From 2fe15953fc285071e2b7c7d281f1afaf93aa2711 Mon Sep 17 00:00:00 2001 From: Nick Eubank Date: Fri, 22 Sep 2023 19:22:27 -0400 Subject: [PATCH] reading link --- docs/html/.doctrees/class_schedule.doctree | Bin 114712 -> 114703 bytes docs/html/.doctrees/environment.pickle | Bin 367485 -> 367360 bytes .../.doctrees/exercises/Exercise_dask.doctree | Bin 94738 -> 94738 bytes .../exercises/Exercise_series.doctree | Bin 38185 -> 37882 bytes .../nbsphinx/exercises/Exercise_series.ipynb | 9 ++++----- .../exercises/Exercise_series.ipynb.txt | 9 ++++----- docs/html/class_schedule.html | 2 +- docs/html/exercises/Exercise_series.html | 4 ++-- docs/html/exercises/Exercise_series.ipynb | 9 ++++----- docs/html/objects.inv | Bin 14039 -> 14048 bytes docs/html/searchindex.js | 2 +- docs/html/sitemap.xml | 2 +- source/class_schedule.csv | 2 +- source/class_schedule_xlsx.xlsx | Bin 13968 -> 13960 bytes source/exercises/Exercise_series.ipynb | 9 ++++----- 15 files changed, 22 insertions(+), 26 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b/docs/html/.doctrees/exercises/Exercise_dask.doctree index 8bb469caa8de51e30b411c30da3ac9018f0dedbd..a30ca229d0b38689f89ee42d20554d5762188ba8 100644 GIT binary patch delta 170 zcmbRAgmuyr)&)!orWWRgrpc)Wsg@~bCMju#7HO%emImfP%EH(v#lUj20@HR5B|@rd i6eim+bxdyLt|Mq*vpmmsc^*dJ1cEBJ_vA3Pu>t_TI59E+ delta 170 zcmbRAgmuyr)&)!oDdy&8sg{W*W@f1tCPqM(fkCQ)c}ikZvZ=A5k-6Ds1*YvBN`zF^ iC``6t>X_WfT}RNsW_h0N@;r>b2?SMc@5y0oV+8aq&%X8-*!ZmKf4hFvisvS0S9j>U5ZHf)U0lyDfwIRn2BzSdD1fMU! z4-ndlVI{LkGdSmw(5jQW7&-JW3H8sd2$Ih2ryO)N8nJGKbmJ_F!*=(6)CGG)k?SV5 zI9zmxa2&&pz&3c~j-o+AH4)i>*zg3N5hXNMhCLoHO;_q~-C(6sgU9kPo?~K| zNgELp!+d%IFL7|A5XO(F*oT(jTP4IWa=^As1amyaDdK*un!aec2jy~8xQ^0p?neUD zz--@nT*EWPL#8;>znfIL)Bmx#gJ-)CGQXjNuk+C4^K*}hMbZu)9WXX)MwQ{6L!56DcLbR1y+f1y(_r`9aB_)jKu%Vsj->{A(t{~6pejICr{|#A%Q=)@G81cb zK~>pD4rvV%Qk6Z_xtgm*RVLtE=~-Iw>(Z9SE97?)Qv^~lrqHr6Rw~9)$5^@;OA#Zs z-%HO&=zA7|;u=ct$zk6VsUi$}^});$7uMl<2FGOJucGcpvbS`KaH4#-(!BBkjk zSJV>OW8gK2*OF5BWmSIqz4C8~yxG7vAzmXUP^oipsp5g$VNmTlHSmy{rCk)r_CmO| 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Class Schedule

Class Time

Class will be Tuesdays and Thursdays from 1:25pm-2:40pm (EDT).

