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155 lines
3.0 KiB
Plaintext
155 lines
3.0 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "bPe8tlE4ShtQ"
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},
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"outputs": [],
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"source": [
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"import pandas as pd"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "0RJHtTTTS8ak"
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},
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"outputs": [],
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"source": [
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"csv_url = '../Dataset/overall_topten_2012-2013.csv'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 359
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},
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"colab_type": "code",
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"id": "V5PMuIMCVv3S",
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"outputId": "59b83999-6dee-4a79-c199-452288f8bc14"
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},
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"outputs": [],
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"source": [
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"csv_df = pd.read_csv(csv_url, skiprows=1)\n",
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"csv_df"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "DzO8pIA3Vwe1"
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},
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"outputs": [],
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"source": [
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"tsv_url = '../Dataset/overall_topten_2012-2013.tsv'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 359
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},
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"colab_type": "code",
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"id": "p8zUaSDuV3zp",
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"outputId": "76be10ef-078f-4efe-839e-184adde7a7eb"
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},
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"outputs": [],
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"source": [
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"tsv_df = pd.read_csv(tsv_url, skiprows=1, sep='\\t')\n",
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"tsv_df"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "wgswI12UV3-m"
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},
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"outputs": [],
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"source": [
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"xlsx_url = '../Dataset/overall_topten_2012-2013.xlsx'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 49
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},
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"colab_type": "code",
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"id": "j2iQakbMYK_t",
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"outputId": "37066af4-21c6-4ea4-9d95-cfa6ccface9c"
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},
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"outputs": [],
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"source": [
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"xlsx_df = pd.read_excel(xlsx_url)\n",
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"xlsx_df"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 359
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},
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"colab_type": "code",
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"id": "OZb8mM7_W5bM",
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"outputId": "4d5968b0-f5ac-4be3-8ace-0fb51bee18f1"
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},
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"outputs": [],
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"source": [
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"xlsx_df1 = pd.read_excel(xlsx_url, skiprows=1, sheet_name=1)\n",
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"xlsx_df1"
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]
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}
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],
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"metadata": {
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"colab": {
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"collapsed_sections": [],
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"name": "Exercise1_02.ipynb",
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}
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