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49 lines
1.5 KiB
Python
49 lines
1.5 KiB
Python
# This is a workaround snippet for reading very large CSV that exceed the
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# machine's memory and dump it into an SQLite database using pandas.
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#
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# Sebastian Raschka, 2015
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#
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# Tested in Python 3.4.2 and pandas 0.15.2
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import pandas as pd
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import sqlite3
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from pandas.io import sql
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import subprocess
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# In and output file paths
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in_csv = '../data/my_large.csv'
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out_sqlite = '../data/my.sqlite'
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table_name = 'my_table' # name for the SQLite database table
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chunksize = 100000 # number of lines to process at each iteration
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# columns that should be read from the CSV file
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columns = ['molecule_id','charge','db','drugsnow','hba','hbd','loc','nrb','smiles']
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# Get number of lines in the CSV file
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nlines = subprocess.check_output(['wc', '-l', in_csv])
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nlines = int(nlines.split()[0])
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# connect to database
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cnx = sqlite3.connect(out_sqlite)
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# Iteratively read CSV and dump lines into the SQLite table
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for i in range(0, nlines, chunksize): # change 0 -> 1 if your csv file contains a column header
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df = pd.read_csv(in_csv,
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header=None, # no header, define column header manually later
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nrows=chunksize, # number of rows to read at each iteration
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skiprows=i) # skip rows that were already read
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# columns to read
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df.columns = columns
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sql.to_sql(df,
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name=table_name,
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con=cnx,
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index=False, # don't use CSV file index
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index_label='molecule_id', # use a unique column from DataFrame as index
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if_exists='append')
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cnx.close()
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