Date, Rm

Topic

Do Before Class

In-Class Exercise

Tues, Aug 29

  • Class Introduction

  • Welcome to VS Code

Thurs, Aug 31

  • Command Line Basics

Fri, Sep 1

SOFTWARE INSTALL DAY

A day of trouble shooting install issues

Tues, Sep 5

  • Advanced Command Line

  • Git

  • Packages

Thurs, Sep 7

  • Git Continued

  • Jupyter

Tues, Sep 12

  • Python Debugger

  • R / Python Differences

  • Packages

Thurs, Sep 14

  • Numpy Basics

Tues, Sep 19

  • Numpy Arrays

More Numpy Concepts:

Matrices:

ND Arrays:

Thurs, Sep 21

  • Pandas: Series

Tues, Sep 26

  • Pandas: DataFrames

Thurs, Sep 28

  • Pandas: Indices & Missing

Tues, Oct 3

  • Pandas: Cleaning

  • Tracebacks

Thurs, Oct 5

  • Grammer of Graphics

  • Intro to Plotting with Altair

Tues, Oct 10

  • Advanced Plotting

Thurs, Oct 12

FALL BREAK

Tues, Oct 17

FALL BREAK

Thurs, Oct 19

  • Pandas: Merging

  • Pandas: Loading and saving data

  • JVP pp 149 - 157

  • WM Chapter 6

Tues, Oct 24

  • Big Data: What is it, how do I work with it?

Thurs, Oct 26

  • Defensive Programming

  • Workflow

  • Backwards Design

  • Getting Help Online

Discuss mid-semester project in class

Tues, Oct 31

  • Defensive Programming

  • Groupby / Split-Apply-Combine

Thurs, Nov 2

  • Pandas: Reshaping

  • Pandas: Categoricals

Tues, Nov 7

  • Speed and Performance in Python

[finish groupby and reshaping exercises]

Thurs, Nov 9

  • Statistics with statsmodels

Tues, Nov 14

  • Machine Learning with sckikit-learn

  • JVP Chapter 5 up to “Hyperparameters and Model Validation” Section (pp 331 - 359)

Thurs, Nov 16

Tues, Nov 21

No Class

Thurs, Nov 23

THANKSGIVING

Tues, Nov 28

  • Defining Your Own Estimators

  • Regex

Thurs, Nov 30

  • Parallelism

  • Distributed Computing

(Note reading includes a 45 minute video to watch) Optional: - Setting Up Cloud Cluster

Fri, Dec 8

Final Project Report and Presentation Due

\ No newline at end of file + Class Schedule — Practical Data Science Skip to content

Class Schedule

Class Time

Class will be Tuesdays and Thursdays from 1:25pm-2:40pm (EDT).

Date, Rm

Topic

Do Before Class

In-Class Exercise

Tues, Aug 29

  • Class Introduction

  • Welcome to VS Code

Thurs, Aug 31

  • Command Line Basics

Fri, Sep 1

SOFTWARE INSTALL DAY

A day of trouble shooting install issues

Tues, Sep 5

  • Advanced Command Line

  • Git

  • Packages

Thurs, Sep 7

  • Git Continued

  • Jupyter

Tues, Sep 12

  • Python Debugger

  • R / Python Differences

  • Packages

Thurs, Sep 14

  • Numpy Basics

Tues, Sep 19

  • Numpy Arrays

More Numpy Concepts:

Matrices:

ND Arrays:

Thurs, Sep 21

  • Pandas: Series

Tues, Sep 26

  • Pandas: DataFrames

Thurs, Sep 28

  • Pandas: Indices & Missing

Tues, Oct 3

  • Pandas: Cleaning

  • Tracebacks

Thurs, Oct 5

  • Grammer of Graphics

  • Intro to Plotting with Altair

Tues, Oct 10

  • Advanced Plotting

Thurs, Oct 12

FALL BREAK

Tues, Oct 17

FALL BREAK

Thurs, Oct 19

  • Pandas: Merging

  • Pandas: Loading and saving data

  • JVP pp 149 - 157

  • WM Chapter 6

Tues, Oct 24

  • Big Data: What is it, how do I work with it?

