{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "730ba509", "metadata": {}, "outputs": [], "source": [ "from IPython.core.interactiveshell import InteractiveShell\n", "InteractiveShell.ast_node_interactivity = \"all\"" ] }, { "cell_type": "code", "execution_count": 2, "id": "d9acd4b6", "metadata": {}, "outputs": [], "source": [ "from pathlib import Path\n", "import sys\n", "proj_dir = Path.cwd().parent\n", "\n", "sys.path.append(str(proj_dir))\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "62452860", "metadata": {}, "outputs": [], "source": [ "from datasets import load_dataset" ] }, { "cell_type": "code", "execution_count": 28, "id": "00affc9a", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a106bb47c1194b15bc289d2ef24258af", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading readme: 0%| | 0.00/804 [00:00\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
scorenum_commentstitlepermalinkselftexturlcreated_utcauthoriddownsupsdatetime
024Reddit, if someone had to describe you to a st.../r/AskReddit/comments/15sn6y/reddit_if_someone...They would be talking about you without your p...http://www.reddit.com/r/AskReddit/comments/15s...2013-01-01 23:59:40+00:00[deleted]15sn6y022013-01-0123:59:40
1524What kind of car does the average \\nRedditor d.../r/AskReddit/comments/15sn6m/what_kind_of_car_...I've always wanted to know what kind of car th...http://www.reddit.com/r/AskReddit/comments/15s...2013-01-01 23:59:31+00:00PaytonAdams15sn6m052013-01-0123:59:31
215What movies have made you go back to the theat.../r/AskReddit/comments/15sn6b/what_movies_have_...http://www.reddit.com/r/AskReddit/comments/15s...2013-01-01 23:59:20+00:00[deleted]15sn6b012013-01-0123:59:20
3018Worst fear(s)?/r/AskReddit/comments/15sn4u/worst_fears/So what is your worst fear, reddit?http://www.reddit.com/r/AskReddit/comments/15s...2013-01-01 23:58:37+00:00[deleted]15sn4u002013-01-0123:58:37
41129If there was a type of ink that lasted only fo.../r/AskReddit/comments/15sn44/if_there_was_a_ty...http://www.reddit.com/r/AskReddit/comments/15s...2013-01-01 23:58:15+00:00Honeybeard15sn440112013-01-0123:58:15
..........................................
3267011Smokers of Reddit- What are your reasons for s.../r/AskReddit/comments/15qzen/smokers_of_reddit...I'm very curious as to what causes someone to ...http://www.reddit.com/r/AskReddit/comments/15q...2013-01-01 00:01:36+00:00kelsofb15qzen002013-01-0100:01:36
326814Hi/r/AskReddit/comments/15qzei/hi/http://www.reddit.com/r/AskReddit/comments/15q...2013-01-01 00:01:34+00:00ImJE5US15qzei012013-01-0100:01:34
326912At the stroke of midnight I was writing this p.../r/AskReddit/comments/15qzdx/at_the_stroke_of_...http://www.reddit.com/r/AskReddit/comments/15q...2013-01-01 00:01:15+00:00Sangfroid_Sonder15qzdx012013-01-0100:01:15
327012With all the rape stories in the news, why don.../r/AskReddit/comments/15qzdc/with_all_the_rape...http://www.reddit.com/r/AskReddit/comments/15q...2013-01-01 00:00:58+00:00[deleted]15qzdc012013-01-0100:00:58
327103Do beautiful people have low entropy?/r/AskReddit/comments/15qzd3/do_beautiful_peop...I have been reading about entropy and arrows o...http://www.reddit.com/r/AskReddit/comments/15q...2013-01-01 00:00:53+00:00[deleted]15qzd3002013-01-0100:00:53
\n", "

3272 rows × 13 columns

\n", "" ], "text/plain": [ " score num_comments title \\\n", "0 2 4 Reddit, if someone had to describe you to a st... \n", "1 5 24 What kind of car does the average \\nRedditor d... \n", "2 1 5 What movies have made you go back to the theat... \n", "3 0 18 Worst fear(s)? \n", "4 11 29 If there was a type of ink that lasted only fo... \n", "... ... ... ... \n", "3267 0 11 Smokers of Reddit- What are your reasons for s... \n", "3268 1 4 Hi \n", "3269 1 2 At the stroke of midnight I was writing this p... \n", "3270 1 2 With all the rape stories in the news, why don... \n", "3271 0 3 Do beautiful people have low entropy? \n", "\n", " permalink \\\n", "0 /r/AskReddit/comments/15sn6y/reddit_if_someone... \n", "1 /r/AskReddit/comments/15sn6m/what_kind_of_car_... \n", "2 /r/AskReddit/comments/15sn6b/what_movies_have_... \n", "3 /r/AskReddit/comments/15sn4u/worst_fears/ \n", "4 /r/AskReddit/comments/15sn44/if_there_was_a_ty... \n", "... ... \n", "3267 /r/AskReddit/comments/15qzen/smokers_of_reddit... \n", "3268 /r/AskReddit/comments/15qzei/hi/ \n", "3269 /r/AskReddit/comments/15qzdx/at_the_stroke_of_... \n", "3270 /r/AskReddit/comments/15qzdc/with_all_the_rape... \n", "3271 /r/AskReddit/comments/15qzd3/do_beautiful_peop... \n", "\n", " selftext \\\n", "0 They would be talking about you without your p... \n", "1 I've always wanted to know what kind of car th... \n", "2 \n", "3 So what is your worst fear, reddit? \n", "4 \n", "... ... \n", "3267 I'm very curious as to what causes someone to ... \n", "3268 \n", "3269 \n", "3270 \n", "3271 I have been reading about entropy and arrows o... \n", "\n", " url \\\n", "0 http://www.reddit.com/r/AskReddit/comments/15s... \n", "1 http://www.reddit.com/r/AskReddit/comments/15s... \n", "2 http://www.reddit.com/r/AskReddit/comments/15s... \n", "3 http://www.reddit.com/r/AskReddit/comments/15s... \n", "4 http://www.reddit.com/r/AskReddit/comments/15s... \n", "... ... \n", "3267 http://www.reddit.com/r/AskReddit/comments/15q... \n", "3268 http://www.reddit.com/r/AskReddit/comments/15q... \n", "3269 http://www.reddit.com/r/AskReddit/comments/15q... \n", "3270 http://www.reddit.com/r/AskReddit/comments/15q... \n", "3271 http://www.reddit.com/r/AskReddit/comments/15q... \n", "\n", " created_utc author id downs ups \\\n", "0 2013-01-01 23:59:40+00:00 [deleted] 15sn6y 0 2 \n", "1 2013-01-01 23:59:31+00:00 PaytonAdams 15sn6m 0 5 \n", "2 2013-01-01 23:59:20+00:00 [deleted] 15sn6b 0 1 \n", "3 2013-01-01 23:58:37+00:00 [deleted] 15sn4u 0 0 \n", "4 2013-01-01 23:58:15+00:00 Honeybeard 15sn44 0 11 \n", "... ... ... ... ... ... \n", "3267 2013-01-01 00:01:36+00:00 kelsofb 15qzen 0 0 \n", "3268 2013-01-01 00:01:34+00:00 ImJE5US 15qzei 0 1 \n", "3269 2013-01-01 00:01:15+00:00 Sangfroid_Sonder 15qzdx 0 1 \n", "3270 2013-01-01 00:00:58+00:00 [deleted] 15qzdc 0 1 \n", "3271 2013-01-01 00:00:53+00:00 [deleted] 15qzd3 0 0 \n", "\n", " date time \n", "0 2013-01-01 23:59:40 \n", "1 2013-01-01 23:59:31 \n", "2 2013-01-01 23:59:20 \n", "3 2013-01-01 23:58:37 \n", "4 2013-01-01 23:58:15 \n", "... ... ... \n", "3267 2013-01-01 00:01:36 \n", "3268 2013-01-01 00:01:34 \n", "3269 2013-01-01 00:01:15 \n", "3270 2013-01-01 00:00:58 \n", "3271 2013-01-01 00:00:53 \n", "\n", "[3272 rows x 13 columns]" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = dataset['all_days'].to_pandas()\n", "df" ] }, { "cell_type": "code", "execution_count": 16, "id": "28df4b06", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "score Int64\n", "num_comments Int64\n", "title string\n", "permalink string\n", "selftext string\n", "url string\n", "created_utc string\n", "author string\n", "id string\n", "downs Int64\n", "ups Int64\n", "dtype: object" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.convert_dtypes().dtypes" ] }, { "cell_type": "code", "execution_count": 18, "id": "e322b6c0", "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 21, "id": "ed1b06c3", "metadata": {}, "outputs": [], "source": [ "df['created_utc'] = pd.to_datetime(df['created_utc'])\n", "df['date'] = df['created_utc'].dt.date\n", "df['time'] = df['created_utc'].dt.time" ] }, { "cell_type": "code", "execution_count": 33, "id": "ff477737", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2013-01-01 3272\n", "Name: date, dtype: int64" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.date.value_counts()" ] }, { "cell_type": "code", "execution_count": 26, "id": "1d11b967", "metadata": {}, "outputs": [], "source": [ "new_df = df.drop_duplicates(subset=['id'], keep=\"first\")" ] }, { "cell_type": "code", "execution_count": 27, "id": "eec00dd6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, { 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\n", 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