{
"metadata": {
"name": "",
"signature": "sha256:14562dd98124ccc1b2c6c1aaeebd7dfb0540432751c05a49b7e6fa000defe67c"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Initialization Download and process all of the data fom Firehose
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Style-Sheet\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"####Notebook Summary\n",
"\n",
"Here we are downloading and processing most of the necissary data to run this analysis pipeline. I have set up a series of scripts to do this in an automated fashon in order to allow for reproduction of this study by others as well as for updating the results obtained here as more TCGA data is collected and reseased. \n",
"\n",
"Downloading this data can take a considerable amount of time (~5 hours) and disk space (~45GB), be prepared. \n",
"\n",
"We use the firehose_get script provided by the Broad to download the data, please see the [firehose_get documentation](ttps://confluence.broadinstitute.org/display/GDAC/Download) for troubleshooting. As we are making using the Broad's initial processing pipeline and data formats we can not promise this initial code will not break upon future update that they make. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%pylab inline"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cd ../src/"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"/cellar/users/agross/TCGA_Code/TCGA/src\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Parameters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Change __OUT_PATH__ to directory on your machine where you want to store the data"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"OUT_PATH = '../Data'"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"RUN_DATE = '2014_01_15'\n",
"CANCER = 'HNSC'\n",
"VERSION = 'all'\n",
"DESCRIPTION = '''Updating analysis for updated dataset.'''\n",
"\n",
"PARAMETERS = {'min_patients' : 12,\n",
" 'pathway_file' : '../Extra_Data/c2.cp.v3.0.symbols_edit.csv'\n",
" }"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"global_params = 'OUT_PATH\\t{}\\n'.format(OUT_PATH)\n",
"global_params += 'RUN_DATE\\t{}\\n'.format(RUN_DATE)\n",
"global_params += 'VERSION\\t{}\\n'.format(VERSION)\n",
"global_params += 'CANCER\\t{}'.format(CANCER)\n",
"f = open('../global_params.txt', 'wb')\n",
"f.write(global_params)\n",
"f.close()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Initialization"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pickle as pickle\n",
"import pandas as pd\n",
"import os as os \n",
"\n",
"from Data.Containers import Run\n",
"\n",
"from Data.Containers import get_run\n",
"from Data.Containers import Cancer\n",
"from Initialization.InitializeCN import initialize_cn\n",
"from Initialization.InitializeReal import initialize_real\n",
"from Initialization.InitializeMut import initialize_mut\n",
"from Initialization.PreprocessMethylation import process_meth"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!curl http://gdac.broadinstitute.org/runs/code/firehose_get_latest.zip -o fh_get.zip\n",
"!unzip fh_get.zip"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" % Total % Received % Xferd Average Speed Time Time Time Current\r\n",
" Dload Upload Total Spent Left Speed\r\n",
"\r",
" 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
"100 6542 100 6542 0 0 22697 0 --:--:-- --:--:-- --:--:-- 22715\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Archive: fh_get.zip\r\n",
" inflating: firehose_get \r\n"
]
}
],
"prompt_number": 7
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Download data"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"d = 'http://gdac.broadinstitute.org/runs/analyses__{}/ingested_data.tsv'.format(RUN_DATE)\n",
"tab = pd.read_table(d, sep='\\n', header=None)\n",
"skip = tab[0].dropna().apply(lambda s: s.startswith('#'))\n",
"skip = list(skip[skip==True].index)\n",
"tab = pd.read_table(d, skiprows=skip, index_col=0).dropna()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cancers = tab[tab.Clinical>0].index[:-1]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Takes about 5 min to download data."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cancer_string = ' '.join(cancers)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!firehose_get -b analyses $RUN_DATE $cancer_string > tmp"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!firehose_get -b -o miR_gene_expression stddata $RUN_DATE $cancer_string > tmp"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!firehose_get -b -o RSEM_genes_normalized stddata $RUN_DATE $cancer_string > tmp"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!firehose_get -b -o rppa stddata $RUN_DATE $cancer_string > tmp"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!firehose_get -b -o clinical stddata $RUN_DATE $cancer_string > tmp"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Takes about 25 min to download methylation data."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!firehose_get -b -o humanmethylation450 stddata $RUN_DATE HNSC"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Validating run selection against Broad Institute website ...\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"You've asked to download archives for the following tasks\r\n",
"\r\n",
" humanmethylation450\r\n",
"\r\n",
"run against the disease cohorts\r\n",
"\r\n",
" HNSC\r\n",
"\r\n",
"from the stddata__2014_01_15 Firehose run. \r\n",
"Attempting to retrieve data for Broad GDAC run stddata__2014_01_15 ...\r\n",
"--2014-02-12 19:57:42-- http://gdac.broadinstitute.org/runs/stddata__2014_01_15/data/HNSC/\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 0K 100% 28.2M=0s\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"2014-02-12 19:57:42 ERROR 404: Not Found.\r\n",
"--2014-02-12 19:57:42-- http://gdac.broadinstitute.org/runs/stddata__2014_01_15/data/HNSC/20140115/\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 0K 101K=0.4s\r\n",
"--2014-02-12 19:57:42-- http://gdac.broadinstitute.org/runs/stddata__2014_01_15/data/HNSC/20140115/gdac.broadinstitute.org_HNSC.Merge_methylation__humanmethylation450__jhu_usc_edu__Level_3__within_bioassay_data_set_function__data.Level_3.2014011500.0.0.tar.gz\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Saving to: `stddata__2014_01_15/HNSC/20140115/gdac.broadinstitute.org_HNSC.Merge_methylation__humanmethylation450__jhu_usc_edu__Level_3__within_bioassay_data_set_function__data.Level_3.2014011500.0.0.tar.gz'\r\n"
]
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{
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},
{
"output_type": "stream",
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]
},
{
"output_type": "stream",
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]
},
{
"output_type": "stream",
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"221184K ........ ........ ........ ........ ........ ........ 11% 942K 29m21s\r\n"
]
},
{
"output_type": "stream",
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]
},
{
"output_type": "stream",
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]
},
{
"output_type": "stream",
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]
},
{
"output_type": "stream",
"stream": "stdout",
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"233472K ........ ........ ........ ........ ........ ........ 12% 998K 29m7s\r\n"
]
},
{
"output_type": "stream",
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"236544K ........ ........ ........ ........ ........ ........ 12% 1021K 29m3s\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"239616K ........ ........ ........ ........ ........ ........ 12% 997K 29m0s\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
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"242688K ........ ........ ........ ........ ........ ........ 12% 996K 28m57s\r\n"
]
},
{
"output_type": "stream",
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"245760K ........ ........ ........ ........ ........ ........ 12% 1.00M 28m53s\r\n"
]
},
{
"output_type": "stream",
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"248832K ........ ........ ........ ........ ........ ........ 12% 1.00M 28m49s\r\n"
]
},
{
"output_type": "stream",
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"251904K ........ ........ ........ ........ ........ ........ 12% 991K 28m46s\r\n"
]
},
{
"output_type": "stream",
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"254976K ........ ........ ........ ........ ........ ........ 13% 994K 28m43s\r\n"
]
},
{
"output_type": "stream",
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"258048K ........ ........ ........ ........ ........ ........ 13% 995K 28m40s\r\n"
]
},
{
"output_type": "stream",
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"text": [
"261120K ........ ........ ........ ........ ........ ........ 13% 995K 28m37s\r\n"
]
},
{
"output_type": "stream",
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"text": [
"264192K ........ ........ ........ ........ ........ ........ 13% 1.02M 28m33s\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"267264K ........ ........ ........ ........ ........ ........ 13% 995K 28m30s\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"270336K ........ ........ ........ ........ ........ ........ 13% 1021K 28m26s\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"273408K ........ ........ ........ ........ ........ ........ 14% 1021K 28m23s\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"276480K ........ ........ ........ ........ ........ ........ 14% 969K 28m20s\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"279552K ........ ........ ........ ........ ........ ........ 14% 1023K 28m16s\r\n"
]
},
{
"output_type": "stream",
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"282624K ........ ........ ........ ........ ........ ........ 14% 990K 28m13s\r\n"
]
},
{
"output_type": "stream",
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"285696K ........ ........ ........ ........ ........ ........ 14% 1020K 28m10s\r\n"
]
},
{
"output_type": "stream",
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"288768K ........ ........ ........ ........ ........ ........ 14% 1022K 28m6s\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"291840K ........ ........ ........ ........ ........ ........ 14% 998K 28m3s\r\n"
]
},
{
"output_type": "stream",
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"294912K ........ ........ ........ ........ ........ ........ 15% 1.00M 27m59s\r\n"
]
},
{
"output_type": "stream",
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"text": [
"297984K ........ ........ ........ ........ ........ ........ 15% 1019K 27m56s\r\n"
]
},
{
"output_type": "stream",
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"text": [
"301056K ........ ........ ........ ........ ........ ........ 15% 995K 27m53s\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"304128K ........ ........ ........ ........ ........ ........ 15% 1.00M 27m49s\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"307200K ........ ........ ........ ........ ........ ........ 15% 1019K 27m46s\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"310272K ........ ........ ........ ........ ........ ........ 15% 996K 27m43s\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"313344K ........ ........ ........ ........ ........ ........ 16% 963K 27m40s\r\n"
]
},
{
"output_type": "stream",
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"text": [
"316416K ........ ........ ........ ........ ........ ........ 16% 993K 27m37s\r\n"
]
},
{
"output_type": "stream",
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"text": [
"319488K ........ ........ ........ ........ ........ ........ 16% 1015K 27m34s\r\n"
]
},
{
"output_type": "stream",
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"text": [
"322560K ........ ........ ........ ........ ........ ........ 16% 996K 27m31s\r\n"
]
},
{
"output_type": "stream",
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"text": [
"325632K ........ ........ ........ ........ ........ ........ 16% 997K 27m28s\r\n"
]
},
{
"output_type": "stream",
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"text": [
"328704K ........ ........ ........ ........ ........ ........ 16% 1023K 27m24s\r\n"
]
},
{
"output_type": "stream",
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"331776K ........ ........ ........ ........ ........ ........ 17% 1022K 27m21s\r\n"
]
},
{
"output_type": "stream",
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"334848K ........ ........ ........ ........ ........ ........ 17% 995K 27m18s\r\n"
]
},
{
"output_type": "stream",
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"337920K ........ ........ ........ ........ ........ ........ 17% 1023K 27m14s\r\n"
]
},
{
"output_type": "stream",
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]
},
{
"output_type": "stream",
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"344064K ........ ........ ........ ........ ........ ........ 17% 1022K 27m7s\r\n"
]
},
{
"output_type": "stream",
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]
},
{
"output_type": "stream",
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"text": [
"350208K ........ ........ ........ ........ ........ ........ 17% 971K 27m2s\r\n"
]
},
{
"output_type": "stream",
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"353280K ........ ........ ........ ........ ........ ........ 18% 1015K 26m58s\r\n"
]
},
{
"output_type": "stream",
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"text": [
"356352K ........ ........ ........ ........ ........ ........ 18% 997K 26m55s\r\n"
]
},
{
"output_type": "stream",
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"text": [
"359424K ........ ........ ........ ........ ........ ........ 18% 1.00M 26m52s\r\n"
]
},
{
"output_type": "stream",
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"text": [
"362496K ........ ........ ........ ........ ........ ........ 18% 1009K 26m49s\r\n"
]
},
{
"output_type": "stream",
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"365568K ........ ........ ........ ........ ........ ........ 18% 1023K 26m45s\r\n"
]
},
{
"output_type": "stream",
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"368640K ........ ........ ........ ........ ........ ........ 18% 1023K 26m42s\r\n"
]
},
{
"output_type": "stream",
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"371712K ........ ........ ........ ........ ........ ........ 19% 954K 26m39s\r\n"
]
},
{
"output_type": "stream",
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"374784K ........ ........ ........ ........ ........ ........ 19% 1020K 26m36s\r\n"
]
},
{
"output_type": "stream",
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"377856K ........ ........ ........ ........ ........ ........ 19% 1021K 26m32s\r\n"
]
},
{
"output_type": "stream",
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"380928K ........ ........ ........ ........ ........ ........ 19% 995K 26m29s\r\n"
]
},
{
"output_type": "stream",
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]
},
{
"output_type": "stream",
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"387072K ........ ........ ........ ........ ........ ........ 19% 1008K 26m23s\r\n"
]
},
{
"output_type": "stream",
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"390144K ........ ........ ........ ........ ........ ........ 19% 996K 26m20s\r\n"
]
},
{
"output_type": "stream",
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"393216K ........ ........ ........ ........ ........ ........ 20% 1022K 26m17s\r\n"
]
},
{
"output_type": "stream",
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"text": [
"396288K ........ ........ ........ ........ ........ ........ 20% 995K 26m14s\r\n"
