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FrancescAlted/MSFT-Dataset-Query-Complex.ipynb

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A comparison of BLZ with Pandas DataFrames
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MSFT-Dataset-Query-Complex.ipynb
{
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"import pandas as pd"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%time df = pd.read_hdf('msft-18-SEP-2013.h5', '/msft')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"CPU times: user 2.22 s, sys: 538 ms, total: 2.76 s\n",
"Wall time: 3.41 s\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<pre>\n",
"&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
"Int64Index: 666754 entries, 0 to 666753\n",
"Data columns (total 29 columns):\n",
"NRIC 666754 non-null values\n",
"Current_RIC 0 non-null values\n",
"Date_G_ 666754 non-null values\n",
"Time_G_ 666754 non-null values\n",
"GMT_Offset 666754 non-null values\n",
"Type 666754 non-null values\n",
"Ex_Cntrb_ID 193946 non-null values\n",
"Price 193946 non-null values\n",
"Volume 193946 non-null values\n",
"Market_VWAP 193176 non-null values\n",
"Buyer_ID 472805 non-null values\n",
"Bid_Price 472806 non-null values\n",
"Bid_Size 472805 non-null values\n",
"Seller_ID 472805 non-null values\n",
"Ask_Price 472806 non-null values\n",
"Ask_Size 472805 non-null values\n",
"Qualifiers 193945 non-null values\n",
"Seq_No 193946 non-null values\n",
"Exch_Time 193946 non-null values\n",
"New_Price 1 non-null values\n",
"New_Vol 1 non-null values\n",
"New_Seq_No 3 non-null values\n",
"Trd_Qte_Date 193945 non-null values\n",
"Quote_Time 472805 non-null values\n",
"Tick_Dir 193174 non-null values\n",
"Open 193945 non-null values\n",
"High 193945 non-null values\n",
"Low 193945 non-null values\n",
"Acc_Volume 193945 non-null values\n",
"dtypes: float64(16), int64(1), object(12)\n",
"</pre>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 666754 entries, 0 to 666753\n",
"Data columns (total 29 columns):\n",
"NRIC 666754 non-null values\n",
"Current_RIC 0 non-null values\n",
"Date_G_ 666754 non-null values\n",
"Time_G_ 666754 non-null values\n",
"GMT_Offset 666754 non-null values\n",
"Type 666754 non-null values\n",
"Ex_Cntrb_ID 193946 non-null values\n",
"Price 193946 non-null values\n",
"Volume 193946 non-null values\n",
"Market_VWAP 193176 non-null values\n",
"Buyer_ID 472805 non-null values\n",
"Bid_Price 472806 non-null values\n",
"Bid_Size 472805 non-null values\n",
"Seller_ID 472805 non-null values\n",
"Ask_Price 472806 non-null values\n",
"Ask_Size 472805 non-null values\n",
"Qualifiers 193945 non-null values\n",
"Seq_No 193946 non-null values\n",
"Exch_Time 193946 non-null values\n",
"New_Price 1 non-null values\n",
"New_Vol 1 non-null values\n",
"New_Seq_No 3 non-null values\n",
"Trd_Qte_Date 193945 non-null values\n",
"Quote_Time 472805 non-null values\n",
"Tick_Dir 193174 non-null values\n",
"Open 193945 non-null values\n",
"High 193945 non-null values\n",
"Low 193945 non-null values\n",
"Acc_Volume 193945 non-null values\n",
"dtypes: float64(16), int64(1), object(12)"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(\"DataFrame: %.1f MB\" % (df.values.nbytes / 2**20.,))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"DataFrame: 147.5 MB\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dfo = df.blocks['object']"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import sys\n",
"overhead = sum(sys.getsizeof(o) for o in dfo.values.flat)\n",
