Skip to content

Instantly share code, notes, and snippets.

@jwillis0720

jwillis0720/Untitled1.ipynb

Created June 21, 2015 00:52
Show Gist options
  • Star 0 You must be signed in to star a gist
  • Fork 0 You must be signed in to fork a gist
  • Save jwillis0720/073ff14fd8f5630b42eb to your computer and use it in GitHub Desktop.
Save jwillis0720/073ff14fd8f5630b42eb to your computer and use it in GitHub Desktop.
Download ZIP
Display the source blob
Display the rendered blob
Raw
Untitled1.ipynb
{"nbformat_minor": 0, "cells": [{"source": "#Load DB", "cell_type": "markdown", "metadata": {}}, {"execution_count": 2, "cell_type": "code", "source": "from pandas import DataFrame", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 5, "cell_type": "code", "source": "import sqlite3 as sql\nconn = sql.connect(\"/ipython_notebooks/germ_database.db3\")\ncursor = conn.cursor()", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"source": "#Query and convert to dataframe", "cell_type": "markdown", "metadata": {}}, {"execution_count": 75, "cell_type": "code", "source": "cursor_execute_long = cursor.execute('''\nSELECT\n\tone.struct_id,\n one.resNum,\n score_types.score_type_name,\n one.score_value,\n rpi.pdb_residue_number,\n rpi.chain_id\n hc.pdbnum as \"mutant_res\",\n hc.germ,\n hc.wt\nFROM\n\tresidue_scores_1b one\nINNER JOIN score_types ON score_types.score_type_id = one.score_type_id\ninner join residues r on r.resNum = one.resNum and one.struct_id = r.struct_id\ninner join residue_pdb_identification rpi on rpi.residue_number = r.resNum and rpi.struct_id = r.struct_id\nleft outer join heavy_chain_mutation_shift hc on hc.chain_id = rpi.chain_id and hc.pdbnum = rpi.pdb_residue_number\nwhere rpi.chain_id = \"H\"\nlimit 10000\n''')", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 133, "cell_type": "code", "source": "cursor_execute_short = cursor.execute('''\nSELECT\n\tone.struct_id,\n one.resNum,\n score_types.score_type_name,\n one.score_value\n -- rpi.pdb_residue_number,\n -- rpi.chain_id\nFROM\n\tresidue_scores_1b one\nINNER JOIN score_types ON score_types.score_type_id = one.score_type_id\ninner join residues r on r.resNum = one.resNum and one.struct_id = r.struct_id\ninner join residue_pdb_identification rpi on rpi.residue_number = r.resNum and rpi.struct_id = r.struct_id\nlimit 10000\n''')", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}, {"execution_count": 134, "cell_type": "code", "source": "df = DataFrame([i for i in cursor_execute_short])", "outputs": [], "metadata": {"collapsed": false, "trusted": true}}, {"execution_count": 140, "cell_type": "code", "source": "df.columns = [i[0] for i in cursor.description]\nprint df.columns\nprint df.head(30)", "outputs": [{"output_type": "stream", "name": "stdout", "text": "Index([u'struct_id', u'resNum', u'score_type_name', u'score_value'], dtype='object')\n struct_id resNum score_type_name score_value\n0 4294967297 1 omega 0.064840\n1 4294967297 1 fa_dun 2.185618\n2 4294967297 1 fa_dun_dev 0.000027\n3 4294967297 1 fa_dun_semi 2.185591\n4 4294967297 1 ref -1.191180\n5 4294967297 2 rama -0.795161\n6 4294967297 2 omega 0.222345\n7 4294967297 2 fa_dun 1.378923\n8 4294967297 2 fa_dun_dev 0.028560\n9 4294967297 2 fa_dun_rot 1.350362\n10 4294967297 2 p_aa_pp -0.442467\n11 4294967297 2 ref 0.249477\n12 4294967297 3 rama 0.267443\n13 4294967297 3 omega 0.005106\n14 4294967297 3 fa_dun 0.020352\n15 4294967297 3 fa_dun_dev 0.025507\n16 4294967297 3 fa_dun_rot -0.005156\n17 4294967297 3 p_aa_pp -0.096847\n18 4294967297 3 ref 0.979644\n19 4294967297 4 rama -1.403292\n20 4294967297 4 