diff --git a/docs/examples.ipynb b/docs/examples.ipynb index a8148c2..f458b22 100644 --- a/docs/examples.ipynb +++ b/docs/examples.ipynb @@ -1,757 +1,788 @@ { - "metadata": { - "name": "", - "signature": "sha256:58bcdd133b74e9fb2a69f4214e4de5d454e8c182bca56d224cede9a477484535" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "%load_ext cypher" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/versae/.virtualenvs/ipython-cypher/lib/python3.4/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead.\n", + " \"You should import from traitlets.config instead.\", ShimWarning)\n", + "/home/versae/.virtualenvs/ipython-cypher/lib/python3.4/site-packages/IPython/utils/traitlets.py:5: UserWarning: IPython.utils.traitlets has moved to a top-level traitlets package.\n", + " warn(\"IPython.utils.traitlets has moved to a top-level traitlets package.\")\n" + ] + } + ], + "source": [ + "%load_ext cypher" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "%config CypherMagic" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "CypherMagic options\n", - "-----------------\n", - "CypherMagic.auto_html=\n", - " Current: False\n", - " Return a D3 representation of the graph instead of regular result sets\n", - "CypherMagic.auto_limit=\n", - " Current: 0\n", - " Automatically limit the size of the returned result sets\n", - "CypherMagic.auto_networkx=\n", - " Current: False\n", - " Return Networkx MultiDiGraph instead of regular result sets\n", - "CypherMagic.auto_pandas=\n", - " Current: False\n", - " Return Pandas DataFrame instead of regular result sets\n", - "CypherMagic.data_contents=\n", - " Current: True\n", - " Bring extra data to render the results as a graph\n", - "CypherMagic.display_limit=\n", - " Current: 0\n", - " Automatically limit the number of rows displayed (full result set is still\n", - " stored)\n", - "CypherMagic.feedback=\n", - " Current: True\n", - " Print number of rows affected\n", - "CypherMagic.rest=\n", - " Current: False\n", - " Return full REST representations of objects inside the result sets\n", - "CypherMagic.short_errors=\n", - " Current: True\n", - " Don't display the full traceback on Neo4j errors\n", - "CypherMagic.style=\n", - " Current: u'DEFAULT'\n", - " Set the table printing style to any of prettytable's defined styles\n", - " (currently DEFAULT, MSWORD_FRIENDLY, PLAIN_COLUMNS, RANDOM)\n" - ] - } - ], - "prompt_number": 2 - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "CypherMagic