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evrenarslan/Matplotlib Box Plot.ipynb

Created April 15, 2020 19:22
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Matplotlib Box Plot.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Box Plot (Kutu Grafiği)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Box Plot (Kutu Grafiği) bize tek bir şekilde 5 adet istatistiki veriyi görselleştirmemizi sağlar. Bunlar:\n",
"\n",
"* ***Minumum:*** Veri seti içerisindeki en küçük değer\n",
"* ***First Quartile (ilk çeyrek):*** En küçük değer ile median'ın tam orta noktadadır. Q1\n",
"* ***Median (medyan):*** Veri setindeki orta noktadır. Önce veri seti küçükten büyüğe sıralanır. Eleman sayının tam orta noktasındaki değerdir. Bu değer aritmetik ortalama değildir.\n",
"* ***Third Quartile (üçüncü çeyrek):*** Median ile en büyük değerin tam orta noktadır. Q3\n",
"* ***Maximum:*** En büyük değer.\n",
"\n",
"Bu grafik bu değerlerin grafik olarak görünmesi dışında outlier olan değerlerin tespitini daha kolay sağlar. Normal dağılımda maximum ve minumum değer ***(maximum=Q3 + 1,5*IQR , minumum=Q1-1,5*IQR)*** değerleri olarak kabul edilir ve bunun dışında kalan değerler outlier olarak kabul edilir. \n",
"\n",
"***IQR= Q1 ile Q3 arasındaki mesafe***\n",
"\n",
"\n",
"Box plot çizmek için <code>df.plot(kind=\"box\")</code> parametresi kullanılır."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Örnekte kullandığımız veri setine ulaşmak için:\n",
"\n",
"https://www.un.org/en/development/desa/population/migration/data/empirical2/migrationflows.asp"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Datanın oluşturulması"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Grafikte kullanacağımız datayı yükleyelim.\n",
"df_can = pd.read_excel('Canada.xlsx',\n",
" sheet_name='Canada by Citizenship',\n",
" skiprows=range(20),\n",
" skipfooter=2\n",
" )\n",
"# Bize gerekli olmayan kolonları silelim\n",
"df_can.drop(['AREA', 'REG', 'DEV', 'Type', 'Coverage'], axis=1, inplace=True)\n",
"# Kolon isimlerini değiştirelim\n",
"df_can.rename(columns={'OdName':'Country', 'AreaName':'Continent','RegName':'Region'}, inplace=True)\n",
"# Kolon isimlerini string yapalım\n",
"df_can.columns = list(map(str, df_can.columns))\n",
"# Country kolonunu index yapalım\n",
"df_can.set_index('Country', inplace=True)\n",
"# Index adını silelim\n",
"df_can.index.name=None\n",
"# Göçmen sayısın toplamını gösteren Total isimli bir kolon ekleyelim\n",
"df_can['Total'] = df_can.sum(axis=1)\n",
"# Grafik çizerken kullanmak üzere years adınsa bir dizi oluşturalım\n",
"years = list(map(str, range(1980, 2014)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Grafiğin oluşturulması"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import matplotlib as mlp\n",
"import matplotlib.pyplot as plt\n",
"\n",
"mlp.style.use(\"ggplot\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Box Plot örneği için Japonya'dan gelen göçmenlerin durumunu inceleyelim."
]
},
{
"cell_type": "code",
"execution_count": 5,
"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>Japan</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1980</th>\n",
" <td>701</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1981</th>\n",
" <td>756</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1982</th>\n",
" <td>598</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1983</th>\n",
" <td>309</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1984</th>\n",
" <td>246</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Japan\n",
"1980 701\n",
"1981 756\n",
"1982 598\n",
"1983 309\n",
"1984 246"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_japan=df_can.loc[[\"Japan\"],years].transpose()\n",
"df_japan.head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df_japan.plot(kind=\"box\",figsize=(8,6))\n",
"\n",
"plt.title(\"Japon Göçmen Sayısı 1980-2013 Arası\")\n",
"plt.ylabel(\"Göçmen sayısı\")\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Yukarıdaki grafiği yorumlayalım.\n",
"* En düşük göçmen sayısının 200 olduğunu, en yükseğinin ~1300 olduğunu görüyoruz.\n",
"* Bu yıllar arasında, yılların %25'inde göçmen sayısının ~500'den küçük olduğunu.\n",
"* Yine bu yıllar arasında, yılların %75'inde göçmen sayısını ~1100'den küçük olduğunu \n",
"* Yılların yarısında göçmen sayısının ~900'den büyük olduğunu \n",
"\n",
"görüyoruz."
