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Tutorial.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import os as os"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from astropy.io import ascii"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data = ascii.read(\"C:\\\\Users\\\\COM\\\\Desktop\\\\data1\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data1=data.to_pandas() #Converting an Astropy table to Data Frame"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 943 entries, 0 to 942\n",
"Data columns (total 4 columns):\n",
"col1 943 non-null object\n",
"col2 943 non-null object\n",
"col3 943 non-null int32\n",
"col4 943 non-null int32\n",
"dtypes: int32(2), object(2)\n",
"memory usage: 14.8+ KB\n"
]
}
],
"source": [
"data1.info()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"astropy.table.table.Table"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(data) "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"pandas.core.frame.DataFrame"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(data1)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data2=data1.drop(1,0) #dropping 1st row"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>col1</th>\n",
" <th>col2</th>\n",
" <th>col3</th>\n",
" <th>col4</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>04-21-1991</td>\n",
" <td>9:09</td>\n",
" <td>58</td>\n",
" <td>100</td>\n",
" </tr>\n",
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" <td>17:08</td>\n",
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" </tr>\n",
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" <td>04-21-1991</td>\n",
" <td>17:08</td>\n",
" <td>33</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>04-21-1991</td>\n",
" <td>22:51</td>\n",
" <td>48</td>\n",
" <td>123</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" col1 col2 col3 col4\n",
"0 04-21-1991 9:09 58 100\n",
"2 04-21-1991 9:09 34 13\n",
"3 04-21-1991 17:08 62 119\n",
"4 04-21-1991 17:08 33 7\n",
"5 04-21-1991 22:51 48 123"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data2.head()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data2=data1.drop('col1',1) #dropping 1st row"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
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],
"text/plain": [
" col2 col3 col4\n",
"0 9:09 58 100\n",
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data2.head()"
]
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{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
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" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
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" <td>33</td>\n",
" <td>9</td>\n",
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" <th>26</th>\n",
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" <td>34</td>\n",
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" </tr>\n",
" <tr>\n",
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" <td>12:49</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>17:24</td>\n",
" <td>62</td>\n",
" <td>206</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" col2 col3 col4\n",
"20 12:00 33 4\n",
"21 17:10 62 129\n",
"22 22:09 48 340\n",
"23 22:09 33 5\n",
"24 7:29 58 67\n",
"25 7:29 33 9\n",
"26 7:29 34 14\n",
"27 12:49 33 4\n",
"28 17:24 62 206"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data2.ix[20:28]"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"20 12:00\n",
"21 17:10\n",
"22 22:09\n",
"23 22:09\n",
"24 7:29\n",
"25 7:29\n",
"Name: col2, dtype: object"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data1.ix[20:25].col2"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>col1</th>\n",
" <th>col2</th>\n",
" <th>col4</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>04-21-1991</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>04-21-1991</td>\n",
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" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>04-21-1991</td>\n",
" <td>9:09</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>04-21-1991</td>\n",
" <td>17:08</td>\n",
" <td>119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>04-21-1991</td>\n",
" <td>17:08</td>\n",
" <td>7</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" col1 col2 col4\n",
"0 04-21-1991 9:09 100\n",
"1 04-21-1991 9:09 9\n",
"2 04-21-1991 9:09 13\n",
"3 04-21-1991 17:08 119\n",
"4 04-21-1991 17:08 7"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data1[['col1','col2','col4']].head()"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>col1</th>\n",
" <th>col2</th>\n",
" <th>col3</th>\n",
" <th>col4</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>04-21-1991</td>\n",
" <td>9:09</td>\n",
" <td>58</td>\n",
" <td>100</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" col1 col2 col3 col4\n",
"0 04-21-1991 9:09 58 100"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data1.query('col3>50 and col1==\"04-21-1991\" and col2==\"9:09\"')"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 943.0000\n",
"mean 44.3807\n",
"std 12.9222\n",
"min 33.0000\n",
"25% 33.0000\n",
"50% 34.0000\n",
"75% 58.0000\n",
"max 65.0000\n",
"Name: col3, dtype: float64"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data1.col3.describe()"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>col3</th>\n",
" <th>col4</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>col3</th>\n",
" <td>1.000000</td>\n",
" <td>0.681254</td>\n",
" </tr>\n",
" <tr>\n",
" <th>col4</th>\n",
" <td>0.681254</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" col3 col4\n",
"col3 1.000000 0.681254\n",
"col4 0.681254 1.000000"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data1.corr() "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"12:00 40\n",
