将csv导入到python数据帧

Question

我正在向数据帧导入包含600列的2GB CSV文件，但每次都会出现内存错误。现在我想在导入时删除一些列。

请告诉我如何实现这一目标。

我的代码：

sourceFileName=r'C:\sunil_plus\dataset\a3rfghj.csv'
data = pd.read_csv(sourceFileName,dtype=object)

示例数据：

"Country Name","Country Code","Indicator Name","Indicator Code","Counterpart Country Name","Counterpart Country Code",Attribute,1948,1949,1950,1951,1952,1953,1954,1955,1956,1957,1958,1959,1960,1960Q1,1960M1,1960M2,1960M3,1960Q2,1960M4,1960M5,1960M6,1960Q3,1960M7,1960M8,1960M9,1960Q4,1960M10,1960M11,1960M12,1961,1961Q1,1961M1,1961M2,1961M3,1961Q2,1961M4,1961M5,1961M6,1961Q3,1961M7,1961M8,1961M9,1961Q4,1961M10,1961M11,1961M12,1962,1962Q1,1962M1,1962M2,1962M3,1962Q2,1962M4,1962M5,1962M6,1962Q3,1962M7,1962M8,1962M9,1962Q4,1962M10,1962M11,1962M12,1963,1963Q1,1963M1,1963M2,1963M3,1963Q2,1963M4,1963M5,1963M6,1963Q3,1963M7,1963M8,1963M9,1963Q4,1963M10,1963M11,1963M12,1964,1964Q1,1964M1,1964M2,1964M3,1964Q2,1964M4,1964M5,1964M6,1964Q3,1964M7,1964M8,1964M9,1964Q4,1964M10,1964M11,1964M12,1965,1965Q1,1965M1,1965M2,1965M3,1965Q2,1965M4,1965M5,1965M6,1965Q3,1965M7,1965M8,1965M9,1965Q4,1965M10,1965M11,1965M12,1966,1966Q1,1966M1,1966M2,1966M3,1966Q2,1966M4,1966M5,1966M6,1966Q3,1966M7,1966M8,1966M9,1966Q4,1966M10,1966M11,1966M12,1967,1967Q1,1967M1,1967M2,1967M3,1967Q2,1967M4,1967M5,1967M6,1967Q3,1967M7,1967M8,1967M9,1967Q4,1967M10,1967M11,1967M12,1968,1968Q1,1968M1,1968M2,1968M3,1968Q2,1968M4,1968M5,1968M6,1968Q3,1968M7,1968M8,1968M9,1968Q4,1968M10,1968M11,1968M12,1969,1969Q1,1969M1,1969M2,1969M3,1969Q2,1969M4,1969M5,1969M6,1969Q3,1969M7,1969M8,1969M9,1969Q4,1969M10,1969M11,1969M12,1970,1970Q1,1970M1,1970M2,1970M3,1970Q2,1970M4,1970M5,1970M6,1970Q3,1970M7,1970M8,1970M9,1970Q4,1970M10,1970M11,1970M12,1971,1971Q1,1971M1,1971M2,1971M3,1971Q2,1971M4,1971M5,1971M6,1971Q3,1971M7,1971M8,1971M9,1971Q4,1971M10,1971M11,1971M12,1972,1972Q1,1972M1,1972M2,1972M3,1972Q2,1972M4,1972M5,1972M6,1972Q3,1972M7,1972M8,1972M9,1972Q4,1972M10,1972M11,1972M12,1973,1973Q1,1973M1,1973M2,1973M3,1973Q2,1973M4,1973M5,1973M6,1973Q3,1973M7,1973M8,1973M9,1973Q4,1973M10,1973M11,1973M12,1974,1974Q1,1974M1,1974M2,1974M3,1974Q2,1974M4,1974M5,1974M6,1974Q3,1974M7,1974M8,1974M9,1974Q4,1974M10,1974M11,1974M12,1975,1975Q1,1975M1,1975M2,1975M3,1975Q2,1975M4,1975M5,1975M6,1975Q3,1975M7,1975M8,1975M9,1975Q4,1975M10,1975M11,1975M12,1976,1976Q1,1976M1,1976M2,1976M3,1976Q2,1976M4,1976M5,1976M6,1976