Junghwan Park / fpm280-data-analysis

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Commit a5d4a032f6cc55c8817b81e7f617c4df0f354f52

Authored by Junghwan Park
1 parent be5a830d6d
Exists in main

added comments, users with less amount of data is removed

Showing 3 changed files with 204 additions and 14 deletions Inline Diff

.vscode/settings.json View file @ a5d4a03
File was created 1 {
2 "python.formatting.provider": "autopep8",
3 "python.linting.enabled": false,
4 "python.analysis.autoImportCompletions": false,
5 "python.analysis.autoSearchPaths": false,
6 "python.analysis.useLibraryCodeForTypes": false
7 }
python-notebook/data_loading.ipynb View file @ a5d4a03
{ 1 1 {
"cells": [ 2 2 "cells": [
{ 3 3 {
"cell_type": "markdown", 4 4 "cell_type": "markdown",
"metadata": {}, 5 5 "metadata": {},
"source": [ 6 6 "source": [
"# Loading libraries" 7 7 "# Loading libraries"
] 8 8 ]
}, 9 9 },
{ 10 10 {
"cell_type": "code", 11 11 "cell_type": "code",
"execution_count": 13, 12 12 "execution_count": 3,
"metadata": {}, 13 13 "metadata": {},
"outputs": [], 14 14 "outputs": [],
"source": [ 15 15 "source": [
"import numpy as np\n", 16 16 "import numpy as np\n",
"import matplotlib.pyplot as plt\n", 17 17 "import matplotlib.pyplot as plt\n",
18 "import seaborn as sns\n",
"from pandas import read_csv\n", 18 19 "from pandas import read_csv\n",
20 "import pandas as pd\n",
"import os\n", 19 21 "import os\n",
"from datetime import datetime" 20 22 "from datetime import datetime, date\n",
23 "# %load_ext line_profiler"
] 21 24 ]
}, 22 25 },
{ 23 26 {
"cell_type": "markdown", 24 27 "cell_type": "markdown",
"metadata": {}, 25 28 "metadata": {},
"source": [ 26 29 "source": [
30 "# Defining Functions and Adjusting Settings"
31 ]
32 },
33 {
34 "cell_type": "code",
35 "execution_count": 4,
36 "metadata": {},
37 "outputs": [],
38 "source": [
39 "pd.options.mode.chained_assignment = None\n",
40 "\n",
41 "def get_date(x):\n",
42 " return date(x.year, x.month, x.day)\n",
43 "\n",
44 "def get_minute_index(x):\n",
45 " return (x.hour * 60) + x.minute"
46 ]
47 },
48 {
49 "cell_type": "markdown",
50 "metadata": {},
51 "source": [
"# Loading data files" 27 52 "# Loading data files"
] 28 53 ]
}, 29 54 },
{ 30 55 {
"cell_type": "code", 31 56 "cell_type": "code",
"execution_count": 16, 32 57 "execution_count": 5,
"metadata": {}, 33 58 "metadata": {},
"outputs": [], 34 59 "outputs": [],
"source": [ 35 60 "source": [
"data_dir = '../data'\n", 36 61 "data_dir = '../data'\n",
"\n", 37 62 "\n",
"daily = read_csv(os.path.join(data_dir, 'daily.csv'))\n", 38 63 "daily = read_csv(os.path.join(data_dir, 'daily.csv'))\n",
"dose = read_csv(os.path.join(data_dir, 'dose.csv'))\n", 39 64 "dose = read_csv(os.path.join(data_dir, 'dose.csv'))\n",
"jawbone = read_csv(os.path.join(data_dir, 'jawbone.csv'), low_memory=False)" 40 65 "jawbone = read_csv(os.path.join(data_dir, 'jawbone.csv'), low_memory=False)\n"
] 41 66 ]
}, 42 67 },
{ 43 68 {
"cell_type": "markdown", 44 69 "cell_type": "markdown",
"metadata": {}, 45 70 "metadata": {},
"source": [ 46 71 "source": [
"# Preprocessing" 47 72 "# Preprocessing\n",
73 "## Picking up the variables"
] 48 74 ]
}, 49 75 },
{ 50 76 {
"cell_type": "code", 51 77 "cell_type": "code",
