Name : ADVAIT GURUNATH CHAVAN¶

Contact No : +91 70214 55852¶

Mail ID : advaitchavan135@gmail.com¶

Oasis Infobyte Data Science Internship¶

Task 2 : Unemployment Analysis with Python¶

1. Importing the necessary dependencies¶

In [1]:

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from warnings import filterwarnings
filterwarnings(action='ignore')
import calendar
import datetime as dt
import plotly.io as plio
plio.templates
import plotly.express as px
import plotly.graph_objects as go
import plotly.figure_factory as ff

from IPython.display import HTML,display

2. Exploring the dataset¶

In [2]:

unemp = pd.read_csv('Unemployment in India.csv')

In [3]:

unemp

Out[3]:

	Region	Date	Frequency	Estimated Unemployment Rate (%)	Estimated Employed	Estimated Labour Participation Rate (%)	Area
0	Andhra Pradesh	31-05-2019	Monthly	3.65	11999139.0	43.24	Rural
1	Andhra Pradesh	30-06-2019	Monthly	3.05	11755881.0	42.05	Rural
2	Andhra Pradesh	31-07-2019	Monthly	3.75	12086707.0	43.50	Rural
3	Andhra Pradesh	31-08-2019	Monthly	3.32	12285693.0	43.97	Rural
4	Andhra Pradesh	30-09-2019	Monthly	5.17	12256762.0	44.68	Rural
...	...	...	...	...	...	...	...
749	West Bengal	29-02-2020	Monthly	7.55	10871168.0	44.09	Urban
750	West Bengal	31-03-2020	Monthly	6.67	10806105.0	43.34	Urban
751	West Bengal	30-04-2020	Monthly	15.63	9299466.0	41.20	Urban
752	West Bengal	31-05-2020	Monthly	15.22	9240903.0	40.67	Urban
753	West Bengal	30-06-2020	Monthly	9.86	9088931.0	37.57	Urban

754 rows × 7 columns

In [4]:

unemp.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 754 entries, 0 to 753
Data columns (total 7 columns):
 #   Column                                   Non-Null Count  Dtype  
---  ------                                   --------------  -----  
 0   Region                                   740 non-null    object 
 1   Date                                     740 non-null    object 
 2   Frequency                                740 non-null    object 
 3   Estimated Unemployment Rate (%)          740 non-null    float64
 4   Estimated Employed                       740 non-null    float64
 5   Estimated Labour Participation Rate (%)  740 non-null    float64
 6   Area                                     740 non-null    object 
dtypes: float64(3), object(4)
memory usage: 41.4+ KB

In [5]:

unemp.shape

Out[5]:

(754, 7)

From the dataset inforrmation section we can infer that there are 740 non-null rows in each of the 7 columns¶

Also from dataset shape we can infer that there are 754 rows (null + non-null) in each of the 7 columns¶

So there are 754 - 740 = 14 null values in each of the 7 columns of the dataset¶

In [6]:

unemp.isna().sum()

Out[6]:

Region                                     14
Date                                       14
Frequency                                  14
Estimated Unemployment Rate (%)            14
Estimated Employed                         14
Estimated Labour Participation Rate (%)    14
Area                                       14
dtype: int64

In [7]:

unemp[unemp.isnull().any(axis=1)]

Out[7]:

	Region	Date	Frequency	Estimated Unemployment Rate (%)	Estimated Employed	Estimated Labour Participation Rate (%)	Area
359	NaN	NaN	NaN	NaN	NaN	NaN	NaN
360	NaN	NaN	NaN	NaN	NaN	NaN	NaN
361	NaN	NaN	NaN	NaN	NaN	NaN	NaN
362	NaN	NaN	NaN	NaN	NaN	NaN	NaN
363	NaN	NaN	NaN	NaN	NaN	NaN	NaN
364	NaN	NaN	NaN	NaN	NaN	NaN	NaN
365	NaN	NaN	NaN	NaN	NaN	NaN	NaN
366	NaN	NaN	NaN	NaN	NaN	NaN	NaN
367	NaN	NaN	NaN	NaN	NaN	NaN	NaN
368	NaN	NaN	NaN	NaN	NaN	NaN	NaN
369	NaN	NaN	NaN	NaN	NaN	NaN	NaN
370	NaN	NaN	NaN	NaN	NaN	NaN	NaN
371	NaN	NaN	NaN	NaN	NaN	NaN	NaN
372	NaN	NaN	NaN	NaN	NaN	NaN	NaN

In [8]:

print(unemp[unemp.isnull().any(axis=1)].index.tolist())

[359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372]

From above we can infer that there are null-values from row number 359 to 372 in each of the seven columns¶

So we will remove these rows ; and make a new dataset¶

In [9]:

unemp_non_null = unemp.iloc[:, :360].dropna()

In [10]:

unemp_non_null

Out[10]:

