{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kích thước dữ liệu nhỏ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## t-statistics\n",
    "Áp dụng khi kích thước mẫu n < 30, ngược lại ta dùng z-score. Khi kích thước mẫu càng lớn thì t-score xấp xỉ z-score.\n",
    "- z-test: so sánh mẫu thu thập với quần thể (sample vs population)\n",
    "- t-test: so sánh 2 mẫu thu thập độc lập (sample_1 vs sample_2, curve_1 vs curve_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "from scipy import stats\n",
    "import math\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t-test cho từng n\n",
      "n = 3: 4.30265272991\n",
      "n = 10: 2.26215716274\n",
      "n = 10: 2.09302405441\n",
      "\n",
      "n = 100: 1.98421695151\n",
      "z-score: 1.95996398454\n"
     ]
    }
   ],
   "source": [
    "print \"t-test cho từng n\"\n",
    "print \"n = 3:\", stats.t.ppf(0.975, 2)\n",
    "print \"n = 10:\", stats.t.ppf(0.975, 9)\n",
    "print \"n = 10:\", stats.t.ppf(0.975, 19)\n",
    "print \"\\nn = 100:\", stats.t.ppf(0.975, 99)\n",
    "print \"z-score:\", stats.norm.ppf(0.975)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Bài toán: tính khoảng tin cậy cho điểm số học sinh**\n",
    "$$SE_{\\bar{X}} = \\frac{s}{\\sqrt{n}} \\ (SE: standard \\ error)$$\n",
    "$$UCL = \\bar{x} + tScore * SE_{\\bar{X}}$$\n",
    "$$LCL = \\bar{x} - tScore * SE_{\\bar{X}}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sample mean: 126.0\n",
      "t-score: 2.39844098471\n",
      "standard error: 8.85437744847\n",
      "Tin cậy trên: 147.236701767\n",
      "Tin cậy dưới: 104.763298233\n"
     ]
    }
   ],
   "source": [
    "# điểm của 10 học sinh\n",
    "scores = np.asarray([120, 105, 100, 130, 115, 100, 185, 105, 130, 170])\n",
    "\n",
    "# chuẩn bị các thông số\n",
    "n = scores.size\n",
    "degree_of_fredom = n - 1\n",
    "confidence_interval = 0.98\n",
    "\n",
    "sample_mean = scores.mean()\n",
    "t_score = stats.t.ppf(confidence_interval, degree_of_fredom)\n",
    "standard_err = scores.std() / math.sqrt(n) \n",
    "\n",
    "print \"sample mean:\", sample_mean\n",
    "print \"t-score:\", t_score\n",
    "print \"standard error:\", standard_err\n",
    "\n",
    "# tính khoảng tin cậy\n",
    "ucl = sample_mean + t_score * standard_err\n",
    "lcl = sample_mean - t_score * standard_err\n",
    "\n",
    "print \"Tin cậy trên:\", ucl\n",
    "print \"Tin cậy dưới:\", lcl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# So sánh hai quần thể\n",
    "**Case study**\n",
    "- Sử dụng **aspirin** có giảm tình trạng đau tim hay không?\n",
    "- **Nam giới** khi lái xe thường gây ra tai nạn nhiều hơn **nữ giới** phải không?\n",
    "- Tình trạng an ninh ở **Hồ Chí Minh** có tốt hơn ở **Hà Nội**?\n",
    "- **Nam giới** thường có mức lương cao hơn **nữ giới** đúng không?\n",
    "- **Ăn kiêng** và không tập luyện hay **tập luyện 2h/ngày** nhưng được ăn đủ thứ thì cái nào giúp giảm béo tốt hơn?\n",
    "- Sử dụng **multi-vitamin** có giúp học sinh tiếp thu môn toán tốt hơn không?\n",
    "- Học **online** hay học qua **sách giáo khoa** cái nào tốt hơn?\n",
    "\n",
    "**Steps**\n",
    "1. Chia quần thể thành experiment và control group\n",