"18:00 35\n",
"17:00 18\n",
"8:45 18\n",
"7:35 18\n",
"17:30 14\n",
"7:00 12\n",
"7:06 12\n",
"8:35 9\n",
"7:30 9\n",
"17:15 9\n",
"8:30 9\n",
"7:52 9\n",
"7:07 9\n",
"17:20 8\n",
"8:00 8\n",
"12:45 8\n",
"16:00 8\n",
"11:30 7\n",
"17:10 7\n",
"Name: col2, dtype: int64"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.value_counts(data1.col2).head(20)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"cutgroup=pd.groupby(data1,data2.col2)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<pandas.core.groupby.DataFrameGroupBy object at 0x07F9FCD0>\n"
]
}
],
"source": [
"print(cutgroup)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"pandas.core.groupby.DataFrameGroupBy"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(cutgroup)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"col2\n",
"08:00 34.0\n",
"10:00 34.0\n",
"10:03 34.0\n",
"11:00 65.0\n",
"11:30 65.0\n",
"11:40 33.0\n",
"11:50 33.0\n",
"11:55 33.0\n",
"12:00 33.0\n",
"12:06 46.5\n",
"12:13 46.5\n",
"12:14 46.5\n",
"12:15 46.5\n",
"12:17 46.5\n",
"12:20 33.0\n",
"12:23 46.5\n",
"12:25 46.5\n",
"12:30 46.5\n",
"12:31 46.5\n",
"12:32 46.5\n",
"12:33 46.5\n",
"12:35 46.5\n",
"12:37 46.5\n",
"12:38 46.5\n",
"12:39 46.5\n",
"12:40 46.5\n",
"12:43 46.5\n",
"12:45 46.5\n",
"12:49 33.0\n",
"12:50 33.0\n",
" ... \n",
"8:33 34.0\n",
"8:34 34.0\n",
"8:35 34.0\n",
"8:38 34.0\n",
"8:42 34.0\n",
"8:45 34.0\n",
"8:50 34.0\n",
"8:51 34.0\n",
"8:53 34.0\n",
"8:55 34.0\n",
"8:58 34.0\n",
"9:02 34.0\n",
"9:04 34.0\n",
"9:05 34.0\n",
"9:07 34.0\n",
"9:08 34.0\n",
"9:09 34.0\n",
"9:10 34.0\n",
"9:15 34.0\n",
"9:17 34.0\n",
"9:19 34.0\n",
"9:20 34.0\n",
"9:25 34.0\n",
"9:35 34.0\n",
"9:38 34.0\n",
"9:42 34.0\n",
"9:43 34.0\n",
"9:45 34.0\n",
"9:46 34.0\n",
"9:50 34.0\n",
"Name: col3, dtype: float64"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cutgroup.col3.median()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>col2</th>\n",
" <th>08:00</th>\n",
" <th>10:00</th>\n",
" <th>10:03</th>\n",
" <th>11:00</th>\n",
" <th>11:30</th>\n",
" <th>11:40</th>\n",
" <th>11:50</th>\n",
" <th>11:55</th>\n",
" <th>12:00</th>\n",
" <th>12:06</th>\n",
" <th>...</th>\n",
" <th>9:20</th>\n",
" <th>9:25</th>\n",
" <th>9:35</th>\n",
" <th>9:38</th>\n",
" <th>9:42</th>\n",
" <th>9:43</th>\n",
" <th>9:45</th>\n",
" <th>9:46</th>\n",
" <th>9:50</th>\n",
" <th>All</th>\n",
" </tr>\n",
" <tr>\n",
" <th>col1</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
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" <tr>\n",
" <th>04-21-1991</th>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>04-22-1991</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>04-23-1991</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>04-24-1991</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>04-25-1991</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 265 columns</p>\n",
"</div>"
],
"text/plain": [
"col2 08:00 10:00 10:03 11:00 11:30 11:40 11:50 11:55 12:00 \\\n",
"col1 \n",
"04-21-1991 0 0 0 0 0 0 0 0 0 \n",
"04-22-1991 0 0 0 0 0 0 0 0 0 \n",
"04-23-1991 0 0 0 0 0 0 0 0 0 \n",
"04-24-1991 0 0 0 0 0 0 0 0 1 \n",
"04-25-1991 0 0 0 0 0 0 0 0 0 \n",
"\n",
"col2 12:06 ... 9:20 9:25 9:35 9:38 9:42 9:43 9:45 9:46 9:50 \\\n",
"col1 ... \n",
"04-21-1991 0 ... 0 0 0 0 0 0 0 0 0 \n",
"04-22-1991 0 ... 0 0 0 0 0 0 0 0 0 \n",
"04-23-1991 0 ... 0 0 0 0 0 0 0 0 0 \n",
"04-24-1991 0 ... 0 0 0 0 0 0 0 0 0 \n",
"04-25-1991 0 ... 0 0 0 0 0 0 0 0 0 \n",
"\n",
"col2 All \n",
"col1 \n",
"04-21-1991 6 \n",
"04-22-1991 6 \n",
"04-23-1991 5 \n",
"04-24-1991 7 \n",
"04-25-1991 8 \n",
"\n",
"[5 rows x 265 columns]"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.crosstab(data1.col1,data1.col2,margins='TRUE').head() #137rows x 265 columns"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>col2</th>\n",
" <th>08:00</th>\n",
" <th>10:00</th>\n",
" <th>10:03</th>\n",
" <th>11:00</th>\n",
" <th>11:30</th>\n",
" <th>11:40</th>\n",
" <th>11:50</th>\n",
" <th>11:55</th>\n",
" <th>12:00</th>\n",
" <th>12:06</th>\n",
" <th>...</th>\n",
" <th>9:19</th>\n",
" <th>9:20</th>\n",
" <th>9:25</th>\n",
" <th>9:35</th>\n",
" <th>9:38</th>\n",
" <th>9:42</th>\n",
" <th>9:43</th>\n",
" <th>9:45</th>\n",
" <th>9:46</th>\n",
" <th>9:50</th>\n",
" </tr>\n",
" <tr>\n",
" <th>col1</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>04-21-1991</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>04-22-1991</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>04-23-1991</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>04-24-1991</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>33.0</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>04-25-1991</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 264 columns</p>\n",
"</div>"
],
"text/plain": [
"col2 08:00 10:00 10:03 11:00 11:30 11:40 11:50 11:55 12:00 \\\n",
"col1 \n",
"04-21-1991 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"04-22-1991 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"04-23-1991 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"04-24-1991 NaN NaN NaN NaN NaN NaN NaN NaN 33.0 \n",
"04-25-1991 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"\n",
"col2 12:06 ... 9:19 9:20 9:25 9:35 9:38 9:42 9:43 9:45 9:46 \\\n",
"col1 ... \n",
"04-21-1991 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"04-22-1991 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"04-23-1991 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"04-24-1991 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"04-25-1991 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"\n",
"col2 9:50 \n",
"col1 \n",
"04-21-1991 NaN \n",
"04-22-1991 NaN \n",
"04-23-1991 NaN \n",
"04-24-1991 NaN \n",
"04-25-1991 NaN \n",
"\n",