Thurs, Oct 26

  • Defensive Programming

  • Workflow

  • Backwards Design

  • Getting Help Online

Discuss mid-semester project in class

Tues, Oct 31

  • Defensive Programming

  • Groupby / Split-Apply-Combine

Thurs, Nov 2

  • Pandas: Reshaping

  • Pandas: Categoricals

Tues, Nov 7

  • Speed and Performance in Python

[finish groupby and reshaping exercises]

Thurs, Nov 9

  • Statistics with statsmodels

Tues, Nov 14

  • Machine Learning with sckikit-learn

  • JVP Chapter 5 up to “Hyperparameters and Model Validation” Section (pp 331 - 359)

Thurs, Nov 16

Tues, Nov 21

No Class

Thurs, Nov 23

THANKSGIVING

Tues, Nov 28

  • Defining Your Own Estimators

  • Regex

Thurs, Nov 30

  • Parallelism

  • Distributed Computing

(Note reading includes a 45 minute video to watch) Optional: - Setting Up Cloud Cluster

Fri, Dec 8

Final Project Report and Presentation Due

\ No newline at end of file diff --git a/docs/html/exercises/Exercise_series.html b/docs/html/exercises/Exercise_series.html index df5e58ef..2abe56fd 100644 --- a/docs/html/exercises/Exercise_series.html +++ b/docs/html/exercises/Exercise_series.html @@ -1,4 +1,4 @@ - Measuring Income Equality with the Gini Coefficient — Practical Data Science Skip to content

Measuring Income Equality with the Gini Coefficient

As we discussed in our numpy exercises, one frequently used measure of inequality is the Gini Coefficient. The Gini Coefficient takes on a value of 1 when the distribution of some property is maximally unequal across a said of entities, and a value of 0 when it is evenly distributed.

In this exercise, we will calculate the Gini Coefficient for income inequality across the countries of the world to get a sense of income inequality across countries.

Gradescope Autograding

Please follow all standard guidance for submitting this assignment to the Gradescope autograder, including storing your solutions in a dictionary called results and ensuring your notebook runs from the start to completion without any errors.

Starting with this assignment, submissions that have not been formatted with ``black`` will be automatically rejected.

For this assignment, please name your file exercise_series.ipynb before uploading.

You can check that you have answers for all questions in your results dictionary with this code:

assert set(results.keys()) == {
+                         Measuring Income Equality with the Gini Coefficient — Practical Data Science                       Skip to content  

Measuring Income Equality with the Gini Coefficient

As we discussed in our numpy exercises, one frequently used measure of inequality is the Gini Coefficient. The Gini Coefficient takes on a value of 1 when the distribution of some property is maximally unequal across a said of entities, and a value of 0 when it is evenly distributed.

In this exercise, we will calculate the Gini Coefficient for income inequality across the countries of the world to get a sense of income inequality across countries.

Gradescope Autograding

Please follow all standard guidance for submitting this assignment to the Gradescope autograder, including storing your solutions in a dictionary called results and ensuring your notebook runs from the start to completion without any errors.

Starting with this assignment, submissions that have not been formatted with ``black`` will be automatically rejected.

For this assignment, please name your file exercise_series.ipynb before uploading.

You can check that you have answers for all questions in your results dictionary with this code:

assert set(results.keys()) == {
     "ex2_mean",
     "ex2_median",
     "ex3_highest_gdp_percap",
@@ -24,7 +24,7 @@
         "Switzerland",
     ],
 )
-

Exercise 2

Find the mean, median, minimum and maximum values of GDP per capita in this data.

Exercise 3

Programmatically, determine which country in our data has the highest income per capita, and which has the lowest income per capita.

(Obviously, this is easier to do by just looking at the data, but that’s only because this dataset is very small. With a real dataset, you would need to do it with code, so please write code to accomplish this task.)

Hint: Country names form the index for this Series, so to get country names you’ll need to access the index.

Store the country names as strings with the keys "ex3_highest_gdp_percap" and "ex3_lowest_gdp_percap"

Exercise 4

Get Python to print out the names of all the countries that have GDP per capita of less than $20,000.

Store these countries in a list, sorted alphabetically, and store it in results under the key "ex4_lessthan20_000"

Exercise 5

Get Python to print out the GDP per capita of Switzerland. Store the result as ex5_switzerland:

Exercise 6

One frequntly used measure of inequality is the Gini Coefficient. The Gini Coefficient takes on a value of 1 when the distribution of some variable is maximally unequal across a population, and a value of 0 when it is evenly distributed. We will calculate the Gini Coefficient for income inequality in our data.

