175 lines
58 KiB
Plaintext
175 lines
58 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Начало лабораборной\n",
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"\n",
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"Выгрузка данных из csv файла в датафрейм"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Index(['gender', 'race/ethnicity', 'parental level of education', 'lunch',\n",
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" 'test preparation course', 'math score', 'reading score',\n",
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" 'writing score'],\n",
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" dtype='object')\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"\n",
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"df = pd.read_csv(\"..//..//static//csv//StudentsPerformance.csv\")\n",
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"print (df.columns)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Данная гистограмма в диапазоне с 10 по 51 строки отображает:\n",
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"На оси X значения оценок по математике, разбитые на 100 интервалов.\n",
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"На оси Y будет указано количество записей (частота) в каждом из этих интервалов. \n",
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"Анализируя гистограмму \"math score\", можно сделать выводы о том, как распределяются оценки.\n",
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"Например, оценку 70 имеет 4 человека, а оценку 18 всего 1 человек из этого диапазона."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<Axes: ylabel='Frequency'>"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"\n",
|
||
"df.iloc[10:51].plot.hist(column=[\"math score\"], bins=100)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Данная гистограмма отображает прцоентное соотношение мужчин и женщин.\n",
|
||
"Что позволяет сделать вывод о том, что женщин среди студентов больше, чем мужчин. "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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6UbHyDWLSBxHdJbM570haSW5ZLbfOX8uc607A6XSYjmM5GlNoggq3h+mvrKFey2i2mYbSveQ8O4Xcv/+Korf+SkNFYbNeL7rbANy7v8VTkgtAfeEO3DlZxPQ5DoCoTr1oKM2loaKQhvJCGkpyierYE09pHlXrPyRpzORmvydpPcu3FvHUR9tMx7AkjSk0wQ3/XsV/NxaYjhE2arevwudxE5ncDW9VCeWfvUpD1T66Tn0WZ/SPyzW6s9dR8Oo9Rzam4PdRtuwlKla+ETgU5fORdOpk2p80KfiYym/eoWLVYgDaHX8hicPPpWDeH0kcMTEwJvHZK+CMIHnCr4lJH9w6b16azOGAWdeewLj+qaajWIoOHx2lmZ/uUCG0sdhjjv/xRmpvorv2I+f5qVRvXkHi0DOb9Jo1mz6lOmspHc+/k8hOPakv2EHpRy/gSkghYch4ABKHn0vi8HODz6la/xGOqFiiu/Un94Ub6TLlCbyV+yh+8zG63fAvHBGRzXqf0rL8frht/lrevmU06cla6/lI6fDRUVi9u1ST3IUAZ0wCkcndaCjb2+TXKF06m/YnXkb8wNOI6tSLhMGnk3jChZR/+fpBH++tKaf8s1dInnAjdXu3EpnclcjkbsT0PBa/twFPaW6Ts0jrKa/1cNPLq3Wo9yioFI5QaXU9t7yiKSxCga++loayPFzxTV+Fy++pA0fjf/4OhxP8B994lH48k8QTLiKiXUfwe/F797ty3ecFnzY6oWpDbgVPfKB10Y+USuEI/WnRBvaWa0ZGE0o//hfu7PU0lBfgztlE0YIZ4HASP/A0ALxVpdQX7MBTGrhwqb5oF/UFO/DW/nhBYcG8e6hY/VbwdmzGSMo/n0/N9q9pKC+gZuvnVHy9iLjMkw74/rU7v8FTkkviiPMAiErLpKEkh9rtq6hc+x44XUQkd2vNH4E00z+Xb2fljn2mY1iCBpqPwOK1udw6b63pGGGraPGj1OVsxFtbgSu2PdHdB5J06hQiO3QBDn5xG0DKubeRMGQCADnPTyVhyHiSRl8NgK+uhrJP51Kz7Qt8NeW4EpKJG3AaSadcgcP149iAz1NH3pz/odMFvyeqc5/g/ZXf/peyT/+NwxVJ8pm/CZ7aKqGrW1Is7902hsQYjf0cikrhMAor3Jz51HLKajymo4hIM10yohtPTBpmOkZI0+Gjw/j9G+tUCCI2sWBNLu+u1/xIh6JSOIR5X2XzyZYi0zFEpAXdvXA9hZUaH/w5KoWfsaekhoeWbDIdQ0RaWFmNhwffyjIdI2SpFH7GHxdtoErL/InY0tvr8vhkS/OmSrErlcJBLFmXx/KtOmwkYmf3LtpAbb1WSvwplcJPVNc18L9va9dSxO5ySmt56kNd1PZTKoWfePKDreRXaBBKJBz8a8VOsvZWmI4RUlQK+9mUV8Gcz3eZjiEibaTB5+fuhevx+XS51g9UCt/z+/3cu2gDDfrHIRJWvt1Txn/W5JiOETJUCt9bsCaXVbtLTccQEQMef38LNfU62xBUCgC4PV4ef3+L6RgiYkhBRR3/XL7DdIyQoFIA5ny+SzOgioS5fy7fQaFOMlEplNXU89wn35mOISKG1dR7efx9naIa9qXw7CffUeHWsUQRgddX72FTXnifohrWpZBTWsOLX+w2HUNEQoTPDw+/G95L7oZ1KTzx/lat3SoijSzfWsSa7PA9EzFsS2F7URWL1mqxdRE50DMfbTMdwZiwLYXnl25H16mJyMEs3VLEupwy0zGMCMtSyCmtYdE32ksQkZ8XrnsLYVkK/1y+Q9NZiMghfbipkI17y03HaHNhVwpFlXXM/3qP6RgiYgHhuLcQdqUwc8UO6nTGkYgcgfezCviusNJ0jDYVVqVQXuvh5S+zTccQEYvw++FfK3aZjtGmwqoUXl+1R+sui8hRWfhNDqXV9aZjtJmwKQW/38/cL3X1sogcHbfHx8srw2fbETalsGxrEbv21ZiOISIWNPfLbBq84TEWGTal8G/NcSQiTZRf4eb9rALTMdpEWJTCnpIaPtlSaDqGiFjYi2GyfntYlMLclbs1pYWINMvKnSVsL6oyHaPV2b4U6hq8vKaL1USkBbyxOsd0hFZn+1L4aFMhpTUe0zFExAYWfpOLz+aHHWxfCgs18Z2ItJC8cjefb99nOkarsnUplNXUs2xLkekYImIjb6yx9yEkW5fC2+vyqA+Tc4tFpG28tyHf1jMj2LoUFmtlNRFpYbUeL++syzMdo9XYthT2lNSwanf4rrMqIq3Hzkv52rYU3vx2L357nyQgIoZ8tbOEshp7TpJn21J4Z719d+9ExKwGn5+PNtlzlgRblkJuWS0b91aYjiEiNvbfjfmmI7QKW5bC+zb9ZYlI6Ph0WzFuj9d0jBZny1L4cFN4zGYoIubUerws22q/66BsVwqVbg9f7SwxHUNEwoAdDyHZrhRWbCvG49VpRyLS+j7ZXGi7uZBsVwofbbbnGQEiEnpKazxk5dnrpBbblcKKbcWmI4hIGPnsO3ttc2xVCrv3VZNf4TYdQ0TCiN1mTbVVKXy5w16/HBEJfV/vKsFjo4k3bVUKK3forCMRaVs19V7W7ikzHaPF2KsUdCqqiBhgp3EF25TCnpIacstqTccQkTD0hY3GFWxTChpPEBFT1uWU47XJ9Qq2KYVVu7R2goiYUevxsq2w0nSMFmGbUlifW246goiEsXV77LENskUp1Df4bNPSImJN3+aUmY7QImxRClvyKzXfkYgYtS5HewohY+Nee/wyRMS6NudXUNdg/fUVbFEKG1QKImKYx+tnU571D2PboxRy7TVLoYhY02YbzJhq+VLw+fxszrf+L0JErG97UZXpCM1m+VLILavF7bHPZFQiYl07iqpNR2g2y5fCrn3W/yWIiD3sKLb+9sgGpVBjOoKICBCYg62+wdpHLqxfCjZoZhGxhwafn+wSa2+TLF8Ku3X4SERCyHaLjytYvhR0+EhEQslOix+9sHQp+Hx+sktUCiISOvLLrb1OvKVLobiqzvKDOiJiL4WVKgVjCivrTEcQEWmkoMLa2yVLl0JxlbV/+CJiPwUV2lMwpkh7CiISYqx+BMPSpVBcVW86gohII/UNPkqqrbttsngpWLuRRcSerHwISaUgItLCymo8piM0maVLYZ8OH4lICKquazAdocksXQpVFv7Bi4h9Vddbd9tk6VKorbf+eqgiYj9W/oPV2qXgUSmISOjR4SNDVAoiEoqq3CoFI9w6fCQiIaiqzrrbJkuXgvYURCQU1Xq0p9DmPF4fDT6/6RgiIgdo8Fp322TZUrDyD11E7M3Kf69athSclk0uInbn91u3FSJMB2gql8NhOoJYjp++8W4GJVSSGVNOr8gyujr20dFfTPv6QiK81p2vRkJLfeKZwDDTMZrEuqXgVClIY91i6hiSWEW/2HJ6R5XRzVFCqr+YJE8hce58IqrycHjroJzAh0griU0fZjpCk1m2FBwOBw4HWHgvTY5Ch8gGjk2sZGB8Bb2jykh3ltKZwAY/oa6AyOq9OOqroZLAh4hJTpfpBE1m2VKAwCGkBrWC5cW7fAxOrGJAXAUZMeWku0pIYx/J3iIS6wqIqs7D6S6FGgIfIqHOad1Nq3WTA06nw9rD/GEg0ulnQEI1A+Mr6RtTTk9XCV0d+0j2FtOuvoCYmjwcNcU43H7QIX2xC5WCGRFOB5o82xyHw0/fODeDEwIb/F6RpXR17qOjt5j2nkJiavNxVeXjqPeiX5SEFZWCGfHREdRoqotW0z2mjiGJlfSLraB3ZBndXCV08mngVuSwNKZgRruYCIosvki2KSlRHoYkVDEgvoI+kaWku0pJ9RfToaGQeHcBkdV5ODwauBVpksg40wmazNqlEBtpOkJIio/wcuz3A7fHRJeT7ioljWKSvUUkuAuIqsnD6S7TwK1Ia0lINZ2gySxdCokx4VcKkU4/g74fuM2IKaNnRBldKN5v4HYvjpp9OGr9UGs6rUiYSkwznaDJLF0K7WIsHf8ABw7cltDNUUJHXxHt6guJrc3HWV2ggVuRUJfQ2XSCJrP0VtVqh496xLoZnFAVGLiNKqWbo4RO319xG1ubT0R1Hg5vvQZuRaxOpWBGuxA6fNQpyhOYYiGunGMiy+nuKtHArUi4UimYkRIf1SbfZ/+B24yoMtIjSgNX3DYUEl9XQHR1Ho66cqgm8CEi4Su2A0S0zbapNVi6