From 16835d750270aa70ec95a2c986174985868ddcd0 Mon Sep 17 00:00:00 2001 From: Milena Perez Date: Mon, 6 Mar 2023 20:20:53 +0000 Subject: [PATCH] comment --- your-code/main.ipynb | 1023 +++++++++++++++++++++++++++++++++++++++--- 1 file changed, 961 insertions(+), 62 deletions(-) diff --git a/your-code/main.ipynb b/your-code/main.ipynb index e196ddb..c8538d7 100755 --- a/your-code/main.ipynb +++ b/your-code/main.ipynb @@ -11,11 +11,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ - "# your code here" + "import pandas as pd\n", + "import numpy as np" ] }, { @@ -27,11 +28,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "# your code here" + "path=pd.read_csv(\"apple_store.csv\")" ] }, { @@ -45,11 +46,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "# your code here" + "data=path.copy()" ] }, { @@ -63,11 +64,136 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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idtrack_namesize_bytespricerating_count_totrating_count_veruser_ratinguser_rating_verprime_genre
0281656475PAC-MAN Premium1007882243.9921292264.04.5Games
1281796108Evernote - stay organized1585786880.00161065264.03.5Productivity
2281940292WeatherBug - Local Weather, Radar, Maps, Alerts1005240320.0018858328223.54.5Weather
3282614216eBay: Best App to Buy, Sell, Save! Online Shop...1285120000.002622416494.04.5Shopping
4282935706Bible927744000.0098592053204.55.0Reference
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" + ], + "text/plain": [ + " id track_name size_bytes \\\n", + "0 281656475 PAC-MAN Premium 100788224 \n", + "1 281796108 Evernote - stay organized 158578688 \n", + "2 281940292 WeatherBug - Local Weather, Radar, Maps, Alerts 100524032 \n", + "3 282614216 eBay: Best App to Buy, Sell, Save! Online Shop... 128512000 \n", + "4 282935706 Bible 92774400 \n", + "\n", + " price rating_count_tot rating_count_ver user_rating user_rating_ver \\\n", + "0 3.99 21292 26 4.0 4.5 \n", + "1 0.00 161065 26 4.0 3.5 \n", + "2 0.00 188583 2822 3.5 4.5 \n", + "3 0.00 262241 649 4.0 4.5 \n", + "4 0.00 985920 5320 4.5 5.0 \n", + "\n", + " prime_genre \n", + "0 Games \n", + "1 Productivity \n", + "2 Weather \n", + "3 Shopping \n", + "4 Reference " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "data.head()" ] }, { @@ -79,11 +205,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " id size_bytes price rating_count_tot \\\n", + "count 7.197000e+03 7.197000e+03 7197.000000 7.197000e+03 \n", + "mean 8.631310e+08 1.991345e+08 1.726218 1.289291e+04 \n", + "std 2.712368e+08 3.592069e+08 5.833006 7.573941e+04 \n", + "min 2.816565e+08 5.898240e+05 0.000000 0.000000e+00 \n", + "25% 6.000937e+08 4.692275e+07 0.000000 2.800000e+01 \n", + "50% 9.781482e+08 9.715302e+07 0.000000 3.000000e+02 \n", + "75% 1.082310e+09 1.819249e+08 1.990000 2.793000e+03 \n", + "max 1.188376e+09 4.025970e+09 299.990000 2.974676e+06 \n", + "\n", + " rating_count_ver user_rating user_rating_ver \n", + "count 7197.000000 7197.000000 7197.000000 \n", + "mean 460.373906 3.526956 3.253578 \n", + "std 3920.455183 1.517948 1.809363 \n", + "min 0.000000 0.000000 0.000000 \n", + "25% 1.000000 3.500000 2.500000 \n", + "50% 23.000000 4.000000 4.000000 \n", + "75% 140.000000 4.500000 4.500000 \n", + "max 177050.000000 5.000000 5.000000 \n" + ] + } + ], "source": [ - "# your code here" + "print(data.describe())" ] }, { @@ -95,11 +247,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "9" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "len(data.columns)" ] }, { @@ -111,11 +274,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['id', 'track_name', 'size_bytes', 'price', 'rating_count_tot',\n", + " 'rating_count_ver', 'user_rating', 'user_rating_ver', 'prime_genre'],\n", + " dtype='object')" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "data.columns" ] }, { @@ -129,11 +305,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "7197" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "data[\"id\"].nunique()" ] }, { @@ -147,11 +334,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ - "# your code here" + "user_rating=data[[\"user_rating\"]]" ] }, { @@ -165,11 +352,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "user_rating 3.526956\n", + "dtype: float64" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "user_rating.mean()" ] }, { @@ -185,11 +384,122 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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iduser_rating
