{"id":50800,"date":"2022-06-02T00:00:00","date_gmt":"2022-06-02T07:00:00","guid":{"rendered":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/%e6%9c%aa%e5%88%86%e9%a1%9e\/visualizing-scraped-google-play-store-data-to-gain-insight-into-the-android-app-market\/"},"modified":"2025-11-14T07:55:25","modified_gmt":"2025-11-14T15:55:25","slug":"visualizing-scraped-google-play-store-data-to-gain-insight-into-the-android-app-market","status":"publish","type":"post","link":"https:\/\/griddb.net\/ja\/%e6%9c%aa%e5%88%86%e9%a1%9e\/visualizing-scraped-google-play-store-data-to-gain-insight-into-the-android-app-market\/","title":{"rendered":"Google Play \u30b9\u30c8\u30a2\u306e\u30b9\u30af\u30ec\u30a4\u30d4\u30f3\u30b0\u30c7\u30fc\u30bf\u3092\u53ef\u8996\u5316\u3057\u3001Android\u30a2\u30d7\u30ea\u5e02\u5834\u3092\u628a\u63e1\u3059\u308b"},"content":{"rendered":"

\u30b9\u30de\u30fc\u30c8\u30d5\u30a9\u30f3\u306e\u666e\u53ca\u306b\u4f34\u3044\u3001\u958b\u767a\u8005\u306fAndroid\u3068iOS\u306e\u4e21\u65b9\u306e\u30d7\u30e9\u30c3\u30c8\u30d5\u30a9\u30fc\u30e0\u3067\u3001\u3088\u308a\u591a\u304f\u306e\u30e2\u30d0\u30a4\u30eb\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u3092\u4f5c\u6210\u3059\u308b\u3088\u3046\u306b\u306a\u308a\u307e\u3057\u305f\u3002\u643a\u5e2f\u96fb\u8a71\u306e\u7d0470\uff05\u304cAndroid\u3092\u642d\u8f09\u3057\u3066\u304a\u308a\u3001Android\u306f\u3088\u308a\u591a\u304f\u306e\u4eba\u3005\u306b\u5229\u7528\u3055\u308c\u3066\u3044\u307e\u3059\u3002 \u3053\u306e\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u3067\u306f\u3001Google Play \u30b9\u30c8\u30a2\u306e\u30c7\u30fc\u30bf\u3092\u8abf\u67fb\u3057\u3001\u767b\u9332\u3055\u308c\u305f\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u306e\u60c5\u5831\u306b\u57fa\u3065\u3044\u3066\u30a2\u30d7\u30ea\u3068\u4eba\u6c17\u5ea6\u306b\u95a2\u3059\u308b\u6d1e\u5bdf\u3092\u5f97\u308b\u4e88\u5b9a\u3067\u3059\u3002<\/p>\n

\u30bd\u30fc\u30b9\u30b3\u30fc\u30c9\u5168\u6587\u306f\u3053\u3061\u3089 <\/a><\/p>\n

GridDB\u3092\u4f7f\u3063\u305f\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u30a8\u30af\u30b9\u30dd\u30fc\u30c8\u3068\u30a4\u30f3\u30dd\u30fc\u30c8<\/h2>\n

GridDB\u306f\u3001\u9ad8\u3044\u30b9\u30b1\u30fc\u30e9\u30d3\u30ea\u30c6\u30a3\u3068\u30a4\u30f3\u30e1\u30e2\u30eaNo SQL\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u3067\u3001\u7279\u306b\u5f53\u793e\u306e\u81a8\u5927\u306a\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u5bfe\u3057\u3066\u3001\u3088\u308a\u9ad8\u3044\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u3068\u52b9\u7387\u6027\u3092\u5b9f\u73fe\u3059\u308b\u305f\u3081\u306e\u4e26\u5217\u51e6\u7406\u3092\u53ef\u80fd\u306b\u3057\u307e\u3059\u3002IoT\u3084\u30d3\u30c3\u30b0\u30c7\u30fc\u30bf\u6280\u8853\u306b\u5bfe\u5fdc\u3057\u305f\u6642\u7cfb\u5217\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306b\u6700\u9069\u5316\u3055\u308c\u3066\u3044\u307e\u3059\u3002GridDB-python\u30af\u30e9\u30a4\u30a2\u30f3\u30c8\u3092\u5229\u7528\u3059\u308b\u3053\u3068\u3067\u3001GridDB\u3068python\u3092\u7c21\u5358\u306b\u63a5\u7d9a\u3057\u3001\u30ea\u30a2\u30eb\u30bf\u30a4\u30e0\u306b\u30c7\u30fc\u30bf\u306e\u30a4\u30f3\u30dd\u30fc\u30c8\u3084\u30a8\u30af\u30b9\u30dd\u30fc\u30c8\u306b\u5229\u7528\u3059\u308b\u3053\u3068\u304c\u53ef\u80fd\u3067\u3059\u3002<\/p>\n