"[5 rows x 264 columns]"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"e=data1.groupby(['col1', \"col2\"]).col3.median().reset_index()\n",
"e.pivot(index='col1', columns='col2', values='col3').head()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>col2</th>\n",
" <th>col3</th>\n",
" <th>col4</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>9:09</td>\n",
" <td>58</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>9:09</td>\n",
" <td>33</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>9:09</td>\n",
" <td>34</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>17:08</td>\n",
" <td>62</td>\n",
" <td>119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>17:08</td>\n",
" <td>33</td>\n",
" <td>7</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" col2 col3 col4\n",
"0 9:09 58 100\n",
"1 9:09 33 9\n",
"2 9:09 34 13\n",
"3 17:08 62 119\n",
"4 17:08 33 7"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from pandasql import sqldf\n",
"pysqldf = lambda q: sqldf(q, globals())\n",
"pysqldf(\"SELECT * FROM data2 LIMIT 5 ; \")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import statsmodels.formula.api as sm"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>crim</th>\n",
" <th>zn</th>\n",
" <th>indus</th>\n",
" <th>chas</th>\n",
" <th>nox</th>\n",
" <th>rm</th>\n",
" <th>age</th>\n",
" <th>dis</th>\n",
" <th>rad</th>\n",
" <th>tax</th>\n",
" <th>ptratio</th>\n",
" <th>black</th>\n",
" <th>lstat</th>\n",
" <th>medv</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.00632</td>\n",
" <td>18.0</td>\n",
" <td>2.31</td>\n",
" <td>0</td>\n",
" <td>0.538</td>\n",
" <td>6.575</td>\n",
" <td>65.2</td>\n",
" <td>4.0900</td>\n",
" <td>1</td>\n",
" <td>296</td>\n",
" <td>15.3</td>\n",
" <td>396.90</td>\n",
" <td>4.98</td>\n",
" <td>24.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.02731</td>\n",
" <td>0.0</td>\n",
" <td>7.07</td>\n",
" <td>0</td>\n",
" <td>0.469</td>\n",
" <td>6.421</td>\n",
" <td>78.9</td>\n",
" <td>4.9671</td>\n",
" <td>2</td>\n",
" <td>242</td>\n",
" <td>17.8</td>\n",
" <td>396.90</td>\n",
" <td>9.14</td>\n",
" <td>21.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.02729</td>\n",
" <td>0.0</td>\n",
" <td>7.07</td>\n",
" <td>0</td>\n",
" <td>0.469</td>\n",
" <td>7.185</td>\n",
" <td>61.1</td>\n",
" <td>4.9671</td>\n",
" <td>2</td>\n",
" <td>242</td>\n",
" <td>17.8</td>\n",
" <td>392.83</td>\n",
" <td>4.03</td>\n",
" <td>34.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.03237</td>\n",
" <td>0.0</td>\n",
" <td>2.18</td>\n",
" <td>0</td>\n",
" <td>0.458</td>\n",
" <td>6.998</td>\n",
" <td>45.8</td>\n",
" <td>6.0622</td>\n",
" <td>3</td>\n",
" <td>222</td>\n",
" <td>18.7</td>\n",
" <td>394.63</td>\n",
" <td>2.94</td>\n",
" <td>33.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.06905</td>\n",
" <td>0.0</td>\n",
" <td>2.18</td>\n",
" <td>0</td>\n",
" <td>0.458</td>\n",
" <td>7.147</td>\n",
" <td>54.2</td>\n",
" <td>6.0622</td>\n",
" <td>3</td>\n",
" <td>222</td>\n",
" <td>18.7</td>\n",
" <td>396.90</td>\n",
" <td>5.33</td>\n",
" <td>36.2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" crim zn indus chas nox rm age dis rad tax ptratio \\\n",
"0 0.00632 18.0 2.31 0 0.538 6.575 65.2 4.0900 1 296 15.3 \n",
"1 0.02731 0.0 7.07 0 0.469 6.421 78.9 4.9671 2 242 17.8 \n",
"2 0.02729 0.0 7.07 0 0.469 7.185 61.1 4.9671 2 242 17.8 \n",
"3 0.03237 0.0 2.18 0 0.458 6.998 45.8 6.0622 3 222 18.7 \n",
"4 0.06905 0.0 2.18 0 0.458 7.147 54.2 6.0622 3 222 18.7 \n",
"\n",
" black lstat medv \n",
"0 396.90 4.98 24.0 \n",
"1 396.90 9.14 21.6 \n",
"2 392.83 4.03 34.7 \n",
"3 394.63 2.94 33.4 \n",
"4 396.90 5.33 36.2 "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"boston=pd.read_csv(\"http://vincentarelbundock.github.io/Rdatasets/csv/MASS/Boston.csv\")\n",
"boston =boston.drop('Unnamed: 0', 1)\n",
"boston.head()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>OLS Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>medv</td> <th> R-squared: </th> <td> 0.631</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.626</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 142.0</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Mon, 12 Dec 2016</td> <th> Prob (F-statistic):</th> <td>1.49e-104</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>02:33:35</td> <th> Log-Likelihood: </th> <td> -1588.2</td> \n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 506</td> <th> AIC: </th> <td> 3190.</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Df Residuals:</th> <td> 499</td> <th> BIC: </th> <td> 3220.</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Df Model:</th> <td> 6</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>Intercept</th> <td> -0.3594</td> <td> 4.863</td> <td> -0.074</td> <td> 0.941</td> <td> -9.915 9.196</td>\n",
"</tr>\n",
"<tr>\n",
" <th>crim</th> <td> -0.0991</td> <td> 0.034</td> <td> -2.890</td> <td> 0.004</td> <td> -0.167 -0.032</td>\n",
"</tr>\n",
"<tr>\n",
" <th>zn</th> <td> -0.0064</td> <td> 0.014</td> <td> -0.470</td> <td> 0.638</td> <td> -0.033 0.020</td>\n",
"</tr>\n",
"<tr>\n",
" <th>nox</th> <td> -10.8653</td> <td> 2.865</td> <td> -3.793</td> <td> 0.000</td> <td> -16.494 -5.237</td>\n",
"</tr>\n",
"<tr>\n",
" <th>ptratio</th> <td> -1.0519</td> <td> 0.135</td> <td> -7.796</td> <td> 0.000</td> <td> -1.317 -0.787</td>\n",
"</tr>\n",
"<tr>\n",
" <th>black</th> <td> 0.0137</td> <td> 0.003</td> <td> 4.453</td> <td> 0.000</td> <td> 0.008 0.020</td>\n",
"</tr>\n",
"<tr>\n",
" <th>rm</th> <td> 6.9796</td> <td> 0.396</td> <td> 17.612</td> <td> 0.000</td> <td> 6.201 7.758</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Omnibus:</th> <td>298.859</td> <th> Durbin-Watson: </th> <td> 0.808</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Omnibus):</th> <td> 0.000</td> <th> Jarque-Bera (JB): </th> <td>3305.426</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Skew:</th> <td> 2.385</td> <th> Prob(JB): </th> <td> 0.00</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Kurtosis:</th> <td>14.577</td> <th> Cond. No. </th> <td>7.66e+03</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: medv R-squared: 0.631\n",