	Region	Date	Frequency	Estimated Unemployment Rate (%)	Estimated Employed	Estimated Labour Participation Rate (%)	Area
0	Andhra Pradesh	31-05-2019	Monthly	3.65	11999139.0	43.24	Rural
1	Andhra Pradesh	30-06-2019	Monthly	3.05	11755881.0	42.05	Rural
2	Andhra Pradesh	31-07-2019	Monthly	3.75	12086707.0	43.50	Rural
3	Andhra Pradesh	31-08-2019	Monthly	3.32	12285693.0	43.97	Rural
4	Andhra Pradesh	30-09-2019	Monthly	5.17	12256762.0	44.68	Rural
...	...	...	...	...	...	...	...
749	West Bengal	29-02-2020	Monthly	7.55	10871168.0	44.09	Urban
750	West Bengal	31-03-2020	Monthly	6.67	10806105.0	43.34	Urban
751	West Bengal	30-04-2020	Monthly	15.63	9299466.0	41.20	Urban
752	West Bengal	31-05-2020	Monthly	15.22	9240903.0	40.67	Urban
753	West Bengal	30-06-2020	Monthly	9.86	9088931.0	37.57	Urban

740 rows × 7 columns

In [11]:

unemp_non_null.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 740 entries, 0 to 753
Data columns (total 7 columns):
 #   Column                                   Non-Null Count  Dtype  
---  ------                                   --------------  -----  
 0   Region                                   740 non-null    object 
 1   Date                                     740 non-null    object 
 2   Frequency                                740 non-null    object 
 3   Estimated Unemployment Rate (%)          740 non-null    float64
 4   Estimated Employed                       740 non-null    float64
 5   Estimated Labour Participation Rate (%)  740 non-null    float64
 6   Area                                     740 non-null    object 
dtypes: float64(3), object(4)
memory usage: 46.2+ KB

In [12]:

unemp_non_null.shape

Out[12]:

(740, 7)

In [13]:

unemp_non_null.isna().sum()

Out[13]:

Region                                     0
Date                                       0
Frequency                                  0
Estimated Unemployment Rate (%)            0
Estimated Employed                         0
Estimated Labour Participation Rate (%)    0
Area                                       0
dtype: int64

In [14]:

unemp_non_null.Date.min(),unemp_non_null.Date.max() 

Out[14]:

(' 29-02-2020', ' 31-12-2019')

Hence, we have removed all the rows from row number 359 to row number 372 that consisted of null values from all of the 7 columns¶

Since, the null values were present in same rows from 359 to 372 in all of the 7 columns; we removed them without following the process the imputing¶

3. Data Transformation¶

In [15]:

unemp_non_null['Date'] = pd.to_datetime(unemp_non_null['Date'],dayfirst=True)

In [16]:

unemp_non_null['Frequency']= unemp_non_null['Frequency'].astype('category')

In [17]:

unemp_non_null['Month'] =  unemp_non_null['Date'].dt.month

In [18]:

unemp_non_null['Month_num'] = unemp_non_null['Month'].apply(lambda x : int(x))

In [19]:

unemp_non_null['Month_name'] =  unemp_non_null['Month_num'].apply(lambda x: calendar.month_abbr[x])

In [20]:

unemp_non_null['Region'] = unemp_non_null['Region'].astype('category')

In [21]:

unemp_non_null['Year'] =  unemp_non_null['Date'].dt.year
unemp_non_null['Year_num'] = unemp_non_null['Year'].apply(lambda x : int(x))

In [22]:

unemp_non_null.drop(columns='Year', inplace=True)

In [23]:

##unemp_non_null.to_csv('unmeployment in India non null.csv', index=False)

In [24]:

unemp_non_null

Out[24]:

	Region	Date	Frequency	Estimated Unemployment Rate (%)	Estimated Employed	Estimated Labour Participation Rate (%)	Area	Month	Month_num	Month_name	Year_num
0	Andhra Pradesh	2019-05-31	Monthly	3.65	11999139.0	43.24	Rural	5	5	May	2019
1	Andhra Pradesh	2019-06-30	Monthly	3.05	11755881.0	42.05	Rural	6	6	Jun	2019
2	Andhra Pradesh	2019-07-31	Monthly	3.75	12086707.0	43.50	Rural	7	7	Jul	2019
3	Andhra Pradesh	2019-08-31	Monthly	3.32	12285693.0	43.97	Rural	8	8	Aug	2019
4	Andhra Pradesh	2019-09-30	Monthly	5.17	12256762.0	44.68	Rural	9	9	Sep	2019
...	...	...	...	...	...	...	...	...	...	...	...
749	West Bengal	2020-02-29	Monthly	7.55	10871168.0	44.09	Urban	2	2	Feb	2020
750	West Bengal	2020-03-31	Monthly	6.67	10806105.0	43.34	Urban	3	3	Mar	2020
751	West Bengal	2020-04-30	Monthly	15.63	9299466.0	41.20	Urban	4	4	Apr	2020
752	West Bengal	2020-05-31	Monthly	15.22	9240903.0	40.67	Urban	5	5	May	2020
753	West Bengal	2020-06-30	Monthly	9.86	9088931.0	37.57	Urban	6	6	Jun	2020

740 rows × 11 columns

4. Statistical Data Exploration¶

Estimated Unemployment Rate (%): This represents the actual unemployment rate you want to calculate. It's the percentage of the labor force that is currently unemployed and seeking employment.
¶