omega 0.212160\n21 4294967297 4 fa_dun 4.218029\n22 4294967297 4 fa_dun_dev 0.003712\n23 4294967297 4 fa_dun_semi 4.214317\n24 4294967297 4 p_aa_pp -0.462765\n25 4294967297 4 ref -1.960940\n26 4294967297 5 rama -0.600053\n27 4294967297 5 omega 0.061867\n28 4294967297 5 fa_dun 3.663050\n29 4294967297 5 fa_dun_dev 0.004953\n"}], "metadata": {"collapsed": false, "trusted": true}}, {"source": "##Pivot\n\n###Why doesn't pivot work here?", "cell_type": "markdown", "metadata": {}}, {"execution_count": 149, "cell_type": "code", "source": "df.pivot(columns='score_type_name',values='score_value',index=['struct_id','resNum'])", "outputs": [{"ename": "ValueError", "evalue": "Wrong number of items passed 10000, placement implies 2", "traceback": ["\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-149-1377278b33f3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpivot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'score_type_name'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'score_value'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'struct_id'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'resNum'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m/dnas/apps/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36mpivot\u001b[1;34m(self, index, columns, values)\u001b[0m\n\u001b[0;32m 3390\u001b[0m \"\"\"\n\u001b[0;32m 3391\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpivot\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3392\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mpivot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3393\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3394\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mstack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdropna\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/dnas/apps/anaconda/lib/python2.7/site-packages/pandas/core/reshape.pyc\u001b[0m in \u001b[0;36mpivot\u001b[1;34m(self, index, columns, values)\u001b[0m\n\u001b[0;32m 368\u001b[0m indexed = Series(self[values].values,\n\u001b[0;32m 369\u001b[0m index=MultiIndex.from_arrays([self[index],\n\u001b[1;32m--> 370\u001b[1;33m self[columns]]))\n\u001b[0m\u001b[0;32m 371\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mindexed\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munstack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 372\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/dnas/apps/anaconda/lib/python2.7/site-packages/pandas/core/series.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, data, index, dtype, name, copy, fastpath)\u001b[0m\n\u001b[0;32m 211\u001b[0m raise_cast_failure=True)\n\u001b[0;32m 212\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 213\u001b[1;33m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSingleBlockManager\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfastpath\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 214\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 215\u001b[0m \u001b[0mgeneric\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mNDFrame\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfastpath\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/dnas/apps/anaconda/lib/python2.7/site-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, block, axis, do_integrity_check, fastpath)\u001b[0m\n\u001b[0;32m 3369\u001b[0m block = make_block(block,\n\u001b[0;32m 3370\u001b[0m \u001b[0mplacement\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mslice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3371\u001b[1;33m