options\n", + "-----------------\n", + "CypherMagic.auto_html=\n", + " Current: False\n", + " Return a D3 representation of the graph instead of regular result sets\n", + "CypherMagic.auto_limit=\n", + " Current: 0\n", + " Automatically limit the size of the returned result sets\n", + "CypherMagic.auto_networkx=\n", + " Current: False\n", + " Return Networkx MultiDiGraph instead of regular result sets\n", + "CypherMagic.auto_pandas=\n", + " Current: False\n", + " Return Pandas DataFrame instead of regular result sets\n", + "CypherMagic.data_contents=\n", + " Current: True\n", + " Bring extra data to render the results as a graph\n", + "CypherMagic.display_limit=\n", + " Current: 0\n", + " Automatically limit the number of rows displayed (full result set is still\n", + " stored)\n", + "CypherMagic.feedback=\n", + " Current: True\n", + " Print number of rows affected\n", + "CypherMagic.rest=\n", + " Current: False\n", + " Return full REST representations of objects inside the result sets\n", + "CypherMagic.short_errors=\n", + " Current: True\n", + " Don't display the full traceback on Neo4j errors\n", + "CypherMagic.style=\n", + " Current: 'DEFAULT'\n", + " Set the table printing style to any of prettytable's defined styles\n", + " (currently DEFAULT, MSWORD_FRIENDLY, PLAIN_COLUMNS, RANDOM)\n", + "CypherMagic.uri=\n", + " Current: 'http://localhost:7474/db/data/'\n", + " Default database URL if none is defined inline\n" + ] + } + ], + "source": [ + "%config CypherMagic" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ { - 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" (Morpheus)-[:KNOWS]->(Cypher),\n", - " (Cypher)-[:KNOWS]->(Smith),\n", - " (Smith)-[:CODED_BY]->(Architect);" - ], - "language": "python", + "data": { + "text/html": [ + "\n", + " \n", + " \n", + "
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s3fJKsSv/i+KsrRKFTskyDF4LbKcsPPgyTLV3nkke03jIqWljOkXsJO1A2s7P\nJ6NbgqzTs3ZE/IiMKvslaVIZBd2fRV5ZvvoHpGntM2QD9yFJJ0XEPyJid1LJ3UCGbz7RBbGWJ/0N\nRNqZW3XWPgHMpzIpVUT8Ndrmg28a9ZWtHwusp1IavsgT5PWclxyZEREvRsSD3ZRxuJR74hxytPsN\n8v44nAxWWKy/n5TfzVUe444FD5Tw6h8CR0bEERGxJ/Al4FFgf0krtm0/OjIMfUGyszG8uXF6ra07\nrG2XIB/yZcvy1cB3Kt+3kr2OIB2ind7/aDJ+/XrSofkGsuHZhdLjLtttQtpwx3VahmGeux/R55M5\niTQLzV+WW8PuMxlkNjHZSFxOW9Y12dA9QlttI3rXIz6CtpIopInnDuDotvWtLOcmkuTWB/Ypn7cg\nlesKbdssSTbC/fZou3CuFiV7swuW1wXAupXvW/fPeZTqATPbizTr/IFXlzTfhezotMK8W2ad1kh7\nATLDfUIHZZmD9IVdSjr8q9+tQ5pDd6isq8pyDbDFcGWY1UYSL5EOm/dIWoS0oy8i6USAiHhW0upk\n7/WRTu88std9OtnbPoGMCPoBOWp5r6RdS8jkd4HvxQyU7W2QR0nnbCvC6iqy0XqHpIUiR2PrAZuS\n9uUBaUX6kD3KP5d1YwEikxXPAt4taVRlfVd7xBWmmQCnJA4+TobfLiVp7krv+ekmBCj/vwRwsLLU\n+s1kg7xb6Um2mER2Rl5sQo5BMIH05X2cdNL+FviapDdImi8iQpmo+gYy7HxmZBXSlHqTpDGtax8R\nrUKOn1Um2r5SiXBagBwtHxYRf+2EEJKWJkdrZ5NBJIcoK+pS5Pk9ObLdr2w/qiLLhWRU0zXDFqTX\nWruD2nZs+bwE2QB9hcygXoGMALmaHLLdSYYxQufKbEwAtqssjyVD0C4lG5+NybyCX5EO2pFUi2mO\ntuXzgMvK57eRvZjbyWStuxl6stxJZE+9ZSNt+X8OpFL8rEfH3uoJbk/6iKrlFERfiZCuJDaW+2Yy\n2QPcodzLPyAdv6eTWct3AGv14Fy1nq/RpIP2eDKjWmSBu0vIDsZnyU5BY7WrGjzG5YGDyucPkP6f\nranY9clRxuVUCkySQQO/pIPBL2Sgws0U/xcZyHAcaamYUNnuIOCDleW5yA7eZh2TpdcXpgMncwIZ\nXncKJVOX7BGfCRwNLFG224QMU1ujLHfESUyGrd5Ahr99t8ixPFkJ8gPl4WnNLDcPfcPBnjupyVIk\nN5PzHrTfje3GAAAgAElEQVSK681BOm5/XNnuLaT5Y7rhnpWHqdWovLX835uZ1rn3vtLQjOn2eeDV\nw/a1SLPJgRRna1k/kYZNO2Qi5bFkZ6ZVCWAyGV68U7ln1iPzUg4GVunBfbICaZ9/O32m3LWKTF8q\n13DZ8v1uwMbTu09G2qs0yrcB76ysOwj4GRmu3bqftybrTS1AXyfjY8DWHZalvdrwaDJU/FhyFDc3\nWRT0XmDLtt9P6OS5mamT6ZTz+K5ARp0sQw59Hydr2dwEvIf0D5waEQ9VftepWkzLkU7viWRv6ntk\n73MU+RBdQjaO/yB7KA8PZ3+dpDhCtyMjju4mTRu/Ip3qp5G9liciYq+239Weu5Io9wGyUuqLZd2c\npMPvdaTD93xSWX8IeEtE3NHE8dVRnHlHkAloP62s35FUEncXOe8jG8BPRcSFDckyhjwfbyaV1LKk\nH+IZ0qx1BJkDcUXtnzRMud57k9Fdt5LPWCsceCFSZpFy/rvtd8N+xrpBSXg9ETg9Ik4vQS4bRc7y\ndzCptA8ny44cD3wiIi6r/L5jSYElgfWnZJt1bDmP5wJnR8SlkrYl525ZmFQah0dJlCOVVscnN5pp\nZ6YrtrmjyRo2B5Hx9guRdYQ+RPa+1iAbwtUl7RulYF4nLmixox8N/CgiLpT0GtI88wXSpLVW2fcL\nZC98CTI/oudIGk+a4/Yiz9e65EPyCnnOjiVLLOxZsqjf3vrtdM7dKLLn+zVJh0VGP70g6ViyB7Y9\ncCRZGLDrCqIwP9mR2FXSfyNiCkBEXCHpCTJyay8yD+QjlQewow2epC2LLIeUVXeRVTuXJ/0+T5C9\nxVMl7RcRV3Vq30OQcQLwnoj4eMn8fheZxLc9OUHXvkXuLXNzHUUW0IyZQTkArZkhzwL+UxSESHPN\nVcC1EXF8WXc6WY34Q9GWXd/hYw3y2j9foqhOI7PVLy37+5mkl8gO8KHlvm3J0szsd50clnTzRZqZ\nWnkQY0ml8H3g05VttiQVxg4NyXA+sEdl+SDSTLBj23Yrl/dem5daI8dNgEsr6w8lfQetks1LkpET\n32EIOQGkklit/Ne3qdTWL98vRiZXjRnOcXTgPCxbjvkU+ma8a5kOxlPxQdBhk0k5R6PIkVorqXN5\nsvd4MGnGGENG0hxNzsHQk8l4SKVwTmX5y+Roc5myvC45CrqatsmiZqYX6Wd4kPQjngN8tZ9t3kIx\n63T6nqjea+V9Aun3uJO25M3yfM1PX3HJxk16Pb9AM3Ai5ybt5muRsfXVTN51SZPPl+ouQAf2vxR9\nSU5nUBy5le8PLA/8zhTbd+Xi91pJzFfeNwJ+2vbdIaQJYWf6EuSm+k+GsI9RZGLaSeTopDUF6wfJ\nXmdPQjf7kbOqKLYu67YgCy02FrpZUUbHU6k2TJb4uIocaVWdovP24Ny0QsXfQUbhVb/7MlkR+FWK\nq9f39xCPcT5SGbeS/MaT1QPuattuIzJkuzqhU6czqVcgzVnfJzPWx5Ojlh+RZuxWGPqGZD2mrlZn\nmKlCYIs9+QzSFifSlANMHWrdSU4DOkHSN6u/jXKWh7l/kQ7p90jag+x9TBPmFxHfIS/uh8nwz6n7\n7oQMM4qkccBPSnLbY8DCJdyzFd7XmttiL2DLYi8fktzFNvsKqQyOJ6/RUZLeQ+ZcvDO6PL9BS67K\n59bx3k+ad+4GdpT0MTLwYM+IuKEhOVYlQ6Mh75txZf2YiLiX9OdMBD4jaeGy3XNNyDKAjAuSIZ4