pandas 写出数据与JSON和Pickle

时间：2020-04-15 11:37:55 收藏：0 阅读：55

一、写出数据

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

二、JSON和Pickle

假设有如下的JSON文件：

[{"a": 1, "b": 2, "c": 3},
 {"a": 4, "b": 5, "c": 6},
 {"a": 7, "b": 8, "c": 9}]

使用read_json函数可以自动将JSON数据集按照指定的顺序转换为Series或者DataFrame对象，其默认做法是假设JSON数据中的每个对象是表里的一行：

In [81]: data = pd.read_json(‘d:/example.json‘)
In [82]: data
Out[82]:
   a  b  c
0  1  2  3
1  4  5  6
2  7  8  9

反之，使用to_json函数，将pandas对象转换为json格式：

In [83]: print(data.to_json())
{"a":{"0":1,"1":4,"2":7},"b":{"0":2,"1":5,"2":8},"c":{"0":3,"1":6,"2":9}}
In [84]: print(data.to_json(orient=‘records‘)) # 与上面的格式不同
[{"a":1,"b":2,"c":3},{"a":4,"b":5,"c":6},{"a":7,"b":8,"c":9}]

我们都知道，Python标准库pickle，可以支持二进制格式的文件读写，且高效方便。

pandas同样设计了用于pickle格式的读写函数read_pickle和to_pickle。

In [85]: df = pd.read_csv(‘d:/ex1.csv‘)
In [86]: df
Out[86]:
   a   b   c   d message
0  1   2   3   4   hello
1  5   6   7   8   world
2  9  10  11  12     foo
In [87]: df.to_pickle(‘d:/df_pickle‘)
In [88]: new_df = pd.read_pickle(‘d:/df_pickle‘)
In [89]: new_df
Out[89]:
   a   b   c   d message
0  1   2   3   4   hello
1  5   6   7   8   world
2  9  10  11  12     foo

原文：https://www.cnblogs.com/lavender1221/p/12703139.html