From c3d8d14accb8e27296a9b58f3672ed7374f227c1 Mon Sep 17 00:00:00 2001
From: VIMAL KUMAR <90466387+vim12345@users.noreply.github.com>
Date: Sat, 12 Oct 2024 07:34:09 +0000
Subject: [PATCH] Initial commit
---
notebooks/05_01b.ipynb | 226 ++++++++++-
notebooks/05_02b.ipynb | 627 +++++++++++++++++++++++++++++-
notebooks/05_03b.ipynb | 416 +++++++++++++++++---
notebooks/05_04b.ipynb | 844 ++++++++++++++++++++++++++++++++++++++++-
notebooks/05_05b.ipynb | 321 +++++++++++++++-
notebooks/05_06b.ipynb | 403 +++++++++++++++++++-
notebooks/05_07b.ipynb | 186 ++++++++-
7 files changed, 2953 insertions(+), 70 deletions(-)
diff --git a/notebooks/05_01b.ipynb b/notebooks/05_01b.ipynb
index 3a9750d..751ab8e 100644
--- a/notebooks/05_01b.ipynb
+++ b/notebooks/05_01b.ipynb
@@ -7,6 +7,13 @@
"## Using NumPy to perform arithmetic operations on data"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
{
"cell_type": "code",
"execution_count": 1,
@@ -17,6 +24,15 @@
"from numpy.random import randn"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "np.set_printoptions(precision=2)"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -25,6 +41,56 @@
"### Creating arrays using a list"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([1, 2, 3, 4, 5, 6])"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "a = np.array([1,2,3,4,5,6])\n",
+ "a"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[10, 20, 30],\n",
+ " [40, 50, 60]])"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "b = np.array([[10,20,30],[40,50,60]])\n",
+ "b"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -32,6 +98,150 @@
"### Creating arrays via assignment"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([31.32, 20.96, 10.04, 6.69, 14.8 , 4.23])"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "np.random.seed(25)\n",
+ "c = 36*np.random.rand(6)\n",
+ "c"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+ " 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34])"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "d=np.arange(1,35)\n",
+ "d"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([10, 20, 30, 40, 50, 60])"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "a*10"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([32.32, 22.96, 13.04, 10.69, 19.8 , 10.23])"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "c+a"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([30.32, 18.96, 7.04, 2.69, 9.8 , -1.77])"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "c-a"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([31.32, 41.92, 30.11, 26.77, 74. , 25.35])"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "c*a"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([31.32, 10.48, 3.35, 1.67, 2.96, 0.7 ])"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "c/a"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {
@@ -40,6 +250,20 @@
"source": [
"### Multiplying matrices and basic linear algebra"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {
@@ -58,7 +282,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.8"
+ "version": "3.12.1"
}
},
"nbformat": 4,
diff --git a/notebooks/05_02b.ipynb b/notebooks/05_02b.ipynb
index 267f293..a519741 100644
--- a/notebooks/05_02b.ipynb
+++ b/notebooks/05_02b.ipynb
@@ -9,7 +9,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
@@ -23,14 +23,151 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 3,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " car_names | \n",
+ " mpg | \n",
+ " cyl | \n",
+ " disp | \n",
+ " hp | \n",
+ " drat | \n",
+ " wt | \n",
+ " qsec | \n",
+ " vs | \n",
+ " am | \n",
+ " gear | \n",
+ " carb | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " Mazda RX4 | \n",
+ " 21.0 | \n",
+ " 6 | \n",
+ " 160.0 | \n",
+ " 110 | \n",
+ " 3.90 | \n",
+ " 2.620 | \n",
+ " 16.46 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 4 | \n",
+ " 4 | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " Mazda RX4 Wag | \n",
+ " 21.0 | \n",
+ " 6 | \n",
+ " 160.0 | \n",
+ " 110 | \n",
+ " 3.90 | \n",
+ " 2.875 | \n",
+ " 17.02 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 4 | \n",
+ " 4 | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " Datsun 710 | \n",
+ " 22.8 | \n",
+ " 4 | \n",
+ " 108.0 | \n",
+ " 93 | \n",
+ " 3.85 | \n",
+ " 2.320 | \n",
+ " 18.61 | \n",
+ " 1 | \n",
+ " 1 | \n",
+ " 4 | \n",
+ " 1 | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " Hornet 4 Drive | \n",
+ " 21.4 | \n",
+ " 6 | \n",
+ " 258.0 | \n",
+ " 110 | \n",
+ " 3.08 | \n",
+ " 3.215 | \n",
+ " 19.44 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " 3 | \n",
+ " 1 | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " Hornet Sportabout | \n",
+ " 18.7 | \n",
+ " 8 | \n",
+ " 360.0 | \n",
+ " 175 | \n",
+ " 3.15 | \n",
+ " 3.440 | \n",
+ " 17.02 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 3 | \n",
+ " 2 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " car_names mpg cyl disp hp drat wt qsec vs am gear \\\n",
+ "0 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 \n",
+ "1 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 \n",
+ "2 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 \n",
+ "3 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 \n",
+ "4 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 \n",
+ "\n",
+ " carb \n",
+ "0 4 \n",
+ "1 4 \n",
+ "2 1 \n",
+ "3 1 \n",
+ "4 2 "
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"address = '/workspaces/python-for-data-science-and-machine-learning-essential-training-part-1-3006708/data/mtcars.csv'\n",
"\n",
"cars = pd.read_csv(address)\n",
- "cars.columns = ['car_names','mpg','cyl','disp', 'hp', 'drat', 'wt', 'qsec', 'vs', 'am', 'gear', 'carb']\n"
+ "cars.columns = ['car_names','mpg','cyl','disp', 'hp', 'drat', 'wt', 'qsec', 'vs', 'am', 'gear', 'carb']\n",
+ "cars.head()"
]
},
{
@@ -40,12 +177,492 @@
"### Looking at summary statistics that decribe a variable's numeric values"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "car_names Mazda RX4Mazda RX4 WagDatsun 710Hornet 4 Drive...\n",
+ "mpg 642.9\n",
+ "cyl 198\n",
+ "disp 7383.1\n",
+ "hp 4694\n",
+ "drat 115.09\n",
+ "wt 102.952\n",
+ "qsec 571.16\n",
+ "vs 14\n",
+ "am 13\n",