Estimated Employed: This is the number of people who are currently employed.
¶

Estimated Labour Participation Rate (%): This represents the percentage of the working-age population that is either employed or actively seeking employment. It includes both employed and unemployed individuals.
¶

In [25]:

round(unemp_non_null[['Estimated Unemployment Rate (%)',
       'Estimated Employed', 'Estimated Labour Participation Rate (%)']].describe(),2)

Out[25]:

	Estimated Unemployment Rate (%)	Estimated Employed	Estimated Labour Participation Rate (%)
count	740.00	740.00	740.00
mean	11.79	7204460.03	42.63
std	10.72	8087988.43	8.11
min	0.00	49420.00	13.33
25%	4.66	1190404.50	38.06
50%	8.35	4744178.50	41.16
75%	15.89	11275489.50	45.50
max	76.74	45777509.00	72.57

(A) Feature vs Region(State)¶

In [26]:

feature_vs_region =  round(unemp_non_null.groupby(['Region'])[['Estimated Unemployment Rate (%)',
       'Estimated Employed', 'Estimated Labour Participation Rate (%)']].mean().reset_index(),2)
feature_vs_region
#feature_vs_region.to_csv('feature_vs_region.csv', index=False)

Out[26]:

	Region	Estimated Unemployment Rate (%)	Estimated Employed	Estimated Labour Participation Rate (%)
0	Andhra Pradesh	7.48	8154093.18	39.38
1	Assam	6.43	5354772.15	44.87
2	Bihar	18.92	12366189.14	38.15
3	Chandigarh	15.99	316831.25	39.34
4	Chhattisgarh	9.24	4303498.57	42.81
5	Delhi	16.50	2627512.86	38.93
6	Goa	9.27	226308.33	39.25
7	Gujarat	6.66	11402012.79	46.10
8	Haryana	26.28	3557072.46	42.74
9	Himachal Pradesh	18.54	1059823.71	44.22
10	Jammu & Kashmir	16.19	1799931.67	41.03
11	Jharkhand	20.58	4469240.43	41.67
12	Karnataka	6.68	10667119.29	41.35
13	Kerala	10.12	4425899.50	34.87
14	Madhya Pradesh	7.41	11115484.32	38.82
15	Maharashtra	7.56	19990195.86	42.30
16	Meghalaya	4.80	689736.81	57.08
17	Odisha	5.66	6545746.96	38.93
18	Puducherry	10.22	212278.08	38.99
19	Punjab	12.03	4539362.00	41.14
20	Rajasthan	14.06	10041064.75	39.97
21	Sikkim	7.25	106880.71	46.07
22	Tamil Nadu	9.28	12269546.75	40.87
23	Telangana	7.74	7939662.75	53.00
24	Tripura	28.35	717002.64	61.82
25	Uttar Pradesh	12.55	28094832.18	39.43
26	Uttarakhand	6.58	1390228.11	33.78
27	West Bengal	8.12	17198538.00	45.42

In [27]:

fig = px.bar(feature_vs_region, x='Region', y='Estimated Unemployment Rate (%)',
             title='Estimated Unemployment Rate (%) vs Region', template='plotly', color_discrete_sequence=['gold'])

# Set the labels for the y-axis
fig.update_yaxes(title_text='Estimated Unemployment Rate (%)')
fig.update_traces(text=feature_vs_region['Estimated Unemployment Rate (%)'], textposition='outside')

# Show the plot
fig.show()

In [68]:

fig = px.bar(feature_vs_region, x='Region', y='Estimated Employed',
             title='Estimated Employed Count vs Region', template='plotly', color_discrete_sequence=['gold'])

# Set the labels for the y-axis
fig.update_yaxes(title_text='Estimated Employed Count')
fig.update_traces(text=feature_vs_region['Estimated Employed'])

fig.update_layout(height=1500, width=1000)

# Show the plot
fig.show()

In [29]:

fig = px.bar(feature_vs_region, x='Region', y='Estimated Labour Participation Rate (%)',
             title='Estimated Labour Participation Rate (%) vs Region', template='plotly', color_discrete_sequence=['gold'])

# Set the labels for the y-axis
fig.update_yaxes(title_text='Estimated Labour Participation Rate (%)')
fig.update_traces(text=feature_vs_region['Estimated Labour Participation Rate (%)'], textposition='outside')

# Show the plot
fig.show()

(B) Feature vs Area(Urban and Rural)¶

In [30]:

feature_vs_area = round(unemp_non_null.groupby(['Area'])[['Estimated Unemployment Rate (%)',
       'Estimated Employed', 'Estimated Labour Participation Rate (%)']].mean().reset_index(),2)
feature_vs_area
#feature_vs_area.to_excel('feature_vs_year.xlsx', index=False)
#feature_vs_area.to_csv('feature_vs_area.csv', index=False)

Out[30]:

	Area	Estimated Unemployment Rate (%)	Estimated Employed	Estimated Labour Participation Rate (%)
0	Rural	10.32	10192852.57	44.46
1	Urban	13.17	4388625.58	40.90