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" + ], + "text/plain": [ + " id user_rating\n", + "0 281656475 4.0\n", + "1 281796108 4.0\n", + "3 282614216 4.0\n", + "4 282935706 4.5\n", + "5 283619399 4.0\n", + "... ... ...\n", + "7192 1187617475 4.5\n", + "7193 1187682390 4.5\n", + "7194 1187779532 4.5\n", + "7195 1187838770 4.5\n", + "7196 1188375727 5.0\n", + "\n", + "[4781 rows x 2 columns]" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "user_rating_high=data.groupby([\"id\"]).agg({\"user_rating\":\"mean\"}).reset_index()\n", + "user_rating_high[user_rating_high[\"user_rating\"]>=4]\n", + "\n", + "\n" ] }, { @@ -201,11 +511,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(4781, 2)" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "user_rating_high[user_rating_high[\"user_rating\"]>=4].shape" ] }, { @@ -219,11 +540,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "metadata": {}, "outputs": [], "source": [ - "# your code here" + "genres=data[[\"prime_genre\"]]" ] }, { @@ -235,11 +556,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 56, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "prime_genre 23\n", + "dtype: int64" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "genres.nunique()" ] }, { @@ -260,11 +593,46 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 58, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "prime_genre \n", + "Games 3862\n", + "Entertainment 535\n", + "Education 453\n", + "Photo & Video 349\n", + "Utilities 248\n", + "Health & Fitness 180\n", + "Productivity 178\n", + "Social Networking 167\n", + "Lifestyle 144\n", + "Music 138\n", + "Shopping 122\n", + "Sports 114\n", + "Book 112\n", + "Finance 104\n", + "Travel 81\n", + "News 75\n", + "Weather 72\n", + "Reference 64\n", + "Food & Drink 63\n", + "Business 57\n", + "Navigation 46\n", + "Medical 23\n", + "Catalogs 10\n", + "dtype: int64" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "data[[\"prime_genre\"]].value_counts()" ] }, { @@ -305,11 +673,46 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 62, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Games 2257\n", + "Entertainment 334\n", + "Photo & Video 167\n", + "Social Networking 143\n", + "Education 132\n", + "Shopping 121\n", + "Utilities 109\n", + "Lifestyle 94\n", + "Finance 84\n", + "Sports 79\n", + "Health & Fitness 76\n", + "Music 67\n", + "Book 66\n", + "Productivity 62\n", + "News 58\n", + "Travel 56\n", + "Food & Drink 43\n", + "Weather 31\n", + "Business 20\n", + "Reference 20\n", + "Navigation 20\n", + "Catalogs 9\n", + "Medical 8\n", + "Name: prime_genre, dtype: int64" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "free_aps=data[data[\"price\"]==0.00]\n", + "free_aps[\"prime_genre\"].value_counts()" ] }, { @@ -352,11 +755,45 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 66, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Shopping 0.991803\n", + "Catalogs 0.900000\n", + "Social Networking 0.856287\n", + "Finance 0.807692\n", + "News 0.773333\n", + "Sports 0.692982\n", + "Travel 0.691358\n", + "Food & Drink 0.682540\n", + "Lifestyle 0.652778\n", + "Entertainment 0.624299\n", + "Book 0.589286\n", + "Games 0.584412\n", + "Music 0.485507\n", + "Photo & Video 0.478510\n", + "Utilities 0.439516\n", + "Navigation 0.434783\n", + "Weather 0.430556\n", + "Health & Fitness 0.422222\n", + "Business 0.350877\n", + "Productivity 0.348315\n", + "Medical 0.347826\n", + "Reference 0.312500\n", + "Education 0.291391\n", + "Name: prime_genre, dtype: float64" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "(free_aps[\"prime_genre\"].value_counts()/data[\"prime_genre\"].value_counts()).sort_values(ascending=False)" ] }, { @@ -397,11 +834,170 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 71, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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prime_genre
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Business5.116316
