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问题:
I have a csv file which isn't coming in correctly with pandas.read_csv when I filter the columns with usecols and use multiple indexes.
import pandas as pd csv = r"""dummy,date,loc,x bar,20090101,a,1 bar,20090102,a,3 bar,20090103,a,5 bar,20090101,b,1 bar,20090102,b,3 bar,20090103,b,5""" f = open('foo.csv', 'w') f.write(csv) f.close() df1 = pd.read_csv('foo.csv', index_col=["date", "loc"], usecols=["dummy", "date", "loc", "x"], parse_dates=["date"], header=0, names=["dummy", "date", "loc", "x"]) print df1 # Ignore the dummy columns df2 = pd.read_csv('foo.csv', index_col=["date", "loc"], usecols=["date", "loc", "x"], # <----------- Changed parse_dates=["date"], header=0, names=["dummy", "date", "loc", "x"]) print df2
I expect that df1 and df2 should be the same except for the missing dummy column, but the columns come in mislabeled. Also the date is getting parsed as a date.
In [118]: %run test.py dummy x date loc 2009-01-01 a bar 1 2009-01-02 a bar 3 2009-01-03 a bar 5 2009-01-01 b bar 1 2009-01-02 b bar 3 2009-01-03 b bar 5 date date loc a 1 20090101 3 20090102 5 20090103 b 1 20090101 3 20090102 5 20090103
Using column numbers instead of names give me the same problem. I can workaround the issue by dropping the dummy column after the read_csv step, but I'm trying to understand what is going wrong. I'm using pandas 0.10.1.
edit: fixed bad header usage.
回答1:
The answer by @chip completely misses the point of two keyword arguments.
- names is only necessary when there is no header and you want to specify other arguments using column names rather than integer indices.
- usecols is supposed to provide a filter before reading the whole DataFrame into memory; if used properly, there should never be a need to delete columns after reading.
This solution corrects those oddities:
import pandas as pd from StringIO import StringIO csv = r"""dummy,date,loc,x bar,20090101,a,1 bar,20090102,a,3 bar,20090103,a,5 bar,20090101,b,1 bar,20090102,b,3 bar,20090103,b,5""" df = pd.read_csv(StringIO(csv), header=0, index_col=["date", "loc"], usecols=["date", "loc", "x"], parse_dates=["date"])
Which gives us:
x date loc 2009-01-01 a 1 2009-01-02 a 3 2009-01-03 a 5 2009-01-01 b 1 2009-01-02 b 3 2009-01-03 b 5
回答2:
This code achieves what you want --- also its weird and certainly buggy:
I observed that it works when:
a) you specify the index_col rel. to the number of columns you really use -- so its three columns in this example, not four (you drop dummy and start counting from then onwards)
b) same for parse_dates
c) not so for usecols ;) for obvious reasons
d) here I adapted the names to mirror this behaviour
import pandas as pd from StringIO import StringIO csv = """dummy,date,loc,x bar,20090101,a,1 bar,20090102,a,3 bar,20090103,a,5 bar,20090101,b,1 bar,20090102,b,3 bar,20090103,b,5 """ df = pd.read_csv(StringIO(csv), index_col=[0,1], usecols=[1,2,3], parse_dates=[0], header=0, names=["date", "loc", "", "x"]) print df
which prints
x date loc 2009-01-01 a 1 2009-01-02 a 3 2009-01-03 a 5 2009-01-01 b 1 2009-01-02 b 3 2009-01-03 b 5
回答3:
If your csv file contains extra data, columns can be deleted from the DataFrame after import.
import pandas as pd from StringIO import StringIO csv = r"""dummy,date,loc,x bar,20090101,a,1 bar,20090102,a,3 bar,20090103,a,5 bar,20090101,b,1 bar,20090102,b,3 bar,20090103,b,5""" df = pd.read_csv(StringIO(csv), index_col=["date", "loc"], usecols=["dummy", "date", "loc", "x"], parse_dates=["date"], header=0, names=["dummy", "date", "loc", "x"]) del df['dummy']
Which gives us:
x date loc 2009-01-01 a 1 2009-01-02 a 3 2009-01-03 a 5 2009-01-01 b 1 2009-01-02 b 3 2009-01-03 b 5
回答4:
import csv first and use csv.DictReader its easy to process...