"Model: OLS Adj. R-squared: 0.626\n",
"Method: Least Squares F-statistic: 142.0\n",
"Date: Mon, 12 Dec 2016 Prob (F-statistic): 1.49e-104\n",
"Time: 02:33:35 Log-Likelihood: -1588.2\n",
"No. Observations: 506 AIC: 3190.\n",
"Df Residuals: 499 BIC: 3220.\n",
"Df Model: 6 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [95.0% Conf. Int.]\n",
"------------------------------------------------------------------------------\n",
"Intercept -0.3594 4.863 -0.074 0.941 -9.915 9.196\n",
"crim -0.0991 0.034 -2.890 0.004 -0.167 -0.032\n",
"zn -0.0064 0.014 -0.470 0.638 -0.033 0.020\n",
"nox -10.8653 2.865 -3.793 0.000 -16.494 -5.237\n",
"ptratio -1.0519 0.135 -7.796 0.000 -1.317 -0.787\n",
"black 0.0137 0.003 4.453 0.000 0.008 0.020\n",
"rm 6.9796 0.396 17.612 0.000 6.201 7.758\n",
"==============================================================================\n",
"Omnibus: 298.859 Durbin-Watson: 0.808\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 3305.426\n",
"Skew: 2.385 Prob(JB): 0.00\n",
"Kurtosis: 14.577 Cond. No. 7.66e+03\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"[2] The condition number is large, 7.66e+03. This might indicate that there are\n",
"strong multicollinearity or other numerical problems.\n",
"\"\"\""
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import statsmodels.formula.api as sm\n",
"result = sm.ols(formula=\"medv ~ crim + zn + nox + ptratio + black + rm \", data=boston).fit()\n",
"result.summary()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Intercept -0.359432\n",
"crim -0.099122\n",
"zn -0.006364\n",
"nox -10.865295\n",
"ptratio -1.051937\n",
"black 0.013737\n",
"rm 6.979587\n",
"dtype: float64"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result.params"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: seaborn in c:\\users\\com\\appdata\\roaming\\python\\python35\\site-packages\n"
]
}
],
"source": [
"! pip install seaborn "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import seaborn as sns\n",
"%matplotlib inline\n",
"sns.distplot(diamonds.price, bins=20, kde=True, rug=False);"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"diamonds =pd.read_csv(\"https://vincentarelbundock.github.io/Rdatasets/csv/ggplot2/diamonds.csv \")"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 53940 entries, 0 to 53939\n",
"Data columns (total 11 columns):\n",
"Unnamed: 0 53940 non-null int64\n",
"carat 53940 non-null float64\n",
"cut 53940 non-null object\n",
"color 53940 non-null object\n",
"clarity 53940 non-null object\n",
"depth 53940 non-null float64\n",
"table 53940 non-null float64\n",
"price 53940 non-null int64\n",
"x 53940 non-null float64\n",
"y 53940 non-null float64\n",
"z 53940 non-null float64\n",
"dtypes: float64(6), int64(2), object(3)\n",
"memory usage: 3.9+ MB\n"
]
}
],
"source": [
"diamonds.info()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>carat</th>\n",
" <th>cut</th>\n",
" <th>color</th>\n",
" <th>clarity</th>\n",
" <th>depth</th>\n",
" <th>table</th>\n",
" <th>price</th>\n",
" <th>x</th>\n",
" <th>y</th>\n",
" <th>z</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.23</td>\n",
" <td>Ideal</td>\n",
" <td>E</td>\n",
" <td>SI2</td>\n",
" <td>61.5</td>\n",
" <td>55.0</td>\n",
" <td>326</td>\n",
" <td>3.95</td>\n",
" <td>3.98</td>\n",
" <td>2.43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.21</td>\n",
" <td>Premium</td>\n",
" <td>E</td>\n",
" <td>SI1</td>\n",
" <td>59.8</td>\n",
" <td>61.0</td>\n",
" <td>326</td>\n",
" <td>3.89</td>\n",
" <td>3.84</td>\n",
" <td>2.31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.23</td>\n",
" <td>Good</td>\n",
" <td>E</td>\n",
" <td>VS1</td>\n",
" <td>56.9</td>\n",
" <td>65.0</td>\n",
" <td>327</td>\n",
" <td>4.05</td>\n",
" <td>4.07</td>\n",
" <td>2.31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.29</td>\n",
" <td>Premium</td>\n",
" <td>I</td>\n",
" <td>VS2</td>\n",
" <td>62.4</td>\n",
" <td>58.0</td>\n",
" <td>334</td>\n",
" <td>4.20</td>\n",
" <td>4.23</td>\n",
" <td>2.63</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.31</td>\n",
" <td>Good</td>\n",
" <td>J</td>\n",
" <td>SI2</td>\n",
" <td>63.3</td>\n",
" <td>58.0</td>\n",
" <td>335</td>\n",
" <td>4.34</td>\n",
" <td>4.35</td>\n",
" <td>2.75</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" carat cut color clarity depth table price x y z\n",
"0 0.23 Ideal E SI2 61.5 55.0 326 3.95 3.98 2.43\n",
"1 0.21 Premium E SI1 59.8 61.0 326 3.89 3.84 2.31\n",
"2 0.23 Good E VS1 56.9 65.0 327 4.05 4.07 2.31\n",
"3 0.29 Premium I VS2 62.4 58.0 334 4.20 4.23 2.63\n",
"4 0.31 Good J SI2 63.3 58.0 335 4.34 4.35 2.75"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"diamonds2=diamonds.drop('Unnamed: 0', 1) #Dropping a particular variable\n",
"diamonds2.head()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Program Files\\Anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
" y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
]
},
{
"data": {
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aaG9vYcGCHC1NdQyPpBkYSdPe3jKr+60F+j4vPbV56anNa1+xgWYn0OGci5pZ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\n1DwLaIwxxhhT8yygMcYYY0zN+39aeo0B0lJV3QAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7c8a0b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import seaborn as sns\n",
"%matplotlib inline\n",
