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Pratiksha_Waghmare_IP-2021-2022 #48

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102 changes: 102 additions & 0 deletions IP_LP3_ Python_Pratiksha_Waghmare_4756/CSV_to_TSV_.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"import tkinter as tk\n",
"\n",
"root= tk.Tk()\n",
"root.title(\"File Converter\")\n",
"canvas1 = tk.Canvas(root, width = 1500, height = 1000, relief = 'raised')\n",
"canvas1.pack()\n",
"\n",
"label1 = tk.Label(root, text='---FILE CONVERTER----') \n",
"label1.config(font=('Times New Roman', 40))\n",
"canvas1.create_window(750, 110, window=label1)\n",
"\n",
"label6 = tk.Label(root, text='CSV to TSV') \n",
"label6.config(font=('helvetica', 28))\n",
"canvas1.create_window(750, 160, window=label6)\n",
"\n",
"label2 = tk.Label(root, text='Enter input file path to convert:') \n",
"label2.config(font=('helvetica', 20))\n",
"canvas1.create_window(750, 250, window=label2)\n",
"\n",
"entry1 = tk.Entry (root) \n",
"canvas1.create_window(750, 300, window=entry1)\n",
"\n",
"label3 = tk.Label(root, text='\\nEnter Destination path for Output file:(using .tsv extension)') \n",
"label3.config(font=('helvetica', 20))\n",
"canvas1.create_window(750, 400, window=label3)\n",
"\n",
"entry2 = tk.Entry (root) \n",
"canvas1.create_window(750, 470, window=entry2)\n",
"\n",
"def converter (): \n",
" \n",
" x1 = entry1.get()\n",
" x2=entry2.get()\n",
" import csv\n",
" try:\n",
" with open(x1,'r') as csvin, open(x2, 'w') as tsvout:\n",
" csvin = csv.reader(csvin)\n",
" tsvout = csv.writer(tsvout, delimiter='\\t')\n",
"\n",
" for row in csvin:\n",
" tsvout.writerow(row)\n",
" label4 = tk.Label(root, text='File Converted') \n",
" label4.config(font=('Times New Roman', 16))\n",
" canvas1.create_window(750, 700, window=label4)\n",
" except:\n",
" label5 = tk.Label(root, text='Oops!!!Invalid Path,\\nPlease enter correct path') \n",
" label5.config(font=('Times New Roman', 16))\n",
" canvas1.create_window(750, 700, window=label5)\n",
" \n",
" \n",
" \n",
"button1 = tk.Button(text='Convert', command=converter, bg='blue', fg='white', font=('Times New Roman', 16, 'bold'))\n",
"canvas1.create_window(750, 600, window=button1)\n",
"\n",
"root.mainloop()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"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": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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170 changes: 170 additions & 0 deletions IP_LP3_ Python_Pratiksha_Waghmare_4756/DATA.csv
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Duration,Pulse,Maxpulse,Calories
60,110,130,409.1
60,117,145,479.0
60,103,135,340.0
45,109,175,282.4
45,117,148,406.0
60,102,127,300.0
60,110,136,374.0
45,104,134,253.3
30,109,133,195.1
60,98,124,269.0
60,103,147,329.3
60,100,120,250.7
60,106,128,345.3
60,104,132,379.3
60,98,123,275.0
60,98,120,215.2
60,100,120,300.0
45,90,112,
60,103,123,323.0
45,97,125,243.0
60,108,131,364.2
45,100,119,282.0
60,130,101,300.0
45,105,132,246.0
60,102,126,334.5
60,100,120,250.0
60,92,118,241.0
60,103,132,
60,100,132,280.0
60,102,129,380.3
60,92,115,243.0
45,90,112,180.1
60,101,124,299.0
60,93,113,223.0
60,107,136,361.0
60,114,140,415.0
60,102,127,300.0
60,100,120,300.0
60,100,120,300.0
45,104,129,266.0
45,90,112,180.1
60,98,126,286.0
60,100,122,329.4
60,111,138,400.0
60,111,131,397.0
60,99,119,273.0
60,109,153,387.6
45,111,136,300.0
45,108,129,298.0
60,111,139,397.6
60,107,136,380.2
80,123,146,643.1
60,106,130,263.0
60,118,151,486.0
30,136,175,238.0
60,121,146,450.7
60,118,121,413.0
45,115,144,305.0
20,153,172,226.4
45,123,152,321.0
210,108,160,1376.0
160,110,137,1034.4
160,109,135,853.0
45,118,141,341.0
20,110,130,131.4
180,90,130,800.4
150,105,135,873.4
150,107,130,816.0
20,106,136,110.4
300,108,143,1500.2
150,97,129,1115.0
60,109,153,387.6
90,100,127,700.0
150,97,127,953.2
45,114,146,304.0
90,98,125,563.2
45,105,134,251.0
45,110,141,300.0
120,100,130,500.4
270,100,131,1729.0
30,159,182,319.2
45,149,169,344.0
30,103,139,151.1
120,100,130,500.0
45,100,120,225.3
30,151,170,300.0
45,102,136,234.0
120,100,157,1000.1
45,129,103,242.0
20,83,107,50.3
180,101,127,600.1
45,107,137,
30,90,107,105.3
15,80,100,50.5
20,150,171,127.4
20,151,168,229.4
30,95,128,128.2
25,152,168,244.2
30,109,131,188.2
90,93,124,604.1
20,95,112,77.7
90,90,110,500.0
90,90,100,500.0
90,90,100,500.4
30,92,108,92.7
30,93,128,124.0
180,90,120,800.3
30,90,120,86.2
90,90,120,500.3
210,137,184,1860.4
60,102,124,325.2
45,107,124,275.0
15,124,139,124.2
45,100,120,225.3
60,108,131,367.6
60,108,151,351.7
60,116,141,443.0
60,97,122,277.4
60,105,125,
60,103,124,332.7
30,112,137,193.9
45,100,120,100.7
60,119,169,336.7
60,107,127,344.9
60,111,151,368.5
60,98,122,271.0
60,97,124,275.3
60,109,127,382.0
90,99,125,466.4
60,114,151,384.0
60,104,134,342.5
60,107,138,357.5
60,103,133,335.0
60,106,132,327.5
60,103,136,339.0
20,136,156,189.0
45,117,143,317.7
45,115,137,318.0
45,113,138,308.0
20,141,162,222.4
60,108,135,390.0
60,97,127,
45,100,120,250.4
45,122,149,335.4
60,136,170,470.2
45,106,126,270.8
60,107,136,400.0
60,112,146,361.9
30,103,127,185.0
60,110,150,409.4
60,106,134,343.0
60,109,129,353.2
60,109,138,374.0
30,150,167,275.8
60,105,128,328.0
60,111,151,368.5
60,97,131,270.4
60,100,120,270.4
60,114,150,382.8
30,80,120,240.9
30,85,120,250.4
45,90,130,260.4
45,95,130,270.0
45,100,140,280.9
60,105,140,290.8
60,110,145,300.0
60,115,145,310.2
75,120,150,320.4
75,125,150,330.4
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