让我们对其进行测试！

数据生成:

sz = 10**3

df = pd.DataFrame(np.random.randint(0, 10**6, (sz, 2)), columns=['i1','i2'])
df['date'] = pd.date_range('2000-01-01', freq='1S', periods=len(df))
df['dt2'] = pd.date_range('1980-01-01', freq='999S', periods=len(df))
df['f1'] = np.random.rand(len(df))
df['f2'] = np.random.rand(len(df))
# generate 10 string columns 
for i in range(1, 11):
    df['s{}'.format(i)] =  pd.util.testing.rands_array(10, len(df))

df = pd.concat([df] * 10**3, ignore_index=True).sample(frac=1)
df = df.set_index(df.pop('date').sort_values())

我们已经生成了以下DF

We have generated the following DF

In [59]: df
Out[59]:
                         i1      i2                 dt2        f1     ...              s7          s8          s9         s10
date                                                                  ...
2000-01-01 00:00:00  216625    4179 1980-01-04 04:35:24  0.679989     ...      7G8rLnoocA  E7Ot7oPsJ6  puQamLn0I2  zxHrATQn0m
2000-01-01 00:00:00  374740  967991 1980-01-09 11:07:48  0.202064     ...      wLETO2g8uL  MhtzNLPXCH  PW1uKxY0df  wTakdCe6nK
2000-01-01 00:00:00  152181  627451 1980-01-10 11:49:39  0.956117     ...      mXOsfUPqOy  6IIst7UFDT  nL6XZxrT3r  BxPCFNdZTK
2000-01-01 00:00:00  915732  730737 1980-01-06 10:25:30  0.854145     ...      Crh94m085p  M1tbrorxGT  XWSKk3b8Pv  M9FWQtPzaa
2000-01-01 00:00:00  590262  248378 1980-01-06 11:48:45  0.307373     ...      wRnMPxeopd  JF24uTUwJC  2CRrs9yB2N  hxYrXFnT1H
2000-01-01 00:00:00  161183  620876 1980-01-08 21:48:36  0.207536     ...      cyN0AExPO2  POaldI6Y0l  TDc13rPdT0  xgoDOW8Y1L
2000-01-01 00:00:00  589696  784856 1980-01-12 02:07:21  0.909340     ...      GIRAAVBRpj  xwcnpwFohz  wqcoTMjQ4S  GTcIWXElo7
...                     ...     ...                 ...       ...     ...             ...         ...         ...         ...
2000-01-01 00:16:39  773606  205714 1980-01-12 07:40:21  0.895944     ...      HEkXfD7pku  1ogy12wBom  OT3KmQRFGz  Dp1cK5R4Gq
2000-01-01 00:16:39  915732  730737 1980-01-06 10:25:30  0.854145     ...      Crh94m085p  M1tbrorxGT  XWSKk3b8Pv  M9FWQtPzaa
2000-01-01 00:16:39  990722  567886 1980-01-03 05:50:06  0.676511     ...      gVO3g0I97R  yCqOhTVeEi  imCCeQa0WG  9tslOJGWDJ
2000-01-01 00:16:39  531778  438944 1980-01-04 20:07:48  0.190714     ...      rbLmkbnO5G  ATm3BpWLC0  moLkyY2Msc  7A2UJERrBG
2000-01-01 00:16:39  880791  245911 1980-01-02 15:57:36  0.014967     ...      bZuKNBvrEF  K84u9HyAmG  4yy2bsUVNn  WZQ5Vvl9zD
2000-01-01 00:16:39  239866  425516 1980-01-10 05:26:42  0.667183     ...      6xukg6TVah  VEUz4d92B8  zHDxty6U3d  ItztnI5LmJ
2000-01-01 00:16:39  338368  804695 1980-01-12 05:27:09  0.084818     ...      NM4fdjKBuW  LXGUbLIuw9  SHdpnttX6q  4oXKMsaOJ5

[1000000 rows x 15 columns]

In [60]: df.shape
Out[60]: (1000000, 15)

In [61]: df.info()
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 1000000 entries, 2000-01-01 00:00:00 to 2000-01-01 00:16:39
Data columns (total 15 columns):
i1     1000000 non-null int32
i2     1000000 non-null int32
dt2    1000000 non-null datetime64[ns]
f1     1000000 non-null float64
f2     1000000 non-null float64
s1     1000000 non-null object
s2     1000000 non-null object
s3     1000000 non-null object
s4     1000000 non-null object
s5     1000000 non-null object
s6     1000000 non-null object
s7     1000000 non-null object
s8     1000000 non-null object
s9     1000000 non-null object
s10    1000000 non-null object
dtypes: datetime64[ns](1), float64(2), int32(2), object(10)
memory usage: 114.4+ MB

#print(df.shape)
#print(df.info())

让我们以不同的格式将其写入磁盘:(CSV，固定的HDF5，HDF5表，羽毛):

Let's write it to disk in different formats: (CSV, HDF5 fixed, HDF5 table, Feather):

# CSV
df.to_csv('c:/tmp/test.csv')
# HDF5 table format
df.to_hdf('c:/tmp/test.h5', 'test', format='t')
#  HDF5 fixed format
df.to_hdf('c:/tmp/test_fix.h5', 'test')
# Feather format
import feather
feather.write_dataframe(df, 'c:/tmp/test.feather')

时间:

现在我们可以测量磁盘读取:

Now we can measure reading from disk:

In [54]: # CSV
    ...: %timeit pd.read_csv('c:/tmp/test.csv', parse_dates=['date', 'dt2'], index_col=0)
1 loop, best of 3: 12.3 s per loop   # 3rd place

In [55]: # HDF5 fixed format
    ...: %timeit pd.read_hdf('c:/tmp/test_fix.h5', 'test')
1 loop, best of 3: 1.85 s per loop   # 1st place

In [56]: # HDF5 table format
    ...: %timeit pd.read_hdf('c:/tmp/test.h5', 'test')
1 loop, best of 3: 24.2 s per loop   # 4th place

In [57]: # Feather
    ...: %timeit feather.read_dataframe('c:/tmp/test.feather')
1 loop, best of 3: 3.21 s per loop   # 2nd place

如果您并非始终需要读取所有数据，则可以将数据以HDF5表格式存储(并使用data_columns参数以便为这些列建立索引) ，将用于过滤).

If you don't always need to read all data, then it would make sense to store your data in HDF5 table format (and make use of data_columns parameter in order to index those columns, that will be used for filtering).