ndim=1, fastpath=True)\n\u001b[0m\u001b[0;32m 3372\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3373\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mblocks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mblock\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/dnas/apps/anaconda/lib/python2.7/site-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36mmake_block\u001b[1;34m(values, placement, klass, ndim, dtype, fastpath)\u001b[0m\n\u001b[0;32m 2097\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2098\u001b[0m return klass(values, ndim=ndim, fastpath=fastpath,\n\u001b[1;32m-> 2099\u001b[1;33m placement=placement)\n\u001b[0m\u001b[0;32m 2100\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2101\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/dnas/apps/anaconda/lib/python2.7/site-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, values, placement, ndim, fastpath)\u001b[0m\n\u001b[0;32m 74\u001b[0m raise ValueError('Wrong number of items passed %d,'\n\u001b[0;32m 75\u001b[0m ' placement implies %d' % (\n\u001b[1;32m---> 76\u001b[1;33m len(self.values), len(self.mgr_locs)))\n\u001b[0m\u001b[0;32m 77\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 78\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mValueError\u001b[0m: Wrong number of items passed 10000, placement implies 2"], "output_type": "error"}], "metadata": {"collapsed": false, "trusted": true}}, {"source": "#Pivot Table\n\n###Does work, but not ideal", "cell_type": "markdown", "metadata": {}}, {"execution_count": 152, "cell_type": "code", "source": "pivoted = df.pivot_table(columns='score_type_name',values='score_value',index=['struct_id','resNum'])", "outputs": [{"execution_count": 152, "output_type": "execute_result", "data": {"text/plain": "score_type_name fa_dun fa_dun_dev fa_dun_rot fa_dun_semi omega \\\nstruct_id resNum \n4294967297 1 2.185618 0.000027 NaN 2.185591 0.064840 \n 2 1.378923 0.028560 1.350362 NaN 0.222345 \n 3 0.020352 0.025507 -0.005156 NaN 0.005106 \n 4 4.218029 0.003712 NaN 4.214317 0.212160 \n 5 3.663050 0.004953 NaN 3.658097 0.061867 \n 6 1.517452 0.220623 1.296829 NaN 0.050372 \n 7 3.698745 0.018365 NaN 3.680381 0.054732 \n 8 0.104812 0.013364 0.091448 NaN 0.000280 \n 9 2.362573 0.000532 NaN 2.362040 0.052061 \n 10 1.844669 0.027023 1.817646 NaN 0.097104 \n 11 0.127562 0.009247 0.118316 NaN 0.020841 \n 12 0.292078 0.034249 0.257829 NaN 0.034202 \n 13 0.648313 0.001905 0.646408 NaN 0.084531 \n 14 2.596477 0.150729 NaN 2.445748 0.514282 \n 15 2.644818 0.159300 NaN 2.485518 0.050967 \n 16 3.561031 0.003633 NaN 3.557399 0.002421 \n 17 0.326576 0.056354 0.270222 NaN 0.207658 \n 18 1.028985 0.044087 0.984898 NaN 1.246412 \n 19 1.423473 0.012313 1.411160 NaN 0.491884 \n 20 0.038964 0.018734 0.020230 NaN 0.000119 \n 21 0.394884 0.012919 0.381965 NaN 0.612963 \n 22 0.037251 0.015745 0.021506 NaN 0.016037 \n 23 2.303942 0.012627 2.291315 NaN 0.033129 \n 24 0.383950 0.095711 0.288238 NaN 0.098923 \n 25 0.130075 0.003686 0.126389 NaN 0.084236 \n 26 0.255410 0.104805 0.150605 NaN 0.168313 \n 27 0.448255 0.064404 0.383851 NaN 0.068534 \n 28 0.482777 0.075748 0.407029 NaN 0.061439 \n 29 0.583948 0.073615 0.510333 NaN 0.040075 \n 30 0.055166 0.003266 0.051899 NaN 0.065008 \n... ... ... ... ... ... \n4294967298 240 4.740174 0.449317 NaN 4.290857 0.344661 \n 241 3.978024 0.118481 NaN 3.859542 0.976365 \n 242 3.452059 0.186282 NaN 3.265777 0.828436 \n 243 0.417471 0.208754 0.208717 NaN 0.012376 \n 244 2.443289 0.019956 NaN 2.423333 0.309896 \n 245 0.019082 0.008125 0.010956 NaN 0.013838 \n 246 0.962801 0.007121 0.955679 NaN 0.000004 \n 