File size: 41,797 Bytes
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"cells": [
{
"cell_type": "code",
"execution_count": 80,
"id": "initial_id",
"metadata": {
"collapsed": true,
"ExecuteTime": {
"end_time": "2024-04-30T14:17:33.633218Z",
"start_time": "2024-04-30T14:17:33.629178Z"
}
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"outputs": [
{
"data": {
"text/plain": " SBD Year Toan Van Ly Hoa Sinh Ngoai ngu Lich su Dia ly GDCD \\\n0 1000001 2022 8.4 8.50 NaN NaN NaN 9.2 6.75 6.00 9.00 \n1 1000002 2022 7.2 8.50 NaN NaN NaN 9.2 8.75 6.50 8.50 \n2 1000003 2022 NaN 6.50 NaN NaN NaN NaN 9.25 7.50 NaN \n3 1000004 2022 7.8 8.25 NaN NaN NaN 7.8 4.50 6.25 8.25 \n4 1000005 2022 7.2 8.00 NaN NaN NaN 7.8 4.75 6.75 8.25 \n\n MaTinh \n0 1 \n1 1 \n2 1 \n3 1 \n4 1 ",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>SBD</th>\n <th>Year</th>\n <th>Toan</th>\n <th>Van</th>\n <th>Ly</th>\n <th>Hoa</th>\n <th>Sinh</th>\n <th>Ngoai ngu</th>\n <th>Lich su</th>\n <th>Dia ly</th>\n <th>GDCD</th>\n <th>MaTinh</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1000001</td>\n <td>2022</td>\n <td>8.4</td>\n <td>8.50</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>9.2</td>\n <td>6.75</td>\n <td>6.00</td>\n <td>9.00</td>\n <td>1</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1000002</td>\n <td>2022</td>\n <td>7.2</td>\n <td>8.50</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>9.2</td>\n <td>8.75</td>\n <td>6.50</td>\n <td>8.50</td>\n <td>1</td>\n </tr>\n <tr>\n <th>2</th>\n <td>1000003</td>\n <td>2022</td>\n <td>NaN</td>\n <td>6.50</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>9.25</td>\n <td>7.50</td>\n <td>NaN</td>\n <td>1</td>\n </tr>\n <tr>\n <th>3</th>\n <td>1000004</td>\n <td>2022</td>\n <td>7.8</td>\n <td>8.25</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>7.8</td>\n <td>4.50</td>\n <td>6.25</td>\n <td>8.25</td>\n <td>1</td>\n </tr>\n <tr>\n <th>4</th>\n <td>1000005</td>\n <td>2022</td>\n <td>7.2</td>\n <td>8.00</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>7.8</td>\n <td>4.75</td>\n <td>6.75</td>\n <td>8.25</td>\n <td>1</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df=pd.read_csv('data/2023.csv')\n",
"df.head()"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-04-30T14:26:25.961690Z",
"start_time": "2024-04-30T14:26:25.510287Z"
}
},
"id": "87c419d6429c43c7",
"execution_count": 91
},
{
"cell_type": "code",
"outputs": [],
"source": [
"df.rename(columns={'sbd':'SBD','toan':'Toan','ngu_van':'Van','ngoai_ngu':'Ngoai ngu','vat_li':'Ly','hoa_hoc':'Hoa','sinh_hoc':'Sinh','lich_su':'Lich su','dia_li':'Dia ly','gdcd':'GDCD'},inplace=True)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-04-30T14:26:26.022927Z",
"start_time": "2024-04-30T14:26:26.017855Z"
}
},
"id": "eeaba2eae3ecb057",
"execution_count": 92
},
{
"cell_type": "code",
"outputs": [
{
"data": {
"text/plain": " SBD Year Toan Van Ly Hoa Sinh Ngoai ngu Lich su \\\n0 1000001 2022 8.4 8.50 NaN NaN NaN 9.2 6.75 \n1 1000002 2022 7.2 8.50 NaN NaN NaN 9.2 8.75 \n2 1000003 2022 NaN 6.50 NaN NaN NaN NaN 9.25 \n3 1000004 2022 7.8 8.25 NaN NaN NaN 7.8 4.50 \n4 1000005 2022 7.2 8.00 NaN NaN NaN 7.8 4.75 \n... ... ... ... ... ... ... ... ... ... \n1022055 64006933 2022 7.8 6.75 NaN NaN NaN 5.4 8.50 \n1022056 64006934 2022 7.4 7.50 6.0 5.75 6.25 6.0 NaN \n1022057 64006935 2022 6.4 7.00 NaN NaN NaN 3.0 5.50 \n1022058 64006936 2022 6.6 7.00 NaN NaN NaN 5.8 6.50 \n1022059 64006937 2022 NaN 5.00 NaN NaN NaN NaN 4.25 \n\n Dia ly GDCD MaTinh \n0 6.00 9.00 1 \n1 6.50 8.50 1 \n2 7.50 NaN 1 \n3 6.25 8.25 1 \n4 6.75 8.25 1 \n... ... ... ... \n1022055 7.75 9.75 64 \n1022056 NaN NaN 64 \n1022057 5.75 7.25 64 \n1022058 6.50 9.25 64 \n1022059 4.75 NaN 64 \n\n[1022060 rows x 12 columns]",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>SBD</th>\n <th>Year</th>\n <th>Toan</th>\n <th>Van</th>\n <th>Ly</th>\n <th>Hoa</th>\n <th>Sinh</th>\n <th>Ngoai ngu</th>\n <th>Lich su</th>\n <th>Dia ly</th>\n <th>GDCD</th>\n <th>MaTinh</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1000001</td>\n <td>2022</td>\n <td>8.4</td>\n <td>8.50</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>9.2</td>\n <td>6.75</td>\n <td>6.00</td>\n <td>9.00</td>\n <td>1</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1000002</td>\n <td>2022</td>\n <td>7.2</td>\n <td>8.50</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>9.2</td>\n <td>8.75</td>\n <td>6.50</td>\n <td>8.50</td>\n <td>1</td>\n </tr>\n <tr>\n <th>2</th>\n <td>1000003</td>\n <td>2022</td>\n <td>NaN</td>\n <td>6.50</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>9.25</td>\n <td>7.50</td>\n <td>NaN</td>\n <td>1</td>\n </tr>\n <tr>\n <th>3</th>\n <td>1000004</td>\n <td>2022</td>\n <td>7.8</td>\n <td>8.25</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>7.8</td>\n <td>4.50</td>\n <td>6.25</td>\n <td>8.25</td>\n <td>1</td>\n </tr>\n <tr>\n <th>4</th>\n <td>1000005</td>\n <td>2022</td>\n <td>7.2</td>\n <td>8.00</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>7.8</td>\n <td>4.75</td>\n <td>6.75</td>\n <td>8.25</td>\n <td>1</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>1022055</th>\n <td>64006933</td>\n <td>2022</td>\n <td>7.8</td>\n <td>6.75</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>5.4</td>\n <td>8.50</td>\n <td>7.75</td>\n <td>9.75</td>\n <td>64</td>\n </tr>\n <tr>\n <th>1022056</th>\n <td>64006934</td>\n <td>2022</td>\n <td>7.4</td>\n <td>7.50</td>\n <td>6.0</td>\n <td>5.75</td>\n <td>6.25</td>\n <td>6.0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>64</td>\n </tr>\n <tr>\n <th>1022057</th>\n <td>64006935</td>\n <td>2022</td>\n <td>6.4</td>\n <td>7.00</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>3.0</td>\n <td>5.50</td>\n <td>5.75</td>\n <td>7.25</td>\n <td>64</td>\n </tr>\n <tr>\n <th>1022058</th>\n <td>64006936</td>\n <td>2022</td>\n <td>6.6</td>\n <td>7.00</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>5.8</td>\n <td>6.50</td>\n <td>6.50</td>\n <td>9.25</td>\n <td>64</td>\n </tr>\n <tr>\n <th>1022059</th>\n <td>64006937</td>\n <td>2022</td>\n <td>NaN</td>\n <td>5.00</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>4.25</td>\n <td>4.75</td>\n <td>NaN</td>\n <td>64</td>\n </tr>\n </tbody>\n</table>\n<p>1022060 rows × 12 columns</p>\n</div>"
},
"execution_count": 93,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-04-30T14:26:26.792751Z",
"start_time": "2024-04-30T14:26:26.764707Z"
}
},
"id": "af4ee84f2b53d9c1",
"execution_count": 93
},
{
"cell_type": "code",
"outputs": [],
"source": [
"df.to_csv('data/2023.csv',index=False)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-04-30T14:26:31.703895Z",
"start_time": "2024-04-30T14:26:28.009264Z"
}
},
"id": "7381ba88b1bc5875",
"execution_count": 94
},
{
"cell_type": "code",
"outputs": [
{
"data": {