To visualize the Gini Coefficient, we plot the cumulative share of the population (ordered from poorest to richest) on the x-axis, and cumulative share of income earned by that group on the y-axis. The Gini Coefficient is then defined as

\[\frac{A}{A + B}\]

, where the areas A and B are labeled below:

gini_coefficient

If income is evenly distributed, then the poorest 20% of a population will also have 20% of the wealth; the poorest 40% will have 40% of the wealth, and so forth, resulting in a perfect 45 degree line. In this situation, there is no area between the 45% line and the actual income distribution, so \(A=0\), and the Gini Coefficient is 0.

If, by contrast, the top 10% of people hold all the wealth in a country, then there will be no wealth for the poorest 90% of people, then wealth will jump up at the far right side of the graph. This will generate a very large gap between the 45% line and actual income for most of the graph, generating a large value for the area \(A\), creating a very high Gini Coefficient.

To illustrate, here are a few different Gini plots. These come from someone studying inequality of participation, so to adapt this to our study of income, just imagine the y-axis plots share of income):

gini_distributions

For discrete data, the Gini Coefficient can be calculated with the following formula:

\[\frac{2 \sum_{i=1}^n i y_i}{n \sum_{i=1}^n y_i} -\frac{n+1}{n}\]

Where \(i\) is each country’s rank ordering from poorest to richest, and \(y_i\) is the income of country \(i\).

Exercise 6

Using this formula, calculate the Gini coefficient for our income data.

Begin by writing a function to calculate the Gini Coefficient for our data by looping over the entries in our Series. In other words, try and embrace the spirit of how you might normally think about interpreting the summation notation written above.

Store the gini coefficient you calculate in results under the key "ex6_gini_loop".

HINT: Be careful with 0-indexing! Python counts from 0, but mathematical formulas (like \(\sum\)) start from 1!

HINT 2: I’ll probalby ask you to use this more than once, so please put it in a function.

Exercise 7

Excellent! But as we’ve seen in our readings, in data science we generally strive to not loop over the entries in our arrays; instead, we aspire to write vectorized code that naturally applies a simple operation to each observation.

So now write a new function to calculate the Gini Coefficient that doesn’t use loops, and instead relies on vectorized code.

Store the result in results under the key "ex7_gini_vectorized".

HINT: you will probably have to create some new series/vectors/arrays.

Exercise 8

The result we just generated offers a snap-shot of inequality for this subset of countries. But what are the dynamics of inequality for these countries?

There is an idea in economics called the “convergence hypothesis”, which argues that poorer countries are likely to grow faster, and as a result global inequality is likely to decline. Economists advocating for this hypothesis pointed out that while rich countries had to invent new technologies in order to grow, many poor countries simply had to take advantage of innovations already developed by rich countries.

To test this hypothesis, let’s do a small analysis of the dynamics of income inequality in our sample. Create the following Series in your Python session, which provides the average growth rate of GDP per capita for all the countries in our sample from 2000 to 2018.

avg_growth = pd.Series(
+

Exercise 2

Find the mean, median, minimum and maximum values of GDP per capita in this data.

Exercise 3

Programmatically, determine which country in our data has the highest income per capita, and which has the lowest income per capita.

(Obviously, this is easier to do by just looking at the data, but that’s only because this dataset is very small. With a real dataset, you would need to do it with code, so please write code to accomplish this task.)

Hint: Country names form the index for this Series, so to get country names you’ll need to access the index.

Store the country names as strings with the keys "ex3_highest_gdp_percap" and "ex3_lowest_gdp_percap"

Exercise 4

Get Python to print out the names of all the countries that have GDP per capita of less than $20,000.