"execution_count": 19, 52 78 "execution_count": 6,
"metadata": {}, 53 79 "metadata": {},
80 "outputs": [],
81 "source": [
82 "# Column names of jawbone data\n",
83 "# 'Var1', 'user', 'start_datetime', 'end_datetime', 'timezone', 'userid',\n",
84 "# 'steps', 'gmtoff', 'tz', 'start_date', 'end_date', 'start_utime',\n",
85 "# 'end_utime', 'start_udate', 'end_udate', 'intake_date', 'intake_utime',\n",
86 "# 'intake_tz', 'intake_gmtoff', 'intake_hour', 'intake_min',\n",
87 "# 'intake_slot', 'travel_start', 'travel_end', 'exit_date',\n",
88 "# 'dropout_date', 'last_date', 'last_utime', 'last_tz', 'last_gmtoff',\n",
89 "# 'last_hour', 'last_min', 'start_utime_local', 'end_utime_local'\n",
90 "\n",
91 "\n",
92 "# duplicate jawbone data\n",
93 "jawbone2 = jawbone.copy(deep=True)\n",
94 "\n",
95 "# convert string datetimes to actual datetime objects\n",
96 "jawbone2[\"start_utime_local\"] = pd.to_datetime(\n",
97 " jawbone2[\"start_utime_local\"], format=\"%Y-%m-%d %H:%M:%S\")\n",
98 "jawbone2[\"start_datetime\"] = pd.to_datetime(\n",
99 " jawbone2[\"start_datetime\"], format=\"%Y-%m-%d %H:%M:%S\")\n",
100 "\n",
101 "# calculate the timezone offset\n",
102 "jawbone2[\"tz_offset\"] = jawbone2[\"start_datetime\"] - \\\n",
103 " jawbone2[\"start_utime_local\"]\n",
104 "\n",
105 "\n",
106 "# selecting only important columns\n",
107 "jawbone3 = jawbone2[[\"user\", \"start_utime_local\",\n",
108 " \"end_utime_local\", \"tz_offset\", \"steps\"]]\n",
109 "\n",
110 "# picking up the local date\n",
111 "jawbone3[\"local_date\"] = jawbone3[\"start_utime_local\"].apply(get_date)\n",
112 "\n",
113 "# picking up the local minute index\n",
114 "jawbone3[\"local_minute_index\"] = jawbone3[\"start_utime_local\"].apply(\n",
115 " get_minute_index)\n"
116 ]
117 },
118 {
119 "cell_type": "markdown",
120 "metadata": {},
121 "source": [
122 "## Making a key info database"
123 ]
124 },
125 {
126 "cell_type": "code",
127 "execution_count": 7,
128 "metadata": {},
129 "outputs": [],
130 "source": [
131 "# picking up the user - date data\n",
132 "user_date = jawbone3[[\"user\", \"local_date\"]].drop_duplicates()"
133 ]
134 },
135 {
136 "cell_type": "markdown",
137 "metadata": {},
138 "source": [
139 "## Removing users with too small amount of data"
140 ]
141 },
142 {
143 "cell_type": "code",
144 "execution_count": 12,
145 "metadata": {},
"outputs": [ 54 146 "outputs": [
{ 55 147 {
148 "name": "stdout",
149 "output_type": "stream",
150 "text": [
151 "Threshold: 10\n",
152 "Users to be removed:[12, 36, 38]\n",
153 "Shape Change: 258889 -> 258363 (-526, -0.2%)\n"
154 ]
155 },
156 {
"data": { 56 157 "data": {
158 "image/png": 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",
"text/plain": [ 57 159 "text/plain": [
"0 1\n", 58 160 "<Figure size 432x288 with 1 Axes>"
"1 1\n", 59
"2 1\n", 60
"3 1\n", 61
"4 1\n", 62
"Name: user, dtype: int64" 63
] 64 161 ]
}, 65 162 },
"execution_count": 19, 66
"metadata": {}, 67 163 "metadata": {},
"output_type": "execute_result" 68 164 "output_type": "display_data"
} 69 165 }
], 70 166 ],
167 "source": [
168 "# making a stat of the number of days per user\n",
169 "stat_user = user_date.groupby(['user'])['local_date'].nunique().sort_values()\n",
170 "\n",
171 "ax = plt.figure()\n",
172 "ax.patch.set_facecolor('white')\n",
