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ankitmishra88/pandasTutorial.ipynb

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pandasTutorial.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Series Object: One dimensional labelled array\n",
"contains data of similar or mixed type"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"C:\\Users\\4WallSpace\\Anaconda\\lib\\site-packages\\pandas\\__init__.py\n"
]
}
],
"source": [
"import pandas as pd\n",
"print(pd.__file__)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 1\n",
"1 2\n",
"2 3\n",
"dtype: int64"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.Series([1,2,3])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# creating Series Object"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 1\n",
"1 2\n",
"2 3\n",
"dtype: int64\n"
]
}
],
"source": [
"srs=pd.Series([1,2,3])\n",
"print(srs)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 [1, 2, 3]\n",
"1 [4, 5, 6]\n",
"2 [6, 7, 8]\n",
"dtype: object"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"srs=pd.Series([[1,2,3],[4,5,6],[6,7,8]])\n",
"srs"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"# Type Of Series"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.series.Series'>\n"
]
}
],
"source": [
"print(type(srs))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"# Change Index of Series"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"a 1\n",
"b 2\n",
"c 3\n",
"d 4\n",
"dtype: int64"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data=[1,2,3,4]\n",
"srs=pd.Series([1,2,3,4],index=['a','b','c','d'])\n",
"srs"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"a 1\n",
"b 2\n",
"c 3\n",
"dtype: int64"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"srs=pd.Series([1,2,3],['a','b','c'])\n",
"srs"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"# Accessing Series element"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1\n"
]
}
],
"source": [
"print(srs['a'])"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"a 1\n",
"b 2\n",
"c 3\n",
"dtype: int64\n"
]
}
],
"source": [
"print(srs['a':'c']) #Not Excludes righthand index"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"a 1\n",
"b 2\n",
"dtype: int64\n"
]
}
],
"source": [
"print(srs[:-1]) #Negative indexing still works after changing"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1\n",
"a 1\n",
"b 2\n",
"c 3\n",
"dtype: int64\n",
"a 1\n",
"b 2\n",
"dtype: int64\n"
]
}
],
"source": [
"print(srs[0])#Elements of series still can be accessed by index value\n",
"print(srs[0:]) \n",
"print(srs[0:2])# While indexing still excludes right hand index"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"#Array,Dictionary and scalar all three can be changed to series"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"name Ankit\n",
"Age 20\n",
"dtype: object\n",
"0 20\n",
"1 30\n",
"2 40\n",
"dtype: int32\n",
"0 1\n",
"1 2\n",
"2 3\n",
"dtype: int64\n",
"0 [1, 2, 3]\n",
"1 [4, 5, 6, 7]\n",
"dtype: object\n"
]
}
],
"source": [
"import numpy as np\n",
"dict={'name':\"Ankit\",'Age':20}\n",
"srs=pd.Series(dict)\n",
"print(srs)\n",
"arr=np.array([20,30,40])\n",
"srs=pd.Series(arr)\n",
"print(srs)\n",
"srs=pd.Series([1,2,3])\n",
"print(srs)\n",
"srs=pd.Series([[1,2,3],[4,5,6,7]])\n",
"print(srs)"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"# DataFrame\n",
"#Two dimensional labelled Data(row labelling,column labelling)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 0 1 2 3 4\n",
"0 1 2 3 4 NaN\n",
"1 1 2 3 4 5.0\n"
]
}
],
"source": [
"data=[[1,2,3,4],[1,2,3,4,5]]\n",
"df=pd.DataFrame(data)\n",
"print(df)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"#Example Using Dictionary"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b\n",
"0 1 4\n",
"1 2 5\n",
"2 3 7\n",
"3 4 9\n",
"4 5 11\n"
]
}
],
"source": [
"data={'a':[1,2,3,4,5],'b':[4,5,7,9,11]}\n",
"dF=pd.DataFrame(data)\n",
"print(dF)"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
"#data in column should be same here otherwise it will show error"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "arrays must all be same length",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-58-1faf53533173>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m'a'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'b'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mdF\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdF\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\Anaconda\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[0;32m 409\u001b[0m )\n\u001b[0;32m 410\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 411\u001b[1;33m \u001b[0mmgr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minit_dict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 412\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMaskedArray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 413\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmrecords\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mmrecords\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\Anaconda\\lib\\site-packages\\pandas\\core\\internals\\construction.py\u001b[0m in \u001b[0;36minit_dict\u001b[1;34m(data, index, columns, dtype)\u001b[0m\n\u001b[0;32m 255\u001b[0m \u001b[0marr\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mis_datetime64tz_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0marr\u001b[0m \u001b[1;32min\u001b[0m \u001b[0marrays\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 256\u001b[0m ]\n\u001b[1;32m--> 257\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0marrays_to_mgr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata_names\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 258\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 259\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\Anaconda\\lib\\site-packages\\pandas\\core\\internals\\construction.py\u001b[0m in \u001b[0;36marrays_to_mgr\u001b[1;34m(arrays, arr_names, index, columns, dtype)\u001b[0m\n\u001b[0;32m 75\u001b[0m \u001b[1;31m# figure out the index, if necessary\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 76\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mindex\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 77\u001b[1;33m \u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mextract_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 78\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 79\u001b[0m \u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mensure_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\Anaconda\\lib\\site-packages\\pandas\\core\\internals\\construction.py\u001b[0m in \u001b[0;36mextract_index\u001b[1;34m(data)\u001b[0m\n\u001b[0;32m 366\u001b[0m \u001b[0mlengths\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mraw_lengths\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 367\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlengths\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 368\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"arrays must all be same length\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 369\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 370\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mhave_dicts\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mValueError\u001b[0m: arrays must all be same length"
]
}
],
"source": [
"data={'a':[1,2,3,4],'b':[3,4,3]}\n",
"dF=pd.DataFrame(data)\n",
"print(dF)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
"#Creating a DataFrame from Series Object"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"a 1\n",
"b 3\n",
"c 4\n",
"dtype: int64\n",
" 0\n",
"a 1\n",
"b 3\n",
"c 4\n"
]
}
],
"source": [
"srs=pd.Series([1,3,4],index=['a','b','c'])\n",
"print(srs)\n",
"df=pd.DataFrame(srs)\n",
"print(df)"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [],
"source": [
"#creating a DataFrame from a numpy ndarray"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[1 2 3 4]\n",
" [3 4 5 5]]\n",
" 0 1 2 3\n",
"0 1 2 3 4\n",
"1 3 4 5 5\n"
]
}
],
"source": [
"nd=np.array([[1,2,3,4],[3,4,5,5]])\n",
"print(nd)\n",
"df=pd.DataFrame(nd)\n",
"print(df)"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b\n",
"0 1 2\n",
"1 3 4\n"
]
}
],
"source": [
"df=pd.DataFrame({'a':nd[:,0],'b':nd[:,1]})\n",
"print(df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Merge, Join,Concatenate"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### DataFrame For Pandas Merge\n",
"1.Left Merge Left Join 2.Right Merge Right Join \n",
"3.Inner Merge Inner Join 4.Outer Merge Outer Join"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Ankit\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:4: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
"of pandas will change to not sort by default.\n",
"\n",
"To accept the future behavior, pass 'sort=False'.\n",
"\n",
"To retain the current behavior and silence the warning, pass 'sort=True'.\n",
"\n",
" after removing the cwd from sys.path.\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Age</th>\n",
" <th>Name</th>\n",
" <th>cricket</th>\n",
" <th>study</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>18</td>\n",
" <td>Ankit</td>\n",
" <td>NaN</td>\n",
" <td>top</td>\n",
" </tr>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>19</td>\n",
" <td>Ayush</td>\n",
" <td>100.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Age Name cricket study\n",
"0 18 Ankit NaN top\n",
"0 19 Ayush 100.0 NaN"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Concatenate\n",
"df1=pd.DataFrame({\"Name\":[\"Ankit\"],\"Age\":[18],\"study\":[\"top\"]})\n",
"df2=pd.DataFrame({\"Name\":[\"Ayush\"],\"Age\":[19],\"cricket\":[100]})\n",
"pd.concat([df1,df2])\n"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [],
"source": [
"#concat is a kind of Outer Join by default"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>Age</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>Ankit</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>Ayush</td>\n",
" <td>19</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Age\n",
"0 Ankit 18\n",
"0 Ayush 19"
]
},
"execution_count": 93,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#inner join\n",
"pd.concat([df1,df2],join=\"inner\")"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>Age</th>\n",
" <th>study</th>\n",
" <th>Name</th>\n",
" <th>Age</th>\n",
" <th>cricket</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>Ankit</td>\n",
" <td>18</td>\n",
" <td>top</td>\n",
" <td>Ayush</td>\n",
" <td>19</td>\n",
" <td>100</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Age study Name Age cricket\n",
"0 Ankit 18 top Ayush 19 100"
]
},
"execution_count": 97,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#use axis=0 or axis=1 to join rowwise or columnwise\n",
"pd.concat([df1,df2],axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Merge : It works similar like sql join"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name_x</th>\n",
" <th>Age</th>\n",
" <th>study</th>\n",
" <th>Name_y</th>\n",
" <th>cricket</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>Ankit</td>\n",
" <td>18</td>\n",
" <td>top</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>19</td>\n",
" <td>NaN</td>\n",
" <td>Ayush</td>\n",
" <td>100.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name_x Age study Name_y cricket\n",
"0 Ankit 18 top NaN NaN\n",
"1 NaN 19 NaN Ayush 100.0"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.merge(df1,df2,on=[\"Age\"],how=\"outer\")"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name_x</th>\n",
" <th>Age</th>\n",
" <th>study</th>\n",
" <th>Name_y</th>\n",
" <th>cricket</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>Ankit</td>\n",
" <td>18</td>\n",
" <td>top</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name_x Age study Name_y cricket\n",
"0 Ankit 18 top NaN NaN"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.merge(df1,df2,on=[\"Age\"],how=\"left\")"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Dataset Import\n",
"## Reading Csv\n",
"### pd.read_csv('file_location')"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>mpg</th>\n",
" <th>cyl</th>\n",
" <th>disp</th>\n",
" <th>hp</th>\n",
" <th>drat</th>\n",
" <th>wt</th>\n",
" <th>qsec</th>\n",
" <th>vs</th>\n",
" <th>am</th>\n",
" <th>gear</th>\n",
" <th>carb</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>Mazda RX4</td>\n",
" <td>21.0</td>\n",
" <td>6</td>\n",
" <td>160.0</td>\n",
" <td>110</td>\n",
" <td>3.90</td>\n",
" <td>2.620</td>\n",
" <td>16.46</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>Mazda RX4 Wag</td>\n",
" <td>21.0</td>\n",
" <td>6</td>\n",
" <td>160.0</td>\n",
" <td>110</td>\n",
" <td>3.90</td>\n",
" <td>2.875</td>\n",
" <td>17.02</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>Datsun 710</td>\n",
" <td>22.8</td>\n",
" <td>4</td>\n",
" <td>108.0</td>\n",
" <td>93</td>\n",
" <td>3.85</td>\n",
" <td>2.320</td>\n",
" <td>18.61</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>Hornet 4 Drive</td>\n",
" <td>21.4</td>\n",
" <td>6</td>\n",
" <td>258.0</td>\n",
" <td>110</td>\n",
" <td>3.08</td>\n",
" <td>3.215</td>\n",
" <td>19.44</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>Hornet Sportabout</td>\n",
" <td>18.7</td>\n",
" <td>8</td>\n",
" <td>360.0</td>\n",
" <td>175</td>\n",
" <td>3.15</td>\n",
" <td>3.440</td>\n",
" <td>17.02</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>Valiant</td>\n",
" <td>18.1</td>\n",
" <td>6</td>\n",
" <td>225.0</td>\n",
" <td>105</td>\n",
" <td>2.76</td>\n",
" <td>3.460</td>\n",
" <td>20.22</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>6</td>\n",
" <td>Duster 360</td>\n",
" <td>14.3</td>\n",
" <td>8</td>\n",
" <td>360.0</td>\n",
" <td>245</td>\n",
" <td>3.21</td>\n",
" <td>3.570</td>\n",
" <td>15.84</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7</td>\n",
" <td>Merc 240D</td>\n",
" <td>24.4</td>\n",
" <td>4</td>\n",
" <td>146.7</td>\n",
" <td>62</td>\n",
" <td>3.69</td>\n",
" <td>3.190</td>\n",
" <td>20.00</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>8</td>\n",
" <td>Merc 230</td>\n",
" <td>22.8</td>\n",
" <td>4</td>\n",
" <td>140.8</td>\n",
" <td>95</td>\n",
" <td>3.92</td>\n",
" <td>3.150</td>\n",
" <td>22.90</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>9</td>\n",
" <td>Merc 280</td>\n",
" <td>19.2</td>\n",
" <td>6</td>\n",
" <td>167.6</td>\n",
" <td>123</td>\n",
" <td>3.92</td>\n",
" <td>3.440</td>\n",
" <td>18.30</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>10</td>\n",
" <td>Merc 280C</td>\n",
" <td>17.8</td>\n",
" <td>6</td>\n",
" <td>167.6</td>\n",
" <td>123</td>\n",
" <td>3.92</td>\n",
" <td>3.440</td>\n",
" <td>18.90</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>11</td>\n",
" <td>Merc 450SE</td>\n",
" <td>16.4</td>\n",
" <td>8</td>\n",
" <td>275.8</td>\n",
" <td>180</td>\n",
" <td>3.07</td>\n",
" <td>4.070</td>\n",
" <td>17.40</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>12</td>\n",
" <td>Merc 450SL</td>\n",
" <td>17.3</td>\n",
" <td>8</td>\n",
" <td>275.8</td>\n",
" <td>180</td>\n",
" <td>3.07</td>\n",
" <td>3.730</td>\n",
" <td>17.60</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>13</td>\n",
" <td>Merc 450SLC</td>\n",
" <td>15.2</td>\n",
" <td>8</td>\n",
" <td>275.8</td>\n",
" <td>180</td>\n",
" <td>3.07</td>\n",
" <td>3.780</td>\n",
" <td>18.00</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>14</td>\n",
" <td>Cadillac Fleetwood</td>\n",
" <td>10.4</td>\n",
" <td>8</td>\n",
" <td>472.0</td>\n",
" <td>205</td>\n",
" <td>2.93</td>\n",
" <td>5.250</td>\n",
" <td>17.98</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>15</td>\n",
" <td>Lincoln Continental</td>\n",
" <td>10.4</td>\n",
" <td>8</td>\n",
" <td>460.0</td>\n",
" <td>215</td>\n",
" <td>3.00</td>\n",
" <td>5.424</td>\n",
" <td>17.82</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16</td>\n",
" <td>Chrysler Imperial</td>\n",
" <td>14.7</td>\n",
" <td>8</td>\n",
" <td>440.0</td>\n",
" <td>230</td>\n",
" <td>3.23</td>\n",
" <td>5.345</td>\n",
" <td>17.42</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>17</td>\n",
" <td>Fiat 128</td>\n",
" <td>32.4</td>\n",
" <td>4</td>\n",
" <td>78.7</td>\n",
" <td>66</td>\n",
" <td>4.08</td>\n",
" <td>2.200</td>\n",
" <td>19.47</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>18</td>\n",
" <td>Honda Civic</td>\n",
" <td>30.4</td>\n",
" <td>4</td>\n",
" <td>75.7</td>\n",
" <td>52</td>\n",
" <td>4.93</td>\n",
" <td>1.615</td>\n",
" <td>18.52</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>19</td>\n",
" <td>Toyota Corolla</td>\n",
" <td>33.9</td>\n",
" <td>4</td>\n",
" <td>71.1</td>\n",
" <td>65</td>\n",
" <td>4.22</td>\n",
" <td>1.835</td>\n",
" <td>19.90</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>20</td>\n",
" <td>Toyota Corona</td>\n",
" <td>21.5</td>\n",
" <td>4</td>\n",
" <td>120.1</td>\n",
" <td>97</td>\n",
" <td>3.70</td>\n",
" <td>2.465</td>\n",
" <td>20.01</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>21</td>\n",
" <td>Dodge Challenger</td>\n",
" <td>15.5</td>\n",
" <td>8</td>\n",
" <td>318.0</td>\n",
" <td>150</td>\n",
" <td>2.76</td>\n",
" <td>3.520</td>\n",
" <td>16.87</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>22</td>\n",
" <td>AMC Javelin</td>\n",
" <td>15.2</td>\n",
" <td>8</td>\n",
" <td>304.0</td>\n",
" <td>150</td>\n",
" <td>3.15</td>\n",
" <td>3.435</td>\n",
" <td>17.30</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>23</td>\n",
" <td>Camaro Z28</td>\n",
" <td>13.3</td>\n",
" <td>8</td>\n",
" <td>350.0</td>\n",
" <td>245</td>\n",
" <td>3.73</td>\n",
" <td>3.840</td>\n",
" <td>15.41</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>24</td>\n",
" <td>Pontiac Firebird</td>\n",
" <td>19.2</td>\n",
" <td>8</td>\n",
" <td>400.0</td>\n",
" <td>175</td>\n",
" <td>3.08</td>\n",
" <td>3.845</td>\n",
" <td>17.05</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>25</td>\n",
" <td>Fiat X1-9</td>\n",
" <td>27.3</td>\n",
" <td>4</td>\n",
" <td>79.0</td>\n",
" <td>66</td>\n",
" <td>4.08</td>\n",
" <td>1.935</td>\n",
" <td>18.90</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>26</td>\n",
" <td>Porsche 914-2</td>\n",
" <td>26.0</td>\n",
" <td>4</td>\n",
" <td>120.3</td>\n",
" <td>91</td>\n",
" <td>4.43</td>\n",
" <td>2.140</td>\n",
" <td>16.70</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>27</td>\n",
" <td>Lotus Europa</td>\n",
" <td>30.4</td>\n",
" <td>4</td>\n",
" <td>95.1</td>\n",
" <td>113</td>\n",
" <td>3.77</td>\n",
" <td>1.513</td>\n",
" <td>16.90</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>28</td>\n",
" <td>Ford Pantera L</td>\n",
" <td>15.8</td>\n",
" <td>8</td>\n",
" <td>351.0</td>\n",
" <td>264</td>\n",
" <td>4.22</td>\n",
" <td>3.170</td>\n",
" <td>14.50</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>29</td>\n",
" <td>Ferrari Dino</td>\n",
" <td>19.7</td>\n",
" <td>6</td>\n",
" <td>145.0</td>\n",
" <td>175</td>\n",
" <td>3.62</td>\n",
" <td>2.770</td>\n",
" <td>15.50</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <td>30</td>\n",
" <td>Maserati Bora</td>\n",
" <td>15.0</td>\n",
" <td>8</td>\n",
" <td>301.0</td>\n",
" <td>335</td>\n",
" <td>3.54</td>\n",
" <td>3.570</td>\n",
" <td>14.60</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <td>31</td>\n",
" <td>Volvo 142E</td>\n",
" <td>21.4</td>\n",
" <td>4</td>\n",
" <td>121.0</td>\n",
" <td>109</td>\n",
" <td>4.11</td>\n",
" <td>2.780</td>\n",
" <td>18.60</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 mpg cyl disp hp drat wt qsec vs am \\\n",
"0 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 \n",
"1 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 \n",
"2 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 \n",
"3 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 \n",
"4 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 \n",
"5 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 \n",
"6 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 \n",
"7 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 \n",
"8 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 \n",
"9 Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 \n",
"10 Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 \n",
"11 Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 \n",
"12 Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 \n",
"13 Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 \n",
"14 Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 \n",
"15 Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 \n",
"16 Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 \n",
"17 Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 \n",
"18 Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 \n",
"19 Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 \n",
"20 Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 \n",
"21 Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 \n",
"22 AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 \n",
"23 Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 \n",
"24 Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 \n",
"25 Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 \n",
"26 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 \n",
"27 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 \n",
"28 Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 \n",
"29 Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 \n",
"30 Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 \n",
"31 Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 \n",
"\n",
" gear carb \n",
"0 4 4 \n",
"1 4 4 \n",
"2 4 1 \n",
"3 3 1 \n",
"4 3 2 \n",
"5 3 1 \n",
"6 3 4 \n",
"7 4 2 \n",
"8 4 2 \n",
"9 4 4 \n",
"10 4 4 \n",
"11 3 3 \n",
"12 3 3 \n",
"13 3 3 \n",
"14 3 4 \n",
"15 3 4 \n",
"16 3 4 \n",
"17 4 1 \n",
"18 4 2 \n",
"19 4 1 \n",
"20 3 1 \n",
"21 3 2 \n",
"22 3 2 \n",
"23 3 4 \n",
"24 3 2 \n",
"25 4 1 \n",
"26 5 2 \n",
"27 5 2 \n",
"28 5 4 \n",
"29 5 6 \n",
"30 5 8 \n",
"31 4 2 "
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cars=pd.read_csv('cars.csv')\n",
"cars"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Analysis"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 21.0\n",
"1 21.0\n",
"2 22.8\n",
"3 21.4\n",
"4 18.7\n",
"5 18.1\n",
"6 14.3\n",
"7 24.4\n",
"8 22.8\n",
"9 19.2\n",
"10 17.8\n",
"11 16.4\n",
"12 17.3\n",
"13 15.2\n",
"14 10.4\n",
"15 10.4\n",
"16 14.7\n",
"17 32.4\n",
"18 30.4\n",
"19 33.9\n",
"20 21.5\n",
"21 15.5\n",
"22 15.2\n",
"23 13.3\n",
"24 19.2\n",
"25 27.3\n",
"26 26.0\n",
"27 30.4\n",
"28 15.8\n",
"29 19.7\n",
"30 15.0\n",
"31 21.4\n",
"Name: mpg, dtype: float64"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#accessing a column\n",
"cars.mpg"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"#changing datatype of a column\n",
"cars.mpg=cars.mpg.astype(str)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"pandas.core.frame.DataFrame"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#checking the type\n",
"type(cars)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>mpg</th>\n",
" <th>cyl</th>\n",
" <th>disp</th>\n",
" <th>hp</th>\n",
" <th>drat</th>\n",
" <th>wt</th>\n",
" <th>qsec</th>\n",
" <th>vs</th>\n",
" <th>am</th>\n",
" <th>gear</th>\n",
" <th>carb</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>Mazda RX4</td>\n",
" <td>21.0</td>\n",
" <td>6</td>\n",
" <td>160.0</td>\n",
" <td>110</td>\n",
" <td>3.90</td>\n",
" <td>2.620</td>\n",
" <td>16.46</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>Mazda RX4 Wag</td>\n",
" <td>21.0</td>\n",
" <td>6</td>\n",
" <td>160.0</td>\n",
" <td>110</td>\n",
" <td>3.90</td>\n",
" <td>2.875</td>\n",
" <td>17.02</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>Datsun 710</td>\n",
" <td>22.8</td>\n",
" <td>4</td>\n",
" <td>108.0</td>\n",
" <td>93</td>\n",
" <td>3.85</td>\n",
" <td>2.320</td>\n",
" <td>18.61</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>Hornet 4 Drive</td>\n",
" <td>21.4</td>\n",
" <td>6</td>\n",
" <td>258.0</td>\n",
" <td>110</td>\n",
" <td>3.08</td>\n",
" <td>3.215</td>\n",
" <td>19.44</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>Hornet Sportabout</td>\n",
" <td>18.7</td>\n",
" <td>8</td>\n",
" <td>360.0</td>\n",
" <td>175</td>\n",
" <td>3.15</td>\n",
" <td>3.440</td>\n",
" <td>17.02</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 mpg cyl disp hp drat wt qsec vs am gear \\\n",
"0 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 \n",
"1 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 \n",
"2 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 \n",
"3 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 \n",
"4 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 \n",
"\n",
" carb \n",
"0 4 \n",
"1 4 \n",
"2 1 \n",
"3 1 \n",
"4 2 "
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#view only the first five row - default\n",
"cars.head()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>mpg</th>\n",
" <th>cyl</th>\n",
" <th>disp</th>\n",
" <th>hp</th>\n",
" <th>drat</th>\n",
" <th>wt</th>\n",
" <th>qsec</th>\n",
" <th>vs</th>\n",
" <th>am</th>\n",
" <th>gear</th>\n",
" <th>carb</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>Mazda RX4</td>\n",
" <td>21.0</td>\n",
" <td>6</td>\n",
" <td>160.0</td>\n",
" <td>110</td>\n",
" <td>3.90</td>\n",
" <td>2.620</td>\n",
" <td>16.46</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>Mazda RX4 Wag</td>\n",
" <td>21.0</td>\n",
" <td>6</td>\n",
" <td>160.0</td>\n",
" <td>110</td>\n",
" <td>3.90</td>\n",
" <td>2.875</td>\n",
" <td>17.02</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>Datsun 710</td>\n",
" <td>22.8</td>\n",
" <td>4</td>\n",
" <td>108.0</td>\n",
" <td>93</td>\n",
" <td>3.85</td>\n",
" <td>2.320</td>\n",
" <td>18.61</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>Hornet 4 Drive</td>\n",
" <td>21.4</td>\n",
" <td>6</td>\n",
" <td>258.0</td>\n",
" <td>110</td>\n",
" <td>3.08</td>\n",
" <td>3.215</td>\n",
" <td>19.44</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>Hornet Sportabout</td>\n",
" <td>18.7</td>\n",
" <td>8</td>\n",
" <td>360.0</td>\n",
" <td>175</td>\n",
" <td>3.15</td>\n",
" <td>3.440</td>\n",
" <td>17.02</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>Valiant</td>\n",
" <td>18.1</td>\n",
" <td>6</td>\n",
" <td>225.0</td>\n",
" <td>105</td>\n",
" <td>2.76</td>\n",
" <td>3.460</td>\n",
" <td>20.22</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>6</td>\n",
" <td>Duster 360</td>\n",
" <td>14.3</td>\n",
" <td>8</td>\n",
" <td>360.0</td>\n",
" <td>245</td>\n",
" <td>3.21</td>\n",
" <td>3.570</td>\n",
" <td>15.84</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7</td>\n",
" <td>Merc 240D</td>\n",
" <td>24.4</td>\n",
" <td>4</td>\n",
" <td>146.7</td>\n",
" <td>62</td>\n",
" <td>3.69</td>\n",
" <td>3.190</td>\n",
" <td>20.00</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>8</td>\n",
" <td>Merc 230</td>\n",
" <td>22.8</td>\n",
" <td>4</td>\n",
" <td>140.8</td>\n",
" <td>95</td>\n",
" <td>3.92</td>\n",
" <td>3.150</td>\n",
" <td>22.90</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>9</td>\n",
" <td>Merc 280</td>\n",
" <td>19.2</td>\n",
" <td>6</td>\n",
" <td>167.6</td>\n",
" <td>123</td>\n",
" <td>3.92</td>\n",
" <td>3.440</td>\n",
" <td>18.30</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 mpg cyl disp hp drat wt qsec vs am gear \\\n",
"0 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 \n",
"1 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 \n",
"2 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 \n",
"3 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 \n",
"4 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 \n",
"5 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 \n",
"6 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 \n",
"7 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 \n",
"8 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 \n",
"9 Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 \n",
"\n",
" carb \n",
"0 4 \n",
"1 4 \n",
"2 1 \n",
"3 1 \n",
"4 2 \n",
"5 1 \n",
"6 4 \n",
"7 2 \n",
"8 2 \n",
"9 4 "
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cars.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>mpg</th>\n",
" <th>cyl</th>\n",
" <th>disp</th>\n",
" <th>hp</th>\n",
" <th>drat</th>\n",
" <th>wt</th>\n",
" <th>qsec</th>\n",
" <th>vs</th>\n",
" <th>am</th>\n",
" <th>gear</th>\n",
" <th>carb</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>27</td>\n",
" <td>Lotus Europa</td>\n",
" <td>30.4</td>\n",
" <td>4</td>\n",
" <td>95.1</td>\n",
" <td>113</td>\n",
" <td>3.77</td>\n",
" <td>1.513</td>\n",
" <td>16.9</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>28</td>\n",
" <td>Ford Pantera L</td>\n",
" <td>15.8</td>\n",
" <td>8</td>\n",
" <td>351.0</td>\n",
" <td>264</td>\n",
" <td>4.22</td>\n",
" <td>3.170</td>\n",
" <td>14.5</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>29</td>\n",
" <td>Ferrari Dino</td>\n",
" <td>19.7</td>\n",
" <td>6</td>\n",
" <td>145.0</td>\n",
" <td>175</td>\n",
" <td>3.62</td>\n",
" <td>2.770</td>\n",
" <td>15.5</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <td>30</td>\n",
" <td>Maserati Bora</td>\n",
" <td>15.0</td>\n",
" <td>8</td>\n",
