I've sent a screenshot of the assignment details in my notepad. (Screenshot 15)

reddit/reddit.ipynb
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{
"cell_type": "code",
"execution_count": 102,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" created_date created_timestamp subreddit \\\n",
"0 2008-07-06 16:00:14 1.215349e+09 artificial \n",
"1 2008-08-27 16:26:50 1.219844e+09 artificial \n",
"2 2008-10-12 00:29:40 1.223761e+09 artificial \n",
"3 2008-10-12 00:40:40 1.223761e+09 artificial \n",
"4 2008-10-14 20:31:01 1.224005e+09 artificial \n",
"\n",
" title id author \\\n",
"0 Man-Machine Poker (Solaris 2) Results (July 3-... 6qgmm IhateEverything \n",
"1 History of artificial intelligence 6y98d [deleted] \n",
"2 Minsky's Critics, Selectors and Resources at a... 76liu liamQ \n",
"3 The Single Layer Perceptron 76ljt liamQ \n",
"4 Siri Raises $8.5 Million for Personal Artifici... 773i4 CuteAlien \n",
"\n",
" author_created_utc full_link \\\n",
"0 1.198203e+09 https://www.reddit.com/r/artificial/comments/6... \n",
"1 NaN https://www.reddit.com/r/artificial/comments/6... \n",
"2 1.223677e+09 https://www.reddit.com/r/artificial/comments/7... \n",
"3 1.223677e+09 https://www.reddit.com/r/artificial/comments/7... \n",
"4 1.179241e+09 https://www.reddit.com/r/artificial/comments/7... \n",
"\n",
" score num_comments num_crossposts subreddit_subscribers post \n",
"0 4.0 1.0 0.0 NaN NaN \n",
"1 5.0 0.0 0.0 NaN NaN \n",
"2 1.0 0.0 0.0 NaN NaN \n",
"3 2.0 1.0 0.0 NaN NaN \n",
"4 4.0 0.0 0.0 NaN NaN "
],
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	created_date	created_timestamp	subreddit	title	id	author	author_created_utc	full_link	score	num_comments	num_crossposts	subreddit_subscribers	post
0	2008-07-06 16:00:14	1.215349e+09	artificial	Man-Machine Poker (Solaris 2) Results (July 3-...	6qgmm	IhateEverything	1.198203e+09	https://www.reddit.com/r/artificial/comments/6...	4.0	1.0	0.0	NaN	NaN
1	2008-08-27 16:26:50	1.219844e+09	artificial	History of artificial intelligence	6y98d	[deleted]	NaN	https://www.reddit.com/r/artificial/comments/6...	5.0	0.0	0.0	NaN	NaN
2	2008-10-12 00:29:40	1.223761e+09	artificial	Minsky's Critics, Selectors and Resources at a...	76liu	liamQ	1.223677e+09	https://www.reddit.com/r/artificial/comments/7...	1.0	0.0	0.0	NaN	NaN
3	2008-10-12 00:40:40	1.223761e+09	artificial	The Single Layer Perceptron	76ljt	liamQ	1.223677e+09	https://www.reddit.com/r/artificial/comments/7...	2.0	1.0	0.0	NaN	NaN
4	2008-10-14 20:31:01	1.224005e+09	artificial	Siri Raises $8.5 Million for Personal Artifici...	773i4	CuteAlien	1.179241e+09	https://www.reddit.com/r/artificial/comments/7...	4.0	0.0	0.0	NaN	NaN

