{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "
Lets start with importing pandas library and read_csv to read the csv file
" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv('College.csv')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " | Unnamed: 0 | \n", "Private | \n", "Apps | \n", "Accept | \n", "Enroll | \n", "Top10perc | \n", "Top25perc | \n", "F.Undergrad | \n", "P.Undergrad | \n", "Outstate | \n", "Room.Board | \n", "Books | \n", "Personal | \n", "PhD | \n", "Terminal | \n", "S.F.Ratio | \n", "perc.alumni | \n", "Expend | \n", "Grad.Rate | \n", "
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | \n", "Abilene Christian University | \n", "Yes | \n", "1660 | \n", "1232 | \n", "721 | \n", "23 | \n", "52 | \n", "2885 | \n", "537 | \n", "7440 | \n", "3300 | \n", "450 | \n", "2200 | \n", "70 | \n", "78 | \n", "18.1 | \n", "12 | \n", "7041 | \n", "60 | \n", "