
</tr>
<tr>
<th>month</th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>2020M01</th>
<td>-10.02</td>
<td>949783.6</td>
<td>-1329.8</td>
<td>3.1</td>
<td>2182450.0</td>
<td>66236.0</td>
<td>3.9</td>
<td>2364841.0</td>
<td>87622.0</td>
<td>3.7</td>
<td>2368402.0</td>
<td>84912.0</td>
</tr>
<tr>
<th>2020M02</th>
<td>3.32</td>
<td>955182.0</td>
<td>3182.0</td>
<td>3.2</td>
<td>2175681.0</td>
<td>66037.0</td>
<td>3.8</td>
<td>2360484.0</td>
<td>86360.0</td>
<td>3.7</td>
<td>2364033.0</td>
<td>83138.0</td>
</tr>
<tr>
<th>2020M03</th>
<td>541.24</td>
<td>999997.4</td>
<td>38556.4</td>
<td>7.0</td>
<td>2300443.0</td>
<td>148469.0</td>
<td>7.5</td>
<td>2489403.0</td>
<td>170692.0</td>
<td>7.3</td>
<td>2492801.0</td>
<td>167960.0</td>
</tr>
<tr>
<th>2020M04</th>
<td>19.36</td>
<td>1017547.0</td>
<td>18928.0</td>
<td>9.8</td>
<td>2340381.0</td>
<td>205486.0</td>
<td>9.5</td>
<td>2522315.0</td>
<td>216155.0</td>
<td>9.4</td>
<td>2525731.0</td>
<td>214558.0</td>
</tr>
<tr>
<th>2020M05</th>
<td>14.82</td>
<td>1032174.2</td>
<td>17398.2</td>
<td>10.9</td>
<td>2374607.0</td>
<td>232311.0</td>
<td>10.2</td>
<td>2552508.0</td>
<td>234581.0</td>
<td>10.1</td>
<td>2555817.0</td>
<td>232003.0</td>
</tr>
</tbody>
</table>
</div>
```python
# SSB has a limit of 300k rows per transaction.
# Some tables have more than that in one period.
ssb_10261 = fetcha.SSB("10261", language="en")
```
```python
# Gives warning and returns None.
df_10261 = ssb_10261.fetch()
```
WARNING:fetcha.ssb:Query exceeds SSB limit of 300k rows per transaction. Current query tries to fetch 607104 rows. User a filter
```python
# Can pass filter to fetch(), but first we need to choose what we want.
# Use variable levels to see which options you have.
ssb_10261.levels
```
0 {'code': 'Region', 'text': 'region', 'values':...
1 {'code': 'Kjonn', 'text': 'sex', 'values': ['0...
2 {'code': 'Alder', 'text': 'age', 'values': ['9...
3 {'code': 'Diagnose3', 'text': 'diagnosis: Chap...
4 {'code': 'ContentsCode', 'text': 'contents', '...
5 {'code': 'Tid', 'text': 'year', 'values': ['20...
Name: variables, dtype: object
```python
# We limit the region to "The whole country".
ssb_10261.levels.iloc[0]
```
{'code': 'Region',
'text': 'region',
'values': ['0',
'30',
'01',
'02',
'03',
'34',
'04',
'05',
'06',
'38',
'07',
'08',
'42',
'09',
'10',
'11',
'46',
'12',
'14',
'15',
'50',
'16',
'17',
'18',
'54',
'19',
'20',
'F00',
'9',
'H03',
'H04',
'H05',
'H12',
'Uoppgitt'],
'valueTexts': ['The whole country',
'Viken',
'Østfold (-2019)',
'Akershus (-2019)',
'Oslo',
'Innlandet',
'Hedmark (-2019)',
'Oppland (-2019)',
'Buskerud (-2019)',
'Vestfold og Telemark',
'Vestfold (-2019)',
'Telemark (-2019)',
'Agder',
'Aust-Agder (-2019)',
'Vest-Agder (-2019)',
'Rogaland',
'Vestland',
'Hordaland (-2019)',
'Sogn og Fjordane (-2019)',
'Møre og Romsdal',
'Trøndelag - Trööndelage',
'Sør-Trøndelag (-2017)',
'Nord-Trøndelag (-2017)',
'Nordland',
'Troms og Finnmark - Romsa ja Finnmárku',
'Troms - Romsa (-2019)',
'Finnmark - Finnmárku (-2019)',
'Total',
'Uoppgitt',
'Helseregion Vest',
'Helseregion Midt-Norge',
'Helseregion Nord',
'Helseregion Sør-Øst',
'Unknown'],
'elimination': True}
```python
fltr = [{"code": "Region", "values": ["0"]}]
```
```python
df_10261 = ssb_10261.fetch(fltr=fltr)
```
```python
df_10261.shape
```
(17856, 1)
```python
df_10261.sample(10)
```
<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th></th>
<th>value</th>
</tr>
<tr>
<th>region</th>
<th>sex</th>
<th>age</th>
<th>diagnosis: Chapter in ICD-10</th>
<th>contents</th>
<th>year</th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th rowspan="10" valign="top">The whole country</th>
<th>Males</th>
<th>40-59 years</th>
<th>Influenza and pneumonia</th>
<th>Number of day cases</th>
<th>2019</th>
<td>76.0</td>
</tr>
<tr>
<th rowspan="2" valign="top">Females</th>
<th>20-39 years</th>
<th>Injuries of upper extremities</th>
<th>Patients with day cases</th>
<th>2019</th>
<td>613.0</td>
</tr>
<tr>
<th>60-69 years</th>
<th>CONGENITAL MALFORMATIONS</th>
<th>Number of bed-days</th>
<th>2019</th>
<td>290.0</td>
</tr>
<tr>
<th rowspan="2" valign="top">Both sexes</th>
<th rowspan="2" valign="top">20-39 years</th>
<th>Cardiac dysrhythmias</th>
<th>Patients with day cases</th>
<th>2019</th>
<td>276.0</td>
</tr>
<tr>
<th>PREGNANCY, CHILDBIRTH AND THE PUERPERIUM</th>
<th>Number of out-patient consultations</th>
<th>2019</th>
<td>109637.0</td>
</tr>
<tr>
<th>Females</th>
<th>60-69 years</th>
<th>Glaucoma</th>
<th>Number of day cases</th>
<th>2019</th>
<td>253.0</td>
</tr>
<tr>
<th rowspan="4" valign="top">Both sexes</th>
<th>Years, total</th>
<th>Other maternal disorders predominantly related to pregnancy</th>
<th>In-patients</th>
<th>2019</th>
<td>1478.0</td>
</tr>
<tr>
<th>60-69 years</th>
<th>Diabetes mellitus</th>
<th>In-patients</th>
<th>2019</th>
<td>488.0</td>
</tr>
<tr>
<th>70-79 years</th>
<th>Other diseases of oesophagus, stomach and duodenum</th>
<th>Number of bed-days</th>
<th>2019</th>
<td>3265.0</td>
</tr>
<tr>
<th>0-9 years</th>
<th>Malignant neoplasms of female genital organs</th>
<th>Out-patients</th>
<th>2019</th>
<td>1.0</td>
</tr>
</tbody>
</table>
</div>
```python
```
نیازمندی
| مقدار | نام |
|---|---|
| - | pyjstat |
نحوه نصب
نصب پکیج whl fetcha-0.0.2:
pip install fetcha-0.0.2.whl
نصب پکیج tar.gz fetcha-0.0.2:
pip install fetcha-0.0.2.tar.gz