Question

当我运行以下代码时：

import pandas as pd

with open('data/training.csv', 'r') as f:

    data2 = pd.read_csv(f, sep='\t', index_col=0)
    EventID = pd.date_range('1/1/2000', periods=250000)
    df = pd.DataFrame(data2, index=EventID, columns=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31])

print df[:3]

print(data2)

我得到以下输出：

            1   2   3   4   5   6   7   8   9   10  11  12  13  14  15  16  \
2000-01-01 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  
2000-01-02 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  
2000-01-03 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN  

            17  18  19  20     
2000-01-01 NaN NaN NaN NaN ... 
2000-01-02 NaN NaN NaN NaN ... 
2000-01-03 NaN NaN NaN NaN ...

我知道CSV中的值不是全部＆＃34; NaN＆＃34;那么为什么输出看起来像这样呢？如何用行数范围内的数字得到正确的输出？

当我注释掉＆＃34; EventID＆＃34;以及添加＆＃34;列的行＃34;就这样：

import pandas as pd

with open('data/training.csv', 'r') as f:

    df = pd.read_csv(f, sep='\t', index_col=0)
    # EventID = pd.date_range('1/1/2000', periods=250000)
    # df = pd.DataFrame(data2, index=EventID, columns=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31])

print df[:3]

我在终端中获得以下输出：

/usr/bin/python2.7 /home/amit/PycharmProjects/HB/Read.py
Empty DataFrame
Columns: []
Index: [100000,138.47,51.655,97.827,27.98,0.91,124.711,2.666,3.064,41.928,197.76,1.582,1.396,0.2,32.638,1.017,0.381,51.626,2.273,-2.414,16.824,-0.277,258.733,2,67.435,2.15,0.444,46.062,1.24,-2.475,113.497,0.00265331133733,s, 100001,160.937,68.768,103.235,48.146,-999.0,-999.0,-999.0,3.473,2.078,125.157,0.879,1.414,-999.0,42.014,2.039,-3.011,36.918,0.501,0.103,44.704,-1.916,164.546,1,46.226,0.725,1.158,-999.0,-999.0,-999.0,46.226,2.23358448717,b, 100002,-999.0,162.172,125.953,35.635,-999.0,-999.0,-999.0,3.148,9.336,197.814,3.776,1.414,-999.0,32.154,-0.705,-2.093,121.409,-0.953,1.052,54.283,-2.186,260.414,1,44.251,2.053,-2.028,-999.0,-999.0,-999.0,44.251,2.34738894364,b]

[3 rows x 0 columns]

Process finished with exit code 0

我不知道该怎么做＆＃34; 3行乘0列＆＃34;部分。

Answer 1

不知道你的数据究竟是什么样子，但我会在OP中采取任何措施：

In [76]:

%%file temp.csv
100000,138.47,51.655,97.827,27.98,0.91,124.711,2.666,3.064,41.928,197.76,1.582,1.396,0.2,32.638,1.017,0.381,51.626,2.273,-2.414,16.824,-0.277,258.733,2,67.435,2.15,0.444,46.062,1.24,-2.475,113.497,0.00265331133733,s, 100001,160.937,68.768,103.235,48.146,-999.0,-999.0,-999.0,3.473,2.078,125.157,0.879,1.414,-999.0,42.014,2.039,-3.011,36.918,0.501,0.103,44.704,-1.916,164.546,1,46.226,0.725,1.158,-999.0,-999.0,-999.0,46.226,2.23358448717,b, 100002,-999.0,162.172,125.953,35.635,-999.0,-999.0,-999.0,3.148,9.336,197.814,3.776,1.414,-999.0,32.154,-0.705,-2.093,121.409,-0.953,1.052,54.283,-2.186,260.414,1,44.251,2.053,-2.028,-999.0,-999.0,-999.0,44.251,2.34738894364,b

In [77]:
#make sure it is tab delimited rather than , delimited
#Change pd.DataFrame(data2 to pd.DataFrame(data2.values
with open('temp.csv', 'r') as f:
    data2 = pd.read_csv(f, sep=',', index_col=0, header=None)
    EventID = pd.date_range('1/1/2000', periods=1)
    df = pd.DataFrame(data2.values, index=EventID, columns=range(98))

print df[:3]

                0       1       2      3     4        5      6      7   \
2000-01-01  138.47  51.655  97.827  27.98  0.91  124.711  2.666  3.064   

                8       9    ...   88      89     90     91   92   93   94  \
2000-01-01  41.928  197.76   ...    1  44.251  2.053 -2.028 -999 -999 -999   

                95        96 97  
2000-01-01  44.251  2.347389  b  

[1 rows x 98 columns]

pd.DataFrame(data2.values是关键所在。 data2是DataFrame并且有自己的索引。现在，您希望将其包含在具有新时间序列索引的新DataFrame中，pandas将尝试匹配并将原始索引与新索引对齐，但没有匹配项。

因此，pd.DataFrame(data2...会导致DataFrame充满nan。解决方案是通过numpy.array将pd.DataFrame(data2.value...中的值传递给构造函数。

使用pandas read_csv时未加载的值

1 个答案: