Intro To distributions_begin.ipynb

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   "source": [
    "## The Engineering WOrld - A Place For Learning And Exploring\n",
    "\n",
    "# Python statistics essential training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Standard imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy.stats\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as pp\n",
    "\n",
    "from IPython import display\n",
    "from ipywidgets import interact, widgets\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import mailbox\n",
    "import csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "china1965 = pd.read_csv('income-1965-china.csv')\n",
    "china2015 = pd.read_csv('income-2015-china.csv')\n",
    "usa1965 = pd.read_csv('income-1965-usa.csv')\n",
    "usa2015 = pd.read_csv('income-2015-usa.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 2 columns):\n",
      "income          1000 non-null float64\n",
      "log10_income    1000 non-null float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 15.7 KB\n"
     ]
    }
   ],
   "source": [
    "china1965.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>income</th>\n",
       "      <th>log10_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.026259</td>\n",
       "      <td>0.011257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.912053</td>\n",
       "      <td>-0.039980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.110699</td>\n",
       "      <td>-0.955857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.469659</td>\n",
       "      <td>-0.328217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.374626</td>\n",
       "      <td>-0.426402</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "     income  log10_income\n",
       "0  1.026259      0.011257\n",
       "1  0.912053     -0.039980\n",
       "2  0.110699     -0.955857\n",
       "3  0.469659     -0.328217\n",
       "4  0.374626     -0.426402"
      ]
     },
     "execution_count": 6,
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   ],
   "source": [
    "china1965.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "income          0.041968\n",
       "log10_income   -1.377078\n",
       "dtype: float64"
      ]
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "china1965.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "income          5.426802\n",
       "log10_income    0.734544\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "china1965.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "income          0.660597\n",
       "log10_income   -0.274157\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "china1965.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "income          0.208846\n",
       "log10_income    0.088610\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "china1965.var(ddof=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>income</th>\n",
       "      <th>log10_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <td>0.344130</td>\n",
       "      <td>-0.463277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.75</th>\n",
       "      <td>0.863695</td>\n",
       "      <td>-0.063640</td>\n",
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      "text/plain": [
       "        income  log10_income\n",
       "0.25  0.344130     -0.463277\n",
       "0.75  0.863695     -0.063640"
      ]
     },
     "execution_count": 12,
     "metadata": {},
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   ],
   "source": [
    "china1965.quantile([0.25, 0.75])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "income          0.557477\n",
       "log10_income   -0.253773\n",
       "Name: 0.5, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "china1965.quantile(0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "income          0.557477\n",
       "log10_income   -0.253773\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "china1965.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "95.5"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.percentileofscore(china1965.income,1.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>income</th>\n",
       "      <th>log10_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.660597</td>\n",
       "      <td>-0.274157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.457226</td>\n",
       "      <td>0.297822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.041968</td>\n",
       "      <td>-1.377078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.344130</td>\n",
       "      <td>-0.463277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.557477</td>\n",
       "      <td>-0.253773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.863695</td>\n",
       "      <td>-0.063640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.426802</td>\n",
       "      <td>0.734544</td>\n",
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      "text/plain": [
       "            income  log10_income\n",
       "count  1000.000000   1000.000000\n",
       "mean      0.660597     -0.274157\n",
       "std       0.457226      0.297822\n",
       "min       0.041968     -1.377078\n",
       "25%       0.344130     -0.463277\n",
       "50%       0.557477     -0.253773\n",
       "75%       0.863695     -0.063640\n",
       "max       5.426802      0.734544"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "china1965.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>income</th>\n",
       "      <th>log10_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>31.587965</td>\n",
       "      <td>1.418835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>22.101531</td>\n",
       "      <td>0.262200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.177852</td>\n",
       "      <td>0.620953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>17.498592</td>\n",
       "      <td>1.243003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>26.069531</td>\n",
       "      <td>1.416133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>39.017113</td>\n",
       "      <td>1.591255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>246.030397</td>\n",
       "      <td>2.390989</td>\n",
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      ],
      "text/plain": [
       "            income  log10_income\n",
       "count  1000.000000   1000.000000\n",
       "mean     31.587965      1.418835\n",
       "std      22.101531      0.262200\n",
       "min       4.177852      0.620953\n",
       "25%      17.498592      1.243003\n",
       "50%      26.069531      1.416133\n",
       "75%      39.017113      1.591255\n",
       "max     246.030397      2.390989"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usa1965.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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