+ "gear 118\n",
+ "carb 90\n",
+ "dtype: object"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "cars.sum()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 328.980\n",
+ "1 329.795\n",
+ "2 259.580\n",
+ "3 426.135\n",
+ "4 590.310\n",
+ "5 385.540\n",
+ "6 656.920\n",
+ "7 270.980\n",
+ "8 299.570\n",
+ "9 350.460\n",
+ "10 349.660\n",
+ "11 510.740\n",
+ "12 511.500\n",
+ "13 509.850\n",
+ "14 728.560\n",
+ "15 726.644\n",
+ "16 725.695\n",
+ "17 213.850\n",
+ "18 195.165\n",
+ "19 206.955\n",
+ "20 273.775\n",
+ "21 519.650\n",
+ "22 506.085\n",
+ "23 646.280\n",
+ "24 631.175\n",
+ "25 208.215\n",
+ "26 272.570\n",
+ "27 273.683\n",
+ "28 670.690\n",
+ "29 379.590\n",
+ "30 694.710\n",
+ "31 288.890\n",
+ "dtype: float64"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "cars.sum(axis=1, numeric_only=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "mpg 19.200\n",
+ "cyl 6.000\n",
+ "disp 196.300\n",
+ "hp 123.000\n",
+ "drat 3.695\n",
+ "wt 3.325\n",
+ "qsec 17.710\n",
+ "vs 0.000\n",
+ "am 0.000\n",
+ "gear 4.000\n",
+ "carb 2.000\n",
+ "dtype: float64"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "cars.median(numeric_only=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "mpg 20.090625\n",
+ "cyl 6.187500\n",
+ "disp 230.721875\n",
+ "hp 146.687500\n",
+ "drat 3.596563\n",
+ "wt 3.217250\n",
+ "qsec 17.848750\n",
+ "vs 0.437500\n",
+ "am 0.406250\n",
+ "gear 3.687500\n",
+ "carb 2.812500\n",
+ "dtype: float64"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "cars.mean(numeric_only=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "car_names Volvo 142E\n",
+ "mpg 33.9\n",
+ "cyl 8\n",
+ "disp 472.0\n",
+ "hp 335\n",
+ "drat 4.93\n",
+ "wt 5.424\n",
+ "qsec 22.9\n",
+ "vs 1\n",
+ "am 1\n",
+ "gear 5\n",
+ "carb 8\n",
+ "dtype: object"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "cars.max()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "19"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mpg = cars.mpg\n",
+ "mpg.idxmax()"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Looking at summary statistics that describe variable distribution"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "mpg 6.026948\n",
+ "cyl 1.785922\n",
+ "disp 123.938694\n",
+ "hp 68.562868\n",
+ "drat 0.534679\n",
+ "wt 0.978457\n",
+ "qsec 1.786943\n",
+ "vs 0.504016\n",
+ "am 0.498991\n",
+ "gear 0.737804\n",
+ "carb 1.615200\n",
+ "dtype: float64"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "cars.std(numeric_only=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "mpg 36.324103\n",
+ "cyl 3.189516\n",
+ "disp 15360.799829\n",
+ "hp 4700.866935\n",
+ "drat 0.285881\n",
+ "wt 0.957379\n",
+ "qsec 3.193166\n",
+ "vs 0.254032\n",
+ "am 0.248992\n",
+ "gear 0.544355\n",
+ "carb 2.608871\n",
+ "dtype: float64"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "cars.var(numeric_only=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "gear\n",
+ "3 15\n",
+ "4 12\n",
+ "5 5\n",
+ "Name: count, dtype: int64"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "gear = cars.gear\n",
+ "gear.value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " mpg | \n",
+ " cyl | \n",
+ " disp | \n",
+ " hp | \n",
+ " drat | \n",
+ " wt | \n",
+ " qsec | \n",
+ " vs | \n",
+ " am | \n",
+ " gear | \n",
+ " carb | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " count | \n",
+ " 32.000000 | \n",
+ " 32.000000 | \n",
+ " 32.000000 | \n",
+ " 32.000000 | \n",
+ " 32.000000 | \n",
+ " 32.000000 | \n",
+ " 32.000000 | \n",
+ " 32.000000 | \n",
+ " 32.000000 | \n",
+ " 32.000000 | \n",
+ " 32.0000 | \n",
+ "
\n",
+ " \n",
+ " mean | \n",
+ " 20.090625 | \n",
+ " 6.187500 | \n",
+ " 230.721875 | \n",
+ " 146.687500 | \n",
+ " 3.596563 | \n",
+ " 3.217250 | \n",
+ " 17.848750 | \n",
+ " 0.437500 | \n",
+ " 0.406250 | \n",
+ " 3.687500 | \n",
+ " 2.8125 | \n",
+ "
\n",
+ " \n",
+ " std | \n",
+ " 6.026948 | \n",
+ " 1.785922 | \n",
+ " 123.938694 | \n",
+ " 68.562868 | \n",
+ " 0.534679 | \n",
+ " 0.978457 | \n",
+ " 1.786943 | \n",
+ " 0.504016 | \n",
+ " 0.498991 | \n",
+ " 0.737804 | \n",
+ " 1.6152 | \n",
+ "
\n",
+ " \n",
+ " min | \n",
+ " 10.400000 | \n",
+ " 4.000000 | \n",
+ " 71.100000 | \n",
+ " 52.000000 | \n",
+ " 2.760000 | \n",
+ " 1.513000 | \n",
+ " 14.500000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 3.000000 | \n",
+ " 1.0000 | \n",
+ "
\n",
+ " \n",
+ " 25% | \n",
+ " 15.425000 | \n",
+ " 4.000000 | \n",
+ " 120.825000 | \n",
+ " 96.500000 | \n",
+ " 3.080000 | \n",
+ " 2.581250 | \n",
+ " 16.892500 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 3.000000 | \n",
+ " 2.0000 | \n",
+ "
\n",
+ " \n",
+ " 50% | \n",
+ " 19.200000 | \n",
+ " 6.000000 | \n",
+ " 196.300000 | \n",
+ " 123.000000 | \n",
+ " 3.695000 | \n",
+ " 3.325000 | \n",
+ " 17.710000 | \n",
+ " 0.000000 | \n",
+ " 0.000000 | \n",
+ " 4.000000 | \n",
+ " 2.0000 | \n",
+ "
\n",
+ " \n",
+ " 75% | \n",
+ " 22.800000 | \n",
+ " 8.000000 | \n",
+ " 326.000000 | \n",
+ " 180.000000 | \n",
+ " 3.920000 | \n",
+ " 3.610000 | \n",
+ " 18.900000 | \n",
+ " 1.000000 | \n",
+ " 1.000000 | \n",
+ " 4.000000 | \n",
+ " 4.0000 | \n",
+ "
\n",
+ " \n",
+ " max | \n",
+ " 33.900000 | \n",
+ " 8.000000 | \n",
+ " 472.000000 | \n",
+ " 335.000000 | \n",
+ " 4.930000 | \n",
+ " 5.424000 | \n",
+ " 22.900000 | \n",
+ " 1.000000 | \n",
+ " 1.000000 | \n",
+ " 5.000000 | \n",
+ " 8.0000 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " mpg cyl disp hp drat wt \\\n",
+ "count 32.000000 32.000000 32.000000 32.000000 32.000000 32.000000 \n",
+ "mean 20.090625 6.187500 230.721875 146.687500 3.596563 3.217250 \n",
+ "std 6.026948 1.785922 123.938694 68.562868 0.534679 0.978457 \n",
+ "min 10.400000 4.000000 71.100000 52.000000 2.760000 1.513000 \n",
+ "25% 15.425000 4.000000 120.825000 96.500000 3.080000 2.581250 \n",
+ "50% 19.200000 6.000000 196.300000 123.000000 3.695000 3.325000 \n",
+ "75% 22.800000 8.000000 326.000000 180.000000 3.920000 3.610000 \n",
+ "max 33.900000 8.000000 472.000000 335.000000 4.930000 5.424000 \n",
+ "\n",
+ " qsec vs am gear carb \n",
+ "count 32.000000 32.000000 32.000000 32.000000 32.0000 \n",
+ "mean 17.848750 0.437500 0.406250 3.687500 2.8125 \n",
