{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Module 7"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Question 1 \n",
"# On the same graph, plot the functions sin(t), cos(t), and tan−1 (t) where t is a vector from −10π to 10π with spacing 0.02. Label the x-axis and the y-axis “x-coordinate” and “ycoordinate” respectively and set the title of the graph to be “Some trigonometric functions”.\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"#creating a linear space\n",
"t = np.arange(-10 * np.pi , 10 * np.pi , 0.02)\n",
"x_sin = np.sin(t)\n",
"x_cos = np.cos(t)\n",
"x_tan = np.arctan(t)\n",
"\n",
"fig , ax = plt.subplots()\n",
"ax.plot(t , x_sin , label = \"$sin(t)$\")\n",
"ax.plot(t , x_cos , label = \"$cos(t)$\")\n",
"ax.plot(t , x_tan , label = \"$arctan(t)$\")\n",
"ax.set(xlabel = \"x-coordinate\",\n",
" ylabel = \"y-coordinate\",\n",
" title = \"Some trigonometric Functions\")\n",
"ax.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Question 2\n",
"# Set a random seed equal to “38472” and generate two arrays “x” and “y” with 15 entries each. Set the size of the marker “s” equal to 90. Plot two subplots with the defined data next to each other. Set the marker for the first plot to be a filled circle and a “plus sign” for the second one. Repeat the exercise above, but now set the sequence of colours for your scatter plot to be p x 2 + y2."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"np.random.seed(38472)\n",
"x = np.random.rand(15)\n",
"y = np.random.rand(15)\n",
"z = np.sqrt(x**2 + y**2)\n",
"\n",
"#first part\n",
"plt.subplot(121)\n",
"plt.scatter(x , y , s = 90 , marker = \"o\")\n",
"plt.subplot(122)\n",
"plt.scatter(x , y , s = 90 , marker = \"*\")\n",
"plt.show()\n",
"\n",
"#second part\n",
"plt.subplot(121)\n",
"plt.scatter(x , y , c = z , marker = \"o\")\n",
"plt.subplot(122)\n",
"plt.scatter(x , y , c = z , marker = \"*\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Question 3\n",
"# For this question, you will need to use the “seaborn” library. Load the seaborn dataset tips and visualize the first 5 rows of this dataset. Set the style of your plot to “ticks” and the colour palette to “pastel”. Display a nested boxplot to show the bills and the tips by day and time, and display whether the server was a smoker or not for each case. Repeat the same exercise, but this time, draw a set of horizontal bars."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"import seaborn as sns\n",
"sns.set(style = \"ticks\" , palette = \"pastel\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
"\n",
"\n",
" | \n",
"total_bill | \n",
"tip | \n",
"sex | \n",
"smoker | \n",
"day | \n",
"time | \n",
"size | \n",
"
\n",
"\n",
"\n",
"\n",
"0 | \n",
"16.99 | \n",
"1.01 | \n",
"Female | \n",
"No | \n",
"Sun | \n",
"Dinner | \n",
"2 | \n",
"
\n",
"\n",
"1 | \n",
"10.34 | \n",
"1.66 | \n",
"Male | \n",
"No | \n",
"Sun | \n",
"Dinner | \n",
"3 | \n",
"
\n",
"\n",
"2 | \n",
"21.01 | \n",
"3.50 | \n",
"Male | \n",
"No | \n",
"Sun | \n",
"Dinner | \n",
"3 | \n",
"
\n",
"\n",
"3 | \n",
"23.68 | \n",
"3.31 | \n",
"Male | \n",
"No | \n",
"Sun | \n",
"Dinner | \n",
"2 | \n",
"
\n",
"\n",
"4 | \n",
"24.59 | \n",
"3.61 | \n",
"Female | \n",
"No | \n",
"Sun | \n",
"Dinner | \n",
"4 | \n",
"
\n",
"\n",
"
\n",
"
"
],
"text/plain": [
" total_bill tip sex smoker day time size\n",
"0 16.99 1.01 Female No Sun Dinner 2\n",
"1 10.34 1.66 Male No Sun Dinner 3\n",
"2 21.01 3.50 Male No Sun Dinner 3\n",
"3 23.68 3.31 Male No Sun Dinner 2\n",
"4 24.59 3.61 Female No Sun Dinner 4"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#load the data\n",
"tips = sns.load_dataset(\"tips\")\n",
"tips.head()"
]
},
{
"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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\n",
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\n",
"text/plain": [
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"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#part2 \n",
"#horizontal bars\n",
"sns.barplot(x = \"total_bill\" , y = \"day\" , hue = \"smoker\" , data = tips)\n",
"plt.show()\n",
"sns.barplot(x = \"tip\" , y = \"day\" , hue = \"smoker\" , data = tips)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Question 4\n",
