[DOCKER] [Explanation with image] Use pickle with Kaggle's NoteBook

things to do

1. Pickle the dataset of the csv file
2. Read the pickle file with another notebook

What is pickle?

The one that saves python objects as binary data https://docs.python.org/ja/3/library/pickle.html

What are you happy about?

Loading is fast Since it is binary data, parsing processing is fast because it is unnecessary. Trained models can be pickled and reused

This verification article is wonderful Python: I investigated the persistence format of pandas

Try with Titanic data

Make train.csv pickle for the time being This is the only code

#pickle is a standard library so no install required
import pickle

import pandas as pd


train = pd.read_csv('../input/titanic/train.csv')

# 'wb'(write binary)Specify
with open('train.pickle', 'wb') as f:
    pickle.dump(train, f)

Save as Dataset

First commit

When the green Complete appears in the upper left, click Open Version.

Scroll to the Output column

If you can see train.pickle, then New Dataset

Enter your favorite Dataset title and create

Dataset is completed

Bring it to another notebook

If you create a new notebook + Add Data

Filter by Your Datasets

Add the guy you just made

Win if displayed here

Let's read

This is the only code

# 'ｒb'(read binary)Specify
with open('../input/titanicdatasetpickles/train.pickle', 'rb') as f:
    train = pickle.load(f)

It is properly loaded as a DataFrame.

train.shape

# (891, 12)

!ls ../input

# titanicdatasetpickles

May be filed

Let's use the dump process

dump_pickles.py

import pickle

import pandas as pd


#Switch path between Kaggle and another environment
if '/kaggle/working' in _dh:
    input_path = '../input'
else:
    input_path = './input'

#Rewrite only here for each competition
data_sets = {
    'train': f'{input_path}/titanic/train.csv',
    'test': f'{input_path}/titanic/test.csv',
    'gender_submission': f'{input_path}/titanic/gender_submission.csv'
}

for name, path in data_sets.items():
    df = pd.read_csv(path)
    with open(f'{name}.pickle', 'wb') as f:
        pickle.dump(df, f)

You can do the same with pandas

#this is
with open('./train.pickle', 'wb') as f:
    pickle.dump(train, f)

#like this
train.to_pickle('./train.pickle')

#this is
with open('../input/titanicdatasetpickles/train.pickle', 'rb') as f:
    df_ss = pickle.load(f)

#like this
train = pd.read_pickle('../input/titanicdatasetpickles/train.pickle')

Sometimes I get this error

ModuleNotFoundError: No module named 'pandas.core.internals.managers'; 'pandas.core.internals' is not a package

It seems to be a problem with the version of pandas

pip install -U pandas

Solved by

• Note that Kaggle's official docker image (kaggle / python) has an error with pandas == 0.23.4 (as of December 09, 2019).

I was saved by this article Inconsistency between pickle and pandas

The end

Thank you for reading to the end