+ "std 1.786943 0.504016 0.498991 0.737804 1.6152 \n",
+ "min 14.500000 0.000000 0.000000 3.000000 1.0000 \n",
+ "25% 16.892500 0.000000 0.000000 3.000000 2.0000 \n",
+ "50% 17.710000 0.000000 0.000000 4.000000 2.0000 \n",
+ "75% 18.900000 1.000000 1.000000 4.000000 4.0000 \n",
+ "max 22.900000 1.000000 1.000000 5.000000 8.0000 "
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "cars.describe()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {
@@ -65,7 +682,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.8"
+ "version": "3.12.1"
}
},
"nbformat": 4,
diff --git a/notebooks/05_03b.ipynb b/notebooks/05_03b.ipynb
index 0606d70..726d42a 100644
--- a/notebooks/05_03b.ipynb
+++ b/notebooks/05_03b.ipynb
@@ -9,7 +9,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
@@ -307,46 +307,335 @@
" 3 | \n",
" 4 | \n",
" \n",
+ " \n",
+ " Lincoln Continental | \n",
+ " Lincoln Continental | \n",
+ " 10.4 | \n",
+ " 8 | \n",
+ " 460.0 | \n",
+ " 215 | \n",
+ " 3.00 | \n",
+ " 5.424 | \n",
+ " 17.82 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 3 | \n",
+ " 4 | \n",
+ "
\n",
+ " \n",
+ " Chrysler Imperial | \n",
+ " Chrysler Imperial | \n",
+ " 14.7 | \n",
+ " 8 | \n",
+ " 440.0 | \n",
+ " 230 | \n",
+ " 3.23 | \n",
+ " 5.345 | \n",
+ " 17.42 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 3 | \n",
+ " 4 | \n",
+ "
\n",
+ " \n",
+ " Fiat 128 | \n",
+ " Fiat 128 | \n",
+ " 32.4 | \n",
+ " 4 | \n",
+ " 78.7 | \n",
+ " 66 | \n",
+ " 4.08 | \n",
+ " 2.200 | \n",
+ " 19.47 | \n",
+ " 1 | \n",
+ " 1 | \n",
+ " 4 | \n",
+ " 1 | \n",
+ "
\n",
+ " \n",
+ " Honda Civic | \n",
+ " Honda Civic | \n",
+ " 30.4 | \n",
+ " 4 | \n",
+ " 75.7 | \n",
+ " 52 | \n",
+ " 4.93 | \n",
+ " 1.615 | \n",
+ " 18.52 | \n",
+ " 1 | \n",
+ " 1 | \n",
+ " 4 | \n",
+ " 2 | \n",
+ "
\n",
+ " \n",
+ " Toyota Corolla | \n",
+ " Toyota Corolla | \n",
+ " 33.9 | \n",
+ " 4 | \n",
+ " 71.1 | \n",
+ " 65 | \n",
+ " 4.22 | \n",
+ " 1.835 | \n",
+ " 19.90 | \n",
+ " 1 | \n",
+ " 1 | \n",
+ " 4 | \n",
+ " 1 | \n",
+ "
\n",
+ " \n",
+ " Toyota Corona | \n",
+ " Toyota Corona | \n",
+ " 21.5 | \n",
+ " 4 | \n",
+ " 120.1 | \n",
+ " 97 | \n",
+ " 3.70 | \n",
+ " 2.465 | \n",
+ " 20.01 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " 3 | \n",
+ " 1 | \n",
+ "
\n",
+ " \n",
+ " Dodge Challenger | \n",
+ " Dodge Challenger | \n",
+ " 15.5 | \n",
+ " 8 | \n",
+ " 318.0 | \n",
+ " 150 | \n",
+ " 2.76 | \n",
+ " 3.520 | \n",
+ " 16.87 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 3 | \n",
+ " 2 | \n",
+ "
\n",
+ " \n",
+ " AMC Javelin | \n",
+ " AMC Javelin | \n",
+ " 15.2 | \n",
+ " 8 | \n",
+ " 304.0 | \n",
+ " 150 | \n",
+ " 3.15 | \n",
+ " 3.435 | \n",
+ " 17.30 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 3 | \n",
+ " 2 | \n",
+ "
\n",
+ " \n",
+ " Camaro Z28 | \n",
+ " Camaro Z28 | \n",
+ " 13.3 | \n",
+ " 8 | \n",
+ " 350.0 | \n",
+ " 245 | \n",
+ " 3.73 | \n",
+ " 3.840 | \n",
+ " 15.41 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 3 | \n",
+ " 4 | \n",
+ "
\n",
+ " \n",
+ " Pontiac Firebird | \n",
+ " Pontiac Firebird | \n",
+ " 19.2 | \n",
+ " 8 | \n",
+ " 400.0 | \n",
+ " 175 | \n",
+ " 3.08 | \n",
+ " 3.845 | \n",
+ " 17.05 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 3 | \n",
+ " 2 | \n",
+ "
\n",
+ " \n",
+ " Fiat X1-9 | \n",
+ " Fiat X1-9 | \n",
+ " 27.3 | \n",
+ " 4 | \n",
+ " 79.0 | \n",
+ " 66 | \n",
+ " 4.08 | \n",
+ " 1.935 | \n",
+ " 18.90 | \n",
+ " 1 | \n",
+ " 1 | \n",
+ " 4 | \n",
+ " 1 | \n",
+ "
\n",
+ " \n",
+ " Porsche 914-2 | \n",
+ " Porsche 914-2 | \n",
+ " 26.0 | \n",
+ " 4 | \n",
+ " 120.3 | \n",
+ " 91 | \n",
+ " 4.43 | \n",
+ " 2.140 | \n",
+ " 16.70 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 5 | \n",
+ " 2 | \n",
+ "
\n",
+ " \n",
+ " Lotus Europa | \n",
+ " Lotus Europa | \n",
+ " 30.4 | \n",
+ " 4 | \n",
+ " 95.1 | \n",
+ " 113 | \n",
+ " 3.77 | \n",
+ " 1.513 | \n",
+ " 16.90 | \n",
+ " 1 | \n",
+ " 1 | \n",
+ " 5 | \n",
+ " 2 | \n",
+ "
\n",
+ " \n",
+ " Ford Pantera L | \n",
+ " Ford Pantera L | \n",
+ " 15.8 | \n",
+ " 8 | \n",
+ " 351.0 | \n",
+ " 264 | \n",
+ " 4.22 | \n",
+ " 3.170 | \n",
+ " 14.50 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 5 | \n",
+ " 4 | \n",
+ "
\n",
+ " \n",
+ " Ferrari Dino | \n",
+ " Ferrari Dino | \n",
+ " 19.7 | \n",
+ " 6 | \n",
+ " 145.0 | \n",
+ " 175 | \n",
+ " 3.62 | \n",
+ " 2.770 | \n",
+ " 15.50 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 5 | \n",
+ " 6 | \n",
+ "
\n",
+ " \n",
+ " Maserati Bora | \n",
+ " Maserati Bora | \n",
+ " 15.0 | \n",
+ " 8 | \n",
+ " 301.0 | \n",
+ " 335 | \n",
+ " 3.54 | \n",
+ " 3.570 | \n",
+ " 14.60 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 5 | \n",
+ " 8 | \n",
+ "
\n",
+ " \n",
+ " Volvo 142E | \n",
+ " Volvo 142E | \n",
+ " 21.4 | \n",
+ " 4 | \n",
+ " 121.0 | \n",
+ " 109 | \n",
+ " 4.11 | \n",
+ " 2.780 | \n",
+ " 18.60 | \n",
+ " 1 | \n",
+ " 1 | \n",
+ " 4 | \n",
+ " 2 | \n",
+ "
\n",
" \n",
"\n",
""
],
"text/plain": [
- " car_names mpg cyl disp hp drat wt \\\n",
- "car_names \n",
- "Mazda RX4 Mazda RX4 21.0 6 160.0 110 3.90 2.620 \n",
- "Mazda RX4 Wag Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 \n",
- "Datsun 710 Datsun 710 22.8 4 108.0 93 3.85 2.320 \n",
- "Hornet 4 Drive Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 \n",
- "Hornet Sportabout Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 \n",
- "Valiant Valiant 18.1 6 225.0 105 2.76 3.460 \n",
- "Duster 360 Duster 360 14.3 8 360.0 245 3.21 3.570 \n",
- "Merc 240D Merc 240D 24.4 4 146.7 62 3.69 3.190 \n",
- "Merc 230 Merc 230 22.8 4 140.8 95 3.92 3.150 \n",
- "Merc 280 Merc 280 19.2 6 167.6 123 3.92 3.440 \n",
- "Merc 280C Merc 280C 17.8 6 167.6 123 3.92 3.440 \n",
- "Merc 450SE Merc 450SE 16.4 8 275.8 180 3.07 4.070 \n",
- "Merc 450SL Merc 450SL 17.3 8 275.8 180 3.07 3.730 \n",
- "Merc 450SLC Merc 450SLC 15.2 8 275.8 180 3.07 3.780 \n",
- "Cadillac Fleetwood Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 \n",
+ " car_names mpg cyl disp hp drat wt \\\n",
+ "car_names \n",
+ "Mazda RX4 Mazda RX4 21.0 6 160.0 110 3.90 2.620 \n",
+ "Mazda RX4 Wag Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 \n",
+ "Datsun 710 Datsun 710 22.8 4 108.0 93 3.85 2.320 \n",
