pandas read_csv and filter columns with usecols
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',
header=0,
names=["dummy", "date", "loc", "x"],
index_col=["date", "loc"],
usecols=["dummy", "date", "loc", "x"],
parse_dates=["date"])
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.
Solution 1:
The solution lies in understanding these two keyword arguments:
-
names is only necessary when there is no header row in your file and you want to specify other arguments (such as
usecols) 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.
So because you have a header row, passing header=0 is sufficient and additionally passing names appears to be confusing pd.read_csv.
Removing names from the second call gives the desired output:
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
Solution 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
Solution 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