파일 IO / pickle

fout = open('school', 'w') # 쓰기 모드로 파일을 오픈 (존재한다면, 덮어씀)

#fout = open('school', 'a') # 쓰기 모드로 파일을 오픈 (존재한다면, 맨 뒤에서 부터 추가)

fout.write(speech)

fout.close()

with open('school', 'r') as fin:

    value = fin.read()

    print value

csv 쓰기

import csv

with open('sample.csv', 'r') as f:

    reader = csv.reader(f)

    reader.next()

        

    with open('result2.csv', 'w') as fw:

        writer = csv.writer(fw)

        writer.writerow(['city', 'population'])

        

        for row in reader:

            writer.writerow([row[1], row[2]])

            print row, type(row)

한줄씩 읽기

f = open('C:\\Python27\\readme.txt') f.readline()

여러줄읽기

f.readlines()

----------------------------------------------

(pickle == 오이지)

import pickle

f = open(path, 'wb')

pickle.dump(data, file)

f.close()

f = open(path, 'rb')

a = pickle.load(f)

print a

df.to_pickle(file_name)

df = pd.read_pickle(file_name)

Although there are already some answers I found a nice comparison in which they tried several ways to serialize Pandas DataFrames: Efficiently Store Pandas DataFrames [Edit: page has been deleted, but still available on web.archive.org].

They compare:

• pickle: original ASCII data format
• cPickle, a C library
• pickle-p2: uses the newer binary format
• json: standardlib json library
• json-no-index: like json, but without index
• msgpack: binary JSON alternative
• CSV
• hdfstore: HDF5 storage format

https://i.stack.imgur.com/T9JEL.png

import pickle

data1 = {'a': [1, 2.0, 3, 4+6j],
         'b': ('string', u'Unicode string'),
         'c': None}

selfref_list = [1, 2, 3]
selfref_list.append(selfref_list)

output = open('data.pkl', 'wb')

# Pickle dictionary using protocol 0.
pickle.dump(data1, output)

# Pickle the list using the highest protocol available.
pickle.dump(selfref_list, output, -1)

output.close()