写出数据

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既然有读,必然有写。

可以使用DataFrame的to_csv方法,将数据导出为逗号分隔的文件:

In [57]: result
Out[57]:
        one       two     three      four key
0  0.467976 -0.038649 -0.295344 -1.824726   L
1 -0.358893  1.404453  0.704965 -0.200638   B
2 -0.501840  0.659254 -0.421691 -0.057688   G
3  0.204886  1.074134  1.388361 -0.982404   R
4  0.354628 -0.133116  0.283763 -0.837063   Q

In [58]: result.to_csv('d:/out.csv')

当然 ,也可以指定为其它分隔符,甚至将数据输出到sys.stdout中:

In [60]: result.to_csv(sys.stdout, sep='|')
|one|two|three|four|key
0|0.467976300189|-0.0386485396255|-0.295344251987|-1.82472622729|L
1|-0.358893469543|1.40445260007|0.704964644926|-0.20063830401500002|B
2|-0.50184039929|0.659253707223|-0.42169061931199997|-0.0576883018364|G
3|0.20488621220199998|1.07413396504|1.38836131252|-0.982404023494|R
4|0.354627914484|-0.13311585229599998|0.283762637978|-0.837062961653|Q

缺失值默认以空字符串出现,当然也可以指定其它标识值对缺失值进行标注,比如使用‘NULL’:

In [70]: data = pd.DataFrame(np.random.randint(9,size=9).reshape(3,3))

In [71]: data
Out[71]:
   0  1  2
0  7  7  3
1  8  1  5
2  2  4  2

In [72]: data.iloc[2,2] = np.nan

In [73]: data.to_csv(sys.stdout, na_rep='NULL')
,0,1,2
0,7,7,3.0
1,8,1,5.0
2,2,4,NULL

在写入的时候,我们还可以禁止将行索引和列索引写入:

In [74]: result.to_csv(sys.stdout, index=False, header=False)
0.467976300189,-0.0386485396255,-0.295344251987,-1.82472622729,L
-0.358893469543,1.40445260007,0.704964644926,-0.20063830401500002,B
-0.50184039929,0.659253707223,,-0.0576883018364,G
0.20488621220199998,1.07413396504,1.38836131252,-0.982404023494,R
0.354627914484,-0.13311585229599998,0.283762637978,-0.837062961653,Q

也可以挑选需要的列写入:

In [75]: result.to_csv(sys.stdout, index=False, columns=['one','three','key'])
one,three,key
0.467976300189,-0.295344251987,L
-0.358893469543,0.704964644926,B
-0.50184039929,,G
0.20488621220199998,1.38836131252,R
0.354627914484,0.283762637978,Q

Series的写入方式也是一样的:

In [76]: dates = pd.date_range('1/1/2019', periods=7) # 生成一个日期Series

In [77]: dates
Out[77]:
DatetimeIndex(['2019-01-01', '2019-01-02', '2019-01-03', '2019-01-04',
               '2019-01-05', '2019-01-06', '2019-01-07'],
              dtype='datetime64[ns]', freq='D')

In [78]: ts = pd.Series(np.arange(7), index=dates) # 将日期作为索引

In [79]: ts
Out[79]:
2019-01-01    0
2019-01-02    1
2019-01-03    2
2019-01-04    3
2019-01-05    4
2019-01-06    5
2019-01-07    6
Freq: D, dtype: int32

In [80]: ts.to_csv('d:/tseries.csv') # 写入文件中

 分块读取 JSON和Pickle 

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