247 NaN NaN NaN NaN 0.004413 \n 248 0.227479 0.107047 0.120432 NaN 0.073280 \n 249 2.470933 0.059415 NaN 2.411518 0.034304 \n 250 2.301918 0.000482 NaN 2.301436 0.485663 \n 251 0.636847 0.000030 0.636817 NaN 0.033697 \n 252 0.052720 0.012307 0.040413 NaN 0.099188 \n 253 3.427858 0.000125 NaN 3.427732 0.206410 \n 254 0.138352 0.008968 0.129384 NaN 0.283981 \n 255 0.271421 0.002089 0.269331 NaN 0.061324 \n 256 2.644857 0.000002 NaN 2.644855 0.034885 \n 257 2.441294 0.000089 NaN 2.441205 0.043497 \n 258 2.722745 0.035252 NaN 2.687493 0.042097 \n 259 0.430576 0.000168 0.430408 NaN 0.032542 \n 260 0.313999 0.026600 0.287399 NaN 0.026820 \n 261 0.091507 0.032129 0.059379 NaN 0.031812 \n 262 1.355400 0.402955 0.952445 NaN 0.021373 \n 263 0.128155 0.007633 0.120522 NaN 0.044180 \n 264 0.209232 0.003300 0.205933 NaN 0.170198 \n 265 3.156909 0.508314 2.648594 NaN 0.109325 \n 266 1.328400 0.016535 1.311864 NaN 0.013423 \n 267 3.462925 0.734232 2.728692 NaN 0.014817 \n 268 3.180541 0.205535 NaN 2.975006 0.021037 \n 269 NaN NaN NaN NaN NaN \n\nscore_type_name p_aa_pp rama ref yhh_planarity \nstruct_id resNum \n4294967297 1 NaN NaN -1.191180 NaN \n 2 -0.442467 -0.795161 0.249477 NaN \n 3 -0.096847 0.267443 0.979644 NaN \n 4 -0.462765 -1.403292 -1.960940 NaN \n 5 -0.275890 -0.600053 -1.517170 NaN \n 6 -0.375813 -0.741903 0.249477 NaN \n 7 0.401857 -0.148868 0.388298 NaN \n 8 -0.796285 -0.403267 0.201340 NaN \n 9 0.195547 -0.349949 -1.630020 NaN \n 10 0.167615 -0.407074 1.080600 NaN \n 11 -0.402554 -0.007522 1.080600 NaN \n 12 -0.242636 -1.393017 0.165383 NaN \n 13 -0.356526 -0.235599 0.761128 NaN \n 14 -0.136966 -0.537316 1.234130 NaN \n 15 0.150995 -0.501137 -1.630020 NaN \n 16 -0.361212 -0.957757 -1.517170 NaN \n 17 -0.477727 -1.271940 0.165383 NaN \n 18 -0.189764 2.146958 0.761128 NaN \n 19 -0.123693 -0.486693 -0.358574 NaN \n 20 -0.358959 1.757088 -0.250485 NaN \n 21 -0.260295 0.388244 0.443793 NaN \n 22 -0.238985 -0.747880 0.979644 NaN \n 23 -0.087818 -0.832301 -0.358574 NaN \n 24 -0.011932 1.308046 0.761128 NaN \n 25 0.129601 -0.032645 0.201340 NaN \n 26 -0.430657 -0.215267 -0.250485 NaN \n 27 -0.176108 0.710819 0.761128 NaN \n 28 -0.507403 0.493257 0.443793 NaN \n 29 0.158187 0.161241 0.979644 NaN \n 30 -0.025786 -0.071459 0.201340 NaN \n... ... ... ... ... \n4294967298 240 -0.269404 -0.268549 0.619370 NaN \n 241 -0.407884 -0.831550 0.619370 NaN \n 242 -0.508247 0.253198 0.162496 0.191642 \n 243 -0.504630 -0.517383 0.443793 NaN \n 244 -0.847634 -0.596147 -1.191180 NaN \n 245 0.079334 0.284252 0.201340 NaN \n 246 0.145112 -0.666483 0.165383 NaN \n 247 0.540807 -0.122814 0.173326 NaN \n 248 -0.333456 0.159415 0.761128 NaN \n 249 -1.534370 0.442499 0.619370 NaN \n 250 -1.949321 1.919370 -1.191180 NaN \n 251 -0.780162 -0.259157 0.165383 NaN \n 252 -0.111913 -0.033058 0.201340 NaN \n 253 -0.182746 -0.111733 1.234130 NaN \n 254 0.638144 0.926719 1.080600 NaN \n 255 -0.459537 -1.105778 0.165383 NaN \n 256 -1.218309 0.336546 -1.191180 NaN \n 257 -1.430593 0.495951 -1.191180 NaN \n 258 -1.603858 0.674321 -1.630020 NaN \n 259 -0.470244 -0.166085 0.165383 NaN \n 260 -1.261095 -0.979981 1.080600 NaN \n 261 0.191408 -0.415607 0.201340 NaN \n 262 -0.315569 0.513188 0.761128 NaN \n 263 -1.523705 -0.601065 -0.250485 NaN \n 264 -0.507001 -0.512832 0.443793 NaN \n 265 -0.298296 -0.552645 -0.324360 NaN \n 266 -0.552644 -0.746187 1.080600 NaN \n 267 -0.020396 -0.372945 -0.358574 NaN \n 268 -0.276916 0.299735 -1.517170 NaN \n 269 NaN -0.691644 NaN NaN \n\n[1505 rows x 9 columns]", "text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>score_type_name</th>\n <th>fa_dun</th>\n <th>fa_dun_dev</th>\n <th>fa_dun_rot</th>\n <th>fa_dun_semi</th>\n <th>omega</th>\n <th>p_aa_pp</th>\n <th>rama</th>\n <th>ref</th>\n <th>yhh_planarity</th>\n </tr>\n <tr>\n <th>struct_id</th>\n <th>resNum</th>\n <th></th>\n <th></th>\n <th></th>\n <th></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 rowspan=\"30\" valign=\"top\">4294967297</th>\n <th>1 </th>\n <td> 2.185618</td>\n <td> 0.000027</td>\n <td> NaN</td>\n <td> 2.185591</td>\n <td> 0.064840</td>\n <td> NaN</td>\n <td> NaN</td>\n <td>-1.191180</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>2 </th>\n <td> 1.378923</td>\n <td> 0.028560</td>\n <td> 1.350362</td>\n <td> NaN</td>\n <td> 0.222345</td>\n <td>-0.442467</td>\n <td>-0.795161</td>\n <td> 0.249477</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>3 </th>\n <td> 0.020352</td>\n <td> 0.025507</td>\n <td>-0.005156</td>\n <td> NaN</td>\n <td> 0.005106</td>\n <td>-0.096847</td>\n <td> 0.267443</td>\n <td> 0.979644</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>4 </th>\n <td> 4.218029</td>\n <td> 0.003712</td>\n <td> NaN</td>\n <td> 4.214317</td>\n <td> 0.212160</td>\n <td>-0.462765</td>\n <td>-1.403292</td>\n <td>-1.960940</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>5 </th>\n <td> 3.663050</td>\n <td> 0.004953</td>\n <td> NaN</td>\n <td> 3.658097</td>\n <td> 0.061867</td>\n <td>-0.275890</td>\n <td>-0.600053</td>\n <td>-1.517170</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>6 </th>\n <td> 1.517452</td>\n <td> 0.220623</td>\n <td> 1.296829</td>\n <td> NaN</td>\n <td> 0.050372</td>\n <td>-0.375813</td>\n <td>-0.741903</td>\n <td> 0.249477</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>7 </th>\n <td> 3.698745</td>\n <td> 0.018365</td>\n <td> NaN</td>\n <td> 3.680381</td>\n <td> 0.054732</td>\n <td> 0.401857</td>\n <td>-0.148868</td>\n <td> 0.388298</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>8 </th>\n <td> 0.104812</td>\n <td> 0.013364</td>\n <td> 0.091448</td>\n <td> NaN</td>\n <td> 0.000280</td>\n <td>-0.796285</td>\n <td>-0.403267</td>\n <td> 0.201340</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>9 </th>\n <td> 2.362573</td>\n <td> 0.000532</td>\n <td> NaN</td>\n <td> 2.362040</td>\n <td> 0.052061</td>\n <td> 0.195547</td>\n <td>-0.349949</td>\n <td>-1.630020</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>10 </th>\n <td> 1.844669</td>\n <td> 0.027023</td>\n <td> 1.817646</td>\n <td> NaN</td>\n <td> 0.097104</td>\n <td> 0.167615</td>\n <td>-0.407074</td>\n <td> 1.080600</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>11 </th>\n <td> 0.127562</td>\n <td> 0.009247</td>\n <td> 0.118316</td>\n <td> NaN</td>\n <td> 0.020841</td>\n <td>-0.402554</td>\n <td>-0.007522</td>\n <td> 1.080600</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>12 </th>\n <td> 0.292078</td>\n <td> 0.034249</td>\n <td> 0.257829</td>\n <td> NaN</td>\n <td> 0.034202</td>\n <td>-0.242636</td>\n <td>-1.393017</td>\n <td> 0.165383</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>13 </th>\n <td> 0.648313</td>\n <td> 0.001905</td>\n <td> 0.646408</td>\n <td> NaN</td>\n <td> 0.084531</td>\n <td>-0.356526</td>\n <td>-0.235599</td>\n <td> 0.761128</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>14 </th>\n <td> 2.596477</td>\n <td> 0.150729</td>\n <td> NaN</td>\n <td> 2.445748</td>\n <td> 0.514282</td>\n <td>-0.136966</td>\n <td>-0.537316</td>\n <td> 1.234130</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>15 </th>\n <td> 2.644818</td>\n <td> 0.159300</td>\n <td> NaN</td>\n <td> 2.485518</td>\n <td> 0.050967</td>\n <td> 