Day 34 - 50 Days of Data Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3a54caa9",
   "metadata": {},
   "source": [
    "## 1.\n",
    "\n",
    "#### Using pandas, write a code to display only 5 rows from the data. How many males and females are in the Dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8a898d0d",
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alice</td>\n",
       "      <td>25</td>\n",
       "      <td>5.5</td>\n",
       "      <td>F</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bob</td>\n",
       "      <td>35</td>\n",
       "      <td>5.9</td>\n",
       "      <td>M</td>\n",
       "      <td>40000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Charlie</td>\n",
       "      <td>45</td>\n",
       "      <td>6.2</td>\n",
       "      <td>M</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Debbie</td>\n",
       "      <td>55</td>\n",
       "      <td>6.6</td>\n",
       "      <td>F</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Edward</td>\n",
       "      <td>65</td>\n",
       "      <td>6.1</td>\n",
       "      <td>M</td>\n",
       "      <td>70000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name  Age  Height Gender  Income\n",
       "0     Alice   25     5.5      F   30000\n",
       "1       Bob   35     5.9      M   40000\n",
       "2  Charlie    45     6.2      M   50000\n",
       "3    Debbie   55     6.6      F   60000\n",
       "4    Edward   65     6.1      M   70000"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"Income_data.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a5367c07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender\n",
       "M    6\n",
       "F    4\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Gender\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1f0f043",
   "metadata": {},
   "source": [
    "## 2. \n",
    "\n",
    "#### What is Edward’s height?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a206a369",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Edward's height: 6.1\n"
     ]
    }
   ],
   "source": [
    "# Find the index for Edward\n",
    "index_edward = df[df[\"Name\"]==\"Edward\"].index\n",
    "\n",
    "# Use index to find Edward's height\n",
    "edward_height = df.loc[index_edward, \"Height\"]\n",
    "print(\"Edward's height:\", float(edward_height.iloc[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae5529e9",
   "metadata": {},
   "source": [
    "## 3. \n",
    "\n",
    "#### Write another code to create a DataFrame of only female names from the DataFrame above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f15e1099",
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alice</td>\n",
       "      <td>25</td>\n",
       "      <td>5.5</td>\n",
       "      <td>F</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Debbie</td>\n",
       "      <td>55</td>\n",
       "      <td>6.6</td>\n",
       "      <td>F</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Greta</td>\n",
       "      <td>76</td>\n",
       "      <td>6.3</td>\n",
       "      <td>F</td>\n",
       "      <td>90000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ivy</td>\n",
       "      <td>60</td>\n",
       "      <td>6.7</td>\n",
       "      <td>F</td>\n",
       "      <td>110000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  Age  Height Gender  Income\n",
       "0   Alice   25     5.5      F   30000\n",
       "1  Debbie   55     6.6      F   60000\n",
       "2   Greta   76     6.3      F   90000\n",
       "3     Ivy   60     6.7      F  110000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using query to filter the DataFrame \n",
    "df_females = df.query('Gender == \"F\"')\n",
    "\n",
    "#Resetting the index\n",
    "df_females.reset_index(inplace=True, drop=True)\n",
    "df_females"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36290d13",
   "metadata": {},
   "source": [
    "## 4. \n",
    "\n",
    "#### What is the average income per female."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e60c898d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average female income is 72500.0\n"
     ]
    }
   ],
   "source": [
    "# Filtering the Dataframe and calculating mean()\n",
    "ave_income_female = df[df['Gender'] == 'F']['Income'].mean()\n",
    "print(f'The average female income is', ave_income_female)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96dd4ef8",
   "metadata": {},
   "source": [
    "## 5.\t\n",
    "\n",
    "####  Your boss suspects that there is a correlation between the person's age and their income. She asks you to create a plot to show this correlation. Using Pandas and Matplotlib, create a scatter plot of age against income."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3e8cc2dc",
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df.plot(x='Age', y='Income',\n",
    "        kind='scatter', \n",
    "        figsize=(12, 8))\n",
    "\n",
    "plt.title(\"Correlation Between Income and Age\", fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ffc4a45",
   "metadata": {},
   "source": [
    "## 6. \n",
    "\n",
    "#### What is the average income of males over 50 compared to average salary of females over 50. Plot a bar plot using Matplotlib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "aa53ab60",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average income for males over 50 is 92500.0\n",
      "Average income for females over 50 is 86666.66666666667\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filter the Dataframe and calculate mean\n",
    "male_over_50 = df[(df['Gender'] == 'M') & (df['Age'] > 50)]['Income'].mean()\n",
    "female_over_50 = df[(df['Gender'] == 'F') & (df['Age'] > 50)]['Income'].mean()\n",
    "\n",
    "print('Average income for males over 50 is', male_over_50)\n",
    "print('Average income for females over 50 is', female_over_50)\n",
    "\n",
    "# Creating a DataFrame of females and males ave income\n",
    "data = {'Gender': ['Male', 'Female'],\n",
    "        'Income': [male_over_50, female_over_50]}\n",
    "\n",
    "\n",
    "# Plotting data\n",
    "bar_df = pd.DataFrame(data)\n",
    "bar_df.plot(x='Gender', y='Income', kind='bar', \n",
    "            figsize=(12, 8))\n",
    "\n",
    "plt.title(\"Average Income Per Gender\", fontsize = 20)\n",
    "plt.legend(loc='upper center')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "502e7727",
   "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
}