173 "ax = sns.histplot(stat_user)\n",
174 "ax.set_title('Distribution of number of days per user')\n",
175 "ax.set_xlabel('Number of days')\n",
176 "ax.set_ylabel('Frequency')\n",
177 "\n",
178 "# cut off values that are not in the range of the data\n",
179 "THRESHOLD_OF_DAYS_PER_USER = 10\n",
180 "\n",
181 "# filter out users that have less days of data than THRESHOLD_OF_DAYS_PER_USER\n",
182 "users_to_be_removed = stat_user[stat_user < THRESHOLD_OF_DAYS_PER_USER].index\n",
183 "\n",
184 "print(\"Threshold: {}\".format(THRESHOLD_OF_DAYS_PER_USER))\n",
185 "print(\"Users to be removed:{}\".format(list(users_to_be_removed)))\n",
186 "\n",
187 "jawbone4 = jawbone3[~jawbone3[\"user\"].isin(users_to_be_removed)]\n",
188 "\n",
189 "\n",
190 "# printing the amount of data removed\n",
191 "jawbone3_count, _ = jawbone3.shape\n",
192 "jawbone4_count, _ = jawbone4.shape\n",
193 "\n",
194 "print(\"Shape Change: {} -> {} (-{}, -{}%)\".format(\n",
195 " jawbone3_count, \n",
196 " jawbone4_count, \n",
197 " jawbone3_count - jawbone4_count, \n",
198 " round((jawbone3_count - jawbone4_count) / jawbone3_count * 100, 2)\n",
199 " )\n",
200 ")"
201 ]
202 },
203 {
204 "cell_type": "code",
205 "execution_count": null,
206 "metadata": {},
207 "outputs": [
208 {
209 "ename": "NameError",
210 "evalue": "name 'users' is not defined",
211 "output_type": "error",
212 "traceback": [
213 "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
214 "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
215 "\u001b[0;32m/var/folders/m6/l3x11zj94l3dp3wnxy1vnscc0000gn/T/ipykernel_50945/4152346818.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mstandard_minute_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"local_minute_index\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1440\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0ma_user\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0musers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0ma_date\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0muser_date2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlocal_date\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
216 "\u001b[0;31mNameError\u001b[0m: name 'users' is not defined"
217 ]
218 }
219 ],
220 "source": [
221 "standard_minute_index = pd.Series(name=\"local_minute_index\", data=np.arange(0, 1440, 1))\n",
222 "\n",
223 "a_user = users[0]\n",
224 "a_date = user_date2.local_date[0]\n",
225 "\n",
226 "a_jawbone3 = jawbone3.loc[(jawbone3.user == a_user) & (jawbone3.local_date == a_date), :]\n",
227 "\n",
228 "vec = a_jawbone3[[\"local_minute_index\", \"steps\"]]\n",
229 "\n",
230 "steps = [0] * 1440\n",
231 "\n",
232 "for index, row in vec.iterrows():\n",
233 " steps[row.local_minute_index] += row.steps\n",
234 "\n",
235 "print(steps)\n",
236 "steps_series = pd.Series(name=\"steps\", data=steps)\n",
237 "steps_series[\"over60\"] = (steps_series > 60) * 1\n",
238 "\n",
239 "steps_series[\"roll\"] = steps_series.rolling(window=5, min_periods=1).sum()\n",
240 "\n",
241 "steps_series.roll.plot()\n",
242 "\n",
243 "\n"
244 ]
245 },
246 {
247 "cell_type": "code",
248 "execution_count": null,
249 "metadata": {},
250 "outputs": [],
"source": [] 71 251 "source": []