T\nSV/a3GV9617+GJmVflXxyU1zfmcGynX4AWnrv0bSoeQzsSEwfzGbIWlDcjR9c7mviUIHZVmZvA/n\nJueh3oqcF2IdspjjxsCBxWd2Amliur6b53ym8klExL8ktUoOf48slreJpD+QPdj/khf7eBrIio2I\nkHQted72Ie3skyRdSo5uRpMX+h6yQezqBDkDETkB0KnkkPo80gE5DphX0nORmdPfJ2/WP0eZYKiO\nEpM9TVZpOT+tuPE7SSfs0WSPfaOIuK3zRzZ9ilybAo9FxL0tGSPifkkXk/NFHEb27C9s0Ok6isyW\n/hpZjfgvZT8vtRSFpI+SZUp65fidn6wFtWeR97GyfnRrg4j4hKT/kKPqv4yETtBgKY3yWeS9eS3Z\ni/8wGbhxNDly+HVxZi8BfCwiftpJ53RFlhXIfIYjI+L8sm4Zsgz9AeR98D5yhP9BcuKgK7qpIICZ\nJ7qp2ihJ+giZKPdaskH+D2nLfbF8fn+U0skNyTIfqSA+RT5UR5IjjCXJ5JafRol+aOLmGg6S3kb6\nCxYl80Ymko3C86TSeGPk/BED/cfUY5K0S1kdEXFJWdcqWSzyIfxPRDzQyAENEklHk8f6piiRV5Xv\nlibNln9uMiqnjGJWJW397ydHameR5+glMsHzLuArUZmVsNuUEUIr2msNMgx4GbIQ5hxkJ2m70vEY\nUff3QJQG+HJy1seTW7Irk0SPBq6OiK+UoJjryCi9RjoNJfDlSHLEcFBEPFCRZxmybbktIr5d7s/F\nIuL3Td6ftXTTtjXcF9PG2e9HOtG2JEPyFiOjmzoaR059PsDcpP3+EuDNg/3dSHiRzsa7SWfcWFJh\nLMkQ50MmIyz+QMaJPwB8tPJdT0uet59/shPxLWDFgeRr+rqRvfNVSZ/NjWTI9AZkPskbO33/DkPO\n8WQC6LlkFNPrSGW2CT1I5uvQMW1IJobuVJ7fauXUzYH/o5RBoc/X2ZhjmMxK/zw5ul+v7bvDSOU8\ntrKuJ3knPb9w0zmJrzohbYrio2QNpK3oc5B21Elc+b+NKclkle/mBd5EluE4oBP769a5JE0stwC7\nVrcZzI1YtlmkHPfaZd1yZGJPI1O/DuE4qxVTtyoNXGveiuOBL/f6GpTztwbp2P8KbVnvvbw32s7f\ncqSZ4wRgq37ugRHbERrgWLejb/bDVvb/GLIywiWU6tCdbkcq+38tWe7m9WS4+GvLPfA5pq2B9U7g\nc70+XxEj2HHd5mxcrcT2E2nvbjkfjyFt6x+nr+Jrx+yjleHfumTuwDROxIh4ltT23yKzNUcM7XbL\nchxjKsvnkjHvX5C0aDERTaWf/xtV/a9If8v/AfOU3/6F9BWt29QxTQ9lZdSNyucJZCz/DsDHJX2V\njHXfTNLyXZDlVc9WuQajy/lt+WyWIJP2ekaRa1NJK0apR1TW/4UsLfM3svrpIlVzRyeesaaRtLKk\nj0g6Xpmb8jvSB7E/WZds3kj/2yqkuXVs1YTWyWMsDvOryWipk8koyBXJkjdzAm9RVpFeiyxUem3d\nf3WVXmupATRuS5PvRPZQ12n7fo7K58biyMke3+nA59tlqywPOCNbl8/b/PSF3u5A2j0PrXzfnruw\nxCD+s9q7nEgqgjGkmekiSvVW0tl2Lm0TwHfx2BcgHX0XAn8iH/o5yjk5mey5v0ApvUBDJjGm7Znv\nUl5vrqxr3S+jyBIM070GXTh3R5NRS68a1ZAjimW7LVMHjqk1M+VHyE7et8nM9lXIvKprSvuyTdnu\nVWbjDsqyNNkx2LcsL0wGBzxFhl4vTibinkXOAtkqk9P7NqXXAkznxK5VTuxqZfl1wKqV76eJ4+/E\nCaVtGF1k+FFpeKrTa/b84vU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s3fJKsSv/i+KsrRKFTskyDF4LbKcsPPgyTLV3nkke03jIqWljOkXsJO1A2s7P\nJ6NbgqzTs3ZE/IiMKvslaVIZBd2fRV5ZvvoHpGntM2QD9yFJJ0XEPyJid1LJ3UCGbz7RBbGWJ/0N\nRNqZW3XWPgHMpzIpVUT8Ndrmg28a9ZWtHwusp1IavsgT5PWclxyZEREvRsSD3ZRxuJR74hxytPsN\n8v44nAxWWKy/n5TfzVUe444FD5Tw6h8CR0bEERGxJ/Al4FFgf0krtm0/OjIMfUGyszG8uXF6ra07\nrG2XIB/yZcvy1cB3Kt+3kr2OIB2ind7/aDJ+/XrSofkGsuHZhdLjLtttQtpwx3VahmGeux/R55M5\niTQLzV+WW8PuMxlkNjHZSFxOW9Y12dA9QlttI3rXIz6CtpIopInnDuDotvWtLOcmkuTWB/Ypn7cg\nlesKbdssSTbC/fZou3CuFiV7swuW1wXAupXvW/fPeZTqATPbizTr/IFXlzTfhezotMK8W