Typo nits. Update cumulative lit graph b/c of loss of cumsum

data-8 · Aug 13, 2023 · 8a5f2d1 · 8a5f2d1
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diff --git a/chapters/01/2/why-data-science.md b/chapters/01/2/why-data-science.md
@@ -15,7 +15,7 @@ their work, their scientific endeavors, and their personal decisions. Critical
 thinking has long been a hallmark of a rigorous education, but critiques are
 often most effective when supported by data. A critical analysis of any aspect
 of the world, may it be business or social science, involves inductive
-reasoning; conclusions can rarely been proven outright, but only supported by
+reasoning; conclusions can rarely be proven outright, but only supported by
 the available evidence. Data science provides the means to make precise,
 reliable, and quantitative arguments about any set of observations. With
 unprecedented access to information and computing, critical thinking about

diff --git a/chapters/01/3/1/Literary_Characters.ipynb b/chapters/01/3/1/Literary_Characters.ipynb
@@ -10,7 +10,7 @@
    },
    "outputs": [],
    "source": [
-    "from datascience import *\n",
+    "from datascience import Table\n",
     "import numpy as np\n",
     "path_data = '../../../'\n",
     "import matplotlib\n",
@@ -74,20 +74,20 @@
     }
    ],
    "source": [
-    "# Count how many times the names Jim, Tom, and Huck appear in each chapter.\n",
+    "# Get the cumulative counts the names Jim, Tom, and Huck appear in each chapter.\n",
     "\n",
     "counts = Table().with_columns([\n",
-    "        'Jim', np.char.count(huck_finn_chapters, 'Jim'),\n",
-    "        'Tom', np.char.count(huck_finn_chapters, 'Tom'),\n",
-    "        'Huck', np.char.count(huck_finn_chapters, 'Huck')\n",
+    "        'Jim', np.cumsum(np.char.count(huck_finn_chapters, 'Jim')),\n",
+    "        'Tom', np.cumsum(np.char.count(huck_finn_chapters, 'Tom')),\n",
+    "        'Huck', np.cumsum(np.char.count(huck_finn_chapters, 'Huck'))\n",
     "    ])\n",
     "\n",
     "# Plot the cumulative counts:\n",
     "# how many times in Chapter 1, how many times in Chapters 1 and 2, and so on.\n",
     "\n",
-    "cum_counts = counts.cumsum().with_column('Chapter', np.arange(1, 44, 1))\n",
+    "cum_counts = counts.with_column('Chapter', np.arange(1, 44, 1))\n",
     "cum_counts.plot(column_for_xticks=3)\n",
-    "plots.title('Cumulative Number of Times Each Name Appears', y=1.08);"
+    "plots.title('Cumulative Number of Times Each Name Appears', y=1.08)"
    ]
   },
   {
@@ -207,22 +207,22 @@
     }
    ],
    "source": [
-    "# Counts of names in the chapters of Little Women\n",
+    "# Get the cumulative counts of the names in the chapters of Little Women\n",
     "\n",
     "counts = Table().with_columns([\n",
-    "        'Amy', np.char.count(little_women_chapters, 'Amy'),\n",
-    "        'Beth', np.char.count(little_women_chapters, 'Beth'),\n",
-    "        'Jo', np.char.count(little_women_chapters, 'Jo'),\n",
-    "        'Meg', np.char.count(little_women_chapters, 'Meg'),\n",
-    "        'Laurie', np.char.count(little_women_chapters, 'Laurie'),\n",
+    "        'Amy', np.cumsum(np.char.count(little_women_chapters, 'Amy')),\n",
+    "        'Beth', np.cumsum(np.char.count(little_women_chapters, 'Beth')),\n",
+    "        'Jo', np.cumsum(np.char.count(little_women_chapters, 'Jo')),\n",
+    "        'Meg', np.cumsum(np.char.count(little_women_chapters, 'Meg')),\n",
+    "        'Laurie', np.cumsum(np.char.count(little_women_chapters, 'Laurie')),\n",
     "\n",
     "    ])\n",
     "\n",
     "# Plot the cumulative counts.\n",
     "\n",
-    "cum_counts = counts.cumsum().with_column('Chapter', np.arange(1, 48, 1))\n",
+    "cum_counts = counts.with_column('Chapter', np.arange(1, 48, 1))\n",
     "cum_counts.plot(column_for_xticks=5)\n",
-    "plots.title('Cumulative Number of Times Each Name Appears', y=1.08);"
+    "plots.title('Cumulative Number of Times Each Name Appears', y=1.08)"
    ]
   },
   {