]
},
{
"output_type": "stream",
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"text": [
"399360K ........ ........ ........ ........ ........ ........ 20% 996K 26m11s\r\n"
]
},
{
"output_type": "stream",
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"402432K ........ ........ ........ ........ ........ ........ 20% 994K 26m8s\r\n"
]
},
{
"output_type": "stream",
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"405504K ........ ........ ........ ........ ........ ........ 20% 1020K 26m4s\r\n"
]
},
{
"output_type": "stream",
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"408576K ........ ........ ........ ........ ........ ........ 20% 997K 26m1s\r\n"
]
},
{
"output_type": "stream",
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"411648K ........ ........ ........ ........ ........ ........ 21% 1022K 25m58s\r\n"
]
},
{
"output_type": "stream",
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"414720K ........ ........ ........ ........ ........ ........ 21% 1022K 25m54s\r\n"
]
},
{
"output_type": "stream",
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]
},
{
"output_type": "stream",
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"420864K ........ ........ ........ ........ ........ ........ 21% 994K 25m48s\r\n"
]
},
{
"output_type": "stream",
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"423936K ........ ........ ........ ........ ........ ........ 21% 981K 25m45s\r\n"
]
},
{
"output_type": "stream",
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"427008K ........ ........ ........ ........ ........ ........ 21% 1.03M 25m42s\r\n"
]
},
{
"output_type": "stream",
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"430080K ........ ........ ........ ........ ........ ........ 21% 997K 25m39s\r\n"
]
},
{
"output_type": "stream",
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]
},
{
"output_type": "stream",
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"436224K ........ ........ ........ ........ ........ ........ 22% 998K 25m32s\r\n"
]
},
{
"output_type": "stream",
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]
},
{
"output_type": "stream",
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"442368K ........ ........ ........ ........ ........ ........ 22% 1024K 25m26s\r\n"
]
},
{
"output_type": "stream",
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"445440K ........ ........ ........ ........ ........ ........ 22% 995K 25m23s\r\n"
]
},
{
"output_type": "stream",
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"448512K ........ ........ ........ ........ ........ ........ 22% 1021K 25m19s\r\n"
]
},
{
"output_type": "stream",
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"451584K ........ ........ ........ ........ ........ ........ 23% 995K 25m16s\r\n"
]
},
{
"output_type": "stream",
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]
},
{
"output_type": "stream",
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"457728K ........ ........ ........ ........ ........ ........ 23% 1009K 25m10s\r\n"
]
},
{
"output_type": "stream",
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]
},
{
"output_type": "stream",
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"463872K ........ ........ ........ ........ ........ ........ 23% 1015K 25m4s\r\n"
]
},
{
"output_type": "stream",
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"466944K ........ ........ ........ ........ ........ ........ 23% 1021K 25m1s\r\n"
]
},
{
"output_type": "stream",
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]
},
{
"output_type": "stream",
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"473088K ........ ........ ........ ........ ........ ........ 24% 1013K 24m54s\r\n"
]
},
{
"output_type": "stream",
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]
},
{
"output_type": "stream",
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]
},
{
"output_type": "stream",
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"482304K ........ ........ ........ ........ ........ ........ 24% 1001K 24m45s\r\n"
]
},
{
"output_type": "stream",
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"485376K ........ ........ ........ ........ ........ ........ 24% 1.02M 24m41s\r\n"
]
},
{
"output_type": "stream",
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]
},
{
"output_type": "stream",
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"491520K ........ ........ ........ ........ ........ ........ 25% 971K 24m36s\r\n"
]
},
{
"output_type": "stream",
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"494592K ........ ........ ........ ........ ........ ........ 25% 1021K 24m32s\r\n"
]
},
{
"output_type": "stream",
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]
},
{
"output_type": "stream",
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"500736K ........ ........ ........ ........ ........ ........ 25% 997K 24m27s\r\n"
]
},
{
"output_type": "stream",
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]
},
{
"output_type": "stream",
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]
},
{
"output_type": "stream",
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"509952K ........ ........ ........ ........ ........ ........ 26% 1022K 24m17s\r\n"
]
},
{
"output_type": "stream",
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"513024K ........ ........ ........ ........ ........ ........ 26% 1022K 24m14s\r\n"
]
},
{
"output_type": "stream",
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]
},
{
"output_type": "stream",
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]
},
{
"output_type": "stream",
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]
},
{
"output_type": "stream",
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]
},
{
"output_type": "stream",
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]
},
{
"output_type": "stream",
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"531456K ........ ........ ........ ........ ........ ........ 27% 1.00M 23m55s\r\n"
]
},
{
"output_type": "stream",
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"534528K ........ ........ ........ ........ ........ ........ 27% 1013K 23m52s\r\n"
]
},
{
"output_type": "stream",
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"537600K ........ ........ ........ ........ ........ ........ 27% 995K 23m49s\r\n"
]
},
{
"output_type": "stream",
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"540672K ........ ........ ........ ........ ........ ........ 27% 1016K 23m45s\r\n"
]
},
{
"output_type": "stream",
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"543744K ........ ........ ........ ........ ........ ........ 27% 997K 23m42s\r\n"
]
},
{
"output_type": "stream",
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"546816K ........ ........ ........ ........ ........ ........ 27% 1003K 23m39s\r\n"
]
},
{
"output_type": "stream",
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"549888K ........ ........ ........ ........ ........ ........ 28% 993K 23m36s\r\n"
]
},
{
"output_type": "stream",
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"552960K ........ ........ ........ ........ ........ ........ 28% 974K 23m33s\r\n"
]
},
{
"output_type": "stream",
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"556032K ........ ........ ........ ........ ........ ........ 28% 994K 23m30s\r\n"
]
},
{
"output_type": "stream",
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"559104K ........ ........ ........ ........ ........ ........ 28% 997K 23m27s\r\n"
]
},
{
"output_type": "stream",
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"text": [
"562176K ........ ........ ........ ........ ........ ........ 28% 993K 23m24s\r\n"
]
},
{
"output_type": "stream",
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"text": [
"565248K ........ ........ ........ ........ ........ ........ 28% 1024K 23m21s\r\n"
]
},
{
"output_type": "stream",
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"568320K ........ ........ ........ ........ ........ ........ 29% 996K 23m18s\r\n"
]
},
{
"output_type": "stream",
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"571392K ........ ........ ........ ........ ........ ........ 29% 1.00M 23m15s\r\n"
]
},
{
"output_type": "stream",
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"574464K ........ ........ ........ ........ ........ ........ 29% 998K 23m12s\r\n"
]
},
{
"output_type": "stream",
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"577536K ........ ........ ........ ........ ........ ........ 29% 1018K 23m8s\r\n"
]
},
{
"output_type": "stream",
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"text": [
"580608K ........ ........ ........ ........ ........ ........ 29% 968K 23m6s\r\n"
]
},
{
"output_type": "stream",
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"text": [
"583680K ........ ........ ........ ........ ........ ........ 29% 1023K 23m2s\r\n"
]
},
{
"output_type": "stream",
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"text": [
"586752K ........ ........ ........ ........ ........ ........ 29% 999K 22m59s\r\n"
]
},
{
"output_type": "stream",
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"text": [
"589824K ........ ........ ........ ........ ........ ........ 30% 1.02M 22m56s\r\n"
]
},
{
"output_type": "stream",
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"text": [
"592896K ........ ........ ........ ........ ........ ........ 30% 996K 22m53s\r\n"
]
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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"1950720K ........ ........ ........ ........ ........ ........ 99% 971K 15s\r\n"
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"1966080K ........ ........ ........ ........ ........ ........ 99% 997K 0s\r\n",
"1969152K 100% 7.69M=32m51s\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"--2014-02-12 20:30:34-- http://gdac.broadinstitute.org/runs/stddata__2014_01_15/data/HNSC/20140115/gdac.broadinstitute.org_HNSC.Merge_methylation__humanmethylation450__jhu_usc_edu__Level_3__within_bioassay_data_set_function__data.Level_3.2014011500.0.0.tar.gz.md5\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Saving to: `stddata__2014_01_15/HNSC/20140115/gdac.broadinstitute.org_HNSC.Merge_methylation__humanmethylation450__jhu_usc_edu__Level_3__within_bioassay_data_set_function__data.Level_3.2014011500.0.0.tar.gz.md5'\r\n",
" 0K 100% 18.8M=0s\r\n",
"--2014-02-12 20:30:34-- http://gdac.broadinstitute.org/runs/stddata__2014_01_15/data/HNSC/20140115/gdac.broadinstitute.org_HNSC.Merge_methylation__humanmethylation450__jhu_usc_edu__Level_3__within_bioassay_data_set_function__data.aux.2014011500.0.0.tar.gz\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Saving to: `stddata__2014_01_15/HNSC/20140115/gdac.broadinstitute.org_HNSC.Merge_methylation__humanmethylation450__jhu_usc_edu__Level_3__within_bioassay_data_set_function__data.aux.2014011500.0.0.tar.gz'\r\n",
" 0K 100% 131M=0s\r\n",
"--2014-02-12 20:30:34-- http://gdac.broadinstitute.org/runs/stddata__2014_01_15/data/HNSC/20140115/gdac.broadinstitute.org_HNSC.Merge_methylation__humanmethylation450__jhu_usc_edu__Level_3__within_bioassay_data_set_function__data.aux.2014011500.0.0.tar.gz.md5\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Saving to: `stddata__2014_01_15/HNSC/20140115/gdac.broadinstitute.org_HNSC.Merge_methylation__humanmethylation450__jhu_usc_edu__Level_3__within_bioassay_data_set_function__data.aux.2014011500.0.0.tar.gz.md5'\r\n",
" 0K 100% 28.0M=0s\r\n",
"--2014-02-12 20:30:34-- http://gdac.broadinstitute.org/runs/stddata__2014_01_15/data/HNSC/20140115/gdac.broadinstitute.org_HNSC.Merge_methylation__humanmethylation450__jhu_usc_edu__Level_3__within_bioassay_data_set_function__data.mage-tab.2014011500.0.0.tar.gz\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Saving to: `stddata__2014_01_15/HNSC/20140115/gdac.broadinstitute.org_HNSC.Merge_methylation__humanmethylation450__jhu_usc_edu__Level_3__within_bioassay_data_set_function__data.mage-tab.2014011500.0.0.tar.gz'\r\n",
" 0K 100% 548M=0s\r\n",
"--2014-02-12 20:30:34-- http://gdac.broadinstitute.org/runs/stddata__2014_01_15/data/HNSC/20140115/gdac.broadinstitute.org_HNSC.Merge_methylation__humanmethylation450__jhu_usc_edu__Level_3__within_bioassay_data_set_function__data.mage-tab.2014011500.0.0.tar.gz.md5\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Saving to: `stddata__2014_01_15/HNSC/20140115/gdac.broadinstitute.org_HNSC.Merge_methylation__humanmethylation450__jhu_usc_edu__Level_3__within_bioassay_data_set_function__data.mage-tab.2014011500.0.0.tar.gz.md5'\r\n",
" 0K 100% 15.5M=0s\r\n",
"FINISHED --2014-02-12 20:30:34--\r\n",
"Total wall clock time: 32m 53s\r\n",
"Downloaded: 8 files, 1.9G in 32m 52s (999 KB/s)\r\n",
"\r\n",
"\r\n",
"Now performing post-processing on retrieved files ...\r\n"
]
}
],
"prompt_number": 15
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Clean Up Downloads"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"No going back from here, so I would check your data to make sure everything got downloaded correctly."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!rm fh_get.zip\n",
"!rm firehose_get"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"if not os.path.isdir(OUT_PATH):\n",
" os.makedirs(OUT_PATH)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"analyses_folder = 'analyses__' + RUN_DATE\n",
"!mv $analyses_folder {OUT_PATH + '/' + analyses_folder}"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"stddata_folder = 'stddata__' + RUN_DATE\n",
"!mv $stddata_folder {OUT_PATH + '/' + stddata_folder}"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 19
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Exctract data and set up file hierarchy for downstream analysis"
]
},
{
"cell_type": "code",
"collapsed": true,
"input": [
"from Initialization.ProcessFirehose import process_all_cancers\n",
"\n",
"process_all_cancers(OUT_PATH, RUN_DATE)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 20
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get rid of all of the downloaded zip files that we processed"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!rm -rf {OUT_PATH + '/' + stddata_folder}\n",
"!rm -rf {OUT_PATH + '/' + analyses_folder}"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ls"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\u001b[0m\u001b[01;34mData\u001b[0m/ \u001b[01;34mFigures\u001b[0m/ \u001b[01;34mInitialization\u001b[0m/ \u001b[01;34mProcessing\u001b[0m/ \u001b[01;34mStats\u001b[0m/ tmp\r\n"
]
}
],
"prompt_number": 21
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Create Run Object for Firehose Download"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data_path = '{}/Firehose__{}/'.format(OUT_PATH, RUN_DATE)\n",
"result_path = data_path + 'ucsd_analyses/'"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cancer_codes = pd.read_table('../Extra_Data/diseaseStudy.txt',\n",
" index_col=0, squeeze=True)\n",
"run_dir = 'http://gdac.broadinstitute.org/runs'\n",
"f = '{}/analyses__{}/ingested_data.tsv'.format(run_dir, RUN_DATE)\n",
"sample_matrix = pd.read_table(f, index_col=0).dropna()\n",
"sample_matrix = sample_matrix.ix[[c for c in sample_matrix.index if \n",
" c not in ['PANCAN12', 'COADREAD','Totals']]]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 101
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"run = Run(RUN_DATE, VERSION, data_path, result_path, PARAMETERS, \n",
" cancer_codes, sample_matrix, DESCRIPTION)\n",
"run.save()\n",
"run"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 119,
"text": [
"Run object for TCGA Analysis\n",
"Firehose run date: 2014_01_15\n",
"Code version: all\n",
"Comment: Updating analysis for updated dataset."