FDq3j2n2a0Q7fQxKrAlOsdDz+w1+ireIxOAVtxq4FZEjZOG9BLB4KaS1O3QpOBx++sXXMih+vytuGw3c5uGsLsRR5wVd7iAiLUGlYE7X9lGc26l4v4HbfaT6A1MsxNXm46rOx9GggVsRaUMqBXO6t4viuarboNJnOoqISECitUvB2otaRkRBYhfTKUREfmTxPQVrlwJAh16mE4iI/CjBulczgx1KIamn6QQiIj+y8LxHYIdS6KBSEJEQktTDdIJmsX4paE9BREJFdHvLH9K2filoT0FEQkXaEHA4TKdoFuuXQuoAwNq/BBGxiS7Hmk7QbNYvhdgOkHKM6RQiIpCmUggN3Y4znUBEBLoMNZ2g2VQKIiItISIGOmaaTtFsKgURkZaQOhBclp45CLBLKaQNAZd15y8XERuwwSAz2KUUIqKh82DTKUQknNlgPAHsUgqgQ0giYlaaSiG0qBRExBSHCzoPNJ2iRagURESaq2MmRMaaTtEi7FMKHftCTHvTKUQkHNlkPAHsVAoOB3QdbjqFiISjXqeYTtBi7FMKoENIImJG3zNNJ2gx9iqF7iNNJxCRcJN2LCRae7W1/dmrFHqfChH2GOwREYvIPMt0ghZlr1KIioM+Y02nEJFw0lelENr6n2c6gYiEi7gU241l2q8U+p0buJBERKS1ZZwBTnttRu31bgDiUyB9lOkUIhIO+p5hOkGLs18pgA4hiUjrc7ggY7zpFC1OpSAi0hTpowLLAduMPUshuTekDjKdQkTsLNM+F6ztz56lADBgoukEImJnNjsV9Qf2LQUdQhKR1tI+3TZTZf+UfUuhy1Bo38N0ChGxIxv/0WnfUgDof67pBCJiRyOmmE7QamxeChpXEJEW1v0E6GzfE1nsXQo9T4EkHUISkRZ03HWmE7Qqe5eC0wnHTzWdQkTsIro9DLrEdIpWZe9SABhxLUTEmE4hInZw7OWB2ZhtzP6lEJds+2YXkTZi80NHEA6lADDyetMJRMTquo6AtCGmU7S68CiFbiNsN+e5iLSxMNhLgHApBYCRvzadQESsKioRhlxmOkWbCJ9SGHQxxHU0nUJErGjIZRAVbzpFmwifUoiItvVViCLSio7/pekEbSZ8SgEC1yxoqU4RORpdhgXmUgsT4VUKSenQ7xzTKUTESkbdaDpBmwqvUgA4YZrpBCJiFSl94dhJplO0qfArhWPGQcd+plOIiBWM/QM4w+uQc/iVAsBpd5lOICKhrtOAsJwNITxLYfClYTVwJCJNMO7uwKSaYSb83jGAwwHj/2w6hYiEqrQhMOAC0ymMCM9SAMgYD71PNZ1CRELRuD8G/ngMQ+FbCgAT7jedQERCTdcRYX3qeniXQrfjYOCFplOISCgZ90fTCYwK71KAwNiCM8J0ChEJBemjoO8E0ymMUimkHAPDJ5tOISKhIMz3EkClEDD2DxBp7yX2ROQweo2BPqeZTmGcSgEgMS3s5jcRkZ84/U+mE4QElcIPRt8GsR1MpxARE4ZdAz1ONJ0iJKgUfhDTHkbfbjqFiLS1+E5w5v+aThEyVAr7G3UDdOpvOoWItKWzH4G4ZNMpQoZKYX8R0XDhc1qIRyRc9D0zbNZePlIqhZ/qfhycfLPpFCLS2iLj4bwnTKcIOSqFgxl7D3TMNJ1CRFrT6X8KrMYojagUDiYy5vvDSPrxiNhS1xE6Df1naKv3c9JPgBN/YzqFiLQ0ZwRc8LewXCvhSOinciin3xtYo1VE7OPkWyBtsOkUIUulcCiRMXDhszqMZFGPrKjD8UAFt73nDt6XX+Vj8sJa0v5fJfF/qWDEP6p4I8tzyNd5+NM6TnihisSHK0j9ayUXzathS7G30WNu/6+b5EcrSH+ykpfXNX691zd6OP/VmpZ7Y9J0yX3gtD+YThHStLU7nB6jYNRNplPIUfo618s/VtdzbOfG/8SnLKxlS7GXN6+MY/1NCVwyIJJJ/6nlmzzvz7wSLNvdwPQTovjyV/F8MDkOjw/OnFtDdb0fgLe2eHhlvYf3J8fz2IQYpr1VS3GND4Byt58/flzHs+fGtN6blSM38anAH3vys1QKR2L8vYG/MMQSqur9XL2glhfOj6VDTOPVsz7f4+WWkVGM7