In [31]:

fig = px.bar(feature_vs_area, x='Area', y='Estimated Unemployment Rate (%)',
             title='Estimated Unemployment Rate (%) vs Area', template='plotly')

# Set the labels for the y-axis
fig.update_yaxes(title_text='Estimated Unemployment Rate (%)')

fig.update_traces(text=feature_vs_area['Estimated Unemployment Rate (%)'], textposition='outside')

# Show the plot
fig.show()

In [32]:

fig = px.bar(feature_vs_area, x='Area', y='Estimated Employed',
             title='Estimated Employed Count vs Area', template='plotly')

# Set the labels for the y-axis
fig.update_yaxes(title_text='Estimated Employed')
fig.update_traces(text=feature_vs_area['Estimated Employed'], textposition='outside')

# Show the plot
fig.show()

In [33]:

fig = px.bar(feature_vs_area, x='Area', y='Estimated Labour Participation Rate (%)',
             title='Estimated Labour Participation Rate (%) vs Area', template='plotly')

# Set the labels for the y-axis
fig.update_yaxes(title_text='Estimated Labour Participation Rate (%)')
fig.update_traces(text=feature_vs_area['Estimated Labour Participation Rate (%)'], textposition='outside')

# Show the plot
fig.show()

(C) Feature vs Year and Month¶

In [34]:

feature_vs_year_month = round(unemp_non_null.groupby(['Year_num','Month_num', 'Month_name'])[['Estimated Unemployment Rate (%)',
       'Estimated Employed', 'Estimated Labour Participation Rate (%)']].mean().reset_index().sort_values(by=['Year_num']),2)
feature_vs_year_month

Out[34]:

	Year_num	Month_num	Month_name	Estimated Unemployment Rate (%)	Estimated Employed	Estimated Labour Participation Rate (%)
0	2019	5	May	8.87	7410148.44	43.90
1	2019	6	Jun	9.30	7358641.57	43.75
2	2019	7	Jul	9.03	7404425.31	43.71
3	2019	8	Aug	9.64	7539815.19	43.65
4	2019	9	Sep	9.05	7739463.96	44.30
5	2019	10	Oct	9.90	7298382.40	44.00
6	2019	11	Nov	9.87	7273660.64	44.11
7	2019	12	Dec	9.50	7377387.83	43.67
8	2020	1	Jan	9.95	7677344.42	44.05
9	2020	2	Feb	9.96	7603996.28	43.72
10	2020	3	Mar	10.70	7516581.13	43.08
11	2020	4	Apr	23.64	5283319.90	35.14
12	2020	5	May	24.88	5879363.02	38.50
13	2020	6	Jun	11.90	7387008.66	40.55

In [35]:

feature_vs_year_month['Month_name'] = feature_vs_year_month['Month_name'].astype(str) 
feature_vs_year_month['Year_num_Month'] = feature_vs_year_month['Year_num'].astype(str) + ' - ' + feature_vs_year_month['Month_name']
feature_vs_year_month = feature_vs_year_month.sort_values(['Year_num', 'Month_name'])
feature_vs_year_month
#feature_vs_year_month.to_csv('feature_vs_year_month.csv', index=False)

Out[35]:

	Year_num	Month_num	Month_name	Estimated Unemployment Rate (%)	Estimated Employed	Estimated Labour Participation Rate (%)	Year_num_Month
3	2019	8	Aug	9.64	7539815.19	43.65	2019 - Aug
7	2019	12	Dec	9.50	7377387.83	43.67	2019 - Dec
2	2019	7	Jul	9.03	7404425.31	43.71	2019 - Jul
1	2019	6	Jun	9.30	7358641.57	43.75	2019 - Jun
0	2019	5	May	8.87	7410148.44	43.90	2019 - May
6	2019	11	Nov	9.87	7273660.64	44.11	2019 - Nov
5	2019	10	Oct	9.90	7298382.40	44.00	2019 - Oct
4	2019	9	Sep	9.05	7739463.96	44.30	2019 - Sep
11	2020	4	Apr	23.64	5283319.90	35.14	2020 - Apr
9	2020	2	Feb	9.96	7603996.28	43.72	2020 - Feb
8	2020	1	Jan	9.95	7677344.42	44.05	2020 - Jan
13	2020	6	Jun	11.90	7387008.66	40.55	2020 - Jun
10	2020	3	Mar	10.70	7516581.13	43.08	2020 - Mar
12	2020	5	May	24.88	5879363.02	38.50	2020 - May

In [36]:

# Create the bar plot
fig = px.bar(feature_vs_year_month, x='Year_num_Month', y='Estimated Unemployment Rate (%)',
             title='Estimated Unemployment Rate (%) vs Year and Month', template='plotly', color_discrete_sequence=['red'])

# Set the labels for the x and y-axes
fig.update_xaxes(title_text='Year and Month')
fig.update_yaxes(title_text='Estimated Unemployment Rate (%)')

# Add values on the bars
fig.update_traces(text=feature_vs_year_month['Estimated Unemployment Rate (%)'], textposition='outside')