0.150995</td>\n <td>-0.501137</td>\n <td>-1.630020</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>16 </th>\n <td> 3.561031</td>\n <td> 0.003633</td>\n <td> NaN</td>\n <td> 3.557399</td>\n <td> 0.002421</td>\n <td>-0.361212</td>\n <td>-0.957757</td>\n <td>-1.517170</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>17 </th>\n <td> 0.326576</td>\n <td> 0.056354</td>\n <td> 0.270222</td>\n <td> NaN</td>\n <td> 0.207658</td>\n <td>-0.477727</td>\n <td>-1.271940</td>\n <td> 0.165383</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>18 </th>\n <td> 1.028985</td>\n <td> 0.044087</td>\n <td> 0.984898</td>\n <td> NaN</td>\n <td> 1.246412</td>\n <td>-0.189764</td>\n <td> 2.146958</td>\n <td> 0.761128</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>19 </th>\n <td> 1.423473</td>\n <td> 0.012313</td>\n <td> 1.411160</td>\n <td> NaN</td>\n <td> 0.491884</td>\n <td>-0.123693</td>\n <td>-0.486693</td>\n <td>-0.358574</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>20 </th>\n <td> 0.038964</td>\n <td> 0.018734</td>\n <td> 0.020230</td>\n <td> NaN</td>\n <td> 0.000119</td>\n <td>-0.358959</td>\n <td> 1.757088</td>\n <td>-0.250485</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>21 </th>\n <td> 0.394884</td>\n <td> 0.012919</td>\n <td> 0.381965</td>\n <td> NaN</td>\n <td> 0.612963</td>\n <td>-0.260295</td>\n <td> 0.388244</td>\n <td> 0.443793</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>22 </th>\n <td> 0.037251</td>\n <td> 0.015745</td>\n <td> 0.021506</td>\n <td> NaN</td>\n <td> 0.016037</td>\n <td>-0.238985</td>\n <td>-0.747880</td>\n <td> 0.979644</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>23 </th>\n <td> 2.303942</td>\n <td> 0.012627</td>\n <td> 2.291315</td>\n <td> NaN</td>\n <td> 0.033129</td>\n <td>-0.087818</td>\n <td>-0.832301</td>\n <td>-0.358574</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>24 </th>\n <td> 0.383950</td>\n <td> 0.095711</td>\n <td> 0.288238</td>\n <td> NaN</td>\n <td> 0.098923</td>\n <td>-0.011932</td>\n <td> 1.308046</td>\n <td> 0.761128</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>25 </th>\n <td> 0.130075</td>\n <td> 0.003686</td>\n <td> 0.126389</td>\n <td> NaN</td>\n <td> 0.084236</td>\n <td> 0.129601</td>\n <td>-0.032645</td>\n <td> 0.201340</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>26 </th>\n <td> 0.255410</td>\n <td> 0.104805</td>\n <td> 0.150605</td>\n <td> NaN</td>\n <td> 0.168313</td>\n <td>-0.430657</td>\n <td>-0.215267</td>\n <td>-0.250485</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>27 </th>\n <td> 0.448255</td>\n <td> 0.064404</td>\n <td> 0.383851</td>\n <td> NaN</td>\n <td> 0.068534</td>\n <td>-0.176108</td>\n <td> 0.710819</td>\n <td> 0.761128</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>28 </th>\n <td> 0.482777</td>\n <td> 0.075748</td>\n <td> 0.407029</td>\n <td> NaN</td>\n <td> 0.061439</td>\n <td>-0.507403</td>\n <td> 0.493257</td>\n <td> 0.443793</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>29 </th>\n <td> 0.583948</td>\n <td> 0.073615</td>\n <td> 0.510333</td>\n <td> NaN</td>\n <td> 0.040075</td>\n <td> 0.158187</td>\n <td> 0.161241</td>\n <td> 0.979644</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>30 </th>\n <td> 0.055166</td>\n <td> 0.003266</td>\n <td> 0.051899</td>\n <td> NaN</td>\n <td> 0.065008</td>\n <td>-0.025786</td>\n <td>-0.071459</td>\n <td> 0.201340</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>...