Manpower EDA Version: 2.0
38 New Insights and Analysis based on Manpower Data Version: 1.0
- Installing Libraries
- Importing Libraries and Dependencies
- Importing Dataset
- Calculating Age
- Data Formatting
- Exploratory Data Analysis
- VISUALIZATIONS
- Insight 1: Top 10 countries from which most of the people are Employeed
- Insight 2 : Religion of the Employees
- Insight 3: Top 10 Highlighted Jobs
- Insight 4 : Top 10 ECONOMIC ACTS
- Insight 5: EDUCATIONAL LEVEL OF THE EMPLOYEES
- Insight 6 : GOVERNORATE RANKING ON THE BASIS OF FREQUENCY FROM HIGHEST TO LEAST
- Insight 7 : MARITAL STATUS OF THE EMPLOYEES
- Insight 8 : TOP 10 COMPANIES IN WHICH THE MAJOR PROPORTION OF PEOPLE IS EMPLOYEED
- Insight 9 : TOP 5 HIRING MONTHS
- Insight 10 : HIGHEST 10 SALARIES OFFERED BY COMPANIES
- Insight 11 : COUNTRIES with 10 HIGHEST MEAN SALARIES
- Insight 12 : JOBS WITH 10 HIGHEST MEAN SALARY
- Insight 13 : 10 HIGHEST MEAN SALARIES ACCORDING TO DIFFERENT EDUCATIONAL BACKGROUNDS
- Insight 14 : GOVERNORATE ACCORDING TO HIGHEST MEAN SALARIES
- Insight 15 : COMPANIES ACCORDING TO COUNTRIES WITH 10 HIGHEST MEAN SALARIES
- Insight 16 : EDUCATIONAL LEVEL OF EMPLOYEES IN COMPANIES THAT ARE OFFERED WITH 10 HIGHEST MEAN SALARIES
- Insight 17 : COMPANIES IN GOVERNORATE WITH 10 HIGHEST MEAN SALARIES
- Insight 18 : Top 10 COMPANIES OFFERING MAXIMUM SALARY
- INSIGHT 19
- INSIGHT 20
- INSIGHT 21
- INSIGHT 22
- INSIGHT 23
- INSIGHT 24
- Insight 25 : Age of 10 Most Aged Employees
- Insight 26 : 10 MAX SALARY ACCORDING TO AGE
- INSIGHT 27
- Insight 28 : Country Wise Top 10 Mean Age
- Insight 29 : EDUCATION WISE TOP 10 MEAN AGE OF EMPLOYEES
- Insight 30 : GOVERNORATE WISE MEAN AGE OF EMPLOYEES
- Insight 31 : ECONOMIC ACT WISE 10 HIGHEST MEAN SALARIES
- Insight 32 : ECONOMIC ACT WISE TOP 10 MOST AGED EMPLOYEES
- Insight 33 : COMPANIES ACCORDING TO ECONOMIC ACT WITH 10 HIGHEST MEAN SALARIES
- INSIGHT 34
- INSIGHT 35
- INSIGHT 36
- INSIGHT 37
df['Age'] = df['Age'].round(2)
df['Age']
df.isnull().sum()
data.columns
data['COUNTRY_DESC'].unique()
data['RLGION_DESC'].unique()
data['JOB_DESC'].value_counts()
data['EDUCATION_DESC'].unique()
data['SALARY'].value_counts()
data['GOVERNORATE_DESC'].unique()
data['MARITAL_STATUS_DESC'].unique()
data['COMPANY_NAME'].unique()
data['HIRE_DATE'].value_counts()
data['Age'].value_counts()
data.info()
insight28 = pd.DataFrame()
insight28 = data.groupby("COUNTRY_DESC")['Age'].mean().round(2)
insight28 = insight28.reset_index("COUNTRY_DESC")
insight28 = insight28.nlargest(10,'Age')
insight28['Country'] = insight28['COUNTRY_DESC']
insight28 = insight28.drop(["COUNTRY_DESC"],axis=1)
insight28['Country'] = insight28['Country'].replace(r'^\s*$', "--", regex=True)
insight28
fig = px.pie(insight28, values='Age', names='Country',title = "Country Wise Top 10 Mean Age", hole=.3)
fig.update_traces(textposition='inside', textinfo='value')
fig.show()
insight29 = pd.DataFrame()
insight29 = data.groupby("EDUCATION_DESC")['Age'].mean().round(2)
insight29 = insight29.reset_index("EDUCATION_DESC")
insight29 = insight29.nlargest(10,'Age')
insight29['Education'] = insight29['EDUCATION_DESC']
insight29 = insight29.drop(["EDUCATION_DESC"],axis=1)
insight29['Education'] = insight29['Education'].replace(r'^\s*$', "--", regex=True)
insight29
insight30 = pd.DataFrame()
insight30 = data.groupby("GOVERNORATE_DESC")['Age'].mean().round(2)
insight30 = insight30.reset_index("GOVERNORATE_DESC")
insight30 = insight30.nlargest(10,'Age')
insight30['Governorate'] = insight30['GOVERNORATE_DESC']
insight30 = insight30.drop(["GOVERNORATE_DESC"],axis=1)
insight30
fig = px.pie(insight32, values='Age', names='Economic Act',title = "ECONOMIC ACT WISE TOP 10 MOST AGED EMPLOYEES", hole=.3)
fig.update_traces(textposition='inside', textinfo='value')
fig.show()
insight33 = pd.DataFrame()
insight33 = data.groupby(["ECONOMIC_ACT_DESC","COMPANY_NAME"])['SALARY'].mean()
insight33 = insight33.reset_index(["ECONOMIC_ACT_DESC","COMPANY_NAME"])
insight33 = insight33.nlargest(10,'SALARY')
insight33['Economic Act'] = insight33['ECONOMIC_ACT_DESC']
insight33['Company'] = insight33['COMPANY_NAME']
insight33['Salary'] = insight33['SALARY']
insight33 = insight33.drop(['ECONOMIC_ACT_DESC','COMPANY_NAME','SALARY'],axis=1)
insight33