istat_unemployment


Notebook: using jsonstat.py to explore ISTAT data (unemployment)

This Jupyter notebook shows how to use jsonstat.py python library to explore Istat data. Istat is the Italian National Institute of Statistics. It publishes a rest api for browsing italian statistics. This api can return results in jsonstat format.


In [1]:
from __future__ import print_function
import os
import pandas as pd
from IPython.core.display import HTML
import matplotlib.pyplot as plt
%matplotlib inline

import istat

Using istat api

Next step is to set a cache dir where to store json files downloaded from Istat. Storing file on disk speeds up development, and assures consistent results over time. Eventually, you can delete donwloaded files to get a fresh copy.


In [2]:
cache_dir = os.path.abspath(os.path.join("..", "tmp", "istat_cached")) # you could choice /tmp
istat.cache_dir(cache_dir)
print("cache_dir is '{}'".format(istat.cache_dir()))


cache_dir is '/Users/26fe_nas/gioprj.on_mac/prj.python/jsonstat.py/tmp/istat_cached'

List all istat areas


In [3]:
istat.areas()


Out[3]:
iddesc
32011 Population and housing census
4Enterprises
7Environment and Energy
8Population and Households
9Households Economic Conditions and Disparities
10Health statistics
11Social Security and Welfare
12Education and training
13Communication, culture and leisure
14Justice and Security
15Citizens' opinions and satisfaction with life
16Social participation
17National Accounts
19Agriculture
20Industry and Construction
21Services
22Public Administrations and Private Institutions
24External Trade and Internationalisation
25Prices
26Labour

List all datasets contained into area LAB (Labour)


In [4]:
istat_area_lab = istat.area('LAB')
istat_area_lab


Out[4]:
IstatArea: cod = LAB description = Labour

List all dimension for dataset DCCV_TAXDISOCCU (Unemployment rate)


In [5]:
istat_dataset_taxdisoccu = istat_area_lab.dataset('DCCV_TAXDISOCCU')
istat_dataset_taxdisoccu


Out[5]:
DCCV_TAXDISOCCU(9):Unemployment rate
nrnamenr. valuesvalues (first 3 values)
0Territory1361:'Italy', 3:'Nord', 4:'Nord-ovest' ...
1Data type16:'unemployment rate'
2Measure11:'percentage values'
3Gender31:'males', 2:'females', 3:'total' ...
4Age class1432:'18-29 years', 3:'20-24 years', 4:'15-24 years' ...
5Highest level of education attained511:'tertiary (university, doctoral and specialization courses)', 12:'total', 3:'primary school certificate, no educational degree' ...
6Citizenship31:'italian', 2:'foreign', 3:'total' ...
7Duration of unemployment22:'12 months and more', 3:'total'
8Time and frequency1931536:'Q4-1980', 2049:'Q4-2007', 1540:'1981' ...

Extract data from dataset DCCV_TAXDISOCCU


In [6]:
spec = { 
    "Territory": 0,                            # 1 Italy
    "Data type": 6,                            # (6:'unemployment rate')
    'Measure': 1,                              # 1 : 'percentage values'
    'Gender': 3,                               # 3 total
    'Age class':31,                            # 31:'15-74 years'
    'Highest level of education attained': 12, # 12:'total', 
    'Citizenship': 3,                          # 3:'total')
    'Duration of unemployment': 3,             # 3:'total'
    'Time and frequency': 0                    # All
}

# convert istat dataset into jsonstat collection and print some info
collection = istat_dataset_taxdisoccu.getvalues(spec)
collection


Out[6]:
JsonstatCollection contains the following JsonStatDataSet:
posdataset
0'IDITTER107*IDTIME'

Print some info of the only dataset contained into the above jsonstat collection


In [7]:
jsonstat_dataset = collection.dataset(0)
jsonstat_dataset


Out[7]:
name: 'IDITTER107*IDTIME'label: 'Unemployment rate by Territory and Time and frequency - unemployment rate - percentage values - 15-74 years'size: 7830
posidlabelsizerole
0IDITTER107Territory135
1IDTIMETime and frequency58

In [8]:
df_all = jsonstat_dataset.to_table(rtype=pd.DataFrame)
df_all.head()


Out[8]:
Territory Time and frequency Value
0 Italy 2004 8.01
1 Italy Q1-2004 8.68
2 Italy Q2-2004 7.88
3 Italy Q3-2004 7.33
4 Italy Q4-2004 8.17

In [9]:
df_all.pivot('Territory', 'Time and frequency', 'Value').head()


Out[9]:
Time and frequency 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 ... Q4-2005 Q4-2006 Q4-2007 Q4-2008 Q4-2009 Q4-2010 Q4-2011 Q4-2012 Q4-2013 Q4-2014
Territory
Abruzzo 7.71 7.88 6.57 6.17 6.63 7.97 8.67 8.59 10.85 11.29 ... 6.95 6.84 5.87 6.67 7.02 9.15 9.48 10.48 11.21 12.08
Agrigento 20.18 17.62 13.40 16.91 16.72 17.43 19.42 17.61 19.48 20.98 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
Alessandria 5.34 5.37 4.65 4.63 4.85 5.81 5.34 6.66 10.48 11.80 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
Ancona 5.11 4.14 4.05 3.49 3.78 5.82 4.94 6.84 9.20 11.27 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
Arezzo 4.55 5.50 4.88 4.61 4.91 5.51 5.87 6.04 7.33 8.04 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

5 rows × 58 columns


In [10]:
spec = { 
    "Territory": 1,                            # 1 Italy
    "Data type": 6,                            # (6:'unemployment rate')
    'Measure': 1,
    'Gender': 3,
    'Age class':0,                             # all classes
    'Highest level of education attained': 12, # 12:'total', 
    'Citizenship': 3,                          # 3:'total')
    'Duration of unemployment': 3,             #  3:'total')
    'Time and frequency': 0                    # All
}

# convert istat dataset into jsonstat collection and print some info
collection_2 = istat_dataset_taxdisoccu.getvalues(spec)
collection_2


Out[10]:
JsonstatCollection contains the following JsonStatDataSet:
posdataset
0'IDCLASETA28*IDTIME'

In [11]:
df = collection_2.dataset(0).to_table(rtype=pd.DataFrame, blocked_dims={'IDCLASETA28':'31'})
df.head(6)


Out[11]:
Age class Time and frequency Value
0 15-74 years Q4-1992 NaN
1 15-74 years 1993 NaN
2 15-74 years Q1-1993 NaN
3 15-74 years Q2-1993 NaN
4 15-74 years Q3-1993 NaN
5 15-74 years Q4-1993 NaN

In [12]:
df = df.dropna()
df = df[df['Time and frequency'].str.contains(r'^Q.*')]
# df = df.set_index('Time and frequency')
df.head(6)


Out[12]:
Age class Time and frequency Value
57 15-74 years Q1-2004 8.68
58 15-74 years Q2-2004 7.88
59 15-74 years Q3-2004 7.33
60 15-74 years Q4-2004 8.17
62 15-74 years Q1-2005 8.27
63 15-74 years Q2-2005 7.54

In [13]:
df.plot(x='Time and frequency',y='Value', figsize=(18,4))


Out[13]:
<matplotlib.axes._subplots.AxesSubplot at 0x1184b1908>

In [14]:
fig = plt.figure(figsize=(18,6))
ax = fig.add_subplot(111)
plt.grid(True)
df.plot(x='Time and frequency',y='Value', ax=ax, grid=True) 
# kind='barh', , alpha=a, legend=False, color=customcmap,
# edgecolor='w', xlim=(0,max(df['population'])), title=ttl)


Out[14]:
<matplotlib.axes._subplots.AxesSubplot at 0x11a898b70>

In [15]:
# plt.figure(figsize=(7,4))
# plt.plot(df['Time and frequency'],df['Value'], lw=1.5, label='1st')
# plt.plot(y[:,1], lw=1.5, label='2st')
# plt.plot(y,'ro')
# plt.grid(True)
# plt.legend(loc=0)
# plt.axis('tight')
# plt.xlabel('index')
# plt.ylabel('value')
# plt.title('a simple plot')

In [16]:
# forza lavoro
istat_forzlv = istat.dataset('LAB', 'DCCV_FORZLV')

spec = { 
    "Territory": 'Italy',                            
    "Data type": 'number of labour force 15 years and more (thousands)',                            # 
    'Measure':   'absolute values',               
    'Gender':    'total',                               
    'Age class': '15 years and over',                            
    'Highest level of education attained': 'total', 
    'Citizenship': 'total',                         
    'Time and frequency': 0                    
}

df_forzlv = istat_forzlv.getvalues(spec).dataset(0).to_table(rtype=pd.DataFrame)
df_forzlv = df_forzlv.dropna()
df_forzlv = df_forzlv[df_forzlv['Time and frequency'].str.contains(r'^Q.*')]
df_forzlv.tail(6)


Out[16]:
Time and frequency Value
187 Q2-2014 25419.15
188 Q3-2014 25373.70
189 Q4-2014 25794.44
190 Q1-2015 25460.25
191 Q2-2015 25598.29
192 Q3-2015 25321.61

In [17]:
istat_inattiv = istat.dataset('LAB', 'DCCV_INATTIV')
# HTML(istat_inattiv.info_dimensions_as_html())

In [18]:
spec = { 
    "Territory": 'Italy',                            
    "Data type": 'number of inactive persons',                           
    'Measure':   'absolute values',               
    'Gender':    'total',                               
    'Age class': '15 years and over',                            
    'Highest level of education attained': 'total', 
    'Time and frequency': 0                    
}

df_inattiv = istat_inattiv.getvalues(spec).dataset(0).to_table(rtype=pd.DataFrame)
df_inattiv = df_inattiv.dropna()
df_inattiv = df_inattiv[df_inattiv['Time and frequency'].str.contains(r'^Q.*')]
df_inattiv.tail(6)


Out[18]:
citizenship Labour status Inactivity reasons Main status Time and frequency Value
24756 total total total total Q2-2014 26594.57
24757 total total total total Q3-2014 26646.90
24758 total total total total Q4-2014 26257.15
24759 total total total total Q1-2015 26608.07
24760 total total total total Q2-2015 26487.67
24761 total total total total Q3-2015 26746.26