" <td>301.0</td>\n",
" <td>335</td>\n",
" <td>3.54</td>\n",
" <td>3.570</td>\n",
" <td>14.6</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <td>31</td>\n",
" <td>Volvo 142E</td>\n",
" <td>21.4</td>\n",
" <td>4</td>\n",
" <td>121.0</td>\n",
" <td>109</td>\n",
" <td>4.11</td>\n",
" <td>2.780</td>\n",
" <td>18.6</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 mpg cyl disp hp drat wt qsec vs am gear \\\n",
"27 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.9 1 1 5 \n",
"28 Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.5 0 1 5 \n",
"29 Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.5 0 1 5 \n",
"30 Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.6 0 1 5 \n",
"31 Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.6 1 1 4 \n",
"\n",
" carb \n",
"27 2 \n",
"28 4 \n",
"29 6 \n",
"30 8 \n",
"31 2 "
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#view first five from bottom\n",
"cars.tail()"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>mpg</th>\n",
" <th>cyl</th>\n",
" <th>disp</th>\n",
" <th>hp</th>\n",
" <th>drat</th>\n",
" <th>wt</th>\n",
" <th>qsec</th>\n",
" <th>vs</th>\n",
" <th>am</th>\n",
" <th>gear</th>\n",
" <th>carb</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>26</td>\n",
" <td>Porsche 914-2</td>\n",
" <td>26.0</td>\n",
" <td>4</td>\n",
" <td>120.3</td>\n",
" <td>91</td>\n",
" <td>4.43</td>\n",
" <td>2.140</td>\n",
" <td>16.7</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>27</td>\n",
" <td>Lotus Europa</td>\n",
" <td>30.4</td>\n",
" <td>4</td>\n",
" <td>95.1</td>\n",
" <td>113</td>\n",
" <td>3.77</td>\n",
" <td>1.513</td>\n",
" <td>16.9</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>28</td>\n",
" <td>Ford Pantera L</td>\n",
" <td>15.8</td>\n",
" <td>8</td>\n",
" <td>351.0</td>\n",
" <td>264</td>\n",
" <td>4.22</td>\n",
" <td>3.170</td>\n",
" <td>14.5</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>29</td>\n",
" <td>Ferrari Dino</td>\n",
" <td>19.7</td>\n",
" <td>6</td>\n",
" <td>145.0</td>\n",
" <td>175</td>\n",
" <td>3.62</td>\n",
" <td>2.770</td>\n",
" <td>15.5</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <td>30</td>\n",
" <td>Maserati Bora</td>\n",
" <td>15.0</td>\n",
" <td>8</td>\n",
" <td>301.0</td>\n",
" <td>335</td>\n",
" <td>3.54</td>\n",
" <td>3.570</td>\n",
" <td>14.6</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <td>31</td>\n",
" <td>Volvo 142E</td>\n",
" <td>21.4</td>\n",
" <td>4</td>\n",
" <td>121.0</td>\n",
" <td>109</td>\n",
" <td>4.11</td>\n",
" <td>2.780</td>\n",
" <td>18.6</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 mpg cyl disp hp drat wt qsec vs am gear \\\n",
"26 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.7 0 1 5 \n",
"27 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.9 1 1 5 \n",
"28 Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.5 0 1 5 \n",
"29 Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.5 0 1 5 \n",
"30 Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.6 0 1 5 \n",
"31 Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.6 1 1 4 \n",
"\n",
" carb \n",
"26 2 \n",
"27 2 \n",
"28 4 \n",
"29 6 \n",
"30 8 \n",
"31 2 "
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cars.tail(6)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(32, 12)"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#shape\n",
"cars.shape"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<bound method DataFrame.info of Unnamed: 0 mpg cyl disp hp drat wt qsec vs am \\\n",
"0 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 \n",
"1 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 \n",
"2 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 \n",
"3 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 \n",
"4 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 \n",
"5 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 \n",
"6 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 \n",
"7 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 \n",
"8 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 \n",
"9 Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 \n",
"10 Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 \n",
"11 Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 \n",
"12 Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 \n",
"13 Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 \n",
"14 Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 \n",
"15 Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 \n",
"16 Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 \n",
"17 Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 \n",
"18 Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 \n",
"19 Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 \n",
"20 Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 \n",
"21 Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 \n",
"22 AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 \n",
"23 Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 \n",
"24 Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 \n",
"25 Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 \n",
"26 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 \n",
"27 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 \n",
"28 Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 \n",
"29 Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 \n",
"30 Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 \n",
"31 Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 \n",
"\n",
" gear carb \n",
"0 4 4 \n",
"1 4 4 \n",
"2 4 1 \n",
"3 3 1 \n",
"4 3 2 \n",
"5 3 1 \n",
"6 3 4 \n",
"7 4 2 \n",
"8 4 2 \n",
"9 4 4 \n",
"10 4 4 \n",
"11 3 3 \n",
"12 3 3 \n",
"13 3 3 \n",
"14 3 4 \n",
"15 3 4 \n",
"16 3 4 \n",
"17 4 1 \n",
"18 4 2 \n",
"19 4 1 \n",
"20 3 1 \n",
"21 3 2 \n",
"22 3 2 \n",
"23 3 4 \n",
"24 3 2 \n",
"25 4 1 \n",
"26 5 2 \n",
"27 5 2 \n",
"28 5 4 \n",
"29 5 6 \n",
"30 5 8 \n",
"31 4 2 >"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#prints a cocise info of car\n",
"cars.info"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 32 entries, 0 to 31\n",
"Data columns (total 12 columns):\n",
"Unnamed: 0 32 non-null object\n",
"mpg 32 non-null object\n",
"cyl 32 non-null int64\n",
"disp 32 non-null float64\n",
"hp 32 non-null int64\n",
"drat 32 non-null float64\n",
"wt 32 non-null float64\n",
"qsec 32 non-null float64\n",
"vs 32 non-null int64\n",
"am 32 non-null int64\n",
"gear 32 non-null int64\n",
"carb 32 non-null int64\n",
"dtypes: float64(4), int64(6), object(2)\n",
"memory usage: 3.1+ KB\n"
]
}
],
"source": [
"#check null counts\n",
"cars.info(null_counts=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Mathematical Analysis"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"cyl 6.187500\n",
"disp 230.721875\n",
"hp 146.687500\n",
"drat 3.596563\n",
"wt 3.217250\n",
"qsec 17.848750\n",
"vs 0.437500\n",
"am 0.406250\n",
"gear 3.687500\n",
"carb 2.812500\n",
"dtype: float64"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#mean \n",
"cars.mean()"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"cyl 6.000\n",
"disp 196.300\n",
"hp 123.000\n",
"drat 3.695\n",
"wt 3.325\n",
"qsec 17.710\n",
"vs 0.000\n",
"am 0.000\n",
"gear 4.000\n",
"carb 2.000\n",
"dtype: float64"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#median\n",
"cars.median()"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"cyl 1.785922\n",
"disp 123.938694\n",
"hp 68.562868\n",
"drat 0.534679\n",
"wt 0.978457\n",
"qsec 1.786943\n",
"vs 0.504016\n",
"am 0.498991\n",
"gear 0.737804\n",
"carb 1.615200\n",
"dtype: float64"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Standard deviation\n",
"cars.std()"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Unnamed: 0 AMC Javelin\n",
"mpg 10.4\n",
"cyl 4\n",
"disp 71.1\n",
"hp 52\n",
"drat 2.76\n",
"wt 1.513\n",
"qsec 14.5\n",
"vs 0\n",
"am 0\n",
"gear 3\n",
"carb 1\n",
"dtype: object"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#minimum\n",
"cars.min()"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Unnamed: 0 Volvo 142E\n",
"mpg 33.9\n",
"cyl 8\n",
"disp 472\n",
"hp 335\n",
"drat 4.93\n",
"wt 5.424\n",
"qsec 22.9\n",
"vs 1\n",
"am 1\n",
"gear 5\n",
"carb 8\n",
"dtype: object"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#max\n",
"cars.max()"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Unnamed: 0 32\n",
"mpg 32\n",
"cyl 32\n",
"disp 32\n",
"hp 32\n",
"drat 32\n",
"wt 32\n",
"qsec 32\n",
"vs 32\n",
"am 32\n",
"gear 32\n",
"carb 32\n",
"dtype: int64"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#count\n",
"cars.count()"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>cyl</th>\n",
" <th>disp</th>\n",
" <th>hp</th>\n",
" <th>drat</th>\n",
" <th>wt</th>\n",
" <th>qsec</th>\n",
" <th>vs</th>\n",
" <th>am</th>\n",
" <th>gear</th>\n",
" <th>carb</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>count</td>\n",
" <td>32.000000</td>\n",
" <td>32.000000</td>\n",
" <td>32.000000</td>\n",
" <td>32.000000</td>\n",
" <td>32.000000</td>\n",
" <td>32.000000</td>\n",
" <td>32.000000</td>\n",
" <td>32.000000</td>\n",
" <td>32.000000</td>\n",
" <td>32.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>mean</td>\n",
" <td>6.187500</td>\n",
" <td>230.721875</td>\n",
" <td>146.687500</td>\n",
" <td>3.596563</td>\n",
" <td>3.217250</td>\n",
" <td>17.848750</td>\n",
" <td>0.437500</td>\n",
" <td>0.406250</td>\n",
" <td>3.687500</td>\n",
" <td>2.8125</td>\n",
" </tr>\n",
" <tr>\n",
" <td>std</td>\n",
" <td>1.785922</td>\n",
" <td>123.938694</td>\n",
" <td>68.562868</td>\n",
" <td>0.534679</td>\n",
" <td>0.978457</td>\n",
" <td>1.786943</td>\n",
" <td>0.504016</td>\n",
" <td>0.498991</td>\n",
" <td>0.737804</td>\n",
" <td>1.6152</td>\n",
" </tr>\n",
" <tr>\n",
" <td>min</td>\n",
" <td>4.000000</td>\n",
" <td>71.100000</td>\n",
" <td>52.000000</td>\n",
" <td>2.760000</td>\n",
" <td>1.513000</td>\n",
" <td>14.500000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>3.000000</td>\n",
" <td>1.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>25%</td>\n",
" <td>4.000000</td>\n",
" <td>120.825000</td>\n",
" <td>96.500000</td>\n",
" <td>3.080000</td>\n",
" <td>2.581250</td>\n",
" <td>16.892500</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>3.000000</td>\n",
" <td>2.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>50%</td>\n",
" <td>6.000000</td>\n",
" <td>196.300000</td>\n",
" <td>123.000000</td>\n",
" <td>3.695000</td>\n",
" <td>3.325000</td>\n",
" <td>17.710000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>4.000000</td>\n",
" <td>2.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>75%</td>\n",
" <td>8.000000</td>\n",
" <td>326.000000</td>\n",
" <td>180.000000</td>\n",
" <td>3.920000</td>\n",
" <td>3.610000</td>\n",
" <td>18.900000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>4.000000</td>\n",
" <td>4.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>max</td>\n",
" <td>8.000000</td>\n",
" <td>472.000000</td>\n",
" <td>335.000000</td>\n",
" <td>4.930000</td>\n",
" <td>5.424000</td>\n",
" <td>22.900000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>5.000000</td>\n",
" <td>8.0000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" cyl disp hp drat wt qsec \\\n",
"count 32.000000 32.000000 32.000000 32.000000 32.000000 32.000000 \n",
"mean 6.187500 230.721875 146.687500 3.596563 3.217250 17.848750 \n",
"std 1.785922 123.938694 68.562868 0.534679 0.978457 1.786943 \n",
"min 4.000000 71.100000 52.000000 2.760000 1.513000 14.500000 \n",
"25% 4.000000 120.825000 96.500000 3.080000 2.581250 16.892500 \n",
"50% 6.000000 196.300000 123.000000 3.695000 3.325000 17.710000 \n",
"75% 8.000000 326.000000 180.000000 3.920000 3.610000 18.900000 \n",
"max 8.000000 472.000000 335.000000 4.930000 5.424000 22.900000 \n",
"\n",
" vs am gear carb \n",
"count 32.000000 32.000000 32.000000 32.0000 \n",
"mean 0.437500 0.406250 3.687500 2.8125 \n",
"std 0.504016 0.498991 0.737804 1.6152 \n",
"min 0.000000 0.000000 3.000000 1.0000 \n",
"25% 0.000000 0.000000 3.000000 2.0000 \n",
"50% 0.000000 0.000000 4.000000 2.0000 \n",
"75% 1.000000 1.000000 4.000000 4.0000 \n",
"max 1.000000 1.000000 5.000000 8.0000 "
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#description : statistics summary\n",
"cars.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cleaning"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>mpg</th>\n",
" <th>cyl</th>\n",
" <th>disp</th>\n",
" <th>hp</th>\n",
" <th>drat</th>\n",
" <th>wt</th>\n",
" <th>qsec</th>\n",
" <th>vs</th>\n",
" <th>am</th>\n",
" <th>gear</th>\n",
" <th>carb</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>Mazda RX4</td>\n",
" <td>21.0</td>\n",
" <td>6</td>\n",
" <td>160.0</td>\n",
" <td>110</td>\n",
" <td>3.9</td>\n",
" <td>2.62</td>\n",
" <td>16.46</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name mpg cyl disp hp drat wt qsec vs am gear carb\n",
"0 Mazda RX4 21.0 6 160.0 110 3.9 2.62 16.46 0 1 4 4"
]
},
"execution_count": 84,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#renaming a column\n",
"cars=cars.rename(columns={'Unnamed: 0':'Name'})\n",
"cars.head(1)"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>mpg</th>\n",
" <th>cyl</th>\n",
" <th>disp</th>\n",
" <th>hp</th>\n",
" <th>drat</th>\n",
" <th>wt</th>\n",
" <th>qsec</th>\n",
" <th>vs</th>\n",
" <th>am</th>\n",
" <th>gear</th>\n",
" <th>carb</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>Mazda RX4</td>\n",
" <td>21.0</td>\n",
" <td>6</td>\n",
" <td>160.0</td>\n",
" <td>110</td>\n",
" <td>3.90</td>\n",
" <td>2.620</td>\n",
" <td>16.46</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>Mazda RX4 Wag</td>\n",
" <td>21.0</td>\n",
" <td>6</td>\n",
" <td>160.0</td>\n",
" <td>110</td>\n",
" <td>3.90</td>\n",
" <td>2.875</td>\n",
" <td>17.02</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>Datsun 710</td>\n",
" <td>22.8</td>\n",
" <td>4</td>\n",
" <td>108.0</td>\n",
" <td>93</td>\n",
" <td>3.85</td>\n",
" <td>2.320</td>\n",
" <td>18.61</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>Hornet 4 Drive</td>\n",
" <td>21.4</td>\n",
" <td>6</td>\n",
" <td>258.0</td>\n",
" <td>110</td>\n",
" <td>3.08</td>\n",
" <td>3.215</td>\n",
" <td>19.44</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>Hornet Sportabout</td>\n",
" <td>18.7</td>\n",
" <td>8</td>\n",
" <td>360.0</td>\n",
" <td>175</td>\n",
" <td>3.15</td>\n",
" <td>3.440</td>\n",
" <td>17.02</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>Valiant</td>\n",
" <td>18.1</td>\n",
" <td>6</td>\n",
" <td>225.0</td>\n",
" <td>105</td>\n",
" <td>2.76</td>\n",
" <td>3.460</td>\n",