"# Load the seaborn dataset titanic and visualize the first 5 rows of this dataset. Set the style of your plot to “whitegrid”. Plot a scatter plot by passing the column names “fare” and “age” to the “x” and “y” axes, respectively. Make sure your plot shows the regression line."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
"\n",
"\n",
" | \n",
"survived | \n",
"pclass | \n",
"sex | \n",
"age | \n",
"sibsp | \n",
"parch | \n",
"fare | \n",
"embarked | \n",
"class | \n",
"who | \n",
"adult_male | \n",
"deck | \n",
"embark_town | \n",
"alive | \n",
"alone | \n",
"
\n",
"\n",
"\n",
"\n",
"0 | \n",
"0 | \n",
"3 | \n",
"male | \n",
"22.0 | \n",
"1 | \n",
"0 | \n",
"7.2500 | \n",
"S | \n",
"Third | \n",
"man | \n",
"True | \n",
"NaN | \n",
"Southampton | \n",
"no | \n",
"False | \n",
"
\n",
"\n",
"1 | \n",
"1 | \n",
"1 | \n",
"female | \n",
"38.0 | \n",
"1 | \n",
"0 | \n",
"71.2833 | \n",
"C | \n",
"First | \n",
"woman | \n",
"False | \n",
"C | \n",
"Cherbourg | \n",
"yes | \n",
"False | \n",
"
\n",
"\n",
"2 | \n",
"1 | \n",
"3 | \n",
"female | \n",
"26.0 | \n",
"0 | \n",
"0 | \n",
"7.9250 | \n",
"S | \n",
"Third | \n",
"woman | \n",
"False | \n",
"NaN | \n",
"Southampton | \n",
"yes | \n",
"True | \n",
"
\n",
"\n",
"3 | \n",
"1 | \n",
"1 | \n",
"female | \n",
"35.0 | \n",
"1 | \n",
"0 | \n",
"53.1000 | \n",
"S | \n",
"First | \n",
"woman | \n",
"False | \n",
"C | \n",
"Southampton | \n",
"yes | \n",
"False | \n",
"
\n",
"\n",
"4 | \n",
"0 | \n",
"3 | \n",
"male | \n",
"35.0 | \n",
"0 | \n",
"0 | \n",
"8.0500 | \n",
"S | \n",
"Third | \n",
"man | \n",
"True | \n",
"NaN | \n",
"Southampton | \n",
"no | \n",
"True | \n",
"
\n",
"\n",
"
\n",
"
"
],
"text/plain": [
" survived pclass sex age sibsp parch fare embarked class \\\n",
"0 0 3 male 22.0 1 0 7.2500 S Third \n",
"1 1 1 female 38.0 1 0 71.2833 C First \n",
"2 1 3 female 26.0 0 0 7.9250 S Third \n",
"3 1 1 female 35.0 1 0 53.1000 S First \n",
"4 0 3 male 35.0 0 0 8.0500 S Third \n",
"\n",
" who adult_male deck embark_town alive alone \n",
"0 man True NaN Southampton no False \n",
"1 woman False C Cherbourg yes False \n",
"2 woman False NaN Southampton yes True \n",
"3 woman False C Southampton yes False \n",
"4 man True NaN Southampton no True "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sns.set(style = \"whitegrid\")\n",
"\n",
"#load dataset\n",
"titanic = sns.load_dataset(\"titanic\")\n",
"titanic.head()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.regplot(x = \"fare\" , y = \"age\" , data = titanic)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Question 5\n",
"# Load the seaborn dataset iris and visualize the first 10 rows of this dataset. Set the style of your plot to “darkgrid” and your palette to “muted”. Use the pandas library to un-pivot the columns “species” in your dataset to “measurements”. Produce a swarmplot by passing the column names “measurements” and “value” to the “x” and “y” axes, respectively"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"sns.set(style = \"darkgrid\" , palette = \"muted\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
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"\n",
"\n",
" | \n",
"sepal_length | \n",
"sepal_width | \n",
"petal_length | \n",
"petal_width | \n",
"species | \n",
"
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"\n",
"\n",
"\n",
"0 | \n",
"5.1 | \n",
"3.5 | \n",
"1.4 | \n",
"0.2 | \n",
"setosa | \n",
"
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"\n",
"1 | \n",
"4.9 | \n",
"3.0 | \n",
"1.4 | \n",
"0.2 | \n",
"setosa | \n",
"
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"\n",
"2 | \n",
"4.7 | \n",
"3.2 | \n",
"1.3 | \n",
"0.2 | \n",
"setosa | \n",
"
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"\n",
"3 | \n",
"4.6 | \n",
"3.1 | \n",
"1.5 | \n",
"0.2 | \n",
"setosa | \n",
"
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"\n",
"4 | \n",
"5.0 | \n",
"3.6 | \n",
"1.4 | \n",
"0.2 | \n",
"setosa | \n",
"
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"\n",
"5 | \n",
"5.4 | \n",
"3.9 | \n",
"1.7 | \n",
"0.4 | \n",
"setosa | \n",
"
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"\n",
"6 | \n",
"4.6 | \n",
"3.4 | \n",
"1.4 | \n",
"0.3 | \n",
"setosa | \n",
"
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"\n",
"7 | \n",
"5.0 | \n",
"3.4 | \n",
"1.5 | \n",
