问题描述
我正在读取一个大型csv，其中包含约1000万行和20个不同的列(带有标题名称).
I am reading a large csv which has around 10 million rows and 20 different columns (with header names).
我有值，两列带有日期和一些字符串.
I have values, 2 columns with dates and some string.
目前，我需要大约1.5分钟的时间来加载数据，如下所示:
Currently it takes me around 1.5 minutes to load the data with something like this:
df = pd.read_csv('data.csv', index_col='date', parse_dates = 'date')
我想问的是，如何使读取速度更快呢?一旦读取数据，就拥有相同的数据框.
I want to ask, how can I make this significantly faster yet, have same dataframe once reading data.
我尝试使用HDF5数据库，但是速度却很慢.
I tried using HDF5 database, but it was just as slow.
我要读取的数据子集(我选择了8列，并从实际的20列和几百万行中给出了3行):
Subset of the data I am trying to read (I chose 8 columns and gave 3 rows out of actual 20 columns and couple million rows):
Date Comp Rating Price Estprice Dividend? Date_earnings Returns
3/12/2017 Apple Buy 100 114 Yes 4/4/2017 0.005646835
3/12/2017 Blackberry Sell 120 97 No 4/25/2017 0.000775331
3/12/2017 Microsoft Hold 140 100 Yes 5/28/2017 0.003028423
感谢您的建议.
推荐答案
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).
这篇关于 pandas read_csv加快的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！