"sns.distplot(diamonds.price, bins=20, kde=True, rug=False);"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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sotqrgC96a7KkRup63ZvpmLegbu9f2PIcAB2zdq3bPgAKG9czsurWna8oTVPS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\nAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xbb9abb0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = sns.boxplot(x=\"color\", y=\"price\", data=diamonds)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.JointGrid at 0x822b870>"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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J5z8uKjqzMCgREeUR3oTzHxcVnVkYlIiI8ghvwvmPq4jPLBzMTUSUR7iyd/7j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\nRETExX8AVzh3OBLoAQIAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7cf2150>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.jointplot('price','carat',data=diamonds2)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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v397o8d69e4xG0VBvetTWql05amt3G9Pf1lm5clm0FavptKZUbDamolXioEGD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\n2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"text/plain": [
"<matplotlib.figure.Figure at 0xcc10df0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.factorplot(x=\"color\", y=\"price\",\n",
"col=\"cut\", data=diamonds, kind=\"box\", size=4, aspect=.5);"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting ggplot\n",
" Downloading ggplot-0.11.5-py2.py3-none-any.whl (2.2MB)\n",
"Requirement already satisfied: six in c:\\program files\\anaconda3\\lib\\site-packages (from ggplot)\n",
"Requirement already satisfied: scipy in c:\\program files\\anaconda3\\lib\\site-packages (from ggplot)\n",
"Collecting brewer2mpl (from ggplot)\n",
" Downloading brewer2mpl-1.4.1-py2.py3-none-any.whl\n",
"Requirement already satisfied: statsmodels in c:\\program files\\anaconda3\\lib\\site-packages (from ggplot)\n",
"Requirement already satisfied: patsy>=0.4 in c:\\program files\\anaconda3\\lib\\site-packages (from ggplot)\n",
"Requirement already satisfied: matplotlib in c:\\program files\\anaconda3\\lib\\site-packages (from ggplot)\n",
"Requirement already satisfied: numpy in c:\\program files\\anaconda3\\lib\\site-packages (from ggplot)\n",
"Requirement already satisfied: pandas in c:\\program files\\anaconda3\\lib\\site-packages (from ggplot)\n",
"Requirement already satisfied: cycler in c:\\program files\\anaconda3\\lib\\site-packages (from ggplot)\n",
"Requirement already satisfied: python-dateutil in c:\\program files\\anaconda3\\lib\\site-packages (from matplotlib->ggplot)\n",
"Requirement already satisfied: pytz in c:\\program files\\anaconda3\\lib\\site-packages (from matplotlib->ggplot)\n",
"Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,>=1.5.6 in c:\\program files\\anaconda3\\lib\\site-packages (from matplotlib->ggplot)\n",
"Installing collected packages: brewer2mpl, ggplot\n",
"Successfully installed brewer2mpl-1.4.1 ggplot-0.11.5\n"
]
}
],
"source": [
"! pip install ggplot\n",
"import ggplot"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'aes' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-31-4898bf0c221f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mggplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'price'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'carat'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"clarity\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdiamonds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mp\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mgeom_point\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'aes' is not defined"
]
}
],
"source": [
"p = ggplot(aes(x='price', y='carat',color=\"clarity\"), data=diamonds)\n",
"p + geom_point()"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data = ascii.read(\"C:\\\\Users\\\\COM\\\\Desktop\\\\auto-mpg.data\", Reader=ascii.NoHeader) #If table isnt read properly"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data1=data.to_pandas()"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 398 entries, 0 to 397\n",
"Data columns (total 9 columns):\n",
"col1 398 non-null float64\n",
"col2 398 non-null int32\n",
"col3 398 non-null float64\n",
"col4 398 non-null object\n",
"col5 398 non-null float64\n",
"col6 398 non-null float64\n",
"col7 398 non-null int32\n",
"col8 398 non-null int32\n",
"col9 398 non-null object\n",
"dtypes: float64(4), int32(3), object(2)\n",
"memory usage: 20.2+ KB\n"
]
}
],
"source": [
"data1.info()"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>col1</th>\n",
" <th>col2</th>\n",
" <th>col3</th>\n",
" <th>col4</th>\n",
" <th>col5</th>\n",
" <th>col6</th>\n",
" <th>col7</th>\n",
" <th>col8</th>\n",
" <th>col9</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>18.0</td>\n",
" <td>8</td>\n",
" <td>307.0</td>\n",
" <td>130.0</td>\n",
" <td>3504.0</td>\n",
" <td>12.0</td>\n",
" <td>70</td>\n",
" <td>1</td>\n",
" <td>chevrolet chevelle malibu</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>15.0</td>\n",
" <td>8</td>\n",
" <td>350.0</td>\n",
" <td>165.0</td>\n",
" <td>3693.0</td>\n",
" <td>11.5</td>\n",
" <td>70</td>\n",
" <td>1</td>\n",
" <td>buick skylark 320</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>18.0</td>\n",
" <td>8</td>\n",
" <td>318.0</td>\n",
" <td>150.0</td>\n",
" <td>3436.0</td>\n",
" <td>11.0</td>\n",
" <td>70</td>\n",
" <td>1</td>\n",
" <td>plymouth satellite</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>16.0</td>\n",
" <td>8</td>\n",
" <td>304.0</td>\n",
" <td>150.0</td>\n",
" <td>3433.0</td>\n",
" <td>12.0</td>\n",
" <td>70</td>\n",
" <td>1</td>\n",
" <td>amc rebel sst</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>17.0</td>\n",
" <td>8</td>\n",
" <td>302.0</td>\n",
" <td>140.0</td>\n",
" <td>3449.0</td>\n",
" <td>10.5</td>\n",
