使用pandas read_table读取csv文件的方法

read_csv是pandas中专门用于csv文件读取的功能，不过这并不是唯一的处理方式。pandas中还有读取表格的通用函数read_table。

接下来使用read_table功能作一下csv文件的读取尝试，使用此功能的时候需要指定文件中的内容分隔符。

查看csv文件的内容如下；

In [10]: cat data.csvindex,name,comment,,,,1,name_01,coment_01,,,,2,name_02,coment_02,,,,3,name_03,coment_03,,,,4,name_04,coment_04,,,,5,name_05,coment_05,,,,6,name_06,coment_06,,,,7,name_07,coment_07,,,,8,name_08,coment_08,,,,9,name_09,coment_09,,,,10,name_10,coment_10,,,,11,name_11,coment_11,,,,12,name_12,coment_12,,,,13,name_13,coment_13,,,,14,name_14,coment_14,,,,15,name_15,coment_15,,,,16,name_16,coment_16,,,,17,name_17,coment_17,,,,18,name_18,coment_18,,,,19,name_19,coment_19,,,,20,name_20,coment_20,,,,21,name_21,coment_21,,,,

使用pandas读取文件内容如下：In [11]: data1 = pd.read_table('data.csv',sep=',')

In [12]: type(data1)Out[12]: pandas.core.frame.DataFrame

In [13]: data1Out[13]:  index  name comment Unnamed: 3 Unnamed: 4 Unnamed: 5 Unnamed: 60  1 name_01 coment_01   NaN   NaN   NaN   NaN1  2 name_02 coment_02   NaN   NaN   NaN   NaN2  3 name_03 coment_03   NaN   NaN   NaN   NaN3  4 name_04 coment_04   NaN   NaN   NaN   NaN4  5 name_05 coment_05   NaN   NaN   NaN   NaN5  6 name_06 coment_06   NaN   NaN   NaN   NaN6  7 name_07 coment_07   NaN   NaN   NaN   NaN7  8 name_08 coment_08   NaN   NaN   NaN   NaN8  9 name_09 coment_09   NaN   NaN   NaN   NaN9  10 name_10 coment_10   NaN   NaN   NaN   NaN10  11 name_11 coment_11   NaN   NaN   NaN   NaN11  12 name_12 coment_12   NaN   NaN   NaN   NaN12  13 name_13 coment_13   NaN   NaN   NaN   NaN13  14 name_14 coment_14   NaN   NaN   NaN   NaN14  15 name_15 coment_15   NaN   NaN   NaN   NaN15  16 name_16 coment_16   NaN   NaN   NaN   NaN16  17 name_17 coment_17   NaN   NaN   NaN   NaN17  18 name_18 coment_18   NaN   NaN   NaN   NaN18  19 name_19 coment_19   NaN   NaN   NaN   NaN19  20 name_20 coment_20   NaN   NaN   NaN   NaN20  21 name_21 coment_21   NaN   NaN   NaN   NaN

不过在几番尝试下来，发现这个分隔符缺省的时候倒是也能够读出数据。

In [16]: data2 = pd.read_table('data.csv')

In [17]: data2Out[17]:   index,name,comment,,,,0 1,name_01,coment_01,,,,1 2,name_02,coment_02,,,,2 3,name_03,coment_03,,,,3 4,name_04,coment_04,,,,4 5,name_05,coment_05,,,,5 6,name_06,coment_06,,,,6 7,name_07,coment_07,,,,7 8,name_08,coment_08,,,,8 9,name_09,coment_09,,,,9 10,name_10,coment_10,,,,10 11,name_11,coment_11,,,,11 12,name_12,coment_12,,,,12 13,name_13,coment_13,,,,13 14,name_14,coment_14,,,,14 15,name_15,coment_15,,,,15 16,name_16,coment_16,,,,16 17,name_17,coment_17,,,,17 18,name_18,coment_18,,,,18 19,name_19,coment_19,,,,19 20,name_20,coment_20,,,,20 21,name_21,coment_21,,,,