removing np import, re-running output

Author: icculp

word_count.ipynb

@@ -5,7 +5,7 @@
     "colab": {
       "name": "Untitled25.ipynb",
       "provenance": [],
-      "authorship_tag": "ABX9TyOJqhnf6/YgfQMx6pfh6Fth",
+      "authorship_tag": "ABX9TyMioTaWOtZ3dcIebltCT4IN",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -33,11 +33,10 @@
         "id": "kT4wyWuy2szs"
       },
       "source": [
-        "import numpy as np\n",
         "import os\n",
         "import pandas as pd"
       ],
-      "execution_count": 7,
+      "execution_count": 1,
       "outputs": []
     },
     {
@@ -47,7 +46,7 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "9J62Uh4H2Y9g",
-        "outputId": "f4147b98-f72a-4ad2-8b5b-c3bb9f0c86db"
+        "outputId": "0ac3147d-fdb4-4b9c-d96b-4da334730a93"
       },
       "source": [
         "!git clone https://github.com/icculp/Learning-Bitcoin-from-the-Command-Line.git"
@@ -58,12 +57,12 @@
           "output_type": "stream",
           "text": [
             "Cloning into 'Learning-Bitcoin-from-the-Command-Line'...\n",
-            "remote: Enumerating objects: 6634, done.\u001b[K\n",
-            "remote: Counting objects: 100% (238/238), done.\u001b[K\n",
-            "remote: Compressing objects: 100% (196/196), done.\u001b[K\n",
-            "remote: Total 6634 (delta 109), reused 82 (delta 42), pack-reused 6396\u001b[K\n",
-            "Receiving objects: 100% (6634/6634), 7.53 MiB | 13.87 MiB/s, done.\n",
-            "Resolving deltas: 100% (4068/4068), done.\n"
+            "remote: Enumerating objects: 6637, done.\u001b[K\n",
+            "remote: Counting objects: 100% (241/241), done.\u001b[K\n",
+            "remote: Compressing objects: 100% (199/199), done.\u001b[K\n",
+            "remote: Total 6637 (delta 110), reused 82 (delta 42), pack-reused 6396\u001b[K\n",
+            "Receiving objects: 100% (6637/6637), 7.53 MiB | 18.46 MiB/s, done.\n",
+            "Resolving deltas: 100% (4069/4069), done.\n"
           ],
           "name": "stdout"
         }
@@ -103,7 +102,7 @@
         "        # print(chapter, count)\n",
         "    return pd.DataFrame(counts, columns=columns)"
       ],
-      "execution_count": 35,
+      "execution_count": 3,
       "outputs": []
     },
     {
@@ -115,7 +114,7 @@
         "chapter_word_counts = count_words()\n",
         "chapter_word_counts.sort_values(by=['Chapter'], inplace=True)"
       ],
-      "execution_count": 38,
+      "execution_count": 4,
       "outputs": []
     },
     {
@@ -126,13 +125,13 @@
           "height": 631
         },
         "id": "IPkG4oQJ6f1e",
-        "outputId": "d057cf19-4d40-477a-964c-d2b6154e1e73"
+        "outputId": "eea2f4b6-c3d3-48b5-dc62-97885c18d48d"
       },
       "source": [
         "from google.colab import data_table\n",
         "data_table.DataTable(chapter_word_counts, include_index=False)"
       ],
-      "execution_count": 39,
+      "execution_count": 5,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -229,7 +228,7 @@
           "metadata": {
             "tags": []
           },
-          "execution_count": 39
+          "execution_count": 5
         }
       ]
     },
@@ -240,13 +239,13 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "4dFD792BBy0S",
-        "outputId": "945da188-2d3b-4061-8f23-08e39e6f97f4"
+        "outputId": "3c4dc4ff-ceb3-4ff5-d40d-c606eec83a8c"
       },
       "source": [
         "total_count = chapter_word_counts['Word Count'].sum()\n",
         "total_count"
       ],
-      "execution_count": 44,
+      "execution_count": 6,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -258,7 +257,7 @@
           "metadata": {
             "tags": []
           },
-          "execution_count": 44
+          "execution_count": 6
         }
       ]
     }