Reference4.836875
Music4.835435
Productivity4.330562
Navigation4.124783
Education4.028234
Health & Fitness1.916444
Book1.790536
Utilities1.647621
Weather1.605417
Food & Drink1.552381
Photo & Video1.473295
Games1.432923
Travel1.120370
Sports0.953070
Entertainment0.889701
Lifestyle0.885417
Catalogs0.799000
News0.517733
Finance0.421154
Social Networking0.339880
Shopping0.016311
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" + ], + "text/plain": [ + " price\n", + "prime_genre \n", + "Medical 8.776087\n", + "Business 5.116316\n", + "Reference 4.836875\n", + "Music 4.835435\n", + "Productivity 4.330562\n", + "Navigation 4.124783\n", + "Education 4.028234\n", + "Health & Fitness 1.916444\n", + "Book 1.790536\n", + "Utilities 1.647621\n", + "Weather 1.605417\n", + "Food & Drink 1.552381\n", + "Photo & Video 1.473295\n", + "Games 1.432923\n", + "Travel 1.120370\n", + "Sports 0.953070\n", + "Entertainment 0.889701\n", + "Lifestyle 0.885417\n", + "Catalogs 0.799000\n", + "News 0.517733\n", + "Finance 0.421154\n", + "Social Networking 0.339880\n", + "Shopping 0.016311" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here" + "data.groupby([\"prime_genre\"]).agg({\"price\":\"mean\"}).sort_values(by=\"price\", ascending=False)\n", + "#Probably Medical or Business genres" ] }, { @@ -417,7 +1013,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 72, "metadata": {}, "outputs": [], "source": [ @@ -438,11 +1034,146 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 80, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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Noyearmonthdayhourpm2.5DEWPTEMPPREScbwdIwsIsIr
012010110NaN-21-11.01021.0NW1.7900
122010111NaN-21-12.01020.0NW4.9200
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" + ], + "text/plain": [ + " No year month day hour pm2.5 DEWP TEMP PRES cbwd Iws Is Ir\n", + "0 1 2010 1 1 0 NaN -21 -11.0 1021.0 NW 1.79 0 0\n", + "1 2 2010 1 1 1 NaN -21 -12.0 1020.0 NW 4.92 0 0\n", + "2 3 2010 1 1 2 NaN -21 -11.0 1019.0 NW 6.71 0 0\n", + "3 4 2010 1 1 3 NaN -21 -14.0 1019.0 NW 9.84 0 0\n", + "4 5 2010 1 1 4 NaN -20 -12.0 1018.0 NW 12.97 0 0" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here:" + "pm25.head()" ] }, { @@ -454,12 +1185,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 75, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "2.0" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "def hourly(x):\n", " '''\n", + " \n", " Input: A numerical value\n", " Output: The value divided by 24\n", " \n", @@ -469,7 +1212,9 @@ " '''\n", " \n", " # Your code here:\n", - " " + " return x/24\n", + "\n", + "hourly(48)" ] }, { @@ -481,11 +1226,132 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 86, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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IwsIsIr
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43824 rows × 3 columns

\n", + "
" + ], + "text/plain": [ + " Iws Is Ir\n", + "0 0.074583 0.0 0.0\n", + "1 0.205000 0.0 0.0\n", + "2 0.279583 0.0 0.0\n", + "3 0.410000 0.0 0.0\n", + "4 0.540417 0.0 0.0\n", + "... ... ... ...\n", + "43819 9.665417 0.0 0.0\n", + "43820 9.907500 0.0 0.0\n", + "43821 10.112500 0.0 0.0\n", + "43822 10.280000 0.0 0.0\n", + "43823 10.410417 0.0 0.0\n", + "\n", + "[43824 rows x 3 columns]" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here:" + "pm25_hourly=pm25[[\"Iws\", \"Is\", \"Ir\"]].apply(hourly)\n", + "pm25_hourly" ] }, { @@ -499,11 +1365,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 87, "metadata": {}, "outputs": [], "source": [ "def sample_sd(x):\n", + " \n", + " std_dev = x.std()\n", + " result = std_dev / (len(x) - 1)\n", + " return result\n", + " \n", " '''\n", " Input: A Pandas series of values\n", " Output: the standard deviation divided by the number of elements in the series\n", @@ -515,11 +1386,39 @@ " \n", " # Your code here:" ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0008105900204213644" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#testing\n", + "sample_sd(data[\"price\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -533,7 +1432,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.9.13" } }, "nbformat": 4,