    "2. Chuẩn bị các thông số\n",
    "    - Tính standard error\n",
    "    - Tính khoảng tin cậy\n",
    "3. Thực hiện kiểm định giả thuyết\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Theo tỉ lệ\n",
    "$$SE = \\sqrt{\\frac{\\hat{p}_1(1 - \\hat{p_1})}{n_1} + \\frac{\\hat{p}_2(1 - \\hat{p_2})}{n_2}}$$\n",
    "$$UCL = (\\hat{p}_1 - \\hat{p}_1) + (Zscore * SE)$$\n",
    "$$LCL = (\\hat{p}_1 - \\hat{p}_1) - (Zscore * SE)$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Sử dụng aspirin có giảm tình trạng đau tim hay không?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 1. Chia quần thể thành experiment và control group\n",
    "group_aspirin = 2219\n",
    "group_control = 2035\n",
    "aspirin_heart_attacks = 26\n",
    "control_heart_attacks = 46"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tỉ lệ phần trăm bị đau tim khi sử dụng aspirin: 1.1716989635\n",
      "Tỉ lệ phần trăm bị đau tim khi không sử dụng aspirin: 2.26044226044\n",
      "Standard error giữa hai nhóm lấy mẫu: 0.00400938535831\n",
      "Differences from samples: 0.0108874329695\n",
      "UCL: 0.0187456838719\n",
      "LCL: 0.00302918206703\n"
     ]
    }
   ],
   "source": [
    "# 2. Chuẩn bị các thông số\n",
    "p_aspirin = aspirin_heart_attacks * 1.0 / group_aspirin\n",
    "p_control = control_heart_attacks * 1.0 / group_control\n",
    "print \"Tỉ lệ phần trăm bị đau tim khi sử dụng aspirin:\", p_aspirin * 100\n",
    "print \"Tỉ lệ phần trăm bị đau tim khi không sử dụng aspirin:\", p_control * 100\n",
    "\n",
    "# tính standard error\n",
    "SE = math.sqrt(abs(p_aspirin * (1 - p_aspirin) / group_aspirin + p_control * (1 - p_control) / group_control))\n",
    "print \"Standard error giữa hai nhóm lấy mẫu:\", SE\n",
    "\n",
    "# tính khoảng tin cậy\n",
    "diff_from_samples = abs(p_aspirin - p_control)\n",
    "critical_value = stats.norm.ppf(0.975)\n",
    "ucl = diff_from_samples + critical_value * SE\n",
    "lcl = diff_from_samples - critical_value * SE\n",
    "print \"Differences from samples:\", diff_from_samples\n",
    "print \"UCL:\", ucl\n",
    "print \"LCL:\", lcl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 3. Thực hiện kiểm định giả thuyết\n",
    "# H0: cho rằng tỉ lệ mắc bệnh tim không thay đỗi. Nghĩa là p1 - p0 = 0\n",
    "# H1: cho rằng tỉ lệ mắc bệnh tim sẽ giảm. Nghĩa là p1 - p0 != 0\n",
    "# Chọn significant level (alpha, mức ý nghĩa) = 5%\n",
    "# Ở đây là population nên ta sẽ chọn Z-statistic để test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$z = \\frac{(\\hat{p}_1 - \\hat{p}_2) - (p_1 - p_2)}{SE} = \\frac{\\hat{p}_1 - \\hat{p}_2}{SE} \\ (assuming \\ no \\ differences: p_1 = p_2)$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Z-score: 2.71548678824\n",
      "Critical value: 1.95996398454\n",
      "Reject H0 => Sử dụng aspirin làm giảm nguy cơ bị đau tim\n"
     ]
    }
   ],
   "source": [
    "z = diff_from_samples / SE\n",
    "print \"Z-score:\", z\n",
    "print \"Critical value:\", critical_value\n",
    "\n",
    "if z > critical_value:\n",
    "    print \"Reject H0 => Sử dụng aspirin làm giảm nguy cơ bị đau tim\"\n",
    "else:\n",
    "    print \"Reject Ha => Sử dụng aspirin không làm giảm nguy cơ bị đau tim\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Theo mean\n",
