more plots
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"cells": [
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{
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"cell_type": "code",
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{
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{
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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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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},
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{
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"execution_count": 9,
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{
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"cell_type": "code",
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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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"execution_count": 11,
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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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"execution_count": 13,
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"metadata": {},
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"outputs": [],
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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='EmojiOne Color')\n",
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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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