" <td>70</td>\n",
" <td>1</td>\n",
" <td>ford torino</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>15.0</td>\n",
" <td>8</td>\n",
" <td>429.0</td>\n",
" <td>198.0</td>\n",
" <td>4341.0</td>\n",
" <td>10.0</td>\n",
" <td>70</td>\n",
" <td>1</td>\n",
" <td>ford galaxie 500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>14.0</td>\n",
" <td>8</td>\n",
" <td>454.0</td>\n",
" <td>220.0</td>\n",
" <td>4354.0</td>\n",
" <td>9.0</td>\n",
" <td>70</td>\n",
" <td>1</td>\n",
" <td>chevrolet impala</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>14.0</td>\n",
" <td>8</td>\n",
" <td>440.0</td>\n",
" <td>215.0</td>\n",
" <td>4312.0</td>\n",
" <td>8.5</td>\n",
" <td>70</td>\n",
" <td>1</td>\n",
" <td>plymouth fury iii</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>14.0</td>\n",
" <td>8</td>\n",
" <td>455.0</td>\n",
" <td>225.0</td>\n",
" <td>4425.0</td>\n",
" <td>10.0</td>\n",
" <td>70</td>\n",
" <td>1</td>\n",
" <td>pontiac catalina</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>15.0</td>\n",
" <td>8</td>\n",
" <td>390.0</td>\n",
" <td>190.0</td>\n",
" <td>3850.0</td>\n",
" <td>8.5</td>\n",
" <td>70</td>\n",
" <td>1</td>\n",
" <td>amc ambassador dpl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>15.0</td>\n",
" <td>8</td>\n",
" <td>383.0</td>\n",
" <td>170.0</td>\n",
" <td>3563.0</td>\n",
" <td>10.0</td>\n",
" <td>70</td>\n",
" <td>1</td>\n",
" <td>dodge challenger se</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>14.0</td>\n",
" <td>8</td>\n",
" <td>340.0</td>\n",
" <td>160.0</td>\n",
" <td>3609.0</td>\n",
" <td>8.0</td>\n",
" <td>70</td>\n",
" <td>1</td>\n",
" <td>plymouth 'cuda 340</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>15.0</td>\n",
" <td>8</td>\n",
" <td>400.0</td>\n",
" <td>150.0</td>\n",
" <td>3761.0</td>\n",
" <td>9.5</td>\n",
" <td>70</td>\n",
" <td>1</td>\n",
" <td>chevrolet monte carlo</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>14.0</td>\n",
" <td>8</td>\n",
" <td>455.0</td>\n",
" <td>225.0</td>\n",
" <td>3086.0</td>\n",
" <td>10.0</td>\n",
" <td>70</td>\n",
" <td>1</td>\n",
" <td>buick estate wagon (sw)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>24.0</td>\n",
" <td>4</td>\n",
" <td>113.0</td>\n",
" <td>95.00</td>\n",
" <td>2372.0</td>\n",
" <td>15.0</td>\n",
" <td>70</td>\n",
" <td>3</td>\n",
" <td>toyota corona mark ii</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>22.0</td>\n",
" <td>6</td>\n",
" <td>198.0</td>\n",
" <td>95.00</td>\n",
" <td>2833.0</td>\n",
" <td>15.5</td>\n",
" <td>70</td>\n",
" <td>1</td>\n",
" <td>plymouth duster</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>18.0</td>\n",
" <td>6</td>\n",
" <td>199.0</td>\n",
" <td>97.00</td>\n",
" <td>2774.0</td>\n",
" <td>15.5</td>\n",
" <td>70</td>\n",
" <td>1</td>\n",
" <td>amc hornet</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>21.0</td>\n",
" <td>6</td>\n",
" <td>200.0</td>\n",
" <td>85.00</td>\n",
" <td>2587.0</td>\n",
" <td>16.0</td>\n",
" <td>70</td>\n",
" <td>1</td>\n",
" <td>ford maverick</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>27.0</td>\n",
" <td>4</td>\n",
" <td>97.0</td>\n",
" <td>88.00</td>\n",
" <td>2130.0</td>\n",
" <td>14.5</td>\n",
" <td>70</td>\n",
" <td>3</td>\n",
" <td>datsun pl510</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>26.0</td>\n",
" <td>4</td>\n",
" <td>97.0</td>\n",
" <td>46.00</td>\n",
" <td>1835.0</td>\n",
" <td>20.5</td>\n",
" <td>70</td>\n",
" <td>2</td>\n",
" <td>volkswagen 1131 deluxe sedan</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>25.0</td>\n",
" <td>4</td>\n",
" <td>110.0</td>\n",
" <td>87.00</td>\n",
" <td>2672.0</td>\n",
" <td>17.5</td>\n",
" <td>70</td>\n",
" <td>2</td>\n",
" <td>peugeot 504</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>24.0</td>\n",
" <td>4</td>\n",
" <td>107.0</td>\n",
" <td>90.00</td>\n",
" <td>2430.0</td>\n",
" <td>14.5</td>\n",
" <td>70</td>\n",
" <td>2</td>\n",
" <td>audi 100 ls</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>25.0</td>\n",
" <td>4</td>\n",
" <td>104.0</td>\n",
" <td>95.00</td>\n",
" <td>2375.0</td>\n",
" <td>17.5</td>\n",
" <td>70</td>\n",
" <td>2</td>\n",
" <td>saab 99e</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>26.0</td>\n",
" <td>4</td>\n",
" <td>121.0</td>\n",
" <td>113.0</td>\n",
" <td>2234.0</td>\n",
" <td>12.5</td>\n",
" <td>70</td>\n",
" <td>2</td>\n",
" <td>bmw 2002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>21.0</td>\n",
" <td>6</td>\n",
" <td>199.0</td>\n",
" <td>90.00</td>\n",
" <td>2648.0</td>\n",
" <td>15.0</td>\n",
" <td>70</td>\n",
" <td>1</td>\n",
" <td>amc gremlin</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>10.0</td>\n",
" <td>8</td>\n",
" <td>360.0</td>\n",
" <td>215.0</td>\n",
" <td>4615.0</td>\n",
" <td>14.0</td>\n",
" <td>70</td>\n",
" <td>1</td>\n",
" <td>ford f250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>10.0</td>\n",
" <td>8</td>\n",
" <td>307.0</td>\n",
" <td>200.0</td>\n",
" <td>4376.0</td>\n",
" <td>15.0</td>\n",
" <td>70</td>\n",
" <td>1</td>\n",
" <td>chevy c20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>11.0</td>\n",
" <td>8</td>\n",
" <td>318.0</td>\n",
" <td>210.0</td>\n",
" <td>4382.0</td>\n",
" <td>13.5</td>\n",
" <td>70</td>\n",
" <td>1</td>\n",
" <td>dodge d200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>9.0</td>\n",
" <td>8</td>\n",
" <td>304.0</td>\n",
" <td>193.0</td>\n",
" <td>4732.0</td>\n",
" <td>18.5</td>\n",
" <td>70</td>\n",
" <td>1</td>\n",
" <td>hi 1200d</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>27.0</td>\n",
" <td>4</td>\n",
" <td>97.0</td>\n",
" <td>88.00</td>\n",
" <td>2130.0</td>\n",
" <td>14.5</td>\n",
" <td>71</td>\n",
" <td>3</td>\n",
" <td>datsun pl510</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>368</th>\n",
" <td>27.0</td>\n",