]
}
],
"prompt_number": 119
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Initialize Data Objects into the File Hierarchy"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def init(c, run):\n",
" try:\n",
" cancer_obj = Cancer(c, run) \n",
" cancer_obj.initialize_data(run, save=True)\n",
" except:\n",
" print c + '\\t' + 'all'\n",
" try:\n",
" initialize_real(c, run.report_path, 'mRNASeq', \n",
" create_meta_features=True)\n",
" except:\n",
" print c + '\\t' + 'mRNASeq'\n",
" try:\n",
" initialize_real(c, run.report_path, 'RPPA', \n",
" create_meta_features=True, create_real_features=False)\n",
" except:\n",
" print c + '\\t' + 'RPPA'\n",
" try:\n",
" initialize_real(c, run.report_path, 'miRNASeq', \n",
" create_meta_features=False)\n",
" except:\n",
" print c + '\\t' + 'miRNASeq'\n",
" try:\n",
" initialize_cn(c, run.report_path, 'CN_broad')\n",
" except:\n",
" print c + '\\t' + 'CN' \n",
" try:\n",
" initialize_mut(c, run.report_path, create_meta_features=True);\n",
" except:\n",
" print c + '\\t' + 'mut' "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 123
},
{
"cell_type": "code",
"collapsed": true,
"input": [
"for cancer in run.cancers:\n",
" init(cancer, run)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"ACC\tmRNASeq\n",
"ACC\tRPPA"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"ACC\tmiRNASeq"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"/cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas-0.13.0_247_g82bcbb8-py2.7-linux-x86_64.egg/pandas/io/parsers.py:1059: DtypeWarning: Columns (1,2,4,5,7,8,10,11,13,14,16,17,19,20,22,23,25,26,28,29,31,32,34,35,37,38,40,41,43,44,46,47,49,50,52,53,55,56,58,59,61,62,64,65,67,68,70,71,73,74,76,77,79,80,82,83,85,86,88,89,91,92,94,95,97,98,100,101,103,104,106,107,109,110,112,113,115,116,118,119,121,122,124,125,127,128,130,131,133,134,136,137,139,140,142,143,145,146,148,149,151,152,154,155,157,158,160,161,163,164,166,167,169,170,172,173,175,176,178,179,181,182,184,185,187,188,190,191,193,194,196,197,199,200,202,203,205,206,208,209,211,212,214,215,217,218,220,221,223,224,226,227,229,230,232,233,235,236,238,239,241,242,244,245,247,248,250,251,253,254,256,257,259,260,262,263,265,266,268,269,271,272,274,275,277,278,280,281,283,284,286,287,289,290,292,293,295,296,298,299,301,302,304,305,307,308,310,311,313,314,316,317,319,320,322,323,325,326,328,329,331,332,334,335,337,338,340,341,343,344,346,347,349,350,352,353,355,356,358,359,361,362,364,365,367,368,370,371,373,374,376,377,379,380,382,383,385,386,388,389,391,392,394,395,397,398,400,401,403,404,406,407,409,410,412,413,415,416,418,419,421,422,424,425,427,428,430,431,433,434,436,437,439,440,442,443,445,446,448,449,451,452,454,455,457,458,460,461,463,464,466,467,469,470,472,473,475,476,478,479,481,482,484,485,487,488,490,491,493,494,496,497,499,500,502,503,505,506,508,509,511,512,514,515,517,518,520,521,523,524,526,527,529,530,532,533,535,536,538,539,541,542,544,545,547,548,550,551,553,554,556,557,559,560,562,563,565,566,568,569,571,572,574,575,577,578,580,581,583,584,586,587,589,590,592,593,595,596,598,599,601,602,604,605,607,608,610,611,613,614,616,617,619,620,622,623,625,626,628,629,631,632,634,635,637,638,640,641,643,644,646,647,649,650,652,653,655,656,658,659,661,662,664,665,667,668,670,671,673,674,676,677,679,680,682,683,685,686,688,689,691,692) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" data = self._reader.read(nrows)\n",
"/cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas-0.13.0_247_g82bcbb8-py2.7-linux-x86_64.egg/pandas/io/parsers.py:1059: DtypeWarning: Columns (1,2,4,5,7,8,10,11,13,14,16,17,19,20,22,23,25,26,28,29,31,32,34,35,37,38,40,41,43,44,46,47,49,50,52,53,55,56,58,59,61,62,64,65,67,68,70,71,73,74,76,77,79,80,82,83,85,86,88,89,91,92,94,95,97,98,100,101,103,104,106,107,109,110,112,113,115,116,118,119,121,122,124,125,127,128,130,131,133,134,136,137,139,140,142,143,145,146,148,149,151,152,154,155,157,158,160,161,163,164,166,167,169,170,172,173,175,176,178,179,181,182,184,185,187,188,190,191,193,194,196,197,199,200,202,203,205,206,208,209,211,212,214,215,217,218,220,221,223,224,226,227,229,230,232,233,235,236,238,239,241,242,244,245,247,248,250,251,253,254,256,257,259,260,262,263,265,266,268,269,271,272,274,275,277,278,280,281,283,284,286,287,289,290,292,293,295,296,298,299,301,302,304,305,307,308,310,311,313,314,316,317,319,320,322,323,325,326,328,329,331,332,334,335,337,338,340,341,343,344,346,347,349,350,352,353,355,356,358,359,361,362,364,365,367,368,370,371,373,374,376,377,379,380,382,383,385,386,388,389,391,392,394,395,397,398,400,401,403,404,406,407,409,410,412,413,415,416,418,419,421,422,424,425,427,428,430,431,433,434,436,437,439,440,442,443,445,446,448,449,451,452,454,455,457,458,460,461,463,464,466,467,469,470,472,473,475,476,478,479,481,482,484,485,487,488,490,491,493,494,496,497,499,500,502,503,505,506,508,509,511,512,514,515,517,518,520,521,523,524,526,527,529,530,532,533,535,536,538,539,541,542,544,545,547,548,550,551,553,554,556,557,559,560,562,563,565,566,568,569,571,572,574,575,577,578,580,581,583,584,586,587,589,590,592,593,595,596,598,599,601,602,604,605,607,608,610,611,613,614,616,617,619,620,622,623,625,626,628,629,631,632,634,635,637,638,640,641,643,644,646,647,649,650,652,653,655,656,658,659,661,662,664,665,667,668,670,671,673,674,676,677,679,680,682,683,685,686,688,689,691,692,694,695,697,698,700,701,703,704,706,707,709,710,712,713,715,716,718,719,721,722,724,725,727,728,730,731,733,734,736,737,739,740,742,743,745,746,748,749,751,752,754,755,757,758,760,761,763,764,766,767,769,770,772,773,775,776,778,779,781,782,784,785,787,788,790,791,793,794,796,797,799,800,802,803,805,806,808,809,811,812,814,815,817,818,820,821,823,824,826,827,829,830,832,833,835,836,838,839,841,842,844,845,847,848,850,851,853,854,856,857,859,860,862,863,865,866,868,869,871,872,874,875,877,878,880,881,883,884,886,887,889,890,892,893,895,896,898,899,901,902,904,905,907,908,910,911,913,914,916,917,919,920,922,923,925,926,928,929,931,932,934,935,937,938,940,941,943,944,946,947,949,950,952,953,955,956,958,959,961,962,964,965,967,968,970,971,973,974,976,977,979,980,982,983,985,986,988,989,991,992,994,995,997,998,1000,1001,1003,1004,1006,1007,1009,1010,1012,1013) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" data = self._reader.read(nrows)\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"CESC\tRPPA"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"DLBC\tmRNASeq"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"DLBC\tRPPA"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"DLBC\tmiRNASeq"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"DLBC\tmut"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"ESCA\tmRNASeq"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"ESCA\tRPPA"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"ESCA\tmut"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"GBM\tmiRNASeq"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"/cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas-0.13.0_247_g82bcbb8-py2.7-linux-x86_64.egg/pandas/io/parsers.py:1059: DtypeWarning: Columns (1,2,4,5,7,8,10,11,13,14,16,17,19,20,22,23,25,26,28,29,31,32,34,35,37,38,40,41,43,44,46,47,49,50,52,53,55,56,58,59,61,62,64,65,67,68,70,71,73,74,76,77,79,80,82,83,85,86,88,89,91,92,94,95,97,98,100,101,103,104,106,107,109,110,112,113,115,116,118,119,121,122,124,125,127,128,130,131,133,134,136,137,139,140,142,143,145,146,148,149,151,152,154,155,157,158,160,161,163,164,166,167,169,170,172,173,175,176,178,179,181,182,184,185,187,188,190,191,193,194,196,197,199,200,202,203,205,206,208,209,211,212,214,215,217,218,220,221,223,224,226,227,229,230,232,233,235,236,238,239,241,242,244,245,247,248,250,251,253,254,256,257,259,260,262,263,265,266,268,269,271,272,274,275,277,278,280,281,283,284,286,287,289,290,292,293,295,296,298,299,301,302,304,305,307,308,310,311,313,314,316,317,319,320,322,323,325,326,328,329,331,332,334,335,337,338,340,341,343,344,346,347,349,350,352,353,355,356,358,359,361,362,364,365,367,368,370,371,373,374,376,377,379,380,382,383,385,386,388,389,391,392,394,395,397,398,400,401,403,404,406,407,409,410,412,413,415,416,418,419,421,422,424,425,427,428,430,431,433,434,436,437,439,440,442,443,445,446,448,449,451,452,454,455,457,458,460,461,463,464,466,467,469,470,472,473,475,476,478,479,481,482,484,485,487,488,490,491,493,494,496,497,499,500,502,503,505,506,508,509,511,512,514,515,517,518,520,521,523,524,526,527,529,530,532,533,535,536,538,539,541,542,544,545,547,548,550,551,553,554,556,557,559,560,562,563,565,566,568,569,571,572,574,575,577,578,580,581,583,584,586,587,589,590,592,593,595,596,598,599,601,602,604,605,607,608,610,611,613,614,616,617,619,620,622,623,625,626,628,629,631,632,634,635,637,638,640,641,643,644,646,647,649,650,652,653,655,656,658,659,661,662,664,665,667,668,670,671,673,674,676,677,679,680,682,683,685,686,688,689,691,692,694,695,697,698,700,701,703,704,706,707,709,710,712,713,715,716,718,719,721,722,724,725,727,728,730,731,733,734,736,737,739,740,742,743,745,746,748,749,751,752,754,755,757,758,760,761,763,764,766,767,769,770,772,773,775,776,778,779,781,782,784,785,787,788,790,791,793,794,796,797,799,800,802,803,805,806,808,809,811,812,814,815,817,818,820,821,823,824,826,827,829,830,832,833,835,836,838,839,841,842,844,845,847,848,850,851,853,854,856,857,859,860,862,863,865,866,868,869,871,872,874,875,877,878,880,881,883,884,886,887,889,890,892,893,895,896,898,899,901,902,904,905,907,908,910,911,913,914,916,917,919,920,922,923,925,926,928,929,931,932,934,935,937,938,940,941,943,944,946,947,949,950,952,953,955,956,958,959,961,962,964,965,967,968,970,971,973,974,976,977,979,980,982,983,985,986,988,989,991,992,994,995,997,998,1000,1001,1003,1004,1006,1007,1009,1010,1012,1013,1015,1016,1018,1019,1021,1022,1024,1025,1027,1028,1030,1031,1033,1034,1036,1037,1039,1040,1042,1043,1045,1046,1048,1049,1051,1052,1054,