199 lines
3.8 KiB
Plaintext
199 lines
3.8 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib ipympl"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Count emoji occurences"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import json\n",
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"import glob\n",
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"import matplotlib\n",
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"import matplotlib.pyplot as plt\n",
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"from __future__ import unicode_literals\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"json_root = \"./emoji_counts/\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"json_files = sorted(glob.glob(json_root + \"/*.json\"))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['./emoji_counts/twitter_emoji_count_2017-12.json',\n",
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" './emoji_counts/twitter_emoji_count_2017_11.json']"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"json_files"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"json_lists = []"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"for path in json_files:\n",
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" with open(path) as f:\n",
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" data = json.load(f)\n",
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" json_lists.append(data)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"merged_dict = {}\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"for j in json_lists:\n",
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" for emoji in j.keys():\n",
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" if emoji in merged_dict:\n",
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" merged_dict[emoji] = merged_dict[emoji] + j[emoji]\n",
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" else:\n",
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" merged_dict[emoji] = j[emoji]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"n_top = 50"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"keysort = np.argsort(list(merged_dict.values()))[-n_top:]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "abd2534a596b4b06af933e5d5d9ba8d7",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"FigureCanvasNbAgg()"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"matplotlib.rc('font', family='symbola')\n",
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"plt.figure(figsize=(10,5))\n",
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"plt.bar(np.array(list(merged_dict.keys()))[keysort], np.array(list(merged_dict.values()))[keysort], color='g')\n",
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"plt.savefig(\"histogram.png\", bbox_inches='tight')\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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