+ "Hornet 4 Drive Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 \n",
+ "Hornet Sportabout Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 \n",
+ "Valiant Valiant 18.1 6 225.0 105 2.76 3.460 \n",
+ "Duster 360 Duster 360 14.3 8 360.0 245 3.21 3.570 \n",
+ "Merc 240D Merc 240D 24.4 4 146.7 62 3.69 3.190 \n",
+ "Merc 230 Merc 230 22.8 4 140.8 95 3.92 3.150 \n",
+ "Merc 280 Merc 280 19.2 6 167.6 123 3.92 3.440 \n",
+ "Merc 280C Merc 280C 17.8 6 167.6 123 3.92 3.440 \n",
+ "Merc 450SE Merc 450SE 16.4 8 275.8 180 3.07 4.070 \n",
+ "Merc 450SL Merc 450SL 17.3 8 275.8 180 3.07 3.730 \n",
+ "Merc 450SLC Merc 450SLC 15.2 8 275.8 180 3.07 3.780 \n",
+ "Cadillac Fleetwood Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 \n",
+ "Lincoln Continental Lincoln Continental 10.4 8 460.0 215 3.00 5.424 \n",
+ "Chrysler Imperial Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 \n",
+ "Fiat 128 Fiat 128 32.4 4 78.7 66 4.08 2.200 \n",
+ "Honda Civic Honda Civic 30.4 4 75.7 52 4.93 1.615 \n",
+ "Toyota Corolla Toyota Corolla 33.9 4 71.1 65 4.22 1.835 \n",
+ "Toyota Corona Toyota Corona 21.5 4 120.1 97 3.70 2.465 \n",
+ "Dodge Challenger Dodge Challenger 15.5 8 318.0 150 2.76 3.520 \n",
+ "AMC Javelin AMC Javelin 15.2 8 304.0 150 3.15 3.435 \n",
+ "Camaro Z28 Camaro Z28 13.3 8 350.0 245 3.73 3.840 \n",
+ "Pontiac Firebird Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 \n",
+ "Fiat X1-9 Fiat X1-9 27.3 4 79.0 66 4.08 1.935 \n",
+ "Porsche 914-2 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 \n",
+ "Lotus Europa Lotus Europa 30.4 4 95.1 113 3.77 1.513 \n",
+ "Ford Pantera L Ford Pantera L 15.8 8 351.0 264 4.22 3.170 \n",
+ "Ferrari Dino Ferrari Dino 19.7 6 145.0 175 3.62 2.770 \n",
+ "Maserati Bora Maserati Bora 15.0 8 301.0 335 3.54 3.570 \n",
+ "Volvo 142E Volvo 142E 21.4 4 121.0 109 4.11 2.780 \n",
"\n",
- " qsec vs am gear carb \n",
- "car_names \n",
- "Mazda RX4 16.46 0 1 4 4 \n",
- "Mazda RX4 Wag 17.02 0 1 4 4 \n",
- "Datsun 710 18.61 1 1 4 1 \n",
- "Hornet 4 Drive 19.44 1 0 3 1 \n",
- "Hornet Sportabout 17.02 0 0 3 2 \n",
- "Valiant 20.22 1 0 3 1 \n",
- "Duster 360 15.84 0 0 3 4 \n",
- "Merc 240D 20.00 1 0 4 2 \n",
- "Merc 230 22.90 1 0 4 2 \n",
- "Merc 280 18.30 1 0 4 4 \n",
- "Merc 280C 18.90 1 0 4 4 \n",
- "Merc 450SE 17.40 0 0 3 3 \n",
- "Merc 450SL 17.60 0 0 3 3 \n",
- "Merc 450SLC 18.00 0 0 3 3 \n",
- "Cadillac Fleetwood 17.98 0 0 3 4 "
+ " qsec vs am gear carb \n",
+ "car_names \n",
+ "Mazda RX4 16.46 0 1 4 4 \n",
+ "Mazda RX4 Wag 17.02 0 1 4 4 \n",
+ "Datsun 710 18.61 1 1 4 1 \n",
+ "Hornet 4 Drive 19.44 1 0 3 1 \n",
+ "Hornet Sportabout 17.02 0 0 3 2 \n",
+ "Valiant 20.22 1 0 3 1 \n",
+ "Duster 360 15.84 0 0 3 4 \n",
+ "Merc 240D 20.00 1 0 4 2 \n",
+ "Merc 230 22.90 1 0 4 2 \n",
+ "Merc 280 18.30 1 0 4 4 \n",
+ "Merc 280C 18.90 1 0 4 4 \n",
+ "Merc 450SE 17.40 0 0 3 3 \n",
+ "Merc 450SL 17.60 0 0 3 3 \n",
+ "Merc 450SLC 18.00 0 0 3 3 \n",
+ "Cadillac Fleetwood 17.98 0 0 3 4 \n",
+ "Lincoln Continental 17.82 0 0 3 4 \n",
+ "Chrysler Imperial 17.42 0 0 3 4 \n",
+ "Fiat 128 19.47 1 1 4 1 \n",
+ "Honda Civic 18.52 1 1 4 2 \n",
+ "Toyota Corolla 19.90 1 1 4 1 \n",
+ "Toyota Corona 20.01 1 0 3 1 \n",
+ "Dodge Challenger 16.87 0 0 3 2 \n",
+ "AMC Javelin 17.30 0 0 3 2 \n",
+ "Camaro Z28 15.41 0 0 3 4 \n",
+ "Pontiac Firebird 17.05 0 0 3 2 \n",
+ "Fiat X1-9 18.90 1 1 4 1 \n",
+ "Porsche 914-2 16.70 0 1 5 2 \n",
+ "Lotus Europa 16.90 1 1 5 2 \n",
+ "Ford Pantera L 14.50 0 1 5 4 \n",
+ "Ferrari Dino 15.50 0 1 5 6 \n",
+ "Maserati Bora 14.60 0 1 5 8 \n",
+ "Volvo 142E 18.60 1 1 4 2 "
]
},
"execution_count": 3,
@@ -359,7 +648,8 @@
"\n",
"cars = pd.read_csv(address)\n",
"cars.columns = ['car_names','mpg','cyl','disp', 'hp', 'drat', 'wt', 'qsec', 'vs', 'am', 'gear', 'carb']\n",
- "cars.index = cars.car_names"
+ "cars.index = cars.car_names\n",
+ "cars"
]
},
{
@@ -370,13 +660,14 @@
{
"data": {
"text/plain": [
+ "carb\n",
"4 10\n",
"2 10\n",
"1 7\n",
"3 3\n",
- "8 1\n",
"6 1\n",
- "Name: carb, dtype: int64"
+ "8 1\n",
+ "Name: count, dtype: int64"
]
},
"execution_count": 4,
@@ -384,7 +675,10 @@
"output_type": "execute_result"
}
],
- "source": []
+ "source": [
+ "carb = cars.carb\n",
+ "carb.value_counts()"
+ ]
},
{
"cell_type": "code",
@@ -489,11 +783,14 @@
"output_type": "execute_result"
}
],
- "source": []
+ "source": [
+ "cars_cat = cars[['cyl','vs','am','gear','carb']]\n",
+ "cars_cat.head()"
+ ]
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {},
"outputs": [
{
@@ -677,7 +974,10 @@
"output_type": "execute_result"
}
],
- "source": []
+ "source": [
+ "gear_group = cars_cat.groupby('gear')\n",
+ "gear_group.describe()"
+ ]
},
{
"cell_type": "markdown",
@@ -691,7 +991,9 @@
"execution_count": 7,
"metadata": {},
"outputs": [],
- "source": []
+ "source": [
+ "cars['group'] = pd.Series(cars.gear, dtype='category')"
+ ]
},
{
"cell_type": "code",
@@ -701,7 +1003,7 @@
{
"data": {
"text/plain": [
- "CategoricalDtype(categories=[3, 4, 5], ordered=False)"
+ "CategoricalDtype(categories=[3, 4, 5], ordered=False, categories_dtype=int64)"
]
},
"execution_count": 8,
@@ -709,7 +1011,9 @@
"output_type": "execute_result"
}
],
- "source": []
+ "source": [
+ "cars['group'].dtypes"
+ ]
},
{
"cell_type": "code",
@@ -719,10 +1023,11 @@
{
"data": {
"text/plain": [
+ "group\n",
"3 15\n",
"4 12\n",
"5 5\n",
- "Name: group, dtype: int64"
+ "Name: count, dtype: int64"
]
},
"execution_count": 9,
@@ -730,7 +1035,9 @@
"output_type": "execute_result"
}
],
- "source": []
+ "source": [
+ "cars['group'].value_counts()"
+ ]
},
{
"cell_type": "markdown",
@@ -741,7 +1048,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
@@ -800,11 +1107,20 @@
"1 0 8 5"
]
},
- "execution_count": 10,
+ "execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
+ "source": [
+ "pd.crosstab(cars['am'], cars['gear'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": []
}
],
@@ -825,7 +1141,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.3"
+ "version": "3.12.1"
}
},
"nbformat": 4,
diff --git a/notebooks/05_04b.ipynb b/notebooks/05_04b.ipynb
index 68dbc04..0d6e49f 100644
--- a/notebooks/05_04b.ipynb
+++ b/notebooks/05_04b.ipynb
@@ -44,14 +44,671 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 3,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " car_names | \n",
