Pandas DataFrame有to_pickle函数，这对于保存DataFrame非常有用:

import pandas as pd

a = pd.DataFrame({'A':[0,1,0,1,0],'B':[True, True, False, False, False]})
print a
#    A      B
# 0  0   True
# 1  1   True
# 2  0  False
# 3  1  False
# 4  0  False

a.to_pickle('my_file.pkl')

b = pd.read_pickle('my_file.pkl')
print b
#    A      B
# 0  0   True
# 1  1   True
# 2  0  False
# 3  1  False
# 4  0  False

Pyarrow跨版本兼容性

总的来说，pyarrow/feather(来自pandas/msgpack的弃用警告)。然而，我有一个挑战与pyarrow的瞬态在规范中的数据序列化pyarrow 0.15.1不能反序列化与0.16.0 ARROW-7961。我使用序列化使用redis，所以必须使用二进制编码。

我重新测试了各种选择(使用jupyter笔记本电脑)

import sys, pickle, zlib, warnings, io
class foocls:
    def pyarrow(out): return pa.serialize(out).to_buffer().to_pybytes()
    def msgpack(out): return out.to_msgpack()
    def pickle(out): return pickle.dumps(out)
    def feather(out): return out.to_feather(io.BytesIO())
    def parquet(out): return out.to_parquet(io.BytesIO())

warnings.filterwarnings("ignore")
for c in foocls.__dict__.values():
    sbreak = True
    try:
        c(out)
        print(c.__name__, "before serialization", sys.getsizeof(out))
        print(c.__name__, sys.getsizeof(c(out)))
        %timeit -n 50 c(out)
        print(c.__name__, "zlib", sys.getsizeof(zlib.compress(c(out))))
        %timeit -n 50 zlib.compress(c(out))
    except TypeError as e:
        if "not callable" in str(e): sbreak = False
        else: raise
    except (ValueError) as e: print(c.__name__, "ERROR", e)
    finally: 
        if sbreak: print("=+=" * 30)        
warnings.filterwarnings("default")

对我的数据帧(在出jupyter变量)具有以下结果

pyarrow before serialization 533366
pyarrow 120805
1.03 ms ± 43.9 µs per loop (mean ± std. dev. of 7 runs, 50 loops each)
pyarrow zlib 20517
2.78 ms ± 81.8 µs per loop (mean ± std. dev. of 7 runs, 50 loops each)
=+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+=
msgpack before serialization 533366
msgpack 109039
1.74 ms ± 72.8 µs per loop (mean ± std. dev. of 7 runs, 50 loops each)
msgpack zlib 16639
3.05 ms ± 71.7 µs per loop (mean ± std. dev. of 7 runs, 50 loops each)
=+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+=
pickle before serialization 533366
pickle 142121
733 µs ± 38.3 µs per loop (mean ± std. dev. of 7 runs, 50 loops each)
pickle zlib 29477
3.81 ms ± 60.4 µs per loop (mean ± std. dev. of 7 runs, 50 loops each)
=+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+=
feather ERROR feather does not support serializing a non-default index for the index; you can .reset_index() to make the index into column(s)
=+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+=
parquet ERROR Nested column branch had multiple children: struct<x: double, y: double>
=+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+==+=

羽毛和拼花不适合我的数据框架。我将继续使用pyarrow。但是我会补充腌黄瓜(没有压缩)。写入缓存时，存储pyarrow和pickle序列化表单。如果pyarrow反序列化失败，则从缓存读取回退到pickle时。

泡菜很好!

import pandas as pd
df.to_pickle('123.pkl')    #to save the dataframe, df to 123.pkl
df1 = pd.read_pickle('123.pkl') #to load 123.pkl back to the dataframe df

https://docs.python.org/3/library/pickle.html

pickle协议格式如下:

协议版本0是原始的“人类可读”协议，并向后兼容Python的早期版本。

协议版本1是一种旧的二进制格式，它也与早期版本的Python兼容。

协议版本2是在Python 2.3中引入的。它提供了更有效的新样式类的pickle。有关协议2带来的改进，请参阅PEP 307。

协议版本3是在Python 3.0中添加的。它显式支持bytes对象，不能被Python 2.x解封。这是默认协议，也是在需要与其他Python 3版本兼容时的推荐协议。

协议版本4是在Python 3.4中添加的。它增加了对非常大的对象、pickle更多类型的对象以及一些数据格式优化的支持。有关协议4带来的改进的信息，请参阅PEP 3154。

这里有很多很棒和充分的答案，但我想发布一个我在Kaggle上使用的测试，这个测试用不同的pandas兼容格式保存和读取大df:

https://www.kaggle.com/pedrocouto39/fast-reading-w-pickle-feather-parquet-jay

我不是作者，也不是作者的朋友，然而，当我读到这个问题时，我觉得值得一提。

CSV: 1分42秒泡菜:4.45秒羽毛:4.35秒拼花:8.31秒杰伦:8.12毫秒 或者0.0812秒(超快的!)

如果我理解正确的话，你已经在使用pandas.read_csv()，但想要加快开发过程，这样你就不必每次编辑脚本时都加载文件，对吗?我有一些建议:

you could load in only part of the CSV file using pandas.read_csv(..., nrows=1000) to only load the top bit of the table, while you're doing the development use ipython for an interactive session, such that you keep the pandas table in memory as you edit and reload your script. convert the csv to an HDF5 table updated use DataFrame.to_feather() and pd.read_feather() to store data in the R-compatible feather binary format that is super fast (in my hands, slightly faster than pandas.to_pickle() on numeric data and much faster on string data).

您可能还会对stackoverflow上的答案感兴趣。