notebook.ipynb

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    "## 1. Google Play Store apps and reviews\n",
    "<p>Mobile apps are everywhere. They are easy to create and can be lucrative. Because of these two factors, more and more apps are being developed. In this notebook, we will do a comprehensive analysis of the Android app market by comparing over ten thousand apps in Google Play across different categories. We'll look for insights in the data to devise strategies to drive growth and retention.</p>\n",
    "<p><img src=\"https://assets.datacamp.com/production/project_619/img/google_play_store.png\" alt=\"Google Play logo\"></p>\n",
    "<p>Let's take a look at the data, which consists of two files:</p>\n",
    "<ul>\n",
    "<li><code>apps.csv</code>: contains all the details of the applications on Google Play. There are 13 features that describe a given app.</li>\n",
    "<li><code>user_reviews.csv</code>: contains 100 reviews for each app, <a href=\"https://www.androidpolice.com/2019/01/21/google-play-stores-redesigned-ratings-and-reviews-section-lets-you-easily-filter-by-star-rating/\">most helpful first</a>. The text in each review has been pre-processed and attributed with three new features: Sentiment (Positive, Negative or Neutral), Sentiment Polarity and Sentiment Subjectivity.</li>\n",
    "</ul>"
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      "Total number of apps in the dataset =  54729564\n",
      "      Unnamed: 0                        App            Category  Rating  \\\n",
      "5131        6127                   BG Score              SPORTS     2.6   \n",
      "8683        9828  Égalité et Réconciliation  NEWS_AND_MAGAZINES     4.9   \n",
      "8910       10070   S.O.L : Stone of Life EX              FAMILY     4.3   \n",
      "2844        3601         Baby Name Together           PARENTING     4.0   \n",
      "5015        6008            BD Provider App  HEALTH_AND_FITNESS     1.4   \n",
      "\n",
      "      Reviews  Size    Installs  Type  Price Content Rating            Genres  \\\n",
      "5131       64   1.6     10,000+  Free      0       Everyone            Sports   \n",
      "8683       58   3.8        500+  Paid  $2.99       Everyone  News & Magazines   \n",
      "8910   171220  42.0  5,000,000+  Free      0   Everyone 10+      Role Playing   \n",
      "2844      464  24.0    100,000+  Free      0       Everyone         Parenting   \n",
      "5015       45  13.0      5,000+  Free      0       Everyone  Health & Fitness   \n",
      "\n",
      "            Last Updated Current Ver   Android Ver  \n",
      "5131       July 26, 2017         2.1  4.0.3 and up  \n",
      "8683        May 26, 2018       1.1.1    5.0 and up  \n",
      "8910       July 28, 2015       1.2.6  2.3.3 and up  \n",
      "2844      August 6, 2018       2.3.1    5.0 and up  \n",
      "5015  September 12, 2016           1    4.1 and up  \n"
     ]
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   ],
   "source": [
    "# Read in dataset\n",
    "import pandas as pd\n",
    "apps_with_duplicates = pd.read_csv('datasets/apps.csv')\n",
    "\n",
    "# Drop duplicates from apps_with_duplicates\n",
    "apps = apps_with_duplicates.drop_duplicates(subset='App')\n",
    "\n",
    "total = apps[\"Unnamed: 0\"].sum()\n",
    "\n",
    "# Print the total number of apps\n",
    "print('Total number of apps in the dataset = ', total)\n",
    "\n",
    "# Have a look at a random sample of 5 rows\n",
    "print(apps.sample(n=5))"
   ]
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    "## 2. Data cleaning\n",
    "<p>Data cleaning is one of the most essential subtask any data science project. Although it can be a very tedious process, it's worth should never be undermined.</p>\n",
    "<p>By looking at a random sample of the dataset rows (from the above task), we observe that some entries in the columns like <code>Installs</code> and <code>Price</code> have a few special characters (<code>+</code> <code>,</code> <code>$</code>) due to the way the numbers have been represented. This prevents the columns from being purely numeric, making it difficult to use them in subsequent future mathematical calculations. Ideally, as their names suggest, we would want these columns to contain only digits from [0-9].</p>\n",
    "<p>Hence, we now proceed to clean our data. Specifically, the special characters <code>,</code> and <code>+</code> present in <code>Installs</code> column and <code>$</code> present in <code>Price</code> column need to be removed.</p>\n",
    "<p>It is also always a good practice to print a summary of your dataframe after completing data cleaning. We will use the <code>info()</code> method to acheive this.</p>"
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      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 9659 entries, 0 to 9658\n",
      "Data columns (total 14 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Unnamed: 0      9659 non-null   int64  \n",
      " 1   App             9659 non-null   object \n",
      " 2   Category        9659 non-null   object \n",