]
},
{
"cell_type": "code",
"execution_count": 10,
"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>Japan</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>34.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>814.911765</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>337.219771</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>198.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>529.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>902.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>1079.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1284.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Japan\n",
"count 34.000000\n",
"mean 814.911765\n",
"std 337.219771\n",
"min 198.000000\n",
"25% 529.000000\n",
"50% 902.000000\n",
"75% 1079.000000\n",
"max 1284.000000"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Yukarıda grafikten yorumlayarak tahmin ettiğimiz değerlerin gerçekte ne olduğunu aşağıdaki gibi görebiliriz.\n",
"df_japan.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Birden fazla ülke için durumu inceleyeceğimizi bir örnek yapalım. Bunun için China ve India örneklerine bakalım."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"df_CI=df_can.loc[[\"China\",\"India\"],years].transpose()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"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>China</th>\n",
" <th>India</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1980</th>\n",
" <td>5123</td>\n",
" <td>8880</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1981</th>\n",
" <td>6682</td>\n",
" <td>8670</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1982</th>\n",
" <td>3308</td>\n",
" <td>8147</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1983</th>\n",
" <td>1863</td>\n",
" <td>7338</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1984</th>\n",
" <td>1527</td>\n",
" <td>5704</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" China India\n",
"1980 5123 8880\n",
"1981 6682 8670\n",
"1982 3308 8147\n",
"1983 1863 7338\n",
"1984 1527 5704"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_CI.head()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x504 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df_CI.plot(kind=\"box\",figsize=(10,7))\n",