"text/plain": " SBD Year Toan Van Ly Hoa Sinh Ngoai ngu Lich su \\\n0 1000001 2022 8.4 8.50 NaN NaN NaN 9.2 6.75 \n1 1000002 2022 7.2 8.50 NaN NaN NaN 9.2 8.75 \n2 1000003 2022 NaN 6.50 NaN NaN NaN NaN 9.25 \n3 1000004 2022 7.8 8.25 NaN NaN NaN 7.8 4.50 \n4 1000005 2022 7.2 8.00 NaN NaN NaN 7.8 4.75 \n... ... ... ... ... ... ... ... ... ... \n1022055 64006933 2022 7.8 6.75 NaN NaN NaN 5.4 8.50 \n1022056 64006934 2022 7.4 7.50 6.0 5.75 6.25 6.0 NaN \n1022057 64006935 2022 6.4 7.00 NaN NaN NaN 3.0 5.50 \n1022058 64006936 2022 6.6 7.00 NaN NaN NaN 5.8 6.50 \n1022059 64006937 2022 NaN 5.00 NaN NaN NaN NaN 4.25 \n\n Dia ly GDCD MaTinh \n0 6.00 9.00 1 \n1 6.50 8.50 1 \n2 7.50 NaN 1 \n3 6.25 8.25 1 \n4 6.75 8.25 1 \n... ... ... ... \n1022055 7.75 9.75 64 \n1022056 NaN NaN 64 \n1022057 5.75 7.25 64 \n1022058 6.50 9.25 64 \n1022059 4.75 NaN 64 \n\n[1022060 rows x 12 columns]",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>SBD</th>\n <th>Year</th>\n <th>Toan</th>\n <th>Van</th>\n <th>Ly</th>\n <th>Hoa</th>\n <th>Sinh</th>\n <th>Ngoai ngu</th>\n <th>Lich su</th>\n <th>Dia ly</th>\n <th>GDCD</th>\n <th>MaTinh</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1000001</td>\n <td>2022</td>\n <td>8.4</td>\n <td>8.50</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>9.2</td>\n <td>6.75</td>\n <td>6.00</td>\n <td>9.00</td>\n <td>1</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1000002</td>\n <td>2022</td>\n <td>7.2</td>\n <td>8.50</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>9.2</td>\n <td>8.75</td>\n <td>6.50</td>\n <td>8.50</td>\n <td>1</td>\n </tr>\n <tr>\n <th>2</th>\n <td>1000003</td>\n <td>2022</td>\n <td>NaN</td>\n <td>6.50</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>9.25</td>\n <td>7.50</td>\n <td>NaN</td>\n <td>1</td>\n </tr>\n <tr>\n <th>3</th>\n <td>1000004</td>\n <td>2022</td>\n <td>7.8</td>\n <td>8.25</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>7.8</td>\n <td>4.50</td>\n <td>6.25</td>\n <td>8.25</td>\n <td>1</td>\n </tr>\n <tr>\n <th>4</th>\n <td>1000005</td>\n <td>2022</td>\n <td>7.2</td>\n <td>8.00</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>7.8</td>\n <td>4.75</td>\n <td>6.75</td>\n <td>8.25</td>\n <td>1</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>1022055</th>\n <td>64006933</td>\n <td>2022</td>\n <td>7.8</td>\n <td>6.75</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>5.4</td>\n <td>8.50</td>\n <td>7.75</td>\n <td>9.75</td>\n <td>64</td>\n </tr>\n <tr>\n <th>1022056</th>\n <td>64006934</td>\n <td>2022</td>\n <td>7.4</td>\n <td>7.50</td>\n <td>6.0</td>\n <td>5.75</td>\n <td>6.25</td>\n <td>6.0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>64</td>\n </tr>\n <tr>\n <th>1022057</th>\n <td>64006935</td>\n <td>2022</td>\n <td>6.4</td>\n <td>7.00</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>3.0</td>\n <td>5.50</td>\n <td>5.75</td>\n <td>7.25</td>\n <td>64</td>\n </tr>\n <tr>\n <th>1022058</th>\n <td>64006936</td>\n <td>2022</td>\n <td>6.6</td>\n <td>7.00</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>5.8</td>\n <td>6.50</td>\n <td>6.50</td>\n <td>9.25</td>\n <td>64</td>\n </tr>\n <tr>\n <th>1022059</th>\n <td>64006937</td>\n <td>2022</td>\n <td>NaN</td>\n <td>5.00</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>4.25</td>\n <td>4.75</td>\n <td>NaN</td>\n <td>64</td>\n </tr>\n </tbody>\n</table>\n<p>1022060 rows × 12 columns</p>\n</div>"
},
"execution_count": 95,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-04-30T14:26:32.671659Z",
"start_time": "2024-04-30T14:26:32.656410Z"
}
},
"id": "b966a61bc9ee9492",
"execution_count": 95
},
{
"cell_type": "code",
"outputs": [
{
"data": {
"text/plain": " SBD Year Toan Van Ly Hoa Sinh Ngoai ngu Lich su \\\n0 1000001 2023 8.4 8.50 NaN NaN NaN 9.2 6.75 \n1 1000002 2023 7.2 8.50 NaN NaN NaN 9.2 8.75 \n2 1000003 2023 NaN 6.50 NaN NaN NaN NaN 9.25 \n3 1000004 2023 7.8 8.25 NaN NaN NaN 7.8 4.50 \n4 1000005 2023 7.2 8.00 NaN NaN NaN 7.8 4.75 \n... ... ... ... ... ... ... ... ... ... \n1022055 64006933 2023 7.8 6.75 NaN NaN NaN 5.4 8.50 \n1022056 64006934 2023 7.4 7.50 6.0 5.75 6.25 6.0 NaN \n1022057 64006935 2023 6.4 7.00 NaN NaN NaN 3.0 5.50 \n1022058 64006936 2023 6.6 7.00 NaN NaN NaN 5.8 6.50 \n1022059 64006937 2023 NaN 5.00 NaN NaN NaN NaN 4.25 \n\n Dia ly GDCD MaTinh \n0 6.00 9.00 1 \n1 6.50 8.50 1 \n2 7.50 NaN 1 \n3 6.25 8.25 1 \n4 6.75 8.25 1 \n... ... ... ... \n1022055 7.75 9.75 64 \n1022056 NaN NaN 64 \n1022057 5.75 7.25 64 \n1022058 6.50 9.25 64 \n1022059 4.75 NaN 64 \n\n[1022060 rows x 12 columns]",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>SBD</th>\n <th>Year</th>\n <th>Toan</th>\n <th>Van</th>\n <th>Ly</th>\n <th>Hoa</th>\n <th>Sinh</th>\n <th>Ngoai ngu</th>\n <th>Lich su</th>\n <th>Dia ly</th>\n <th>GDCD</th>\n <th>MaTinh</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1000001</td>\n <td>2023</td>\n <td>8.4</td>\n <td>8.50</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>9.2</td>\n <td>6.75</td>\n <td>6.00</td>\n <td>9.00</td>\n <td>1</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1000002</td>\n <td>2023</td>\n <td>7.2</td>\n <td>8.50</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>9.2</td>\n <td>8.75</td>\n <td>6.50</td>\n <td>8.50</td>\n <td>1</td>\n </tr>\n <tr>\n <th>2</th>\n <td>1000003</td>\n <td>2023</td>\n <td>NaN</td>\n <td>6.50</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>9.25</td>\n <td>7.50</td>\n <td>NaN</td>\n <td>1</td>\n </tr>\n <tr>\n <th>3</th>\n <td>1000004</td>\n <td>2023</td>\n <td>7.8</td>\n <td>8.25</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>7.8</td>\n <td>4.50</td>\n <td>6.25</td>\n <td>8.25</td>\n <td>1</td>\n </tr>\n <tr>\n <th>4</th>\n <td>1000005</td>\n <td>2023</td>\n <td>7.2</td>\n <td>8.00</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>7.8</td>\n <td>4.75</td>\n <td>6.75</td>\n <td>8.25</td>\n <td>1</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>1022055</th>\n <td>64006933</td>\n <td>2023</td>\n <td>7.8</td>\n <td>6.75</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>5.4</td>\n <td>8.50</td>\n <td>7.75</td>\n <td>9.75</td>\n <td>64</td>\n </tr>\n <tr>\n <th>1022056</th>\n <td>64006934</td>\n <td>2023</td>\n <td>7.4</td>\n <td>7.50</td>\n <td>6.0</td>\n <td>5.75</td>\n <td>6.25</td>\n <td>6.0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>64</td>\n </tr>\n <tr>\n <th>1022057</th>\n <td>64006935</td>\n <td>2023</td>\n <td>6.4</td>\n <td>7.00</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>3.0</td>\n <td>5.50</td>\n <td>5.75</td>\n <td>7.25</td>\n <td>64</td>\n </tr>\n <tr>\n <th>1022058</th>\n <td>64006936</td>\n <td>2023</td>\n <td>6.6</td>\n <td>7.00</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>5.8</td>\n <td>6.50</td>\n <td>6.50</td>\n <td>9.25</td>\n <td>64</td>\n </tr>\n <tr>\n <th>1022059</th>\n <td>64006937</td>\n <td>2023</td>\n <td>NaN</td>\n <td>5.00</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>4.25</td>\n <td>4.75</td>\n <td>NaN</td>\n <td>64</td>\n </tr>\n </tbody>\n</table>\n<p>1022060 rows × 12 columns</p>\n</div>"