1055,1057,1058,1060,1061,1063,1064,1066,1067,1069,1070,1072,1073,1075,1076,1078,1079,1081,1082,1084,1085,1087,1088,1090,1091,1093,1094,1096,1097,1099,1100,1102,1103,1105,1106,1108,1109,1111,1112,1114,1115,1117,1118,1120,1121,1123,1124,1126,1127,1129,1130,1132,1133,1135,1136,1138,1139,1141,1142,1144,1145,1147,1148,1150,1151,1153,1154,1156,1157,1159,1160,1162,1163,1165,1166,1168,1169,1171,1172,1174,1175,1177,1178,1180,1181,1183,1184,1186,1187,1189,1190,1192,1193,1195,1196,1198,1199,1201,1202,1204,1205,1207,1208,1210,1211,1213,1214,1216,1217,1219,1220,1222,1223,1225,1226,1228,1229,1231,1232,1234,1235,1237,1238,1240,1241,1243,1244,1246,1247,1249,1250,1252,1253,1255,1256,1258,1259,1261,1262,1264,1265,1267,1268,1270,1271,1273,1274,1276,1277,1279,1280,1282,1283,1285,1286,1288,1289,1291,1292,1294,1295,1297,1298,1300,1301,1303,1304,1306,1307,1309,1310,1312,1313,1315,1316,1318,1319,1321,1322,1324,1325,1327,1328,1330,1331,1333,1334,1336,1337,1339,1340,1342,1343,1345,1346,1348,1349,1351,1352,1354,1355,1357,1358,1360,1361,1363,1364,1366,1367,1369,1370,1372,1373,1375,1376,1378,1379,1381,1382,1384,1385,1387,1388,1390,1391,1393,1394,1396,1397,1399,1400,1402,1403,1405,1406,1408,1409,1411,1412,1414,1415,1417,1418,1420,1421,1423,1424,1426,1427,1429,1430,1432,1433,1435,1436,1438,1439,1441,1442,1444,1445,1447,1448,1450,1451,1453,1454,1456,1457,1459,1460,1462,1463,1465,1466,1468,1469,1471,1472,1474,1475,1477,1478,1480,1481,1483,1484,1486,1487,1489,1490,1492,1493,1495,1496,1498,1499,1501,1502,1504,1505,1507,1508,1510,1511,1513,1514,1516,1517,1519,1520,1522,1523,1525,1526,1528,1529,1531,1532,1534,1535,1537,1538,1540,1541,1543,1544,1546,1547,1549,1550,1552,1553,1555,1556,1558,1559,1561,1562,1564,1565,1567,1568,1570,1571,1573,1574,1576,1577,1579,1580,1582,1583,1585,1586,1588,1589,1591,1592,1594,1595,1597,1598,1600,1601,1603,1604,1606,1607,1609,1610,1612,1613,1615,1616,1618,1619,1621,1622,1624,1625,1627,1628,1630,1631,1633,1634,1636,1637,1639,1640,1642,1643,1645,1646,1648,1649,1651,1652,1654,1655,1657,1658,1660,1661,1663,1664,1666,1667,1669,1670,1672,1673,1675,1676,1678,1679,1681,1682,1684,1685,1687,1688,1690,1691,1693,1694,1696,1697,1699,1700,1702,1703,1705,1706,1708,1709,1711,1712,1714,1715,1717,1718,1720,1721,1723,1724,1726,1727,1729,1730,1732,1733,1735,1736,1738,1739,1741,1742,1744,1745,1747,1748,1750,1751,1753,1754,1756,1757,1759,1760,1762,1763,1765,1766,1768,1769,1771,1772,1774,1775,1777,1778,1780,1781,1783,1784,1786,1787,1789,1790,1792,1793,1795,1796,1798,1799,1801,1802,1804,1805,1807,1808,1810,1811,1813,1814,1816,1817,1819,1820,1822,1823,1825,1826,1828,1829,1831,1832,1834,1835,1837,1838,1840,1841,1843,1844,1846,1847,1849,1850,1852,1853,1855,1856,1858,1859,1861,1862,1864,1865,1867,1868,1870,1871,1873,1874,1876,1877,1879,1880,1882,1883,1885,1886,1888,1889,1891,1892,1894,1895,1897,1898,1900,1901,1903,1904,1906,1907,1909,1910,1912,1913,1915,1916,1918,1919,1921,1922,1924,1925,1927,1928,1930,1931,1933,1934,1936,1937,1939,1940,1942,1943,1945,1946,1948,1949,1951,1952,1954,1955,1957,1958,1960,1961,1963,1964,1966,1967,1969,1970,1972,1973,1975,1976,1978,1979,1981,1982,1984,1985,1987,1988,1990,1991,1993,1994,1996,1997,1999,2000,2002,2003,2005,2006,2008,2009,2011,2012,2014,2015,2017,2018,2020,2021,2023,2024,2026,2027,2029,2030,2032,2033,2035,2036,2038,2039,2041,2042,2044,2045,2047,2048,2050,2051,2053,2054,2056,2057,2059,2060,2062,2063,2065,2066,2068,2069,2071,2072,2074,2075,2077,2078,2080,2081,2083,2084,2086,2087,2089,2090,2092,2093,2095,2096,2098,2099,2101,2102,2104,2105,2107,2108,2110,2111,2113,2114,2116,2117,2119,2120,2122,2123,2125,2126,2128,2129,2131,2132,2134,2135,2137,2138,2140,2141,2143,2144,2146,2147,2149,2150,2152,2153,2155,2156,2158,2159,2161,2162,2164,2165,2167,2168,2170,2171,2173,2174,2176,2177,2179,2180,2182,2183,2185,2186,2188,2189,2191,2192,2194,2195,2197,2198,2200,2201,2203,2204,2206,2207,2209,2210,2212,2213,2215,2216,2218,2219,2221,2222,2224,2225,2227,2228,2230,2231,2233,2234,2236,2237,2239,2240,2242,2243,2245,2246,2248,2249,2251,2252,2254,2255,2257,2258,2260,2261,2263,2264,2266,2267,2269,2270,2272,2273,2275,2276,2278,2279,2281,2282,2284,2285,2287,2288,2290,2291,2293,2294,2296,2297) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" data = self._reader.read(nrows)\n",
"/cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas-0.13.0_247_g82bcbb8-py2.7-linux-x86_64.egg/pandas/io/parsers.py:1059: DtypeWarning: Columns (1,2,4,5,7,8,10,11,13,14,16,17,19,20,22,23,25,26,28,29,31,32,34,35,37,38,40,41,43,44,46,47,49,50,52,53,55,56,58,59,61,62,64,65,67,68,70,71,73,74,76,77,79,80,82,83,85,86,88,89,91,92,94,95,97,98,100,101,103,104,106,107,109,110,112,113,115,116,118,119,121,122,124,125,127,128,130,131,133,134,136,137,139,140,142,143,145,146,148,149,151,152,154,155,157,158,160,161,163,164,166,167,169,170,172,173,175,176,178,179,181,182,184,185,187,188,190,191,193,194,196,197,199,200,202,203,205,206,208,209,211,212,214,215,217,218,220,221,223,224,226,227,229,230,232,233,235,236,238,239,241,242,244,245,247,248,250,251,253,254,256,257,259,260,262,263,265,266,268,269,271,272,274,275,277,278,280,281,283,284,286,287,289,290,292,293,295,296,298,299,301,302,304,305,307,308,310,311,313,314,316,317,319,320,322,323,325,326,328,329,331,332,334,335,337,338,340,341,343,344,346,347,349,350,352,353,355,356,358,359,361,362,364,365,367,368,370,371,373,374,376,377,379,380,382,383,385,386,388,389,391,392,394,395,397,398,400,401,403,404,406,407,409,410,412,413,415,416,418,419,421,422,424,425,427,428,430,431,433,434,436,437,439,440,442,443,445,446,448,449,451,452,454,455,457,458,460,461,463,464,466,467,469,470,472,473,475,476,478,479,481,482,484,485,487,488,490,491,493,494,496,497,499,500,502,503,505,506,508,509,511,512,514,515,517,518,520,521,523,524,526,527,529,530,532,533,535,536,538,539,541,542,544,545,547,548,550,551,553,554,556,557,559,560,562,563,565,566,568,569,571,572,574,575,577,578,580,581,583,584,586,587,589,590,592,593,595,596,598,599,601,602,604,605,607,608,610,611,613,614,616,617,619,620,622,623,625,626,628,629,631,632,634,635,637,638,640,641,643,644,646,647,649,650,652,653,655,656,658,659,661,662,664,665,667,668,670,671,673,674,676,677,679,680,682,683,685,686,688,689,691,692,694,695,697,698,700,701,703,704,706,707,709,710,712,713,715,716,718,719,721,722,724,725,727,728,730,731,733,734,736,737,739,740,742,743,745,746,748,749,751,752,754,755,757,758,760,761,763,764,766,767,769,770,772,773,775,776,778,779,781,782,784,785,787,788,790,791,793,794,796,797,799,800,802,803,805,806,808,809,811,812,814,815,817,818,820,821,823,824,826,827,829,830,832,833,835,836,838,839,841,842,844,845,847,848,850,851,853,854,856,857,859,860,862,863,865,866,868,869,871,872,874,875,877,878,880,881,883,884,886,887,889,890,892,893,895,896,898,899,901,902,904,905,907,908,910,911,913,914,916,917,919,920,922,923,925,926,928,929,931,932,934,935,937,938,940,941,943,944,946,947,949,950,952,953,955,956,958,959,961,962,964,965,967,968,970,971,973,974,976,977,979,980,982,983,985,986,988,989,991,992,994,995,997,998,1000,1001,1003,1004,1006,1007,1009,1010,1012,1013,1015,1016,1018,1019,1021,1022,1024,1025,1027,1028,1030,1031,1033,1034,1036,1037,1039,1040,1042,1043,1045,1046,1048,1049,1051,1052,1054,1055,1057,1058,1060,1061,1063,1064,1066,1067,1069,1070,1072,1073,1075,1076,1078,1079,1081,1082,1084,1085,1087,1088,1090,1091,1093,1094,1096,1097,1099,1100,1102,1103,1105,1106,1108,1109,1111,1112,1114,1115,1117,1118,1120,1121,1123,1124,1126,1127,1129,1130,1132,1133,1135,1136,1138,1139,1141,1142,1144,1145,1147,1148,1150,1151,1153,1154,1156,1157,1159,1160,1162,1163,1165,1166,1168,1169,1171,1172,1174,1175,1177,1178,1180,1181,1183,1184,1186,1187,1189,1190,1192,1193,1195,1196,1198,1199,1201,1202,1204,1205,1207,1208,1210,1211,1213,1214,1216,1217,1219,1220,1222,1223,1225,1226,1228,1229,1231,1232,1234,1235,1237,1238,1240,1241,1243,1244,1246,1247,1249,1250,1252,1253,1255,1256,1258,1259,1261,1262,1264,1265,1267,1268,1270,1271,1273,1274,1276,1277,1279,1280,1282,1283,1285,1286,1288,1289,1291,1292,1294,1295,1297,1298,1300,1301,1303,1304,1306,1307,1309,1310,1312,1313,1315,1316,1318,1319,1321,1322,1324,1325,1327,1328,1330,1331,1333,1334,1336,1337,1339,1340,1342,1343,1345,1346,1348,1349,1351,1352,1354,1355,1357,1358,1360,1361,1363,1364,1366,1367,1369,1370,1372,1373,1375,1376,1378,1379,1381,1382,1384,1385,1387,1388) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" data = self._reader.read(nrows)\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"KICH\tRPPA"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"/cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas-0.13.0_247_g82bcbb8-py2.7-linux-x86_64.egg/pandas/io/parsers.py:1059: DtypeWarning: Columns (1,2,4,5,7,8,10,11,13,14,16,17,19,20,22,23,25,26,28,29,31,32,34,35,37,38,40,41,43,44,46,47,49,50,52,53,55,56,58,59,61,62,64,65,67,68,70,71,73,74,76,77,79,80,82,83,85,86,88,89,91,92,94,95,97,98,100,101,103,104,106,107,109,110,112,113,115,116,118,119,121,122,124,125,127,128,130,131,133,134,136,137,139,140,142,143,145,146,148,149,151,152,154,155,157,158,160,161,163,164,166,167,169,170,172,173,175,176,178,179,181,182,184,185,187,188,190,191,193,194,196,197,199,200,202,203,205,206,208,209,211,212,214,215,217,218,220,221,223,224,226,227,229,230,232,233,235,236,238,239,241,242,244,245,247,248,250,251,253,254,256,257,259,260,262,263,265,266,268,269,271,272,274,275,277,278,280,281,283,284,286,287,289,290,292,293,295,296,298,299,301,302,304,305,307,308,310,311,313,314,316,317,319,320,322,323,325,326,328,329,331,332,334,335,337,338,340,341,343,344,346,347,349,350,352,353,355,356,358,359,361,362,364,365,367,368,370,371,373,374,376,377,379,380,382,383,385,386,388,389,391,392,394,395,397,398,400,401,403,404,406,407,409,410,412,413,415,416,418,419,421,422,424,425,427,428,430,431,433,434,436,437,439,440,442,443,445,446,448,449,451,452,454,455,457,458,460,461,463,464,466,467,469,470,472,473,475,476,478,479,481,482,484,485,487,488,490,491,493,494,496,497,499,500,502,503,505,506,508,509,511,512,514,515,517,518,520,521,523,524,526,527,529,530,532,533,535,536,538,539,541,542,544,545,547,548,550,551,553,554,556,557,559,560,562,563,565,566,568,569,571,572,574,575,577,578,580,581,583,584,586,587,589,590,592,593,595,596,598,599,601,602,604,605,607,608,610,611,613,614,616,617,619,620,622,623,625,626,628,629,631,632,634,635,637,638,640,641,643,644,646,647,649,650,652,653,655,656,658,659,661,662,664,665,667,668,670,671,673,674,676,677,679,680,682,683,685,686,688,689,691,692,694,695,697,698,700,701,703,704,706,707,709,710,712,713,715,716,718,719,721,722,724,725,727,728,730,731,733,734,736,737,739,740,742,743,745,746,748,749,751,752,754,755,757,758,760,761,763,764,766,767,769,770,772,773,775,776,778,779,781,782,784,785,787,788,790,791,793,794,796,797,799,800) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" data = self._reader.read(nrows)\n",
