We have chosen the dataset related to various career fields in different emirates in various years. We will explore the various career opportunities to find deeper insights.
We have chosen this dataset because employment is the primary concern in any nation. It is necessary for the government to keep a check on various career opportunities throughout.
As far as columns and rows are concerned, our dataset has various meaningful data columns that provide data related to the nationality of citizens, career field, year, emirate, type of sector, etc. Our dataset has 730 rows in total.
Codes of Employment Trends Of Private & Government Sector in UAE | Data Analysis
We are importing multiple libraries such as pandas, seaborn, matplotlib
We are reading the data using the pandas library to read the excel data. We have used the read_excel( ) function to read the excel file data.
We have displayed the data as we can see above
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Next is data cleaning for which we have used dropna( ) function. We have dropped the rows and columns that have all the values as null.
We have printed the dimensions of the data. We can see that many null columns are dropped(that had all the values as null).
In the above screenshot, we are using the info ( ) function to print the non null count and data type of each column
Employment Trends Of Private & Government Sector in UAE | Data Analysis
In the above piece of code, we are using countplot ( ) function where we have passed nationality on the y-axis. We can see that our dataset has more records for “national” people.
In the above piece of code, we are using countplot ( ) function where we have passed the year on the y-axis. We can see that year 2012 had the minimum number of employees whereas the year 2013 has the highest number of males. The year 2014 and 2015 has an equal number of males and females.
We are grouping the data on the basis of emirates using the group_by ( ) function. We are calculating the mean value column for each emirate group.
In the above piece of code, we are using the lineplot ( ) function where we have passed the value on the y axis. We can see that the “health authority” emirate has a high average value.
Employment Trends Of Private & Government Sector in UAE | Data Analysis using Python Project and final year project and assignment of ML, Ai, and Data Science.
In the above piece of code, we are using countplot ( ) function where we have passed sex_EN on y-axis. We can see that our dataset has more records for “male” individuals.
In the above piece of code, we are using the scatterplot ( ) function where we have passed nationality on the y-axis. We can see that our dataset has more records for “national” people. We are using xticks to rotate the orientation of y-axis labels.
We are grouping the data on the basis of sector_EN using the group_by ( ) function. We are considering the max of value column for each sector group.
Employment Trends Of Private & Government Sector in UAE | Data Analysis
We are first, filtering the data for all the females in the private and government sectors. We are using the countplot ( ) function where we have passed the sector on the y-axis. We can see that the government sector has more females than the private sector.
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