+ " mpg | \n",
+ " cyl | \n",
+ " disp | \n",
+ " hp | \n",
+ " drat | \n",
+ " wt | \n",
+ " qsec | \n",
+ " vs | \n",
+ " am | \n",
+ " gear | \n",
+ " carb | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " Mazda RX4 | \n",
+ " 21.0 | \n",
+ " 6 | \n",
+ " 160.0 | \n",
+ " 110 | \n",
+ " 3.90 | \n",
+ " 2.620 | \n",
+ " 16.46 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 4 | \n",
+ " 4 | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " Mazda RX4 Wag | \n",
+ " 21.0 | \n",
+ " 6 | \n",
+ " 160.0 | \n",
+ " 110 | \n",
+ " 3.90 | \n",
+ " 2.875 | \n",
+ " 17.02 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 4 | \n",
+ " 4 | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " Datsun 710 | \n",
+ " 22.8 | \n",
+ " 4 | \n",
+ " 108.0 | \n",
+ " 93 | \n",
+ " 3.85 | \n",
+ " 2.320 | \n",
+ " 18.61 | \n",
+ " 1 | \n",
+ " 1 | \n",
+ " 4 | \n",
+ " 1 | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " Hornet 4 Drive | \n",
+ " 21.4 | \n",
+ " 6 | \n",
+ " 258.0 | \n",
+ " 110 | \n",
+ " 3.08 | \n",
+ " 3.215 | \n",
+ " 19.44 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " 3 | \n",
+ " 1 | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " Hornet Sportabout | \n",
+ " 18.7 | \n",
+ " 8 | \n",
+ " 360.0 | \n",
+ " 175 | \n",
+ " 3.15 | \n",
+ " 3.440 | \n",
+ " 17.02 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 3 | \n",
+ " 2 | \n",
+ "
\n",
+ " \n",
+ " 5 | \n",
+ " Valiant | \n",
+ " 18.1 | \n",
+ " 6 | \n",
+ " 225.0 | \n",
+ " 105 | \n",
+ " 2.76 | \n",
+ " 3.460 | \n",
+ " 20.22 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " 3 | \n",
+ " 1 | \n",
+ "
\n",
+ " \n",
+ " 6 | \n",
+ " Duster 360 | \n",
+ " 14.3 | \n",
+ " 8 | \n",
+ " 360.0 | \n",
+ " 245 | \n",
+ " 3.21 | \n",
+ " 3.570 | \n",
+ " 15.84 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 3 | \n",
+ " 4 | \n",
+ "
\n",
+ " \n",
+ " 7 | \n",
+ " Merc 240D | \n",
+ " 24.4 | \n",
+ " 4 | \n",
+ " 146.7 | \n",
+ " 62 | \n",
+ " 3.69 | \n",
+ " 3.190 | \n",
+ " 20.00 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " 4 | \n",
+ " 2 | \n",
+ "
\n",
+ " \n",
+ " 8 | \n",
+ " Merc 230 | \n",
+ " 22.8 | \n",
+ " 4 | \n",
+ " 140.8 | \n",
+ " 95 | \n",
+ " 3.92 | \n",
+ " 3.150 | \n",
+ " 22.90 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " 4 | \n",
+ " 2 | \n",
+ "
\n",
+ " \n",
+ " 9 | \n",
+ " Merc 280 | \n",
+ " 19.2 | \n",
+ " 6 | \n",
+ " 167.6 | \n",
+ " 123 | \n",
+ " 3.92 | \n",
+ " 3.440 | \n",
+ " 18.30 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " 4 | \n",
+ " 4 | \n",
+ "
\n",
+ " \n",
+ " 10 | \n",
+ " Merc 280C | \n",
+ " 17.8 | \n",
+ " 6 | \n",
+ " 167.6 | \n",
+ " 123 | \n",
+ " 3.92 | \n",
+ " 3.440 | \n",
+ " 18.90 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " 4 | \n",
+ " 4 | \n",
+ "
\n",
+ " \n",
+ " 11 | \n",
+ " Merc 450SE | \n",
+ " 16.4 | \n",
+ " 8 | \n",
+ " 275.8 | \n",
+ " 180 | \n",
+ " 3.07 | \n",
+ " 4.070 | \n",
+ " 17.40 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 3 | \n",
+ " 3 | \n",
+ "
\n",
+ " \n",
+ " 12 | \n",
+ " Merc 450SL | \n",
+ " 17.3 | \n",
+ " 8 | \n",
+ " 275.8 | \n",
+ " 180 | \n",
+ " 3.07 | \n",
+ " 3.730 | \n",
+ " 17.60 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 3 | \n",
+ " 3 | \n",
+ "
\n",
+ " \n",
+ " 13 | \n",
+ " Merc 450SLC | \n",
+ " 15.2 | \n",
+ " 8 | \n",
+ " 275.8 | \n",
+ " 180 | \n",
+ " 3.07 | \n",
+ " 3.780 | \n",
+ " 18.00 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 3 | \n",
+ " 3 | \n",
+ "
\n",
+ " \n",
+ " 14 | \n",
+ " Cadillac Fleetwood | \n",
+ " 10.4 | \n",
+ " 8 | \n",
+ " 472.0 | \n",
+ " 205 | \n",
+ " 2.93 | \n",
+ " 5.250 | \n",
+ " 17.98 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 3 | \n",
+ " 4 | \n",
+ "
\n",
+ " \n",
+ " 15 | \n",
+ " Lincoln Continental | \n",
+ " 10.4 | \n",
+ " 8 | \n",
+ " 460.0 | \n",
+ " 215 | \n",
+ " 3.00 | \n",
+ " 5.424 | \n",
+ " 17.82 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 3 | \n",
+ " 4 | \n",
+ "
\n",
+ " \n",
+ " 16 | \n",
+ " Chrysler Imperial | \n",
+ " 14.7 | \n",
+ " 8 | \n",
+ " 440.0 | \n",
+ " 230 | \n",
+ " 3.23 | \n",
+ " 5.345 | \n",
+ " 17.42 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 3 | \n",
+ " 4 | \n",
+ "
\n",
+ " \n",
+ " 17 | \n",
+ " Fiat 128 | \n",
+ " 32.4 | \n",
+ " 4 | \n",
+ " 78.7 | \n",
+ " 66 | \n",
+ " 4.08 | \n",
+ " 2.200 | \n",
+ " 19.47 | \n",
+ " 1 | \n",
+ " 1 | \n",
+ " 4 | \n",
+ " 1 | \n",
+ "
\n",
+ " \n",
+ " 18 | \n",
+ " Honda Civic | \n",
+ " 30.4 | \n",
+ " 4 | \n",
+ " 75.7 | \n",
+ " 52 | \n",
+ " 4.93 | \n",
+ " 1.615 | \n",
+ " 18.52 | \n",
+ " 1 | \n",
+ " 1 | \n",
+ " 4 | \n",
+ " 2 | \n",
+ "
\n",
+ " \n",
+ " 19 | \n",
+ " Toyota Corolla | \n",
+ " 33.9 | \n",
+ " 4 | \n",
+ " 71.1 | \n",
+ " 65 | \n",
+ " 4.22 | \n",
+ " 1.835 | \n",
+ " 19.90 | \n",
+ " 1 | \n",
+ " 1 | \n",
+ " 4 | \n",
+ " 1 | \n",
+ "
\n",
+ " \n",
+ " 20 | \n",
+ " Toyota Corona | \n",
+ " 21.5 | \n",
+ " 4 | \n",
+ " 120.1 | \n",
+ " 97 | \n",
+ " 3.70 | \n",
+ " 2.465 | \n",
+ " 20.01 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " 3 | \n",
+ " 1 | \n",
+ "
\n",
+ " \n",
+ " 21 | \n",
+ " Dodge Challenger | \n",
+ " 15.5 | \n",
+ " 8 | \n",
+ " 318.0 | \n",
+ " 150 | \n",
+ " 2.76 | \n",
+ " 3.520 | \n",
+ " 16.87 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 3 | \n",
+ " 2 | \n",
+ "
\n",
+ " \n",
+ " 22 | \n",
+ " AMC Javelin | \n",
+ " 15.2 | \n",
+ " 8 | \n",
+ " 304.0 | \n",
+ " 150 | \n",
+ " 3.15 | \n",
+ " 3.435 | \n",
+ " 17.30 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 3 | \n",
+ " 2 | \n",
+ "
\n",
+ " \n",
+ " 23 | \n",
+ " Camaro Z28 | \n",
+ " 13.3 | \n",
+ " 8 | \n",
+ " 350.0 | \n",
+ " 245 | \n",
+ " 3.73 | \n",
+ " 3.840 | \n",
+ " 15.41 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 3 | \n",
+ " 4 | \n",
+ "
\n",
+ " \n",
+ " 24 | \n",
+ " Pontiac Firebird | \n",
+ " 19.2 | \n",
+ " 8 | \n",
+ " 400.0 | \n",
+ " 175 | \n",
+ " 3.08 | \n",
+ " 3.845 | \n",
+ " 17.05 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 3 | \n",
+ " 2 | \n",
+ "
\n",
+ " \n",
+ " 25 | \n",
+ " Fiat X1-9 | \n",
+ " 27.3 | \n",