"print(\"Overhead of objects: %.1f MB\" % (overhead / 2**20.,))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Overhead of objects: 286.8 MB\n"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(\"DataFrame: %.1f MB\" % ((df.values.nbytes + overhead) / 2**20.,))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"DataFrame: 434.3 MB\n"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import blz"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!ptdump -v msft-18-SEP-2013.h5"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"/ (RootGroup) ''\r\n",
"/msft (Group) ''\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"/msft/table (Table(666754,)) ''\r\n",
" description := {\r\n",
" \"index\": Int64Col(shape=(), dflt=0, pos=0),\r\n",
" \"values_block_0\": StringCol(itemsize=49, shape=(12,), dflt='', pos=1),\r\n",
" \"values_block_1\": Int64Col(shape=(1,), dflt=0, pos=2),\r\n",
" \"values_block_2\": Float64Col(shape=(16,), dflt=0.0, pos=3)}\r\n",
" byteorder := 'little'\r\n",
" chunkshape := (358,)\r\n",
" autoindex := True\r\n",
" colindexes := {\r\n",
" \"index\": Index(6, medium, shuffle, zlib(1)).is_csi=False}\r\n"
]
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dt = np.dtype([(k, v if v.kind!='O' else 'S49') for k,v in df.dtypes.iteritems()])\n",
"dt"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
"dtype([('NRIC', 'S49'), ('Current_RIC', '<f8'), ('Date_G_', 'S49'), ('Time_G_', 'S49'), ('GMT_Offset', '<i8'), ('Type', 'S49'), ('Ex_Cntrb_ID', 'S49'), ('Price', '<f8'), ('Volume', '<f8'), ('Market_VWAP', '<f8'), ('Buyer_ID', 'S49'), ('Bid_Price', '<f8'), ('Bid_Size', '<f8'), ('Seller_ID', 'S49'), ('Ask_Price', '<f8'), ('Ask_Size', '<f8'), ('Qualifiers', 'S49'), ('Seq_No', '<f8'), ('Exch_Time', 'S49'), ('New_Price', '<f8'), ('New_Vol', '<f8'), ('New_Seq_No', '<f8'), ('Trd_Qte_Date', 'S49'), ('Quote_Time', 'S49'), ('Tick_Dir', 'S49'), ('Open', '<f8'), ('High', '<f8'), ('Low', '<f8'), ('Acc_Volume', '<f8')])"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%time bt = blz.fromiter((i[1:] for i in df.itertuples()), dtype=dt, count=len(df))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"CPU times: user 4.2 s, sys: 57 ms, total: 4.25 s\n",
"Wall time: 4.26 s\n"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"bt"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 13,
"text": [
"btable((666754,), [('NRIC', 'S49'), ('Current_RIC', '<f8'), ('Date_G_', 'S49'), ('Time_G_', 'S49'), ('GMT_Offset', '<i8'), ('Type', 'S49'), ('Ex_Cntrb_ID', 'S49'), ('Price', '<f8'), ('Volume', '<f8'), ('Market_VWAP', '<f8'), ('Buyer_ID', 'S49'), ('Bid_Price', '<f8'), ('Bid_Size', '<f8'), ('Seller_ID', 'S49'), ('Ask_Price', '<f8'), ('Ask_Size', '<f8'), ('Qualifiers', 'S49'), ('Seq_No', '<f8'), ('Exch_Time', 'S49'), ('New_Price', '<f8'), ('New_Vol', '<f8'), ('New_Seq_No', '<f8'), ('Trd_Qte_Date', 'S49'), ('Quote_Time', 'S49'), ('Tick_Dir', 'S49'), ('Open', '<f8'), ('High', '<f8'), ('Low', '<f8'), ('Acc_Volume', '<f8')])\n",
" nbytes: 460.37 MB; cbytes: 26.53 MB; ratio: 17.35\n",
" bparams := bparams(clevel=5, shuffle=True, cname=blosclz)\n",
"[ ('MSFT.O', nan, '18-SEP-2013', '00:00:00.201', -4, 'Quote', 'nan', nan, nan, nan, 'PSE ', 32.92, 5.0, 'PSE ', 32.93, 32.0, 'nan', nan, 'nan', nan, nan, nan, 'nan', '00:00:00.135', 'nan', nan, nan, nan, nan)\n",
" ('MSFT.O', nan, '18-SEP-2013', '00:00:00.291', -4, 'Quote', 'nan', nan, nan, nan, ' ', 0.0, 0.0, ' ', 0.0, 0.0, 'nan', nan, 'nan', nan, nan, nan, 'nan', '00:00:00.215', 'nan', nan, nan, nan, nan)\n",
" ('MSFT.O', nan, '18-SEP-2013', '07:00:01.111', -4, 'Correction', ' ', 0.0, 0.0, nan, 'nan', nan, nan, 'nan', nan, nan, 'nan', 0.0, '00:00:00.000', 0.0, 0.0, 0.0, 'nan', 'nan', 'nan', nan, nan, nan, nan)\n",
" ...,\n",
" ('MSFT.O', nan, '18-SEP-2013', '23:36:11.846', -4, 'Quote', 'nan', nan, nan, nan, 'PSE ', 33.24, 3.0, 'NAS ', 33.33, 2.0, 'nan', nan, 'nan', nan, nan, nan, 'nan', '23:36:11.795', 'nan', nan, nan, nan, nan)\n",