},
"execution_count": 98,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = df.assign(Year=2023)\n",
"df"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-04-30T14:26:44.976827Z",
"start_time": "2024-04-30T14:26:44.930869Z"
}
},
"id": "900e6641d27e3088",
"execution_count": 98
},
{
"cell_type": "code",
"outputs": [],
"source": [
"df.to_csv('data/2023.csv',index=False)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-04-30T14:26:49.959578Z",
"start_time": "2024-04-30T14:26:46.456016Z"
}
},
"id": "12e11e07fa727e48",
"execution_count": 99
},
{
"cell_type": "code",
"outputs": [
{
"data": {
"text/plain": "SBD int64\nToan float64\nVan float64\nNgoai ngu float64\nvat_li float64\nHoa float64\nSinh float64\nLich su float64\nDia ly float64\nGDCD float64\nma_ngoai_ngu object\nYear int64\nMaTinh int64\ndtype: object"
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.dtypes"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-04-30T12:34:06.647891Z",
"start_time": "2024-04-30T12:34:06.639239Z"
}
},
"id": "2121a36203aea2f6",
"execution_count": 17
},
{
"cell_type": "code",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2\n"
]
}
],
"source": [
"sbd_str = str(2000001)\n",
"len(sbd_str[:6])\n",
"print(sbd_str[:1])"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-04-30T14:10:46.778010Z",
"start_time": "2024-04-30T14:10:46.774354Z"
}
},
"id": "7b3a1198b88b8942",
"execution_count": 73
},
{
"cell_type": "code",
"outputs": [],
"source": [
"def get_province_code(sbd):\n",
" sbd_str = str(sbd)\n",
" if(len(sbd_str)==7):\n",
" return sbd_str[:1]\n",
" else:\n",
" return sbd_str[:2]\n",
" \n",
"df['MaTinh'] = df['SBD'].apply(get_province_code)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-04-30T14:11:48.217361Z",
"start_time": "2024-04-30T14:11:47.923886Z"
}
},
"id": "b017ce0eb2bdc15f",
"execution_count": 75
},
{
"cell_type": "code",
"outputs": [
{
"data": {
"text/plain": " SBD Toan Van Ngoai ngu Ly Hoa Sinh Lich su Dia ly GDCD \\\n0 1000001 8.4 8.50 9.2 NaN NaN NaN 6.75 6.00 9.00 \n1 1000002 7.2 8.50 9.2 NaN NaN NaN 8.75 6.50 8.50 \n2 1000003 NaN 6.50 NaN NaN NaN NaN 9.25 7.50 NaN \n3 1000004 7.8 8.25 7.8 NaN NaN NaN 4.50 6.25 8.25 \n4 1000005 7.2 8.00 7.8 NaN NaN NaN 4.75 6.75 8.25 \n\n ma_ngoai_ngu MaTinh \n0 N1 1 \n1 N1 1 \n2 NaN 1 \n3 N1 1 \n4 N1 1 ",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>SBD</th>\n <th>Toan</th>\n <th>Van</th>\n <th>Ngoai ngu</th>\n <th>Ly</th>\n <th>Hoa</th>\n <th>Sinh</th>\n <th>Lich su</th>\n <th>Dia ly</th>\n <th>GDCD</th>\n <th>ma_ngoai_ngu</th>\n <th>MaTinh</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1000001</td>\n <td>8.4</td>\n <td>8.50</td>\n <td>9.2</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>6.75</td>\n <td>6.00</td>\n <td>9.00</td>\n <td>N1</td>\n <td>1</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1000002</td>\n <td>7.2</td>\n <td>8.50</td>\n <td>9.2</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>8.75</td>\n <td>6.50</td>\n <td>8.50</td>\n <td>N1</td>\n <td>1</td>\n </tr>\n <tr>\n <th>2</th>\n <td>1000003</td>\n <td>NaN</td>\n <td>6.50</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>9.25</td>\n <td>7.50</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>1</td>\n </tr>\n <tr>\n <th>3</th>\n <td>1000004</td>\n <td>7.8</td>\n <td>8.25</td>\n <td>7.8</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>4.50</td>\n <td>6.25</td>\n <td>8.25</td>\n <td>N1</td>\n <td>1</td>\n </tr>\n <tr>\n <th>4</th>\n <td>1000005</td>\n <td>7.2</td>\n <td>8.00</td>\n <td>7.8</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>4.75</td>\n <td>6.75</td>\n <td>8.25</td>\n <td>N1</td>\n <td>1</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-04-30T14:12:05.610489Z",
