{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Examples**"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" animal | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" snake | \n",
"
\n",
" \n",
" 1 | \n",
" bat | \n",
"
\n",
" \n",
" 2 | \n",
" tiger | \n",
"
\n",
" \n",
" 3 | \n",
" lion | \n",
"
\n",
" \n",
" 4 | \n",
" fox | \n",
"
\n",
" \n",
" 5 | \n",
" eagle | \n",
"
\n",
" \n",
" 6 | \n",
" shark | \n",
"
\n",
" \n",
" 7 | \n",
" dog | \n",
"
\n",
" \n",
" 8 | \n",
" deer | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" animal\n",
"0 snake\n",
"1 bat\n",
"2 tiger\n",
"3 lion\n",
"4 fox\n",
"5 eagle\n",
"6 shark\n",
"7 dog\n",
"8 deer"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.DataFrame({'animal':['snake', 'bat', 'tiger', 'lion',\n",
" 'fox', 'eagle', 'shark', 'dog', 'deer']})\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Viewing the first 5 lines"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" animal | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" snake | \n",
"
\n",
" \n",
" 1 | \n",
" bat | \n",
"
\n",
" \n",
" 2 | \n",
" tiger | \n",
"
\n",
" \n",
" 3 | \n",
" lion | \n",
"
\n",
" \n",
" 4 | \n",
" fox | \n",
"
\n",
" \n",
"
\n",
"
"
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"text/plain": [
" animal\n",
"0 snake\n",
"1 bat\n",
"2 tiger\n",
"3 lion\n",
"4 fox"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Display the first n lines (three in this case):"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" animal | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" snake | \n",
"
\n",
" \n",
" 1 | \n",
" bat | \n",
"
\n",
" \n",
" 2 | \n",
" tiger | \n",
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\n",
" \n",
"
\n",
"
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"text/plain": [
" animal\n",
"0 snake\n",
"1 bat\n",
"2 tiger"
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},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head(3)"
]
}
],
"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": "3.7.3"
}
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"nbformat_minor": 4
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