Day 36 - 50 Days of Data Analysis .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fd0dcf4a",
   "metadata": {},
   "source": [
    "## 1. \n",
    "\n",
    "#### Load the data using pandas, which location has the most active users based on total posts?. What is the total number of posts in this location?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d1465b62",
   "metadata": {},
   "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>username</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>location</th>\n",
       "      <th>followers</th>\n",
       "      <th>posts</th>\n",
       "      <th>friends</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>user1</td>\n",
       "      <td>28</td>\n",
       "      <td>male</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>5493</td>\n",
       "      <td>56</td>\n",
       "      <td>605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>user2</td>\n",
       "      <td>22</td>\n",
       "      <td>female</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>9530</td>\n",
       "      <td>25</td>\n",
       "      <td>683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>user3</td>\n",
       "      <td>22</td>\n",
       "      <td>male</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>7200</td>\n",
       "      <td>20</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>user4</td>\n",
       "      <td>32</td>\n",
       "      <td>female</td>\n",
       "      <td>New York</td>\n",
       "      <td>7164</td>\n",
       "      <td>90</td>\n",
       "      <td>364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>user5</td>\n",
       "      <td>33</td>\n",
       "      <td>male</td>\n",
       "      <td>San Diego</td>\n",
       "      <td>4034</td>\n",
       "      <td>44</td>\n",
       "      <td>624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>user6</td>\n",
       "      <td>27</td>\n",
       "      <td>male</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>1389</td>\n",
       "      <td>58</td>\n",
       "      <td>765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>user7</td>\n",
       "      <td>28</td>\n",
       "      <td>male</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>1937</td>\n",
       "      <td>25</td>\n",
       "      <td>923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>user8</td>\n",
       "      <td>34</td>\n",
       "      <td>female</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>5344</td>\n",
       "      <td>70</td>\n",
       "      <td>452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>user9</td>\n",
       "      <td>34</td>\n",
       "      <td>female</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>8655</td>\n",
       "      <td>51</td>\n",
       "      <td>980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>user10</td>\n",
       "      <td>27</td>\n",
       "      <td>female</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>9825</td>\n",
       "      <td>60</td>\n",
       "      <td>575</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  username  age  gender      location  followers  posts  friends\n",
       "0    user1   28    male  Philadelphia       5493     56      605\n",
       "1    user2   22  female   Los Angeles       9530     25      683\n",
       "2    user3   22    male  Philadelphia       7200     20      100\n",
       "3    user4   32  female      New York       7164     90      364\n",
       "4    user5   33    male     San Diego       4034     44      624\n",
       "5    user6   27    male       Phoenix       1389     58      765\n",
       "6    user7   28    male       Phoenix       1937     25      923\n",
       "7    user8   34  female        Dallas       5344     70      452\n",
       "8    user9   34  female       Chicago       8655     51      980\n",
       "9   user10   27  female   Los Angeles       9825     60      575"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('social_media.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e21d40a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The most active location based on posts is New York\n",
      "The total number of posts is 90\n"
     ]
    }
   ],
   "source": [
    "# Group the data by location and sum the number of posts per location\n",
    "location_counts = df.groupby('location').sum()['posts']\n",
    "\n",
    "# Find the location with the maximum number of posts\n",
    "most_active_loc = location_counts.idxmax()\n",
    "print(f'The most active location based on posts is {most_active_loc}')\n",
    "print(f'The total number of posts is {location_counts.max()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4017abb9",
   "metadata": {},
   "source": [
    "## 2. \n",
    "\n",
    "#### Which gender has the least number of friends and what is the total number of friends for this gender?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e3a2fc5f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The gender with the least friends is male\n",
      "The total number of friends is  3017\n"
     ]
    }
   ],
   "source": [
    "# Group the data by gender and sum the number friends\n",
    "gender_counts = df.groupby('gender').sum()['friends']\n",
    "\n",
    "# Find the gender with the least friends\n",
    "least_active_gender = gender_counts.idxmin()\n",
    "print(f'The gender with the least friends is {least_active_gender}')\n",
    "print(f'The total number of friends is  {gender_counts.male}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "288595b1",
   "metadata": {},
   "source": [
    "## 3. \n",
    "\n",
    "#### What is the gender, location and age of the most active user based on posts?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b462953e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The age of the most active is 32\n",
      "The gender of the most active user is female\n",
      "The city with the most active user is New York\n"
     ]
    }
   ],
   "source": [
    "# Finding the age of most active user based on posts\n",
    "age_of_user = df.sort_values(by=\"posts\", ascending=False).loc[:,\"age\"].head(1)\n",
    "\n",
    "# Finding the gender of most active user based on posts\n",
    "gender_of_user = df.sort_values(by=\"posts\", ascending=False).loc[:,\"gender\"].head(1)\n",
    "\n",
    "# Finding the city of most active user based on posts\n",
    "city_of_user = df.sort_values(by=\"posts\", ascending=False).loc[:,\"location\"].head(1)\n",
    "\n",
    "print(f'The age of the most active is {age_of_user.iloc[0]}')\n",
    "print(f'The gender of the most active user is {gender_of_user.iloc[0]}')\n",
    "print(f'The city with the most active user is {city_of_user.iloc[0]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8270a31c",
   "metadata": {},
   "source": [
    "## 4. \n",
    "\n",
    "#### Which city has the highest number of female users?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ac82e99c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The city with the most female users is Los Angeles\n"
     ]
    }
   ],
   "source": [
    "# Group the data by city and gender, and count the number of users in each group\n",
    "city_gender_counts = df.groupby([\"location\",\"gender\"]).size(). reset_index(name = \"count\")\n",
    "\n",
    "# Filter the data to only include rows with female users\n",
    "female_users = city_gender_counts[city_gender_counts[\"gender\"] == \"female\"]\n",
    "\n",
    "# Find the city with the highest number of female users\n",
    "most_female_users_city = female_users[\"count\"].idxmax()\n",
    "\n",
    "# Return the city with the highets number of female \n",
    "city = female_users.iloc[most_female_users_city][\"location\"]\n",
    "\n",
    "print(f'The city with the most female users is {city}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50e9653f",
   "metadata": {},
   "source": [
    "## 5. \n",
    "\n",
    "#### Using a pie chart, compare the number of male and female users. Present your answer in percentages. Use Matplotlib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dfedbdea",
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# Count the number of male and female users\n",
    "gender_counts = df['gender'].value_counts()\n",
    "\n",
    "# Create the pie chart\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.pie(gender_counts, labels=['Male', 'Female'], \n",
    "        autopct='%1.1f%%',\n",
    "        shadow=True)\n",
    "\n",
    "plt.title('Gender Distribution', fontsize= 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b185550",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}