\n

"
},
"metadata": {},
"execution_count": 7
}
],
"source": [
"WORK_DIR = 'data/'\n",
"all_data = pd.DataFrame()\n",
"\n",
"for dataset in os.listdir(WORK_DIR):\n",
" all_data = pd.concat([all_data, pd.read_csv(WORK_DIR + dataset, index_col = 0)])\n",
" \n",
"all_data = all_data.reset_index(drop = True)\n",
"all_data['created_date'] = all_data['created_date'].astype('datetime64')\n",
"all_data.head()"
]
},
{
"cell_type": "code",
"execution_count": 153,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" created_date created_timestamp subreddit \\\n",
"162616 2020-01-20 16:00:26 1.579529e+09 dataengineering \n",
"748 2013-09-19 05:07:26 1.379556e+09 artificial \n",
"54980 2016-02-07 00:05:10 1.454796e+09 AskStatistics \n",
"270585 2019-07-29 23:45:21 1.564433e+09 learnmachinelearning \n",
"82331 2021-03-14 03:40:36 1.615686e+09 AskStatistics \n",
"\n",
" title id \\\n",
"162616 SAP cloud Data Warehouse erdjne \n",
"748 Markov extension for Chrome 1moo58 \n",
"54980 Can you apply the same statistic to different ... 44ieeg \n",
"270585 How do Histogram of Oriented Gradients descrip... cjh5ay \n",
"82331 The topic is \"level of satisfaction on governm... m4kvs1 \n",
"\n",
" author author_created_utc \\\n",
"162616 Boozmork NaN \n",
"748 EmoryM 1.211877e+09 \n",
"54980 hello30303049 1.454796e+09 \n",
"270585 EverydayQuestion NaN \n",
"82331 pearsonsigma NaN \n",
"\n",
" full_link score \\\n",
"162616 https://www.reddit.com/r/dataengineering/comme... 1.0 \n",
"748 https://www.reddit.com/r/artificial/comments/1... 19.0 \n",
"54980 https://www.reddit.com/r/AskStatistics/comment... 1.0 \n",
"270585 https://www.reddit.com/r/learnmachinelearning/... 1.0 \n",
"82331 https://www.reddit.com/r/AskStatistics/comment... 1.0 \n",
"\n",
" num_comments num_crossposts subreddit_subscribers \\\n",
"162616 6.0 0.0 9356.0 \n",
"748 7.0 NaN NaN \n",
"54980 5.0 NaN NaN \n",
"270585 0.0 0.0 82047.0 \n",
"82331 2.0 0.0 39909.0 \n",
"\n",
" post author_created_date \n",
"162616 Hi Engineers, \\n\\nThe company I work for is at... NaT \n",
"748 I think the results of markov chains are great... 2008-05-27 08:31:47 \n",
"54980 Sorry if my question is worded badly. Here's a... 2016-02-06 22:00:26 \n",
"270585 I'm looking through this tutorial on creating ... NaT \n",
"82331 NaN NaT "
],
"text/html": "

\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

	created_date	created_timestamp	subreddit	title	id	author	author_created_utc	full_link	score	num_comments	num_crossposts	subreddit_subscribers	post	author_created_date
162616	2020-01-20 16:00:26	1.579529e+09	dataengineering	SAP cloud Data Warehouse	erdjne	Boozmork	NaN	https://www.reddit.com/r/dataengineering/comme...	1.0	6.0	0.0	9356.0	Hi Engineers, \\n\\nThe company I work for is at...	NaT
748	2013-09-19 05:07:26	1.379556e+09	artificial	Markov extension for Chrome	1moo58	EmoryM	1.211877e+09	https://www.reddit.com/r/artificial/comments/1...	19.0	7.0	NaN	NaN	I think the results of markov chains are great...	2008-05-27 08:31:47
54980	2016-02-07 00:05:10	1.454796e+09	AskStatistics	Can you apply the same statistic to different ...	44ieeg	hello30303049	1.454796e+09	https://www.reddit.com/r/AskStatistics/comment...	1.0	5.0	NaN	NaN	Sorry if my question is worded badly. Here's a...	2016-02-06 22:00:26
270585	2019-07-29 23:45:21	1.564433e+09	learnmachinelearning	How do Histogram of Oriented Gradients descrip...	cjh5ay	EverydayQuestion	NaN	https://www.reddit.com/r/learnmachinelearning/...	1.0	0.0	0.0	82047.0	I'm looking through this tutorial on creating ...	NaT
82331	2021-03-14 03:40:36	1.615686e+09	AskStatistics	The topic is \"level of satisfaction on governm...	m4kvs1	pearsonsigma	NaN	https://www.reddit.com/r/AskStatistics/comment...	1.0	2.0	0.0	39909.0	NaN	NaT

