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']
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
（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
dates = pd.date_range("",periods=3)
df = pd.DataFrame(np.random.randn(3,4),index = dates,columns=['A','B','C','D'])
df
dates = pd.date_range("",periods=3)
df = pd.DataFrame(np.random.randn(3,4),index = dates,columns=['A','B','C','D'])
df
Out[36]:
A B C D
2015-01-01 -0.888495 -0.983042 0.162524 -0.768370
2015-01-02 0.954982 0.777860 -0.635805 -0.271617
2015-01-03 1.778827 1.052819 0.090116 -1.822029
-
DataFrame.iteritems() :Iterator over (column name, Series) pairs.
for colName,colSeries in df.iteritems():
print colName
print colSeriesA
2015-01-01 -0.888495
2015-01-02 0.954982
2015-01-03 1.778827
Freq: D, Name: A, dtype: float64
B
2015-01-01 -0.983042
2015-01-02 0.777860
2015-01-03 1.052819
Freq: D, Name: B, dtype: float64
C
2015-01-01 0.162524
2015-01-02 -0.635805
2015-01-03 0.090116
Freq: D, Name: C, dtype: float64
D
2015-01-01 -0.768370
2015-01-02 -0.271617
2015-01-03 -1.822029
Freq: D, Name: D, dtype: float64 -
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.
for index,rowSeries in df.iterrows():
print index
print rowSeries2015-01-01 00:00:00
A -0.888495
B -0.983042
C 0.162524
D -0.768370
Name: 2015-01-01 00:00:00, dtype: float64
2015-01-02 00:00:00
A 0.954982
B 0.777860
C -0.635805
D -0.271617
Name: 2015-01-02 00:00:00, dtype: float64
2015-01-03 00:00:00
A 1.778827
B 1.052819
C 0.090116
D -1.822029
Name: 2015-01-03 00:00:00, dtype: float64 -
DataFrame.itertuples(index=True) :Iterate over the rows of DataFrame as tuples, with index value as first element of the tuple.
for rowTuple in df.itertuples():
print rowTuple[0]
print rowTuple[1:]2015-01-01 00:00:00
(-0.88849501182393553, -0.98304167749573845, 0.1625244406175089, -0.76836987403165646)
2015-01-02 00:00:00
(0.95498214900986345, 0.77786021238601544, -0.635805031818656, -0.27161684716624435)
2015-01-03 00:00:00
(1.7788269763069902, 1.0528194112440166, 0.09011643978723563, -1.82202928954011)
Series
-
Series.iteritems() :Lazily iterate over (index, value) tuples
In [51]: s = pd.Series(['a','b','c','d','e'])
s
s = pd.Series(['a','b','c','d','e'])
s
Out[51]:
0 a
1 b
2 c
3 d
4 e
dtype: objectfor index,value in s.iteritems():
print index,value
0 a
1 b
2 c
3 d
4 e