"/cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas-0.13.0_247_g82bcbb8-py2.7-linux-x86_64.egg/pandas/io/parsers.py:1059: DtypeWarning: Columns (1,2,4,5,7,8,10,11,13,14,16,17,19,20,22,23,25,26,28,29,31,32,34,35,37,38,40,41,43,44,46,47,49,50,52,53,55,56,58,59,61,62,64,65,67,68,70,71,73,74,76,77,79,80,82,83,85,86,88,89,91,92,94,95,97,98,100,101,103,104,106,107,109,110,112,113,115,116,118,119,121,122,124,125,127,128,130,131,133,134,136,137,139,140,142,143,145,146,148,149,151,152,154,155,157,158,160,161,163,164,166,167,169,170,172,173,175,176,178,179,181,182,184,185,187,188,190,191,193,194,196,197,199,200,202,203,205,206,208,209,211,212,214,215,217,218,220,221,223,224,226,227,229,230,232,233,235,236,238,239,241,242,244,245,247,248,250,251,253,254,256,257,259,260,262,263,265,266,268,269,271,272,274,275,277,278,280,281,283,284,286,287,289,290,292,293,295,296,298,299,301,302,304,305,307,308,310,311,313,314,316,317,319,320,322,323,325,326,328,329,331,332,334,335,337,338,340,341,343,344,346,347,349,350,352,353,355,356,358,359,361,362,364,365,367,368,370,371,373,374,376,377,379,380,382,383,385,386,388,389,391,392,394,395,397,398,400,401,403,404,406,407,409,410,412,413,415,416,418,419,421,422,424,425,427,428,430,431,433,434,436,437,439,440,442,443,445,446,448,449,451,452,454,455,457,458,460,461,463,464,466,467,469,470,472,473,475,476,478,479,481,482,484,485,487,488,490,491,493,494,496,497,499,500,502,503,505,506,508,509,511,512,514,515,517,518,520,521,523,524,526,527,529,530,532,533,535,536,538,539,541,542,544,545,547,548,550,551,553,554,556,557,559,560,562,563,565,566,568,569,571,572,574,575,577,578,580,581,583,584,586,587,589,590,592,593,595,596,598,599,601,602,604,605,607,608,610,611,613,614,616,617,619,620,622,623,625,626,628,629,631,632,634,635,637,638,640,641,643,644,646,647,649,650,652,653,655,656,658,659,661,662,664,665,667,668,670,671,673,674,676,677,679,680,682,683,685,686,688,689,691,692,694,695,697,698,700,701,703,704,706,707,709,710,712,713,715,716,718,719,721,722,724,725,727,728,730,731,733,734,736,737,739,740,742,743,745,746,748,749,751,752,754,755,757,758,760,761,763,764,766,767,769,770,772,773,775,776,778,779,781,782,784,785,787,788,790,791,793,794,796,797,799,800,802,803,805,806,808,809,811,812,814,815,817,818,820,821,823,824,826,827,829,830,832,833,835,836,838,839,841,842,844,845,847,848,850,851,853,854,856,857,859,860,862,863,865,866,868,869,871,872,874,875,877,878,880,881,883,884,886,887,889,890,892,893,895,896,898,899,901,902,904,905) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" data = self._reader.read(nrows)\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"KIRP\tRPPA"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"LAML\tmRNASeq"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"LAML\tRPPA"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"LAML\tmiRNASeq"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"/cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas-0.13.0_247_g82bcbb8-py2.7-linux-x86_64.egg/pandas/io/parsers.py:1059: DtypeWarning: Columns (1,2,4,5,7,8,10,11,13,14,16,17,19,20,22,23,25,26,28,29,31,32,34,35,37,38,40,41,43,44,46,47,49,50,52,53,55,56,58,59,61,62,64,65,67,68,70,71,73,74,76,77,79,80,82,83,85,86,88,89,91,92,94,95,97,98,100,101,103,104,106,107,109,110,112,113,115,116,118,119,121,122,124,125,127,128,130,131,133,134,136,137,139,140,142,143,145,146,148,149,151,152,154,155,157,158,160,161,163,164,166,167,169,170,172,173,175,176,178,179,181,182,184,185,187,188,190,191,193,194,196,197,199,200,202,203,205,206,208,209,211,212,214,215,217,218,220,221,223,224,226,227,229,230,232,233,235,236,238,239,241,242,244,245,247,248,250,251,253,254,256,257,259,260,262,263,265,266,268,269,271,272,274,275,277,278,280,281,283,284,286,287,289,290,292,293,295,296,298,299,301,302,304,305,307,308,310,311,313,314,316,317,319,320,322,323,325,326,328,329,331,332,334,335,337,338,340,341,343,344,346,347,349,350,352,353,355,356,358,359,361,362,364,365,367,368,370,371,373,374,376,377,379,380,382,383,385,386,388,389,391,392,394,395,397,398,400,401,403,404,406,407,409,410,412,413,415,416,418,419,421,422,424,425,427,428,430,431,433,434,436,437,439,440,442,443,445,446,448,449,451,452,454,455,457,458,460,461,463,464,466,467,469,470,472,473,475,476,478,479,481,482,484,485,487,488,490,491,493,494,496,497,499,500,502,503,505,506,508,509,511,512,514,515,517,518,520,521,523,524,526,527,529,530,532,533,535,536,538,539,541,542,544,545,547,548,550,551,553,554,556,557,559,560,562,563,565,566,568,569,571,572,574,575,577,578,580,581,583,584,586,587,589,590,592,593,595,596,598,599,601,602,604,605,607,608,610,611) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" data = self._reader.read(nrows)\n",
"/cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas-0.13.0_247_g82bcbb8-py2.7-linux-x86_64.egg/pandas/io/parsers.py:1059: DtypeWarning: Columns (1,2,4,5,7,8,10,11,13,14,16,17,19,20,22,23,25,26,28,29,31,32,34,35,37,38,40,41,43,44,46,47,49,50,52,53,55,56,58,59,61,62,64,65,67,68,70,71,73,74,76,77,79,80,82,83,85,86,88,89,91,92,94,95,97,98,100,101,103,104,106,107,109,110,112,113,115,116,118,119,121,122,124,125,127,128,130,131,133,134,136,137,139,140,142,143,145,146,148,149,151,152,154,155,157,158,160,161,163,164,166,167,169,170,172,173,175,176,178,179,181,182,184,185,187,188,190,191,193,194,196,197,199,200,202,203,205,206,208,209,211,212,214,215,217,218,220,221,223,224,226,227,229,230,232,233,235,236,238,239,241,242,244,245,247,248,250,251,253,254,256,257,259,260,262,263,265,266,268,269,271,272,274,275,277,278,280,281,283,284,286,287,289,290,292,293,295,296,298,299,301,302,304,305,307,308,310,311,313,314,316,317,319,320,322,323,325,326,328,329,331,332,334,335,337,338,340,341,343,344,346,347,349,350,352,353,355,356,358,359,361,362,364,365,367,368,370,371,373,374,376,377,379,380,382,383,385,386,388,389,391,392,394,395,397,398,400,401,403,404,406,407,409,410,412,413,415,416,418,419,421,422,424,425,427,428,430,431,433,434,436,437,439,440,442,443,445,446,448,449,451,452,454,455,457,458,460,461,463,464,466,467,469,470,472,473,475,476,478,479,481,482,484,485,487,488,490,491,493,494,496,497,499,500,502,503,505,506,508,509,511,512,514,515,517,518,520,521,523,524,526,527,529,530,532,533,535,536,538,539,541,542,544,545,547,548,550,551,553,554,556,557,559,560,562,563,565,566,568,569,571,572,574,575,577,578,580,581,583,584,586,587,589,590,592,593,595,596,598,599,601,602,604,605,607,608,610,611,613,614,616,617,619,620,622,623,625,626,628,629,631,632,634,635,637,638,640,641,643,644,646,647,649,650,652,653,655,656,658,659,661,662,664,665,667,668,670,671,673,674,676,677,679,680,682,683,685,686,688,689,691,692,694,695,697,698,700,701,703,704,706,707,709,710,712,713,715,716,718,719,721,722,724,725,727,728,730,731,733,734,736,737,739,740,742,743,745,746,748,749,751,752,754,755,757,758,760,761,763,764,766,767,769,770,772,773,775,776,778,779,781,782,784,785,787,788,790,791,793,794,796,797,799,800,802,803,805,806,808,809,811,812,814,815,817,818,820,821,823,824,826,827,829,830,832,833,835,836,838,839,841,842,844,845,847,848,850,851,853,854,856,857,859,860,862,863,865,866,868,869,871,872,874,875,877,878,880,881,883,884,886,887,889,890,892,893,895,896,898,899,901,902,904,905,907,908,910,911,913,914,916,917,919,920,922,923,925,926,928,929) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" data = self._reader.read(nrows)\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"LIHC\tRPPA"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"LIHC\tmut"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"/cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas-0.13.0_247_g82bcbb8-py2.7-linux-x86_64.egg/pandas/io/parsers.py:1059: DtypeWarning: Columns (1,2,4,5,7,8,10,11,13,14,16,17,19,20,22,23,25,26,28,29,31,32,34,35,37,38,40,41,43,44,46,47,49,50,52,53,55,56,58,59,61,62,64,65,67,68,70,71,73,74,76,77,79,80,82,83,85,86,88,89,91,92,94,95,97,98,100,101,103,104,106,107,109,110,112,113,115,116,118,119,121,122,124,125,127,128,130,131,133,134,136,137,139,140,142,143,145,146,148,149,151,152,154,155,157,158,160,161,163,164,166,167,169,170,172,173,175,176,178,179,181,182,184,185,187,188,190,191,193,194,196,197,199,200,202,203,205,206,208,209,211,212,214,215,217,218,220,221,223,224,226,227,229,230,232,233,235,236,238,239,241,242,244,245,247,248,250,251,253,254,256,257,259,260,262,263,265,266,268,269,271,272,274,275,277,278,280,281,283,284,286,287,289,290,292,293,295,296,298,299,301,302,304,305,307,308,310,311,313,314,316,317,319,320,322,323,325,326,328,329,331,332,334,335,337,338,340,341,343,344,346,347,349,350,352,353,355,356,358,359,361,362,364,365,367,368,370,371,373,374,376,377,379,380,382,383,385,386,388,389,391,392,394,395,397,398,400,401,403,404,406,407,409,410,412,413,415,416,418,419,421,422,424,425,427,428,430,431,433,434,436,437,439,440,442,443,445,446,448,449,451,452,454,455,457,458,460,461,463,464,466,467,469,470,472,473,475,476,478,479,481,482,484,485,487,488,490,491,493,494,496,497,499,500,502,503,505,506,508,509,511,512,514,515,517,518,520,521,523,524,526,527,529,530,532,533,535,536,538,539,541,542,544,545,547,548,550,551,553,554,556,557,559,560,562,563,565,566,568,569,571,572,574,575,577,578,580,581,583,584,586,587,589,590) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" data = self._reader.read(nrows)\n",
