अलगाव के प्रभाव: बेरोज़गारी

Python में US Census डेटा का विश्लेषण

Lee Hachadoorian

Asst. Professor of Instruction, Temple University

ACS सब्जेक्ट टेबल IDs समझें

[B|C]ssnnn[A-I]

B या C = "Base Table" या "Collapsed Table"

B15002	C15002[A-I]
No schooling	High school से कम
Nursery to 4th grade	High school grad, GED, या विकल्प
5th and 6th grade	कुछ college या associate's
7th and 8th grade	Bachelor's या उससे ऊपर
9th grade
etc...

[B|C]ssnnn[A-I]

A = White alone
B = Black or African American Alone
C = American Indian and Alaska Native Alone
D = Asian Alone
E = Native Hawaiian and Other Pacific Islander Alone
F = Some Other Race Alone
G = Two or More Races
H = White Alone, Not Hispanic or Latino
I = Hispanic or Latino

[B|C]ssnnn[A-I]

01 = Age and Sex
02 = Race
03 = Hispanic or Latino Origin
05 = Foreign Born; Citizenship; Year of Entry; Nativity
15 = Educational Attainment
19 = Income (Households and Families)
23 = Employment Status; Work Experience; Labor Force

Source: https://www.census.gov/programs-surveys/acs/guidance/which-data-tool/table-ids-explained.html

अलगाव के प्रभावों की तुलना

अफ्रीकी-अमेरिकी बेरोज़गारी बनाम अलगाव का रिग्रेशन प्लॉट, जहाँ पुरुष और महिला ऑब्ज़र्वेशन अलग रंगों में हैं. पुरुष बेरोज़गारी की रेखा महिला की तुलना में अधिक तेज़ी से बढ़ती है.

Tidy डेटा

चौड़ा DataFrame: msa_labor_force

     msa  male_lf  female_lf
0  12060   400843     481425
1  25540    30656      35046
2  26420   231346     268923
3  26900    55943      71036
...

msa_labor_force.columns = 
    ["msa", "male", "female"]

Tidy DataFrame: tidy_msa_labor_force

      msa     sex  labor_force
0   12060    male       400843
1   25540    male        30656
2   26420    male       231346
3   26900    male        55943
...
49  12060  female       481425
50  25540  female        35046
51  26420  female       268923
52  26900  female        71036
...

pandas.melt

tidy_msa_labor_force = msa_labor_force.melt(

    id_vars = ["msa"],

    value_vars = ["male", "female"],

    var_name = "sex",

    value_name = "labor_force"
    )

pandas.melt

tidy_msa_labor_force

      msa     sex  labor_force
0   12060    male       400843
1   25540    male        30656
2   26420    male       231346
3   26900    male        55943
...
49  12060  female       481425
50  25540  female        35046
51  26420  female       268923
52  26900  female        71036
...

अभ्यास करते हैं

Python में US Census डेटा का विश्लेषण