{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Iterators"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = DataFrame({'A' : np.random.randn(10), 'B' : 'foo'})\n",
"df.to_csv('data/test_iterator.csv',mode='w')\n",
"df.to_hdf('data/test_iterator.h5','df',mode='w',format='table',data_columns=True)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" A B\n",
"0 0.548203 foo\n",
"1 0.513688 foo\n",
"2 0.260623 foo\n",
" A B\n",
"3 -1.474517 foo\n",
"4 -2.653109 foo\n",
"5 -0.201879 foo\n",
" A B\n",
"6 0.850183 foo\n",
"7 -0.796159 foo\n",
"8 -0.874545 foo\n",
" A B\n",
"9 -0.272888 foo\n"
]
}
],
"source": [
"for df in pd.read_csv('data/test_iterator.csv',chunksize=3,index_col=0):\n",
" print df"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" A B\n",
"0 0.548203 foo\n",
"1 0.513688 foo\n",
"2 0.260623 foo\n",
" A B\n",
"3 -1.474517 foo\n",
"4 -2.653109 foo\n",
"5 -0.201879 foo\n",
" A B\n",
"6 0.850183 foo\n",
"7 -0.796159 foo\n",
"8 -0.874545 foo\n",
" A B\n",
"9 -0.272888 foo\n"
]
}
],
"source": [
"for df in pd.read_hdf('data/test_iterator.h5','df',chunksize=3):\n",
" print df"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# usecols"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"
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"
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" \n",
" \n",
" | \n",
" B | \n",
"
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" \n",
" \n",
" \n",
" 0 | \n",
" foo | \n",
"
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" 1 | \n",
" foo | \n",
"
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" 2 | \n",
" foo | \n",
"
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" 3 | \n",
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"
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" 4 | \n",
" foo | \n",
"
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" 5 | \n",
" foo | \n",
"
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" 6 | \n",
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"
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" 7 | \n",
" foo | \n",
"
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" \n",
" 8 | \n",
" foo | \n",
"
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" \n",
" 9 | \n",
" foo | \n",
"
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" \n",
"
\n",
"
"
],
"text/plain": [
" B\n",
"0 foo\n",
"1 foo\n",
"2 foo\n",
"3 foo\n",
"4 foo\n",
"5 foo\n",
"6 foo\n",
"7 foo\n",
"8 foo\n",
"9 foo"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.read_csv('data/test_iterator.csv',usecols=[0,'B'],index_col=0)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" B | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" foo | \n",
"
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" \n",
" 1 | \n",
" foo | \n",
"
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" \n",
" 2 | \n",
" foo | \n",
"
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" \n",
" 3 | \n",
" foo | \n",
"
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" \n",
" 4 | \n",
" foo | \n",
"
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" \n",
" 5 | \n",
" foo | \n",
"
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" \n",
" 6 | \n",
" foo | \n",
"
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" \n",
" 7 | \n",
" foo | \n",
"
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" \n",
" 8 | \n",
" foo | \n",
"
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" \n",
" 9 | \n",
" foo | \n",
"
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" \n",
"
\n",
"
"
],
"text/plain": [
" B\n",
"0 foo\n",
"1 foo\n",
"2 foo\n",
"3 foo\n",
"4 foo\n",
"5 foo\n",
"6 foo\n",
"7 foo\n",
"8 foo\n",
"9 foo"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# this is actually a reindex\n",
"pd.read_hdf('data/test_iterator.h5','df',columns=['B'])"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 foo\n",
"1 foo\n",
"2 foo\n",
"3 foo\n",
"4 foo\n",
"5 foo\n",
"6 foo\n",
"7 foo\n",
"8 foo\n",
"9 foo\n",
"dtype: object\n"
]
}
],
"source": [
"with pd.HDFStore('data/test_iterator.h5') as store:\n",
" print store.select_column('df','B')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}