OaiTwcnfzo1mqQYB6sPUQrvXRPPdcOiGJTqYmiaizkXxpBd7g8+Z1Oxj7G9XBzf1cWVQyJpF+1gZ2mgMO76wM1Nx0fSo73+VzPuuOs04d0R0L/UIxEZq7ORLGT6O27O6xvBhD4HrpNxcrqL+RsbKKn14/P7mbfBg7vBz9heR76mRnld4HNybKBwhnZ2sWqvl9JaP6v3eqn1+MlIdrIiu4E1+V7+Z1RUi7wvaYYuw+DsR02nsAStLnOkep4UOIz05bOmk8ghzNvgYU2el6+vjz/o11+7PI5f/KeGlMcqiXBCXCQs/EUcGclHVvg+v5/b3nNzSrqLwamBK9/PyojgmmMjOeGFKmIjHbx4USzxUXDTEjdzLozl+VUe/vZVPR3jHPxzYgyDUnXFfJuK7QCTXtJhoyOkUjgaZzwAe7+B7M9NJ5GD2FPu49b33HwwOY6YiIMvun7vx27K3H4+nBxHxzgHizY3MOn1Gj79ZTxDOh9+Yz19iZsNhV5WTG1cOvePjeH+sT9udB5YWseE3hFEuuCh5XWsvymet7c2MGVRLat/ndC8NypHzuGES2ZCh56mk1iGw+/3+02HsJSqIvjnWKjIMZ1EfmLRZg8Xz6/FtV8feP3gAJwO2HJzAhl/q2LDTfGN/lqf8FI1GclO/j4x9pCvf/M7tSze0sDy6+Lp3eHn9yw2F3s5/9Vavrkhnlnf1LMi28trl8dRXe8n4eFKKv6QSGL0wUtLWtjYuwOLaMkR057C0UroBFfMhVnnQEOt6TSyn/G9I1h/U+O/4H+5uJb+HV38/pQoajyBv3+cP9keu5zgO8SfRn6/n1vedbNwcwNLr407ZCH4/X5ueNvNE2dGkxDlwOsDT+BEpOBnr/4Maxt9z4TTfm86heVo5LQpug6HC54xnUJ+IjHaweBUV6OP+EgHKbGB+/t3dJKR7OSGt918letle4mPxz+v44PtXi7q/+PfR+Nfqub/vqoP3p7+jpu56zy8ckksidEO8qt85Ff5qPUcuHWfucZDpzgH5/eLBOCUHhF8vLOBL3MaePKLOgZ2cpIUo72EVpfUEy75Jzj0sz5a2lNoqmMnQd638MX/mU4iRyjS5eCdq2L5w0d1nP9qDVX1gbOEXrwohnP7RgYft73EF7zOAOD5VYGL0ca+2PgCtNkXxnDdsB/PLCqo8jHj0zo+/9WPeysju7m446RoznulltT4wCC0tLKIGPjFv7WSYhNpTKE5fF54+TLY/rHpJCLygwufheHXmE5hWTp81BxOF1w2Czr0Np1ERABGXKtCaCaVQnPFdoArX4UonWYoYlTX4XDuX02nsDyVQktIHQAX/4PAyY8i0ubiU2HSvwNL6kqzqBRayoCJOv1NxITodnDNG1pFrYWoFFrS2D/AwAtNpxAJH67owOHbLseaTmIbKoWW5HDApf+CzLNNJxGxP8f3J3r0Gm06ia2oFFqaKzIw+VbGBNNJROzt/KcCh22lRakUWkNENPziZegzznQSEXsa/2cYMcV0CltSKbSWyJjAsc5eY0wnEbGXU38HY243ncK2VAqtKTIWrpoPPU42nUTEHk65DU7/k+kUtqZSaG1R8XD169B9pOkkItZ24vTAmibSqlQKbSE6IXAeddcRppOIWNMJ18PZfzGdIiyoFNpKTDuYvBDSdD61yFE57jpNX9GGVAptKTYJpiyGzoNNJxGxhlPvgvOf1roIbUhTZ5tQXQwvXQQF600nEQlNzgiY+BSMmGw6SdhRKZhSVwmvTdFaDCI/FZUIk16EjPGmk4QllYJJ3gZ4+1b4Zq7pJCKhIbFL4Gy9tCGmk4QtlUIoWPZX+OQh0ylEzEodGCiE9t1NJwlrKoVQse41WDwdvPWHf6yI3fQ+LbCuckx700nCnkohlOz6LDDOUFNsOolI2xl6FVzwTGAySTFOpRBqyrLh1at0ZpKEh9N+D+PuMZ1C9qNSCEX11bDoJshabDqJSOtwRgamvh5+jekk8hMqhVDl98Pyv8InfwH0KxIbSeoBl8yEHqNMJ5GDUCmEus1LYOFNUFduOolI8w2+FCY+qQHlEKZSsILynMDhpJ3LTScRaZqohMD8RcOuMp1EDkOlYBV+P3z5PHz0ADS4TacROXJdR8ClMyHlGNNJ5AioFKymcDMs/DXkfWs6icihOZxwyq0w7o863dRCVApW5PXAskfh0yfA7zWdRuRAiV3h4r9Dn9NMJ5GjpFKwsj1fw8IboGS76SQiP+o/ES74G8Qlm04iTaBSsLr6anj/T7BqlukkEu4i4+CsGXD8VNNJpBlUCnax7QNYfDNU5ZtOIuHomPFwzqPQsa/pJNJMKgU7qSmBd34HG/5jOomEiw694ay/QP9zTSeRFqJSsKPsL+G/f4TcVaaTiF1FxsOpd8BJN0NEtOk00oJUCnbl98OGN+DDB6A823QasZMhl8MZD0K7rqaTSCtQKdidxw0rnw+cvlpXYTqNWFnasXDOY9DzJNNJpBWpFMJFdXFgcr3Vc3RtgxyduBQ4/V4YcS04nabTSCtTKYSboi3w/r2w7b+mk0ioc0bACdNg7N0Qm2Q6jbQRlUK42v5J4PqGgg2mk0iocUbA4MtgzO3QqZ/pNNLGVArhzOeDb1+BFU/Bvm2m04hpkfEwYgqcNB2S0k2nEUNUChI4U2nb+/DFs7Bzmek00tbiUmDkDTDyek1NISoF+Yn8DYEpute/Dt4602mkNSX1gJNuCSyJGRVnOo2ECJWCHFxVIXz9L1j1L6guMp1GWlLnwXDKbTDoYnBFmE4jIUalIIfWUAfr5gf2HgqzTKeR5ug1JlAGfSeYTiIhTKUgR277x/DFc/Ddh4D+2VhCu26BdZGP/QWkDTadRixApSBHb9922LgANi6GgvWm08hPxSTBwAvh2EnQ8xRwOEwnEgtRKUjzFH8HWQth4yJd82BSRAxkngVDJkHfMyEiynQisSiVgrSc4u9g40LIWqSCaAsOZ2Cc4NhJMOACiGlnOpHYgEpBWocKonU4XNBtRODMocGXQmKa6URiMyoFaX3F2wLlsGsF5KyC+irTiazD4YIuxwb2CHqNCcxQGp1oOpXYmEpB2pbPC/nrYc9KyP4CsldC5V7TqUKHwxmYorrX6B9LIKa96VQSRlQKYl5ZdmC1uB8+ijaB32c6VdtwOAMXk/UaA73HQM+TVQJilEpBQo+7HPZ8HdiT2PsNlO6Esj3g85hO1nQOZ2BaiZS+gcXtUzKgY2bg2oHYDqbTiQSpFMQafF6oyIWSnVC66/uP7/+7ZCe4y8zm+0F0e+iY8ePGv2PfwH+nHKO1jMUSVApiD7VljYuiqhA8NeCp3e9z7c/cV3vganQOJ0TGQWTs95/jIDoBYpMDf9nHff/5h/9OSAsUQEKqgTcv0nJUCiIQmOPJUxOYRjwyDiJjTCcSMUKlICIiQVqFW0REglQKIiISpFIQEZEglYKIiASpFEREJEilICIiQSoFEREJUimIHKXrrrsOh8PBjTfeeMDXpk+fjsPh4Lrrrmv7YCItQKUg0gTp6enMmzeP2tra4H1ut5tXXnmFHj16GEwm0jwqBZEmGDFiBOnp6SxYsCB434IFC+jRowfDhw8H4KWXXiIlJYW6urpGz73ooouYPHkyAI8//jjdu3cnJycHgF27duFwOFi7di0ARUVF9O3bl/vuu++gXwe4//77GTZsWPD2ddddx0UXXRS8vW/fPjp06EBSUtLPPgdg6dKlOBwOysrKAJgzZ06j5+xv7dq1OBwOdu3adYifkliRSkGkiaZOncrs2bODt2fNmsUvf/nL4O3LL78cr9fLm2++GbyvsLCQJUuWMHXqVADuuOMOLr30Us455xzKy8sbvX5NTQ0TJ05k9OjRPPjgg03O+cADD9DQ0NDk50t4USmINNE111zDihUr2L17N7t37+azzz7jmmuuCX49NjaWq666qlFxzJ07lx49ejB27NjgfU8++SSZmZlcfPHF1NfXA+D1erniiitISkrihRdeaPSaQKPDVoeydetWZs2axW9/+9vmvFUJIyoFkSbq1KkT5513HnPmzGH27Nmcd955dOzYsdFjrr/+et5//31yc3OBwCGZHwaqf+B0Ohk9ejSffPIJv/71r4HAHsRbb73FiSeeSERERPCxqampdOrUifnz5+PzHX51urvuuosbbriBPn36HPC19evXk5CQEPw455xzDnhMeXk5CQkJtGvXjr59+3LnnXfi8Vh