"start_time": "2024-04-30T14:12:05.597835Z"
}
},
"id": "515e136f58e4c69b",
"execution_count": 77
},
{
"cell_type": "code",
"outputs": [],
"source": [
"df.to_csv('data/2023.csv',index=False)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-04-30T14:15:32.253515Z",
"start_time": "2024-04-30T14:15:29.200187Z"
}
},
"id": "30fe858f2e263521",
"execution_count": 78
},
{
"cell_type": "code",
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" SBD Year Toan Van Ly Hoa Sinh Ngoai ngu Lich su Dia ly GDCD \\\n",
"0 1000001 2022 8.4 8.50 NaN NaN NaN 9.2 6.75 6.00 9.00 \n",
"1 1000002 2022 7.2 8.50 NaN NaN NaN 9.2 8.75 6.50 8.50 \n",
"2 1000003 2022 NaN 6.50 NaN NaN NaN NaN 9.25 7.50 NaN \n",
"3 1000004 2022 7.8 8.25 NaN NaN NaN 7.8 4.50 6.25 8.25 \n",
"4 1000005 2022 7.2 8.00 NaN NaN NaN 7.8 4.75 6.75 8.25 \n",
"\n",
" MaTinh ma_ngoai_ngu \n",
"0 1 N1 \n",
"1 1 N1 \n",
"2 1 NaN \n",
"3 1 N1 \n",
"4 1 N1 \n"
]
}
],
"source": [
"new_column_order = ['SBD', 'Year', 'Toan', 'Van', 'Ly', 'Hoa', 'Sinh', 'Ngoai ngu', 'Lich su', 'Dia ly', 'GDCD', 'MaTinh', 'ma_ngoai_ngu']\n",
"\n",
"df = df[new_column_order]\n",
"\n",
"print(df.head())"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-04-30T14:18:18.321251Z",
"start_time": "2024-04-30T14:18:18.274248Z"
}
},
"id": "216acc0f7a904835",
"execution_count": 86
},
{
"cell_type": "code",
"outputs": [
{
"data": {
"text/plain": " SBD Year Toan Van Ly Hoa Sinh Ngoai ngu Lich su \\\n0 1000001 2022 8.4 8.50 NaN NaN NaN 9.2 6.75 \n1 1000002 2022 7.2 8.50 NaN NaN NaN 9.2 8.75 \n2 1000003 2022 NaN 6.50 NaN NaN NaN NaN 9.25 \n3 1000004 2022 7.8 8.25 NaN NaN NaN 7.8 4.50 \n4 1000005 2022 7.2 8.00 NaN NaN NaN 7.8 4.75 \n... ... ... ... ... ... ... ... ... ... \n1022055 64006933 2022 7.8 6.75 NaN NaN NaN 5.4 8.50 \n1022056 64006934 2022 7.4 7.50 6.0 5.75 6.25 6.0 NaN \n1022057 64006935 2022 6.4 7.00 NaN NaN NaN 3.0 5.50 \n1022058 64006936 2022 6.6 7.00 NaN NaN NaN 5.8 6.50 \n1022059 64006937 2022 NaN 5.00 NaN NaN NaN NaN 4.25 \n\n Dia ly GDCD MaTinh ma_ngoai_ngu \n0 6.00 9.00 1 N1 \n1 6.50 8.50 1 N1 \n2 7.50 NaN 1 NaN \n3 6.25 8.25 1 N1 \n4 6.75 8.25 1 N1 \n... ... ... ... ... \n1022055 7.75 9.75 64 N1 \n1022056 NaN NaN 64 N1 \n1022057 5.75 7.25 64 N1 \n1022058 6.50 9.25 64 N1 \n1022059 4.75 NaN 64 NaN \n\n[1022060 rows x 13 columns]",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>SBD</th>\n <th>Year</th>\n <th>Toan</th>\n <th>Van</th>\n <th>Ly</th>\n <th>Hoa</th>\n <th>Sinh</th>\n <th>Ngoai ngu</th>\n <th>Lich su</th>\n <th>Dia ly</th>\n <th>GDCD</th>\n <th>MaTinh</th>\n <th>ma_ngoai_ngu</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1000001</td>\n <td>2022</td>\n <td>8.4</td>\n <td>8.50</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>9.2</td>\n <td>6.75</td>\n <td>6.00</td>\n <td>9.00</td>\n <td>1</td>\n <td>N1</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1000002</td>\n <td>2022</td>\n <td>7.2</td>\n <td>8.50</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>9.2</td>\n <td>8.75</td>\n <td>6.50</td>\n <td>8.50</td>\n <td>1</td>\n <td>N1</td>\n </tr>\n <tr>\n <th>2</th>\n <td>1000003</td>\n <td>2022</td>\n <td>NaN</td>\n <td>6.50</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>9.25</td>\n <td>7.50</td>\n <td>NaN</td>\n <td>1</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>3</th>\n <td>1000004</td>\n <td>2022</td>\n <td>7.8</td>\n <td>8.25</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>7.8</td>\n <td>4.50</td>\n <td>6.25</td>\n <td>8.25</td>\n <td>1</td>\n <td>N1</td>\n </tr>\n <tr>\n <th>4</th>\n <td>1000005</td>\n <td>2022</td>\n <td>7.2</td>\n <td>8.00</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>7.8</td>\n <td>4.75</td>\n <td>6.75</td>\n <td>8.25</td>\n <td>1</td>\n <td>N1</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>1022055</th>\n <td>64006933</td>\n <td>2022</td>\n <td>7.8</td>\n <td>6.75</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>5.4</td>\n <td>8.50</td>\n <td>7.75</td>\n <td>9.75</td>\n <td>64</td>\n <td>N1</td>\n </tr>\n <tr>\n <th>1022056</th>\n <td>64006934</td>\n <td>2022</td>\n <td>7.4</td>\n <td>7.50</td>\n <td>6.0</td>\n <td>5.75</td>\n <td>6.25</td>\n <td>6.0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>64</td>\n <td>N1</td>\n </tr>\n <tr>\n <th>1022057</th>\n <td>64006935</td>\n <td>2022</td>\n <td>6.4</td>\n <td>7.00</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>3.0</td>\n <td>5.50</td>\n <td>5.75</td>\n <td>7.25</td>\n <td>64</td>\n <td>N1</td>\n </tr>\n <tr>\n <th>1022058</th>\n <td>64006936</td>\n <td>2022</td>\n <td>6.6</td>\n <td>7.00</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>5.8</td>\n <td>6.50</td>\n <td>6.50</td>\n <td>9.25</td>\n <td>64</td>\n <td>N1</td>\n </tr>\n <tr>\n <th>1022059</th>\n <td>64006937</td>\n <td>2022</td>\n <td>NaN</td>\n <td>5.00</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>4.25</td>\n <td>4.75</td>\n <td>NaN</td>\n <td>64</td>\n <td>NaN</td>\n </tr>\n </tbody>\n</table>\n<p>1022060 rows × 13 columns</p>\n</div>"
},
"execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-04-30T14:18:25.393362Z",
"start_time": "2024-04-30T14:18:25.382952Z"
}
},
"id": "bdf730249480c3a1",
"execution_count": 87
},
{
"cell_type": "code",
"outputs": [],
"source": [
"df=df.drop(columns=['ma_ngoai_ngu'],axis=1)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-04-30T14:18:47.869655Z",
"start_time": "2024-04-30T14:18:47.837068Z"
}
},
"id": "28f446b3294d3694",
"execution_count": 88
},
{
"cell_type": "code",
"outputs": [
{
"data": {
"text/plain": " SBD Year Toan Van Ly Hoa Sinh Ngoai ngu Lich su \\\n0 1000001 2022 8.4 8.50 NaN NaN NaN 9.2 6.75 \n1 1000002 2022 7.2 8.50 NaN NaN NaN 9.2 8.75 \n2 1000003 2022 NaN 6.50 NaN NaN NaN NaN 9.25 \n3 1000004 2022 7.8 8.25 NaN NaN NaN 7.8 4.50 \n4 1000005 2022 7.2 8.00 NaN NaN NaN 7.8 4.75 \n... ... ... ... ... ... ... ... ... ... \n1022055 64006933 2022 7.8 6.75 NaN NaN NaN 5.4 8.50 \n1022056 64006934 2022 7.4 7.50 6.0 5.75 6.25 6.0 NaN \n1022057 64006935 2022 6.4 7.00 NaN NaN NaN 3.0 5.50 \n1022058 64006936 2022 6.6 7.00 NaN NaN NaN 5.8 6.50 \n1022059 64006937 2022 NaN 5.00 NaN NaN NaN NaN 4.25 \n\n Dia ly GDCD MaTinh \n0 6.00 9.00 1 \n1 6.50 8.50 1 \n2 7.50 NaN 1 \n3 6.25 8.25 1 \n4 6.75 8.25 1 \n... ... ... ... \n1022055 7.75 9.75 64 \n1022056 NaN NaN 64 \n1022057 5.75 7.25 64 \n1022058 6.50 9.25 64 \n1022059 4.75 NaN 64 \n\n[1022060 