"/cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas-0.13.0_247_g82bcbb8-py2.7-linux-x86_64.egg/pandas/io/parsers.py:1059: DtypeWarning: Columns (1,2,4,5,7,8,10,11,13,14,16,17,19,20,22,23,25,26,28,29,31,32,34,35,37,38,40,41,43,44,46,47,49,50,52,53,55,56,58,59,61,62,64,65,67,68,70,71,73,74,76,77,79,80,82,83,85,86,88,89,91,92,94,95,97,98,100,101,103,104,106,107,109,110,112,113,115,116,118,119,121,122,124,125,127,128,130,131,133,134,136,137,139,140,142,143,145,146,148,149,151,152,154,155,157,158,160,161,163,164,166,167,169,170,172,173,175,176,178,179,181,182,184,185,187,188,190,191,193,194,196,197,199,200,202,203,205,206,208,209,211,212,214,215,217,218,220,221,223,224,226,227,229,230,232,233,235,236,238,239,241,242,244,245,247,248,250,251,253,254,256,257,259,260,262,263,265,266,268,269,271,272,274,275,277,278,280,281,283,284,286,287,289,290,292,293,295,296,298,299,301,302,304,305,307,308,310,311,313,314,316,317,319,320,322,323,325,326,328,329,331,332,334,335,337,338,340,341,343,344,346,347,349,350,352,353,355,356,358,359,361,362,364,365,367,368,370,371,373,374,376,377,379,380,382,383,385,386,388,389,391,392,394,395,397,398,400,401,403,404,406,407,409,410,412,413,415,416,418,419,421,422,424,425,427,428,430,431,433,434,436,437,439,440,442,443,445,446,448,449,451,452,454,455,457,458,460,461,463,464,466,467,469,470,472,473,475,476,478,479,481,482,484,485,487,488,490,491,493,494,496,497,499,500,502,503,505,506,508,509,511,512,514,515,517,518,520,521,523,524,526,527,529,530,532,533,535,536,538,539,541,542,544,545,547,548,550,551,553,554,556,557,559,560,562,563,565,566,568,569,571,572,574,575,577,578,580,581,583,584,586,587,589,590,592,593,595,596,598,599,601,602,604,605,607,608,610,611,613,614,616,617,619,620,622,623,625,626,628,629,631,632,634,635,637,638,640,641,643,644,646,647,649,650,652,653,655,656,658,659,661,662,664,665,667,668,670,671,673,674,676,677,679,680,682,683,685,686,688,689,691,692,694,695,697,698,700,701,703,704,706,707,709,710,712,713,715,716,718,719,721,722,724,725,727,728,730,731,733,734,736,737,739,740,742,743,745,746,748,749,751,752,754,755,757,758,760,761,763,764,766,767,769,770,772,773,775,776,778,779,781,782,784,785,787,788,790,791,793,794,796,797,799,800,802,803,805,806,808,809,811,812,814,815,817,818,820,821,823,824,826,827,829,830,832,833,835,836,838,839,841,842,844,845,847,848,850,851,853,854,856,857,859,860,862,863,865,866,868,869,871,872,874,875,877,878,880,881,883,884,886,887,889,890,892,893,895,896,898,899,901,902,904,905,907,908,910,911,913,914,916,917,919,920,922,923,925,926,928,929,931,932,934,935,937,938,940,941,943,944,946,947,949,950,952,953,955,956,958,959,961,962,964,965,967,968,970,971,973,974,976,977,979,980,982,983,985,986,988,989,991,992,994,995,997,998,1000,1001,1003,1004,1006,1007,1009,1010,1012,1013,1015,1016,1018,1019,1021,1022,1024,1025,1027,1028,1030,1031,1033,1034,1036,1037,1039,1040,1042,1043,1045,1046,1048,1049,1051,1052,1054,1055,1057,1058,1060,1061,1063,1064,1066,1067,1069,1070,1072,1073,1075,1076,1078,1079,1081,1082,1084,1085,1087,1088,1090,1091,1093,1094,1096,1097,1099,1100,1102,1103,1105,1106,1108,1109,1111,1112,1114,1115,1117,1118,1120,1121,1123,1124,1126,1127,1129,1130,1132,1133,1135,1136,1138,1139,1141,1142,1144,1145,1147,1148,1150,1151,1153,1154,1156,1157,1159,1160,1162,1163,1165,1166,1168,1169,1171,1172,1174,1175,1177,1178,1180,1181,1183,1184,1186,1187,1189,1190,1192,1193,1195,1196,1198,1199,1201,1202,1204,1205,1207,1208,1210,1211,1213,1214,1216,1217,1219,1220,1222,1223,1225,1226,1228,1229,1231,1232,1234,1235,1237,1238,1240,1241,1243,1244,1246,1247,1249,1250,1252,1253,1255,1256,1258,1259,1261,1262,1264,1265,1267,1268,1270,1271,1273,1274,1276,1277,1279,1280,1282,1283,1285,1286,1288,1289,1291,1292,1294,1295,1297,1298,1300,1301,1303,1304,1306,1307,1309,1310,1312,1313,1315,1316,1318,1319,1321,1322,1324,1325,1327,1328,1330,1331,1333,1334,1336,1337,1339,1340,1342,1343,1345,1346,1348,1349,1351,1352,1354,1355,1357,1358,1360,1361,1363,1364,1366,1367,1369,1370,1372,1373,1375,1376,1378,1379,1381,1382,1384,1385,1387,1388,1390,1391,1393,1394,1396,1397,1399,1400,1402,1403,1405,1406,1408,1409,1411,1412,1414,1415,1417,1418,1420,1421,1423,1424,1426,1427) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" data = self._reader.read(nrows)\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"/cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas-0.13.0_247_g82bcbb8-py2.7-linux-x86_64.egg/pandas/io/parsers.py:1059: DtypeWarning: Columns (1,2,4,5,7,8,10,11,13,14,16,17,19,20,22,23,25,26,28,29,31,32,34,35,37,38,40,41,43,44,46,47,49,50,52,53,55,56,58,59,61,62,64,65,67,68,70,71,73,74,76,77,79,80,82,83,85,86,88,89,91,92,94,95,97,98,100,101,103,104,106,107,109,110,112,113,115,116,118,119,121,122,124,125,127,128,130,131,133,134,136,137,139,140,142,143,145,146,148,149,151,152,154,155,157,158,160,161,163,164,166,167,169,170,172,173,175,176,178,179,181,182,184,185,187,188,190,191,193,194,196,197,199,200,202,203,205,206,208,209,211,212,214,215,217,218,220,221,223,224,226,227,229,230,232,233,235,236,238,239,241,242,244,245,247,248,250,251,253,254,256,257,259,260,262,263,265,266,268,269,271,272,274,275,277,278,280,281,283,284,286,287,289,290,292,293,295,296,298,299,301,302,304,305,307,308,310,311,313,314,316,317,319,320,322,323,325,326,328,329,331,332,334,335,337,338,340,341,343,344,346,347,349,350,352,353,355,356,358,359,361,362,364,365,367,368,370,371,373,374,376,377,379,380,382,383,385,386,388,389,391,392,394,395,397,398,400,401,403,404,406,407,409,410,412,413,415,416,418,419,421,422,424,425,427,428,430,431,433,434,436,437,439,440,442,443,445,446,448,449,451,452,454,455,457,458,460,461,463,464,466,467,469,470,472,473,475,476,478,479,481,482,484,485,487,488,490,491,493,494,496,497,499,500,502,503,505,506,508,509,511,512,514,515,517,518,520,521,523,524,526,527,529,530,532,533,535,536,538,539,541,542,544,545,547,548,550,551,553,554,556,557,559,560,562,563,565,566,568,569,571,572,574,575,577,578,580,581,583,584,586,587,589,590,592,593,595,596,598,599,601,602,604,605,607,608,610,611,613,614,616,617,619,620,622,623,625,626,628,629,631,632,634,635,637,638,640,641,643,644,646,647,649,650,652,653,655,656,658,659,661,662,664,665,667,668,670,671,673,674,676,677,679,680,682,683,685,686,688,689,691,692,694,695,697,698,700,701,703,704,706,707,709,710,712,713,715,716,718,719,721,722,724,725,727,728,730,731,733,734,736,737,739,740,742,743,745,746,748,749,751,752,754,755,757,758,760,761,763,764,766,767,769,770,772,773,775,776,778,779,781,782,784,785,787,788,790,791,793,794,796,797,799,800,802,803,805,806,808,809,811,812,814,815,817,818,820,821,823,824,826,827,829,830,832,833,835,836,838,839,841,842,844,845,847,848,850,851,853,854,856,857,859,860,862,863,865,866,868,869,871,872,874,875,877,878,880,881,883,884,886,887,889,890,892,893,895,896,898,899,901,902,904,905,907,908,910,911,913,914,916,917,919,920,922,923,925,926,928,929,931,932,934,935,937,938,940,941,943,944,946,947,949,950,952,953,955,956,958,959,961,962,964,965,967,968,970,971,973,974,976,977,979,980,982,983,985,986,988,989,991,992,994,995,997,998,1000,1001,1003,1004,1006,1007,1009,1010,1012,1013,1015,1016,1018,1019,1021,1022,1024,1025,1027,1028,1030,1031,1033,1034,1036,1037,1039,1040,1042,1043,1045,1046,1048,1049,1051,1052,1054,1055,1057,1058,1060,1061,1063,1064,1066,1067,1069,1070,1072,1073,1075,1076,1078,1079,1081,1082,1084,1085,1087,1088,1090,1091,1093,1094,1096,1097,1099,1100,1102,1103,1105,1106,1108,1109,1111,1112,1114,1115,1117,1118,1120,1121,1123,1124,1126,1127) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" data = self._reader.read(nrows)\n",
"/cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas-0.13.0_247_g82bcbb8-py2.7-linux-x86_64.egg/pandas/io/parsers.py:1059: DtypeWarning: Columns (1,2,4,5,7,8,10,11,13,14,16,17,19,20,22,23,25,26,28,29,31,32,34,35,37,38,40,41,43,44,46,47,49,50,52,53,55,56,58,59,61,62,64,65,67,68,70,71,73,74,76,77,79,80,82,83,85,86,88,89,91,92,94,95,97,98,100,101,103,104,106,107,109,110,112,113,115,116,118,119,121,122,124,125,127,128,130,131,133,134,136,137,139,140,142,143,145,146,148,149,151,152,154,155,157,158,160,161,163,164,166,167,169,170,172,173,175,176,178,179,181,182,184,185,187,188,190,191,193,194,196,197,199,200,202,203,205,206,208,209,211,212,214,215,217,218,220,221,223,224,226,227,229,230,232,233,235,236,238,239,241,242,244,245,247,248,250,251,253,254,256,257,259,260,262,263,265,266,268,269,271,272,274,275,277,278,280,281,283,284,286,287,289,290,292,293,295,296,298,299,301,302,304,305,307,308,310,311,313,314,316,317,319,320,322,323,325,326,328,329,331,332,334,335,337,338,340,341,343,344,346,347,349,350,352,353,355,356,358,359,361,362,364,365,367,368,370,371,373,374,376,377,379,380,382,383,385,386,388,389,391,392,394,395,397,398,400,401,403,404,406,407,409,410,412,413,415,416,418,419,421,422,424,425,427,428,430,431,433,434,436,437,439,440,442,443,445,446,448,449,451,452,454,455,457,458,460,461,463,464,466,467,469,470,472,473,475,476,478,479,481,482,484,485,487,488,490,491,493,494,496,497,499,500,502,503,505,506,508,509,511,512,514,515,517,518,520,521,523,524,526,527,529,530,532,533,535,536,538,539,541,542,544,545,547,548,550,551,553,554,556,557,559,560,562,563,565,566,568,569,571,572,574,575,577,578,580,581,583,584,586,587,589,590,592,593,595,596,598,599,601,602,604,605,607,608,610,611,613,614,616,617,619,620,622,623,625,626,628,629,631,632,634,635,637,638,640,641,643,644,646,647,649,650,652,653,655,656,658,659,661,662,664,665,667,668,670,671,673,674,676,677,679,680,682,683,685,686,688,689,691,692,694,695,697,698,700,701,703,704,706,707,709,710,712,713,715,716