    "$$SE = \\sqrt{\\frac{\\sigma_1^2}{n_1} + \\frac{\\sigma_2^2}{n_2}}$$\n",
    "$$UCL = (\\hat{\\mu}_1 - \\hat{\\mu}_1) + (Zscore * SE)$$\n",
    "$$LCL = (\\hat{\\mu}_1 - \\hat{\\mu}_1) - (Zscore * SE)$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mô phỏng quá trình lấy mẫu ngẫu nhiên\n",
    "Học **online** hay học qua **sách giáo khoa** cái nào tốt hơn?\n",
    "- Chọn ngẫu nhiên 100 học sinh để học online\n",
    "- Chọn ngẫu nhiên 100 học sinh để học qua sách giáo khoa\n",
    "- Cho 2 nhóm này học cùng một kiến thức toán nhưng từ hai nguồn khác nhau (online, sách giáo khoa)\n",
    "- Làm bài kiểm tra toán trắc nghiệm để đánh giá 2 nhóm này."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c1709d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Kết quả làm kiểm tra toán từ 2 nhóm\n",
    "# A: học online\n",
    "# B: học sách giáo khoa\n",
    "# 30 câu trắc nghiệm, mỗi câu 1 điểm\n",
    "\n",
    "def do_the_test():\n",
    "    n = 100\n",
    "    \n",
    "    # cho từng nhóm làm kiểm tra\n",
    "    group_A_scores = np.asarray([np.random.randint(1, 30) for p in range(0, n)])\n",
    "    group_B_scores = np.asarray([np.random.randint(1, 30) for p in range(0, n)])\n",
    "\n",
    "    # tính số điểm median của hai nhóm\n",
    "    group_A_median_scores = np.median(group_A_scores)\n",
    "    group_B_median_scores = np.median(group_B_scores)\n",
    "    \n",
    "    # so sánh sự khác biệt điểm số\n",
    "    diff_AB = group_A_median_scores - group_B_median_scores\n",
    "    return diff_AB\n",
    "\n",
    "# Thực hiện kiểm tra nhiều lần\n",
    "median_diffs = []\n",
    "for i in range(0, 100):\n",
    "    median_diffs.append(do_the_test())\n",
    "\n",
    "# biểu diễn bằng histogram    \n",
    "plt.hist(median_diffs)\n",
    "plt.xlabel(\"Med diff\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference mean scores: 2.1\n"
     ]
    }
   ],
   "source": [
    "# 1. Chia quần thể thành digital và text group\n",
    "n_digital_group = 120\n",
    "n_text_group = 80\n",
    "\n",
    "digital_mean = 16.2\n",
    "text_mean = 14.1\n",
    "\n",
    "digital_std = 2.5\n",
    "text_std = 3.6\n",
    "\n",
    "diff_of_mean = digital_mean - text_mean\n",
    "print \"Difference mean scores:\", diff_of_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standard error giữa hai nhóm lấy mẫu: 0.462691401836\n",
      "UCL: 3.00685848356\n",
      "LCL: 1.19314151644\n"
     ]
    }
   ],
   "source": [
    "# 2. Chuẩn bị các thông số\n",
    "\n",
    "# tính standard error\n",
    "SE = math.sqrt(digital_std**2 / n_digital_group + text_std**2 / n_text_group)\n",
    "print \"Standard error giữa hai nhóm lấy mẫu:\", SE\n",
    "\n",
    "# tính khoảng tin cậy\n",
    "critical_value = stats.norm.ppf(0.975)\n",
    "ucl = diff_of_mean + critical_value * SE\n",
    "lcl = diff_of_mean - critical_value * SE\n",
    "print \"UCL:\", ucl\n",
    "print \"LCL:\", lcl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 3. Thực hiện kiểm định giả thuyết\n",
    "# H0: cho rằng học online không có gì khác học sách giáo khoa. Nghĩa là mean1 - mean2 = 0\n",