"0.2 | \n",
"setosa | \n",
"
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"\n",
"8 | \n",
"4.4 | \n",
"2.9 | \n",
"1.4 | \n",
"0.2 | \n",
"setosa | \n",
"
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"\n",
"9 | \n",
"4.9 | \n",
"3.1 | \n",
"1.5 | \n",
"0.1 | \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\n",
"5 5.4 3.9 1.7 0.4 setosa\n",
"6 4.6 3.4 1.4 0.3 setosa\n",
"7 5.0 3.4 1.5 0.2 setosa\n",
"8 4.4 2.9 1.4 0.2 setosa\n",
"9 4.9 3.1 1.5 0.1 setosa"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iris = sns.load_dataset(\"iris\")\n",
"iris.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"iris = pd.melt(iris , \"species\" , var_name = \"measurement\")"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
"\n",
"\n",
" | \n",
"species | \n",
"measurement | \n",
"value | \n",
"
\n",
"\n",
"\n",
"\n",
"0 | \n",
"setosa | \n",
"sepal_length | \n",
"5.1 | \n",
"
\n",
"\n",
"1 | \n",
"setosa | \n",
"sepal_length | \n",
"4.9 | \n",
"
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"\n",
"2 | \n",
"setosa | \n",
"sepal_length | \n",
"4.7 | \n",
"
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"\n",
"3 | \n",
"setosa | \n",
"sepal_length | \n",
"4.6 | \n",
"
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"\n",
"4 | \n",
"setosa | \n",
"sepal_length | \n",
"5.0 | \n",
"
\n",
"\n",
"
\n",
"
"
],
"text/plain": [
" species measurement value\n",
"0 setosa sepal_length 5.1\n",
"1 setosa sepal_length 4.9\n",
"2 setosa sepal_length 4.7\n",
"3 setosa sepal_length 4.6\n",
"4 setosa sepal_length 5.0"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iris.head()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.swarmplot(x = \"measurement\" , y = \"value\" , data = iris)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Question 6\n",
"# Set a random seed equal to 2973 and define a random matrix “M” with dimension 10 × 15 and print it to screen. Produce two heatmaps of “M” using both the seaborn and the plotly library. Set the title on both plots to be “Matrix M”, the x-axis to be labeled “x values”and the y-axis to be labeled “y-values”.\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[0.0876024 0.41232239 0.87656989 0.56750629 0.93491933 0.09954808\n",
" 0.62852921 0.04798192 0.70626034 0.56597304 0.01237379 0.5432333\n",
" 0.29414638 0.41939833 0.79163008]\n",
" [0.59712153 0.21809528 0.77521831 0.25775332 0.75333644 0.49901496\n",
" 0.08928535 0.09728087 0.29050879 0.15664534 0.64741518 0.37813052\n",
" 0.06842041 0.86419701 0.08275132]\n",
" [0.738645 0.04756234 0.65284642 0.90054164 0.92731343 0.45818133\n",
" 0.82967829 0.69371545 0.07190966 0.66219733 0.50844901 0.44347898\n",
" 0.18137114 0.96835626 0.53027778]\n",
" [0.98690911 0.02342536 0.92391269 0.99403292 0.16137839 0.16983025\n",
" 0.8275861 0.24203479 0.06913303 0.03343447 0.96919964 0.40533429\n",
" 0.44641583 0.48417301 0.46467999]\n",
" [0.33978249 0.89383287 0.86490424 0.4238323 0.68331851 0.80420626\n",
" 0.00507141 0.6212552 0.2184031 0.63554857 0.22163575 0.23730916\n",
" 0.3780322 0.23424225 0.94709841]\n",
" [0.41934381 0.62052923 0.09147514 0.8524753 0.2811296 0.58956832\n",
" 0.11040354 0.49918421 0.49760294 0.58402194 0.64206802 0.2713312\n",
" 0.02030717 0.44088877 0.00214313]\n",
" [0.38433481 0.73196066 0.00264607 0.48197315 0.9275724 0.12486831\n",
" 0.24378882 0.34505733 0.89243826 0.53150586 0.6936207 0.2800514\n",
" 0.83489799 0.77063504 0.67220736]\n",
" [0.55764345 0.69180634 0.85312145 0.26707357 0.99981026 0.81290144\n",
" 0.21548192 0.53762304 0.49259547 0.57918545 0.26319786 0.24799732\n",
" 0.50596571 0.02357364 0.76147212]\n",
" [0.34152884 0.25799099 0.25989923 0.17945435 0.32194949 0.43154712\n",
" 0.89191554 0.06581954 0.06860743 0.97390071 0.60549639 0.91369244\n",
" 0.46274295 0.4419909 0.55900419]\n",
" [0.23139278 0.72173172 0.42278556 0.08767223 0.21940505 0.56890646\n",
" 0.4548838 0.49042464 0.67773125 0.20376524 0.50505733 0.68159045\n",
" 0.57288822 0.18958613 0.13755121]]\n"
]
}
],
"source": [
"np.random.seed(2973)\n",
"M = np.random.rand(10,15)\n",
"print(M)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = sns.heatmap(M).set_title(\"Matrix M\")\n",
"plt.xlabel(\"x values\")\n",
"plt.ylabel(\"y values\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"