      " 3   Rating          8196 non-null   float64\n",
      " 4   Reviews         9659 non-null   int64  \n",
      " 5   Size            8432 non-null   float64\n",
      " 6   Installs        9659 non-null   object \n",
      " 7   Type            9659 non-null   object \n",
      " 8   Price           9659 non-null   object \n",
      " 9   Content Rating  9659 non-null   object \n",
      " 10  Genres          9659 non-null   object \n",
      " 11  Last Updated    9659 non-null   object \n",
      " 12  Current Ver     9651 non-null   object \n",
      " 13  Android Ver     9657 non-null   object \n",
      "dtypes: float64(2), int64(2), object(10)\n",
      "memory usage: 1.1+ MB\n",
      "None\n"
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    "# List of characters to remove\n",
    "chars_to_remove = ['+', ',', '$']\n",
    "# List of column names to clean\n",
    "cols_to_clean = apps[['Installs', 'Price']]\n",
    "\n",
    "# Loop for each column in cols_to_clean\n",
    "for col in cols_to_clean:\n",
    "    # Loop for each char in chars_to_remove\n",
    "    for char in chars_to_remove:\n",
    "        # Replace the character with an empty string\n",
    "        apps[col] = apps[col].apply(lambda x: x.replace(char, ''))\n",
    "        \n",
    "# Print a summary of the apps dataframe\n",
    "print(apps.info())"
   ]
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    "## 3. Correcting data types\n",
    "<p>From the previous task we noticed that <code>Installs</code> and <code>Price</code> were categorized as <code>object</code> data type (and not <code>int</code> or <code>float</code>) as we would like. This is because these two columns originally had mixed input types: digits and special characters. To know more about Pandas data types, read <a href=\"https://datacarpentry.org/python-ecology-lesson/04-data-types-and-format/\">this</a>.</p>\n",
    "<p>The four features that we will be working with most frequently henceforth are <code>Installs</code>, <code>Size</code>, <code>Rating</code> and <code>Price</code>. While <code>Size</code> and <code>Rating</code> are both <code>float64</code> (i.e. purely numerical data types), we still need to work on <code>Installs</code> and <code>Price</code> to make them numeric.</p>"
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      "\n"
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    "# Convert Installs to float data type\n",
    "apps['Installs'] = apps['Installs'].astype('float64')\n",
    "\n",
    "# Convert Price to float data type\n",
    "apps['Price'] = apps['Price'].astype('float64')\n",
    "\n",
    "# Checking dtypes of the apps dataframe\n",
    "print()"
   ]
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    "## 4. Exploring app categories\n",
    "<p>With more than 1 billion active users in 190 countries around the world, Google Play continues to be an important distribution platform to build a global audience. For businesses to get their apps in front of users, it's important to make them more quickly and easily discoverable on Google Play. To improve the overall search experience, Google has introduced the concept of grouping apps into categories.</p>\n",
    "<p>This brings us to the following questions:</p>\n",
    "<ul>\n",
    "<li>Which category has the highest share of (active) apps in the market? </li>\n",
    "<li>Is any specific category dominating the market?</li>\n",
    "<li>Which categories have the fewest number of apps?</li>\n",
    "</ul>\n",
    "<p>We will see that there are <code>33</code> unique app categories present in our dataset. <em>Family</em> and <em>Game</em> apps have the highest market prevalence. Interestingly, <em>Tools</em>, <em>Business</em> and <em>Medical</em> apps are also at the top.</p>"
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   ],
   "source": [
    "import plotly\n",
    "plotly.offline.init_notebook_mode(connected=True)\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "# Print the total number of unique categories\n",
    "num_categories = len(apps['Category'].unique())\n",
    "print('Number of categories = ', num_categories)\n",
    "\n",
    "# Count the number of apps in each 'Category'. \n",
    "num_apps_in_category = apps['Category'].value_counts()\n",
    "# Sort num_apps_in_category in descending order based on the count of apps in each category\n",
    "sorted_num_apps_in_category = num_apps_in_category.sort_values(ascending=False)\n",
    "\n",
    "data = [go.Bar(\n",
    "        x = num_apps_in_category.index, # index = category name\n",
    "        y = num_apps_in_category.values, # value = count\n",
    ")]\n",
    "\n",
    "plotly.offline.iplot(data)"
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    "## 5. Distribution of app ratings\n",
    "<p>After having witnessed the market share for each category of apps, let's see how all these apps perform on an average. App ratings (on a scale of 1 to 5) impact the discoverability, conversion of apps as well as the company's overall brand image. Ratings are a key performance indicator of an app.</p>\n",