Q3,1976M7,1976M8,1976M9,1976Q4,1976M10,1976M11,1976M12,1977,1977Q1,1977M1,1977M2,1977M3,1977Q2,1977M4,1977M5,1977M6,1977Q3,1977M7,1977M8,1977M9,1977Q4,1977M10,1977M11,1977M12,1978,1978Q1,1978M1,1978M2,1978M3,1978Q2,1978M4,1978M5,1978M6,1978Q3,1978M7,1978M8,1978M9,1978Q4,1978M10,1978M11,1978M12,1979,1979Q1,1979M1,1979M2,1979M3,1979Q2,1979M4,1979M5,1979M6,1979Q3,1979M7,1979M8,1979M9,1979Q4,1979M10,1979M11,1979M12,1980,1980Q1,1980M1,1980M2,1980M3,1980Q2,1980M4,1980M5,1980M6,1980Q3,1980M7,1980M8,1980M9,1980Q4,1980M10,1980M11,1980M12,1981,1981Q1,1981M1,1981M2,1981M3,1981Q2,1981M4,1981M5,1981M6,1981Q3,1981M7,1981M8,1981M9,1981Q4,1981M10,1981M11,1981M12,1982,1982Q1,1982M1,1982M2,1982M3,1982Q2,1982M4,1982M5,1982M6,1982Q3,1982M7,1982M8,1982M9,1982Q4,1982M10,1982M11,1982M12,1983,1983Q1,1983M1,1983M2,1983M3,1983Q2,1983M4,1983M5,1983M6,1983Q3,1983M7,1983M8,1983M9,1983Q4,1983M10,1983M11,1983M12,1984,1984Q1,1984M1,1984M2,1984M3,1984Q2,1984M4,1984M5,1984M6,1984Q3,1984M7,1984M8,1984M9,1984Q4,1984M10,1984M11,1984M12,1985,1985Q1,1985M1,1985M2,1985M3,1985Q2,1985M4,1985M5,1985M6,1985Q3,1985M7,1985M8,1985M9,1985Q4,1985M10,1985M11,1985M12,1986,1986Q1,1986M1,1986M2,1986M3,1986Q2,1986M4,1986M5,1986M6,1986Q3,1986M7,1986M8,1986M9,1986Q4,1986M10,1986M11,1986M12,1987,1987Q1,1987M1,1987M2,1987M3,1987Q2,1987M4,1987M5,1987M6,1987Q3,1987M7,1987M8,1987M9,1987Q4,1987M10,1987M11,1987M12,1988,1988Q1,1988M1,1988M2,1988M3,1988Q2,1988M4,1988M5,1988M6,1988Q3,1988M7,1988M8,1988M9,1988Q4,1988M10,1988M11,1988M12,1989,1989Q1,1989M1,1989M2,1989M3,1989Q2,1989M4,1989M5,1989M6,1989Q3,1989M7,1989M8,1989M9,1989Q4,1989M10,1989M11,1989M12,1990,1990Q1,1990M1,1990M2,1990M3,1990Q2,1990M4,1990M5,1990M6,1990Q3,1990M7,1990M8,1990M9,1990Q4,1990M10,1990M11,1990M12,1991,1991Q1,1991M1,1991M2,1991M3,1991Q2,1991M4,1991M5,1991M6,1991Q3,1991M7,1991M8,1991M9,1991Q4,1991M10,1991M11,1991M12,1992,1992Q1,1992M1,1992M2,1992M3,1992Q2,1992M4,1992M5,1992M6,1992Q3,1992M7,1992M8,1992M9,1992Q4,1992M10,1992M11,1992M12,1993,1993Q1,1993M1,1993M2,1993M3,1993Q2,1993M4,1993M5,1993M6,1993Q3,1993M7,1993M8,1993M9,1993Q4,1993M10,1993M11,1993M12,1994,1994Q1,1994M1,1994M2,1994M3,1994Q2,1994M4,1994M5,1994M6,1994Q3,1994M7,1994M8,1994M9,1994Q4,1994M10,1994M11,1994M12,1995,1995Q1,1995M1,1995M2,1995M3,1995Q2,1995M4,1995M5,1995M6,1995Q3,1995M7,1995M8,1995M9,1995Q4,1995M10,1995M11,1995M12,1996,1996Q1,1996M1,1996M2,1996M3,1996Q2,1996M4,1