GridDB\u306e\u30bb\u30c3\u30c8\u30a2\u30c3\u30d7<\/h3>\n

\u307e\u305a\u7b2c\u4e00\u306b\u3001GridDB\u304c\u30b7\u30b9\u30c6\u30e0\u306b\u6b63\u3057\u304f\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3055\u308c\u3066\u3044\u308b\u3053\u3068\u3092\u78ba\u8a8d\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002\u5f53\u30b5\u30a4\u30c8\u3067\u516c\u958b\u3057\u3066\u3044\u308b\u30b9\u30c6\u30c3\u30d7\u30d0\u30a4\u30b9\u30c6\u30c3\u30d7\u30ac\u30a4\u30c9\u306f\u3001\u7570\u306a\u308bOS\u3067\u306eGridDB\u306e\u30bb\u30c3\u30c8\u30a2\u30c3\u30d7\u3092\u652f\u63f4\u3059\u308b\u3082\u306e\u3067\u3059\u3002<\/p>\n

\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8<\/h3>\n

CSV ‘apps.csv’ \u306b\u683c\u7d0d\u3055\u308c\u305f Google Play \u30b9\u30c8\u30a2\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002\u3053\u308c\u306f\u3001Google Play\u4e0a\u306e\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u306e\u3059\u3079\u3066\u306e\u8a73\u7d30\u304c\u542b\u307e\u308c\u3066\u3044\u307e\u3059\u3002\u8a55\u4fa1\u3001\u30b5\u30a4\u30ba\u3001\u30c0\u30a6\u30f3\u30ed\u30fc\u30c9\u6570\u306a\u3069\u300113\u306e\u7279\u5fb4\u3067\u4e0e\u3048\u3089\u308c\u305f\u30a2\u30d7\u30ea\u3092\u8aac\u660e\u3057\u307e\u3059\u3002<\/p>\n

\u524d\u51e6\u7406<\/h3>\n

GridDB\u306b\u30c7\u30fc\u30bf\u3092\u30a8\u30af\u30b9\u30dd\u30fc\u30c8\u3059\u308b\u524d\u306b\u3001\u3044\u304f\u3064\u304b\u306e\u524d\u51e6\u7406\u3092\u884c\u3044\u3001GridDB\u304c\u6700\u9069\u306a\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u3092\u767a\u63ee\u3067\u304d\u308b\u3088\u3046\u306b\u30c7\u30fc\u30bf\u3092\u30af\u30ea\u30fc\u30cb\u30f3\u30b0\u3057\u307e\u3059\u3002\u307e\u305a\u3001\u751f\u30c7\u30fc\u30bf\u3092 CSV \u30d5\u30a1\u30a4\u30eb\u3068\u3057\u3066\u8aad\u307f\u8fbc\u307f\u307e\u3059\u3002<\/p>\n