,718,719,721,722,724,725,727,728,730,731,733,734,736,737,739,740,742,743,745,746,748,749,751,752,754,755,757,758,760,761,763,764,766,767,769,770,772,773,775,776,778,779,781,782,784,785,787,788,790,791,793,794,796,797,799,800,802,803,805,806,808,809,811,812,814,815,817,818,820,821,823,824,826,827,829,830,832,833,835,836,838,839,841,842,844,845,847,848,850,851,853,854,856,857,859,860,862,863,865,866,868,869,871,872,874,875,877,878,880,881,883,884,886,887,889,890,892,893,895,896,898,899,901,902,904,905,907,908,910,911,913,914,916,917,919,920,922,923,925,926,928,929,931,932,934,935,937,938,940,941,943,944,946,947,949,950,952,953,955,956,958,959,961,962,964,965,967,968,970,971,973,974,976,977,979,980,982,983,985,986,988,989,991,992,994,995,997,998,1000,1001,1003,1004,1006,1007,1009,1010,1012,1013,1015,1016,1018,1019,1021,1022,1024,1025,1027,1028,1030,1031,1033,1034,1036,1037,1039,1040,1042,1043,1045,1046,1048,1049,1051,1052,1054,1055,1057,1058,1060,1061,1063,1064,1066,1067,1069,1070,1072,1073,1075,1076,1078,1079,1081,1082,1084,1085,1087,1088,1090,1091,1093,1094,1096,1097,1099,1100,1102,1103,1105,1106,1108,1109,1111,1112,1114,1115,1117,1118,1120,1121,1123,1124,1126,1127,1129,1130,1132,1133,1135,1136,1138,1139,1141,1142,1144,1145,1147,1148,1150,1151,1153,1154,1156,1157,1159,1160,1162,1163,1165,1166,1168,1169,1171,1172,1174,1175,1177,1178,1180,1181,1183,1184,1186,1187,1189,1190,1192,1193,1195,1196,1198,1199,1201,1202,1204,1205,1207,1208,1210,1211,1213,1214,1216,1217,1219,1220,1222,1223,1225,1226,1228,1229,1231,1232,1234,1235,1237,1238,1240,1241,1243,1244,1246,1247,1249,1250,1252,1253,1255,1256,1258,1259,1261,1262,1264,1265,1267,1268,1270,1271,1273,1274,1276,1277,1279,1280,1282,1283,1285,1286,1288,1289,1291,1292,1294,1295,1297,1298,1300,1301,1303,1304,1306,1307,1309,1310,1312,1313,1315,1316,1318,1319,1321,1322,1324,1325,1327,1328,1330,1331,1333,1334,1336,1337,1339,1340,1342,1343,1345,1346,1348,1349,1351,1352,1354,1355,1357,1358,1360,1361,1363,1364,1366,1367,1369,1370,1372,1373,1375,1376,1378,1379,1381,1382) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" data = self._reader.read(nrows)\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"PAAD\tRPPA"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"SARC\tRPPA"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"SARC\tmut"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"/cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas-0.13.0_247_g82bcbb8-py2.7-linux-x86_64.egg/pandas/io/parsers.py:1059: DtypeWarning: Columns (1,2,4,5,7,8,10,11,13,14,16,17,19,20,22,23,25,26,28,29,31,32,34,35,37,38,40,41,43,44,46,47,49,50,52,53,55,56,58,59,61,62,64,65,67,68,70,71,73,74,76,77,79,80,82,83,85,86,88,89,91,92,94,95,97,98,100,101,103,104,106,107,109,110,112,113,115,116,118,119,121,122,124,125,127,128,130,131,133,134,136,137,139,140,142,143,145,146,148,149,151,152,154,155,157,158,160,161,163,164,166,167,169,170,172,173,175,176,178,179,181,182,184,185,187,188,190,191,193,194,196,197,199,200,202,203,205,206,208,209,211,212,214,215,217,218,220,221,223,224,226,227,229,230,232,233,235,236,238,239,241,242,244,245,247,248,250,251,253,254,256,257,259,260,262,263,265,266,268,269,271,272,274,275,277,278,280,281,283,284,286,287,289,290,292,293,295,296,298,299,301,302,304,305,307,308,310,311,313,314,316,317,319,320,322,323,325,326,328,329,331,332,334,335,337,338,340,341,343,344,346,347,349,350,352,353,355,356,358,359,361,362,364,365,367,368,370,371,373,374,376,377,379,380,382,383,385,386,388,389,391,392,394,395,397,398,400,401,403,404,406,407,409,410,412,413,415,416,418,419,421,422,424,425,427,428,430,431,433,434,436,437,439,440,442,443,445,446,448,449,451,452,454,455,457,458,460,461,463,464,466,467,469,470,472,473,475,476,478,479,481,482,484,485,487,488,490,491,493,494,496,497,499,500,502,503,505,506,508,509,511,512,514,515,517,518,520,521,523,524,526,527,529,530,532,533,535,536,538,539,541,542,544,545,547,548,550,551,553,554,556,557,559,560,562,563,565,566,568,569,571,572,574,575,577,578,580,581,583,584,586,587,589,590,592,593,595,596,598,599,601,602,604,605,607,608,610,611,613,614,616,617,619,620,622,623,625,626,628,629,631,632,634,635,637,638,640,641,643,644,646,647,649,650,652,653,655,656,658,659,661,662,664,665,667,668,670,671,673,674,676,677,679,680,682,683,685,686,688,689,691,692,694,695,697,698,700,701,703,704,706,707,709,710,712,713,715,716,718,719,721,722,724,725,727,728,730,731,733,734,736,737,739,740,742,743,745,746,748,749,751,752,754,755,757,758,760,761,763,764,766,767,769,770,772,773,775,776,778,779,781,782,784,785,787,788,790,791,793,794,796,797,799,800,802,803,805,806,808,809,811,812,814,815,817,818,820,821,823,824,826,827,829,830,832,833,835,836,838,839,841,842,844,845,847,848,850,851,853,854,856,857,859,860,862,863,865,866,868,869,871,872,874,875,877,878,880,881,883,884,886,887,889,890,892,893,895,896,898,899,901,902,904,905,907,908,910,911,913,914,916,917,919,920,922,923,925,926) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" data = self._reader.read(nrows)\n",
"/cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas-0.13.0_247_g82bcbb8-py2.7-linux-x86_64.egg/pandas/io/parsers.py:1059: DtypeWarning: Columns (1,2,4,5,7,8,10,11,13,14,16,17,19,20,22,23,25,26,28,29,31,32,34,35,37,38,40,41,43,44,46,47,49,50,52,53,55,56,58,59,61,62,64,65,67,68,70,71,73,74,76,77,79,80,82,83,85,86,88,89,91,92,94,95,97,98,100,101,103,104,106,107,109,110,112,113,115,116,118,119,121,122,124,125,127,128,130,131,133,134,136,137,139,140,142,143,145,146,148,149,151,152,154,155,157,158,160,161,163,164,166,167,169,170,172,173,175,176,178,179,181,182,184,185,187,188,190,191,193,194,196,197,199,200,202,203,205,206,208,209,211,212,214,215,217,218,220,221,223,224,226,227,229,230,232,233,235,236,238,239,241,242,244,245,247,248,250,251,253,254,256,257,259,260,262,263,265,266,268,269,271,272,274,275,277,278,280,281,283,284,286,287,289,290,292,293,295,296,298,299,301,302,304,305,307,308,310,311,313,314,316,317,319,320,322,323,325,326,328,329,331,332,334,335,337,338,340,341,343,344,346,347,349,350,352,353,355,356,358,359,361,362,364,365,367,368,370,371,373,374,376,377,379,380,382,383,385,386,388,389,391,392,394,395,397,398,400,401,403,404,406,407,409,410,412,413,415,416,418,419,421,422,424,425,427,428,430,431,433,434,436,437,439,440,442,443,445,446,448,449,451,452,454,455,457,458,460,461,463,464,466,467,469,470,472,473,475,476,478,479,481,482,484,485,487,488,490,491,493,494,496,497,499,500,502,503,505,506,508,509,511,512,514,515,517,518,520,521,523,524,526,527,529,530,532,533,535,536,538,539,541,542,544,545,547,548,550,551,553,554,556,557,559,560,562,563,565,566,568,569,571,572,574,575,577,578,580,581,583,584,586,587,589,590,592,593,595,596,598,599,601,602,604,605,607,608,610,611,613,614,616,617,619,620,622,623,625,626,628,629,631,632,634,635,637,638,640,641,643,644,646,647,649,650,652,653,655,656,658,659,661,662,664,665,667,668,670,671,673,674,676,677,679,680,682,683,685,686,688,689,691,692,694,695,697,698,700,701,703,704,706,707,709,710,712,713,715,716,718,719,721,722,724,725,727,728,730,731,733,734,736,737,739,740,742,743,745,746,748,749,751,752,754,755,757,758,760,761,763,764,766,767,769,770,772,773,775,776,778,779,781,782,784,785,787,788,790,791,793,794,796,797,799,800,802,803,805,806,808,809,811,812,814,815,817,818,820,821,823,824,826,827,829,830,832,833,835,836,838,839,841,842,844,845,847,848,850,851,853,854,856,857,859,860,862,863,865,866,868,869,871,872,874,875,877,878,880,881,883,884,886,887,889,890,892,893,895,896,898,899,901,902,904,905,907,908,910,911,913,914,916,917,919,920,922,923,925,926,928,929,931,932,934,935,937,938,940,941,943,944,946,947,949,950,952,953,955,956,958,959,961,962,964,965,967,968,970,971,973,974,976,977,979,980,982,983,985,986,988,989,991,992,994,995,997,998,1000,1001,1003,1004,1006,1007,1009,1010,1012,1013,1015,1016,1018,1019,1021,1022,1024,1025,1027,1028,1030,1031,1033,1034) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" data = self._reader.read(nrows)\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"STAD\tmRNASeq"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"STAD\tRPPA"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"/cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas-0.13.0_247_g82bcbb8-py2.7-linux-x86_64.egg/pandas/io/parsers.py:1059: DtypeWarning: Columns (1,2,4,5,7,8,10,11,13,14,16,17,19,20,22,23,25,26,28,29,31,32,34,35,37,38,40,41,43,44,46,47,49,50,52,53,55,56,58,59,61,62,64,65,67,68,70,71,73,74,76,77,79,80,82,83,85,86,88,89,91,92,94,95,97,98,100,101,103,104,106,107,109,110,112,113,115,116,118,119,121,122,124,125,127,128,130,131,133,134,136,137,139,140,142,143,145,146,148,149,151,152,154,155,157,158,160,161,163,164,166,167,169,170,172,173,175,176,178,179,181,182,184,185,187,188,190,191,193,194,196,197,199,200,202,203,205,206,208,209,211,212,214,215,217,218,220,221,223,224,226,227,229,230,232,233,235,236,238,239,241,242,244,245,247,248,250,251,253,254,256,257,259,260,262,263,265,266,268,269,271,272,274,275,277,278,280,281,283,284,286,287,289,290,292,293,295,296,298,299,301,302,304,305,307,308,310,311,313,314,316,317,319,320,322,323,325,326,328,329,331,332,334,335,337,338,340,341,343,344,346,347,349,350,352,353,355,356,358,359,361,362,364,365,367,368,370,371,373,374,376,377,379,380,382,383,385,386,388,389,391,392,394,395,397,398,400,401,403,404,406,407,409,410,412,413,415,416,418,419,421,422,424,425,427,428,430,431,433,434,436,437,439,440,442,443,445,446,448,449,451,452,454,455,457,458,460,461,463,464,466,467,469,470,472,473,475,476,478,479,481,482,484,485,487,488,490,491,493,494,496,497,499,500,502,503,505,506,508,509,511,512,514,515,517,518,520,521,523,524,526,527,529,530,532,533,535,536,538,539,541,542,544,545,547,548,550,551,553,554,556,557,559,560,562,563,565,566,568,569,571,572,574,575,577,578,580,581,583,584,586,587,589,590,592,593,595,596,598,599,601,602,604,605,607,608,610,611,613,614,616,617,619,620,622,623,625,626,628,629,631,632,634,635,637,638,640,641,643,644,646,647,649,650,652,653,655,656,658,659,661,662,664,665,667,668,670,671,673,674,676,677,679,680,682,683,685,686,688,689,691,692,694,695,697,698,700,701,703,704,706,707,709,710,712,713,715,716,718,719,721,722,724,725,727,728,730,731,733,734,736,737,739,740,742,743,745,746,748,749,751,752,754,755,757,758,760,761,763,764,766,767,769,770,772,773,775,776,778,779,781,782,784,785,787,788,790,791,793,794,796,797,799,800,802,803,805,806,808,809,811,812,814,815,817,818,820,821,823,824,826,827,829,830,832,833,835,836,838,839,841,842,844,845,847,848,850,851,853,854,856,857,859,860,862,863,865,866,868,869,871,872,874,875,877,878,880,881,883,884,886,887,889,890,892,893,895,896,898,899,901,902,904,905,907,908,910,911,913,914,916,917,919,920,922,923,925,926,928,929,931,932,934,935,937,938,940,941) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" data = self._reader.read(nrows)\n",