    "# H1: cho rằng học online tốt hơn học sách giáo khoa. Nghĩa là mean1 - mean2 > 0\n",
    "# Chọn significant level (alpha, mức ý nghĩa) = 1%\n",
    "# Ở đây là population nên ta sẽ chọn Z-statistic để test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$z = \\frac{(\\bar{x_1} - \\bar{x_2}) - (\\mu_1 - \\mu_2)}{SE} = \\frac{\\bar{x_1} - \\bar{x_2}}{SE} \\ (assuming \\ no \\ differences: \\mu_1 = \\mu_2)$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Z-score: 4.53866225235\n",
      "Critical value: 2.32634787404\n",
      "Reject H0 => học online tốt hơn học sách giáo khoa\n"
     ]
    }
   ],
   "source": [
    "z = diff_of_mean / SE\n",
    "print \"Z-score:\", z\n",
    "\n",
    "critical_value = stats.norm.ppf(0.99)\n",
    "print \"Critical value:\", critical_value\n",
    "\n",
    "if z > critical_value:\n",
    "    print \"Reject H0 => học online tốt hơn học sách giáo khoa\"\n",
    "else:\n",
    "    print \"Reject Ha => học online không có gì khác học sách giáo khoa\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chi-square\n",
    "Còn gọi là goodness-of-fit test: dùng để test các thống kê có phân hoạch theo chu kỳ hay nhóm\n",
    "$$X^2 = \\sum \\frac{(observed - expected)^2}{expected}$$\n",
    "**Bài toán: báo cáo lượt view trong tuần từ phía Facebook có khác Google không?**\n",
    "- H0: hai báo cáo là như nhau\n",
    "- Ha: hai báo cáo là khác nhau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi-square: 9.06333333333\n"
     ]
    }
   ],
   "source": [
    "# số lượt view trong tuần phía Facebook\n",
    "facebook_page_view_report = np.asarray([30, 50, 60, 60])\n",
    "\n",
    "# số lượt view trong tuần phía Google\n",
    "google_page_view_report = np.asarray([45, 48, 55, 52])\n",
    "\n",
    "# calculate Chi-square\n",
    "diff_observed = (google_page_view_report - facebook_page_view_report)**2\n",
    "facebook_inverse = 1.0 / facebook_page_view_report\n",
    "X2 = np.inner(diff_observed, facebook_inverse)\n",
    "print \"Chi-square:\", X2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Critical value: 7.81472790325\n",
      "Reject H0 => hai báo cáo là khác nhau\n"
     ]
    }
   ],
   "source": [
    "# compare with critical value\n",
    "k = facebook_page_view_report.size\n",
    "dof = k - 1\n",
    "critical_value = stats.chi2.ppf(0.95, dof)\n",
    "print \"Critical value:\", critical_value\n",
    "\n",
    "if X2 > critical_value:\n",
    "    print \"Reject H0 => hai báo cáo là khác nhau\"\n",
    "else:\n",
    "    print \"Reject Ha => hai báo cáo là như nhau\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ANOVA\n",
    "Giả định của ANOVA\n",
    "- Normal distribution\n",
    "- Independent observations\n",
    "- Equivalent variance\n",
    "\n",
    "SST: Total sum of squares\n",
    "$$SST = \\sum_{i=1}^n (y_i - \\bar{y})^2$$\n",
    "\n",
    "SSW: Sum of squares within\n",
    "$$SSW = \\sum_{i=1}^k \\sum_{j=1}^{n_i} (x_{ij} - \\bar{x}_i)^2$$\n",
    "\n",
    "SSB: Sum of squares between\n",
    "$$SSB = n \\sum_{i=1}^k \\sum_{j=1}^n (\\bar{x}_i - \\bar{x})^2$$\n",
    "\n",
    "f-statistics\n",
    "$$SST = SSW + SSB$$\n",
    "$$F-stat = \\frac{\\frac{SSB}{m - 1}}{\\frac{SSW}{n_t - m}}$$\n",
    "\n",