# Show the plot
fig.show()

In [37]:

# Create the bar plot
fig = px.bar(feature_vs_year_month, x='Year_num_Month', y='Estimated Employed',
             title='Estimated Employed Count vs Year and Month', template='plotly', color_discrete_sequence=['red'])

# Set the labels for the x and y-axes
fig.update_xaxes(title_text='Year and Month')
fig.update_yaxes(title_text='Estimated Employed')

# Add values on the bars
fig.update_traces(text=feature_vs_year_month['Estimated Employed'], textposition='outside')

# Show the plot
fig.show()

In [38]:

# Create the bar plot
fig = px.bar(feature_vs_year_month, x='Year_num_Month', y='Estimated Labour Participation Rate (%)',
             title='Estimated Labour Participation Rate (%) vs Year and Month', template='plotly', color_discrete_sequence=['red'])

# Set the labels for the x and y-axes
fig.update_xaxes(title_text='Year and Month')
fig.update_yaxes(title_text='Estimated Labour Participation Rate (%)')

# Add values on the bars
fig.update_traces(text=feature_vs_year_month['Estimated Labour Participation Rate (%)'], textposition='outside')

# Show the plot
fig.show()

(D) Feature vs Year¶

In [39]:

feature_vs_year =  round(unemp_non_null.groupby(['Year_num'])[['Estimated Unemployment Rate (%)',
       'Estimated Employed', 'Estimated Labour Participation Rate (%)']].mean().reset_index(),2)
feature_vs_year
#feature_vs_year.to_csv('feature_vs_year.csv', index=False)

Out[39]:

	Year_num	Estimated Unemployment Rate (%)	Estimated Employed	Estimated Labour Participation Rate (%)
0	2019	9.4	7422976.47	43.89
1	2020	15.1	6901356.57	40.89

In [40]:

fig = px.bar(feature_vs_year, x='Year_num', y='Estimated Unemployment Rate (%)',
             title='Estimated Unemployment Rate (%) vs Year', template='plotly', color_discrete_sequence=['aqua'])

# Set the labels for the y-axis
fig.update_yaxes(title_text='Estimated Unemployment Rate (%)')
fig.update_traces(text=feature_vs_year['Estimated Unemployment Rate (%)'], textposition='outside')

# Show the plot
fig.show()

In [41]:

fig = px.bar(feature_vs_year, x='Year_num', y='Estimated Employed',
             title='Estimated Employed Count vs Year', template='plotly', color_discrete_sequence=['aqua'])

# Set the labels for the y-axis
fig.update_yaxes(title_text='Estimated Employed')
fig.update_traces(text=feature_vs_year['Estimated Employed'], textposition='outside')

# Show the plot
fig.show()

In [42]:

fig = px.bar(feature_vs_year, x='Year_num', y='Estimated Labour Participation Rate (%)',
             title='Estimated Labour Participation Rate (%) vs Year', template='plotly', color_discrete_sequence=['aqua'])

# Set the labels for the y-axis
fig.update_yaxes(title_text='Estimated Labour Participation Rate (%)')
fig.update_traces(text=feature_vs_year['Estimated Labour Participation Rate (%)'], textposition='outside')

# Show the plot
fig.show()

5. Using correlation, pairplot and scatterplot to understand the relation between the features¶

In [43]:

unemp_non_null.corr()

Out[43]:

	Estimated Unemployment Rate (%)	Estimated Employed	Estimated Labour Participation Rate (%)	Month	Month_num	Year_num
Estimated Unemployment Rate (%)	1.000000	-0.222876	0.002558	-0.122938	-0.122938	0.262602
Estimated Employed	-0.222876	1.000000	0.011300	0.011285	0.011285	-0.031841
Estimated Labour Participation Rate (%)	0.002558	0.011300	1.000000	0.087257	0.087257	-0.182460
Month	-0.122938	0.011285	0.087257	1.000000	1.000000	-0.768484
Month_num	-0.122938	0.011285	0.087257	1.000000	1.000000	-0.768484
Year_num	0.262602	-0.031841	-0.182460	-0.768484	-0.768484	1.000000

In [44]:

sns.heatmap(unemp_non_null.corr(), annot=True)

Out[44]:

<AxesSubplot:>

In [45]:

plt.figure(figsize=(20,6))
sns.pairplot(unemp_non_null)
plt.show()

<Figure size 2000x600 with 0 Axes>

In [46]:

plt.figure(figsize=(20,20))
fig = px.scatter_matrix(unemp_non_null,template='plotly',
    dimensions=['Estimated Unemployment Rate (%)','Estimated Employed',
                'Estimated Labour Participation Rate (%)'],
    color='Region')
fig.show()

<Figure size 2000x2000 with 0 Axes>

In [47]:

plt.figure(figsize=(20,16))
fig = px.scatter_matrix(unemp_non_null,template='plotly',
    dimensions=['Estimated Unemployment Rate (%)','Estimated Employed',
                'Estimated Labour Participation Rate (%)'],
    color='Area')
fig.show()