+ " 4 | \n",
+ " 79.0 | \n",
+ " 66 | \n",
+ " 4.08 | \n",
+ " 1.935 | \n",
+ " 18.90 | \n",
+ " 1 | \n",
+ " 1 | \n",
+ " 4 | \n",
+ " 1 | \n",
+ "
\n",
+ " \n",
+ " 26 | \n",
+ " Porsche 914-2 | \n",
+ " 26.0 | \n",
+ " 4 | \n",
+ " 120.3 | \n",
+ " 91 | \n",
+ " 4.43 | \n",
+ " 2.140 | \n",
+ " 16.70 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 5 | \n",
+ " 2 | \n",
+ "
\n",
+ " \n",
+ " 27 | \n",
+ " Lotus Europa | \n",
+ " 30.4 | \n",
+ " 4 | \n",
+ " 95.1 | \n",
+ " 113 | \n",
+ " 3.77 | \n",
+ " 1.513 | \n",
+ " 16.90 | \n",
+ " 1 | \n",
+ " 1 | \n",
+ " 5 | \n",
+ " 2 | \n",
+ "
\n",
+ " \n",
+ " 28 | \n",
+ " Ford Pantera L | \n",
+ " 15.8 | \n",
+ " 8 | \n",
+ " 351.0 | \n",
+ " 264 | \n",
+ " 4.22 | \n",
+ " 3.170 | \n",
+ " 14.50 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 5 | \n",
+ " 4 | \n",
+ "
\n",
+ " \n",
+ " 29 | \n",
+ " Ferrari Dino | \n",
+ " 19.7 | \n",
+ " 6 | \n",
+ " 145.0 | \n",
+ " 175 | \n",
+ " 3.62 | \n",
+ " 2.770 | \n",
+ " 15.50 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 5 | \n",
+ " 6 | \n",
+ "
\n",
+ " \n",
+ " 30 | \n",
+ " Maserati Bora | \n",
+ " 15.0 | \n",
+ " 8 | \n",
+ " 301.0 | \n",
+ " 335 | \n",
+ " 3.54 | \n",
+ " 3.570 | \n",
+ " 14.60 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 5 | \n",
+ " 8 | \n",
+ "
\n",
+ " \n",
+ " 31 | \n",
+ " Volvo 142E | \n",
+ " 21.4 | \n",
+ " 4 | \n",
+ " 121.0 | \n",
+ " 109 | \n",
+ " 4.11 | \n",
+ " 2.780 | \n",
+ " 18.60 | \n",
+ " 1 | \n",
+ " 1 | \n",
+ " 4 | \n",
+ " 2 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " car_names mpg cyl disp hp drat wt qsec vs am \\\n",
+ "0 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 \n",
+ "1 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 \n",
+ "2 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 \n",
+ "3 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 \n",
+ "4 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 \n",
+ "5 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 \n",
+ "6 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 \n",
+ "7 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 \n",
+ "8 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 \n",
+ "9 Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 \n",
+ "10 Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 \n",
+ "11 Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 \n",
+ "12 Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 \n",
+ "13 Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 \n",
+ "14 Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 \n",
+ "15 Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 \n",
+ "16 Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 \n",
+ "17 Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 \n",
+ "18 Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 \n",
+ "19 Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 \n",
+ "20 Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 \n",
+ "21 Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 \n",
+ "22 AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 \n",
+ "23 Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 \n",
+ "24 Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 \n",
+ "25 Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 \n",
+ "26 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 \n",
+ "27 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 \n",
+ "28 Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 \n",
+ "29 Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 \n",
+ "30 Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 \n",
+ "31 Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 \n",
+ "\n",
+ " gear carb \n",
+ "0 4 4 \n",
+ "1 4 4 \n",
+ "2 4 1 \n",
+ "3 3 1 \n",
+ "4 3 2 \n",
+ "5 3 1 \n",
+ "6 3 4 \n",
+ "7 4 2 \n",
+ "8 4 2 \n",
+ "9 4 4 \n",
+ "10 4 4 \n",
+ "11 3 3 \n",
+ "12 3 3 \n",
+ "13 3 3 \n",
+ "14 3 4 \n",
+ "15 3 4 \n",
+ "16 3 4 \n",
+ "17 4 1 \n",
+ "18 4 2 \n",
+ "19 4 1 \n",
+ "20 3 1 \n",
+ "21 3 2 \n",
+ "22 3 2 \n",
+ "23 3 4 \n",
+ "24 3 2 \n",
+ "25 4 1 \n",
+ "26 5 2 \n",
+ "27 5 2 \n",
+ "28 5 4 \n",
+ "29 5 6 \n",
+ "30 5 8 \n",
+ "31 4 2 "
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"address = '/workspaces/python-for-data-science-and-machine-learning-essential-training-part-1-3006708/data/mtcars.csv'\n",
"\n",
"cars = pd.read_csv(address)\n",
- "cars.columns = ['car_names','mpg','cyl','disp', 'hp', 'drat', 'wt', 'qsec', 'vs', 'am', 'gear', 'carb']\n"
+ "cars.columns = ['car_names','mpg','cyl','disp', 'hp', 'drat', 'wt', 'qsec', 'vs', 'am', 'gear', 'carb']\n",
+ "cars"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.pairplot(cars)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "x = cars[['mpg','hp','qsec','wt']]\n",
+ "sns.pairplot(x)"
]
},
{
@@ -61,12 +718,191 @@
"### Using scipy to calculate the Pearson correlation coefficient"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "PearsonR Correlation Coefficient -0.776\n"
+ ]
+ }
+ ],
+ "source": [
+ "mpg = cars['mpg']\n",
+ "hp = cars['hp']\n",
+ "qsec = cars['qsec']\n",
+ "wt = cars['wt']\n",
+ "\n",
+ "pearsonr_coefficient, p_value = pearsonr(mpg,hp)\n",
+ "print('PearsonR Correlation Coefficient %0.3f' % (pearsonr_coefficient))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "PearsonR Correlation Coefficient 0.419\n"
+ ]
+ }
+ ],
+ "source": [
+ "pearsonr_coefficient, p_value = pearsonr(mpg,qsec)\n",
+ "print('PearsonR Correlation Coefficient %0.3f' % (pearsonr_coefficient))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "PearsonR Correlation Coefficient -0.868\n"
+ ]
+ }
+ ],
+ "source": [
+ "pearsonr_coefficient, p_value = pearsonr(mpg,wt)\n",
+ "print('PearsonR Correlation Coefficient %0.3f' % (pearsonr_coefficient))"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Using Seaborn to visualize the Pearson correlation coefficient"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " mpg | \n",
+ " hp | \n",
+ " qsec | \n",
+ " wt | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " mpg | \n",
+ " 1.000000 | \n",
+ " -0.776168 | \n",
+ " 0.418684 | \n",
+ " -0.867659 | \n",
+ "
\n",
+ " \n",
+ " hp | \n",
+ " -0.776168 | \n",