" <td>20.22</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>6</td>\n",
" <td>Duster 360</td>\n",
" <td>14.3</td>\n",
" <td>8</td>\n",
" <td>360.0</td>\n",
" <td>245</td>\n",
" <td>3.21</td>\n",
" <td>3.570</td>\n",
" <td>15.84</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7</td>\n",
" <td>Merc 240D</td>\n",
" <td>24.4</td>\n",
" <td>4</td>\n",
" <td>146.7</td>\n",
" <td>62</td>\n",
" <td>3.69</td>\n",
" <td>3.190</td>\n",
" <td>20.00</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>8</td>\n",
" <td>Merc 230</td>\n",
" <td>22.8</td>\n",
" <td>4</td>\n",
" <td>140.8</td>\n",
" <td>95</td>\n",
" <td>3.92</td>\n",
" <td>3.150</td>\n",
" <td>22.90</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>9</td>\n",
" <td>Merc 280</td>\n",
" <td>19.2</td>\n",
" <td>6</td>\n",
" <td>167.6</td>\n",
" <td>123</td>\n",
" <td>3.92</td>\n",
" <td>3.440</td>\n",
" <td>18.30</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>10</td>\n",
" <td>Merc 280C</td>\n",
" <td>17.8</td>\n",
" <td>6</td>\n",
" <td>167.6</td>\n",
" <td>123</td>\n",
" <td>3.92</td>\n",
" <td>3.440</td>\n",
" <td>18.90</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>11</td>\n",
" <td>Merc 450SE</td>\n",
" <td>16.4</td>\n",
" <td>8</td>\n",
" <td>275.8</td>\n",
" <td>180</td>\n",
" <td>3.07</td>\n",
" <td>4.070</td>\n",
" <td>17.40</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>12</td>\n",
" <td>Merc 450SL</td>\n",
" <td>17.3</td>\n",
" <td>8</td>\n",
" <td>275.8</td>\n",
" <td>180</td>\n",
" <td>3.07</td>\n",
" <td>3.730</td>\n",
" <td>17.60</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>13</td>\n",
" <td>Merc 450SLC</td>\n",
" <td>15.2</td>\n",
" <td>8</td>\n",
" <td>275.8</td>\n",
" <td>180</td>\n",
" <td>3.07</td>\n",
" <td>3.780</td>\n",
" <td>18.00</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>14</td>\n",
" <td>Cadillac Fleetwood</td>\n",
" <td>10.4</td>\n",
" <td>8</td>\n",
" <td>472.0</td>\n",
" <td>205</td>\n",
" <td>2.93</td>\n",
" <td>5.250</td>\n",
" <td>17.98</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>15</td>\n",
" <td>Lincoln Continental</td>\n",
" <td>10.4</td>\n",
" <td>8</td>\n",
" <td>460.0</td>\n",
" <td>215</td>\n",
" <td>3.00</td>\n",
" <td>5.424</td>\n",
" <td>17.82</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16</td>\n",
" <td>Chrysler Imperial</td>\n",
" <td>14.7</td>\n",
" <td>8</td>\n",
" <td>440.0</td>\n",
" <td>230</td>\n",
" <td>3.23</td>\n",
" <td>5.345</td>\n",
" <td>17.42</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>17</td>\n",
" <td>Fiat 128</td>\n",
" <td>32.4</td>\n",
" <td>4</td>\n",
" <td>78.7</td>\n",
" <td>66</td>\n",
" <td>4.08</td>\n",
" <td>2.200</td>\n",
" <td>19.47</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>18</td>\n",
" <td>Honda Civic</td>\n",
" <td>30.4</td>\n",
" <td>4</td>\n",
" <td>75.7</td>\n",
" <td>52</td>\n",
" <td>4.93</td>\n",
" <td>1.615</td>\n",
" <td>18.52</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>19</td>\n",
" <td>Toyota Corolla</td>\n",
" <td>33.9</td>\n",
" <td>4</td>\n",
" <td>71.1</td>\n",
" <td>65</td>\n",
" <td>4.22</td>\n",
" <td>1.835</td>\n",
" <td>19.90</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>20</td>\n",
" <td>Toyota Corona</td>\n",
" <td>21.5</td>\n",
" <td>4</td>\n",
" <td>120.1</td>\n",
" <td>97</td>\n",
" <td>3.70</td>\n",
" <td>2.465</td>\n",
" <td>20.01</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>21</td>\n",
" <td>Dodge Challenger</td>\n",
" <td>15.5</td>\n",
" <td>8</td>\n",
" <td>318.0</td>\n",
" <td>150</td>\n",
" <td>2.76</td>\n",
" <td>3.520</td>\n",
" <td>16.87</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>22</td>\n",
" <td>AMC Javelin</td>\n",
" <td>15.2</td>\n",
" <td>8</td>\n",
" <td>304.0</td>\n",
" <td>150</td>\n",
" <td>3.15</td>\n",
" <td>3.435</td>\n",
" <td>17.30</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>23</td>\n",
" <td>Camaro Z28</td>\n",
" <td>13.3</td>\n",
" <td>8</td>\n",
" <td>350.0</td>\n",
" <td>245</td>\n",
" <td>3.73</td>\n",
" <td>3.840</td>\n",
" <td>15.41</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>24</td>\n",
" <td>Pontiac Firebird</td>\n",
" <td>19.2</td>\n",
" <td>8</td>\n",
" <td>400.0</td>\n",
" <td>175</td>\n",
" <td>3.08</td>\n",
" <td>3.845</td>\n",
" <td>17.05</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>25</td>\n",
" <td>Fiat X1-9</td>\n",
" <td>27.3</td>\n",
" <td>4</td>\n",
" <td>79.0</td>\n",
" <td>66</td>\n",
" <td>4.08</td>\n",
" <td>1.935</td>\n",
" <td>18.90</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>26</td>\n",
" <td>Porsche 914-2</td>\n",
" <td>26.0</td>\n",
" <td>4</td>\n",
" <td>120.3</td>\n",
" <td>91</td>\n",
" <td>4.43</td>\n",
" <td>2.140</td>\n",
" <td>16.70</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>27</td>\n",
" <td>Lotus Europa</td>\n",
" <td>30.4</td>\n",
" <td>4</td>\n",
" <td>95.1</td>\n",
" <td>113</td>\n",
" <td>3.77</td>\n",
" <td>1.513</td>\n",
" <td>16.90</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>28</td>\n",
" <td>Ford Pantera L</td>\n",
" <td>15.8</td>\n",
" <td>8</td>\n",
" <td>351.0</td>\n",
" <td>264</td>\n",
" <td>4.22</td>\n",
" <td>3.170</td>\n",
" <td>14.50</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>29</td>\n",
" <td>Ferrari Dino</td>\n",
" <td>19.7</td>\n",
" <td>6</td>\n",
" <td>145.0</td>\n",
" <td>175</td>\n",
" <td>3.62</td>\n",
" <td>2.770</td>\n",
" <td>15.50</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <td>30</td>\n",
" <td>Maserati Bora</td>\n",
" <td>15.0</td>\n",
" <td>8</td>\n",
" <td>301.0</td>\n",
" <td>335</td>\n",
" <td>3.54</td>\n",
" <td>3.570</td>\n",
" <td>14.60</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <td>31</td>\n",
" <td>Volvo 142E</td>\n",
" <td>21.4</td>\n",
" <td>4</td>\n",
" <td>121.0</td>\n",
" <td>109</td>\n",
" <td>4.11</td>\n",
" <td>2.780</td>\n",
" <td>18.60</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name mpg cyl disp hp drat wt qsec vs am \\\n",
"0 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 \n",
"1 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 \n",
"2 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 \n",
"3 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 \n",
"4 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 \n",
"5 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 \n",
"6 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 \n",
"7 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 \n",
"8 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 \n",
"9 Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 \n",
"10 Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 \n",
"11 Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 \n",
"12 Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 \n",
"13 Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 \n",
"14 Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 \n",
"15 Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 \n",
"16 Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 \n",
"17 Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 \n",
"18 Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 \n",
"19 Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 \n",
"20 Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 \n",
"21 Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 \n",
"22 AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 \n",
"23 Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 \n",
"24 Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 \n",
"25 Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 \n",
"26 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 \n",
"27 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 \n",
"28 Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 \n",
"29 Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 \n",
"30 Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 \n",
"31 Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 \n",
"\n",
" gear carb \n",
"0 4 4 \n",
"1 4 4 \n",
"2 4 1 \n",
"3 3 1 \n",
"4 3 2 \n",
"5 3 1 \n",
"6 3 4 \n",
"7 4 2 \n",
"8 4 2 \n",
"9 4 4 \n",
"10 4 4 \n",
"11 3 3 \n",
"12 3 3 \n",
"13 3 3 \n",
"14 3 4 \n",
"15 3 4 \n",
"16 3 4 \n",
"17 4 1 \n",
"18 4 2 \n",
"19 4 1 \n",
"20 3 1 \n",
"21 3 2 \n",
"22 3 2 \n",
"23 3 4 \n",
"24 3 2 \n",
"25 4 1 \n",
"26 5 2 \n",
"27 5 2 \n",
"28 5 4 \n",
"29 5 6 \n",
"30 5 8 \n",
"31 4 2 "
]
},
"execution_count": 85,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#flling the mean value\n",
"cars.qsec=cars.qsec.fillna(cars.qsec.mean)\n",
"cars"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>mpg</th>\n",
" <th>cyl</th>\n",
" <th>disp</th>\n",
" <th>hp</th>\n",
" <th>drat</th>\n",
" <th>wt</th>\n",
" <th>qsec</th>\n",
" <th>vs</th>\n",
" <th>am</th>\n",
" <th>gear</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>Mazda RX4</td>\n",
" <td>21.0</td>\n",
" <td>6</td>\n",
" <td>160.0</td>\n",
" <td>110</td>\n",
" <td>3.90</td>\n",
" <td>2.620</td>\n",
" <td>16.46</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>Mazda RX4 Wag</td>\n",
" <td>21.0</td>\n",
" <td>6</td>\n",
" <td>160.0</td>\n",
" <td>110</td>\n",
" <td>3.90</td>\n",
" <td>2.875</td>\n",
" <td>17.02</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>Datsun 710</td>\n",
" <td>22.8</td>\n",
" <td>4</td>\n",
" <td>108.0</td>\n",
" <td>93</td>\n",
" <td>3.85</td>\n",
" <td>2.320</td>\n",
" <td>18.61</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>Hornet 4 Drive</td>\n",
" <td>21.4</td>\n",
" <td>6</td>\n",
" <td>258.0</td>\n",
" <td>110</td>\n",
" <td>3.08</td>\n",
" <td>3.215</td>\n",
" <td>19.44</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>Hornet Sportabout</td>\n",
" <td>18.7</td>\n",
" <td>8</td>\n",
" <td>360.0</td>\n",
" <td>175</td>\n",
" <td>3.15</td>\n",
" <td>3.440</td>\n",
" <td>17.02</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>Valiant</td>\n",
" <td>18.1</td>\n",
" <td>6</td>\n",
" <td>225.0</td>\n",
" <td>105</td>\n",
" <td>2.76</td>\n",
" <td>3.460</td>\n",
" <td>20.22</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>6</td>\n",
" <td>Duster 360</td>\n",
" <td>14.3</td>\n",
" <td>8</td>\n",
" <td>360.0</td>\n",
" <td>245</td>\n",
" <td>3.21</td>\n",
" <td>3.570</td>\n",
" <td>15.84</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7</td>\n",
" <td>Merc 240D</td>\n",
" <td>24.4</td>\n",
" <td>4</td>\n",
" <td>146.7</td>\n",
" <td>62</td>\n",
" <td>3.69</td>\n",
" <td>3.190</td>\n",
" <td>20.00</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>8</td>\n",
" <td>Merc 230</td>\n",
" <td>22.8</td>\n",
" <td>4</td>\n",
" <td>140.8</td>\n",
" <td>95</td>\n",
" <td>3.92</td>\n",
" <td>3.150</td>\n",
" <td>22.90</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>9</td>\n",
" <td>Merc 280</td>\n",
" <td>19.2</td>\n",
" <td>6</td>\n",
" <td>167.6</td>\n",
" <td>123</td>\n",
" <td>3.92</td>\n",
" <td>3.440</td>\n",
" <td>18.30</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>10</td>\n",
" <td>Merc 280C</td>\n",
" <td>17.8</td>\n",
" <td>6</td>\n",
" <td>167.6</td>\n",
" <td>123</td>\n",
" <td>3.92</td>\n",
" <td>3.440</td>\n",
" <td>18.90</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>11</td>\n",
" <td>Merc 450SE</td>\n",
" <td>16.4</td>\n",
" <td>8</td>\n",
" <td>275.8</td>\n",
" <td>180</td>\n",
" <td>3.07</td>\n",
" <td>4.070</td>\n",
" <td>17.40</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>12</td>\n",
" <td>Merc 450SL</td>\n",
" <td>17.3</td>\n",
" <td>8</td>\n",
" <td>275.8</td>\n",
" <td>180</td>\n",
" <td>3.07</td>\n",
" <td>3.730</td>\n",
" <td>17.60</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>13</td>\n",
" <td>Merc 450SLC</td>\n",
" <td>15.2</td>\n",
" <td>8</td>\n",
" <td>275.8</td>\n",
" <td>180</td>\n",
" <td>3.07</td>\n",
" <td>3.780</td>\n",
" <td>18.00</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>14</td>\n",
" <td>Cadillac Fleetwood</td>\n",
" <td>10.4</td>\n",
" <td>8</td>\n",
" <td>472.0</td>\n",
" <td>205</td>\n",
" <td>2.93</td>\n",
" <td>5.250</td>\n",
" <td>17.98</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>15</td>\n",
" <td>Lincoln Continental</td>\n",
" <td>10.4</td>\n",
" <td>8</td>\n",
" <td>460.0</td>\n",
" <td>215</td>\n",
" <td>3.00</td>\n",
" <td>5.424</td>\n",
" <td>17.82</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16</td>\n",
" <td>Chrysler Imperial</td>\n",
" <td>14.7</td>\n",
" <td>8</td>\n",
" <td>440.0</td>\n",
" <td>230</td>\n",
" <td>3.23</td>\n",
" <td>5.345</td>\n",
" <td>17.42</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>17</td>\n",
" <td>Fiat 128</td>\n",
" <td>32.4</td>\n",
" <td>4</td>\n",
" <td>78.7</td>\n",
" <td>66</td>\n",
" <td>4.08</td>\n",
" <td>2.200</td>\n",
" <td>19.47</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>18</td>\n",
" <td>Honda Civic</td>\n",
" <td>30.4</td>\n",
" <td>4</td>\n",
" <td>75.7</td>\n",
" <td>52</td>\n",
" <td>4.93</td>\n",
" <td>1.615</td>\n",
" <td>18.52</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>19</td>\n",
" <td>Toyota Corolla</td>\n",
" <td>33.9</td>\n",
" <td>4</td>\n",
" <td>71.1</td>\n",
" <td>65</td>\n",
" <td>4.22</td>\n",
" <td>1.835</td>\n",
" <td>19.90</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>20</td>\n",
" <td>Toyota Corona</td>\n",
" <td>21.5</td>\n",
" <td>4</td>\n",
" <td>120.1</td>\n",
" <td>97</td>\n",
" <td>3.70</td>\n",
" <td>2.465</td>\n",
" <td>20.01</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>21</td>\n",
" <td>Dodge Challenger</td>\n",
" <td>15.5</td>\n",
" <td>8</td>\n",
" <td>318.0</td>\n",
" <td>150</td>\n",
" <td>2.76</td>\n",
" <td>3.520</td>\n",
" <td>16.87</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>22</td>\n",
" <td>AMC Javelin</td>\n",
" <td>15.2</td>\n",
" <td>8</td>\n",
" <td>304.0</td>\n",
" <td>150</td>\n",
" <td>3.15</td>\n",
" <td>3.435</td>\n",
" <td>17.30</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>23</td>\n",
" <td>Camaro Z28</td>\n",
" <td>13.3</td>\n",
" <td>8</td>\n",
" <td>350.0</td>\n",
" <td>245</td>\n",
" <td>3.73</td>\n",
" <td>3.840</td>\n",
" <td>15.41</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>24</td>\n",
" <td>Pontiac Firebird</td>\n",
" <td>19.2</td>\n",
" <td>8</td>\n",
" <td>400.0</td>\n",
" <td>175</td>\n",
" <td>3.08</td>\n",
" <td>3.845</td>\n",
" <td>17.05</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>25</td>\n",
" <td>Fiat X1-9</td>\n",
" <td>27.3</td>\n",
" <td>4</td>\n",
" <td>79.0</td>\n",
" <td>66</td>\n",
" <td>4.08</td>\n",
" <td>1.935</td>\n",
" <td>18.90</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>26</td>\n",
" <td>Porsche 914-2</td>\n",
" <td>26.0</td>\n",
" <td>4</td>\n",
" <td>120.3</td>\n",
" <td>91</td>\n",
" <td>4.43</td>\n",
" <td>2.140</td>\n",
" <td>16.70</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <td>27</td>\n",
" <td>Lotus Europa</td>\n",
" <td>30.4</td>\n",
" <td>4</td>\n",
" <td>95.1</td>\n",
" <td>113</td>\n",
" <td>3.77</td>\n",
" <td>1.513</td>\n",
" <td>16.90</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <td>28</td>\n",
" <td>Ford Pantera L</td>\n",
" <td>15.8</td>\n",
" <td>8</td>\n",
" <td>351.0</td>\n",
" <td>264</td>\n",
" <td>4.22</td>\n",
" <td>3.170</td>\n",
" <td>14.50</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <td>29</td>\n",
" <td>Ferrari Dino</td>\n",
" <td>19.7</td>\n",
" <td>6</td>\n",
" <td>145.0</td>\n",
" <td>175</td>\n",
" <td>3.62</td>\n",
" <td>2.770</td>\n",
" <td>15.50</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <td>30</td>\n",
" <td>Maserati Bora</td>\n",
" <td>15.0</td>\n",
" <td>8</td>\n",
" <td>301.0</td>\n",
" <td>335</td>\n",
" <td>3.54</td>\n",
" <td>3.570</td>\n",
" <td>14.60</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <td>31</td>\n",
" <td>Volvo 142E</td>\n",
" <td>21.4</td>\n",
" <td>4</td>\n",
" <td>121.0</td>\n",
" <td>109</td>\n",
" <td>4.11</td>\n",
" <td>2.780</td>\n",
" <td>18.60</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name mpg cyl disp hp drat wt qsec vs am \\\n",
"0 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 \n",
"1 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 \n",
"2 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 \n",
"3 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 \n",
"4 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 \n",
"5 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 \n",
"6 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 \n",
"7 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 \n",
"8 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 \n",
"9 Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 \n",
"10 Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 \n",
"11 Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 \n",
"12 Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 \n",
"13 Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 \n",
"14 Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 \n",
"15 Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 \n",
"16 Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 \n",
"17 Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 \n",
"18 Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 \n",
"19 Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 \n",
"20 Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 \n",
"21 Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 \n",
"22 AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 \n",
"23 Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 \n",
"24 Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 \n",
"25 Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 \n",
"26 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 \n",
"27 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 \n",
"28 Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 \n",
"29 Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 \n",
"30 Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 \n",
"31 Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 \n",
"\n",
" gear \n",
"0 4 \n",
"1 4 \n",
"2 4 \n",
"3 3 \n",
"4 3 \n",
"5 3 \n",
"6 3 \n",
"7 4 \n",
"8 4 \n",
"9 4 \n",
"10 4 \n",
"11 3 \n",
"12 3 \n",
"13 3 \n",
"14 3 \n",
"15 3 \n",
"16 3 \n",
"17 4 \n",
"18 4 \n",
"19 4 \n",
"20 3 \n",
"21 3 \n",
"22 3 \n",
"23 3 \n",
"24 3 \n",
"25 4 \n",
"26 5 \n",
"27 5 \n",
"28 5 \n",
"29 5 \n",
"30 5 \n",
"31 4 "
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Removing Unnecessary columns\n",
"#cars=cars.drop(columns=['carb'])\n",
"cars"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>mpg</th>\n",
" <th>cyl</th>\n",
" <th>disp</th>\n",
" <th>hp</th>\n",
" <th>drat</th>\n",
" <th>wt</th>\n",
" <th>qsec</th>\n",
" <th>vs</th>\n",
" <th>am</th>\n",
" <th>gear</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>mpg</td>\n",
" <td>1.000000</td>\n",
" <td>-0.852162</td>\n",
" <td>-0.847551</td>\n",
" <td>-0.776168</td>\n",
" <td>0.681172</td>\n",
" <td>-0.867659</td>\n",
" <td>0.418684</td>\n",
" <td>0.664039</td>\n",
" <td>0.599832</td>\n",
" <td>0.480285</td>\n",
" </tr>\n",
" <tr>\n",
" <td>cyl</td>\n",
" <td>-0.852162</td>\n",
" <td>1.000000</td>\n",
" <td>0.902033</td>\n",
" <td>0.832447</td>\n",
" <td>-0.699938</td>\n",
" <td>0.782496</td>\n",
" <td>-0.591242</td>\n",
" <td>-0.810812</td>\n",
" <td>-0.522607</td>\n",
" <td>-0.492687</td>\n",
" </tr>\n",
" <tr>\n",
" <td>disp</td>\n",
" <td>-0.847551</td>\n",
" <td>0.902033</td>\n",
" <td>1.000000</td>\n",
" <td>0.790949</td>\n",
" <td>-0.710214</td>\n",
" <td>0.887980</td>\n",
" <td>-0.433698</td>\n",
" <td>-0.710416</td>\n",
" <td>-0.591227</td>\n",
" <td>-0.555569</td>\n",
" </tr>\n",
" <tr>\n",
" <td>hp</td>\n",
" <td>-0.776168</td>\n",
" <td>0.832447</td>\n",
" <td>0.790949</td>\n",
" <td>1.000000</td>\n",
" <td>-0.448759</td>\n",
" <td>0.658748</td>\n",
" <td>-0.708223</td>\n",
" <td>-0.723097</td>\n",
" <td>-0.243204</td>\n",
" <td>-0.125704</td>\n",
" </tr>\n",
" <tr>\n",
" <td>drat</td>\n",
" <td>0.681172</td>\n",
" <td>-0.699938</td>\n",
" <td>-0.710214</td>\n",
" <td>-0.448759</td>\n",
" <td>1.000000</td>\n",
" <td>-0.712441</td>\n",
" <td>0.091205</td>\n",
" <td>0.440278</td>\n",
" <td>0.712711</td>\n",
" <td>0.699610</td>\n",
" </tr>\n",
" <tr>\n",
" <td>wt</td>\n",
" <td>-0.867659</td>\n",
" <td>0.782496</td>\n",
" <td>0.887980</td>\n",
" <td>0.658748</td>\n",
" <td>-0.712441</td>\n",
" <td>1.000000</td>\n",
" <td>-0.174716</td>\n",
" <td>-0.554916</td>\n",
" <td>-0.692495</td>\n",
" <td>-0.583287</td>\n",
" </tr>\n",
" <tr>\n",
" <td>qsec</td>\n",
" <td>0.418684</td>\n",
" <td>-0.591242</td>\n",
" <td>-0.433698</td>\n",
" <td>-0.708223</td>\n",
" <td>0.091205</td>\n",
" <td>-0.174716</td>\n",
" <td>1.000000</td>\n",
" <td>0.744535</td>\n",
" <td>-0.229861</td>\n",
" <td>-0.212682</td>\n",
" </tr>\n",
" <tr>\n",
" <td>vs</td>\n",
" <td>0.664039</td>\n",
" <td>-0.810812</td>\n",
" <td>-0.710416</td>\n",
" <td>-0.723097</td>\n",
" <td>0.440278</td>\n",
" <td>-0.554916</td>\n",
" <td>0.744535</td>\n",
" <td>1.000000</td>\n",
" <td>0.168345</td>\n",
" <td>0.206023</td>\n",
" </tr>\n",
" <tr>\n",
" <td>am</td>\n",
" <td>0.599832</td>\n",
" <td>-0.522607</td>\n",
" <td>-0.591227</td>\n",
" <td>-0.243204</td>\n",
" <td>0.712711</td>\n",
" <td>-0.692495</td>\n",
" <td>-0.229861</td>\n",
" <td>0.168345</td>\n",
" <td>1.000000</td>\n",
" <td>0.794059</td>\n",
" </tr>\n",
" <tr>\n",
" <td>gear</td>\n",
" <td>0.480285</td>\n",
" <td>-0.492687</td>\n",
" <td>-0.555569</td>\n",
" <td>-0.125704</td>\n",
" <td>0.699610</td>\n",
" <td>-0.583287</td>\n",
" <td>-0.212682</td>\n",
" <td>0.206023</td>\n",
" <td>0.794059</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" mpg cyl disp hp drat wt qsec \\\n",
"mpg 1.000000 -0.852162 -0.847551 -0.776168 0.681172 -0.867659 0.418684 \n",
"cyl -0.852162 1.000000 0.902033 0.832447 -0.699938 0.782496 -0.591242 \n",
"disp -0.847551 0.902033 1.000000 0.790949 -0.710214 0.887980 -0.433698 \n",
"hp -0.776168 0.832447 0.790949 1.000000 -0.448759 0.658748 -0.708223 \n",
"drat 0.681172 -0.699938 -0.710214 -0.448759 1.000000 -0.712441 0.091205 \n",
"wt -0.867659 0.782496 0.887980 0.658748 -0.712441 1.000000 -0.174716 \n",
"qsec 0.418684 -0.591242 -0.433698 -0.708223 0.091205 -0.174716 1.000000 \n",
"vs 0.664039 -0.810812 -0.710416 -0.723097 0.440278 -0.554916 0.744535 \n",
"am 0.599832 -0.522607 -0.591227 -0.243204 0.712711 -0.692495 -0.229861 \n",
"gear 0.480285 -0.492687 -0.555569 -0.125704 0.699610 -0.583287 -0.212682 \n",
"\n",
" vs am gear \n",
"mpg 0.664039 0.599832 0.480285 \n",
"cyl -0.810812 -0.522607 -0.492687 \n",
"disp -0.710416 -0.591227 -0.555569 \n",
"hp -0.723097 -0.243204 -0.125704 \n",
"drat 0.440278 0.712711 0.699610 \n",
"wt -0.554916 -0.692495 -0.583287 \n",
"qsec 0.744535 -0.229861 -0.212682 \n",
"vs 1.000000 0.168345 0.206023 \n",
"am 0.168345 1.000000 0.794059 \n",
"gear 0.206023 0.794059 1.000000 "
]
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#correlation matrix\n",
"cars.corr()"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>cyl</th>\n",
" <th>mpg</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>cyl</td>\n",
" <td>1.000000</td>\n",
" <td>-0.852162</td>\n",
" </tr>\n",
" <tr>\n",
" <td>mpg</td>\n",
" <td>-0.852162</td>\n",
" <td>1.000000</td>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" cyl mpg\n",
"cyl 1.000000 -0.852162\n",
"mpg -0.852162 1.000000"
]
},
"execution_count": 104,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Find correlation between some columns only\n",
"df=cars[['Name','cyl','mpg']].corr()\n",
"df\n",
"#We can see name is of type string so it didn't come in corr matrix"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "could not convert string to float: 'Mazda RX4'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-107-963fccace553>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcars\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mName\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m~\\Anaconda\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mastype\u001b[1;34m(self, dtype, copy, errors, **kwargs)\u001b[0m\n\u001b[0;32m 5880\u001b[0m \u001b[1;31m# else, only a single dtype is given\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5881\u001b[0m new_data = self._data.astype(\n\u001b[1;32m-> 5882\u001b[1;33m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 5883\u001b[0m )\n\u001b[0;32m 5884\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\Anaconda\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36mastype\u001b[1;34m(self, dtype, **kwargs)\u001b[0m\n\u001b[0;32m 579\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 580\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 581\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"astype\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 582\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 583\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mconvert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\Anaconda\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36mapply\u001b[1;34m(self, f, axes, filter, do_integrity_check, consolidate, **kwargs)\u001b[0m\n\u001b[0;32m 436\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb_items\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0malign_copy\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 437\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 438\u001b[1;33m \u001b[0mapplied\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 439\u001b[0m \u001b[0mresult_blocks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_extend_blocks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 440\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\Anaconda\\lib\\site-packages\\pandas\\core\\internals\\blocks.py\u001b[0m in \u001b[0;36mastype\u001b[1;34m(self, dtype, copy, errors, values, **kwargs)\u001b[0m\n\u001b[0;32m 557\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 558\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"raise\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 559\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_astype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 560\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 561\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_astype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"raise\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\Anaconda\\lib\\site-packages\\pandas\\core\\internals\\blocks.py\u001b[0m in \u001b[0;36m_astype\u001b[1;34m(self, dtype, copy, errors, values, **kwargs)\u001b[0m\n\u001b[0;32m 641\u001b[0m \u001b[1;31m# _astype_nansafe works fine with 1-d only\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 642\u001b[0m \u001b[0mvals1d\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 643\u001b[1;33m \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mastype_nansafe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvals1d\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 644\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 645\u001b[0m \u001b[1;31m# TODO(extension)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\Anaconda\\lib\\site-packages\\pandas\\core\\dtypes\\cast.py\u001b[0m in \u001b[0;36mastype_nansafe\u001b[1;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[0;32m 727\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcopy\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 728\u001b[0m \u001b[1;31m# Explicit copy, or required since NumPy can't view from / to object.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 729\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 730\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 731\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mview\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mValueError\u001b[0m: could not convert string to float: 'Mazda RX4'"
]
}
],
"source": [
"cars.Name.astype(float)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Manipulation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Indexing\n",
"#### By Position"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 21.0\n",
"1 21.0\n",
"2 22.8\n",
"3 21.4\n",
"4 18.7\n",
"5 18.1\n",
"6 14.3\n",
"7 24.4\n",
"8 22.8\n",
"9 19.2\n",
"10 17.8\n",
"11 16.4\n",
"12 17.3\n",
"13 15.2\n",
"14 10.4\n",
"15 10.4\n",
"16 14.7\n",
"17 32.4\n",
"18 30.4\n",
"19 33.9\n",
"20 21.5\n",
"21 15.5\n",
"22 15.2\n",
"23 13.3\n",
"24 19.2\n",
"25 27.3\n",
"26 26.0\n",
"27 30.4\n",
"28 15.8\n",
"29 19.7\n",
"30 15.0\n",
"31 21.4\n",
"Name: mpg, dtype: float64"
]
},
"execution_count": 109,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#single column\n",
"cars.iloc[:,1]"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Name Mazda RX4 Wag\n",
"mpg 21\n",
"cyl 6\n",
"disp 160\n",
"hp 110\n",
"drat 3.9\n",
"wt 2.875\n",
"qsec 17.02\n",
"vs 0\n",
"am 1\n",
"gear 4\n",
"Name: 1, dtype: object"
]
},
"execution_count": 110,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#single row\n",
"cars.iloc[1,:]"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>mpg</th>\n",
" <th>cyl</th>\n",
" <th>disp</th>\n",
" <th>hp</th>\n",
" <th>drat</th>\n",
" <th>wt</th>\n",
" <th>qsec</th>\n",
" <th>vs</th>\n",
" <th>am</th>\n",
" <th>gear</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>Mazda RX4</td>\n",
" <td>21.0</td>\n",
" <td>6</td>\n",
" <td>160.0</td>\n",
" <td>110</td>\n",
" <td>3.90</td>\n",
" <td>2.620</td>\n",
" <td>16.46</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>Mazda RX4 Wag</td>\n",
" <td>21.0</td>\n",
" <td>6</td>\n",
" <td>160.0</td>\n",
" <td>110</td>\n",
" <td>3.90</td>\n",
" <td>2.875</td>\n",
" <td>17.02</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>Datsun 710</td>\n",
" <td>22.8</td>\n",
" <td>4</td>\n",
" <td>108.0</td>\n",
" <td>93</td>\n",
" <td>3.85</td>\n",
" <td>2.320</td>\n",
" <td>18.61</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>Hornet 4 Drive</td>\n",
" <td>21.4</td>\n",
" <td>6</td>\n",
" <td>258.0</td>\n",
" <td>110</td>\n",
" <td>3.08</td>\n",
" <td>3.215</td>\n",
" <td>19.44</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>Hornet Sportabout</td>\n",
" <td>18.7</td>\n",
" <td>8</td>\n",
" <td>360.0</td>\n",
" <td>175</td>\n",
" <td>3.15</td>\n",
" <td>3.440</td>\n",
" <td>17.02</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>Valiant</td>\n",
" <td>18.1</td>\n",
" <td>6</td>\n",
" <td>225.0</td>\n",
" <td>105</td>\n",
" <td>2.76</td>\n",
" <td>3.460</td>\n",
" <td>20.22</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>6</td>\n",
" <td>Duster 360</td>\n",
" <td>14.3</td>\n",
" <td>8</td>\n",
" <td>360.0</td>\n",
" <td>245</td>\n",
" <td>3.21</td>\n",
" <td>3.570</td>\n",
" <td>15.84</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7</td>\n",
" <td>Merc 240D</td>\n",
" <td>24.4</td>\n",
" <td>4</td>\n",
" <td>146.7</td>\n",
" <td>62</td>\n",
" <td>3.69</td>\n",
" <td>3.190</td>\n",
" <td>20.00</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>8</td>\n",
" <td>Merc 230</td>\n",
" <td>22.8</td>\n",
" <td>4</td>\n",
" <td>140.8</td>\n",
" <td>95</td>\n",
" <td>3.92</td>\n",
" <td>3.150</td>\n",
" <td>22.90</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>9</td>\n",
" <td>Merc 280</td>\n",
" <td>19.2</td>\n",
" <td>6</td>\n",
" <td>167.6</td>\n",
" <td>123</td>\n",
" <td>3.92</td>\n",
" <td>3.440</td>\n",
" <td>18.30</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>10</td>\n",
" <td>Merc 280C</td>\n",
" <td>17.8</td>\n",
" <td>6</td>\n",
" <td>167.6</td>\n",
" <td>123</td>\n",
" <td>3.92</td>\n",
" <td>3.440</td>\n",
" <td>18.90</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>11</td>\n",
" <td>Merc 450SE</td>\n",
" <td>16.4</td>\n",
" <td>8</td>\n",
" <td>275.8</td>\n",
" <td>180</td>\n",
" <td>3.07</td>\n",
" <td>4.070</td>\n",
" <td>17.40</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>12</td>\n",
" <td>Merc 450SL</td>\n",
" <td>17.3</td>\n",
" <td>8</td>\n",
" <td>275.8</td>\n",
" <td>180</td>\n",
" <td>3.07</td>\n",
" <td>3.730</td>\n",
" <td>17.60</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>13</td>\n",
" <td>Merc 450SLC</td>\n",
" <td>15.2</td>\n",
" <td>8</td>\n",
" <td>275.8</td>\n",
" <td>180</td>\n",
" <td>3.07</td>\n",
" <td>3.780</td>\n",
" <td>18.00</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>14</td>\n",
" <td>Cadillac Fleetwood</td>\n",
" <td>10.4</td>\n",
" <td>8</td>\n",
" <td>472.0</td>\n",
" <td>205</td>\n",
" <td>2.93</td>\n",
" <td>5.250</td>\n",
" <td>17.98</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>15</td>\n",
" <td>Lincoln Continental</td>\n",
" <td>10.4</td>\n",
" <td>8</td>\n",
" <td>460.0</td>\n",
" <td>215</td>\n",
" <td>3.00</td>\n",
" <td>5.424</td>\n",
" <td>17.82</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16</td>\n",
" <td>Chrysler Imperial</td>\n",
" <td>14.7</td>\n",
" <td>8</td>\n",
" <td>440.0</td>\n",
" <td>230</td>\n",
" <td>3.23</td>\n",
" <td>5.345</td>\n",
" <td>17.42</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>17</td>\n",
" <td>Fiat 128</td>\n",
" <td>32.4</td>\n",
" <td>4</td>\n",
" <td>78.7</td>\n",
" <td>66</td>\n",
" <td>4.08</td>\n",
" <td>2.200</td>\n",
" <td>19.47</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>18</td>\n",
" <td>Honda Civic</td>\n",
" <td>30.4</td>\n",
" <td>4</td>\n",
" <td>75.7</td>\n",
" <td>52</td>\n",
" <td>4.93</td>\n",
" <td>1.615</td>\n",
" <td>18.52</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>19</td>\n",
" <td>Toyota Corolla</td>\n",
" <td>33.9</td>\n",
" <td>4</td>\n",
" <td>71.1</td>\n",
" <td>65</td>\n",
" <td>4.22</td>\n",
" <td>1.835</td>\n",
" <td>19.90</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>20</td>\n",
" <td>Toyota Corona</td>\n",
" <td>21.5</td>\n",
" <td>4</td>\n",
" <td>120.1</td>\n",
" <td>97</td>\n",
" <td>3.70</td>\n",
" <td>2.465</td>\n",
" <td>20.01</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>21</td>\n",
" <td>Dodge Challenger</td>\n",
" <td>15.5</td>\n",
" <td>8</td>\n",
" <td>318.0</td>\n",
" <td>150</td>\n",
" <td>2.76</td>\n",
" <td>3.520</td>\n",
" <td>16.87</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>22</td>\n",
" <td>AMC Javelin</td>\n",
" <td>15.2</td>\n",
" <td>8</td>\n",
" <td>304.0</td>\n",
" <td>150</td>\n",
" <td>3.15</td>\n",
" <td>3.435</td>\n",
" <td>17.30</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>23</td>\n",
" <td>Camaro Z28</td>\n",
" <td>13.3</td>\n",
" <td>8</td>\n",
" <td>350.0</td>\n",
" <td>245</td>\n",
" <td>3.73</td>\n",
" <td>3.840</td>\n",
" <td>15.41</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>24</td>\n",
" <td>Pontiac Firebird</td>\n",
" <td>19.2</td>\n",
" <td>8</td>\n",
" <td>400.0</td>\n",
" <td>175</td>\n",
" <td>3.08</td>\n",
" <td>3.845</td>\n",
" <td>17.05</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>25</td>\n",
" <td>Fiat X1-9</td>\n",
" <td>27.3</td>\n",
" <td>4</td>\n",
" <td>79.0</td>\n",
" <td>66</td>\n",
" <td>4.08</td>\n",
" <td>1.935</td>\n",
" <td>18.90</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>26</td>\n",
" <td>Porsche 914-2</td>\n",
" <td>26.0</td>\n",
" <td>4</td>\n",
" <td>120.3</td>\n",
" <td>91</td>\n",
" <td>4.43</td>\n",
" <td>2.140</td>\n",
" <td>16.70</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <td>27</td>\n",
" <td>Lotus Europa</td>\n",
" <td>30.4</td>\n",
" <td>4</td>\n",
" <td>95.1</td>\n",
" <td>113</td>\n",
" <td>3.77</td>\n",
" <td>1.513</td>\n",
" <td>16.90</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <td>28</td>\n",
" <td>Ford Pantera L</td>\n",
" <td>15.8</td>\n",
" <td>8</td>\n",
" <td>351.0</td>\n",
" <td>264</td>\n",
" <td>4.22</td>\n",
" <td>3.170</td>\n",
" <td>14.50</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <td>29</td>\n",
" <td>Ferrari Dino</td>\n",
" <td>19.7</td>\n",
" <td>6</td>\n",
" <td>145.0</td>\n",
" <td>175</td>\n",
" <td>3.62</td>\n",
" <td>2.770</td>\n",
" <td>15.50</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <td>30</td>\n",
" <td>Maserati Bora</td>\n",
" <td>15.0</td>\n",
" <td>8</td>\n",
" <td>301.0</td>\n",
" <td>335</td>\n",
" <td>3.54</td>\n",
" <td>3.570</td>\n",
" <td>14.60</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <td>31</td>\n",
" <td>Volvo 142E</td>\n",
" <td>21.4</td>\n",
" <td>4</td>\n",
" <td>121.0</td>\n",
" <td>109</td>\n",
" <td>4.11</td>\n",
" <td>2.780</td>\n",
" <td>18.60</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name mpg cyl disp hp drat wt qsec vs am \\\n",
"0 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 \n",
"1 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 \n",
"2 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 \n",
"3 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 \n",
"4 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 \n",
"5 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 \n",
"6 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 \n",
"7 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 \n",
"8 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 \n",
"9 Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 \n",
"10 Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 \n",
"11 Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 \n",
"12 Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 \n",
"13 Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 \n",
"14 Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 \n",
"15 Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 \n",
"16 Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 \n",
"17 Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 \n",
"18 Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 \n",
"19 Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 \n",
"20 Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 \n",
"21 Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 \n",
"22 AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 \n",
"23 Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 \n",
"24 Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 \n",
"25 Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 \n",
"26 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 \n",
"27 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 \n",
"28 Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 \n",
"29 Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 \n",
"30 Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 \n",
"31 Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 \n",
"\n",
" gear \n",
"0 4 \n",
"1 4 \n",
"2 4 \n",
"3 3 \n",
"4 3 \n",
"5 3 \n",
"6 3 \n",
"7 4 \n",
"8 4 \n",
"9 4 \n",
"10 4 \n",
"11 3 \n",
"12 3 \n",
"13 3 \n",
"14 3 \n",
"15 3 \n",
"16 3 \n",
"17 4 \n",
"18 4 \n",
"19 4 \n",
"20 3 \n",
"21 3 \n",
"22 3 \n",
"23 3 \n",
"24 3 \n",
"25 4 \n",
"26 5 \n",
"27 5 \n",
"28 5 \n",
"29 5 \n",
"30 5 \n",
"31 4 "
]
},
"execution_count": 111,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#all rows and columns\n",
"cars.iloc[:,:]"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>hp</th>\n",
" <th>drat</th>\n",
" <th>wt</th>\n",
" <th>qsec</th>\n",
" <th>vs</th>\n",
" <th>am</th>\n",
" <th>gear</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>6</td>\n",
" <td>245</td>\n",
" <td>3.21</td>\n",
" <td>3.570</td>\n",
" <td>15.84</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7</td>\n",
" <td>62</td>\n",
" <td>3.69</td>\n",
" <td>3.190</td>\n",
" <td>20.00</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>8</td>\n",
" <td>95</td>\n",
" <td>3.92</td>\n",
" <td>3.150</td>\n",
" <td>22.90</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>9</td>\n",
" <td>123</td>\n",
" <td>3.92</td>\n",
" <td>3.440</td>\n",
" <td>18.30</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>10</td>\n",
" <td>123</td>\n",
" <td>3.92</td>\n",
" <td>3.440</td>\n",
" <td>18.90</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>11</td>\n",
" <td>180</td>\n",
" <td>3.07</td>\n",
" <td>4.070</td>\n",
" <td>17.40</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>12</td>\n",
" <td>180</td>\n",
" <td>3.07</td>\n",
" <td>3.730</td>\n",
" <td>17.60</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>13</td>\n",
" <td>180</td>\n",
" <td>3.07</td>\n",
" <td>3.780</td>\n",
" <td>18.00</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>14</td>\n",
" <td>205</td>\n",
" <td>2.93</td>\n",
" <td>5.250</td>\n",
" <td>17.98</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>15</td>\n",
" <td>215</td>\n",
" <td>3.00</td>\n",
" <td>5.424</td>\n",
" <td>17.82</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16</td>\n",
" <td>230</td>\n",
" <td>3.23</td>\n",
" <td>5.345</td>\n",
" <td>17.42</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>17</td>\n",
" <td>66</td>\n",
" <td>4.08</td>\n",
" <td>2.200</td>\n",
" <td>19.47</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>18</td>\n",
" <td>52</td>\n",
" <td>4.93</td>\n",
" <td>1.615</td>\n",
" <td>18.52</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>19</td>\n",
" <td>65</td>\n",
" <td>4.22</td>\n",
" <td>1.835</td>\n",
" <td>19.90</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>20</td>\n",
" <td>97</td>\n",
" <td>3.70</td>\n",
" <td>2.465</td>\n",
" <td>20.01</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>21</td>\n",
" <td>150</td>\n",
" <td>2.76</td>\n",
" <td>3.520</td>\n",
" <td>16.87</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>22</td>\n",
" <td>150</td>\n",
" <td>3.15</td>\n",
" <td>3.435</td>\n",
" <td>17.30</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>23</td>\n",
" <td>245</td>\n",
" <td>3.73</td>\n",
" <td>3.840</td>\n",
" <td>15.41</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>24</td>\n",
" <td>175</td>\n",
" <td>3.08</td>\n",
" <td>3.845</td>\n",
" <td>17.05</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>25</td>\n",
" <td>66</td>\n",
" <td>4.08</td>\n",
" <td>1.935</td>\n",
" <td>18.90</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>26</td>\n",
" <td>91</td>\n",
" <td>4.43</td>\n",
" <td>2.140</td>\n",
" <td>16.70</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <td>27</td>\n",
" <td>113</td>\n",
" <td>3.77</td>\n",
" <td>1.513</td>\n",
" <td>16.90</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <td>28</td>\n",
" <td>264</td>\n",
" <td>4.22</td>\n",
" <td>3.170</td>\n",
" <td>14.50</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <td>29</td>\n",
" <td>175</td>\n",
" <td>3.62</td>\n",
" <td>2.770</td>\n",
" <td>15.50</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <td>30</td>\n",
" <td>335</td>\n",
" <td>3.54</td>\n",
" <td>3.570</td>\n",
" <td>14.60</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <td>31</td>\n",
" <td>109</td>\n",
" <td>4.11</td>\n",
" <td>2.780</td>\n",
" <td>18.60</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" hp drat wt qsec vs am gear\n",
"6 245 3.21 3.570 15.84 0 0 3\n",
"7 62 3.69 3.190 20.00 1 0 4\n",
"8 95 3.92 3.150 22.90 1 0 4\n",
"9 123 3.92 3.440 18.30 1 0 4\n",
"10 123 3.92 3.440 18.90 1 0 4\n",
"11 180 3.07 4.070 17.40 0 0 3\n",
"12 180 3.07 3.730 17.60 0 0 3\n",
"13 180 3.07 3.780 18.00 0 0 3\n",
"14 205 2.93 5.250 17.98 0 0 3\n",
"15 215 3.00 5.424 17.82 0 0 3\n",
"16 230 3.23 5.345 17.42 0 0 3\n",
"17 66 4.08 2.200 19.47 1 1 4\n",
"18 52 4.93 1.615 18.52 1 1 4\n",
"19 65 4.22 1.835 19.90 1 1 4\n",
"20 97 3.70 2.465 20.01 1 0 3\n",
"21 150 2.76 3.520 16.87 0 0 3\n",
"22 150 3.15 3.435 17.30 0 0 3\n",
"23 245 3.73 3.840 15.41 0 0 3\n",
"24 175 3.08 3.845 17.05 0 0 3\n",
"25 66 4.08 1.935 18.90 1 1 4\n",
"26 91 4.43 2.140 16.70 0 1 5\n",
"27 113 3.77 1.513 16.90 1 1 5\n",
"28 264 4.22 3.170 14.50 0 1 5\n",
"29 175 3.62 2.770 15.50 0 1 5\n",
"30 335 3.54 3.570 14.60 0 1 5\n",
"31 109 4.11 2.780 18.60 1 1 4"
]
},
"execution_count": 113,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cars.iloc[6:,4:]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### By Label"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 21.0\n",
"1 21.0\n",
"2 22.8\n",
"3 21.4\n",
"4 18.7\n",
"5 18.1\n",
"6 14.3\n",
"7 24.4\n",
"8 22.8\n",
"9 19.2\n",
"10 17.8\n",
"11 16.4\n",
"12 17.3\n",
"13 15.2\n",
"14 10.4\n",
"15 10.4\n",
"16 14.7\n",
"17 32.4\n",
"18 30.4\n",
"19 33.9\n",
"20 21.5\n",
"21 15.5\n",
"22 15.2\n",
"23 13.3\n",
"24 19.2\n",
"25 27.3\n",
"26 26.0\n",
"27 30.4\n",
"28 15.8\n",
"29 19.7\n",
"30 15.0\n",
"31 21.4\n",
"Name: mpg, dtype: float64"
]
},
"execution_count": 115,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#see all the rows of mpg col\n",
"cars.loc[:,'mpg']"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>mpg</th>\n",
" <th>cyl</th>\n",
" <th>disp</th>\n",
" <th>hp</th>\n",
" <th>drat</th>\n",
" <th>wt</th>\n",
" <th>qsec</th>\n",
" <th>vs</th>\n",
" <th>am</th>\n",
" <th>gear</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>22.8</td>\n",
" <td>4</td>\n",
" <td>108.0</td>\n",
" <td>93</td>\n",
" <td>3.85</td>\n",
" <td>2.320</td>\n",
" <td>18.61</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>21.4</td>\n",
" <td>6</td>\n",
" <td>258.0</td>\n",
" <td>110</td>\n",
" <td>3.08</td>\n",
" <td>3.215</td>\n",
" <td>19.44</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>18.7</td>\n",
" <td>8</td>\n",
" <td>360.0</td>\n",
" <td>175</td>\n",
" <td>3.15</td>\n",
" <td>3.440</td>\n",
" <td>17.02</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" mpg cyl disp hp drat wt qsec vs am gear\n",
"2 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4\n",
"3 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3\n",
"4 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3"
]
},
"execution_count": 121,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#loc can take both index or string arg\n",
"cars.loc[2:4,'mpg':'gear']"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>mpg</th>\n",
" <th>cyl</th>\n",
" <th>disp</th>\n",
" <th>hp</th>\n",
" <th>drat</th>\n",
" <th>wt</th>\n",
" <th>qsec</th>\n",
" <th>vs</th>\n",
" <th>am</th>\n",
" <th>gear</th>\n",
" <th>gm</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>Mazda RX4</td>\n",
" <td>21.0</td>\n",
" <td>6</td>\n",
" <td>160.0</td>\n",
" <td>110</td>\n",
" <td>3.90</td>\n",
" <td>2.620</td>\n",
" <td>16.46</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>Mazda RX4 Wag</td>\n",
" <td>21.0</td>\n",
" <td>6</td>\n",
" <td>160.0</td>\n",
" <td>110</td>\n",
" <td>3.90</td>\n",
" <td>2.875</td>\n",
" <td>17.02</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>Datsun 710</td>\n",
" <td>22.8</td>\n",
" <td>4</td>\n",
" <td>108.0</td>\n",
" <td>93</td>\n",
" <td>3.85</td>\n",
" <td>2.320</td>\n",
" <td>18.61</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>Hornet 4 Drive</td>\n",
" <td>21.4</td>\n",
" <td>6</td>\n",
" <td>258.0</td>\n",
" <td>110</td>\n",
" <td>3.08</td>\n",
" <td>3.215</td>\n",
" <td>19.44</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>Hornet Sportabout</td>\n",
" <td>18.7</td>\n",
" <td>8</td>\n",
" <td>360.0</td>\n",
" <td>175</td>\n",
" <td>3.15</td>\n",
" <td>3.440</td>\n",
" <td>17.02</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>Valiant</td>\n",
" <td>18.1</td>\n",
" <td>6</td>\n",
" <td>225.0</td>\n",
" <td>105</td>\n",
" <td>2.76</td>\n",
" <td>3.460</td>\n",
" <td>20.22</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>6</td>\n",
" <td>Duster 360</td>\n",
" <td>14.3</td>\n",
" <td>8</td>\n",
" <td>360.0</td>\n",
" <td>245</td>\n",
" <td>3.21</td>\n",
" <td>3.570</td>\n",
" <td>15.84</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7</td>\n",
" <td>Merc 240D</td>\n",
" <td>24.4</td>\n",
" <td>4</td>\n",
" <td>146.7</td>\n",
" <td>62</td>\n",
" <td>3.69</td>\n",
" <td>3.190</td>\n",
" <td>20.00</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>8</td>\n",
" <td>Merc 230</td>\n",
" <td>22.8</td>\n",
" <td>4</td>\n",
" <td>140.8</td>\n",
" <td>95</td>\n",
" <td>3.92</td>\n",
" <td>3.150</td>\n",
" <td>22.90</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>9</td>\n",
" <td>Merc 280</td>\n",
" <td>19.2</td>\n",
" <td>6</td>\n",
" <td>167.6</td>\n",
" <td>123</td>\n",
" <td>3.92</td>\n",
" <td>3.440</td>\n",
" <td>18.30</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>10</td>\n",
" <td>Merc 280C</td>\n",
" <td>17.8</td>\n",
" <td>6</td>\n",
" <td>167.6</td>\n",
" <td>123</td>\n",
" <td>3.92</td>\n",
" <td>3.440</td>\n",
" <td>18.90</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>11</td>\n",
" <td>Merc 450SE</td>\n",
" <td>16.4</td>\n",
" <td>8</td>\n",
" <td>275.8</td>\n",
" <td>180</td>\n",
" <td>3.07</td>\n",
" <td>4.070</td>\n",
" <td>17.40</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>12</td>\n",
" <td>Merc 450SL</td>\n",
" <td>17.3</td>\n",
" <td>8</td>\n",
" <td>275.8</td>\n",
" <td>180</td>\n",
" <td>3.07</td>\n",
" <td>3.730</td>\n",
" <td>17.60</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>13</td>\n",
" <td>Merc 450SLC</td>\n",
" <td>15.2</td>\n",
" <td>8</td>\n",
" <td>275.8</td>\n",
" <td>180</td>\n",
" <td>3.07</td>\n",
" <td>3.780</td>\n",
" <td>18.00</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>14</td>\n",
" <td>Cadillac Fleetwood</td>\n",
" <td>10.4</td>\n",
" <td>8</td>\n",
" <td>472.0</td>\n",
" <td>205</td>\n",
" <td>2.93</td>\n",
" <td>5.250</td>\n",
" <td>17.98</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>15</td>\n",
" <td>Lincoln Continental</td>\n",
" <td>10.4</td>\n",
" <td>8</td>\n",
" <td>460.0</td>\n",
" <td>215</td>\n",
" <td>3.00</td>\n",
" <td>5.424</td>\n",
" <td>17.82</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16</td>\n",
" <td>Chrysler Imperial</td>\n",
" <td>14.7</td>\n",
" <td>8</td>\n",
" <td>440.0</td>\n",
" <td>230</td>\n",
" <td>3.23</td>\n",
" <td>5.345</td>\n",
" <td>17.42</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>17</td>\n",
" <td>Fiat 128</td>\n",
" <td>32.4</td>\n",
" <td>4</td>\n",
" <td>78.7</td>\n",
" <td>66</td>\n",
" <td>4.08</td>\n",
" <td>2.200</td>\n",
" <td>19.47</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>18</td>\n",
" <td>Honda Civic</td>\n",
" <td>30.4</td>\n",
" <td>4</td>\n",
" <td>75.7</td>\n",
" <td>52</td>\n",
" <td>4.93</td>\n",
" <td>1.615</td>\n",
" <td>18.52</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>19</td>\n",
" <td>Toyota Corolla</td>\n",
" <td>33.9</td>\n",
" <td>4</td>\n",
" <td>71.1</td>\n",
" <td>65</td>\n",
" <td>4.22</td>\n",
" <td>1.835</td>\n",
" <td>19.90</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>20</td>\n",
" <td>Toyota Corona</td>\n",
" <td>21.5</td>\n",
" <td>4</td>\n",
" <td>120.1</td>\n",
" <td>97</td>\n",
" <td>3.70</td>\n",
" <td>2.465</td>\n",
" <td>20.01</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>21</td>\n",
" <td>Dodge Challenger</td>\n",
" <td>15.5</td>\n",
" <td>8</td>\n",
" <td>318.0</td>\n",
" <td>150</td>\n",
" <td>2.76</td>\n",
" <td>3.520</td>\n",
" <td>16.87</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>22</td>\n",
" <td>AMC Javelin</td>\n",
" <td>15.2</td>\n",
" <td>8</td>\n",
" <td>304.0</td>\n",
" <td>150</td>\n",
" <td>3.15</td>\n",
" <td>3.435</td>\n",
" <td>17.30</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>23</td>\n",
" <td>Camaro Z28</td>\n",
" <td>13.3</td>\n",
" <td>8</td>\n",
" <td>350.0</td>\n",
" <td>245</td>\n",
" <td>3.73</td>\n",
" <td>3.840</td>\n",
" <td>15.41</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>24</td>\n",
" <td>Pontiac Firebird</td>\n",
" <td>19.2</td>\n",
" <td>8</td>\n",
" <td>400.0</td>\n",
" <td>175</td>\n",
" <td>3.08</td>\n",
" <td>3.845</td>\n",
" <td>17.05</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>25</td>\n",
" <td>Fiat X1-9</td>\n",
" <td>27.3</td>\n",
" <td>4</td>\n",
" <td>79.0</td>\n",
" <td>66</td>\n",
" <td>4.08</td>\n",
" <td>1.935</td>\n",
" <td>18.90</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>26</td>\n",
" <td>Porsche 914-2</td>\n",
" <td>26.0</td>\n",
" <td>4</td>\n",
" <td>120.3</td>\n",
" <td>91</td>\n",
" <td>4.43</td>\n",
" <td>2.140</td>\n",
" <td>16.70</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>27</td>\n",
" <td>Lotus Europa</td>\n",
" <td>30.4</td>\n",
" <td>4</td>\n",
" <td>95.1</td>\n",
" <td>113</td>\n",
" <td>3.77</td>\n",
" <td>1.513</td>\n",
" <td>16.90</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>28</td>\n",
" <td>Ford Pantera L</td>\n",
" <td>15.8</td>\n",
" <td>8</td>\n",
" <td>351.0</td>\n",
" <td>264</td>\n",
" <td>4.22</td>\n",
" <td>3.170</td>\n",
" <td>14.50</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>29</td>\n",
" <td>Ferrari Dino</td>\n",
" <td>19.7</td>\n",
" <td>6</td>\n",
" <td>145.0</td>\n",
" <td>175</td>\n",
" <td>3.62</td>\n",
" <td>2.770</td>\n",
" <td>15.50</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>30</td>\n",
" <td>Maserati Bora</td>\n",
" <td>15.0</td>\n",
" <td>8</td>\n",
" <td>301.0</td>\n",
" <td>335</td>\n",
" <td>3.54</td>\n",
" <td>3.570</td>\n",
" <td>14.60</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>31</td>\n",
" <td>Volvo 142E</td>\n",
" <td>21.4</td>\n",
" <td>4</td>\n",
" <td>121.0</td>\n",
" <td>109</td>\n",
" <td>4.11</td>\n",
" <td>2.780</td>\n",
" <td>18.60</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name mpg cyl disp hp drat wt qsec vs am \\\n",
"0 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 \n",
"1 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 \n",
"2 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 \n",
"3 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 \n",
"4 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 \n",
"5 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 \n",
"6 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 \n",
"7 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 \n",
"8 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 \n",
"9 Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 \n",
"10 Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 \n",
"11 Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 \n",
"12 Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 \n",
"13 Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 \n",
"14 Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 \n",
"15 Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 \n",
"16 Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 \n",
"17 Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 \n",
"18 Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 \n",
"19 Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 \n",
"20 Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 \n",
"21 Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 \n",
"22 AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 \n",
"23 Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 \n",
"24 Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 \n",
"25 Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 \n",
"26 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 \n",
"27 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 \n",
"28 Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 \n",
"29 Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 \n",
"30 Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 \n",
"31 Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 \n",
"\n",
" gear gm \n",
"0 4 1 \n",
"1 4 1 \n",
"2 4 1 \n",
"3 3 1 \n",
"4 3 1 \n",
"5 3 1 \n",
"6 3 1 \n",
"7 4 1 \n",
"8 4 1 \n",
"9 4 1 \n",
"10 4 1 \n",
"11 3 1 \n",
"12 3 1 \n",
"13 3 1 \n",
"14 3 1 \n",
"15 3 1 \n",
"16 3 1 \n",
"17 4 1 \n",
"18 4 1 \n",
"19 4 1 \n",
"20 3 1 \n",
"21 3 1 \n",
"22 3 1 \n",
"23 3 1 \n",
"24 3 1 \n",
"25 4 1 \n",
"26 5 1 \n",
"27 5 1 \n",
"28 5 1 \n",
"29 5 1 \n",
"30 5 1 \n",
"31 4 1 "
]
},
"execution_count": 124,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#set values of column to a value\n",
"cars['gm']=1\n",
"cars"
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>mpg</th>\n",
" <th>cyl</th>\n",
" <th>disp</th>\n",
" <th>hp</th>\n",
" <th>drat</th>\n",
" <th>wt</th>\n",
" <th>qsec</th>\n",
" <th>vs</th>\n",
" <th>am</th>\n",
" <th>gear</th>\n",
" <th>gm</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>Mazda RX4</td>\n",
" <td>21.0</td>\n",
" <td>6</td>\n",
" <td>160.0</td>\n",
" <td>110</td>\n",
" <td>3.90</td>\n",
" <td>2.620</td>\n",
" <td>16.46</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>Mazda RX4 Wag</td>\n",
" <td>21.0</td>\n",
" <td>6</td>\n",
" <td>160.0</td>\n",
" <td>110</td>\n",
" <td>3.90</td>\n",
" <td>2.875</td>\n",
" <td>17.02</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>Hornet Sportabout</td>\n",
" <td>18.7</td>\n",
" <td>8</td>\n",
" <td>360.0</td>\n",
" <td>175</td>\n",
" <td>3.15</td>\n",
" <td>3.440</td>\n",
" <td>17.02</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>6</td>\n",
" <td>Duster 360</td>\n",
" <td>14.3</td>\n",
" <td>8</td>\n",
" <td>360.0</td>\n",
" <td>245</td>\n",
" <td>3.21</td>\n",
" <td>3.570</td>\n",
" <td>15.84</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>11</td>\n",
" <td>Merc 450SE</td>\n",
" <td>16.4</td>\n",
" <td>8</td>\n",
" <td>275.8</td>\n",
" <td>180</td>\n",
" <td>3.07</td>\n",
" <td>4.070</td>\n",
" <td>17.40</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>12</td>\n",
" <td>Merc 450SL</td>\n",
" <td>17.3</td>\n",
" <td>8</td>\n",
" <td>275.8</td>\n",
" <td>180</td>\n",
" <td>3.07</td>\n",
" <td>3.730</td>\n",
" <td>17.60</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>13</td>\n",
" <td>Merc 450SLC</td>\n",
" <td>15.2</td>\n",
" <td>8</td>\n",
" <td>275.8</td>\n",
" <td>180</td>\n",
" <td>3.07</td>\n",
" <td>3.780</td>\n",
" <td>18.00</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>14</td>\n",
" <td>Cadillac Fleetwood</td>\n",
" <td>10.4</td>\n",
" <td>8</td>\n",
" <td>472.0</td>\n",
" <td>205</td>\n",
" <td>2.93</td>\n",
" <td>5.250</td>\n",
" <td>17.98</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>15</td>\n",
" <td>Lincoln Continental</td>\n",
" <td>10.4</td>\n",
" <td>8</td>\n",
" <td>460.0</td>\n",
" <td>215</td>\n",
" <td>3.00</td>\n",
" <td>5.424</td>\n",
" <td>17.82</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16</td>\n",
" <td>Chrysler Imperial</td>\n",
" <td>14.7</td>\n",
" <td>8</td>\n",
" <td>440.0</td>\n",
" <td>230</td>\n",
" <td>3.23</td>\n",
" <td>5.345</td>\n",
" <td>17.42</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>21</td>\n",
" <td>Dodge Challenger</td>\n",
" <td>15.5</td>\n",
" <td>8</td>\n",
" <td>318.0</td>\n",
" <td>150</td>\n",
" <td>2.76</td>\n",
" <td>3.520</td>\n",
" <td>16.87</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>22</td>\n",
" <td>AMC Javelin</td>\n",
" <td>15.2</td>\n",
" <td>8</td>\n",
" <td>304.0</td>\n",
" <td>150</td>\n",
" <td>3.15</td>\n",
" <td>3.435</td>\n",
" <td>17.30</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>23</td>\n",
" <td>Camaro Z28</td>\n",
" <td>13.3</td>\n",
" <td>8</td>\n",
" <td>350.0</td>\n",
" <td>245</td>\n",
" <td>3.73</td>\n",
" <td>3.840</td>\n",
" <td>15.41</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>24</td>\n",
" <td>Pontiac Firebird</td>\n",
" <td>19.2</td>\n",
" <td>8</td>\n",
" <td>400.0</td>\n",
" <td>175</td>\n",
" <td>3.08</td>\n",
" <td>3.845</td>\n",
" <td>17.05</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>26</td>\n",
" <td>Porsche 914-2</td>\n",
" <td>26.0</td>\n",
" <td>4</td>\n",
" <td>120.3</td>\n",
" <td>91</td>\n",
" <td>4.43</td>\n",
" <td>2.140</td>\n",
" <td>16.70</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>28</td>\n",
" <td>Ford Pantera L</td>\n",
" <td>15.8</td>\n",
" <td>8</td>\n",
" <td>351.0</td>\n",
" <td>264</td>\n",
" <td>4.22</td>\n",
" <td>3.170</td>\n",
" <td>14.50</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>29</td>\n",
" <td>Ferrari Dino</td>\n",
" <td>19.7</td>\n",
" <td>6</td>\n",
" <td>145.0</td>\n",
" <td>175</td>\n",
" <td>3.62</td>\n",
" <td>2.770</td>\n",
" <td>15.50</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>30</td>\n",
" <td>Maserati Bora</td>\n",
" <td>15.0</td>\n",
" <td>8</td>\n",
" <td>301.0</td>\n",
" <td>335</td>\n",
" <td>3.54</td>\n",
" <td>3.570</td>\n",
" <td>14.60</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name mpg cyl disp hp drat wt qsec vs am \\\n",
"0 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 \n",
"1 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 \n",
"4 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 \n",
"6 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 \n",
"11 Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 \n",
"12 Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 \n",
"13 Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 \n",
"14 Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 \n",
"15 Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 \n",
"16 Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 \n",
"21 Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 \n",
"22 AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 \n",
"23 Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 \n",
"24 Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 \n",
"26 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 \n",
"28 Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 \n",
"29 Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 \n",
"30 Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 \n",
"\n",
" gear gm \n",
"0 4 1 \n",
"1 4 1 \n",
"4 3 1 \n",
"6 3 1 \n",
"11 3 1 \n",
"12 3 1 \n",
"13 3 1 \n",
"14 3 1 \n",
"15 3 1 \n",
"16 3 1 \n",
"21 3 1 \n",
"22 3 1 \n",
"23 3 1 \n",
"24 3 1 \n",
"26 5 1 \n",
"28 5 1 \n",
"29 5 1 \n",
"30 5 1 "
]
},
"execution_count": 126,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#filtering\n",
"cars[cars['vs']==0]"
]
},
{
"cell_type": "code",
"execution_count": 133,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>mpg</th>\n",
" <th>cyl</th>\n",
" <th>disp</th>\n",
" <th>hp</th>\n",
" <th>drat</th>\n",
" <th>wt</th>\n",
" <th>qsec</th>\n",
" <th>vs</th>\n",
" <th>am</th>\n",
" <th>gear</th>\n",
" <th>gm</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>Mazda RX4</td>\n",
" <td>21.0</td>\n",
" <td>6</td>\n",
" <td>160.0</td>\n",
" <td>110</td>\n",
" <td>3.90</td>\n",
" <td>2.620</td>\n",
" <td>16.46</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>Mazda RX4 Wag</td>\n",
" <td>21.0</td>\n",
" <td>6</td>\n",
" <td>160.0</td>\n",
" <td>110</td>\n",
" <td>3.90</td>\n",
" <td>2.875</td>\n",
" <td>17.02</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>Datsun 710</td>\n",
" <td>22.8</td>\n",
" <td>4</td>\n",
" <td>108.0</td>\n",
" <td>93</td>\n",
" <td>3.85</td>\n",
" <td>2.320</td>\n",
" <td>18.61</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>Hornet 4 Drive</td>\n",
" <td>21.4</td>\n",
" <td>6</td>\n",
" <td>258.0</td>\n",
" <td>110</td>\n",
" <td>3.08</td>\n",
" <td>3.215</td>\n",
" <td>19.44</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>Hornet Sportabout</td>\n",
" <td>18.7</td>\n",
" <td>8</td>\n",
" <td>360.0</td>\n",
" <td>175</td>\n",
" <td>3.15</td>\n",
" <td>3.440</td>\n",
" <td>17.02</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name mpg cyl disp hp drat wt qsec vs am gear \\\n",
"0 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 4 1 4 \n",
"1 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 4 1 4 \n",
"2 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 5 1 4 \n",
"3 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 5 0 3 \n",
"4 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 4 0 3 \n",
"\n",
" gm \n",
"0 1 \n",
"1 1 \n",
"2 1 \n",
"3 1 \n",
"4 1 "
]
},
"execution_count": 133,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Applying lambda function\n",
"f=lambda x:x+2\n",
"cars['vs']=cars['vs'].apply(f)\n",
"cars.head()"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>mpg</th>\n",
" <th>cyl</th>\n",
" <th>disp</th>\n",
" <th>hp</th>\n",
" <th>drat</th>\n",
" <th>wt</th>\n",
" <th>qsec</th>\n",
" <th>vs</th>\n",
" <th>am</th>\n",
" <th>gear</th>\n",
" <th>gm</th>\n",
" <th>2am</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>Mazda RX4</td>\n",
" <td>21.0</td>\n",
" <td>6</td>\n",
" <td>160.0</td>\n",
" <td>110</td>\n",
" <td>3.90</td>\n",
" <td>2.620</td>\n",
" <td>16.46</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>Mazda RX4 Wag</td>\n",
" <td>21.0</td>\n",
" <td>6</td>\n",
" <td>160.0</td>\n",
" <td>110</td>\n",
" <td>3.90</td>\n",
" <td>2.875</td>\n",
" <td>17.02</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>Datsun 710</td>\n",
" <td>22.8</td>\n",
" <td>4</td>\n",
" <td>108.0</td>\n",
" <td>93</td>\n",
" <td>3.85</td>\n",
" <td>2.320</td>\n",
" <td>18.61</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>Hornet 4 Drive</td>\n",
" <td>21.4</td>\n",
" <td>6</td>\n",
" <td>258.0</td>\n",
" <td>110</td>\n",
" <td>3.08</td>\n",
" <td>3.215</td>\n",
" <td>19.44</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>Hornet Sportabout</td>\n",
" <td>18.7</td>\n",
" <td>8</td>\n",
" <td>360.0</td>\n",
" <td>175</td>\n",
" <td>3.15</td>\n",
" <td>3.440</td>\n",
" <td>17.02</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name mpg cyl disp hp drat wt qsec vs am gear \\\n",
"0 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 4 1 4 \n",
"1 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 4 1 4 \n",
"2 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 5 1 4 \n",
"3 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 5 0 3 \n",
"4 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 4 0 3 \n",
"\n",
" gm 2am \n",
"0 1 3 \n",
"1 1 3 \n",
"2 1 3 \n",
"3 1 2 \n",
"4 1 2 "
]
},
"execution_count": 134,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#creating new column and applying lambda function\n",
"cars['2am']=cars['am'].apply(f)\n",
"cars.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sorting"
]
},
{
"cell_type": "code",
"execution_count": 145,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>mpg</th>\n",
" <th>cyl</th>\n",
" <th>disp</th>\n",
" <th>hp</th>\n",
" <th>drat</th>\n",
" <th>wt</th>\n",
" <th>qsec</th>\n",
" <th>vs</th>\n",
" <th>am</th>\n",
" <th>gear</th>\n",
" <th>gm</th>\n",
" <th>2am</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>15</td>\n",
" <td>Lincoln Continental</td>\n",
" <td>10.4</td>\n",
" <td>8</td>\n",
" <td>460.0</td>\n",
" <td>215</td>\n",
" <td>3.00</td>\n",
" <td>5.424</td>\n",
" <td>17.82</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>14</td>\n",
" <td>Cadillac Fleetwood</td>\n",
" <td>10.4</td>\n",
" <td>8</td>\n",
" <td>472.0</td>\n",
" <td>205</td>\n",
" <td>2.93</td>\n",
" <td>5.250</td>\n",
" <td>17.98</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>23</td>\n",
" <td>Camaro Z28</td>\n",
" <td>13.3</td>\n",
" <td>8</td>\n",
" <td>350.0</td>\n",
" <td>245</td>\n",
" <td>3.73</td>\n",
" <td>3.840</td>\n",
" <td>15.41</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>6</td>\n",
" <td>Duster 360</td>\n",
" <td>14.3</td>\n",
" <td>8</td>\n",
" <td>360.0</td>\n",
" <td>245</td>\n",
" <td>3.21</td>\n",
" <td>3.570</td>\n",
" <td>15.84</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16</td>\n",
" <td>Chrysler Imperial</td>\n",
" <td>14.7</td>\n",
" <td>8</td>\n",
" <td>440.0</td>\n",
" <td>230</td>\n",
" <td>3.23</td>\n",
" <td>5.345</td>\n",
" <td>17.42</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name mpg cyl disp hp drat wt qsec vs am \\\n",
"15 Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 4 0 \n",
"14 Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 4 0 \n",
"23 Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 4 0 \n",
"6 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 4 0 \n",
"16 Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 4 0 \n",
"\n",
" gear gm 2am \n",
"15 3 1 2 \n",
"14 3 1 2 \n",
"23 3 1 2 \n",
"6 3 1 2 \n",
"16 3 1 2 "
]
},
"execution_count": 145,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#sort by value of a column\n",
"cars=cars.sort_values(by='mpg')\n",
"cars.head()"
]
},
{
"cell_type": "code",
"execution_count": 151,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>mpg</th>\n",
" <th>cyl</th>\n",
" <th>disp</th>\n",
" <th>hp</th>\n",
" <th>drat</th>\n",
" <th>wt</th>\n",
" <th>qsec</th>\n",
" <th>vs</th>\n",
" <th>am</th>\n",
" <th>gear</th>\n",
" <th>gm</th>\n",
" <th>2am</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>19</td>\n",
" <td>Toyota Corolla</td>\n",
" <td>33.9</td>\n",
" <td>4</td>\n",
" <td>71.1</td>\n",
" <td>65</td>\n",
" <td>4.22</td>\n",
" <td>1.835</td>\n",
" <td>19.90</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>17</td>\n",
" <td>Fiat 128</td>\n",
" <td>32.4</td>\n",
" <td>4</td>\n",
" <td>78.7</td>\n",
" <td>66</td>\n",
" <td>4.08</td>\n",
" <td>2.200</td>\n",
" <td>19.47</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>18</td>\n",
" <td>Honda Civic</td>\n",
" <td>30.4</td>\n",
" <td>4</td>\n",
" <td>75.7</td>\n",
" <td>52</td>\n",
" <td>4.93</td>\n",
" <td>1.615</td>\n",
" <td>18.52</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>27</td>\n",
" <td>Lotus Europa</td>\n",
" <td>30.4</td>\n",
" <td>4</td>\n",
" <td>95.1</td>\n",
" <td>113</td>\n",
" <td>3.77</td>\n",
" <td>1.513</td>\n",
" <td>16.90</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>25</td>\n",
" <td>Fiat X1-9</td>\n",
" <td>27.3</td>\n",
" <td>4</td>\n",
" <td>79.0</td>\n",
" <td>66</td>\n",
" <td>4.08</td>\n",
" <td>1.935</td>\n",
" <td>18.90</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name mpg cyl disp hp drat wt qsec vs am gear \\\n",
"19 Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 5 1 4 \n",
"17 Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 5 1 4 \n",
"18 Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 5 1 4 \n",
"27 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 5 1 5 \n",
"25 Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 5 1 4 \n",
"\n",
" gm 2am \n",
"19 1 3 \n",
"17 1 3 \n",
"18 1 3 \n",
"27 1 3 \n",
"25 1 3 "
]
},
"execution_count": 151,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#sort in descending order\n",
"cars=cars.sort_values(by='mpg',ascending=False)\n",
"cars.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Filtering"
]
},
{
"cell_type": "code",
"execution_count": 157,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"19 True\n",
"17 True\n",
"18 True\n",
"27 True\n",
"25 False\n",
"26 False\n",
"7 False\n",
"2 False\n",
"8 False\n",
"20 False\n",
"31 False\n",
"3 False\n",
"0 False\n",
"1 False\n",
"29 False\n",
"24 False\n",
"9 False\n",
"4 False\n",
"5 False\n",
"10 False\n",
"12 False\n",
"11 False\n",
"28 False\n",
"21 False\n",
"13 False\n",
"22 False\n",
"30 False\n",
"16 False\n",
"6 False\n",
"23 False\n",
"14 False\n",
"15 False\n",
"Name: mpg, dtype: bool"
]
},
"execution_count": 157,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#filtering\n",
"cars['mpg']>30"
]
},
{
"cell_type": "code",
"execution_count": 170,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>mpg</th>\n",
" <th>cyl</th>\n",
" <th>disp</th>\n",
" <th>hp</th>\n",
" <th>drat</th>\n",
" <th>wt</th>\n",
" <th>qsec</th>\n",
" <th>vs</th>\n",
" <th>am</th>\n",
" <th>gear</th>\n",
" <th>gm</th>\n",
" <th>2am</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>19</td>\n",
" <td>Toyota Corolla</td>\n",
" <td>33.9</td>\n",
" <td>4</td>\n",
" <td>71.1</td>\n",
" <td>65</td>\n",
" <td>4.22</td>\n",
" <td>1.835</td>\n",
" <td>19.90</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>17</td>\n",
" <td>Fiat 128</td>\n",
" <td>32.4</td>\n",
" <td>4</td>\n",
" <td>78.7</td>\n",
" <td>66</td>\n",
" <td>4.08</td>\n",
" <td>2.200</td>\n",
" <td>19.47</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name mpg cyl disp hp drat wt qsec vs am gear gm \\\n",
"19 Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 5 1 4 1 \n",
"17 Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 5 1 4 1 \n",
"\n",
" 2am \n",
"19 3 \n",
"17 3 "
]
},
"execution_count": 170,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#filter array with multiple conditions\n",
"filters=(cars['mpg']>30)&(cars['qsec']>19)\n",
"#Applying filter to car\n",
"new_cars=cars[filters]\n",
"#view filtered data\n",
"new_cars\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Visualization"
]
},
{
"cell_type": "code",
"execution_count": 175,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x21d48c74d08>]"
]
},
"execution_count": 175,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"y1=cars['mpg']\n",
"x=range(32)\n",
"plt.plot(x,y1)"
]
},
{
"cell_type": "code",
"execution_count": 177,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x21d48d6efc8>]"
]
},
"execution_count": 177,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#plotting curve\n",
"y2=cars['hp']\n",
"plt.plot(x,y2)"
]
},
{
"cell_type": "code",
"execution_count": 180,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"No handles with labels found to put in legend.\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x21d49e7d208>"
]
},
"execution_count": 180,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#plotting 2 curve\n",
"plt.plot(x,y1)\n",
"plt.plot(x,y2)\n",
"plt.legend()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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