reading link

docs/html/.doctrees/nbsphinx/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",

docs/html/_sources/exercises/Exercise_series.ipynb.txt

@@ -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",

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",

docs/html/sitemap.xml

@@ -1 +1 @@
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+<urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9"><url><loc>http://cm4ss.comclass_schedule.html</loc></url><url><loc>http://cm4ss.comdistributed_starting_dask_cluster.html</loc></url><url><loc>http://cm4ss.comexercises/Exercise_bigdata.html</loc></url><url><loc>http://cm4ss.comexercises/Exercise_cloud_dukesubscription.html</loc></url><url><loc>http://cm4ss.comexercises/Exercise_dask.html</loc></url><url><loc>http://cm4ss.comexercises/Exercise_dask_realdata.html</loc></url><url><loc>http://cm4ss.comexercises/Exercise_git.html</loc></url><url><loc>http://cm4ss.comexercises/Exercise_jupyterlab.html</loc></url><url><loc>http://cm4ss.comexercises/Exercise_merging.html</loc></url><url><loc>http://cm4ss.comexercises/Exercise_scikit_learn.html</loc></url><url><loc>http://cm4ss.comexercises/Exercise_sklearn.html</loc></url><url><loc>http://cm4ss.comexercises/Solutions_merging.html</loc></url><url><loc>http://cm4ss.comindex.html</loc></url><url><loc>http://cm4ss.comparallelism.html</loc></url><url><loc>http://cm4ss.compr_review.html</loc></url><url><loc>http://cm4ss.comwhat_is_big_data.html</loc></url><url><loc>http://cm4ss.comgenindex.html</loc></url><url><loc>http://cm4ss.comsearch.html</loc></url></urlset>

source/class_schedule.csv

@@ -52,7 +52,7 @@
 - `Vectorization <https://nickeubank.github.io/practicaldatascience_book/notebooks/class_2/week_4/11_vectorization.html>`_
 - `Pandas: From Working with Tabular Data through Subsetting and Indexing with Single Square Brackets <https://nickeubank.github.io/practicaldatascience_book/notebooks/class_3/week_2/00_intro_to_pandas.html>`_",- `Link <exercises/Exercise_series.ipynb>`_
 "Tues, Sep 26",- Pandas: DataFrames,"- `Pandas DataFrames <https://nickeubank.github.io/practicaldatascience_book/notebooks/class_3/week_2/30_pandas_dataframes.html>`_
-- `Pandas DataFrame Gotchas <40_https://nickeubank.github.io/practicaldatascience_book/notebooks/class_3/week_2/35_pandas_dataframe_gotchas.html>`_",- `Link <exercises/Exercise_dataframe.ipynb>`_
+- `Pandas DataFrame Gotchas <https://nickeubank.github.io/practicaldatascience_book/notebooks/class_3/week_2/35_pandas_dataframe_gotchas.html>`_",- `Link <exercises/Exercise_dataframe.ipynb>`_
 "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>`_

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",