    "**Đánh giá các bộ phim có giống nhau?**\n",
    "- H0: đánh giá các bộ phim là như nhau\n",
    "- Ha: đánh giá các bộ phim là khác nhau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grand mean: 5.0\n",
      "Total sum of squares: 52.0\n"
     ]
    }
   ],
   "source": [
    "film_A = np.asarray([5, 3, 5, 3])\n",
    "film_B = np.asarray([8, 3, 4, 5])\n",
    "film_C = np.asarray([5, 6, 5, 8])\n",
    "film_D = np.asarray([4, 6, 8, 2])\n",
    "\n",
    "# SST: Total sum of squares\n",
    "mean_A = film_A.mean()\n",
    "mean_B = film_B.mean()\n",
    "mean_C = film_C.mean()\n",
    "mean_D = film_D.mean()\n",
    "\n",
    "mean_collect = np.asarray([mean_A, mean_B, mean_C, mean_D])\n",
    "grand_mean = mean_collect.mean()\n",
    "print \"Grand mean:\", grand_mean\n",
    "\n",
    "ss_A = np.inner(film_A - grand_mean, film_A - grand_mean)\n",
    "ss_B = np.inner(film_B - grand_mean, film_B - grand_mean)\n",
    "ss_C = np.inner(film_C - grand_mean, film_C - grand_mean)\n",
    "ss_D = np.inner(film_D - grand_mean, film_D - grand_mean)\n",
    "SST = ss_A + ss_B + ss_C + ss_D\n",
    "print \"Total sum of squares:\", SST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of squares within: 44.0\n"
     ]
    }
   ],
   "source": [
    "# SSW: Sum of squares within\n",
    "sw_A = np.inner(film_A - mean_A, film_A - mean_A)\n",
    "sw_B = np.inner(film_B - mean_B, film_B - mean_B)\n",
    "sw_C = np.inner(film_C - mean_C, film_C - mean_C)\n",
    "sw_D = np.inner(film_D - mean_D, film_D - mean_D)\n",
    "SSW = sw_A + sw_B + sw_C + sw_D\n",
    "print \"Sum of squares within:\", SSW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of squares between: 8.0\n"
     ]
    }
   ],
   "source": [
    "# SSB: Sum of squares between\n",
    "n = film_A.size\n",
    "SSB = n * np.inner(mean_collect - grand_mean, mean_collect - grand_mean)\n",
    "print \"Sum of squares between:\", SSB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F statistics: 0.727272727273\n",
      "Critical value: 3.4902948195\n",
      "Reject Ha => đánh giá các bộ phim là như nhau\n"
     ]
    }
   ],
   "source": [
    "n = film_A.size + film_B.size + film_C.size + film_D.size\n",
    "m = mean_collect.size\n",
    "f_stat = (SSB / (m - 1)) / (SSW / (n - m))\n",
    "print \"F statistics:\", f_stat\n",
    "\n",
    "# get critical value\n",
    "dfn = m - 1\n",
    "dfd = n - m\n",
    "critical_value = stats.f.ppf(0.95, dfn, dfd)\n",
    "print \"Critical value:\", critical_value\n",
    "\n",
    "if f_stat > critical_value:\n",
    "    print \"Reject H0 => đánh giá các bộ phim là khác nhau\"\n",
    "else:\n",
    "    print \"Reject Ha => đánh giá các bộ phim là như nhau\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pair plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jazcWo808GJSSZPWXu7Gu1Aj72DisvSPISo2FXheN+sveffwlU59kScxLpj7cvH5hwNon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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112264b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "sns.set(style=\"ticks\", color_codes=True)\n",
    "iris = sns.load_dataset(\"iris\")\n",
    "g = sns.pairplot(iris)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}