{
"cells": [
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting pandas-profiling\n",
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"\u001b[?25h Created wheel for confuse: filename=confuse-1.1.0-py3-none-any.whl size=17573 sha256=c75ad1ae731e6e17e670156bbc556c84e54278f025205c6a10e373178dd7334e\n",
" Stored in directory: /home/jovyan/.cache/pip/wheels/18/e0/b3/79594ba4a96afaf41d39916ed83ce7d5ec031874c66a76eabb\n",
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" Stored in directory: /home/jovyan/.cache/pip/wheels/16/72/b0/e5be34699908d9ff25dcb3debbb717987b766af61bbddffdfe\n",
"Successfully built confuse htmlmin pyyaml imagehash\n",
"Installing collected packages: missingno, llvmlite, numba, joblib, phik, tqdm, pyyaml, confuse, astropy, htmlmin, networkx, tangled-up-in-unicode, Pillow, PyWavelets, imagehash, visions, pandas-profiling\n",
"Successfully installed Pillow-7.1.2 PyWavelets-1.1.1 astropy-4.0.1.post1 confuse-1.1.0 htmlmin-0.1.12 imagehash-4.1.0 joblib-0.15.1 llvmlite-0.33.0 missingno-0.4.2 networkx-2.4 numba-0.50.0 pandas-profiling-2.8.0 phik-0.10.0 pyyaml-5.3.1 tangled-up-in-unicode-0.0.6 tqdm-4.46.1 visions-0.4.4\n"
]
}
],
"source": [
"!pip install pandas-profiling "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd \n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"import seaborn as sns\n",
"import pandas_profiling "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"path =\"https://raw.githubusercontent.com/reddyprasade/Data-Sets-For-Machine-Learnig-and-Data-Science/master/DataSets/height-weight.csv\""
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv(path,names=['Height',\"Weight\"],)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
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"
\n",
" \n",
" | 4997 | \n",
" 67.013795 | \n",
" 199.195400 | \n",
"
\n",
" \n",
" | 4998 | \n",
" 71.557718 | \n",
" 185.905910 | \n",
"
\n",
" \n",
" | 4999 | \n",
" 70.351880 | \n",
" 198.903012 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Height | \n",
" Weight | \n",
"
\n",
" \n",
" \n",
" \n",
" | count | \n",
" 5000.000000 | \n",
" 5000.000000 | \n",
"
\n",
" \n",
" | mean | \n",
" 69.026346 | \n",
" 187.020621 | \n",
"
\n",
" \n",
" | std | \n",
" 2.863362 | \n",
" 19.781155 | \n",
"
\n",
" \n",
" | min | \n",
" 58.406905 | \n",
" 112.902939 | \n",
"
\n",
" \n",
" | 25% | \n",
" 67.174679 | \n",
" 173.887767 | \n",
"
\n",
" \n",
" | 50% | \n",
" 69.027709 | \n",
" 187.033546 | \n",
"
\n",
" \n",
" | 75% | \n",
" 70.988744 | \n",
" 200.357802 | \n",
"
\n",
" \n",
" | max | \n",
" 78.998742 | \n",
" 269.989698 | \n",
"
\n",
" \n",
"
\n",
"