rows x 12 columns]",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>SBD</th>\n <th>Year</th>\n <th>Toan</th>\n <th>Van</th>\n <th>Ly</th>\n <th>Hoa</th>\n <th>Sinh</th>\n <th>Ngoai ngu</th>\n <th>Lich su</th>\n <th>Dia ly</th>\n <th>GDCD</th>\n <th>MaTinh</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1000001</td>\n <td>2022</td>\n <td>8.4</td>\n <td>8.50</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>9.2</td>\n <td>6.75</td>\n <td>6.00</td>\n <td>9.00</td>\n <td>1</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1000002</td>\n <td>2022</td>\n <td>7.2</td>\n <td>8.50</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>9.2</td>\n <td>8.75</td>\n <td>6.50</td>\n <td>8.50</td>\n <td>1</td>\n </tr>\n <tr>\n <th>2</th>\n <td>1000003</td>\n <td>2022</td>\n <td>NaN</td>\n <td>6.50</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>9.25</td>\n <td>7.50</td>\n <td>NaN</td>\n <td>1</td>\n </tr>\n <tr>\n <th>3</th>\n <td>1000004</td>\n <td>2022</td>\n <td>7.8</td>\n <td>8.25</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>7.8</td>\n <td>4.50</td>\n <td>6.25</td>\n <td>8.25</td>\n <td>1</td>\n </tr>\n <tr>\n <th>4</th>\n <td>1000005</td>\n <td>2022</td>\n <td>7.2</td>\n <td>8.00</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>7.8</td>\n <td>4.75</td>\n <td>6.75</td>\n <td>8.25</td>\n <td>1</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>1022055</th>\n <td>64006933</td>\n <td>2022</td>\n <td>7.8</td>\n <td>6.75</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>5.4</td>\n <td>8.50</td>\n <td>7.75</td>\n <td>9.75</td>\n <td>64</td>\n </tr>\n <tr>\n <th>1022056</th>\n <td>64006934</td>\n <td>2022</td>\n <td>7.4</td>\n <td>7.50</td>\n <td>6.0</td>\n <td>5.75</td>\n <td>6.25</td>\n <td>6.0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>64</td>\n </tr>\n <tr>\n <th>1022057</th>\n <td>64006935</td>\n <td>2022</td>\n <td>6.4</td>\n <td>7.00</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>3.0</td>\n <td>5.50</td>\n <td>5.75</td>\n <td>7.25</td>\n <td>64</td>\n </tr>\n <tr>\n <th>1022058</th>\n <td>64006936</td>\n <td>2022</td>\n <td>6.6</td>\n <td>7.00</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>5.8</td>\n <td>6.50</td>\n <td>6.50</td>\n <td>9.25</td>\n <td>64</td>\n </tr>\n <tr>\n <th>1022059</th>\n <td>64006937</td>\n <td>2022</td>\n <td>NaN</td>\n <td>5.00</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>4.25</td>\n <td>4.75</td>\n <td>NaN</td>\n <td>64</td>\n </tr>\n </tbody>\n</table>\n<p>1022060 rows × 12 columns</p>\n</div>"
},
"execution_count": 89,
"metadata": {},
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}
],
"source": [
"df"
],
"metadata": {
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"ExecuteTime": {
"end_time": "2024-04-30T14:18:49.898951Z",
"start_time": "2024-04-30T14:18:49.889087Z"
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},
"id": "fe3254e3a718f7f6",
"execution_count": 89
},
{
"cell_type": "code",
"outputs": [],
"source": [
"df.to_csv('data/2023.csv',index=False)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2024-04-30T14:19:20.824556Z",
"start_time": "2024-04-30T14:19:17.362497Z"
}
},
"id": "27c407a58e045190",
"execution_count": 90
},
{
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"outputs": [],
"source": [],
"metadata": {
"collapsed": false
},
"id": "528e143bf5f95d4e"
}
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