    "<p>From our research, we found that the average volume of ratings across all app categories is <code>4.17</code>. The histogram plot is skewed to the left indicating that the majority of the apps are highly rated with only a few exceptions in the low-rated apps.</p>"
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     "text": [
      "Average app rating =  4.173243045387998\n"
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          4,
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          4,
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          4,
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          4,
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          4,
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          3,
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          null,
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          5,
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          5,
          5,
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          4,
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          4,
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          4,
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          4,
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          4,
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          4,
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          4,
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          4,
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          3,
          3.9,
          4.5,
          3.4,
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          4,
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          4,
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          4,
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          4,
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          4,
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          4,
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          4,
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          4,
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          4,
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          5,
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          1,
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          4,
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          3,
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          4,
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          4,
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          4,
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[0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2d\"}], \"histogram2dcontour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2dcontour\"}], \"mesh3d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"mesh3d\"}], \"parcoords\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"parcoords\"}], \"pie\": [{\"automargin\": true, \"type\": \"pie\"}], \"scatter\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter\"}], \"scatter3d\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter3d\"}], \"scattercarpet\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattercarpet\"}], \"scattergeo\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergeo\"}], \"scattergl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergl\"}], \"scattermapbox\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattermapbox\"}], \"scatterpolar\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolar\"}], \"scatterpolargl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolargl\"}], \"scatterternary\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterternary\"}], \"surface\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"surface\"}], \"table\": [{\"cells\": {\"fill\": {\"color\": \"#EBF0F8\"}, \"line\": {\"color\": \"white\"}}, \"header\": {\"fill\": {\"color\": \"#C8D4E3\"}, \"line\": {\"color\": \"white\"}}, \"type\": \"table\"}]}, \"layout\": {\"annotationdefaults\": {\"arrowcolor\": \"#2a3f5f\", \"arrowhead\": 0, \"arrowwidth\": 1}, \"autotypenumbers\": \"strict\", \"coloraxis\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"colorscale\": {\"diverging\": [[0, \"#8e0152\"], [0.1, \"#c51b7d\"], [0.2, \"#de77ae\"], [0.3, \"#f1b6da\"], [0.4, \"#fde0ef\"], [0.5, \"#f7f7f7\"], [0.6, \"#e6f5d0\"], [0.7, \"#b8e186\"], [0.8, \"#7fbc41\"], [0.9, \"#4d9221\"], [1, \"#276419\"]], \"sequential\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"sequentialminus\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]]}, \"colorway\": [\"#636efa\", \"#EF553B\", \"#00cc96\", \"#ab63fa\", \"#FFA15A\", \"#19d3f3\", \"#FF6692\", \"#B6E880\", \"#FF97FF\", \"#FECB52\"], \"font\": {\"color\": \"#2a3f5f\"}, \"geo\": {\"bgcolor\": \"white\", \"lakecolor\": \"white\", \"landcolor\": \"#E5ECF6\", \"showlakes\": true, \"showland\": true, \"subunitcolor\": \"white\"}, \"hoverlabel\": {\"align\": \"left\"}, \"hovermode\": \"closest\", \"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('a3d92e47-91ad-4b68-98fd-145b8645b5e9');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Average rating of apps\n",