"\n",
"plt.title(\"Çin ve Hindistan Göçmen Sayısı 1980-2013 Arası\")\n",
"plt.ylabel(\"Göçmen sayısı\")\n",
"\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"China ve India için median değerleri yaklaşık olarak oaynı olmakla birlikte diğer değerlerde China daha yüksek değerler ulaşmış.\n",
"\n",
"***Grafiği yatay olarak çizdirebiliriz***"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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YsEB9+vTR2rVrFRsbq379+um3337Tq6++qssuu0yVKlXS3Xff7RK6OnfurOrVq+udd95RTEyMNm7cqLfeeqvA52zevLlSUlL01VdfKT4+Xo888ohGjx6t77//XlWrVi203gtRp04drVy5UsePH1dmZqYiIiL05JNPqmrVqs7dmc8884xuv/12DR06VNKZQF2lShXFxMTo+eefV3R0tGrWrKnU1FSXZZ8+fVoZGRnO0L18+XLnLtpz+1M6swv1lltu0enTp7Vy5UqX3bI1a9aUJB04cED169d3Tj948OB/dPKMl5eX83++adOmOnz4sJ599tl8z1Q/a+bMmUpPT1elSpWc04wx8vb21sGDB1W9enXn9PMPJ1m4cKGGDh2qsWPHKjY2VlWrVtXChQv1zDPPONv0799fnTp10ooVKxQfH6/OnTvrtttuuyiXNULpwEgcSoXg4GB17txZU6ZMcR67cq7Tp08XeVmB/+Q5u3btqrffflsffPCBqlWrps6dOzvnt2rVSps3b1bdunUVHR3t8nf2eLLidnakYf369c5pWVlZ2rBhQ5GPjYqK0pAhQ7Ro0SK98MILevPNN53zfH19lZOT49J+7dq16tSpkx544AG1aNFC0dHRRY5AuqNZs2Zq3Lixxo4d6/zwLcjZ9T1/BGHdunXOeS1btlRSUpJ+//33f1zbuYq7/y+//HJJ/z8SuHbtWg0ZMkTdunVTkyZNVLNmTe3Zs8flMV5eXhowYIBmzZqlWbNm6eqrr853NOpcVapU0W233aY33nhDCQkJ2r59u8txdReTv7+/IiIilJWVpUWLFql79+7y8jrzsXPs2DHn7bPOjtKd/RLQvn17fffddzpy5IizzVdffaXc3Fy1b99e0pnAeLZf6tSp42yXnp7uPCbtq6++UmBgoMtzXXrppYqIiHCeCXvWihUrdM011/zjdc/NzdWpU6cKnL97926tXr1aS5YscRmx//nnnxUVFaV58+YVuvy1a9eqRYsWeuyxx9SyZUvVq1dPe/fuzdOuZs2a6t+/v95++23NmTNH7777rsv2hN0IcSg1pk2bpgoVKqhly5Z67733tG3bNu3evVsLFixQq1atLkqAON99992nzz//XNOmTVOvXr1cvsn/61//0vbt23Xvvffqxx9/VEpKinP04vwP0+ISHR2tbt26aejQoYqPj9e2bds0YMAAHT16tMDH/P333xo6dKhWr16tlJQU/fTTT1qxYoWuuOIKZ5vLLrtM8fHx2rdvn/MabQ0aNNDXX3+t+Ph47dq1S88++6x++OGHf7wODodDb731ln7//Xe1bt1aixYt0vbt2/XLL7/o448/1jfffOP8MK9bt6569OihIUOG6IsvvtCOHTv0yCOPaOvWrRo5cqSkMydt1KlTR926ddPKlSuVkpKiVatW6cMPP/xHdXqy/2NjYzV9+nQlJCTo119/1apVqzRkyBAFBgY6g0eDBg307rvvasuWLUpMTFSvXr3yBG1JeuCBB7Rjxw7Nnj1bgwYNKvR5X3vtNb377rtKSkpSSkqK5s6dK29vb5eRqKJkZGQ4A4d05ppsiYmJLpdH+eqrr/TZZ59pz549WrNmjW688UadOHHC5aD77t27a968eXrrrbeUkpKidevWafjw4WratKnq1q0rSerdu7dCQ0PVu3dv/fzzz4qPj9fQoUN11113OUdu87N//37FxMQ4X2vHjx/XgQMHdODAAedIpsPh0MiRIzVhwgQtWLBAO3bs0KhRo