996M5,1996M6,1996Q3,1996M7,1996M8,1996M9,1996Q4,1996M10,1996M11,1996M12,1997,1997Q1,1997M1,1997M2,1997M3,1997Q2,1997M4,1997M5,1997M6,1997Q3,1997M7,1997M8,1997M9,1997Q4,1997M10,1997M11,1997M12,1998,1998Q1,1998M1,1998M2,1998M3,1998Q2,1998M4,1998M5,1998M6,1998Q3,1998M7,1998M8,1998M9,1998Q4,1998M10,1998M11,1998M12,1999,1999Q1,1999M1,1999M2,1999M3,1999Q2,1999M4,1999M5,1999M6,1999Q3,1999M7,1999M8,1999M9,1999Q4,1999M10,1999M11,1999M12,2000,2000Q1,2000M1,2000M2,2000M3,2000Q2,2000M4,2000M5,2000M6,2000Q3,2000M7,2000M8,2000M9,2000Q4,2000M10,2000M11,2000M12,2001,2001Q1,2001M1,2001M2,2001M3,2001Q2,2001M4,2001M5,2001M6,2001Q3,2001M7,2001M8,2001M9,2001Q4,2001M10,2001M11,2001M12,2002,2002Q1,2002M1,2002M2,2002M3,2002Q2,2002M4,2002M5,2002M6,2002Q3,2002M7,2002M8,2002M9,2002Q4,2002M10,2002M11,2002M12,2003,2003Q1,2003M1,2003M2,2003M3,2003Q2,2003M4,2003M5,2003M6,2003Q3,2003M7,2003M8,2003M9,2003Q4,2003M10,2003M11,2003M12,2004,2004Q1,2004M1,2004M2,2004M3,2004Q2,2004M4,2004M5,2004M6,2004Q3,2004M7,2004M8,2004M9,2004Q4,2004M10,2004M11,2004M12,2005,2005Q1,2005M1,2005M2,2005M3,2005Q2,2005M4,2005M5,2005M6,2005Q3,2005M7,2005M8,2005M9,2005Q4,2005M10,2005M11,2005M12,2006,2006Q1,2006M1,2006M2,2006M3,2006Q2,2006M4,2006M5,2006M6,2006Q3,2006M7,2006M8,2006M9,2006Q4,2006M10,2006M11,2006M12,2007,2007Q1,2007M1,2007M2,2007M3,2007Q2,2007M4,2007M5,2007M6,2007Q3,2007M7,2007M8,2007M9,2007Q4,2007M10,2007M11,2007M12,2008,2008Q1,2008M1,2008M2,2008M3,2008Q2,2008M4,2008M5,2008M6,2008Q3,2008M7,2008M8,2008M9,2008Q4,2008M10,2008M11,2008M12,2009,2009Q1,2009M1,2009M2,2009M3,2009Q2,2009M4,2009M5,2009M6,2009Q3,2009M7,2009M8,2009M9,2009Q4,2009M10,2009M11,2009M12,2010,2010Q1,2010M1,2010M2,2010M3,2010Q2,2010M4,2010M5,2010M6,2010Q3,2010M7,2010M8,2010M9,2010Q4,2010M10,2010M11,2010M12,2011,2011Q1,2011M1,2011M2,2011M3,2011Q2,2011M4,2011M5,2011M6,2011Q3,2011M7,2011M8,2011M9,2011Q4,2011M10,2011M11,2011M12,2012,2012Q1,2012M1,2012M2,2012M3,2012Q2,2012M4,2012M5,2012M6,2012Q3,2012M7,2012M8,2012M9,2012Q4,2012M10,2012M11,2012M12,2013,2013Q1,2013M1,2013M2,2013M3,2013Q2,2013M4,2013M5,2013M6,2013Q3,2013M7,2013M8,2013M9,2013Q4,2013M10,2013M11,2013M12,2014,2014Q1,2014M1,2014M2,2014M3,2014Q2,2014M4,2014M5,2014M6,2014Q3,2014M7,2014M8,2014M9,2014Q4,2014M10,2014M11,2014M12,2015,2015Q1,2015M1,2015M2,2015M3,2015Q2,2015M4,2015M5,2015M6,2015Q3,2015M7,2015M8,2015M9,2015Q4,2015M10,2015M11,2015M12,2016,2016Q1,2016M1,2016M2,2016M3,2016Q2,2016M4,2016M5,2016M6,2016Q3,2016M7,2016M8,2016M9,2016Q4,2016M10,2016M11,2016M12,2017Q1,2017M1,2017M2,2017M3,
"Advanced Economies","110","Goods, Value of Exports, Free on board (FOB), US Dollars","TXG_FOB_USD","Lao People's Democratic Republic","544","Value",,,,,,,,"600000","7500000","11200000","10100000","9900000","13590000",,,,,,,,,,,,,,,,,"31800000",,,,,,,,,,,,,,,,,"22190000",,,,,,,,,,,,,,,,,"19310000",,,,,,,,,,,,,,,,,"13400000",,,,,,,,,,,,,,,,,"17500000",,,,,,,,,,,,,,,,,"20000000",,,,,,,,,,,,,,,,,"21000000",,,,,,,,,,,,,,,,,"28220000",,,,,,,,,,,,,,,,,"46444000",,,,,,,,,,,,,,,,,"42244000",,,,,,,,,,,,,,,,,"28035000",,,,,,,,,,,,,,,,,"22672000",,,,,,,,,,,,,,,,,"35970000",,,,,,,,,,,,,,,,,"52015000",,,,,,,,,,,,,,,,,"24440000",,,,,,,,,,,,,,,,,"14690000",,,,,,,,,,,,,,,,,"32230000",,,,,,,,,,,,,,,,,"38550000",,,,,,,,,,,,,,,,,"34860000",,,,,,,,,,,,,,,,,"55760000",,,,,,,,,,,,,,,,,"37930000",,,,,,,,,,,,,,,,,"35560000",,,,,,,,,,,,,,,,,"39910000",,,,,,,,,,,,,,,,,"15670000",,,,,,,,,,,,,,,,,"26490000",,,,,,,,,,,,,,,,,"20760000",,,,,,,,,,,,,,,,,"26680000",,,,,,,,,,,,,,,,,"35095211.66717",,,,,,,,,,,,,,,,,"36724275.976975",,,,,,,,,,,,,,,,,"36915312.0753272",,,,,,,,,,,,,,,,,"46665636.4768923",,,,,,,,,,,,,,,,,"66109300.8664534",,,,,,,,,,,,,,,,,"87913998.4608552",,,,,,,,,,,,,,,,,"148354775.637297",,,,,,,,,,,,,,,,,"163819040.691337",,,,,,,,,,,,,,,,,"156024282.72955",,,,,,,,,,,,,,,,,"133137318.554441",,,,,,,,,,,,,,,,,"94399137.7518639",,,,,,,,,,,,,,,,,"117343069.557997",,,,,,,,,,,,,,,,,"117587881.503864",,,,,,,,,,,,,,,,,"98762873.9625738",,,,,,,,,,,,,,,,,"109005712.389615",,,,,,,,,,,,,,,,,"106595482.789968",,,,,,,,,,,,,,,,,"172941555.708648",,,,,,,,,,,,,,,,,"160810422.300987",,,,,,,,,,,,,,,,,"166500301.797628",,,,,,,,,,,,,,,,,"259543675.819615",,,,,,,,,,,,,,,,,"312728794.020366",,,,,,,,,,,,,,,,,"353281591.775144",,,,,,,,,,,,,,,,,"398101531.917045",,,,,,,,,,,,,,,,,"624671291.89195",,,,,,,,,,,,,,,,,"745336092.052549",,,,,,,,,,,,,,,,,"630395816.514461",,,,,,,,,,,,,,,,,"777975419.209323",,,,,,,,,,,,,,,,,"733654481.53557",,,,,,,,,,,,,,,,,"503096458.712403",,,,,,,,,,,,,,,,,,,,,

Answer 1

要在导入时排除3列，您可以执行以下操作：

data = pd.read_csv(
 sourceFileName,
 usecols=lambda col: col not in ["Country Name","Indicator Name","Counterpart Country Name"]
)

Answer 2

columns_to_be_removed = ['a', 'b']
data = pd.read_csv(sourceFileName).drop(columns_to_be_removed, axis = 'columns')