"/cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas-0.13.0_247_g82bcbb8-py2.7-linux-x86_64.egg/pandas/io/parsers.py:1059: DtypeWarning: Columns (1,2,4,5,7,8,10,11,13,14,16,17,19,20,22,23,25,26,28,29,31,32,34,35,37,38,40,41,43,44,46,47,49,50,52,53,55,56,58,59,61,62,64,65,67,68,70,71,73,74,76,77,79,80,82,83,85,86,88,89,91,92,94,95,97,98,100,101,103,104,106,107,109,110,112,113,115,116,118,119,121,122,124,125,127,128,130,131,133,134,136,137,139,140,142,143,145,146,148,149,151,152,154,155,157,158,160,161,163,164,166,167,169,170,172,173,175,176,178,179,181,182,184,185,187,188,190,191,193,194,196,197,199,200,202,203,205,206,208,209,211,212,214,215,217,218,220,221,223,224,226,227,229,230,232,233,235,236,238,239,241,242,244,245,247,248,250,251,253,254,256,257,259,260,262,263,265,266,268,269,271,272,274,275,277,278,280,281,283,284,286,287,289,290,292,293,295,296,298,299,301,302,304,305,307,308,310,311,313,314,316,317,319,320,322,323,325,326,328,329,331,332,334,335,337,338,340,341,343,344,346,347,349,350,352,353,355,356,358,359,361,362,364,365,367,368,370,371,373,374,376,377,379,380,382,383,385,386,388,389,391,392,394,395,397,398,400,401,403,404,406,407,409,410,412,413,415,416,418,419,421,422,424,425,427,428,430,431,433,434,436,437,439,440,442,443,445,446,448,449,451,452,454,455,457,458,460,461,463,464,466,467,469,470,472,473,475,476,478,479,481,482,484,485,487,488,490,491,493,494,496,497,499,500,502,503,505,506,508,509,511,512,514,515,517,518,520,521,523,524,526,527,529,530,532,533,535,536,538,539,541,542,544,545,547,548,550,551,553,554,556,557,559,560,562,563,565,566,568,569,571,572,574,575,577,578,580,581,583,584,586,587,589,590,592,593,595,596,598,599,601,602,604,605,607,608,610,611,613,614,616,617,619,620,622,623,625,626,628,629,631,632,634,635,637,638,640,641,643,644,646,647,649,650,652,653,655,656,658,659,661,662,664,665,667,668,670,671,673,674,676,677,679,680,682,683,685,686,688,689,691,692,694,695,697,698,700,701,703,704,706,707,709,710,712,713,715,716,718,719,721,722,724,725,727,728,730,731,733,734,736,737,739,740,742,743,745,746,748,749,751,752,754,755,757,758,760,761,763,764,766,767,769,770,772,773,775,776,778,779,781,782,784,785,787,788,790,791,793,794,796,797,799,800,802,803,805,806,808,809,811,812,814,815,817,818,820,821,823,824,826,827,829,830,832,833,835,836,838,839,841,842,844,845,847,848,850,851,853,854,856,857,859,860,862,863,865,866,868,869,871,872,874,875,877,878,880,881,883,884,886,887,889,890,892,893,895,896,898,899,901,902,904,905,907,908,910,911,913,914,916,917,919,920,922,923,925,926,928,929,931,932,934,935,937,938,940,941,943,944,946,947,949,950,952,953,955,956,958,959,961,962,964,965,967,968,970,971,973,974,976,977,979,980,982,983,985,986,988,989,991,992,994,995,997,998,1000,1001,1003,1004,1006,1007,1009,1010,1012,1013,1015,1016,1018,1019,1021,1022,1024,1025,1027,1028,1030,1031,1033,1034,1036,1037,1039,1040,1042,1043,1045,1046,1048,1049,1051,1052,1054,1055,1057,1058,1060,1061,1063,1064,1066,1067,1069,1070,1072,1073,1075,1076,1078,1079,1081,1082,1084,1085,1087,1088,1090,1091,1093,1094,1096,1097,1099,1100,1102,1103,1105,1106,1108,1109,1111,1112,1114,1115,1117,1118,1120,1121,1123,1124,1126,1127,1129,1130,1132,1133,1135,1136,1138,1139,1141,1142,1144,1145,1147,1148,1150,1151,1153,1154,1156,1157,1159,1160,1162,1163,1165,1166,1168,1169,1171,1172,1174,1175,1177,1178,1180,1181,1183,1184,1186,1187,1189,1190,1192,1193,1195,1196,1198,1199,1201,1202,1204,1205,1207,1208,1210,1211,1213,1214,1216,1217,1219,1220,1222,1223,1225,1226,1228,1229,1231,1232,1234,1235,1237,1238,1240,1241,1243,1244,1246,1247,1249,1250,1252,1253,1255,1256,1258,1259,1261,1262,1264,1265,1267,1268,1270,1271,1273,1274,1276,1277,1279,1280,1282,1283,1285,1286,1288,1289,1291,1292,1294,1295,1297,1298,1300,1301,1303,1304,1306,1307,1309,1310,1312,1313,1315,1316,1318,1319,1321,1322,1324,1325,1327,1328,1330,1331,1333,1334,1336,1337,1339,1340,1342,1343,1345,1346,1348,1349,1351,1352,1354,1355,1357,1358,1360,1361,1363,1364,1366,1367,1369,1370,1372,1373,1375,1376,1378,1379,1381,1382,1384,1385,1387,1388,1390,1391,1393,1394,1396,1397,1399,1400,1402,1403,1405,1406,1408,1409,1411,1412,1414,1415,1417,1418,1420,1421,1423,1424,1426,1427,1429,1430,1432,1433,1435,1436,1438,1439,1441,1442,1444,1445,1447,1448,1450,1451,1453,1454,1456,1457,1459,1460,1462,1463,1465,1466,1468,1469,1471,1472,1474,1475,1477,1478,1480,1481,1483,1484,1486,1487,1489,1490,1492,1493,1495,1496,1498,1499,1501,1502,1504,1505,1507,1508,1510,1511,1513,1514,1516,1517,1519,1520,1522,1523,1525,1526,1528,1529,1531,1532,1534,1535,1537,1538,1540,1541,1543,1544,1546,1547,1549,1550,1552,1553,1555,1556,1558,1559,1561,1562,1564,1565,1567,1568,1570,1571,1573,1574,1576,1577,1579,1580,1582,1583,1585,1586,1588,1589,1591,1592,1594,1595,1597,1598,1600,1601,1603,1604,1606,1607,1609,1610,1612,1613,1615,1616,1618,1619,1621,1622,1624,1625,1627,1628,1630,1631,1633,1634,1636,1637,1639,1640,1642,1643,1645,1646,1648,1649,1651,1652,1654,1655,1657,1658,1660,1661,1663,1664,1666,1667,1669,1670,1672,1673,1675,1676,1678,1679,1681,1682,1684,1685) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" data = self._reader.read(nrows)\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"/cellar/users/agross/anaconda2/lib/python2.7/site-packages/pandas-0.13.0_247_g82bcbb8-py2.7-linux-x86_64.egg/pandas/io/parsers.py:1059: DtypeWarning: Columns (1,2,4,5,7,8,10,11,13,14,16,17,19,20,22,23,25,26,28,29,31,32,34,35,37,38,40,41,43,44,46,47,49,50,52,53,55,56,58,59,61,62,64,65,67,68,70,71,73,74,76,77,79,80,82,83,85,86,88,89,91,92,94,95,97,98,100,101,103,104,106,107,109,110,112,113,115,116,118,119,121,122,124,125,127,128,130,131,133,134,136,137,139,140,142,143,145,146,148,149,151,152,154,155,157,158,160,161,163,164,166,167,169,170,172,173,175,176,178,179,181,182,184,185,187,188,190,191,193,194,196,197,199,200,202,203,205,206,208,209,211,212,214,215,217,218,220,221,223,224,226,227,229,230,232,233,235,236,238,239,241,242,244,245,247,248,250,251,253,254,256,257,259,260,262,263,265,266,268,269,271,272,274,275,277,278,280,281,283,284,286,287,289,290,292,293,295,296,298,299,301,302,304,305,307,308,310,311,313,314,316,317,319,320,322,323,325,326,328,329,331,332,334,335,337,338,340,341,343,344,346,347,349,350,352,353,355,356,358,359,361,362,364,365,367,368,370,371,373,374,376,377,379,380,382,383,385,386,388,389,391,392,394,395,397,398,400,401,403,404,406,407,409,410,412,413,415,416,418,419,421,422,424,425,427,428,430,431,433,434,436,437,439,440,442,443,445,446,448,449,451,452,454,455,457,458,460,461,463,464,466,467,469,470,472,473,475,476,478,479,481,482,484,485,487,488,490,491,493,494,496,497,499,500,502,503,505,506,508,509,511,512,514,515,517,518,520,521,523,524,526,527,529,530,532,533,535,536,538,539,541,542,544,545,547,548,550,551,553,554,556,557,559,560,562,563,565,566,568,569,571,572,574,575,577,578,580,581,583,584,586,587,589,590,592,593,595,596,598,599,601,602,604,605,607,608,610,611,613,614,616,617,619,620,622,623,625,626,628,629,631,632,634,635,637,638,640,641,643,644,646,647,649,650,652,653,655,656,658,659,661,662,664,665,667,668,670,671,673,674,676,677,679,680,682,683,685,686,688,689,691,692,694,695,697,698,700,701,703,704,706,707,709,710,712,713,715,716,718,719,721,722,724,725,727,728,730,731,733,734,736,737,739,740,742,743,745,746,748,749,751,752,754,755,757,758,760,761,763,764,766,767,769,770,772,773,775,776,778,779,781,782,784,785,787,788,790,791,793,794,796,797,799,800,802,803,805,806,808,809,811,812,814,815,817,818,820,821,823,824,826,827,829,830,832,833,835,836,838,839,841,842,844,845,847,848,850,851,853,854,856,857,859,860,862,863,865,866,868,869,871,872,874,875,877,878,880,881,883,884,886,887,889,890,892,893,895,896,898,899,901,902,904,905,907,908,910,911,913,914,916,917,919,920,922,923,925,926,928,929,931,932,934,935,937,938,940,941,943,944,946,947,949,950,952,953,955,956,958,959,961,962,964,965,967,968,970,971,973,974,976,977,979,980,982,983,985,986,988,989,991,992,994,995,997,998,1000,1001,1003,1004,1006,1007,1009,1010,1012,1013,1015,1016,1018,1019,1021,1022,1024,1025,1027,1028,1030,1031,1033,1034,1036,1037,1039,1040,1042,1043,1045,1046,1048,1049,1051,1052,1054,1055,1057,1058,1060,1061,1063,1064,1066,1067,1069,1070,1072,1073,1075,1076,1078,1079,1081,1082,1084,1085,1087,1088,1090,1091,1093,1094,1096,1097,1099,1100,1102,1103,1105,1106,1108,1109,1111,1112,1114,1115,1117,1118,1120,1121,1123,1124,1126,1127,1129,1130,1132,1133,1135,1136,1138,1139,1141,1142,1144,1145,1147,1148,1150,1151,1153,1154,1156,1157,1159,1160,1162,1163,1165,1166,1168,1169,1171,1172,1174,1175,1177,1178,1180,1181,1183,1184,1186,1187,1189,1190,1192,1193,1195,1196,1198,1199,1201,1202,1204,1205,1207,1208,1210,1211,1213,1214,1216,1217,1219,1220,1222,1223,1225,1226,1228,1229,1231,1232,1234,1235,1237,1238,1240,1241,1243,1244,1246,1247,1249,1250,1252,1253,1255,1256) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" data = self._reader.read(nrows)\n"
]
}
],
"prompt_number": 124
}
],
"metadata": {}
}
]
}