\n
apps_with_duplicates = pd.read_csv('datasets\/apps.csv', index_col=0)    <\/code><\/pre>\n<\/div>\n
\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092dataframe\u3068\u3057\u3066\u4fdd\u5b58\u3057\u3001\u30af\u30ea\u30fc\u30cb\u30f3\u30b0\u51e6\u7406\u3092\u958b\u59cb\u3057\u307e\u3059\u3002\u30a2\u30d7\u30ea\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u304b\u3089\u3001\u91cd\u8907\u3084NULL\u5024\u3092\u524a\u9664\u3057\u3066\u3044\u304d\u307e\u3059\u3002\u307e\u305f\u3001\u4eca\u56de\u306e\u5206\u6790\u306b\u4e0d\u8981\u306a\u5217\u3092\u524a\u9664\u3057\u3001\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u5217\u3092\u30ea\u30bb\u30c3\u30c8\u3057\u3066\u30c7\u30fc\u30bf\u306e\u4e0d\u4e00\u81f4\u3092\u56de\u907f\u3057\u307e\u3059\u3002<\/p>\n
\n
apps = apps_with_duplicates.drop_duplicates()\napps.dropna(inplace=True)\napps.drop(['Genres','Content Rating', 'Last Updated', 'Current Ver', 'Android Ver'],axis = 1)   \napps.reset_index(drop=True, inplace=True)\napps.index.name = 'ID' <\/code><\/pre>\n<\/div>\n
\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4fdd\u5b58\u3059\u308b\u524d\u306e\u6700\u5f8c\u306e\u30b9\u30c6\u30c3\u30d7\u306f\u3001\u6570\u5024\u30c7\u30fc\u30bf\u3092\u7c21\u5358\u306b\u8aad\u3081\u308b\u3088\u3046\u306b\u3059\u308b\u305f\u3081\u306b\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u304b\u3089\u7279\u6b8a\u6587\u5b57\u3092\u9664\u53bb\u3059\u308b\u3053\u3068\u3067\u3059\u3002\u6570\u5024\u306e\u5217\u304b\u3089\u9664\u53bb\u3059\u308b\u7279\u6b8a\u6587\u5b57\u306e\u30ea\u30b9\u30c8\u306f\u65e2\u306b\u77e5\u3089\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n
\n
# List of characters to remove\nchars_to_remove = ['+', ',', 'M', '$']\n# List of column names to clean\ncols_to_clean = ['Installs', 'Size', 'Price']\n\n\n\n# Loop for each column\nfor col in cols_to_clean:\n    # Replace each character with an empty string\n    for char in chars_to_remove:\n        #print(col)\n        #print(char)\n        apps[col] = apps[col].astype(str).str.replace(char, '')\n    apps[col] = pd.to_numeric(apps[col])\n<\/code><\/pre>\n<\/div>\n
GridDB\u306b\u30a2\u30c3\u30d7\u30ed\u30fc\u30c9\u3059\u308b\u524d\u306b\u3001\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u7aef\u672b\u306e\u30ed\u30fc\u30ab\u30eb\u30b3\u30d4\u30fc\u3068\u3057\u3066\u4fdd\u5b58\u3057\u3066\u304a\u304d\u307e\u3059\u3002<\/p>\n
\n
apps.to_csv(\"apps_processed.csv\")<\/code><\/pre>\n<\/div>\n
\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092GridDB\u306b\u30a8\u30af\u30b9\u30dd\u30fc\u30c8\u3059\u308b<\/h3>\n
\u3067\u306f\u3001GridDB\u306b\u30c7\u30fc\u30bf\u3092\u30a2\u30c3\u30d7\u30ed\u30fc\u30c9\u3057\u3066\u3044\u304d\u307e\u3059\u3002\u305d\u306e\u305f\u3081\u306b\u3001\u30ed\u30fc\u30ab\u30eb\u30c9\u30e9\u30a4\u30d6\u304b\u3089\u51e6\u7406\u6e08\u307f\u306eCSV\u30d5\u30a1\u30a4\u30eb\u3092\u8aad\u307f\u8fbc\u3093\u3067\u3001\u7570\u306a\u308bdataframe\u306b\u4fdd\u5b58\u3057\u307e\u3059\u3002<\/p>\n
\n