<Figure size 2000x1600 with 0 Axes>

6. Exploratory Data Analysis¶

[A] Estimated Unemployment Rate(%)¶

In [48]:

plt.figure(figsize=(20,15))
fig = px.box(unemp_non_null,x='Region',y='Estimated Unemployment Rate (%)',color='Region',title='Estimated Unemployment rate(%) vs State',template='plotly')
fig.update_layout(xaxis={'categoryorder':'total descending'})
fig.show()

<Figure size 2000x1500 with 0 Axes>

In [49]:

plt.figure(figsize=(20,15))
fig = px.box(unemp_non_null,x='Area',y='Estimated Unemployment Rate (%)',color='Area',title='Estimated Unemployment rate(%) vs Area',template='plotly')
fig.update_layout(xaxis={'categoryorder':'total descending'})
fig.show()

<Figure size 2000x1500 with 0 Axes>

In [50]:

df_est_unemp_rate_vs_region = unemp_non_null[['Estimated Unemployment Rate (%)','Region']]

df_est_unemp_rate_vs_region = df_est_unemp_rate_vs_region.groupby('Region').mean().reset_index()

df_est_unemp_rate_vs_region = df_est_unemp_rate_vs_region.sort_values('Estimated Unemployment Rate (%)')

fig = px.bar(df_est_unemp_rate_vs_region, x='Region',y='Estimated Unemployment Rate (%)',color='Region',
            title='Average Estimated Unemployment Rate(%) in each state',template='plotly', text='Estimated Unemployment Rate (%)', height=1000)

fig.show()

In [51]:

unemp_2020 = unemp_non_null[unemp_non_null['Year_num'] == 2020]
unemp_2019 = unemp_non_null[unemp_non_null['Year_num'] == 2019]

In [52]:

plt.figure(figsize=(20,30))
fig = px.bar(unemp_2020, x='Region',y='Estimated Unemployment Rate (%)',animation_frame = 'Month_name' ,color='Area',
        title='Estimated Unemployment rate across Region(States) during year 2020', height=900,template='plotly')
fig.update_layout(xaxis={'categoryorder':'total descending'})
fig.layout.updatemenus[0].buttons[0].args[1]["frame"]["duration"] = 2000
fig.show()

<Figure size 2000x3000 with 0 Axes>

In [53]:

plt.figure(figsize=(20,30))
fig = px.bar(unemp_2019, x='Region',y='Estimated Unemployment Rate (%)',animation_frame = 'Month_name' ,color='Area',
        title='Estimated Unemployment rate across Region(States) during year 2019', height=700,template='plotly')
fig.update_layout(xaxis={'categoryorder':'total descending'})
fig.layout.updatemenus[0].buttons[0].args[1]["frame"]["duration"] = 2000
fig.show()

<Figure size 2000x3000 with 0 Axes>

In [54]:

fig = px.sunburst(unemp_non_null.groupby(['Region'])['Estimated Unemployment Rate (%)'].mean().reset_index(),
                  path=['Region'], values='Estimated Unemployment Rate (%)',
                  color_continuous_scale='Plasma', title='Estimated Unemployment Rate (%) by Region(State)',
                  height=950, template='ggplot2',custom_data=['Estimated Unemployment Rate (%)'])

fig.update_traces(textinfo='label+value')

fig.show()

From the pieplot, avg. unemployment rate(%) bar plot and box plots we can infer the following:-
¶

The top 5 regions(states) in India having the highest unemployement rate (%) during COVID-19 lockdown are:
¶

1. Tripura = 28.35%
¶

2. Haryana = 26.28%
¶

3. Jharkhand = 20.59%
¶

4. Bihar = 18.92%
¶

5. Himachal Pradesh = 18.54%
¶

[B] Estimated Employed Count¶

In [55]:

plt.figure(figsize=(20,15))
fig = px.box(unemp_non_null,x='Region',y='Estimated Employed',color='Region',title='Estimated Employed Count vs State',template='plotly')
fig.update_layout(xaxis={'categoryorder':'total descending'})
fig.show()

<Figure size 2000x1500 with 0 Axes>

In [56]:

plt.figure(figsize=(20,15))
fig = px.box(unemp_non_null,x='Area',y='Estimated Employed',color='Area',title='Estimated Employed Count vs Area',template='plotly')
fig.update_layout(xaxis={'categoryorder':'total descending'})
fig.show()

<Figure size 2000x1500 with 0 Axes>

In [57]:

df_est_emp_vs_region = unemp_non_null[['Estimated Employed','Region']]

df_est_emp_vs_region = df_est_emp_vs_region.groupby('Region').mean().reset_index()

df_est_emp_vs_region = df_est_emp_vs_region.sort_values('Estimated Employed')

fig = px.bar(df_est_emp_vs_region, x='Region',y='Estimated Employed',color='Region',
            title='Average Estimated Employed in each state',template='plotly', text='Estimated Employed', height=1000)

fig.show()