\n

"
},
"metadata": {},
"execution_count": 153
}
],
"source": [
"all_data['author_created_date'] = pd.to_datetime(all_data['author_created_utc'], unit='s')\n",
"all_data['author_created_date'].head()\n",
"\n",
"all_data['created_date'] = pd.to_datetime(all_data['created_date'])\n",
"\n",
"all_data = all_data.sample(1000)\n",
"all_data.head()"
]
},
{
"cell_type": "code",
"execution_count": 154,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" created_timestamp author_created_utc score num_comments \\\n",
"count 1.000000e+03 2.160000e+02 1000.000000 1000.000000 \n",
"mean 1.539772e+09 1.384704e+09 4.006000 4.005000 \n",
"std 6.565712e+07 8.450600e+07 19.502931 12.309691 \n",
"min 1.249206e+09 1.122350e+09 0.000000 0.000000 \n",
"25% 1.501587e+09 1.332197e+09 1.000000 0.000000 \n",
"50% 1.556755e+09 1.407746e+09 1.000000 1.000000 \n",
"75% 1.591825e+09 1.447657e+09 1.000000 4.000000 \n",
"max 1.615972e+09 1.498049e+09 519.000000 207.000000 \n",
"\n",
" num_crossposts subreddit_subscribers \n",
"count 753.000000 6.920000e+02 \n",
"mean 0.009296 2.463465e+05 \n",
"std 0.109003 3.605919e+05 \n",
"min 0.000000 1.517000e+03 \n",
"25% 0.000000 4.200400e+04 \n",
"50% 0.000000 9.736050e+04 \n",
"75% 0.000000 2.191938e+05 \n",
"max 2.000000 1.736778e+06 "
],
"text/html": ""
},
"metadata": {},
"execution_count": 154
}
],
"source": [
"all_data.describe()\n"
]
},
{
"source": [
"1. Summarize the data (4 points)\n",
"\n",
"## Which subreddit has the most posts (top 5)? ##\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 155,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"MachineLearning 225\n",
"statistics 118\n",
"datascience 115\n",
"learnmachinelearning 93\n",
"computerscience 82\n",
"Name: subreddit, dtype: int64"
]
},
"metadata": {},
"execution_count": 155
}
],
"source": [
"all_data['subreddit'].value_counts().head(5)"
]
},
{
"source": [
"## Which user has the most posts (top 5)? ##\n",
" \n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 156,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[deleted] 36\n",
"ai_jobs 14\n",
"aijobs-com 8\n",
"Yuqing7 5\n",
"AutoModerator 5\n",
"Name: author, dtype: int64"
]
},
"metadata": {},
"execution_count": 156
}
],
"source": [
"all_data['author'].value_counts().head(5)"
]
},
{
"source": [
"## Which subreddit has the most distinct post authors? ##"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 157,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" author\n",
"subreddit \n",
"MachineLearning 209\n",
"statistics 114\n",
"datascience 108\n",
"learnmachinelearning 89\n",
"computerscience 78"
],
"text/html": ""
},
"metadata": {},
"execution_count": 157
}
],
"source": [
"grouped_df = all_data.groupby(\"subreddit\")\n",
"\n",
"grouped_df = grouped_df.agg({\"author\": \"nunique\"})\n",
"grouped_df.sort_values('author', ascending=False).head(5)\n"
]
},
{
"source": [
"## Which subreddit contains the greatest percentage of posts with a post body (i.e. contains a value in the post column)? ##"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 158,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" post\n",
"subreddit \n",
"datascienceproject 100.000000\n",
"DataScienceJobs 92.000000\n",
"artificial 78.205128\n",
"data 76.923077\n",
"MachineLearning 69.777778"
],
"text/html": ""
},
"metadata": {},
"execution_count": 158
}
],
"source": [
"# grouped_df = all_data.groupby(\"subreddit\")\n",
"\n",
"# grouped_df = grouped_df.agg({\"post\": \"isna\"})\n",
"#grouped_df.sort_values('author', ascending=False)\n",
"grouped_df = all_data.groupby(\"subreddit\")\n",
"#all_data['post'].isnull().sum(axis = 0)\n",
"grouped_df = grouped_df.agg({'post': lambda x: x.isnull().sum()*100 / (x.notnull().sum() + x.isnull().sum())})\n",
"grouped_df.sort_values('post', ascending=False).head(5)\n"
]
},
{
"source": [
"## 2. Visualize the data (4 points) ##\n",
"\n",
"### Plot the total number of posts across all subreddits over time (line plot). ### \n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 159,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "

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\n"
},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"plt.rcParams[\"figure.figsize\"]=30,20\n",
"plt.plot(all_data['subreddit'].value_counts())\n",
"plt.show()"
]
},
{
"source": [
"### Plot a histogram showing the distribution of post scores. ### \n",
"\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 160,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "

",
"image/svg+xml": "\n\n\n\n",
"image/png":...