#read the cleaned data from csv\napps = pd.read_csv(\"apps_processed.csv\")<\/code><\/pre>\n<\/div>\n
\u6b21\u306b\u3001\u884c\u60c5\u5831\u3092\u633f\u5165\u3059\u308b\u524d\u306b\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u306e\u30c7\u30b6\u30a4\u30f3\u3092\u751f\u6210\u3067\u304d\u308b\u3088\u3046\u306b\u3001\u30a2\u30d7\u30ea\u306e\u30b3\u30e9\u30e0\u60c5\u5831\u3092GridDB\u306b\u6e21\u3059\u305f\u3081\u306e\u30b3\u30f3\u30c6\u30ca\u3092\u4f5c\u6210\u3057\u307e\u3059\u3002<\/p>\n
\n
#Create container \napps_container = \"apps_container\"\n\n# Create containerInfo\napps_containerInfo = griddb.ContainerInfo(apps_container,\n                    [[\"ID\", griddb.Type.INTEGER],\n                    [\"App\", griddb.Type.STRING],\n                    [\"Category\", griddb.Type.STRING],\n                    [\"Rating\", griddb.Type.FLOAT],\n                    [\"Reviews\", griddb.Type.FLOAT],\n                    [\"Size\", griddb.Type.FLOAT],\n                    [\"Installs\", griddb.Type.STRING],\n                    [\"Type\", griddb.Type.STRING],\n                    [\"Price\", griddb.Type.FLOAT]],\n                    griddb.ContainerType.COLLECTION, True)\n                    \napps_columns = gridstore.put_container(apps_containerInfo)\n<\/code><\/pre>\n<\/div>\n
\u30b9\u30ad\u30fc\u30de\u304c\u5b8c\u6210\u3057\u305f\u3089\u3001\u884c\u5358\u4f4d\u306e\u30c7\u30fc\u30bf\u3092GridDB\u306b\u633f\u5165\u3057\u3066\u3044\u304d\u307e\u3059\u3002<\/p>\n
\n
# Put rows\napps_columns.put_rows(apps)\n<\/code><\/pre>\n<\/div>\n
\u3053\u308c\u3067\u3001GridDB\u30d7\u30e9\u30c3\u30c8\u30d5\u30a9\u30fc\u30e0\u3078\u306e\u30c7\u30fc\u30bf\u30a2\u30c3\u30d7\u30ed\u30fc\u30c9\u306b\u6210\u529f\u3057\u307e\u3057\u305f\u3002<\/p>\n
GridDB\u304b\u3089\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u3059\u308b<\/h3>\n
\u30b3\u30f3\u30c6\u30ca\u3092\u4f5c\u6210\u3057\u3001SQL\u306b\u4f3c\u305f\u554f\u3044\u5408\u308f\u305b\u8a00\u8a9e\u3067\u3042\u308bTQL\u30b3\u30de\u30f3\u30c9\u3092\u4f7f\u3063\u3066\u95a2\u9023\u3059\u308b\u884c\u3092\u554f\u3044\u5408\u308f\u305b\u308b\u3053\u3068\u3067\u3001\u7c21\u5358\u306bGridDB\u304b\u3089\u30c7\u30fc\u30bf\u3092\u62bd\u51fa\u3059\u308b\u3053\u3068\u304c\u51fa\u6765\u307e\u3059\u30022\u3064\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u5225\u3005\u306b\u4fdd\u5b58\u3059\u308b\u305f\u3081\u306b\u3001\u7570\u306a\u308b\u30b3\u30f3\u30c6\u30ca\u3068\u5909\u6570\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/p>\n
\n
# Define the container names\napps_container = \"apps_container\"\n\n# Get the containers\nobtained_apps = gridstore.get_container(apps_container)\n    \n# Fetch all rows - language_tag_container\nquery = obtained_apps.query(\"select *\")\n    \nrs = query.fetch(False)\nprint(f\"{apps_container} Data\")\n    \n# Iterate and create a list\nretrieved_data= []\nwhile rs.has_next():\n    data = rs.next()\n    retrieved_data.append(data)\n\n# Convert the list to a pandas data frame\napps = pd.DataFrame(retrieved_data, columns=[\"ID\",\"App\",\"Category\",\"Rating\",\"Reviews\",\"Size\",\"Installs\",\"Type\",\"Price\"])\n\n# Get the data frame details\nprint(apps)\napps.info()<\/code><\/pre>\n<\/div>\n
\u3053\u308c\u3067\u3001\u89e3\u6790\u306b\u4f7f\u3048\u308b\u30c7\u30fc\u30bf\u304c\u63c3\u3044\u307e\u3057\u305f\u3002<\/p>\n