Store these countries in a list, sorted alphabetically, and store it in results under the key "ex4_lessthan20_000"

Exercise 5

Get Python to print out the GDP per capita of Switzerland. Store the result as ex5_switzerland:

Gini in Detail

One frequently used measure of inequality is the Gini Coefficient. The Gini Coefficient takes on a value of 1 when the distribution of some variable is maximally unequal across a population, and a value of 0 when it is evenly distributed. We will calculate the Gini Coefficient for income inequality in our data.

To visualize the Gini Coefficient, we plot the cumulative share of the population (ordered from poorest to richest) on the x-axis, and cumulative share of income earned by that group on the y-axis. The Gini Coefficient is then defined as

\[\frac{A}{A + B}\]

, where the areas A and B are labeled below:

gini_coefficient

If income is evenly distributed, then the poorest 20% of a population will also have 20% of the wealth; the poorest 40% will have 40% of the wealth, and so forth, resulting in a perfect 45 degree line. In this situation, there is no area between the 45% line and the actual income distribution, so \(A=0\), and the Gini Coefficient is 0.

If, by contrast, the top 10% of people hold all the wealth in a country, then there will be no wealth for the poorest 90% of people, then wealth will jump up at the far right side of the graph. This will generate a very large gap between the 45% line and actual income for most of the graph, generating a large value for the area \(A\), creating a very high Gini Coefficient.

To illustrate, here are a few different Gini plots. These come from someone studying inequality of participation, so to adapt this to our study of income, just imagine the y-axis plots share of income):

gini_distributions

For discrete data, the Gini Coefficient can be calculated with the following formula:

\[\frac{2 \sum_{i=1}^n i y_i}{n \sum_{i=1}^n y_i} -\frac{n+1}{n}\]

Where \(i\) is each country’s rank ordering from poorest to richest, and \(y_i\) is the income of country \(i\).

Exercise 6

Using this formula, calculate the Gini coefficient for our income data.

Begin by writing a function to calculate the Gini Coefficient for our data by looping over the entries in our Series. In other words, try and embrace the spirit of how you might normally think about interpreting the summation notation written above.

Store the gini coefficient you calculate in results under the key "ex6_gini_loop".

HINT: Be careful with 0-indexing! Python counts from 0, but mathematical formulas (like \(\sum\)) start from 1!

HINT 2: I’ll probalby ask you to use this more than once, so please put it in a function.

Exercise 7

Excellent! But as we’ve seen in our readings, in data science we generally strive to not loop over the entries in our arrays; instead, we aspire to write vectorized code that naturally applies a simple operation to each observation.

So now write a new function to calculate the Gini Coefficient that doesn’t use loops, and instead relies on vectorized code.

Store the result in results under the key "ex7_gini_vectorized".

HINT: you will probably have to create some new series/vectors/arrays.

Exercise 8

The result we just generated offers a snap-shot of inequality for this subset of countries. But what are the dynamics of inequality for these countries?

There is an idea in economics called the “convergence hypothesis”, which argues that poorer countries are likely to grow faster, and as a result global inequality is likely to decline. Economists advocating for this hypothesis pointed out that while rich countries had to invent new technologies in order to grow, many poor countries simply had to take advantage of innovations already developed by rich countries.

To test this hypothesis, let’s do a small analysis of the dynamics of income inequality in our sample. Create the following Series in your Python session, which provides the average growth rate of GDP per capita for all the countries in our sample from 2000 to 2018.