    "avg_app_rating = apps['Rating'].mean()\n",
    "print('Average app rating = ', avg_app_rating)\n",
    "\n",
    "# Distribution of apps according to their ratings\n",
    "data = [go.Histogram(\n",
    "        x = apps['Rating']\n",
    ")]\n",
    "\n",
    "# Vertical dashed line to indicate the average app rating\n",
    "layout = {'shapes': [{\n",
    "              'type' :'line',\n",
    "              'x0': avg_app_rating,\n",
    "              'y0': 0,\n",
    "              'x1': avg_app_rating,\n",
    "              'y1': 1000,\n",
    "              'line': { 'dash': 'dashdot'}\n",
    "          }]\n",
    "          }\n",
    "\n",
    "plotly.offline.iplot({'data': data, 'layout': layout})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "39"
    },
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 6. Size and price of an app\n",
    "<p>Let's now examine app size and app price. For size, if the mobile app is too large, it may be difficult and/or expensive for users to download. Lengthy download times could turn users off before they even experience your mobile app. Plus, each user's device has a finite amount of disk space. For price, some users expect their apps to be free or inexpensive. These problems compound if the developing world is part of your target market; especially due to internet speeds, earning power and exchange rates.</p>\n",
    "<p>How can we effectively come up with strategies to size and price our app?</p>\n",
    "<ul>\n",
    "<li>Does the size of an app affect its rating? </li>\n",
    "<li>Do users really care about system-heavy apps or do they prefer light-weighted apps? </li>\n",
    "<li>Does the price of an app affect its rating? </li>\n",
    "<li>Do users always prefer free apps over paid apps?</li>\n",
    "</ul>\n",
    "<p>We find that the majority of top rated apps (rating over 4) range from 2 MB to 20 MB. We also find that the vast majority of apps price themselves under \\$10.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "dc": {
     "key": "39"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set_style(\"darkgrid\")\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# Select rows where both 'Rating' and 'Size' values are present (ie. the two values are not null)\n",
    "apps_with_size_and_rating_present = apps[~(apps['Rating'].isnull()) & (~apps['Size'].isnull())]\n",
    "\n",
    "# Subset for categories with at least 250 apps\n",
    "large_categories = apps_with_size_and_rating_present.groupby(['Category']).filter(lambda x: len(x) >= 250)\n",
    "\n",
    "# Plot size vs. rating\n",
    "plt1 = sns.jointplot(x = large_categories['Size'], y = large_categories['Rating'])\n",
    "\n",
    "# Select apps whose 'Type' is 'Paid'\n",
    "paid_apps = apps_with_size_and_rating_present[apps_with_size_and_rating_present['Type'] == 'Paid']\n",
    "\n",
    "# Plot price vs. rating\n",
    "plt2 = sns.jointplot(x = paid_apps['Price'], y = paid_apps['Rating'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "46"
    },
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 7. Relation between app category and app price\n",
    "<p>So now comes the hard part. How are companies and developers supposed to make ends meet? What monetization strategies can companies use to maximize profit? The costs of apps are largely based on features, complexity, and platform.</p>\n",
    "<p>There are many factors to consider when selecting the right pricing strategy for your mobile app. It is important to consider the willingness of your customer to pay for your app. A wrong price could break the deal before the download even happens. Potential customers could be turned off by what they perceive to be a shocking cost, or they might delete an app they’ve downloaded after receiving too many ads or simply not getting their money's worth.</p>\n",
    "<p>Different categories demand different price ranges. Some apps that are simple and used daily, like the calculator app, should probably be kept free. However, it would make sense to charge for a highly-specialized medical app that diagnoses diabetic patients. Below, we see that <em>Medical and Family</em> apps are the most expensive. Some medical apps extend even up to \\$80! All game apps are reasonably priced below \\$20.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "dc": {
     "key": "46"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>App</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3327</th>\n",
       "      <td>FAMILY</td>\n",
       "      <td>most expensive app (H)</td>\n",
       "      <td>399.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3465</th>\n",
       "      <td>LIFESTYLE</td>\n",
       "      <td>💎 I'm rich</td>\n",
       "      <td>399.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3469</th>\n",
       "      <td>LIFESTYLE</td>\n",