In [58]:

plt.figure(figsize=(20,25))
fig = px.bar(unemp_2020, x='Region',y='Estimated Employed',animation_frame = 'Month_name' ,color='Area',
            title='Estimated Employed count across Region(States) in India in 2020', height=700,template='plotly')

fig.update_layout(xaxis={'categoryorder':'total descending'})

fig.layout.updatemenus[0].buttons[0].args[1]["frame"]["duration"] = 2000

fig.show()

<Figure size 2000x2500 with 0 Axes>

In [59]:

plt.figure(figsize=(20,25))
fig = px.bar(unemp_2019, x='Region',y='Estimated Employed',animation_frame = 'Month_name' ,color='Area',
            title='Estimated Employed count across Region(States) of India in 2019', height=700,template='plotly')

fig.update_layout(xaxis={'categoryorder':'total descending'})

fig.layout.updatemenus[0].buttons[0].args[1]["frame"]["duration"] = 2000

fig.show()

<Figure size 2000x2500 with 0 Axes>

In [60]:

fig = px.sunburst(unemp_non_null.groupby(['Region'])['Estimated Employed'].mean().reset_index(),
                  path=['Region'], values='Estimated Employed',
                  color_continuous_scale='Plasma', title='Estimated Employed Count by Region(State)',
                  height=1050, template='ggplot2',custom_data=['Estimated Employed'])

fig.update_traces(textinfo='label+value')

fig.show()

From the pieplot, avg. employed count bar plot and box plots we can infer the following:-
¶

The top 5 regions(states) in India having the highest employed count during COVID-19 lockdown are:
¶

1. Uttar Pradesh = 28.09 Million
¶

2. Maharashtra = 19.99 Million
¶

3. West Bengal = 17.19 Million
¶

4. Bihar = 12.37 Million
¶

5. Tamil Nadu = 12.27 Million
¶

[C] Estimated Labour Participation Rate (%)¶

In [61]:

plt.figure(figsize=(20,15))
fig = px.box(unemp_non_null,x='Region',y='Estimated Labour Participation Rate (%)',color='Region',title='Estimated Labour Participation Rate (%) vs State',template='plotly')
fig.update_layout(xaxis={'categoryorder':'total descending'})
fig.show()

<Figure size 2000x1500 with 0 Axes>

In [62]:

plt.figure(figsize=(20,15))
fig = px.box(unemp_non_null,x='Area',y='Estimated Labour Participation Rate (%)',color='Area',title='Estimated Labour Participation rate(%) vs Area',template='plotly')
fig.update_layout(xaxis={'categoryorder':'total descending'})
fig.show()

<Figure size 2000x1500 with 0 Axes>

In [63]:

df_est_lab_par_rate_vs_region = unemp_non_null[['Estimated Labour Participation Rate (%)','Region']]

df_est_lab_par_rate_vs_region = df_est_lab_par_rate_vs_region.groupby('Region').mean().reset_index()

df_est_lab_par_rate_vs_region = df_est_lab_par_rate_vs_region.sort_values('Estimated Labour Participation Rate (%)')

fig = px.bar(df_est_lab_par_rate_vs_region, x='Region',y='Estimated Labour Participation Rate (%)',color='Region',
            title='Average Estimated Labour Participation Rate (%) in each state',template='plotly', text='Estimated Labour Participation Rate (%)', height=1000)

fig.show()

In [64]:

plt.figure(figsize=(20,30))
fig = px.bar(unemp_2020, x='Region',y='Estimated Labour Participation Rate (%)',animation_frame = 'Month_name' ,color='Area',
        title='Estimated Labour Participation Rate (%) across Region(States) during year 2020', height=900,template='plotly')
fig.update_layout(xaxis={'categoryorder':'total descending'})
fig.layout.updatemenus[0].buttons[0].args[1]["frame"]["duration"] = 2000
fig.show()

<Figure size 2000x3000 with 0 Axes>

In [65]:

plt.figure(figsize=(20,30))
fig = px.bar(unemp_2019, x='Region',y='Estimated Labour Participation Rate (%)',animation_frame = 'Month_name' ,color='Area',
        title='Estimated Labour Participation Rate (%) across Region(States) during year 2019', height=700,template='plotly')
fig.update_layout(xaxis={'categoryorder':'total descending'})
fig.layout.updatemenus[0].buttons[0].args[1]["frame"]["duration"] = 2000
fig.show()

<Figure size 2000x3000 with 0 Axes>

In [66]:

fig = px.sunburst(unemp_non_null.groupby(['Region'])['Estimated Labour Participation Rate (%)'].mean().reset_index(),
                  path=['Region'], values='Estimated Labour Participation Rate (%)',
                  color_continuous_scale='Plasma', title='Estimated Labour Participation Rate (%) by Region(State)',
                  height=950, template='ggplot2',custom_data=['Estimated Labour Participation Rate (%)'])

fig.update_traces(textinfo='label+value')

fig.show()

