pandas的札记

导入导出数据

在导入，导出DataFrame数据时，会用到各种格式，分为 to_csv ;to_excel;to_hdf;to_sql;to_json;to_msgpack ;to_html;to_gbq ;to_stata;to_clipboard;to_pickle

可参照IO Tools 分类。

输出指定colums是，会用到arg colums，例如

to_csv(filename,columns=["col1","col2"],......)
# 此处注意的是要使用双引号，单引号不起效果，不知道为什么，另外
# index,header设置为False会不写入行号（索引好）和列标
#也可如下方式使用list函数
to_csv(filename,columns = list('col1','col2'),......)

如果想要保存为ascii文本则可以使用to_csv,可以对是否保存索引（行号）等参数进设置。

调换colums顺序

若原始数据是这样的：

In [6]: df
Out[6]:
          0         1         2         3         4      mean
0  0.445598  0.173835  0.343415  0.682252  0.582616  0.445543
1  0.881592  0.696942  0.702232  0.696724  0.373551  0.670208
2  0.662527  0.955193  0.131016  0.609548  0.804694  0.632596
3  0.260919  0.783467  0.593433  0.033426  0.512019  0.436653
4  0.131842  0.799367  0.182828  0.683330  0.019485  0.363371
5  0.498784  0.873495  0.383811  0.699289  0.480447  0.587165
6  0.388771  0.395757  0.745237  0.628406  0.784473  0.588529
7  0.147986  0.459451  0.310961  0.706435  0.100914  0.345149
8  0.394947  0.863494  0.585030  0.565944  0.356561  0.553195
9  0.689260  0.865243  0.136481  0.386582  0.730399  0.561593

In [7]: cols = df.columns.tolist()

In [8]: cols
Out[8]: [0L, 1L, 2L, 3L, 4L, 'mean']

View Code

通过调换columns更改顺序

In [12]: cols = cols[-1:] + cols[:-1] 
In [13]: cols
Out[13]: ['mean', 0L, 1L, 2L, 3L, 4L]

进而可以达到如下效果

In [16]: df = df[cols]  #    OR    df = df.ix[:, cols]

In [17]: df
Out[17]:
       mean         0         1         2         3         4
0  0.445543  0.445598  0.173835  0.343415  0.682252  0.582616
1  0.670208  0.881592  0.696942  0.702232  0.696724  0.373551
2  0.632596  0.662527  0.955193  0.131016  0.609548  0.804694
3  0.436653  0.260919  0.783467  0.593433  0.033426  0.512019
4  0.363371  0.131842  0.799367  0.182828  0.683330  0.019485
5  0.587165  0.498784  0.873495  0.383811  0.699289  0.480447
6  0.588529  0.388771  0.395757  0.745237  0.628406  0.784473
7  0.345149  0.147986  0.459451  0.310961  0.706435  0.100914
8  0.553195  0.394947  0.863494  0.585030  0.565944  0.356561
9  0.561593  0.689260  0.865243  0.136481  0.386582  0.730399

View Code

（参考来源）

pandas DataFrame 中指定位置数据的修改：

df['one']['second'] = value
# 由于DataFrame在索引数据是得到的是副本copy所以，此时原数据df并没有修改,并会抛出警告Warning: SettingWithCopy 

df.loc['one','second'] = value
#如上会修改原数据df
#或是：
dfmi.loc[:,('one','second')] = value

具体参考SettingWithCopy

pandas DataFrame & Series 遍历数据(loop iterate on data)

DataFrame

 1 dates = pd.date_range("20150101",periods=3)
 2 df = pd.DataFrame(np.random.randn(3,4),index = dates,columns=['A','B','C','D'])
 3 df
 4 dates = pd.date_range("20150101",periods=3)
 5 df = pd.DataFrame(np.random.randn(3,4),index = dates,columns=['A','B','C','D'])
 6 df
 7 Out[36]:
 8 A    B    C    D
 9 2015-01-01    -0.888495    -0.983042    0.162524    -0.768370
10 2015-01-02    0.954982    0.777860    -0.635805    -0.271617
11 2015-01-03    1.778827    1.052819    0.090116    -1.822029

DataFrame.iteritems() :Iterator over (column name, Series) pairs.

1 for colName,colSeries in df.iteritems():
2     print colName
3     print colSeries

 1 A
 2 2015-01-01   -0.888495
 3 2015-01-02    0.954982
 4 2015-01-03    1.778827
 5 Freq: D, Name: A, dtype: float64
 6 B
 7 2015-01-01   -0.983042
 8 2015-01-02    0.777860
 9 2015-01-03    1.052819
10 Freq: D, Name: B, dtype: float64
11 C
12 2015-01-01    0.162524
13 2015-01-02   -0.635805
14 2015-01-03    0.090116
15 Freq: D, Name: C, dtype: float64
16 D
17 2015-01-01   -0.768370
18 2015-01-02   -0.271617
19 2015-01-03   -1.822029
20 Freq: D, Name: D, dtype: float64

View Code

DataFrame.iterrows() :Iterate over the rows of a DataFrame as (index, Series) pairs. 数据一致是对列来说的，所以此方法迭代时数据类型会改变，如果想使用原始数据类型，最好使用itertuples，且速度快于Itetuples.

1 for index,rowSeries in df.iterrows():
2     print index
3     print rowSeries

 1 2015-01-01 00:00:00
 2 A   -0.888495
 3 B   -0.983042
 4 C    0.162524
 5 D   -0.768370
 6 Name: 2015-01-01 00:00:00, dtype: float64
 7 2015-01-02 00:00:00
 8 A    0.954982
 9 B    0.777860
10 C   -0.635805
11 D   -0.271617
12 Name: 2015-01-02 00:00:00, dtype: float64
13 2015-01-03 00:00:00
14 A    1.778827
15 B    1.052819
16 C    0.090116
17 D   -1.822029
18 Name: 2015-01-03 00:00:00, dtype: float64

View Code

DataFrame.itertuples(index=True) :Iterate over the rows of DataFrame as tuples, with index value as first element of the tuple.

1 for rowTuple in df.itertuples():
2     print rowTuple[0]
3     print rowTuple[1:]

1 2015-01-01 00:00:00
2 (-0.88849501182393553, -0.98304167749573845, 0.1625244406175089, -0.76836987403165646)
3 2015-01-02 00:00:00
4 (0.95498214900986345, 0.77786021238601544, -0.635805031818656, -0.27161684716624435)
5 2015-01-03 00:00:00
6 (1.7788269763069902, 1.0528194112440166, 0.09011643978723563, -1.82202928954011)

View Code

Series

Series.iteritems() 　　 :Lazily iterate over (index, value) tuples

 1 In [51]:
 2 
 3 s = pd.Series(['a','b','c','d','e'])
 4 s
 5 s = pd.Series(['a','b','c','d','e'])
 6 s
 7 Out[51]:
 8 0    a
 9 1    b
10 2    c
11 3    d
12 4    e
13 dtype: object

1 for index,value in s.iteritems():
2     print index,value
3 0 a
4 1 b
5 2 c
6 3 d
7 4 e

View Code