/Tzzz/r0UcfvaD1jYuL08qVK7Vnzx7t3LlTs2bN0iuvvKK+ffsWWOPMmTMVFRWl7t27q3Hjxi5/PXv2LPAEh7MaNGigLVu26JNPPlFycrImTZqkxYsXu7QZNmyYli9fruTkZCUlJWnx4sWqXbu2AgICClwuLFNCx+IB+UpLSzOPPfaYqVevnpFkqlSpYmJiYsz8+fNNVlaWMabgS4yc65133jHuvLyzsrJMWFiYkWQSEhLyzN+8ebPp1q2bCQwMNH5+fqZu3bpm4MCBzpMf8vNPLzFy/oHTHTp0cLnkQXp6uunRo4fx9/c3oaGhZtSoUYVeYuTEiROmV69e5tJLLzUVK1Y0YWFhpmfPnua3335ztv/8889Nw4YNja+vr3O7ZWZmmh49epiAgAATHBxshgwZYp599llTp06dfJ/nLHe3/a+//moeeughExUVZXx9fY2/v79p3ry5efbZZ83Bgwed7f766y/nJUZ8fX3zvcTI/v37TZ8+fUxgYKCRZBo0aOA8UeP8g/uNMWbdunVGkklJSXFO++6774wk88svvzinFdX/+Z0IkN+yz/fyyy+ba665xoSFhZmKFSua2rVrm3vuucflEiebN2827dq1M35+fqZOnTpm6tSpeV4LZ3Xv3t1Uq1bNHD9+3GX6+es+ffp0c+WVV5qAgADnZUvOHlRf0Pqcb968eXkuSSPJpa5FixaZ6Oho4+vra4KDg02vXr3yXJonOzvbjBkzxtSvX9/4+fmZmjVrmt69e5tff/3Vpd2OHTvMjTfeaCpVqmSCg4PNoEGDnJcbutAaJZn4+HiXtq+88oqpXbu28fX1Nc2aNTMrVqy44PUdMWKEqVu3rvHz8zOBgYHmyiuvNFOmTDHZ2dn51nfq1CkTFhbmPGHpfFu3bjWSzFdffZXv+4YxZ967Bg0aZIKCgkxAQIDp1auXmTx5ssv/3pAhQ0y9evWMn5+fCQ4ONl26dDFbt251zufEBvs5jPkPD7IBPGz16tV67733ijw4GDhXp06dnBd5LS+uuuoqtWnTRpMnTy7pUgAUI3anolRKSkqSt7d3vj/VA+Rn37592rt3r/z9/fX555+XdDnFIi0tTbNmzdKmTZs0YsSIki4HQDHj7FSUSr1799auXbvUr1+/ki4Flvjuu+907733Kjw8XHFxcSVdTrGoXr26goKCNGnSJOdxZADKD3anAgAAWIjdqQAAABYixAEAAFioXB4Tt2/fvjzTQkNDndfHQtlHf5cf9HX5Qn+XL+WhvyMiIgqcx0gcAACAhQhxAAAAFiLEAQAAWIgQBwAAYCFCHAAAgIUIcQAAABYixAEAAFiIEAcAAGAhQhwAAICFCHEAAAAWIsQBAABYiBAHAABgIUIcAACAhQhxAAAAFiLEAQAAWIgQBwAAYCFCHAAAgIUIcQAAABYixAEAAFiIEAcAAGAhQhwAAICFCHEAAAAWIsQBAABYiBAHAABgIUIcAACAhQhxAAAAFiLEAQAAWIgQBwAAYCFCHAAAgIUIcQAAABYixAEAAFiIEAcAAGAhQhwAAICFCHEAAAAWIsQBAABYiBAHAABgIUIcAACAhQhxAAAAFiLEAQAAWIgQBwAAYCFCHAAAgIUIcQAAABYixAEAAFiIEAcAAGAhQhwAAICFCHEAAAAWIsQBAABYiBAHAABgIUIcAACAhQhxAAAAFiLEAQAAWIgQBwAAYCFCHAAAgIUIcQAAABYixAEAAFiIEAcAAGAhQhwAAICFCHEAAAAWIsQBAABYiBAHAABgIUIcAACAhQhxAAAAFiLEAQAAWIgQBwAAYCFCHAAAgIUIcQAAABYixAEAAFiIEAcAAGAhQhwAAICFCHEAAAAWIsQBAABYiBAHAABgIUIcAACAhQhxAAAAFiLEAQAAWIgQBwAAYCFCHAAAgIUIcQAAABYixAEAAFjIp6QLAEqjRo1qKDOT7zhlR0RJFwA3BQbmKinpQEmXAViBEAfkIzPTS3/8sa+ky8BFEBoaqvT0dElSZGQE/VrKRUYSuAF3MdQAAABgIUIcAACAhQhxAAAAFiLEAQAAWIgQBwAAYCFCXBnFGV4AAHfweWEvQhwAAICFCHEAAAAWIsQBAABYqERCXJ8+fS6ofVJSksaOHStJSkhI0NKlSz1RFgAAgDWs+9mtVq1aqVWrViVdBgAAQIkq0RCXlJSkhQsXKiAgQKmpqYqKitLw4cPlcDiUmJio+fPnKyAgQJdddpnzMV9//bWSk5P1wAMPKCEhQYsXL1Z2drYCAgI0fPhwBQYGluAaAQAAFI8SH4lLSUnR+PHjFRQUpNGjR2vnzp2KiorSjBkz9O9//1s1atTQhAkT8n1sw4YNNWbMGDkcDq1atUrLli1T375987RbuXKlVq5cKUkaO3asQkND87Tx8fHJd7rNOG28KIVvn7L2eiivzv/fpl9Lv3/+3sV734Wy9f+iLH52X4gSD3HR0dEKCQmRJF166aVKS0uTn5+fwsPDVbNmTUlSTEyMM4SdKyMjQxMnTtThw4eVnZ2t8PDwfJ+jY8eO6tixo/N+enp6njahoaH5TrdXhP74Y19JF1FqFdXfkZERZez1UH659jX9Wvr9s/eusvde7nk2v9+Vh/6OiCj4S0mJh7gKFSo4b3t5eSk3N9ftx86dO1ddu3ZVq1atnLtmAQAAyoNSeYmRiIgIpaWl6cCBA5Kkb775Jt92x48fV3BwsCRpzZo1xVYfAABASSvxkbj8+Pr66sEHH9TYsWMVEBCghg0bKjU1NU+7Hj16aPz48QoODla9evWUlpZWAtUCAAAUP4cxxpR0EcVt3768x1uUtf3qkZEcE1cYd46JY/uVDef2Nf1a+v3TPipr7+XFweb/i/LQ34UdE1cqd6cCAACgcIQ4AAAACxHiAAAALESIK6NsPb4BAFC8+LywFyEOAADAQoQ4AAAACxHiAAAALESIAwAAsBAhDgAAwEKl8me3gNIgMrLgq2TDNv/fl/Rr6RYYmFvSJQDWIMQB+eCU+7KjPPwsD4Dyid2pAAAAFiLEAQAAWIgQBwAAYCFCHAAAgIUIcQAAABYixAEAAFiIEAcAAGAhQhwAAICFCHEAAAAWIsQBAABYiBAHAABgIUIcAACAhQhxAAAAFiLEAQAAWIgQBwAAYCFCHAAAgIUIcQAAABYixAEAAFiIEAcAAGAhQhwAAICFCHEAAAAWIsQBAABYiBAHAABgIUIcAACAhQhxAAAAFiLEAQAAWIgQBwAAYCFCHAAAgIUIcQAAABYixAEAAFiIEAcAAGAhQhwAAICFCHEAAAAWIsQBAABYiBAHAABgIUIcAACAhQhxAAAAFvJxp9HkyZPlcDjynTds2LCLWhAAAACK5laIq1GjhqfrAAAAwAVwK8T16NHD03UAAADgArgV4rZu3arw8HCFh4crMzNTCxYskJeXl3r37q3AwEBP1wgAAIDzuHViw5w5c+TldabpW2+9pZycHDkcDs2YMcOjxQEAACB/bo3EZWRkKDQ0VDk5Ofr55581bdo0+fj46MEHH/R0fQAAAMiHWyGuUqVKyszMVGpqqmrVqiU/Pz9lZ2crOzvb0/UBAAAgH26FuE6dOunpp59Wdna2+vXrJ0nasWOHIiMjPVkbAAAACuBWiOvevbuuuuoqeXl5OS83EhwcrMGDB3u0OAAAAOTPrRAnSREREYXeBwAAQPFxK8Q99NBDBc578803L1oxAAAAcI9bIW748OGergMAAAAXwK0QV7t2bQUEBHi6FgAAALjJ7d2pTZs2VUxMjFq1aiUfH7cPpQMAAIAHuPWLDdOmTVPjxo31ySefaODAgZoxY4Z27Njh6doAAABQALeG1KpWraouXbqoS5cu2rdvn9auXavJkyfL4XDo2muv1Q033KCwsDBP1woAAID/49ZI3LkyMzOVmZmpEydOqHr16srIyNCTTz6ppUuXeqI+AAAA5MOtkbjU1FStW7dO69atk5+fn2JjY/X6668rODhYknTHHXdo5MiR6t69u0eLBQAAwBluhbi4uDi1b99ejz/+uKKjo/PMDw8PV5cuXS56cQAAAMifWyFu5syZRZ6Retddd12UggAAAFA0t0Kcj4+PMjMztXv3bh09elTGGOe8G264wWPFAQAAIH9uhbgff/xRkydPVs2aNZWamqratWsrNTVVDRs2JMQBAACUALdC3IcffqghQ4aoXbt26t+/v1599VXFx8crNTXV0/UBAAAgH25dYiQ9PV3t2rVzmRYbG6u1a9d6pCgAAAAUzq0QV7VqVWVmZkqSwsLCtGvXLh08eFC5ubkeLQ4AAAD5c2t3aocOHbRjxw61bdtWt9xyi55//nk5HA517drV0/UBAAAgH26FuHMv4hsbGzL3lxsAABLMSURBVKtGjRrp5MmTqlWrlscKAwAAQMHcCnHnO3DggLy8LvgXuwAAAHCRuJXE4uLitGPHDknS0qVLNWnSJE2aNEmLFy/2aHEAAADIn1shLjU1VfXr15ckrVq1SnFxcRozZoy++uorjxYHAACA/Lm1O/XsLzQcOHBAkpzHwh07dsxDZQEAAKAwboW4Bg0aaO7cuTp8+LBat24t6UygCwgI8GhxAAAAyJ9bu1OHDh0qf39/1alTRz179pQk7du3T126dPFocQAAAMifWyNxAQEB6t27t8u0K6+80iMFAQAAoGhcJwQAAMBChDgAAAALEeIAAAAsRIgDAACwkFsnNvz9999atmyZfv31V508edJl3vPPP++RwgAAAFAwt0LcpEmTlJ2drXbt2snX19fTNQEAAKAIboW4Xbt2afbs2apQoYKn6wEAAIAb3Dom7pJLLtGhQ4c8XQsAAADc5NZIXOPGjfXSSy/puuuuU2BgoMu8G264wSOFAQAAoGBuhbgdO3YoJCREW7ZsyTOPEAcAAFD83ApxcXFxnq4DAAAAF8Dt68QdPXpUa9eu1bJlyyRJGRkZHCcHAABQQtwKcdu2bdOIESO0bt06LVq0SJJ04MABzZo1y6PFAQAAIH9uhbj58+drxIgReuaZZ+Tt7S1Jio6OVnJyskeLAwAAQP7cCnF//vmnmjRp4jLNx8dHOTk5HikKAAAAhXMrxNWqVUuJiYku07Zs2aJLLrnEI0UBAACgcG6dndqnTx+98soratGihbKysjRz5kxt3LhRI0eO9HR9AAAAyIdbIa5+/fp67bXXtG7dOvn5+Sk0NFQvvfSSQkJCPF0fAAAA8uFWiJOk4OBg/dd//ZcnawEAAICb3Apxx48f1/Lly7V3716dPHnSZd6zzz7rkcIAAABQMLdC3Pjx45Wbm6urrrpKvr6+nq4JAAAARXArxP3yyy+aM2eOfHzc3vsKAAAAD3LrEiMNGzbUH3/84elaAAAA4Ca3htaGDBmil19+WdHR0QoMDHSZd+edd3qkMAAAABTMrRD3/vvv69ChQwoLC9OJEyec0x0Oh8cKAwAAQMHcCnHr16/XpEmTFBQU5Ol6AAAA4Aa3jomrXr2684fvAQAAUPLcGom79tpr9eqrr6pTp055jolr3LixRwoDAABAwdwKcV988YWkM8fGncvhcGjKlCkXvyoAAAAUyq0QN3XqVE/XAQAAgAvg1jFxe/fuVXp6usu09PR07d271xM1AQAAoAhuhbjJkycrJyfHZVp2dramTJmiAwcOaPXq1VwMGAAAoBi5FeLS09NVvXp1l2k1atRQamqqli1bpoyMDL3wwgseKRAAAAB5uRXigoODtWfPHpdpe/bsUc2aNTVo0CB17dpVubm5HikQAAAAebl1YsMtt9yi1157Td26dVP16tV18OBBffrpp7r99tslSX5+fpo1a5ZHCwUAAMD/cyvEdezYUZUrV9bq1at16NAhhYSEqG/fvmrbtq2n6wMAAEA+3ApxktSuXTu1a9fOk7UAAADATW6HuPj4eK1du1YZGRkKDg5WTEyMrr/+ek/WBgAAgAIUeGLD7NmznbcXL16spUuXqn379urfv7/at2+vZcuWafHixcVSJAAAAFwVOBIXFBTkvL1q1So999xzCgsLc05r1qyZ4uLinCc3AAAAoPgUOBJ3+eWXO2+fOnVKVatWdZkfEBCgrKwsz1UGAACAAhUY4ubMmeO83bx5c73xxhvat2+fsrKy9Mcff2jKlClq1qxZsRQJAAAAVwXuTh03bpzz9v3336+5c+dq5MiRys7Olo+Pj9q2bav777+/WIoEAACAK7fOTvX399ewYcM0ZMgQHT16VAEBAfLycuvHHgAAAOABRYa4syNvkrRr1y6Xn9dq0KCBvL29PVcdAAAA8lVoiPvyyy+1c+dODR8+XJL04osvKiAgQNKZkx3uvfde3XDDDZ6vEgAAAC4KDXFr1qzRwIEDnfcrVKigN998U5K0d+9ezZo1ixAHAABQAgo9sC0tLU2XXnqp836tWrWct+vUqaO0tDSPFQYAAICCFRriTp48qZMnTzrv//d//7fz9qlTp1zmAQAAoPgUGuIuueQSbd68Od95iYmJql27tkeKAgAAQOEKDXFdunTR7Nmz9eOPPzrPSs3NzdWPP/6ouXPnqkuXLsVSJAAAAFwVemJD+/btlZGRocmTJys7O1tVq1bVkSNHVKFCBd1555265ppriqtOAAAAnKPI68Tdeuut6tChg3bt2uW80G/9+vXl7+9fHPUBAAAgH27/YkPz5s09XQsAAADcxG9nAQAAWIgQBwAAYCFCHAAAgIUIcQAAABYixAEAAFiIEAcAAGAhQhwAAICFCHEAAAAWIsQBAABYiBAHAABgIUIcAACAhQhxAAAAFiLEAQAAWIgQBwAAYCFCHAAAgIUIcQAAABYixAEAAFiIEAcAAGAhQhwAAICFCHEAAAAWIsQBAABYiBAHAABgIUIcAACAhQhxAAAAFiLEAQAAWIgQBwAAYCFCHAAAgIUIcQAAABYixAEAAFiIEAcAAGAhQhwAAICFCHEAAAAWIsQBAABYiBAHAABgIZ+SLgClU6NGNZSZWdYzfkRJF4BiQ1+XLxenvwMDc5WUdOCiLAvwBEIc8pWZ6aU//thX0mV4TGhoqNLT00u6DBSDyMiIMv1ahquL+b8dGUn4R+lW1odaAAAAyiRCHAAAgIUIcQAAABYixAEAAFiIEAcAAGAhQhwAAICFCHEewGnpAACUbaXhs54QBwAAYCFCHAAAgIUIcQAAABYqtp/dyszM1Pz585WcnCwfHx+Fh4erdevWSkhI0KhRo/K0nz59urp27apatWoVV4kAAADWKJYQZ4zRa6+9ptjYWI0YMUKStHfvXiUkJBT4mMGDBxdHaQAAAFYqlhCXlJQkHx8f3XTTTc5pl156qY4dO6atW7dq3LhxSk1NVVRUlIYPHy6Hw6HnnntOffr0Ud26ddWnTx916dJFmzZtkq+vr0aOHKnAwEAlJCRo8eLFys7OVkBAgIYPH67AwMDiWCUAAIASVSwh7rffftNll12W77yUlBSNHz9eQUFBGj16tHbu3KmGDRu6tDl16pTq1aunXr16acGCBVq1apXuuOMONWzYUGPGjJHD4dCqVau0bNky9e3bN89zrFy5UitXrpQkjR07VqGhoXna+Pj45Dv9P1UaTj3+py7m9ihtLnZ/o3Sjr8sP3svLo5Lro5J+bym2Y+IKEh0drZCQEElnRufS0tLyhDgfHx+1bNlSkhQVFaXNmzdLkjIyMjRx4kQdPnxY2dnZCg8Pz/c5OnbsqI4dOzrvp6en52kTGhqa7/T/TIT++GPfRVpWyYiMjLiI26P0ubj9jdKtbL+W4Yr38vKlJN/Li+tzMiKi4JBaLCGudu3a+uGHH/KdV6FCBedtLy8v5ebm5mnj7e0th8PhbJOTkyNJmjt3rrp27apWrVopKSlJCxcu9ED1AAAApU+xXGKkcePGOn36tHOXpiTt3r1b27Zt+0fLPX78uIKDgyVJa9as+UfLAgAAsEmxjMQ5HA498cQTmj9/vj755BNVqFBBYWFhat269T9abo8ePTR+/HgFBwerXr16SktLu0gVAwAAlG4OY4wp6SKK2759eY9xuJj71SMj7T+OoiysQ2E4Jq78KOuvZbjivbx8Kelj4orj9VHYMXH8YgMAAICFCHEAAAAWIsR5AMPvAACUbaXhs54QBwAAYCFCHAAAgIUIcQAAABYixAEAAFiIEAcAAGChYvnFBtgpMrLgCwyWDWV9/XBW2X8tw9XF6e/AwLy/5Q2UJoQ45Ks0nDrtSfxiQ/lBX5cv9DfKE3anAgAAWIgQBwAAYCFCHAAAgIUIcQAAABYixAEAAFiIEAcAAGAhQhwAAICFCHEAAAAWIsQBAABYiBAHAABgIUIcAACAhQhxAAAAFiLEAQAAWIgQBwAAYCFCHAAAgIUIcQAAABYixAEAAFiIEAcAAGAhQhwAAICFCHEAAAAWIsQBAABYiBAHAABgIUIcAACAhQhxAAAAFiLEAQAAWIgQBwAAYCFCHAAAgIUIcQAAABYixAEAAFiIEAcAAGAhQhwAAICFCHEAAAAWIsQBAABYiBAHAABgIUIcAACAhQhxAAAAFiLEAQAAWIgQBwAAYCFCHAAAgIUIcQAAABYixAEAAFiIEAcAAGAhQhwAAICFCHEAAAAWIsQBAABYiBAHAABgIUIcAACAhQhxAAAAFiLEAQAAWIgQBwAAYCFCHAAAgIUIcQAAABYixAEAAFiIEAcAAGAhQhwAAICFCHEAAAAWIsQBAABYiBAHAABgIUIcAACAhQhxAAAAFiLEAQAAWIgQBwAAYCFCHAAAgIUIcQAAABYixAEAAFiIEAcAAGAhQhwAAICFCHEAAAAWIsQBAABYiBAHAABgIUIcAACAhQhxAAAAFiLEAQAAWIgQBwAAYCFCHAAAgIUIcQAAABYixAEAAFiIEAcAAGAhQhwAAICFHMYYU9JFAAAA4MIwEvd/Ro0aVdIloBjR3+UHfV2+0N/lS3nvb0IcAACAhQhxAAAAFiLE/Z+OHTuWdAkoRvR3+UFfly/0d/lS3vubExsAAAAsxEgcAACAhQhxAAAAFvIp6QJKg8TERM2bN0+5ubnq0KGDunfvXtIlwQ3Tpk3Tpk2bVK1aNY0bN06S9Pfff2vChAn6888/FRYWpkcffVRVqlSRJC1ZskSrV6+Wl5eX+vfvr+bNm0uS9uzZo6lTpyorK0stWrRQ//795XA4dPr0aU2ZMkV79uxRQECARowYofDw8BJb3/IsPT1dU6dOVWZmphwOhzp27KguXbrQ32VUVlaW4uLilJ2drZycHLVt21Y9e/akv8uw3NxcjRo1SsHBwRo1ahR97S5TzuXk5Jhhw4aZAwcOmNOnT5snnnjCpKamlnRZcENSUpJJTk42jz32mHPaO++8Y5YsWWKMMWbJkiXmnXfeMcYYk5qaap544gmTlZVlDh48aIYNG2ZycnKMMcaMGjXK7Ny50+Tm5poxY8aYTZs2GWOMWbFihZkxY4YxxphvvvnGjB8/vjhXD+fIyMgwycnJxhhjjh8/bh5++GGTmppKf5dRubm55sSJE8YYY06fPm2efvpps3PnTvq7DPv000/NxIkTzcsvv2yM4b3cXeV+d+ru3btVo0YNVa9eXT4+Prr66qu1YcOGki4Lbrjiiiuc38zO2rBhg2JjYyVJsbGxzr7csGGDrr76alWoUEHh4eGqUaOGdu/ercOHD+vEiROqX7++HA6HYmJinI9JSEjQddddJ0lq27attm7dKsN5QCUiKChIUVFRkqRKlSopMjJSGRkZ9HcZ5XA45OfnJ0nKyclRTk6OHA4H/V1GHTp0SJs2bVKHDh2c0+hr95T7EJeRkaGQkBDn/ZCQEGVkZJRgRfgn/vrrLwUFBUk688F/5MgRSXn7OTg4WBkZGYX2/7nzvL295e/vr6NHjxbXqqAAaWlpSklJUXR0NP1dhuXm5mrkyJEaMGCAmjRponr16tHfZdT8+fN17733yuFwOKfR1+4p9yEuvzR+7gsJZUNB37oK+zbGa6P0OXnypMaNG6d+/frJ39+/wHb0t/28vLz02muvafr06UpOTtZvv/1WYFv6214bN25UtWrVnCPtRaGvXZX7ExtCQkJ06NAh5/1Dhw450z/sU61aNR0+fFhBQUE6fPiwqlatKilvP2dkZCg4ODjf/g8ODnZ5TEhIiHJycnT8+PE8u29RfLKzszVu3Dhde+21atOmjST6uzyoXLmyrrjiCiUmJtLfZdDOnTuVkJCgn376SVlZWTpx4oTeeOMN+tpN5X4krm7dutq/f7/S0tKUnZ2t9evXq1WrViVdFv5DrVq10po1ayRJa9asUevWrZ3T169fr9OnTystLU379+9XdHS0goKCVKlSJe3atUvGGK1du9bZ/y1bttTXX38tSfr+++/VqFGjMvPtzTbGGE2fPl2RkZHq2rWrczr9XTYdOXJEx44dk3TmTNUtW7YoMjKS/i6DevfurenTp2vq1KkaMWKEGjdurIcffpi+dhO/2CBp06ZNeuutt5Sbm6vrr79et99+e0mXBDdMnDhR27Zt09GjR1WtWjX17NlTrVu31oQJE5Senq7Q0FA99thjzm9cixcvVnx8vLy8vNSvXz+1aNFCkpScnKxp06YpKytLzZs31/333y+Hw6GsrCxNmTJFKSkpqlKlikaMGKHq1auX5CqXWzt27NC///1vXXLJJc433169eqlevXr0dxn066+/aurUqcrNzZUxRu3atdOdd96po0eP0t9lWFJSkj799FONGjWKvnYTIQ4AAMBC5X53KgAAgI0IcQAAABYixAEAAFiIEAcAAGAhQhwAAICFCHEAAAAWIsQBAABY6H8BlbHlKG/Ty8sAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 720x504 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df_CI.plot(kind=\"box\",figsize=(10,7),color=\"blue\",vert=False) # Renk değiştirdim ve yatay olarak çizdim. \n",
"\n",
"plt.title(\"Çin ve Hindistan Göçmen Sayısı 1980-2013 Arası\")\n",
"plt.ylabel(\"Göçmen sayısı\")\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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