<p>我会使用<a href="http://pandas.pydata.org/pandas-docs/stable/10min.html" rel="nofollow">pandas</a>来处理这样大量的数据:</p>
<pre><code>import io
import pandas as pd
data = """\
date, animal, color, junk
3/14/2015, cat, blue, aaa
3/24/2015, dog, green, bbb
"""
num_cols = 4
all_cols = set(range(num_cols))
skip_cols = set([2,3])
# replace `io.StringIO(data)` with the CSV filename
df = pd.read_csv(io.StringIO(data),
sep=',',
skipinitialspace=True,
parse_dates=[0],
usecols=(all_cols - skip_cols))
print(df)
# save DF as CSV file
df.to_csv('/path/to/new.csv', index=False)
# save DF to SQLite DB
import sqlalchemy
engine = sqlalchemy.create_engine('sqlite:///my_db.sqlite')
df.to_sql('my_table', engine, if_exists='replace')
</code></pre>
<p>示例:</p>
<pre><code>In [150]: data = """\
.....: date, animal, color, junk
.....: 3/14/2015, cat, blue, aaa
.....: 3/24/2015, dog, green, bbb
.....: """
In [151]: num_cols = 4
In [152]: all_cols = set(range(num_cols))
In [153]: skip_cols = set([2,3])
In [154]: df = pd.read_csv(io.StringIO(data),
.....: sep=',',
.....: skipinitialspace=True,
.....: parse_dates=['date'],
.....: usecols=(all_cols - skip_cols))
In [155]: print(df)
date animal
0 2015-03-14 cat
1 2015-03-24 dog
</code></pre>