       "      <td>I'm Rich - Trump Edition</td>\n",
       "      <td>400.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4396</th>\n",
       "      <td>LIFESTYLE</td>\n",
       "      <td>I am rich</td>\n",
       "      <td>399.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4398</th>\n",
       "      <td>FAMILY</td>\n",
       "      <td>I am Rich Plus</td>\n",
       "      <td>399.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4399</th>\n",
       "      <td>LIFESTYLE</td>\n",
       "      <td>I am rich VIP</td>\n",
       "      <td>299.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4400</th>\n",
       "      <td>FINANCE</td>\n",
       "      <td>I Am Rich Premium</td>\n",
       "      <td>399.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4401</th>\n",
       "      <td>LIFESTYLE</td>\n",
       "      <td>I am extremely Rich</td>\n",
       "      <td>379.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4402</th>\n",
       "      <td>FINANCE</td>\n",
       "      <td>I am Rich!</td>\n",
       "      <td>399.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4403</th>\n",
       "      <td>FINANCE</td>\n",
       "      <td>I am rich(premium)</td>\n",
       "      <td>399.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4406</th>\n",
       "      <td>FAMILY</td>\n",
       "      <td>I Am Rich Pro</td>\n",
       "      <td>399.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4408</th>\n",
       "      <td>FINANCE</td>\n",
       "      <td>I am rich (Most expensive app)</td>\n",
       "      <td>399.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4410</th>\n",
       "      <td>FAMILY</td>\n",
       "      <td>I Am Rich</td>\n",
       "      <td>389.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4413</th>\n",
       "      <td>FINANCE</td>\n",
       "      <td>I am Rich</td>\n",
       "      <td>399.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4417</th>\n",
       "      <td>FINANCE</td>\n",
       "      <td>I AM RICH PRO PLUS</td>\n",
       "      <td>399.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8763</th>\n",
       "      <td>FINANCE</td>\n",
       "      <td>Eu Sou Rico</td>\n",
       "      <td>394.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8780</th>\n",
       "      <td>LIFESTYLE</td>\n",
       "      <td>I'm Rich/Eu sou Rico/أنا غني/我很有錢</td>\n",
       "      <td>399.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Category                                App   Price\n",
       "3327     FAMILY             most expensive app (H)  399.99\n",
       "3465  LIFESTYLE                         💎 I'm rich  399.99\n",
       "3469  LIFESTYLE           I'm Rich - Trump Edition  400.00\n",
       "4396  LIFESTYLE                          I am rich  399.99\n",
       "4398     FAMILY                     I am Rich Plus  399.99\n",
       "4399  LIFESTYLE                      I am rich VIP  299.99\n",
       "4400    FINANCE                  I Am Rich Premium  399.99\n",
       "4401  LIFESTYLE                I am extremely Rich  379.99\n",
       "4402    FINANCE                         I am Rich!  399.99\n",
       "4403    FINANCE                 I am rich(premium)  399.99\n",
       "4406     FAMILY                      I Am Rich Pro  399.99\n",
       "4408    FINANCE     I am rich (Most expensive app)  399.99\n",
       "4410     FAMILY                          I Am Rich  389.99\n",
       "4413    FINANCE                          I am Rich  399.99\n",
       "4417    FINANCE                 I AM RICH PRO PLUS  399.99\n",
       "8763    FINANCE                        Eu Sou Rico  394.99\n",
       "8780  LIFESTYLE  I'm Rich/Eu sou Rico/أنا غني/我很有錢  399.99"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(15, 8)\n",
    "\n",
    "# Select a few popular app categories\n",
    "popular_app_cats = apps[apps.Category.isin(['GAME', 'FAMILY', 'PHOTOGRAPHY',\n",
    "                                            'MEDICAL', 'TOOLS', 'FINANCE',\n",
    "                                            'LIFESTYLE','BUSINESS'])]\n",
    "\n",
    "# Examine the price trend by plotting Price vs Category\n",
    "ax = sns.stripplot(x = popular_app_cats['Price'], y = popular_app_cats['Category'], jitter=True, linewidth=1)\n",
    "ax.set_title('App pricing trend across categories')\n",
    "\n",
    "# Apps whose Price is greater than 200\n",
    "apps_above_200 = apps.loc[apps['Price'] > 200]\n",
    "apps_above_200[['Category', 'App', 'Price']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "53"
    },
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 8. Filter out \"junk\" apps\n",
    "<p>It looks like a bunch of the really expensive apps are \"junk\" apps. That is, apps that don't really have a purpose. Some app developer may create an app called <em>I Am Rich Premium</em> or <em>most expensive app (H)</em> just for a joke or to test their app development skills. Some developers even do this with malicious intent and try to make money by hoping people accidentally click purchase on their app in the store.</p>\n",