" ('MSFT.O', nan, '18-SEP-2013', '23:59:54.120', -4, 'Trade', 'ADF', 33.33, 159.0, nan, 'nan', nan, nan, 'nan', nan, nan, ' T [GV4_TEXT];132[IRGCOND]; [PRC_QL2]', 1507323.0, '23:59:54.063', nan, nan, nan, '18-SEP-2013', 'nan', 'nan', 32.99, 33.4, 32.83, 64102503.0)\n",
" ('MSFT.O', nan, '18-SEP-2013', '23:59:54.120', -4, 'Trade', 'ADF', 33.33, 841.0, nan, 'nan', nan, nan, 'nan', nan, nan, ' T [GV4_TEXT];132[IRGCOND]; [PRC_QL2]', 1507324.0, '23:59:54.071', nan, nan, nan, '18-SEP-2013', 'nan', 'nan', 32.99, 33.4, 32.83, 64103344.0)]"
]
}
],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(\"Decomp: %.1f MB, Comp: %.1f MB\" % (bt.nbytes / 2**20., bt.cbytes / 2**20.))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Decomp: 460.4 MB, Comp: 26.5 MB\n"
]
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(\"DataFrame: %.1f MB\" % ((df.values.nbytes + overhead) / 2**20.,))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"DataFrame: 434.3 MB\n"
]
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%time bt = blz.btable(bt[:], bparams=blz.bparams(shuffle=False))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"CPU times: user 1.29 s, sys: 187 ms, total: 1.48 s\n",
"Wall time: 1.48 s\n"
]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(\"Decomp: %.1f MB, Comp: %.1f MB\" % (bt.nbytes / 2**20., bt.cbytes / 2**20.))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Decomp: 460.4 MB, Comp: 20.2 MB\n"
]
}
],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%time bt = blz.btable(bt[:], bparams=blz.bparams(shuffle=False, cname='lz4'))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"CPU times: user 1.18 s, sys: 223 ms, total: 1.4 s\n",
"Wall time: 1.4 s\n"
]
}
],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(\"Decomp: %.1f MB, Comp: %.1f MB\" % (bt.nbytes / 2**20., bt.cbytes / 2**20.))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Decomp: 460.4 MB, Comp: 21.0 MB\n"
]
}
],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%time bt = blz.btable(bt[:], bparams=blz.bparams(shuffle=False, cname='snappy'))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"CPU times: user 1.02 s, sys: 271 ms, total: 1.29 s\n",
"Wall time: 1.29 s\n"
]
}
],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(\"Decomp: %.1f MB, Comp: %.1f MB\" % (bt.nbytes / 2**20., bt.cbytes / 2**20.))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Decomp: 460.4 MB, Comp: 386.1 MB\n"
]
}
],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%time bt = blz.btable(bt[:], bparams=blz.bparams(shuffle=False, cname='zlib'))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"CPU times: user 3.95 s, sys: 229 ms, total: 4.18 s\n",
"Wall time: 4.19 s\n"
]
}
],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(\"Decomp: %.1f MB, Comp: %.1f MB\" % (bt.nbytes / 2**20., bt.cbytes / 2**20.))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Decomp: 460.4 MB, Comp: 12.9 MB\n"
]
}
],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%time bt = blz.btable(bt[:], bparams=blz.bparams(shuffle=False, cname='zlib', clevel=9))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"CPU times: user 13.5 s, sys: 190 ms, total: 13.7 s\n",
"Wall time: 13.7 s\n"
]
}
],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(\"Decomp: %.1f MB, Comp: %.1f MB\" % (bt.nbytes / 2**20., bt.cbytes / 2**20.))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Decomp: 460.4 MB, Comp: 10.4 MB\n"
]
}
],
"prompt_number": 25
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Querying"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 26
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%timeit [r for r in bt.where('(Bid_Price < 3) & (Bid_Size < 20)')]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"10 loops, best of 3: 29.4 ms per loop\n"