" <td>4</td>\n",
" <td>112.0</td>\n",
" <td>88.00</td>\n",
" <td>2640.0</td>\n",
" <td>18.6</td>\n",
" <td>82</td>\n",
" <td>1</td>\n",
" <td>chevrolet cavalier wagon</td>\n",
" </tr>\n",
" <tr>\n",
" <th>369</th>\n",
" <td>34.0</td>\n",
" <td>4</td>\n",
" <td>112.0</td>\n",
" <td>88.00</td>\n",
" <td>2395.0</td>\n",
" <td>18.0</td>\n",
" <td>82</td>\n",
" <td>1</td>\n",
" <td>chevrolet cavalier 2-door</td>\n",
" </tr>\n",
" <tr>\n",
" <th>370</th>\n",
" <td>31.0</td>\n",
" <td>4</td>\n",
" <td>112.0</td>\n",
" <td>85.00</td>\n",
" <td>2575.0</td>\n",
" <td>16.2</td>\n",
" <td>82</td>\n",
" <td>1</td>\n",
" <td>pontiac j2000 se hatchback</td>\n",
" </tr>\n",
" <tr>\n",
" <th>371</th>\n",
" <td>29.0</td>\n",
" <td>4</td>\n",
" <td>135.0</td>\n",
" <td>84.00</td>\n",
" <td>2525.0</td>\n",
" <td>16.0</td>\n",
" <td>82</td>\n",
" <td>1</td>\n",
" <td>dodge aries se</td>\n",
" </tr>\n",
" <tr>\n",
" <th>372</th>\n",
" <td>27.0</td>\n",
" <td>4</td>\n",
" <td>151.0</td>\n",
" <td>90.00</td>\n",
" <td>2735.0</td>\n",
" <td>18.0</td>\n",
" <td>82</td>\n",
" <td>1</td>\n",
" <td>pontiac phoenix</td>\n",
" </tr>\n",
" <tr>\n",
" <th>373</th>\n",
" <td>24.0</td>\n",
" <td>4</td>\n",
" <td>140.0</td>\n",
" <td>92.00</td>\n",
" <td>2865.0</td>\n",
" <td>16.4</td>\n",
" <td>82</td>\n",
" <td>1</td>\n",
" <td>ford fairmont futura</td>\n",
" </tr>\n",
" <tr>\n",
" <th>374</th>\n",
" <td>23.0</td>\n",
" <td>4</td>\n",
" <td>151.0</td>\n",
" <td>?</td>\n",
" <td>3035.0</td>\n",
" <td>20.5</td>\n",
" <td>82</td>\n",
" <td>1</td>\n",
" <td>amc concord dl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>375</th>\n",
" <td>36.0</td>\n",
" <td>4</td>\n",
" <td>105.0</td>\n",
" <td>74.00</td>\n",
" <td>1980.0</td>\n",
" <td>15.3</td>\n",
" <td>82</td>\n",
" <td>2</td>\n",
" <td>volkswagen rabbit l</td>\n",
" </tr>\n",
" <tr>\n",
" <th>376</th>\n",
" <td>37.0</td>\n",
" <td>4</td>\n",
" <td>91.0</td>\n",
" <td>68.00</td>\n",
" <td>2025.0</td>\n",
" <td>18.2</td>\n",
" <td>82</td>\n",
" <td>3</td>\n",
" <td>mazda glc custom l</td>\n",
" </tr>\n",
" <tr>\n",
" <th>377</th>\n",
" <td>31.0</td>\n",
" <td>4</td>\n",
" <td>91.0</td>\n",
" <td>68.00</td>\n",
" <td>1970.0</td>\n",
" <td>17.6</td>\n",
" <td>82</td>\n",
" <td>3</td>\n",
" <td>mazda glc custom</td>\n",
" </tr>\n",
" <tr>\n",
" <th>378</th>\n",
" <td>38.0</td>\n",
" <td>4</td>\n",
" <td>105.0</td>\n",
" <td>63.00</td>\n",
" <td>2125.0</td>\n",
" <td>14.7</td>\n",
" <td>82</td>\n",
" <td>1</td>\n",
" <td>plymouth horizon miser</td>\n",
" </tr>\n",
" <tr>\n",
" <th>379</th>\n",
" <td>36.0</td>\n",
" <td>4</td>\n",
" <td>98.0</td>\n",
" <td>70.00</td>\n",
" <td>2125.0</td>\n",
" <td>17.3</td>\n",
" <td>82</td>\n",
" <td>1</td>\n",
" <td>mercury lynx l</td>\n",
" </tr>\n",
" <tr>\n",
" <th>380</th>\n",
" <td>36.0</td>\n",
" <td>4</td>\n",
" <td>120.0</td>\n",
" <td>88.00</td>\n",
" <td>2160.0</td>\n",
" <td>14.5</td>\n",
" <td>82</td>\n",
" <td>3</td>\n",
" <td>nissan stanza xe</td>\n",
" </tr>\n",
" <tr>\n",
" <th>381</th>\n",
" <td>36.0</td>\n",
" <td>4</td>\n",
" <td>107.0</td>\n",
" <td>75.00</td>\n",
" <td>2205.0</td>\n",
" <td>14.5</td>\n",
" <td>82</td>\n",
" <td>3</td>\n",
" <td>honda accord</td>\n",
" </tr>\n",
" <tr>\n",
" <th>382</th>\n",
" <td>34.0</td>\n",
" <td>4</td>\n",
" <td>108.0</td>\n",
" <td>70.00</td>\n",
" <td>2245.0</td>\n",
" <td>16.9</td>\n",
" <td>82</td>\n",
" <td>3</td>\n",
" <td>toyota corolla</td>\n",
" </tr>\n",
" <tr>\n",
" <th>383</th>\n",
" <td>38.0</td>\n",
" <td>4</td>\n",
" <td>91.0</td>\n",
" <td>67.00</td>\n",
" <td>1965.0</td>\n",
" <td>15.0</td>\n",
" <td>82</td>\n",
" <td>3</td>\n",
" <td>honda civic</td>\n",
" </tr>\n",
" <tr>\n",
" <th>384</th>\n",
" <td>32.0</td>\n",
" <td>4</td>\n",
" <td>91.0</td>\n",
" <td>67.00</td>\n",
" <td>1965.0</td>\n",
" <td>15.7</td>\n",
" <td>82</td>\n",
" <td>3</td>\n",
" <td>honda civic (auto)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>385</th>\n",
" <td>38.0</td>\n",
" <td>4</td>\n",
" <td>91.0</td>\n",
" <td>67.00</td>\n",
" <td>1995.0</td>\n",
" <td>16.2</td>\n",
" <td>82</td>\n",
" <td>3</td>\n",
" <td>datsun 310 gx</td>\n",
" </tr>\n",
" <tr>\n",
" <th>386</th>\n",
" <td>25.0</td>\n",
" <td>6</td>\n",
" <td>181.0</td>\n",
" <td>110.0</td>\n",
" <td>2945.0</td>\n",
" <td>16.4</td>\n",
" <td>82</td>\n",
" <td>1</td>\n",
" <td>buick century limited</td>\n",
" </tr>\n",
" <tr>\n",
" <th>387</th>\n",
" <td>38.0</td>\n",
" <td>6</td>\n",
" <td>262.0</td>\n",
" <td>85.00</td>\n",
" <td>3015.0</td>\n",
" <td>17.0</td>\n",
" <td>82</td>\n",
" <td>1</td>\n",
" <td>oldsmobile cutlass ciera (diesel)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>388</th>\n",
" <td>26.0</td>\n",
" <td>4</td>\n",
" <td>156.0</td>\n",
" <td>92.00</td>\n",
" <td>2585.0</td>\n",
" <td>14.5</td>\n",
" <td>82</td>\n",
" <td>1</td>\n",
" <td>chrysler lebaron medallion</td>\n",
" </tr>\n",
" <tr>\n",
" <th>389</th>\n",
" <td>22.0</td>\n",
" <td>6</td>\n",
" <td>232.0</td>\n",
" <td>112.0</td>\n",
" <td>2835.0</td>\n",
" <td>14.7</td>\n",
" <td>82</td>\n",
" <td>1</td>\n",
" <td>ford granada l</td>\n",
" </tr>\n",
" <tr>\n",
" <th>390</th>\n",
" <td>32.0</td>\n",
" <td>4</td>\n",
" <td>144.0</td>\n",
" <td>96.00</td>\n",
" <td>2665.0</td>\n",
" <td>13.9</td>\n",
" <td>82</td>\n",
" <td>3</td>\n",
" <td>toyota celica gt</td>\n",
" </tr>\n",
" <tr>\n",
" <th>391</th>\n",
" <td>36.0</td>\n",
" <td>4</td>\n",
" <td>135.0</td>\n",
" <td>84.00</td>\n",
" <td>2370.0</td>\n",
" <td>13.0</td>\n",
" <td>82</td>\n",
" <td>1</td>\n",
" <td>dodge charger 2.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>392</th>\n",
" <td>27.0</td>\n",
" <td>4</td>\n",
" <td>151.0</td>\n",
" <td>90.00</td>\n",
" <td>2950.0</td>\n",
" <td>17.3</td>\n",
" <td>82</td>\n",
" <td>1</td>\n",
