Employment and the Labor Force

Analyzing US Census Data in Python

Lee Hachadoorian

Asst. Professor of Instruction, Temple University

Employment Concepts

Labor Force: People who are working or looking for work
Unemployed: People unable to find work
Unemployment Rate: $$Unemployed / LaborForce$$
Labor Force Participation Rate: $$LaborForce / WorkingAgePop$$

Creating a Bar Plot

   year  pct_unemployed
0  2011       10.264992
0  2012        9.373092
0  2013        8.435212
0  2014        7.226895
0  2015        6.297886
0  2016        5.750313
0  2017        5.281027

sns.barplot(
  x = "year", y = "pct_unemployed", 
  color = "cornflowerblue", 
  data = employment)

A barplot showing unemployment by year. The bars decrease in size from 2011 to 2017.

pandas.melt

print(hispanic_unemployment)

   year  pct_hisp_male_25to54_unemp  pct_hisp_female_25to54_unemp
0  2011                    9.352638                     11.426135
0  2012                    8.062535                     10.751855
0  2013                    6.915451                      9.524808
0  2014                    5.724187                      8.285590
0  2015                    5.040303                      7.070101
0  2016                    4.568206                      6.521980
0  2017                    4.184646                      5.706956

pandas.melt

# Rename columns
col_rename = {"pct_hisp_male_25to54_unemp": "male",
             "pct_hisp_female_25to54_unemp": "female"}
hispanic_unemployment.rename(columns = col_rename, inplace = True)

# Melt DataFrame
tidy_unemp = hispanic_unemployment.melt(
        id_vars = "year",
        value_vars = ["male", "female"],
        var_name = "sex",
        value_name = "pct_unemployed")

pandas.melt

# Rename columns
col_rename = {"pct_hisp_male_25to54_unemp": "male",
             "pct_hisp_female_25to54_unemp": "female"}
hispanic_unemployment.rename(columns = col_rename, inplace = True)

# Melt DataFrame
tidy_unemp = hispanic_unemployment.melt(
        id_vars = "year",
        # value_vars = ["male", "female"],
        var_name = "sex",
        value_name = "pct_unemployed")

pandas.melt

    year     sex  pct_unemployed
0   2011    male        9.352638
1   2012    male        8.062535
2   2013    male        6.915451
3   2014    male        5.724187
4   2015    male        5.040303
5   2016    male        4.568206
6   2017    male        4.184646
7   2011  female       11.426135
8   2012  female       10.751855
9   2013  female        9.524808
10  2014  female        8.285590
11  2015  female        7.070101
12  2016  female        6.521980
13  2017  female        5.706956

Creating a Grouped Bar Chart

sns.barplot(x = "year", y = "pct_unemployed", hue = "sex", 
            data = tidy_unemp)

A barplot showing unemployment by year, with separate bars for male and female unemployment. The bars decrease in size from 2011 to 2017. Male unemployment is consistently lower than female unemployment.

Let's practice!

Analyzing US Census Data in Python