	Region	Date	Frequency	Estimated Unemployment Rate (%)	Estimated Employed	Estimated Labour Participation Rate (%)	Area
359	NaN	NaN	NaN	NaN	NaN	NaN	NaN
360	NaN	NaN	NaN	NaN	NaN	NaN	NaN
361	NaN	NaN	NaN	NaN	NaN	NaN	NaN
362	NaN	NaN	NaN	NaN	NaN	NaN	NaN
363	NaN	NaN	NaN	NaN	NaN	NaN	NaN
364	NaN	NaN	NaN	NaN	NaN	NaN	NaN
365	NaN	NaN	NaN	NaN	NaN	NaN	NaN
366	NaN	NaN	NaN	NaN	NaN	NaN	NaN
367	NaN	NaN	NaN	NaN	NaN	NaN	NaN
368	NaN	NaN	NaN	NaN	NaN	NaN	NaN
369	NaN	NaN	NaN	NaN	NaN	NaN	NaN
370	NaN	NaN	NaN	NaN	NaN	NaN	NaN
371	NaN	NaN	NaN	NaN	NaN	NaN	NaN
372	NaN	NaN	NaN	NaN	NaN	NaN	NaN

	Region	Date	Frequency	Estimated Unemployment Rate (%)	Estimated Employed	Estimated Labour Participation Rate (%)	Area
359	NaN	NaN	NaN	NaN	NaN	NaN	NaN
360	NaN	NaN	NaN	NaN	NaN	NaN	NaN
361	NaN	NaN	NaN	NaN	NaN	NaN	NaN
362	NaN	NaN	NaN	NaN	NaN	NaN	NaN
363	NaN	NaN	NaN	NaN	NaN	NaN	NaN
364	NaN	NaN	NaN	NaN	NaN	NaN	NaN
365	NaN	NaN	NaN	NaN	NaN	NaN	NaN
366	NaN	NaN	NaN	NaN	NaN	NaN	NaN
367	NaN	NaN	NaN	NaN	NaN	NaN	NaN
368	NaN	NaN	NaN	NaN	NaN	NaN	NaN
369	NaN	NaN	NaN	NaN	NaN	NaN	NaN
370	NaN	NaN	NaN	NaN	NaN	NaN	NaN
371	NaN	NaN	NaN	NaN	NaN	NaN	NaN
372	NaN	NaN	NaN	NaN	NaN	NaN	NaN

Name : ADVAIT GURUNATH CHAVAN¶

Contact No : +91 70214 55852¶

Mail ID : advaitchavan135@gmail.com¶

Oasis Infobyte Data Science Internship¶

Task 2 : Unemployment Analysis with Python¶

1. Importing the necessary dependencies¶

2. Exploring the dataset¶

From the dataset inforrmation section we can infer that there are 740 non-null rows in each of the 7 columns¶

Also from dataset shape we can infer that there are 754 rows (null + non-null) in each of the 7 columns¶

So there are 754 - 740 = 14 null values in each of the 7 columns of the dataset¶

From above we can infer that there are null-values from row number 359 to 372 in each of the seven columns¶

So we will remove these rows ; and make a new dataset¶

Hence, we have removed all the rows from row number 359 to row number 372 that consisted of null values from all of the 7 columns¶

Since, the null values were present in same rows from 359 to 372 in all of the 7 columns; we removed them without following the process the imputing¶

3. Data Transformation¶

4. Statistical Data Exploration¶

Estimated Unemployment Rate (%): This represents the actual unemployment rate you want to calculate. It's the percentage of the labor force that is currently unemployed and seeking employment. ¶

Estimated Employed: This is the number of people who are currently employed.¶

Estimated Labour Participation Rate (%): This represents the percentage of the working-age population that is either employed or actively seeking employment. It includes both employed and unemployed individuals.¶

(A) Feature vs Region(State)¶

(B) Feature vs Area(Urban and Rural)¶

(C) Feature vs Year and Month¶

(D) Feature vs Year¶

5. Using correlation, pairplot and scatterplot to understand the relation between the features¶

6. Exploratory Data Analysis¶

[A] Estimated Unemployment Rate(%)¶

From the pieplot, avg. unemployment rate(%) bar plot and box plots we can infer the following:-¶

The top 5 regions(states) in India having the highest unemployement rate (%) during COVID-19 lockdown are:¶

1. Tripura = 28.35%¶

2. Haryana = 26.28%¶

3. Jharkhand = 20.59%¶

4. Bihar = 18.92%¶

5. Himachal Pradesh = 18.54%¶

[B] Estimated Employed Count¶

From the pieplot, avg. employed count bar plot and box plots we can infer the following:-¶

The top 5 regions(states) in India having the highest employed count during COVID-19 lockdown are:¶

1. Uttar Pradesh = 28.09 Million ¶

2. Maharashtra = 19.99 Million¶

3. West Bengal = 17.19 Million ¶

4. Bihar = 12.37 Million¶

5. Tamil Nadu = 12.27 Million¶

[C] Estimated Labour Participation Rate (%)¶

From the pieplot, avg. labour participation rate(%) bar plot and box plots we can infer the following:-¶

The top 5 regions(states) in India having the highest labour participation rate (%) during COVID-19 lockdown are:¶

1. Tripura = 61.82%¶

2. Meghalaya = 57.08%¶

3. Telangana = 53.00%¶

4. Gujarat = 46.10%¶

5. Sikkim = 46.07%¶