avg_growth = pd.Series(
     [
         -0.29768835,
         0.980299584,
diff --git a/docs/html/exercises/Exercise_series.ipynb b/docs/html/exercises/Exercise_series.ipynb
index f8ac1ece..e5da3f09 100644
--- a/docs/html/exercises/Exercise_series.ipynb
+++ b/docs/html/exercises/Exercise_series.ipynb
@@ -87,7 +87,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Exercise 3\n",
+    "### Exercise 3\n",
     "\n",
     "Programmatically, determine which country in our data has the highest income per capita, and which has the lowest income per capita.\n",
     "\n",
@@ -113,7 +113,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Exercise 5 \n",
+    "### Exercise 5 \n",
     "\n",
     "Get Python to print out the GDP per capita of Switzerland. Store the result as `ex5_switzerland`:"
    ]
@@ -122,9 +122,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Exercise 6\n",
+    "## Gini in Detail\n",
     "\n",
-    "One frequntly used measure of inequality is the Gini Coefficient. The Gini Coefficient takes on a value of 1 when the distribution of some variable is maximally unequal across a population, and a value of 0 when it is evenly distributed. We will calculate the Gini Coefficient for income inequality in our data. \n",
+    "One frequently used measure of inequality is the Gini Coefficient. The Gini Coefficient takes on a value of 1 when the distribution of some variable is maximally unequal across a population, and a value of 0 when it is evenly distributed. We will calculate the Gini Coefficient for income inequality in our data. \n",
     "\n",
     "To visualize the Gini Coefficient, we plot the cumulative share of the population (ordered from poorest to richest) on the x-axis, and cumulative share of income earned by that group on the y-axis. The Gini Coefficient is then defined as $$\\frac{A}{A + B}$$, where the areas A and B are labeled below: \n",
     "\n",
@@ -154,7 +154,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n",
     "### Exercise 6\n",
     "\n",
     "Using this formula, calculate the Gini coefficient for our income data. \n",
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diff --git a/source/class_schedule.csv b/source/class_schedule.csv
index f97159c7..51bccbaa 100644
--- a/source/class_schedule.csv
+++ b/source/class_schedule.csv
@@ -52,7 +52,7 @@
 - `Vectorization `_
 - `Pandas: From Working with Tabular Data through Subsetting and Indexing with Single Square Brackets `_",- `Link `_
 "Tues, Sep 26",- Pandas: DataFrames,"- `Pandas DataFrames `_
-- `Pandas DataFrame Gotchas <40_https://nickeubank.github.io/practicaldatascience_book/notebooks/class_3/week_2/35_pandas_dataframe_gotchas.html>`_",- `Link `_
+- `Pandas DataFrame Gotchas `_",- `Link `_
 "Thurs, Sep 28",- Pandas: Indices & Missing,"- WM pp 136-142 (Indices, Section 5.2 up to MultiIndexes)
 - JVP pp 119-127 (""Handling Missing Data"" in Chpt 3)
 - `Numpy Views and Copies Review <40_pandas_basics/10_views_and_copies_numpy_review.ipynb>`_
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diff --git a/source/exercises/Exercise_series.ipynb b/source/exercises/Exercise_series.ipynb
index f8ac1ece..e5da3f09 100644
--- a/source/exercises/Exercise_series.ipynb
+++ b/source/exercises/Exercise_series.ipynb
@@ -87,7 +87,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Exercise 3\n",
+    "### Exercise 3\n",
     "\n",
     "Programmatically, determine which country in our data has the highest income per capita, and which has the lowest income per capita.\n",
     "\n",
@@ -113,7 +113,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Exercise 5 \n",
+    "### Exercise 5 \n",
     "\n",
     "Get Python to print out the GDP per capita of Switzerland. Store the result as `ex5_switzerland`:"
    ]
@@ -122,9 +122,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Exercise 6\n",
+    "## Gini in Detail\n",
     "\n",
-    "One frequntly used measure of inequality is the Gini Coefficient. The Gini Coefficient takes on a value of 1 when the distribution of some variable is maximally unequal across a population, and a value of 0 when it is evenly distributed. We will calculate the Gini Coefficient for income inequality in our data. \n",
+    "One frequently used measure of inequality is the Gini Coefficient. The Gini Coefficient takes on a value of 1 when the distribution of some variable is maximally unequal across a population, and a value of 0 when it is evenly distributed. We will calculate the Gini Coefficient for income inequality in our data. \n",
     "\n",
     "To visualize the Gini Coefficient, we plot the cumulative share of the population (ordered from poorest to richest) on the x-axis, and cumulative share of income earned by that group on the y-axis. The Gini Coefficient is then defined as $$\\frac{A}{A + B}$$, where the areas A and B are labeled below: \n",
     "\n",
@@ -154,7 +154,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n",
     "### Exercise 6\n",
     "\n",
     "Using this formula, calculate the Gini coefficient for our income data. \n",