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tomokishii/example1.csv

Created March 29, 2016 02:04
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Pandas Date index parse
Raw
example1.csv
Date Open High Low Close
2014/12/31 17702.11914 17713.75977 17450.76953 17450.76953
2014/12/30 17702.11914 17713.75977 17450.76953 17450.76953
2014/12/29 17914.55078 17914.55078 17525.66016 17729.83984
2014/12/26 17778.91016 17843.73047 17769.00977 17818.96094
2014/12/25 17804.83984 17832.4707 17773.58984 17808.75
2014/12/24 17842.03906 17875.41016 17800.41992 17854.23047
2014/12/22 17685.5293 17692.58008 17572.61914 17635.14062
2014/12/19 17511.00977 17621.40039 17471.59961 17621.40039
2014/12/18 17210.05078 17210.05078 17210.05078 17210.05078
Raw
example2.csv
Date Open High Low Close Volume
15-Mar-16 103.96 105.18 103.85 104.58 40067734
14-Mar-16 101.91 102.91 101.78 102.52 25027426
11-Mar-16 102.24 102.28 101.50 102.26 27200833
10-Mar-16 101.41 102.24 100.15 101.17 33513577
9-Mar-16 101.31 101.58 100.27 101.12 27130729
8-Mar-16 100.78 101.76 100.40 101.03 31274161
7-Mar-16 102.39 102.83 100.96 101.87 35828909
4-Mar-16 102.37 103.75 101.37 103.01 45936485
3-Mar-16 100.58 101.71 100.45 101.50 36792245
2-Mar-16 100.51 100.89 99.64 100.75 33084941
Raw
example3.csv
Date Open High Low Close
2014年12月31日 17702.11914 17713.75977 17450.76953 17450.76953
2014年12月30日 17702.11914 17713.75977 17450.76953 17450.76953
2014年12月29日 17914.55078 17914.55078 17525.66016 17729.83984
2014年12月26日 17778.91016 17843.73047 17769.00977 17818.96094
2014年12月25日 17804.83984 17832.4707 17773.58984 17808.75
2014年12月24日 17842.03906 17875.41016 17800.41992 17854.23047
2014年12月22日 17685.5293 17692.58008 17572.61914 17635.14062
2014年12月19日 17511.00977 17621.40039 17471.59961 17621.40039
2014年12月18日 17210.05078 17210.05078 17210.05078 17210.05078
Display the source blob
Display the rendered blob
Raw
pandas_date_index.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Pandas Date Index handling\n",
"\n",
"python ver. 3.5.1 \n",
"pandas ver. 0.18.0 "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\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",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2014/12/31</td>\n",
" <td>17702.11914</td>\n",
" <td>17713.75977</td>\n",
" <td>17450.76953</td>\n",
" <td>17450.76953</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2014/12/30</td>\n",
" <td>17702.11914</td>\n",
" <td>17713.75977</td>\n",
" <td>17450.76953</td>\n",
" <td>17450.76953</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2014/12/29</td>\n",
" <td>17914.55078</td>\n",
" <td>17914.55078</td>\n",
" <td>17525.66016</td>\n",
" <td>17729.83984</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2014/12/26</td>\n",
" <td>17778.91016</td>\n",
" <td>17843.73047</td>\n",
" <td>17769.00977</td>\n",
" <td>17818.96094</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2014/12/25</td>\n",
" <td>17804.83984</td>\n",
" <td>17832.47070</td>\n",
" <td>17773.58984</td>\n",
" <td>17808.75000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Open High Low Close\n",
"0 2014/12/31 17702.11914 17713.75977 17450.76953 17450.76953\n",
"1 2014/12/30 17702.11914 17713.75977 17450.76953 17450.76953\n",
"2 2014/12/29 17914.55078 17914.55078 17525.66016 17729.83984\n",
"3 2014/12/26 17778.91016 17843.73047 17769.00977 17818.96094\n",
"4 2014/12/25 17804.83984 17832.47070 17773.58984 17808.75000"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df10 = pd.read_csv('./pandas_date_ex/example1.csv')\n",
"df10.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2014-12-31</th>\n",
" <td>17702.11914</td>\n",
" <td>17713.75977</td>\n",
" <td>17450.76953</td>\n",