2ad1kh7\nATLDfUIHZZmD9IVdSjr8q9+tQ5pDd6isq8pyDbDFcGWY1UYSL5EOm/dIWoS0oy8i6USAiHhW0upk\n7/WRTu88std9OtnbPoGMCPoBOWp5r6RdS8jkd4HvxQyU7W2QR0nnbCvC6iqy0XqHpIUiR2PrAZuS\n9uUBaUX6kD3KP5d1YwEikxXPAt4taVRlfVd7xBWmmQCnJA4+TobfLiVp7krv+ekmBCj/vwRwsLLU\n+s1kg7xb6Um2mER2Rl5sQo5BMIH05X2cdNL+FviapDdImi8iQpmo+gYy7HxmZBXSlHqTpDGtax8R\nrUKOn1Um2r5SiXBagBwtHxYRf+2EEJKWJkdrZ5NBJIcoK+pS5Pk9ObLdr2w/qiLLhWRU0zXDFqTX\nWruD2nZs+bwE2QB9hcygXoGMALmaHLLdSYYxQufKbEwAtqssjyVD0C4lG5+NybyCX5EO2pFUi2mO\ntuXzgMvK57eRvZjbyWStuxl6stxJZE+9ZSNt+X8OpFL8rEfH3uoJbk/6iKrlFERfiZCuJDaW+2Yy\n2QPcodzLPyAdv6eTWct3AGv14Fy1nq/RpIP2eDKjWmSBu0vIDsZnyU5BY7WrGjzG5YGDyucPkP6f\nranY9clRxuVUCkySQQO/pIPBL2Sgws0U/xcZyHAcaamYUNnuIOCDleW5yA7eZh2TpdcXpgMncwIZ\nXncKJVOX7BGfCRwNLFG224QMU1ujLHfESUyGrd5Ahr99t8ixPFkJ8gPl4WnNLDcPfcPBnjupyVIk\nN5PzHrTfje3GAAAgAElEQVSK681BOm5/XNnuLaT5Y7rhnpWHqdWovLX835uZ1rn3vtLQjOn2eeDV\nw/a1SLPJgRRna1k/kYZNO2Qi5bFkZ6ZVCWAyGV68U7ln1iPzUg4GVunBfbICaZ9/O32m3LWKTF8q\n13DZ8v1uwMbTu09G2qs0yrcB76ysOwj4GRmu3bqftybrTS1AXyfjY8DWHZalvdrwaDJU/FhyFDc3\nWRT0XmDLtt9P6OS5mamT6ZTz+K5ARp0sQw59Hydr2dwEvIf0D5waEQ9VftepWkzLkU7viWRv6ntk\n73MU+RBdQjaO/yB7KA8PZ3+dpDhCtyMjju4mTRu/Ip3qp5G9liciYq+239Weu5Io9wGyUuqLZd2c\npMPvdaTD93xSWX8IeEtE3NHE8dVRnHlHkAloP62s35FUEncXOe8jG8BPRcSFDckyhjwfbyaV1LKk\nH+IZ0qx1BJkDcUXtnzRMud57k9Fdt5LPWCsceCFSZpFy/rvtd8N+xrpBSXg9ETg9Ik4vQS4bRc7y\ndzCptA8ny44cD3wiIi6r/L5jSYElgfWnZJt1bDmP5wJnR8SlkrYl525ZmFQah0dJlCOVVscnN5pp\nZ6YrtrmjyRo2B5Hx9guRdYQ+RPa+1iAbwtUl7RulYF4nLmixox8N/CgiLpT0GtI88wXSpLVW2fcL\nZC98CTI/oudIGk+a4/Yiz9e65EPyCnnOjiVLLOxZsqjf3vrtdM7dKLLn+zVJh0VGP70g6ViyB7Y9\ncCRZGLDrCqIwP9mR2FXSfyNiCkBEXCHpCTJyay8yD+QjlQewow2epC2LLIeUVXeRVTuXJ/0+T5C9\nxVMl7RcRV3Vq30OQcQLwnoj4eMn8fheZxLc9OUHXvkXuLXNzHUUW0IyZQTkArZkhzwL+UxSESHPN\nVcC1EXF8WXc6WY34Q9GWXd/hYw3y2j9foqhOI7PVLy37+5mkl8gO8KHlvm3J0szsd50clnTzRZqZ\nWnkQY0ml8H3g05VttiQVxg4NyXA+sEdl+SDSTLBj23Yrl/dem5daI8dNgEsr6w8lfQetks1LkpET\n32EIOQGkklit/Ne3qdTWL98vRiZXjRnOcXTgPCxbjvkU+ma8a5kOxlPxQdBhk0k5R6PIkVorqXN5\nsvd4MGnGGENG0hxNzsHQk8l4SKVwTmX5y+Roc5myvC45CrqatsmiZqYX6Wd4kPQjngN8tZ9t3kIx\n63T6nqjea+V9Aun3uJO25M3yfM1PX3HJxk16Pb9AM3Ai5ybt5muRsfXVTN51SZPPl+ouQAf2vxR9\nSU5nUBy5le8PLA/8zhTbd+Xi91pJzFfeNwJ+2vbdIaQJYWf6EuSm+k+GsI9RZGLaSeTopDUF6wfJ\nXmdPQjf7kbOqKLYu67YgCy02FrpZUUbHU6k2TJb4uIocaVWdovP24Ny0QsXfQUbhVb/7MlkR+FWK\nq9f39xCPcT5SGbeS/MaT1QPuattuIzJkuzqhU6czqVcgzVnfJzPWx5Ojlh+RZuxWGPqGZD2mrlZn\nmKlCYIs9+QzSFifSlANMHWrdSU4DOkHSN6u/jXKWh7l/kQ7p90jag+x9TBPmFxHfIS/uh8nwz6n7\n7oQMM4qkccBPSnLbY8DCJdyzFd7XmttiL2DLYi8fktzFNvsKqQyOJ6/RUZLeQ+ZcvDO6PL9BS67K\n59bx3k+ad+4GdpT0MTLwYM+IuKEhOVYlQ6Mh75txZf2YiLiX9OdMBD4jaeGy3XNNyDKAjAuSIZ4T\nSV/a3GV9617+GJmVflXxyU1zfmcGynX4AWnrv0bSoeQzsSEwfzGbIWlDcjR9c7mviUIHZVmZvA/n\nJueh3oqcF2IdspjjxsCBxWd2Amliur6b53ym8klExL8ktUoOf48slreJpD+QPdj/khf7eBrIio2I\nkHQted72Ie3skyRdSo5uRpMX+h6yQezqBDkDETkB0KnkkPo80gE5DphX0nORmdPfJ2/WP0eZYKiO\nEpM9TVZpOT+tuPE7SSfs0WSPfaOIuK3zRzZ9ilybAo9FxL0tGSPifkkXk/NFHEb27C9s0Ok6isyW\n/hpZjfgvZT8vtRSFpI+SZUp65fidn6wFtWeR97GyfnRrg4j4hKT/kKPqv4yETtBgKY3yWeS9eS3Z\ni/8wGbhxNDly+HVxZi8BfCwiftpJ53RFlhXIfIYjI+L8sm4Zsgz9AeR98D5yhP9BcuKgK7qpIICZ\nJ7qp2ihJ+giZKPdaskH+D2nLfbF8fn+U0skNyTIfqSA+RT5UR5IjjCXJ5JafRol+aOLmGg6S3kb6\nCxYl80Ymko3C86TSeGPk/BED/cfUY5K0S1kdEXFJWdcqWSzyIfxPRDzQyAENEklHk8f6piiRV5Xv\nlibNln9uMiqnjGJWJW397ydHameR5+glMsHzLuArUZmVsNuUEUIr2msNMgx4GbIQ5hxkJ2m70vEY\nUff3QJQG+HJy1seTW7Irk0SPBq6OiK+UoJjryCi9RjoNJfDlSHLEcFBEPFCRZxmybbktIr5d7s/F\nIuL3Td6ftXTTtjXcF9PG2e9HOtG2JEPyFiOjmzoaR059PsDcpP3+EuDNg/3dSHiRzsa7SWfcWFJh\nLMkQ50MmIyz+QMaJPwB8tPJdT0uet59/shPxLWDFgeRr+rqRvfNVSZ/NjWTI9AZkPskbO33/DkPO\n8WQC6LlkFNPrSGW2CT1I5uvQMW1IJobuVJ7fauXUzYH/o5RBoc/X2ZhjmMxK/zw5ul+v7bvDSOU8\ntrKuJ3knPb9w0zmJrzohbYrio2QNpK3oc5B21Elc+b+NKclkle/mBd5EluE4oBP769a5JE0stwC7\nVrcZzI1YtlmkHPfaZd1yZGJPI1O/DuE4qxVTtyoNXGveiuOBL/f6GpTztwbp2P8KbVnvvbw32s7f\ncqSZ4wRgq37ugRHbERrgWLejb/bDVvb/GLIywiWU6tCdbkcq+38tWe7m9WS4+GvLPfA5pq2B9U7g\nc70+XxEj2HHd5mxcrcT2E2nvbjkfjyFt6x+nr+Jrx+yjleHfumTuwDROxIh4ltT23yKzNUcM7XbL\nchxjKsvnkjHvX5C0aDERTaWf/xtV/a9If8v/AfOU3/6F9BWt29QxTQ9lZdSNyucJZCz/DsDHJX2V\njHXfTNLyXZDlVc9WuQajy/lt+WyWIJP2ekaRa1NJK0apR1TW/4UsLfM3svrpIlVzRyeesaaRtLKk\nj0g6Xpmb8jvSB7E/WZds3kj/2yqkuXVs1YTWyWMsDvOryWipk8koyBXJkjdzAm9RVpFeiyxUem3d\nf3WVXmupATRuS5PvRPZQ12n7fo7K58biyMke3+nA59tlqywPOCNbl8/b/PSF3u5A2j0PrXzfnruw\nxCD+s9q7nEgqgjGkmekiSvVW0tl2Lm0TwHfx2BcgHX0XAn8iH/o5yjk5mey5v0ApvUBDJjGm7Znv\nUl5vrqxr3S+jyBIM070GXTh3R5NRS68a1ZAjimW7LVMHjqk1M+VHyE7et8nM9lXIvKprSvuyTdnu\nVWbjDsqyNNkx2LcsL0wGBzxFhl4vTibinkXOAtkqk9P7NqXXAkznxK5VTuxqZfl1wKqV76eJ4+/E\nCaVtGF1k+FFpeKrTa/b84vU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DwrPpz/ij0K6ixFn8WKBtFQWJWwHvSvo8fI9Pzey5CvtXbC/5uSU9fwK4RNKk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\nRDea/sCbkvYowLCZFRVb8R3FFB+7gAV/HMGI+OM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DwrPpz/ij0K6ixFn8WKBtFQWJWwHvSvo8fI9Pzey5CvtXbC/5uSU9fwK4RNKk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\nqYkhlckBQF5eXqxz58688cYb0Tq9npJ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namereldegree
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2Agent SmithKNOWS1
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namereldegree
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" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 14, - "text": [ - " name rel degree\n", - "0 Morpheus KNOWS 3\n", - "1 Cypher KNOWS 2\n", - "2 Agent Smith KNOWS 1\n", - "3 Neo LOVES 1\n", - "4 Trinity LOVES 1\n", - "5 Neo KNOWS 1\n", - "6 Trinity KNOWS 1\n", - "7 The Architect CODED_BY 1\n", - "8 Agent Smith CODED_BY 1" - ] - } - ], - "prompt_number": 14 + "output_type": "execute_result" }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "print(results.csv())" - ], - "language": "python", + "data": { + "image/png": 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76bl/h81k5wBol25CmjlquZHmxlpfY+EAMEO07AcwcABw2LiMCZmVMWdg6XpfxOJHVvTb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\nhGhM7MCKujb07duXhQsXUlRUxNKlSzl58iSRkZFYrVaeffbZqvtcGOu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The ArchitectCODED_BY1
Agent SmithCODED_BY1
" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 16, - "text": [ - "[[u'Morpheus', u'KNOWS', 3],\n", - " [u'Cypher', u'KNOWS', 2],\n", - " [u'Agent Smith', u'KNOWS', 1],\n", - " [u'Neo', u'LOVES', 1],\n", - " [u'Trinity', u'LOVES', 1],\n", - " [u'Neo', u'KNOWS', 1],\n", - " [u'Trinity', u'KNOWS', 1],\n", - " [u'The Architect', u'CODED_BY', 1],\n", - " [u'Agent Smith', u'CODED_BY', 1]]" - ] - } - ], - "prompt_number": 16 + "output_type": "execute_result" } ], - "metadata": {} + "source": [ + "from cypher import run\n", + "run(\"match (n)-[r]-() return n.name as name, type(r) as rel, count(r) as degree order by degree desc\")" + ] + } + ], + "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": "3.4.0" } - ] -} \ No newline at end of file + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/setup.py b/setup.py index 542ec7a..ea35d88 100644 --- a/setup.py +++ b/setup.py @@ -6,7 +6,7 @@ NEWS = open(os.path.join(here, 'NEWS.txt')).read() -version = '0.2.2' +version = '0.2.3' install_requires = [ 'neo4jrestclient>=2.1.0', diff --git a/src/cypher/run.py b/src/cypher/run.py index 355d352..ac83a0d 100644 --- a/src/cypher/run.py +++ b/src/cypher/run.py @@ -1,6 +1,7 @@ import codecs from collections import defaultdict import csv +from functools import reduce import json import prettytable import operator @@ -25,7 +26,7 @@ from cypher.column_guesser import ColumnGuesserMixin from cypher.connection import Connection from cypher.utils import ( - DefaultConfigurable, DEFAULT_CONFIGURABLE, StringIO, + DefaultConfigurable, DEFAULT_CONFIGURABLE, PY2, StringIO, string_types ) @@ -58,15 +59,16 @@ def __init__(self, f, dialect=csv.excel, encoding="utf-8", **kwds): def writerow(self, row): _row = [s.encode("utf-8") - if hasattr(s, "encode") + if PY2 else s for s in row] self.writer.writerow(_row) # Fetch UTF-8 output from the queue ... data = self.queue.getvalue() - data = data.decode("utf-8") - # ... and reencode it into the target encoding - data = self.encoder.encode(data) + if PY2: + data = data.decode("utf-8") + # ... and reencode it into the target encoding + data = self.encoder.encode(data) # write to the target stream self.stream.write(data) # empty queue @@ -111,16 +113,20 @@ def __init__(self, results, query, config): self.limit = config.auto_limit style_name = config.style self.style = prettytable.__dict__[style_name.upper()] - if len(results) > 0: + if results is not None: if not config.rest: _results = results.rows else: _results = results + _results = _results or [] if self.limit: list.__init__(self, _results[:self.limit]) else: list.__init__(self, _results) - self.field_names = unduplicate_field_names(self.keys) + if self.keys is not None: + self.field_names = unduplicate_field_names(self.keys) + else: + self.field_names = [] self.pretty = prettytable.PrettyTable(self.field_names) if not config.auto_pandas: for row in self[:config.display_limit or None]: @@ -272,7 +278,7 @@ def draw(self, directed=True, layout="spring", if node_label_attr is None: node_labels[node] = "$:{}$\n{}".format( ":".join(labels), - props.values()[0] if props else "", + next(iter(props.values())) if props else "", ) else: props_list = ["{}: {}".format(k, v) @@ -283,7 +289,7 @@ def draw(self, directed=True, layout="spring", node_color = [] node_colors = list(node_colors) legend_colors = [] - colors = plt.matplotlib.colors.ColorConverter().cache.items() + colors = list(plt.matplotlib.colors.ColorConverter().cache.items()) for color_name, color_rgb in colors[:len(node_colors)]: node_color.append(color_rgb) legend_colors.append(color_name) @@ -442,7 +448,7 @@ def interpret_stats(results): the Cypher query """ stats = results.stats - contains_updates = stats.pop("contains_updates", False) + contains_updates = stats.pop("contains_updates", False) if stats else False if not contains_updates: result = '{} rows affected.'.format(len(results)) else: