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