mirror of
https://github.com/ChristopherA/Learning-Bitcoin-from-the-Command-Line.git
synced 2025-06-08 08:26:17 +00:00
266 lines
20 KiB
Plaintext
266 lines
20 KiB
Plaintext
{
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"id": "view-in-github",
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"source": [
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"<a href=\"https://colab.research.google.com/github/icculp/Learning-Bitcoin-from-the-Command-Line/blob/master/word_count.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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{
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"metadata": {
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"id": "kT4wyWuy2szs"
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},
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"source": [
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"import numpy as np\n",
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"import os\n",
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"import pandas as pd"
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],
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"execution_count": 7,
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"outputs": []
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},
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{
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"outputId": "f4147b98-f72a-4ad2-8b5b-c3bb9f0c86db"
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},
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"source": [
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"!git clone https://github.com/icculp/Learning-Bitcoin-from-the-Command-Line.git"
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],
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"execution_count": 2,
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"outputs": [
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{
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"text": [
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"Cloning into 'Learning-Bitcoin-from-the-Command-Line'...\n",
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"remote: Enumerating objects: 6634, done.\u001b[K\n",
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"remote: Counting objects: 100% (238/238), done.\u001b[K\n",
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"remote: Compressing objects: 100% (196/196), done.\u001b[K\n",
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"remote: Total 6634 (delta 109), reused 82 (delta 42), pack-reused 6396\u001b[K\n",
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"Receiving objects: 100% (6634/6634), 7.53 MiB | 13.87 MiB/s, done.\n",
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"Resolving deltas: 100% (4068/4068), done.\n"
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"metadata": {
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"id": "k_g4-Fvn2yPy"
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},
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"source": [
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"def count_words():\n",
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" \"\"\" Counts words ignoring code blocks and digits \"\"\"\n",
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" columns=['Chapter', 'Word Count']\n",
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" counts = []\n",
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" repo_path = '/content/Learning-Bitcoin-from-the-Command-Line/'\n",
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" for chapter in os.listdir(repo_path):\n",
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" if not chapter.endswith('.md'):\n",
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" continue\n",
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" count = 0\n",
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" flag = 0 # ignores words between ``` code markdown\n",
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" with open(repo_path + chapter) as ch:\n",
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" for line in ch.readlines():\n",
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" if flag:\n",
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" if '```' in line:\n",
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" flag = 0\n",
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" continue\n",
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" continue\n",
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" if '```' in line:\n",
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" flag = 1\n",
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" continue\n",
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" for word in line.split():\n",
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" if any(ch.isdigit() for ch in word):\n",
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" continue\n",
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" count += 1\n",
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" counts.append((chapter, count))\n",
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" # print(chapter, count)\n",
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" return pd.DataFrame(counts, columns=columns)"
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],
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"execution_count": 35,
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"outputs": []
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},
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{
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"metadata": {
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"id": "2-pkEaYF3Uxm"
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},
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"source": [
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"chapter_word_counts = count_words()\n",
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"chapter_word_counts.sort_values(by=['Chapter'], inplace=True)"
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],
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"execution_count": 38,
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"outputs": []
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},
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{
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"outputId": "d057cf19-4d40-477a-964c-d2b6154e1e73"
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},
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"source": [
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"from google.colab import data_table\n",
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"data_table.DataTable(chapter_word_counts, include_index=False)"
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],
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"execution_count": 39,
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"outputs": [
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"output_type": "execute_result",
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||
]
|
||
},
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"execution_count": 39
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "4dFD792BBy0S",
|
||
"outputId": "945da188-2d3b-4061-8f23-08e39e6f97f4"
|
||
},
|
||
"source": [
|
||
"total_count = chapter_word_counts['Word Count'].sum()\n",
|
||
"total_count"
|
||
],
|
||
"execution_count": 44,
|
||
"outputs": [
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": [
|
||
"89946"
|
||
]
|
||
},
|
||
"metadata": {
|
||
"tags": []
|
||
},
|
||
"execution_count": 44
|
||
}
|
||
]
|
||
}
|
||
]
|
||
} |