" <td>chevrolet camaro</td>\n",
" </tr>\n",
" <tr>\n",
" <th>393</th>\n",
" <td>27.0</td>\n",
" <td>4</td>\n",
" <td>140.0</td>\n",
" <td>86.00</td>\n",
" <td>2790.0</td>\n",
" <td>15.6</td>\n",
" <td>82</td>\n",
" <td>1</td>\n",
" <td>ford mustang gl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>394</th>\n",
" <td>44.0</td>\n",
" <td>4</td>\n",
" <td>97.0</td>\n",
" <td>52.00</td>\n",
" <td>2130.0</td>\n",
" <td>24.6</td>\n",
" <td>82</td>\n",
" <td>2</td>\n",
" <td>vw pickup</td>\n",
" </tr>\n",
" <tr>\n",
" <th>395</th>\n",
" <td>32.0</td>\n",
" <td>4</td>\n",
" <td>135.0</td>\n",
" <td>84.00</td>\n",
" <td>2295.0</td>\n",
" <td>11.6</td>\n",
" <td>82</td>\n",
" <td>1</td>\n",
" <td>dodge rampage</td>\n",
" </tr>\n",
" <tr>\n",
" <th>396</th>\n",
" <td>28.0</td>\n",
" <td>4</td>\n",
" <td>120.0</td>\n",
" <td>79.00</td>\n",
" <td>2625.0</td>\n",
" <td>18.6</td>\n",
" <td>82</td>\n",
" <td>1</td>\n",
" <td>ford ranger</td>\n",
" </tr>\n",
" <tr>\n",
" <th>397</th>\n",
" <td>31.0</td>\n",
" <td>4</td>\n",
" <td>119.0</td>\n",
" <td>82.00</td>\n",
" <td>2720.0</td>\n",
" <td>19.4</td>\n",
" <td>82</td>\n",
" <td>1</td>\n",
" <td>chevy s-10</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>398 rows × 9 columns</p>\n",
"</div>"
],
"text/plain": [
" col1 col2 col3 col4 col5 col6 col7 col8 \\\n",
"0 18.0 8 307.0 130.0 3504.0 12.0 70 1 \n",
"1 15.0 8 350.0 165.0 3693.0 11.5 70 1 \n",
"2 18.0 8 318.0 150.0 3436.0 11.0 70 1 \n",
"3 16.0 8 304.0 150.0 3433.0 12.0 70 1 \n",
"4 17.0 8 302.0 140.0 3449.0 10.5 70 1 \n",
"5 15.0 8 429.0 198.0 4341.0 10.0 70 1 \n",
"6 14.0 8 454.0 220.0 4354.0 9.0 70 1 \n",
"7 14.0 8 440.0 215.0 4312.0 8.5 70 1 \n",
"8 14.0 8 455.0 225.0 4425.0 10.0 70 1 \n",
"9 15.0 8 390.0 190.0 3850.0 8.5 70 1 \n",
"10 15.0 8 383.0 170.0 3563.0 10.0 70 1 \n",
"11 14.0 8 340.0 160.0 3609.0 8.0 70 1 \n",
"12 15.0 8 400.0 150.0 3761.0 9.5 70 1 \n",
"13 14.0 8 455.0 225.0 3086.0 10.0 70 1 \n",
"14 24.0 4 113.0 95.00 2372.0 15.0 70 3 \n",
"15 22.0 6 198.0 95.00 2833.0 15.5 70 1 \n",
"16 18.0 6 199.0 97.00 2774.0 15.5 70 1 \n",
"17 21.0 6 200.0 85.00 2587.0 16.0 70 1 \n",
"18 27.0 4 97.0 88.00 2130.0 14.5 70 3 \n",
"19 26.0 4 97.0 46.00 1835.0 20.5 70 2 \n",
"20 25.0 4 110.0 87.00 2672.0 17.5 70 2 \n",
"21 24.0 4 107.0 90.00 2430.0 14.5 70 2 \n",
"22 25.0 4 104.0 95.00 2375.0 17.5 70 2 \n",
"23 26.0 4 121.0 113.0 2234.0 12.5 70 2 \n",
"24 21.0 6 199.0 90.00 2648.0 15.0 70 1 \n",
"25 10.0 8 360.0 215.0 4615.0 14.0 70 1 \n",
"26 10.0 8 307.0 200.0 4376.0 15.0 70 1 \n",
"27 11.0 8 318.0 210.0 4382.0 13.5 70 1 \n",
"28 9.0 8 304.0 193.0 4732.0 18.5 70 1 \n",
"29 27.0 4 97.0 88.00 2130.0 14.5 71 3 \n",
".. ... ... ... ... ... ... ... ... \n",
"368 27.0 4 112.0 88.00 2640.0 18.6 82 1 \n",
"369 34.0 4 112.0 88.00 2395.0 18.0 82 1 \n",
"370 31.0 4 112.0 85.00 2575.0 16.2 82 1 \n",
"371 29.0 4 135.0 84.00 2525.0 16.0 82 1 \n",
"372 27.0 4 151.0 90.00 2735.0 18.0 82 1 \n",
"373 24.0 4 140.0 92.00 2865.0 16.4 82 1 \n",
"374 23.0 4 151.0 ? 3035.0 20.5 82 1 \n",
"375 36.0 4 105.0 74.00 1980.0 15.3 82 2 \n",
"376 37.0 4 91.0 68.00 2025.0 18.2 82 3 \n",
"377 31.0 4 91.0 68.00 1970.0 17.6 82 3 \n",
"378 38.0 4 105.0 63.00 2125.0 14.7 82 1 \n",
"379 36.0 4 98.0 70.00 2125.0 17.3 82 1 \n",
"380 36.0 4 120.0 88.00 2160.0 14.5 82 3 \n",
"381 36.0 4 107.0 75.00 2205.0 14.5 82 3 \n",
"382 34.0 4 108.0 70.00 2245.0 16.9 82 3 \n",
"383 38.0 4 91.0 67.00 1965.0 15.0 82 3 \n",
"384 32.0 4 91.0 67.00 1965.0 15.7 82 3 \n",
"385 38.0 4 91.0 67.00 1995.0 16.2 82 3 \n",
"386 25.0 6 181.0 110.0 2945.0 16.4 82 1 \n",
"387 38.0 6 262.0 85.00 3015.0 17.0 82 1 \n",
"388 26.0 4 156.0 92.00 2585.0 14.5 82 1 \n",
"389 22.0 6 232.0 112.0 2835.0 14.7 82 1 \n",
"390 32.0 4 144.0 96.00 2665.0 13.9 82 3 \n",
"391 36.0 4 135.0 84.00 2370.0 13.0 82 1 \n",
"392 27.0 4 151.0 90.00 2950.0 17.3 82 1 \n",
"393 27.0 4 140.0 86.00 2790.0 15.6 82 1 \n",
"394 44.0 4 97.0 52.00 2130.0 24.6 82 2 \n",
"395 32.0 4 135.0 84.00 2295.0 11.6 82 1 \n",
"396 28.0 4 120.0 79.00 2625.0 18.6 82 1 \n",
"397 31.0 4 119.0 82.00 2720.0 19.4 82 1 \n",
"\n",
" col9 \n",
"0 chevrolet chevelle malibu \n",
"1 buick skylark 320 \n",
"2 plymouth satellite \n",
"3 amc rebel sst \n",
"4 ford torino \n",
"5 ford galaxie 500 \n",
"6 chevrolet impala \n",
"7 plymouth fury iii \n",
"8 pontiac catalina \n",
"9 amc ambassador dpl \n",
"10 dodge challenger se \n",
"11 plymouth 'cuda 340 \n",
"12 chevrolet monte carlo \n",
"13 buick estate wagon (sw) \n",
"14 toyota corona mark ii \n",
"15 plymouth duster \n",
"16 amc hornet \n",
"17 ford maverick \n",
"18 datsun pl510 \n",
"19 volkswagen 1131 deluxe sedan \n",
"20 peugeot 504 \n",
"21 audi 100 ls \n",
"22 saab 99e \n",
"23 bmw 2002 \n",
"24 amc gremlin \n",
"25 ford f250 \n",
"26 chevy c20 \n",
"27 dodge d200 \n",
"28 hi 1200d \n",
"29 datsun pl510 \n",
".. ... \n",
"368 chevrolet cavalier wagon \n",
"369 chevrolet cavalier 2-door \n",
"370 pontiac j2000 se hatchback \n",
"371 dodge aries se \n",
"372 pontiac phoenix \n",
"373 ford fairmont futura \n",
"374 amc concord dl \n",
"375 volkswagen rabbit l \n",
"376 mazda glc custom l \n",
"377 mazda glc custom \n",
"378 plymouth horizon miser \n",
"379 mercury lynx l \n",
"380 nissan stanza xe \n",
"381 honda accord \n",
"382 toyota corolla \n",
"383 honda civic \n",
"384 honda civic (auto) \n",
"385 datsun 310 gx \n",
"386 buick century limited \n",
"387 oldsmobile cutlass ciera (diesel) \n",
"388 chrysler lebaron medallion \n",
"389 ford granada l \n",
"390 toyota celica gt \n",
"391 dodge charger 2.2 \n",
"392 chevrolet camaro \n",
"393 ford mustang gl \n",
"394 vw pickup \n",
"395 dodge rampage \n",
"396 ford ranger \n",
"397 chevy s-10 \n",
"\n",
"[398 rows x 9 columns]"
]
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