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audhiaprilliant/concave_function.ipynb

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concave_function.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# IHSG Pre-processing Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Audhi Aprilliant"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1 Import Libraries"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"ExecuteTime": {
"end_time": "2020-03-17T20:35:29.132953Z",
"start_time": "2020-03-17T20:35:29.126113Z"
}
},
"outputs": [],
"source": [
"import pandas as pd # Dataframe manipulation\n",
"import numpy as np # Mathematics operation\n",
"import datetime"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load the Data"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"ExecuteTime": {
"end_time": "2020-03-17T20:35:29.210984Z",
"start_time": "2020-03-17T20:35:29.138470Z"
}
},
"outputs": [],
"source": [
"ihsg_data = pd.read_csv('Datasets/^JKSE.csv')"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"ExecuteTime": {
"end_time": "2020-03-17T20:35:29.315803Z",
"start_time": "2020-03-17T20:35:29.215699Z"
},
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dimension of financial news:\n",
"257 rows and 7 columns\n"
]
},
{
"data": {
"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>Date</th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Adj Close</th>\n",
" <th>Volume</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2019-03-08</td>\n",
" <td>6439.840820</td>\n",
" <td>6442.204102</td>\n",
" <td>6381.730957</td>\n",
" <td>6383.067871</td>\n",
" <td>6383.067871</td>\n",
" <td>92588600.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2019-03-11</td>\n",
" <td>6418.882813</td>\n",
" <td>6420.782227</td>\n",
" <td>6366.039063</td>\n",
" <td>6366.434082</td>\n",
" <td>6366.434082</td>\n",
" <td>97316700.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2019-03-12</td>\n",
" <td>6395.880859</td>\n",
" <td>6404.430176</td>\n",
" <td>6339.967773</td>\n",
" <td>6353.773926</td>\n",
" <td>6353.773926</td>\n",
" <td>84514000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2019-03-13</td>\n",
" <td>6343.121094</td>\n",
" <td>6377.575195</td>\n",
" <td>6337.874023</td>\n",
" <td>6377.575195</td>\n",
" <td>6377.575195</td>\n",
" <td>100029400.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2019-03-14</td>\n",
" <td>6374.916992</td>\n",
" <td>6413.266113</td>\n",
" <td>6372.964844</td>\n",
" <td>6413.266113</td>\n",
" <td>6413.266113</td>\n",
" <td>96692700.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2019-03-15</td>\n",
" <td>6420.182129</td>\n",
" <td>6489.194824</td>\n",
" <td>6419.644043</td>\n",
" <td>6461.183105</td>\n",
" <td>6461.183105</td>\n",
" <td>122938800.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Open High Low Close \\\n",
"0 2019-03-08 6439.840820 6442.204102 6381.730957 6383.067871 \n",
"1 2019-03-11 6418.882813 6420.782227 6366.039063 6366.434082 \n",
"2 2019-03-12 6395.880859 6404.430176 6339.967773 6353.773926 \n",
"3 2019-03-13 6343.121094 6377.575195 6337.874023 6377.575195 \n",
"4 2019-03-14 6374.916992 6413.266113 6372.964844 6413.266113 \n",
"5 2019-03-15 6420.182129 6489.194824 6419.644043 6461.183105 \n",
"\n",
" Adj Close Volume \n",
"0 6383.067871 92588600.0 \n",
"1 6366.434082 97316700.0 \n",
"2 6353.773926 84514000.0 \n",
"3 6377.575195 100029400.0 \n",
"4 6413.266113 96692700.0 \n",
"5 6461.183105 122938800.0 "
]
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print('Dimension of financial news:\\n{}'.format(ihsg_data.shape[0]),\n",
" 'rows and {}'.format(ihsg_data.shape[1]),'columns')\n",
"ihsg_data.head(6)"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"ExecuteTime": {
"end_time": "2020-03-17T20:35:29.406594Z",
"start_time": "2020-03-17T20:35:29.324102Z"