\u30c7\u30fc\u30bf\u89e3\u6790\u3068\u53ef\u8996\u5316<\/h2>\n
\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u63a2\u7d22\u3057\u3001\u3044\u304f\u3064\u304b\u306e\u7279\u5fb4\u3092\u7406\u89e3\u3059\u308b\u3053\u3068\u304b\u3089\u5206\u6790\u3092\u958b\u59cb\u3057\u307e\u3059\u3002<\/p>\n
1. \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u542b\u307e\u308c\u308b\u5225\u500b\u306e\u30a2\u30d7\u30ea\u306e\u7dcf\u6570<\/p>\n
\n
# Print the total number of apps\nprint('Total number of apps in the dataset = ', len(apps))<\/code><\/pre>\n<\/div>\n
\"\"<\/a><\/p>\n
2. \u6700\u3082\u9ad8\u4fa1\u306a\u30a2\u30d7\u30ea<\/p>\n
\n
Expensiveapp = apps.iloc[appdata['Price'].idxmax()]<\/code><\/pre>\n<\/div>\n
\"\"<\/a><\/p>\n
3. \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u542b\u307e\u308c\u308b\u30ab\u30c6\u30b4\u30ea\u30fc\u306e\u7dcf\u6570<\/p>\n
\n
# Print the total number of unique categories\nnum_categories = len(apps['Category'].unique())\nprint('Number of categories = ', num_categories)<\/code><\/pre>\n<\/div>\n
\"\"<\/a><\/p>\n
4. \u30a2\u30d7\u30ea\u306e\u5e73\u5747\u8a55\u4fa1<\/p>\n
\n
avg_app_rating = apps['Rating'].mean()\nprint('Average app rating = ', avg_app_rating)<\/code><\/pre>\n<\/div>\n
\"\"<\/a><\/p>\n
\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u306e\u3055\u307e\u3056\u307e\u306a\u4ed5\u69d8\u306b\u5bfe\u3057\u3066\u3044\u304f\u3064\u304b\u306e\u30b0\u30e9\u30d5\u3092\u30d7\u30ed\u30c3\u30c8\u3059\u308b\u3053\u3068\u3067\u3001\u5206\u6790\u3092\u7d9a\u884c\u3057\u307e\u3059\u3002\u4f8b\u3048\u3070\u3001\u30a2\u30d7\u30ea\u3092\u30ab\u30c6\u30b4\u30ea\u306b\u5206\u985e\u3057\u3001\u5404\u30ab\u30c6\u30b4\u30ea\u306e\u30a2\u30d7\u30ea\u306e\u6570\u3092\u30d7\u30ed\u30c3\u30c8\u3057\u3066\u3001Play\u30b9\u30c8\u30a2\u4e0a\u306e\u30a2\u30d7\u30ea\u306e\u4e2d\u3067\u6700\u3082\u4eba\u6c17\u306e\u3042\u308b\u30ab\u30c6\u30b4\u30ea\u3092\u63a2\u308a\u307e\u3059\u3002<\/p>\n
\n
# Count the number of apps in each 'Category' and sort them in descending order\nnum_apps_in_category = apps['Category'].value_counts().sort_values(ascending = False)\nnum_apps_in_category.plot.bar()\nplt.show()<\/code><\/pre>\n<\/div>\n
\"\"<\/a><\/p>\n
\u307b\u3068\u3093\u3069\u306e\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u304c\u300c\u30d5\u30a1\u30df\u30ea\u30fc\u300d\u30ab\u30c6\u30b4\u30ea\u30fc\u306b\u5c5e\u3057\u3066\u3044\u308b\u3053\u3068\u304c\u5206\u304b\u308a\u307e\u3059\u3002\u3053\u306e\u30ab\u30c6\u30b4\u30ea\u30fc\u306b\u5c5e\u3059\u308b\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u306e\u6570\u304c\u591a\u3044\u7406\u7531\u306e1\u3064\u306f\u3001\u307b\u3068\u3093\u3069\u306e\u8996\u8074\u8005\u3092\u5bfe\u8c61\u3068\u3057\u3066\u3044\u308b\u305f\u3081\u3001\u5e02\u5834\u3067\u306e\u6210\u529f\u306e\u53ef\u80fd\u6027\u304c\u9ad8\u3044\u3068\u3044\u3046\u3053\u3068\u304b\u3082\u3057\u308c\u307e\u305b\u3093\u3002<\/p>\n