+ " 1.000000 | \n",
+ " -0.708223 | \n",
+ " 0.658748 | \n",
+ "
\n",
+ " \n",
+ " qsec | \n",
+ " 0.418684 | \n",
+ " -0.708223 | \n",
+ " 1.000000 | \n",
+ " -0.174716 | \n",
+ "
\n",
+ " \n",
+ " wt | \n",
+ " -0.867659 | \n",
+ " 0.658748 | \n",
+ " -0.174716 | \n",
+ " 1.000000 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " mpg hp qsec wt\n",
+ "mpg 1.000000 -0.776168 0.418684 -0.867659\n",
+ "hp -0.776168 1.000000 -0.708223 0.658748\n",
+ "qsec 0.418684 -0.708223 1.000000 -0.174716\n",
+ "wt -0.867659 0.658748 -0.174716 1.000000"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "corr = x.corr()\n",
+ "corr"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.heatmap(corr, xticklabels=corr.columns.values, yticklabels=corr.columns.values)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {
@@ -86,7 +922,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.8"
+ "version": "3.12.1"
}
},
"nbformat": 4,
diff --git a/notebooks/05_05b.ipynb b/notebooks/05_05b.ipynb
index 72426dd..a37eb7f 100644
--- a/notebooks/05_05b.ipynb
+++ b/notebooks/05_05b.ipynb
@@ -44,9 +44,145 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 3,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " car_names | \n",
+ " mpg | \n",
+ " cyl | \n",
+ " disp | \n",
+ " hp | \n",
+ " drat | \n",
+ " wt | \n",
+ " qsec | \n",
+ " vs | \n",
+ " am | \n",
+ " gear | \n",
+ " carb | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " Mazda RX4 | \n",
+ " 21.0 | \n",
+ " 6 | \n",
+ " 160.0 | \n",
+ " 110 | \n",
+ " 3.90 | \n",
+ " 2.620 | \n",
+ " 16.46 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 4 | \n",
+ " 4 | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " Mazda RX4 Wag | \n",
+ " 21.0 | \n",
+ " 6 | \n",
+ " 160.0 | \n",
+ " 110 | \n",
+ " 3.90 | \n",
+ " 2.875 | \n",
+ " 17.02 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 4 | \n",
+ " 4 | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " Datsun 710 | \n",
+ " 22.8 | \n",
+ " 4 | \n",
+ " 108.0 | \n",
+ " 93 | \n",
+ " 3.85 | \n",
+ " 2.320 | \n",
+ " 18.61 | \n",
+ " 1 | \n",
+ " 1 | \n",
+ " 4 | \n",
+ " 1 | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " Hornet 4 Drive | \n",
+ " 21.4 | \n",
+ " 6 | \n",
+ " 258.0 | \n",
+ " 110 | \n",
+ " 3.08 | \n",
+ " 3.215 | \n",
+ " 19.44 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " 3 | \n",
+ " 1 | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " Hornet Sportabout | \n",
+ " 18.7 | \n",
+ " 8 | \n",
+ " 360.0 | \n",
+ " 175 | \n",
+ " 3.15 | \n",
+ " 3.440 | \n",
+ " 17.02 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 3 | \n",
+ " 2 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " car_names mpg cyl disp hp drat wt qsec vs am gear \\\n",
+ "0 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 \n",
+ "1 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 \n",
+ "2 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 \n",
+ "3 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 \n",
+ "4 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 \n",
+ "\n",
+ " carb \n",
+ "0 4 \n",
+ "1 4 \n",
+ "2 1 \n",
+ "3 1 \n",
+ "4 2 "
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"address = '/workspaces/python-for-data-science-and-machine-learning-essential-training-part-1-3006708/data/mtcars.csv'\n",
"\n",
@@ -55,12 +191,191 @@
"cars.head()"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.pairplot(cars)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "x = cars[['cyl','vs','am','gear']]\n",
+ "sns.pairplot(x)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "spearmanr correlation coeffient -0.814\n"
+ ]
+ }
+ ],
+ "source": [
+ "cyl = cars['cyl']\n",
+ "vs = cars['vs']\n",
+ "am = cars['am']\n",
+ "gear = cars['gear']\n",
+ "spearmanr_coefficient , p_value = spearmanr(cyl,vs)\n",
+ "print('spearmanr correlation coeffient %0.3f' % (spearmanr_coefficient))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "spearmanr correlation coeffient -0.564\n"
+ ]
+ }
+ ],
+ "source": [
+ "spearmanr_coefficient , p_value = spearmanr(cyl,gear)\n",
+ "print('spearmanr correlation coeffient %0.3f' % (spearmanr_coefficient))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "spearmanr correlation coeffient -0.522\n"
+ ]
+ }
+ ],
+ "source": [
+ "spearmanr_coefficient , p_value = spearmanr(cyl,am)\n",
+ "print('spearmanr correlation coeffient %0.3f' % (spearmanr_coefficient))"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Chi-square test for independence"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "chi_square Statistic 8.741, p_value 0.013\n"
+ ]
+ }
+ ],
+ "source": [
+ "table = pd.crosstab(cyl, am)\n",
+ "from scipy.stats import chi2_contingency\n",
+ "chi2, p, dof, expected = chi2_contingency(table.values)\n",
+ "print('chi_square Statistic % 0.3f, p_value %0.3f' % (chi2,p))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "chi_square Statistic 21.340, p_value 0.000\n"
+ ]
+ }
+ ],
+ "source": [
+ "table = pd.crosstab(cyl, vs)\n",
+ "from scipy.stats import chi2_contingency\n",
+ "chi2, p, dof, expected = chi2_contingency(table.values)\n",
+ "print('chi_square Statistic % 0.3f, p_value %0.3f' % (chi2,p))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "chi_square Statistic 18.036, p_value 0.001\n"
+ ]
+ }
+ ],
+ "source": [
+ "table = pd.crosstab(cyl, gear)\n",
+ "from scipy.stats import chi2_contingency\n",
+ "chi2, p, dof, expected = chi2_contingency(table.values)\n",
+ "print('chi_square Statistic % 0.3f, p_value %0.3f' % (chi2,p))"
+ ]
}
],
"metadata": {
@@ -79,7 +394,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.8"
+ "version": "3.12.1"
}
},
"nbformat": 4,
diff --git a/notebooks/05_06b.ipynb b/notebooks/05_06b.ipynb
index 875a7ac..cb4d584 100644
--- a/notebooks/05_06b.ipynb
+++ b/notebooks/05_06b.ipynb
@@ -30,6 +30,107 @@
"rcParams['figure.figsize'] = 5,4"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Sepal length | \n",
+ " Sepal Width | \n",
+ " Petal Length | \n",
+ " Petal Width | \n",
+ " Species | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " 5.1 | \n",
+ " 3.5 | \n",
+ " 1.4 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " 4.9 | \n",
+ " 3.0 | \n",