},
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 257 entries, 0 to 256\n",
"Data columns (total 7 columns):\n",
"Date 257 non-null object\n",
"Open 247 non-null float64\n",
"High 247 non-null float64\n",
"Low 247 non-null float64\n",
"Close 247 non-null float64\n",
"Adj Close 247 non-null float64\n",
"Volume 247 non-null float64\n",
"dtypes: float64(6), object(1)\n",
"memory usage: 14.2+ KB\n"
]
}
],
"source": [
"ihsg_data.info()"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"ExecuteTime": {
"end_time": "2020-03-17T20:35:29.472012Z",
"start_time": "2020-03-17T20:35:29.412660Z"
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"Date 0\n",
"Open 10\n",
"High 10\n",
"Low 10\n",
"Close 10\n",
"Adj Close 10\n",
"Volume 10\n",
"dtype: int64"
]
},
"execution_count": 93,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Check missing value\n",
"ihsg_data.isna().sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2 Function to Imput the Date and Time"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"ExecuteTime": {
"end_time": "2020-03-17T20:35:29.527054Z",
"start_time": "2020-03-17T20:35:29.477958Z"
}
},
"outputs": [],
"source": [
"def imput_date(df):\n",
" start = datetime.datetime.strptime(df.loc[0,'Date'],'%Y-%m-%d')\n",
" end = datetime.datetime.strptime(df.loc[df.shape[0] - 1,'Date'],'%Y-%m-%d')\n",
" list_date = pd.date_range(start,end).strftime('%Y-%m-%d').tolist()\n",
" pd_date = pd.DataFrame(df,list_date)\n",
" pd_date['Date'] = pd_date.index.astype(object)\n",
" pd_date = pd_date.reset_index(drop=True)\n",
" df_date = pd.merge(pd_date['Date'],df,on='Date',how='left')\n",
" return df_date"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2 Function to Calculate the Return of IHSG"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"ExecuteTime": {
"end_time": "2020-03-17T20:35:29.595903Z",
"start_time": "2020-03-17T20:35:29.538917Z"
}
},
"outputs": [],
"source": [
"def return_ihsg(df):\n",
" df_no_na = df.dropna().reset_index(drop=True)\n",
" return_data = [0]\n",
" index_data = list(df_no_na.index)\n",
" for i in range(1,len(index_data)):\n",
" return_i = (df_no_na['Adj Close'][index_data[i]] - \n",
" df_no_na['Adj Close'][index_data[i-1]])/df_no_na['Adj Close'][index_data[i]]\n",
" return_data.append(return_i)\n",
" df_no_na = pd.concat([df_no_na['Date'],pd.DataFrame(return_data)],axis=1)\n",
" df_full = pd.merge(df,df_no_na,on='Date',how='left')\n",
" df_full = df_full.rename(columns = {0:'Return'})\n",
" return df_full"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2020-03-04T09:24:24.082912Z",
"start_time": "2020-03-04T09:24:24.066687Z"
}
},
"source": [
"## 3 Function to Apply Curve Function"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"ExecuteTime": {
"end_time": "2020-03-17T20:35:29.664193Z",
"start_time": "2020-03-17T20:35:29.603110Z"
},
"scrolled": true
},
"outputs": [],
"source": [
"def curve_function(df):\n",
" for i in df.columns:\n",
" while df[i].isna().sum() > 0:\n",
" for j in range(df.shape[0]):\n",
" if pd.isnull(df.loc[j,i]):\n",
" seq_k = [j]\n",
" k = j\n",
" while pd.isnull(df.loc[k,i]):\n",
" k = k + 1\n",
" seq_k.append(k)\n",
" if len(seq_k) % 2 == 0:\n",
" df.loc[seq_k[int((len(seq_k) - 1)/2)],i] = (df.loc[j - 1,i] + \n",
" df.loc[seq_k[len(seq_k) - 1],i])/2\n",
" else:\n",
" df.loc[seq_k[int((len(seq_k) - 1)/2)],i] = (df.loc[j - 1,i] + \n",
" df.loc[seq_k[len(seq_k) - 1],i])/2\n",
" else:\n",
" df.loc[j,i] = df.loc[j,i]\n",
" return(df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4 Apply Functions Above"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"ExecuteTime": {
"end_time": "2020-03-17T20:35:29.875304Z",
"start_time": "2020-03-17T20:35:29.670020Z"
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dimension of financial news:\n",
"257 rows and 8 columns\n"
]
},
{