\u305d\u3057\u3066\u3001\u3053\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f7f\u3063\u3066\u3001\u30a2\u30d7\u30ea\u306e\u30b3\u30b9\u30c8\u306b\u5bfe\u3059\u308b\u30e6\u30fc\u30b6\u30fc\u306e\u55dc\u597d\u3092\u6d1e\u5bdf\u3059\u308b\u306e\u3067\u3059\u3002Google Play \u30b9\u30c8\u30a2\u3067\u5229\u7528\u3067\u304d\u308b\u30a2\u30d7\u30ea\u306b\u306f2\u7a2e\u985e\u3042\u308a\u3001\u307b\u3068\u3093\u3069\u304c\u7121\u6599\u30a2\u30d7\u30ea\uff080\u5186\uff09\u3067\u3059\u304c\u3001\u4e2d\u306b\u306f\u6709\u6599\u30a2\u30d7\u30ea\u3082\u3042\u308a\u307e\u3059\u3002\u3053\u3053\u3067\u3001\u6709\u6599\u30a2\u30d7\u30ea\u3068\u7121\u6599\u30a2\u30d7\u30ea\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u6570\u3092\u6bd4\u8f03\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n
\n
type_installs = apps[[\"Installs\",\"Type\"]].groupby(by = \"Type\").mean()\nsns.barplot(data=type_installs, x=type_installs.index , y = \"Installs\" )<\/code><\/pre>\n<\/div>\n
\"\"<\/a><\/p>\n
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\u7570\u306a\u308b\u30ab\u30c6\u30b4\u30ea\u9593\u3067\u30a2\u30d7\u30ea\u306e\u4fa1\u683c\u3092\u6bd4\u8f03\u3057\u307e\u3059\u3002\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u4e2d\u304b\u3089\u4eba\u6c17\u306e\u3042\u308b\u30ab\u30c6\u30b4\u30ea\u3092\u3044\u304f\u3064\u304b\u9078\u629e\u3057\u3001\u4fa1\u683c\u304c100\u30c9\u30eb\u672a\u6e80\u306e\u30a2\u30d7\u30ea\u3092\u30d7\u30ed\u30c3\u30c8\u3059\u308b\u3068\u3001\u30e2\u30d0\u30a4\u30eb\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u306e\u958b\u767a\u6642\u306b\u4f7f\u7528\u3055\u308c\u308b\u4fa1\u683c\u6226\u7565\u306b\u3064\u3044\u3066\u3088\u308a\u3088\u304f\u7406\u89e3\u3059\u308b\u3053\u3068\u304c\u51fa\u6765\u307e\u3059\u3002<\/p>\n
\n
# Select a few popular app categories\npopular_app_cats = apps[apps.Category.isin(['GAME', 'FAMILY', 'PHOTOGRAPHY',\n                                            'MEDICAL', 'TOOLS', 'FINANCE',\n                                            'LIFESTYLE','BUSINESS'])]\n\n# Select apps priced below $100\napps_under_100 = popular_app_cats[popular_app_cats['Price']<100]\n\nfig, ax = plt.subplots()\nfig.set_size_inches(15, 8)\n\n# Examine price vs category with the authentic apps (apps_under_100)\nax = sns.stripplot(x='Price', y='Category', data=apps_under_100,\n                   jitter=True, linewidth=1)\nax.set_title('App pricing trend across categories after filtering for outliers')\nplt.show()<\/code><\/pre>\n<\/div>\n
\"\"<\/a><\/p>\n
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 \u30bd\u30fc\u30b9\u30b3\u30fc\u30c9\u5168\u6587\u306f\u3053\u3061\u3089 <\/a><\/p>\n
\u7d50\u8ad6<\/strong><\/h2>\n
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