} 72 252 }
], 73 253 ],
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100 280
requirements.txt View file @ a5d4a03
absl-py==1.0.0 1 1 absl-py==1.0.0
appnope==0.1.2 2 2 appnope==0.1.2
astunparse==1.6.3 3 3 astunparse==1.6.3
backcall==0.2.0 4 4 backcall==0.2.0
cached-property==1.5.2 5 5 cached-property==1.5.2
cachetools==4.2.4 6 6 cachetools==4.2.4
certifi==2021.10.8 7 7 certifi==2021.10.8
charset-normalizer==2.0.10 8 8 charset-normalizer==2.0.10
cycler==0.11.0 9 9 cycler==0.11.0
debugpy==1.5.1 10 10 debugpy==1.5.1
decorator==5.1.1 11 11 decorator==5.1.1
entrypoints==0.3 12 12 entrypoints==0.3
flatbuffers==2.0 13 13 flatbuffers==2.0
fonttools==4.29.0 14 14 fonttools==4.29.0
gast==0.4.0 15 15 gast==0.4.0
google-auth==2.3.3 16 16 google-auth==2.3.3
google-auth-oauthlib==0.4.6 17 17 google-auth-oauthlib==0.4.6
google-pasta==0.2.0 18 18 google-pasta==0.2.0
grpcio==1.43.0 19 19 grpcio==1.43.0
h5py==3.6.0 20 20 h5py==3.6.0
idna==3.3 21 21 idna==3.3
importlib-metadata==4.10.0 22 22 importlib-metadata==4.10.0
ipykernel==6.7.0 23 23 ipykernel==6.7.0
ipython==7.31.1 24 24 ipython==7.31.1
jedi==0.18.1 25 25 jedi==0.18.1
jupyter-client==7.1.2 26 26 jupyter-client==7.1.2
jupyter-core==4.9.1 27 27 jupyter-core==4.9.1
keras==2.7.0 28 28 keras==2.7.0
Keras-Preprocessing==1.1.2 29 29 Keras-Preprocessing==1.1.2
kiwisolver==1.3.2 30 30 kiwisolver==1.3.2
libclang==12.0.0 31 31 libclang==12.0.0
32 line-profiler==3.4.0
Markdown==3.3.6 32 33 Markdown==3.3.6
matplotlib==3.5.1 33 34 matplotlib==3.5.1
matplotlib-inline==0.1.3 34 35 matplotlib-inline==0.1.3
nest-asyncio==1.5.4 35 36 nest-asyncio==1.5.4
numpy==1.21.5 36 37 numpy==1.21.5
oauthlib==3.1.1 37 38 oauthlib==3.1.1
opt-einsum==3.3.0 38 39 opt-einsum==3.3.0
packaging==21.3 39 40 packaging==21.3
pandas==1.3.5 40 41 pandas==1.3.5
parso==0.8.3 41 42 parso==0.8.3
pexpect==4.8.0 42 43 pexpect==4.8.0
pickleshare==0.7.5 43 44 pickleshare==0.7.5
Pillow==9.0.0 44 45 Pillow==9.0.0
prompt-toolkit==3.0.24 45 46 prompt-toolkit==3.0.24
protobuf==3.19.1 46 47 protobuf==3.19.1
ptyprocess==0.7.0 47 48 ptyprocess==0.7.0
pyasn1==0.4.8 48 49 pyasn1==0.4.8
pyasn1-modules==0.2.8 49 50 pyasn1-modules==0.2.8
Pygments==2.11.2 50 51 Pygments==2.11.2
pyparsing==3.0.7 51 52 pyparsing==3.0.7
python-dateutil==2.8.2 52 53 python-dateutil==2.8.2
pytz==2021.3 53 54 pytz==2021.3
pyzmq==22.3.0 54 55 pyzmq==22.3.0
requests==2.27.1 55 56 requests==2.27.1
requests-oauthlib==1.3.0 56 57 requests-oauthlib==1.3.0
rsa==4.8 57 58 rsa==4.8
59 scipy==1.7.3
60 seaborn==0.11.2
six==1.16.0 58 61 six==1.16.0
tensorboard==2.7.0 59 62 tensorboard==2.7.0
tensorboard-data-server==0.6.1 60 63 tensorboard-data-server==0.6.1
tensorboard-plugin-wit==1.8.1 61 64 tensorboard-plugin-wit==1.8.1
tensorflow==2.7.0 62 65 tensorflow==2.7.0
tensorflow-estimator==2.7.0 63 66 tensorflow-estimator==2.7.0
tensorflow-io-gcs-filesystem==0.23.1 64 67 tensorflow-io-gcs-filesystem==0.23.1
termcolor==1.1.0 65 68 termcolor==1.1.0
tornado==6.1 66 69 tornado==6.1
traitlets==5.1.1 67 70 traitlets==5.1.1
typing-extensions==4.0.1 68 71 typing-extensions==4.0.1
urllib3==1.26.8 69 72 urllib3==1.26.8
wcwidth==0.2.5 70 73 wcwidth==0.2.5
Werkzeug==2.0.2 71 74 Werkzeug==2.0.2
wrapt==1.13.3 72 75 wrapt==1.13.3
zipp==3.7.0 73 76 zipp==3.7.0