+ " 1.4 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " 4.7 | \n",
+ " 3.2 | \n",
+ " 1.3 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " 4.6 | \n",
+ " 3.1 | \n",
+ " 1.5 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " 5.0 | \n",
+ " 3.6 | \n",
+ " 1.4 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Sepal length Sepal Width Petal Length Petal Width Species\n",
+ "0 5.1 3.5 1.4 0.2 setosa\n",
+ "1 4.9 3.0 1.4 0.2 setosa\n",
+ "2 4.7 3.2 1.3 0.2 setosa\n",
+ "3 4.6 3.1 1.5 0.2 setosa\n",
+ "4 5.0 3.6 1.4 0.2 setosa"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "address = '/workspaces/python-for-data-science-and-machine-learning-essential-training-part-1-3006708/data/iris.data.csv'\n",
+ "df = pd.read_csv(filepath_or_buffer=address, header=None, sep=',')\n",
+ "df.columns = ['Sepal length','Sepal Width','Petal Length','Petal Width ', 'Species']\n",
+ "x = df.iloc[:,0:4].values\n",
+ "y = df.iloc[:,4].values\n",
+ "df[:5]"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -37,12 +138,312 @@
"### Identifying outliers from Tukey boxplots"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "df.boxplot(return_type='dict')\n",
+ "plt.plot()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Sepal length | \n",
+ " Sepal Width | \n",
+ " Petal Length | \n",
+ " Petal Width | \n",
+ " Species | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 15 | \n",
+ " 5.7 | \n",
+ " 4.4 | \n",
+ " 1.5 | \n",
+ " 0.4 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " 32 | \n",
+ " 5.2 | \n",
+ " 4.1 | \n",
+ " 1.5 | \n",
+ " 0.1 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " 33 | \n",
+ " 5.5 | \n",
+ " 4.2 | \n",
+ " 1.4 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Sepal length Sepal Width Petal Length Petal Width Species\n",
+ "15 5.7 4.4 1.5 0.4 setosa\n",
+ "32 5.2 4.1 1.5 0.1 setosa\n",
+ "33 5.5 4.2 1.4 0.2 setosa"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Sepal_width = x[:,1]\n",
+ "iris_outliers = (Sepal_width>4)\n",
+ "df[iris_outliers]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Sepal length | \n",
+ " Sepal Width | \n",
+ " Petal Length | \n",
+ " Petal Width | \n",
+ " Species | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " 5.1 | \n",
+ " 3.5 | \n",
+ " 1.4 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " 4.9 | \n",
+ " 3.0 | \n",
+ " 1.4 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " 4.7 | \n",
+ " 3.2 | \n",
+ " 1.3 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " 4.6 | \n",
+ " 3.1 | \n",
+ " 1.5 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " 5.0 | \n",
+ " 3.6 | \n",
+ " 1.4 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ "
\n",
+ " \n",
+ " 145 | \n",
+ " 6.7 | \n",
+ " 3.0 | \n",
+ " 5.2 | \n",
+ " 2.3 | \n",
+ " virginica | \n",
+ "
\n",
+ " \n",
+ " 146 | \n",
+ " 6.3 | \n",
+ " 2.5 | \n",
+ " 5.0 | \n",
+ " 1.9 | \n",
+ " virginica | \n",
+ "
\n",
+ " \n",
+ " 147 | \n",
+ " 6.5 | \n",
+ " 3.0 | \n",
+ " 5.2 | \n",
+ " 2.0 | \n",
+ " virginica | \n",
+ "
\n",
+ " \n",
+ " 148 | \n",
+ " 6.2 | \n",
+ " 3.4 | \n",
+ " 5.4 | \n",
+ " 2.3 | \n",
+ " virginica | \n",
+ "
\n",
+ " \n",
+ " 149 | \n",
+ " 5.9 | \n",
+ " 3.0 | \n",
+ " 5.1 | \n",
+ " 1.8 | \n",
+ " virginica | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
149 rows × 5 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Sepal length Sepal Width Petal Length Petal Width Species\n",
+ "0 5.1 3.5 1.4 0.2 setosa\n",
+ "1 4.9 3.0 1.4 0.2 setosa\n",
+ "2 4.7 3.2 1.3 0.2 setosa\n",
+ "3 4.6 3.1 1.5 0.2 setosa\n",
+ "4 5.0 3.6 1.4 0.2 setosa\n",
+ ".. ... ... ... ... ...\n",
+ "145 6.7 3.0 5.2 2.3 virginica\n",
+ "146 6.3 2.5 5.0 1.9 virginica\n",
+ "147 6.5 3.0 5.2 2.0 virginica\n",
+ "148 6.2 3.4 5.4 2.3 virginica\n",
+ "149 5.9 3.0 5.1 1.8 virginica\n",
+ "\n",
+ "[149 rows x 5 columns]"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Sepal_width = x[:,1]\n",
+ "iris_outliers = (Sepal_width>2.05)\n",
+ "df[iris_outliers]"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Applying Tukey outlier labeling"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " 0 1 2 3\n",
+ "count 150.0 150.0 150.0 150.0\n",
+ "mean 5.8 3.1 3.8 1.2\n",
+ "std 0.8 0.4 1.8 0.8\n",
+ "min 4.3 2.0 1.0 0.1\n",
+ "25% 5.1 2.8 1.6 0.3\n",
+ "50% 5.8 3.0 4.3 1.3\n",
+ "75% 6.4 3.3 5.1 1.8\n",
+ "max 7.9 4.4 6.9 2.5\n"
+ ]
+ }
+ ],
+ "source": [
+ "pd.options.display.float_format = '{:.1f}'.format\n",
+ "X_df = pd.DataFrame(x)\n",
+ "print(X_df.describe())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {
@@ -61,7 +462,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.8"
+ "version": "3.12.1"
}
},
"nbformat": 4,
diff --git a/notebooks/05_07b.ipynb b/notebooks/05_07b.ipynb
index a92f8cf..6f07dfa 100644
--- a/notebooks/05_07b.ipynb
+++ b/notebooks/05_07b.ipynb
@@ -40,9 +40,96 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 3,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Sepal Length | \n",
+ " Sepal Width | \n",
+ " Petal Length | \n",
+ " Petal Width | \n",
+ " Species | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " 5.1 | \n",
+ " 3.5 | \n",
+ " 1.4 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " 4.9 | \n",
+ " 3.0 | \n",
+ " 1.4 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " 4.7 | \n",
+ " 3.2 | \n",
+ " 1.3 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " 4.6 | \n",
+ " 3.1 | \n",
+ " 1.5 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " 5.0 | \n",
+ " 3.6 | \n",
+ " 1.4 | \n",
+ " 0.2 | \n",
+ " setosa | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Sepal Length Sepal Width Petal Length Petal Width Species\n",
+ "0 5.1 3.5 1.4 0.2 setosa\n",
+ "1 4.9 3.0 1.4 0.2 setosa\n",
+ "2 4.7 3.2 1.3 0.2 setosa\n",
+ "3 4.6 3.1 1.5 0.2 setosa\n",
+ "4 5.0 3.6 1.4 0.2 setosa"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"address = '/workspaces/python-for-data-science-and-machine-learning-essential-training-part-1-3006708/data/iris.data.csv'\n",
"df= pd.read_csv(filepath_or_buffer=address, header=None, sep=',')\n",
@@ -55,10 +142,33 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 4,
"metadata": {},
- "outputs": [],
- "source": []
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "