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@Pabla007
Created March 4, 2019 17:46
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Simple Plot of CSV File With Column 1 Vs Column 2 .
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{
"cells": [
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['Year', 'Population']"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#C:\\Users\\pabla\\OneDrive\\Desktop\\Plot\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"df1= pd.read_csv('C:\\Users\\pabla\\OneDrive\\Desktop\\Plot\\sample.csv')\n",
"df1\n",
"list(df1)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0xb318be0>"
]
},
"execution_count": 14,
"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.pyplot as plt\n",
"\n",
"\n",
"df1.plot(x='Population', y='Year',color='r')\n",
"#df1.plot()\n",
"#sa1.plot(color='r',lw=1.3)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.15"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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