]
}
],
"prompt_number": 38
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%timeit df[(df['Bid_Price'] < 3) | (df['Bid_Size'] == 0)]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"100 loops, best of 3: 14.1 ms per loop\n"
]
}
],
"prompt_number": 39
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%time bt = blz.btable(bt[:], bparams=blz.bparams(shuffle=False))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"CPU times: user 1.07 s, sys: 210 ms, total: 1.28 s\n",
"Wall time: 1.28 s\n"
]
}
],
"prompt_number": 40
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%timeit [r for r in bt.where('(Bid_Price < 3) & (Bid_Size < 20)')]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"10 loops, best of 3: 58.2 ms per loop\n"
]
}
],
"prompt_number": 41
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%time bt = blz.btable(bt[:], bparams=blz.bparams(shuffle=False, cname='lz4'))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"CPU times: user 1.2 s, sys: 171 ms, total: 1.37 s\n",
"Wall time: 1.37 s\n"
]
}
],
"prompt_number": 42
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%timeit [r for r in bt.where('(Bid_Price < 3) & (Bid_Size < 20)')]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"10 loops, best of 3: 29.2 ms per loop\n"
]
}
],
"prompt_number": 43
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%time bt = blz.btable(bt[:], bparams=blz.bparams(shuffle=False, cname='lz4hc', clevel=9))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"CPU times: user 15.3 s, sys: 218 ms, total: 15.5 s\n",
"Wall time: 15.5 s\n"
]
}
],
"prompt_number": 44
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(\"Decomp: %.1f MB, Comp: %.1f MB\" % (bt.nbytes / 2**20., bt.cbytes / 2**20.))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Decomp: 460.4 MB, Comp: 13.9 MB\n"
]
}
],
"prompt_number": 45
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%timeit [r for r in bt.where('(Bid_Price < 3) & (Bid_Size < 20)')]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"10 loops, best of 3: 26.9 ms per loop\n"
]
}
],
"prompt_number": 72
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%timeit df[(df['Bid_Price'] < 3) & (df['Bid_Size'] < 20)]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"100 loops, best of 3: 14.1 ms per loop\n"
]
}
],
"prompt_number": 69
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df['Bid_Price']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 71,
"text": [
"0 32.92\n",
"1 0.00\n",
"2 NaN\n",
"3 1.00\n",
"4 31.01\n",
"5 33.00\n",
"6 33.00\n",
"7 32.98\n",
"8 32.99\n",
"9 32.95\n",
"10 32.97\n",
"11 32.98\n",
"12 32.98\n",
"13 32.99\n",
"14 32.99\n",
"...\n",
"666739 NaN\n",
"666740 33.23\n",
"666741 33.23\n",
"666742 33.23\n",
"666743 NaN\n",
"666744 33.23\n",
"666745 33.27\n",
"666746 33.27\n",
"666747 33.27\n",
"666748 33.23\n",
"666749 33.24\n",
"666750 33.25\n",
"666751 33.24\n",
"666752 NaN\n",
"666753 NaN\n",
"Name: Bid_Price, Length: 666754, dtype: float64"
]
}
],
"prompt_number": 71
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df['Bid_Size']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 36,
"text": [
"0 5\n",
"1 0\n",
"2 NaN\n",
"3 1\n",
"4 20\n",
"5 10\n",
"6 10\n",
"7 5\n",
"8 5\n",
"9 10\n",
"10 5\n",
"11 5\n",
"12 5\n",
"13 10\n",
"14 2\n",
"...\n",
"666739 NaN\n",
"666740 7\n",
"666741 7\n",
"666742 7\n",
"666743 NaN\n",
"666744 7\n",
"666745 1\n",
"666746 4\n",
"666747 1\n",
"666748 6\n",
"666749 3\n",
"666750 10\n",
"666751 3\n",
"666752 NaN\n",
"666753 NaN\n",
"Name: Bid_Size, Length: 666754, dtype: float64"
]
}
],
"prompt_number": 36
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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