</th>\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 </tr>\n <tr>\n <th rowspan=\"30\" valign=\"top\">4294967298</th>\n <th>240</th>\n <td> 4.740174</td>\n <td> 0.449317</td>\n <td> NaN</td>\n <td> 4.290857</td>\n <td> 0.344661</td>\n <td>-0.269404</td>\n <td>-0.268549</td>\n <td> 0.619370</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>241</th>\n <td> 3.978024</td>\n <td> 0.118481</td>\n <td> NaN</td>\n <td> 3.859542</td>\n <td> 0.976365</td>\n <td>-0.407884</td>\n <td>-0.831550</td>\n <td> 0.619370</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>242</th>\n <td> 3.452059</td>\n <td> 0.186282</td>\n <td> NaN</td>\n <td> 3.265777</td>\n <td> 0.828436</td>\n <td>-0.508247</td>\n <td> 0.253198</td>\n <td> 0.162496</td>\n <td> 0.191642</td>\n </tr>\n <tr>\n <th>243</th>\n <td> 0.417471</td>\n <td> 0.208754</td>\n <td> 0.208717</td>\n <td> NaN</td>\n <td> 0.012376</td>\n <td>-0.504630</td>\n <td>-0.517383</td>\n <td> 0.443793</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>244</th>\n <td> 2.443289</td>\n <td> 0.019956</td>\n <td> NaN</td>\n <td> 2.423333</td>\n <td> 0.309896</td>\n <td>-0.847634</td>\n <td>-0.596147</td>\n <td>-1.191180</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>245</th>\n <td> 0.019082</td>\n <td> 0.008125</td>\n <td> 0.010956</td>\n <td> NaN</td>\n <td> 0.013838</td>\n <td> 0.079334</td>\n <td> 0.284252</td>\n <td> 0.201340</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>246</th>\n <td> 0.962801</td>\n <td> 0.007121</td>\n <td> 0.955679</td>\n <td> NaN</td>\n <td> 0.000004</td>\n <td> 0.145112</td>\n <td>-0.666483</td>\n <td> 0.165383</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>247</th>\n <td> NaN</td>\n <td> NaN</td>\n <td> NaN</td>\n <td> NaN</td>\n <td> 0.004413</td>\n <td> 0.540807</td>\n <td>-0.122814</td>\n <td> 0.173326</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>248</th>\n <td> 0.227479</td>\n <td> 0.107047</td>\n <td> 0.120432</td>\n <td> NaN</td>\n <td> 0.073280</td>\n <td>-0.333456</td>\n <td> 0.159415</td>\n <td> 0.761128</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>249</th>\n <td> 2.470933</td>\n <td> 0.059415</td>\n <td> NaN</td>\n <td> 2.411518</td>\n <td> 0.034304</td>\n <td>-1.534370</td>\n <td> 0.442499</td>\n <td> 0.619370</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>250</th>\n <td> 2.301918</td>\n <td> 0.000482</td>\n <td> NaN</td>\n <td> 2.301436</td>\n <td> 0.485663</td>\n <td>-1.949321</td>\n <td> 1.919370</td>\n <td>-1.191180</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>251</th>\n <td> 0.636847</td>\n <td> 0.000030</td>\n <td> 0.636817</td>\n <td> NaN</td>\n <td> 0.033697</td>\n <td>-0.780162</td>\n <td>-0.259157</td>\n <td> 0.165383</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>252</th>\n <td> 0.052720</td>\n <td> 0.012307</td>\n <td> 0.040413</td>\n <td> NaN</td>\n <td> 0.099188</td>\n <td>-0.111913</td>\n <td>-0.033058</td>\n <td> 0.201340</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>253</th>\n <td> 3.427858</td>\n <td> 0.000125</td>\n <td> NaN</td>\n <td> 3.427732</td>\n <td> 0.206410</td>\n <td>-0.182746</td>\n <td>-0.111733</td>\n <td> 1.234130</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>254</th>\n <td> 0.138352</td>\n <td> 0.008968</td>\n <td> 0.129384</td>\n <td> NaN</td>\n <td> 0.283981</td>\n <td> 0.638144</td>\n <td> 0.926719</td>\n <td> 1.080600</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>255</th>\n <td> 0.271421</td>\n <td> 0.002089</td>\n <td> 0.269331</td>\n <td> NaN</td>\n <td> 0.061324</td>\n <td>-0.459537</td>\n <td>-1.105778</td>\n <td> 0.165383</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>256</th>\n <td> 2.644857</td>\n <td> 0.000002</td>\n <td> NaN</td>\n <td> 2.644855</td>\n <td> 0.034885</td>\n <td>-1.218309</td>\n <td> 0.336546</td>\n <td>-1.191180</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>257</th>\n <td> 2.441294</td>\n <td> 0.000089</td>\n <td> NaN</td>\n <td> 2.441205</td>\n <td> 0.043497</td>\n <td>-1.430593</td>\n <td> 0.495951</td>\n <td>-1.191180</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>258</th>\n <td> 2.722745</td>\n <td> 0.035252</td>\n <td> NaN</td>\n <td> 2.687493</td>\n <td> 0.042097</td>\n <td>-1.603858</td>\n <td> 0.674321</td>\n <td>-1.630020</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>259</th>\n <td> 0.430576</td>\n <td> 0.000168</td>\n <td> 0.430408</td>\n <td> NaN</td>\n <td> 0.032542</td>\n <td>-0.470244</td>\n <td>-0.166085</td>\n <td> 0.165383</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>260</th>\n <td> 0.313999</td>\n <td> 0.026600</td>\n <td> 0.287399</td>\n <td> NaN</td>\n <td> 0.026820</td>\n <td>-1.261095</td>\n <td>-0.979981</td>\n <td> 1.080600</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>261</th>\n <td> 0.091507</td>\n <td> 0.032129</td>\n <td> 0.059379</td>\n <td> NaN</td>\n <td> 0.031812</td>\n <td> 0.191408</td>\n <td>-0.415607</td>\n <td> 0.201340</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>262</th>\n <td> 1.355400</td>\n <td> 0.402955</td>\n <td> 0.952445</td>\n <td> NaN</td>\n <td> 0.021373</td>\n <td>-0.315569</td>\n <td> 0.513188</td>\n <td> 0.761128</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>263</th>\n <td> 0.128155</td>\n <td> 0.007633</td>\n <td> 0.120522</td>\n <td> NaN</td>\n <td> 0.044180</td>\n <td>-1.523705</td>\n <td>-0.601065</td>\n <td>-0.250485</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>264</th>\n <td> 0.209232</td>\n <td> 0.003300</td>\n <td> 0.205933</td>\n <td> NaN</td>\n <td> 0.170198</td>\n <td>-0.507001</td>\n <td>-0.512832</td>\n <td> 0.443793</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>265</th>\n <td> 3.156909</td>\n <td> 0.508314</td>\n <td> 2.648594</td>\n <td> NaN</td>\n <td> 0.109325</td>\n <td>-0.298296</td>\n <td>-0.552645</td>\n <td>-0.324360</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>266</th>\n <td> 1.328400</td>\n <td> 0.016535</td>\n <td> 1.311864</td>\n <td> NaN</td>\n <td> 0.013423</td>\n <td>-0.552644</td>\n <td>-0.746187</td>\n <td> 1.080600</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>267</th>\n <td> 3.462925</td>\n <td> 0.734232</td>\n <td> 2.728692</td>\n <td> NaN</td>\n <td> 0.014817</td>\n <td>-0.020396</td>\n <td>-0.372945</td>\n <td>-0.358574</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>268</th>\n <td> 3.180541</td>\n <td> 0.205535</td>\n <td> NaN</td>\n <td> 2.975006</td>\n <td> 0.021037</td>\n <td>-0.276916</td>\n <td> 0.299735</td>\n <td>-1.517170</td>\n <td> NaN</td>\n </tr>\n <tr>\n <th>269</th>\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>-0.691644</td>\n <td> NaN</td>\n <td> NaN</td>\n </tr>\n </tbody>\n</table>\n<p>1505 rows \u00d7 9 columns</p>\n</div>"}, "metadata": {}}], "metadata": {"scrolled": true, "collapsed": false, "trusted": true}}, {"execution_count": null, "cell_type": "code", "source": "", "outputs": [], "metadata": {"collapsed": true, "trusted": true}}], "nbformat": 4, "metadata": {"kernelspec": {"display_name": "Python 2", "name": "python2", "language": "python"}, "language_info": {"mimetype": "text/x-python", "nbconvert_exporter": "python", "version": "2.7.9", "name": "python", "file_extension": ".py", "pygments_lexer": "ipython2", "codemirror_mode": {"version": 2, "name": "ipython"}}, "css": [""]}}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment