UNEMPLOYMENT ANALYSIS WITH PYTHON¶

In [ ]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as pt
import seaborn as sb
In [ ]:
data_unemp = pd.read_csv("/content/Unemployment_Rate_upto_11_2020.csv")
In [ ]:
data_unemp
Out[ ]:
Region Date Frequency Estimated Unemployment Rate (%) Estimated Employed Estimated Labour Participation Rate (%) Region.1 longitude latitude
0 Andhra Pradesh 31-01-2020 M 5.48 16635535 41.02 South 15.9129 79.740
1 Andhra Pradesh 29-02-2020 M 5.83 16545652 40.90 South 15.9129 79.740
2 Andhra Pradesh 31-03-2020 M 5.79 15881197 39.18 South 15.9129 79.740
3 Andhra Pradesh 30-04-2020 M 20.51 11336911 33.10 South 15.9129 79.740
4 Andhra Pradesh 31-05-2020 M 17.43 12988845 36.46 South 15.9129 79.740
... ... ... ... ... ... ... ... ... ...
262 West Bengal 30-06-2020 M 7.29 30726310 40.39 East 22.9868 87.855
263 West Bengal 31-07-2020 M 6.83 35372506 46.17 East 22.9868 87.855
264 West Bengal 31-08-2020 M 14.87 33298644 47.48 East 22.9868 87.855
265 West Bengal 30-09-2020 M 9.35 35707239 47.73 East 22.9868 87.855
266 West Bengal 31-10-2020 M 9.98 33962549 45.63 East 22.9868 87.855

267 rows × 9 columns

In [ ]:
data_unemp.head(20)
Out[ ]:
Region Date Frequency Estimated Unemployment Rate (%) Estimated Employed Estimated Labour Participation Rate (%) Region.1 longitude latitude
0 Andhra Pradesh 31-01-2020 M 5.48 16635535 41.02 South 15.9129 79.7400
1 Andhra Pradesh 29-02-2020 M 5.83 16545652 40.90 South 15.9129 79.7400
2 Andhra Pradesh 31-03-2020 M 5.79 15881197 39.18 South 15.9129 79.7400
3 Andhra Pradesh 30-04-2020 M 20.51 11336911 33.10 South 15.9129 79.7400
4 Andhra Pradesh 31-05-2020 M 17.43 12988845 36.46 South 15.9129 79.7400
5 Andhra Pradesh 30-06-2020 M 3.31 19805400 47.41 South 15.9129 79.7400
6 Andhra Pradesh 31-07-2020 M 8.34 15431615 38.91 South 15.9129 79.7400
7 Andhra Pradesh 31-08-2020 M 6.96 15251776 37.83 South 15.9129 79.7400
8 Andhra Pradesh 30-09-2020 M 6.40 15220312 37.47 South 15.9129 79.7400
9 Andhra Pradesh 31-10-2020 M 6.59 15157557 37.34 South 15.9129 79.7400
10 Assam 31-01-2020 M 4.66 13051904 52.98 Northeast 26.2006 92.9376
11 Assam 29-02-2020 M 4.41 10088268 40.77 Northeast 26.2006 92.9376
12 Assam 31-03-2020 M 4.77 11542888 46.73 Northeast 26.2006 92.9376
13 Assam 30-04-2020 M 11.06 6830817 29.55 Northeast 26.2006 92.9376
14 Assam 31-05-2020 M 9.55 11367897 48.26 Northeast 26.2006 92.9376
15 Assam 30-06-2020 M 0.60 9095944 35.07 Northeast 26.2006 92.9376
16 Assam 31-07-2020 M 3.77 10286757 40.88 Northeast 26.2006 92.9376
17 Assam 31-08-2020 M 5.53 9781310 39.52 Northeast 26.2006 92.9376
18 Assam 30-09-2020 M 1.19 14107641 54.38 Northeast 26.2006 92.9376
19 Assam 31-10-2020 M 3.02 11949329 46.84 Northeast 26.2006 92.9376
In [ ]:
data_unemp.tail(30)
Out[ ]:
Region Date Frequency Estimated Unemployment Rate (%) Estimated Employed Estimated Labour Participation Rate (%) Region.1 longitude latitude
237 Uttar Pradesh 31-01-2020 M 7.58 59433759 39.63 North 26.8467 80.9462
238 Uttar Pradesh 29-02-2020 M 8.98 58060531 39.23 North 26.8467 80.9462
239 Uttar Pradesh 31-03-2020 M 10.11 56976338 38.89 North 26.8467 80.9462
240 Uttar Pradesh 30-04-2020 M 21.54 50915056 39.73 North 26.8467 80.9462
241 Uttar Pradesh 31-05-2020 M 20.41 49801902 38.22 North 26.8467 80.9462
242 Uttar Pradesh 30-06-2020 M 9.47 55380649 37.29 North 26.8467 80.9462
243 Uttar Pradesh 31-07-2020 M 5.56 56201654 36.19 North 26.8467 80.9462
244 Uttar Pradesh 31-08-2020 M 5.79 55831744 35.96 North 26.8467 80.9462
245 Uttar Pradesh 30-09-2020 M 4.18 56106836 35.45 North 26.8467 80.9462
246 Uttar Pradesh 31-10-2020 M 3.75 56539521 35.49 North 26.8467 80.9462
247 Uttarakhand 31-01-2020 M 5.49 2711639 32.99 North 30.0668 79.0193
248 Uttarakhand 29-02-2020 M 4.99 3020931 36.48 North 30.0668 79.0193
249 Uttarakhand 31-03-2020 M 19.85 2539302 36.27 North 30.0668 79.0193
250 Uttarakhand 30-04-2020 M 6.48 2720115 33.23 North 30.0668 79.0193
251 Uttarakhand 31-05-2020 M 8.01 2694072 33.38 North 30.0668 79.0193
252 Uttarakhand 30-06-2020 M 8.61 2656071 33.06 North 30.0668 79.0193
253 Uttarakhand 31-07-2020 M 12.38 2938552 38.07 North 30.0668 79.0193
254 Uttarakhand 31-08-2020 M 14.26 2717528 35.90 North 30.0668 79.0193
255 Uttarakhand 30-09-2020 M 22.26 2695230 39.18 North 30.0668 79.0193
256 Uttarakhand 31-10-2020 M 9.23 2739309 34.03 North 30.0668 79.0193
257 West Bengal 31-01-2020 M 6.94 35820789 47.35 East 22.9868 87.8550
258 West Bengal 29-02-2020 M 4.92 36964178 47.74 East 22.9868 87.8550
259 West Bengal 31-03-2020 M 6.92 35903917 47.27 East 22.9868 87.8550
260 West Bengal 30-04-2020 M 17.41 26938836 39.90 East 22.9868 87.8550
261 West Bengal 31-05-2020 M 17.41 28356675 41.92 East 22.9868 87.8550
262 West Bengal 30-06-2020 M 7.29 30726310 40.39 East 22.9868 87.8550
263 West Bengal 31-07-2020 M 6.83 35372506 46.17 East 22.9868 87.8550
264 West Bengal 31-08-2020 M 14.87 33298644 47.48 East 22.9868 87.8550
265 West Bengal 30-09-2020 M 9.35 35707239 47.73 East 22.9868 87.8550
266 West Bengal 31-10-2020 M 9.98 33962549 45.63 East 22.9868 87.8550

Basic information of dataset¶

In [ ]:
data_unemp.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 267 entries, 0 to 266
Data columns (total 9 columns):
 #   Column                                    Non-Null Count  Dtype  
---  ------                                    --------------  -----  
 0   Region                                    267 non-null    object 
 1    Date                                     267 non-null    object 
 2    Frequency                                267 non-null    object 
 3    Estimated Unemployment Rate (%)          267 non-null    float64
 4    Estimated Employed                       267 non-null    int64  
 5    Estimated Labour Participation Rate (%)  267 non-null    float64
 6   Region.1                                  267 non-null    object 
 7   longitude                                 267 non-null    float64
 8   latitude                                  267 non-null    float64
dtypes: float64(4), int64(1), object(4)
memory usage: 18.9+ KB
In [ ]:
data_unemp.describe()
Out[ ]:
Estimated Unemployment Rate (%) Estimated Employed Estimated Labour Participation Rate (%) longitude latitude
count 267.000000 2.670000e+02 267.000000 267.000000 267.000000
mean 12.236929 1.396211e+07 41.681573 22.826048 80.532425
std 10.803283 1.336632e+07 7.845419 6.270731 5.831738
min 0.500000 1.175420e+05 16.770000 10.850500 71.192400
25% 4.845000 2.838930e+06 37.265000 18.112400 76.085600
50% 9.650000 9.732417e+06 40.390000 23.610200 79.019300
75% 16.755000 2.187869e+07 44.055000 27.278400 85.279900
max 75.850000 5.943376e+07 69.690000 33.778200 92.937600
In [ ]:
data_unemp.size
Out[ ]:
2403
In [ ]:
data_unemp.shape
Out[ ]:
(267, 9)
In [ ]:
data_unemp.columns
Out[ ]:
Index(['Region', ' Date', ' Frequency', ' Estimated Unemployment Rate (%)',
       ' Estimated Employed', ' Estimated Labour Participation Rate (%)',
       'Region.1', 'longitude', 'latitude'],
      dtype='object')
In [ ]:
data_unemp.isnull().sum()
Out[ ]:
Region                                      0
 Date                                       0
 Frequency                                  0
 Estimated Unemployment Rate (%)            0
 Estimated Employed                         0
 Estimated Labour Participation Rate (%)    0
Region.1                                    0
longitude                                   0
latitude                                    0
dtype: int64
In [ ]:
#for checking duplicacy
data_unemp.duplicated().sum()
Out[ ]:
0

Data Visualization¶

In [ ]:
data_unemp=pd.DataFrame(data_unemp)
y=data_unemp[' Estimated Unemployment Rate (%)']
x=data_unemp['Region']
pr= pt.figure(figsize=(40, 15))
pt.xlabel("States",fontweight='bold',fontsize=20)
pt.ylabel("Estimated Unemployment rate",fontweight='bold',fontsize=20)
pt.xticks(fontweight='bold',rotation='vertical',fontsize=20)
pt.yticks(fontweight='bold',fontsize=15)
pt.bar(x,y, color='b',align='center')
Out[ ]:
<BarContainer object of 267 artists>
In [ ]:
# State wise rate of unemplyement

u_emp= data_unemp[['Region',' Estimated Unemployment Rate (%)']].groupby('Region').sum().sort_values(by=' Estimated Unemployment Rate (%)', ascending =False)
u_emp
Out[ ]:
Estimated Unemployment Rate (%)
Region
Haryana 274.77
Tripura 250.55
Jharkhand 195.39
Bihar 194.71
Delhi 184.14
Puducherry 179.42
Himachal Pradesh 160.65
Rajasthan 158.68
Jammu & Kashmir 148.30
Tamil Nadu 121.87
Goa 121.67
Punjab 119.81
Uttarakhand 111.56
West Bengal 101.92
Uttar Pradesh 97.37
Kerala 94.34
Andhra Pradesh 86.64
Maharashtra 79.79
Sikkim 78.34
Chhattisgarh 78.19
Karnataka 76.68
Madhya Pradesh 68.54
Telangana 68.33
Odisha 64.62
Gujarat 63.76
Assam 48.56
Meghalaya 38.66

Display unemployement rate¶

In [ ]:
import plotly.express as pl
In [70]:
!pip install kaleido

Requirement already satisfied: kaleido in /usr/local/lib/python3.10/dist-packages (0.2.1)
In [71]:
unemp_data= data_unemp[["Region", "Region.1", ' Estimated Unemployment Rate (%)']]
figure= pl.sunburst(unemp_data, path=["Region.1", "Region"],values=' Estimated Unemployment Rate (%)',width=700, height=700, color_continuous_scale="spectral",title="Rate of unemployment in India")

figure.show(renderer='colab')
figure.show(renderer='notebook')

Scatterplot¶

In [ ]:
import plotly.express as px
In [72]:
df = pd.read_csv('/content/Unemployment_Rate_upto_11_2020.csv', encoding='UTF-8')

fig = px.scatter(df, x="Region", y=' Estimated Unemployment Rate (%)', color=' Estimated Labour Participation Rate (%)',
                 title="Scatterplot")


fig.show(renderer='colab')
fig.show(renderer='notebook')

Heatmap¶

In [ ]:
pt.figure(figsize=(8,4))
sb.heatmap(data_unemp.corr())
pt.show()

<ipython-input-21-1f1227181cc7>:2: FutureWarning:

The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.

Histogram Plot¶

In [ ]:
pt.figure(figsize=(12,10))
pt.title('Unemployment In India')
sb.histplot(x=' Estimated Unemployment Rate (%)', hue="Region.1", data=data_unemp)
pt.show()

State wise unemployemnt rate¶

In [ ]:
pt.figure(figsize=(12,10))
pt.title('Unemployment In India State Wise')
sb.histplot(x=' Estimated Unemployment Rate (%)', hue="Region", data=data_unemp)
pt.show()

Export to html¶

!sudo apt-get install texlive-xetex texlive-fonts-recommended texlive-plain-generic¶

!jupyter nbconvert --to html /content/UNEMPLOYMENT_ANALYSIS_WITH_PYTHON.ipynb¶

In [65]:
!jupyter nbconvert --to html /content/UNEMPLOYMENT_ANALYSIS_WITH_PYTHON.ipynb

[NbConvertApp] Converting notebook /content/UNEMPLOYMENT_ANALYSIS_WITH_PYTHON.ipynb to html
/usr/local/lib/python3.10/dist-packages/nbconvert/filters/widgetsdatatypefilter.py:71: UserWarning: Your element with mimetype(s) dict_keys(['application/vnd.plotly.v1+json']) is not able to be represented.
  warn(
[NbConvertApp] Writing 1057890 bytes to /content/UNEMPLOYMENT_ANALYSIS_WITH_PYTHON.html