    "<p>Let's filter out these junk apps and re-do our visualization.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "dc": {
     "key": "53"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'App pricing trend across categories after filtering for junk apps')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select apps priced below $100\n",
    "apps_under_100 = popular_app_cats.loc[popular_app_cats['Price'] < 100]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(15, 8)\n",
    "\n",
    "# Examine price vs category with the authentic apps (apps_under_100)\n",
    "ax = sns.stripplot(x = apps_under_100['Price'], y = apps_under_100['Category'], data = apps, jitter = True, linewidth = 1)\n",
    "ax.set_title('App pricing trend across categories after filtering for junk apps')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "60"
    },
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 9. Popularity of paid apps vs free apps\n",
    "<p>For apps in the Play Store today, there are five types of pricing strategies: free, freemium, paid, paymium, and subscription. Let's focus on free and paid apps only. Some characteristics of free apps are:</p>\n",
    "<ul>\n",
    "<li>Free to download.</li>\n",
    "<li>Main source of income often comes from advertisements.</li>\n",
    "<li>Often created by companies that have other products and the app serves as an extension of those products.</li>\n",
    "<li>Can serve as a tool for customer retention, communication, and customer service.</li>\n",
    "</ul>\n",
    "<p>Some characteristics of paid apps are:</p>\n",
    "<ul>\n",
    "<li>Users are asked to pay once for the app to download and use it.</li>\n",
    "<li>The user can't really get a feel for the app before buying it.</li>\n",
    "</ul>\n",
    "<p>Are paid apps installed as much as free apps? It turns out that paid apps have a relatively lower number of installs than free apps, though the difference is not as stark as I would have expected!</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "dc": {
     "key": "60"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
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   "source": [
    "trace0 = go.Box(\n",
    "    # Data for paid apps\n",
    "    y = apps[apps['Type'] == 'Paid']['Installs'],\n",
    "    name = 'Paid'\n",
    ")\n",
    "\n",
    "trace1 = go.Box(\n",
    "    # Data for free apps\n",
    "    y = apps[apps['Type'] == 'Free']['Installs'],\n",
    "    name = 'Free'\n",
    ")\n",
    "\n",
    "layout = go.Layout(\n",
    "    title = \"Number of downloads of paid apps vs. free apps\",\n",
    "    yaxis = dict(title = \"Log number of downloads\",\n",
    "                type = 'log',\n",
    "                autorange = True)\n",
    ")\n",
    "\n",
    "# Add trace0 and trace1 to a list for plotting\n",
    "data = [trace0, trace1]\n",
    "plotly.offline.iplot({'data': data, 'layout': layout})"
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   "source": [
    "## 10. Sentiment analysis of user reviews\n",
    "<p>Mining user review data to determine how people feel about your product, brand, or service can be done using a technique called sentiment analysis. User reviews for apps can be analyzed to identify if the mood is positive, negative or neutral about that app. For example, positive words in an app review might include words such as 'amazing', 'friendly', 'good', 'great', and 'love'. Negative words might be words like 'malware', 'hate', 'problem', 'refund', and 'incompetent'.</p>\n",
    "<p>By plotting sentiment polarity scores of user reviews for paid and free apps, we observe that free apps receive a lot of harsh comments, as indicated by the outliers on the negative y-axis. Reviews for paid apps appear never to be extremely negative. This may indicate something about app quality, i.e., paid apps being of higher quality than free apps on average. The median polarity score for paid apps is a little higher than free apps, thereby syncing with our previous observation.</p>\n",
    "<p>In this notebook, we analyzed over ten thousand apps from the Google Play Store. We can use our findings to inform our decisions should we ever wish to create an app ourselves.</p>"
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     "data": {
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\n",
      "text/plain": [
       "<Figure size 792x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load user_reviews.csv\n",
    "reviews_df = pd.read_csv('datasets/user_reviews.csv')\n",
    "\n",
    "# Join the two dataframes\n",
    "merged_df = apps.merge(reviews_df)\n",
    "\n",
    "# Drop NA values from Sentiment and Review columns\n",
    "merged_df = merged_df.dropna(subset = ['Sentiment', 'Review'])\n",
    "\n",
    "sns.set_style('ticks')\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(11, 8)\n",
    "\n",
    "# User review sentiment polarity for paid vs. free apps\n",
    "ax = sns.boxplot(x = 'Type', y = 'Sentiment_Polarity', data = merged_df)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}