"data": {
"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>Date</th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Adj Close</th>\n",
" <th>Volume</th>\n",
" <th>Return</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2019-03-08</td>\n",
" <td>6439.840820</td>\n",
" <td>6442.204102</td>\n",
" <td>6381.730957</td>\n",
" <td>6383.067871</td>\n",
" <td>6383.067871</td>\n",
" <td>92588600.0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2019-03-11</td>\n",
" <td>6418.882813</td>\n",
" <td>6420.782227</td>\n",
" <td>6366.039063</td>\n",
" <td>6366.434082</td>\n",
" <td>6366.434082</td>\n",
" <td>97316700.0</td>\n",
" <td>-0.002613</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2019-03-12</td>\n",
" <td>6395.880859</td>\n",
" <td>6404.430176</td>\n",
" <td>6339.967773</td>\n",
" <td>6353.773926</td>\n",
" <td>6353.773926</td>\n",
" <td>84514000.0</td>\n",
" <td>-0.001993</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2019-03-13</td>\n",
" <td>6343.121094</td>\n",
" <td>6377.575195</td>\n",
" <td>6337.874023</td>\n",
" <td>6377.575195</td>\n",
" <td>6377.575195</td>\n",
" <td>100029400.0</td>\n",
" <td>0.003732</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2019-03-14</td>\n",
" <td>6374.916992</td>\n",
" <td>6413.266113</td>\n",
" <td>6372.964844</td>\n",
" <td>6413.266113</td>\n",
" <td>6413.266113</td>\n",
" <td>96692700.0</td>\n",
" <td>0.005565</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Open High Low Close \\\n",
"0 2019-03-08 6439.840820 6442.204102 6381.730957 6383.067871 \n",
"1 2019-03-11 6418.882813 6420.782227 6366.039063 6366.434082 \n",
"2 2019-03-12 6395.880859 6404.430176 6339.967773 6353.773926 \n",
"3 2019-03-13 6343.121094 6377.575195 6337.874023 6377.575195 \n",
"4 2019-03-14 6374.916992 6413.266113 6372.964844 6413.266113 \n",
"\n",
" Adj Close Volume Return \n",
"0 6383.067871 92588600.0 0.000000 \n",
"1 6366.434082 97316700.0 -0.002613 \n",
"2 6353.773926 84514000.0 -0.001993 \n",
"3 6377.575195 100029400.0 0.003732 \n",
"4 6413.266113 96692700.0 0.005565 "
]
},
"execution_count": 97,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ihsg_data_clean = return_ihsg(ihsg_data)\n",
"print('Dimension of financial news:\\n{}'.format(ihsg_data_clean.shape[0]),\n",
" 'rows and {}'.format(ihsg_data_clean.shape[1]),'columns')\n",
"ihsg_data_clean.head()"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"ExecuteTime": {
"end_time": "2020-03-17T20:35:29.963673Z",
"start_time": "2020-03-17T20:35:29.881112Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dimension of financial news:\n",
"365 rows and 8 columns\n"
]
},
{
"data": {
"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>Date</th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Adj Close</th>\n",
" <th>Volume</th>\n",
" <th>Return</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2019-03-08</td>\n",
" <td>6439.840820</td>\n",
" <td>6442.204102</td>\n",
" <td>6381.730957</td>\n",
" <td>6383.067871</td>\n",
" <td>6383.067871</td>\n",
" <td>92588600.0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2019-03-09</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2019-03-10</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2019-03-11</td>\n",
" <td>6418.882813</td>\n",
" <td>6420.782227</td>\n",
" <td>6366.039063</td>\n",
" <td>6366.434082</td>\n",
" <td>6366.434082</td>\n",
" <td>97316700.0</td>\n",
" <td>-0.002613</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2019-03-12</td>\n",
" <td>6395.880859</td>\n",
" <td>6404.430176</td>\n",
" <td>6339.967773</td>\n",
" <td>6353.773926</td>\n",
" <td>6353.773926</td>\n",
" <td>84514000.0</td>\n",
" <td>-0.001993</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Open High Low Close \\\n",
"0 2019-03-08 6439.840820 6442.204102 6381.730957 6383.067871 \n",
"1 2019-03-09 NaN NaN NaN NaN \n",
"2 2019-03-10 NaN NaN NaN NaN \n",
"3 2019-03-11 6418.882813 6420.782227 6366.039063 6366.434082 \n",
"4 2019-03-12 6395.880859 6404.430176 6339.967773 6353.773926 \n",
"\n",
" Adj Close Volume Return \n",
"0 6383.067871 92588600.0 0.000000 \n",
"1 NaN NaN NaN \n",
"2 NaN NaN NaN \n",
"3 6366.434082 97316700.0 -0.002613 \n",
"4 6353.773926 84514000.0 -0.001993 "
]
},
"execution_count": 98,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ihsg_data_clean = imput_date(ihsg_data_clean)\n",
"print('Dimension of financial news:\\n{}'.format(ihsg_data_clean.shape[0]),\n",
" 'rows and {}'.format(ihsg_data_clean.shape[1]),'columns')\n",
"ihsg_data_clean.head()"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"ExecuteTime": {
"end_time": "2020-03-17T20:35:42.479250Z",
"start_time": "2020-03-17T20:35:29.969846Z"
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dimension of financial news:\n",
"365 rows and 8 columns\n"
]
},
{
"data": {
"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",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Adj Close</th>\n",
" <th>Volume</th>\n",
" <th>Return</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2019-03-08</td>\n",
" <td>6439.840820</td>\n",
" <td>6442.204102</td>\n",
" <td>6381.730957</td>\n",
" <td>6383.067871</td>\n",
" <td>6383.067871</td>\n",
" <td>92588600.0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2019-03-09</td>\n",
" <td>6434.601318</td>\n",
" <td>6436.848633</td>\n",
" <td>6377.807983</td>\n",
" <td>6378.909424</td>\n",
" <td>6378.909424</td>\n",
" <td>93770625.0</td>\n",
" <td>-0.000653</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2019-03-10</td>\n",
" <td>6429.361817</td>\n",
" <td>6431.493165</td>\n",
" <td>6373.885010</td>\n",
" <td>6374.750976</td>\n",
" <td>6374.750976</td>\n",
" <td>94952650.0</td>\n",
" <td>-0.001306</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2019-03-11</td>\n",
" <td>6418.882813</td>\n",
" <td>6420.782227</td>\n",
" <td>6366.039063</td>\n",
" <td>6366.434082</td>\n",
" <td>6366.434082</td>\n",
" <td>97316700.0</td>\n",
" <td>-0.002613</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2019-03-12</td>\n",
" <td>6395.880859</td>\n",
" <td>6404.430176</td>\n",
" <td>6339.967773</td>\n",
" <td>6353.773926</td>\n",
" <td>6353.773926</td>\n",
" <td>84514000.0</td>\n",
" <td>-0.001993</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Open High Low Close \\\n",
"0 2019-03-08 6439.840820 6442.204102 6381.730957 6383.067871 \n",
"1 2019-03-09 6434.601318 6436.848633 6377.807983 6378.909424 \n",
"2 2019-03-10 6429.361817 6431.493165 6373.885010 6374.750976 \n",
"3 2019-03-11 6418.882813 6420.782227 6366.039063 6366.434082 \n",
"4 2019-03-12 6395.880859 6404.430176 6339.967773 6353.773926 \n",
"\n",
" Adj Close Volume Return \n",
"0 6383.067871 92588600.0 0.000000 \n",
"1 6378.909424 93770625.0 -0.000653 \n",
"2 6374.750976 94952650.0 -0.001306 \n",
"3 6366.434082 97316700.0 -0.002613 \n",
"4 6353.773926 84514000.0 -0.001993 "
]
},
"execution_count": 99,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ihsg_data_curve = curve_function(ihsg_data_clean)\n",
"print('Dimension of financial news:\\n{}'.format(ihsg_data_curve.shape[0]),\n",
" 'rows and {}'.format(ihsg_data_curve.shape[1]),'columns')\n",
"ihsg_data_curve.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Save Data"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"ExecuteTime": {
"end_time": "2020-03-17T20:35:42.592819Z",
"start_time": "2020-03-17T20:35:42.484353Z"
}
},
"outputs": [],
"source": [
"ihsg_data_curve.to_csv(\"Datasets/interim/Clean Data of JCI 2019.csv\",index=False)"
]
}
],
"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": "3.6.9"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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