4sSM5rIjDP0REfs7UqVO5+eabAXj22WcP+Prw4cMZOnQoL730EmeeeSYbN25kyZIljR6TnZ3Nfffdx2uvvdZo7GDRokVceeWVXHnllfTv3x8Ah8PB3//+d6ZMmcLzzz9PZGQk9fX1DBw48IDvvWzZMj799FNmz57N4sWLD/h6v379Gh3aWrlyZaM9HYDExETWrFmD3+8nKyuLa6+9lrS0NCZMmHCUPymxCpWCSDOcffbZ1NfX43A4OOussw76mGnTpvHUU0+Rm5vLhAkTSE9Pb/T16dOnc8kll3D55ZeTkZHBiBEjmD17NuPGjWPatGnccMMNwUFggEsuuYSJEyeSk5ODz+fjmWeeYfny5Y1e0+/3c8cdd3DvvffSocPBl/uMiooiIyMjePuHwe79OZ3O4GP69u3LGWecwdq1a1UKNqbDRyLN4HK52LRpE1lZWbhcroM+5qqrriInJ4cXXnghOMD8g9dee40vv/ySJ554AiC4Af/h84wZM9ixYwczZ85s9LyoqCj69OlDRkYGycnJB3zPjz76iPLycqZPn97s9+h2u6mtrWX16tWsWLGCwYMHN/s1JXSpFESaqV27drRr1+5nv96+fXsuvfRSEhISGp0qWlZWxq233soTTzxBSkrKQZ+bmJjIc889x1133UVBQcERZ6quruaRRx4hMjLyiJ9zMOXl5cTGxhIfH8/EiRO5+OKLuf3225v1mhLatPKaSBsYP348gwYN4plnnjEdReSQVAoirai0tJSlS5dy2WWXkZWVRb9+/UxHEjkkDTSLtKLhw4dTWlrKo48+qkIQS9CegoiIBGmgWUREglQKIiISpFIQEZEglYKIiASpFEREJEilICIiQSoFEREJUimIiEiQSkFERIJUCiIiEqRSEBGRIJWCiIgEqRRERCRIpSAiIkEqBRERCVIpiIhIkEpBRESCVAoiIhKkUhARkSCVgoiIBKkUREQkSKUgIiJBKgUREQlSKYiISND/B+/x//DPQusOAAAAAElFTkSuQmCC",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt \n",
|
||
"\n",
|
||
"labels = 'Женщины', 'Мужчины'\n",
|
||
"sizes = [len(df[df['gender']== 'female']),\n",
|
||
" len(df[df['gender']== 'male'])]\n",
|
||
"\n",
|
||
"plt.pie(sizes, labels=labels, autopct='%1.1f%%')\n",
|
||
"plt.show()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Данная диаграмма отображает соотношение студентов, которые прошли курс подготовки к тестированию по группам.\n",
|
||
"Что позволяет сделать вывод о том, что, например, больше всего неподготовленных студентов в группе С."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<matplotlib.legend.Legend at 0x2d8c973ba70>"
|
||
]
|
||
},
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plot = df.groupby([\"race/ethnicity\", \"test preparation course\"]).size().unstack().plot.bar(color=[\"pink\", \"green\"])\n",
|
||
"plot.legend([\"Прошёл\", \"Не прошёл\"])"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "aimenv",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.12.6"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
}
|