" <td>17450.76953</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-12-30</th>\n",
" <td>17702.11914</td>\n",
" <td>17713.75977</td>\n",
" <td>17450.76953</td>\n",
" <td>17450.76953</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-12-29</th>\n",
" <td>17914.55078</td>\n",
" <td>17914.55078</td>\n",
" <td>17525.66016</td>\n",
" <td>17729.83984</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-12-26</th>\n",
" <td>17778.91016</td>\n",
" <td>17843.73047</td>\n",
" <td>17769.00977</td>\n",
" <td>17818.96094</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-12-25</th>\n",
" <td>17804.83984</td>\n",
" <td>17832.47070</td>\n",
" <td>17773.58984</td>\n",
" <td>17808.75000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Open High Low Close\n",
"Date \n",
"2014-12-31 17702.11914 17713.75977 17450.76953 17450.76953\n",
"2014-12-30 17702.11914 17713.75977 17450.76953 17450.76953\n",
"2014-12-29 17914.55078 17914.55078 17525.66016 17729.83984\n",
"2014-12-26 17778.91016 17843.73047 17769.00977 17818.96094\n",
"2014-12-25 17804.83984 17832.47070 17773.58984 17808.75000"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1 = pd.read_csv('./pandas_date_ex/example1.csv', index_col='Date', parse_dates='Date')\n",
"df1.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"pandas.tslib.Timestamp"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# what type of date?\n",
"type(df1.index[0])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\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>Volume</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>15-Mar-16</td>\n",
" <td>103.96</td>\n",
" <td>105.18</td>\n",
" <td>103.85</td>\n",
" <td>104.58</td>\n",
" <td>40067734</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>14-Mar-16</td>\n",
" <td>101.91</td>\n",
" <td>102.91</td>\n",
" <td>101.78</td>\n",
" <td>102.52</td>\n",
" <td>25027426</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>11-Mar-16</td>\n",
" <td>102.24</td>\n",
" <td>102.28</td>\n",
" <td>101.50</td>\n",
" <td>102.26</td>\n",
" <td>27200833</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>10-Mar-16</td>\n",
" <td>101.41</td>\n",
" <td>102.24</td>\n",
" <td>100.15</td>\n",
" <td>101.17</td>\n",
" <td>33513577</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>9-Mar-16</td>\n",
" <td>101.31</td>\n",
" <td>101.58</td>\n",
" <td>100.27</td>\n",
" <td>101.12</td>\n",
" <td>27130729</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Open High Low Close Volume\n",
"0 15-Mar-16 103.96 105.18 103.85 104.58 40067734\n",
"1 14-Mar-16 101.91 102.91 101.78 102.52 25027426\n",
"2 11-Mar-16 102.24 102.28 101.50 102.26 27200833\n",
"3 10-Mar-16 101.41 102.24 100.15 101.17 33513577\n",
"4 9-Mar-16 101.31 101.58 100.27 101.12 27130729"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df20 = pd.read_csv('./pandas_date_ex/example2.csv')\n",
"df20.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Volume</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2016-03-15</th>\n",
" <td>103.96</td>\n",
" <td>105.18</td>\n",
" <td>103.85</td>\n",
" <td>104.58</td>\n",
" <td>40067734</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-03-14</th>\n",
" <td>101.91</td>\n",
" <td>102.91</td>\n",
" <td>101.78</td>\n",
" <td>102.52</td>\n",
" <td>25027426</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-03-11</th>\n",
" <td>102.24</td>\n",
" <td>102.28</td>\n",
" <td>101.50</td>\n",
" <td>102.26</td>\n",
" <td>27200833</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-03-10</th>\n",
" <td>101.41</td>\n",
" <td>102.24</td>\n",
" <td>100.15</td>\n",
" <td>101.17</td>\n",
" <td>33513577</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-03-09</th>\n",
" <td>101.31</td>\n",
" <td>101.58</td>\n",
" <td>100.27</td>\n",
" <td>101.12</td>\n",
" <td>27130729</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Open High Low Close Volume\n",
"Date \n",
"2016-03-15 103.96 105.18 103.85 104.58 40067734\n",
"2016-03-14 101.91 102.91 101.78 102.52 25027426\n",
"2016-03-11 102.24 102.28 101.50 102.26 27200833\n",
"2016-03-10 101.41 102.24 100.15 101.17 33513577\n",
"2016-03-09 101.31 101.58 100.27 101.12 27130729"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2 = pd.read_csv('./pandas_date_ex/example2.csv', index_col=0, parse_dates=0)\n",
"df2.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 日付表記のいろいろなケース\n",
"- 年，月，日の順番が変わるケース．\n",
"- 月，日が１桁の数字になるとき，ゼロ埋めして桁数を２桁にしている，or １桁で表示している．\n",
"- 年の表示が4桁，または下２桁．\n",
"- 月が数字表記，英字（３文字の略称）のケース\n",
"\n",
"ほとんどのケースにread_csv()のパーサは対応可能のようだ．"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\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",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2014年12月31日</td>\n",
" <td>17702.11914</td>\n",
" <td>17713.75977</td>\n",
" <td>17450.76953</td>\n",
" <td>17450.76953</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2014年12月30日</td>\n",
" <td>17702.11914</td>\n",
" <td>17713.75977</td>\n",
" <td>17450.76953</td>\n",
" <td>17450.76953</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2014年12月29日</td>\n",
" <td>17914.55078</td>\n",
" <td>17914.55078</td>\n",
" <td>17525.66016</td>\n",
" <td>17729.83984</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2014年12月26日</td>\n",
" <td>17778.91016</td>\n",
" <td>17843.73047</td>\n",
" <td>17769.00977</td>\n",
" <td>17818.96094</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2014年12月25日</td>\n",
" <td>17804.83984</td>\n",
" <td>17832.47070</td>\n",
" <td>17773.58984</td>\n",
" <td>17808.75000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Open High Low Close\n",
"0 2014年12月31日 17702.11914 17713.75977 17450.76953 17450.76953\n",
"1 2014年12月30日 17702.11914 17713.75977 17450.76953 17450.76953\n",
"2 2014年12月29日 17914.55078 17914.55078 17525.66016 17729.83984\n",
"3 2014年12月26日 17778.91016 17843.73047 17769.00977 17818.96094\n",
"4 2014年12月25日 17804.83984 17832.47070 17773.58984 17808.75000"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Japanese Date Expression?\n",
"df30 = pd.read_csv('./pandas_date_ex/example3.csv')\n",
"df30.head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2014年12月31日</th>\n",
" <td>17702.11914</td>\n",
" <td>17713.75977</td>\n",
" <td>17450.76953</td>\n",
" <td>17450.76953</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014年12月30日</th>\n",
" <td>17702.11914</td>\n",
" <td>17713.75977</td>\n",
" <td>17450.76953</td>\n",
" <td>17450.76953</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014年12月29日</th>\n",
" <td>17914.55078</td>\n",
" <td>17914.55078</td>\n",
" <td>17525.66016</td>\n",
" <td>17729.83984</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014年12月26日</th>\n",
" <td>17778.91016</td>\n",
" <td>17843.73047</td>\n",
" <td>17769.00977</td>\n",
" <td>17818.96094</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014年12月25日</th>\n",
" <td>17804.83984</td>\n",
" <td>17832.47070</td>\n",
" <td>17773.58984</td>\n",
" <td>17808.75000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Open High Low Close\n",
"Date \n",
"2014年12月31日 17702.11914 17713.75977 17450.76953 17450.76953\n",
"2014年12月30日 17702.11914 17713.75977 17450.76953 17450.76953\n",
"2014年12月29日 17914.55078 17914.55078 17525.66016 17729.83984\n",
"2014年12月26日 17778.91016 17843.73047 17769.00977 17818.96094\n",
"2014年12月25日 17804.83984 17832.47070 17773.58984 17808.75000"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df31 = pd.read_csv('./pandas_date_ex/example3.csv', index_col='Date', parse_dates='Date')\n",
"df31.head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"str"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# In this case, index is not TimeStamp type.\n",
"type(df31.index[0])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2014-12-31</th>\n",
" <td>17702.11914</td>\n",
" <td>17713.75977</td>\n",
" <td>17450.76953</td>\n",
" <td>17450.76953</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-12-30</th>\n",
" <td>17702.11914</td>\n",
" <td>17713.75977</td>\n",
" <td>17450.76953</td>\n",
" <td>17450.76953</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-12-29</th>\n",
" <td>17914.55078</td>\n",
" <td>17914.55078</td>\n",
" <td>17525.66016</td>\n",
" <td>17729.83984</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-12-26</th>\n",
" <td>17778.91016</td>\n",
" <td>17843.73047</td>\n",
" <td>17769.00977</td>\n",
" <td>17818.96094</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-12-25</th>\n",
" <td>17804.83984</td>\n",
" <td>17832.47070</td>\n",
" <td>17773.58984</td>\n",
" <td>17808.75000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Open High Low Close\n",
"Date \n",
"2014-12-31 17702.11914 17713.75977 17450.76953 17450.76953\n",
"2014-12-30 17702.11914 17713.75977 17450.76953 17450.76953\n",
"2014-12-29 17914.55078 17914.55078 17525.66016 17729.83984\n",
"2014-12-26 17778.91016 17843.73047 17769.00977 17818.96094\n",
"2014-12-25 17804.83984 17832.47070 17773.58984 17808.75000"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f3 = '%Y年%m月%d日'\n",
"my_parser = lambda date: pd.datetime.strptime(date, f3)\n",
"df3 = pd.read_csv('./pandas_date_ex/example3.csv', index_col=0, parse_dates=0, \n",
" date_parser=my_parser)\n",
"df3.head()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"pandas.tslib.Timestamp"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(df3.index[0])"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Volume</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2016-03-15</th>\n",
" <td>103.96</td>\n",
" <td>105.18</td>\n",
" <td>103.85</td>\n",
" <td>104.58</td>\n",
" <td>40067734</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-03-14</th>\n",
" <td>101.91</td>\n",
" <td>102.91</td>\n",
" <td>101.78</td>\n",
" <td>102.52</td>\n",
" <td>25027426</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-03-11</th>\n",
" <td>102.24</td>\n",
" <td>102.28</td>\n",
" <td>101.50</td>\n",
" <td>102.26</td>\n",
" <td>27200833</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-03-10</th>\n",
" <td>101.41</td>\n",
" <td>102.24</td>\n",
" <td>100.15</td>\n",
" <td>101.17</td>\n",
" <td>33513577</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-03-09</th>\n",
" <td>101.31</td>\n",
" <td>101.58</td>\n",
" <td>100.27</td>\n",
" <td>101.12</td>\n",
" <td>27130729</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Open High Low Close Volume\n",
"Date \n",
"2016-03-15 103.96 105.18 103.85 104.58 40067734\n",
"2016-03-14 101.91 102.91 101.78 102.52 25027426\n",
"2016-03-11 102.24 102.28 101.50 102.26 27200833\n",
"2016-03-10 101.41 102.24 100.15 101.17 33513577\n",
"2016-03-09 101.31 101.58 100.27 101.12 27130729"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f2 = '%d-%b-%y'\n",
"my_parser = lambda date: pd.datetime.strptime(date, f2)\n",
"df21 = pd.read_csv('./pandas_date_ex/example2.csv', index_col=0, parse_dates=0, \n",
" date_parser=my_parser)\n",
"df21.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 日付フォーマットについて\n",
"日付フォーマットの仕様は，C言語から来ているとのことで，Python Datetimeのドキュメントに記載されている． \n",
"\n",
"日付関連でよく使いそうなフォーマットについて，ドキュメントから抜粋してみる． \n",
"\n",
"| ディレクティブ | 意味 | 例 |\n",
"|:----------------:|:--------|:-------|\n",
"| %d\t| 0埋め(zero padding)した10進数で表記した月中の日にち． | 01, 02, ..., 31 |\n",
"| %b | ロケールの月名を短縮形で表示します． | Jan, Feb, ..., Dec (en_US); Jan, Feb, ..., Dez (de_DE) |\n",
"| %B | ロケールの月名を表示します． | January, February, ..., |\n",
"| %m | 0埋めした10進数で表記した月. | 01, 02, ..., 12 |\n",
"| %y | 0埋めした10進数で表記した世紀あり（2桁）の年． | 00, 01, ..., 99 |\n",
"| %Y | 西暦 ( 4桁) の 10 進表記を表します． | 2011, 2012, 2013, ... |"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2016-03-28 00:00:00\n"
]
}
],
"source": [
"# Confirmation of the table\n",
"from datetime import datetime\n",
"s2 = '28-Mar-16'\n",
"f2 = '%d-%b-%y'\n",
"print(datetime.strptime(s2, f2))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2009-12-01 00:00:00\n"
]
}
],
"source": [
"s3 = '1-Dec-09'\n",
"f3 = '%d-%b-%y'\n",
"print(datetime.strptime(s3, f3))\n",
"# s3 = '1-Dec-9' is No good"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2015-09-01 00:00:00\n"
]
}
],
"source": [
"s4 = '1-9-2015'\n",
"f4 = '%d-%m-%Y'\n",
"print(datetime.strptime(s4, f4))"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"datetime.datetime"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(datetime.strptime(s4, f4))"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Volume</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2016-03-15</th>\n",
" <td>103.96</td>\n",
" <td>105.18</td>\n",
" <td>103.85</td>\n",
" <td>104.58</td>\n",
" <td>40067734</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-03-14</th>\n",
" <td>101.91</td>\n",
" <td>102.91</td>\n",
" <td>101.78</td>\n",
" <td>102.52</td>\n",
" <td>25027426</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-03-11</th>\n",
" <td>102.24</td>\n",
" <td>102.28</td>\n",
" <td>101.50</td>\n",
" <td>102.26</td>\n",
" <td>27200833</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-03-10</th>\n",
" <td>101.41</td>\n",
" <td>102.24</td>\n",
" <td>100.15</td>\n",
" <td>101.17</td>\n",
" <td>33513577</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-03-09</th>\n",
" <td>101.31</td>\n",
" <td>101.58</td>\n",
" <td>100.27</td>\n",
" <td>101.12</td>\n",
" <td>27130729</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Open High Low Close Volume\n",
"Date \n",
"2016-03-15 103.96 105.18 103.85 104.58 40067734\n",
"2016-03-14 101.91 102.91 101.78 102.52 25027426\n",
"2016-03-11 102.24 102.28 101.50 102.26 27200833\n",
"2016-03-10 101.41 102.24 100.15 101.17 33513577\n",
"2016-03-09 101.31 101.58 100.27 101.12 27130729"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f2 = '%d-%b-%y'\n",
"my_parser = lambda date: pd.datetime.strptime(date, f2)\n",
"df21 = pd.read_csv('./pandas_date_ex/example2.csv', index_col=0, parse_dates=0, \n",
" date_parser=my_parser)\n",
"df21.head()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fedd7e7f518>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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4G8/yvcvp8nUXhrUaxr/b/FsnZ8knZm+ZzdC5Q2laqSlv3PwGNYLT6gsp5ZTW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\nBeCtW9/KpgiVUkqp7JNvx8DTOny++9R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FsP0oqaLePaet2nlzLPAK6f5tlevWr5BWTbsm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\nAA+CIAiCHuR/B6i3g6XmmugAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fedd7e63a20>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"df3[['High', 'Low']].plot(figsize=(8,4), grid=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"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": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
@tomokishii
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You need to locate 3 example files in sub-directory ('pandas_date_ex').

プログラムを動かす際，３つのexampleファイルは，サブディレクトリ 'pandas_date_ex' にいれて下さい．

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