'''

Created on Dec 9, 2015

@author: Shikai

'''

#############################################################

'''

import sys

def test():

if (len(sys.argv) == 1):

print 'Not enough arguments'

else:

args = sys.argv[1:]

print 'Hello ' + ' '.join(args)

if __name__ == '__main__':

test()

'''

x = raw_input('what are the first 10 perfect squares? ')

# >>> map(lambda x : x*x, range(10))

# x = eval(x)

x = input('what are the first 10 perfect squares? ')

# >>> map(lambda x : x*x, range(10))

# input() will attempt to evaluate it as if it were a python program

# input() = raw_input() + eval()

print '{} and {}'.format('spam', 'eggs')

print '{0} and {1}'.format('spam', 'eggs')

print '{1} and {0}'.format('spam', 'eggs')

print 'This {food} is {adjective}.'.format(

food='spam', adjective='absolutely horrible')

import math

# !s (str()), !r (repr())

print 'The value of PI is approximately {!r}.'.format(math.pi)

print 'The value of PI is approximately {0:.3f}.'.format(math.pi)

table = {'Sjoerd': 4127, 'Jack': 4098, 'Dcab': 7678}

for name, phone in table.items():

print '{0:10} ==> {1:10d}'.format(name, phone)

#############################################################

import os

os.chdir('/Users/Shikai/Documents/workspace/python_io/src')

f = open('sample.txt','r')

for line in f:

print line,

# Note the trailing comma on previous line,

# it is to avoid extra empty line

f.close()

lines = list(open('sample.txt'))

# better use 'with' to deal with file objects

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

lines = f.readlines()

'''

import fileinput

import glob # unix style pathname pattern expansion

import string, sys

# fileinput.input(sys.argv[1:])

for line in fileinput.input(glob.glob("samples/*.txt")):

if fileinput.isfirstline(): # first in a file?

sys.stderr.write("-- reading %s --\n" % fileinput.filename())

sys.stdout.write(str(fileinput.lineno()) + " " + string.upper(line))

https://docs.python.org/2/library/fileinput.html#module-fileinput

'''

'''

numpy input and output

# numpy binary files

load(file, ...) # load arrays from .npy or .npz files

save(file, ...) # save one array to a binary file .npy format

savez(file, ...) # save several arrays into a single file in uncompressed .npz format

savez_compressed(file, ...) # in compressed .npz format

# text files

loadtxt(fname, ...)

savetxt(fname, ...)

genfromtxt(file_name, # local file or url or gz or bz2

dtype, # 1) a single type,

# 2) a sequence of types corresponding to each column

# 3) a sequence of tuples (name, type)

delimiter, # 1) default: whitespaces

# 2) str, ',', ';'

# 3) int, width of each field

# 4) a sequence

skip_header, # number of lines to skip at the beginning of the file

skip_footer, # number of lines to skip at the end of the file

missing_values, # 1) a string or a comma-separated string

# 2) a sequence of strings

# 3) a dictionary

filling_values, # 1) a single value for all columns

# 2) a sequence of values, for corresponding column

# 3) a dictionary

comments, # str, '#'

usecols, # sequence of ints or names

autostrip, # strip white spaces from the variables

max_rows) # maximum number of rows to read

Note: refer to help(numpy.genfromtxt) for more details

http://docs.scipy.org/doc/numpy/user/basics.io.genfromtxt.html

http://docs.scipy.org/doc/numpy-1.10.0/reference/routines.io.html

'''

'''

refer to pandas IO tools, fancy

http://pandas.pydata.org/pandas-docs/stable/io.html

### appending to a csv

with open(filename, 'a') as f:

df.to_csv(f, header=False)

# header=False so as not to append column names

### read in multiple files together

df = []

for file in files:

df.append(process_your_file(file))

df = pd.concat(df)

### read in a large file chunk by chunk to avoid memory issue

df = pd.read_csv(filename, iterator=True, chunksize=1000)

df = pd.concat(df, ignore_index=True)

### inferring dtypes from a file

chunker = pd.read_csv(filename, chunksize=1000, ...)

dtypes = pd.DataFrame([chunk.dtypes for chunk in chunker])

types = dtypes.max().to_dic()

chunker = pd.read_csv(filename, chunksize=1000, dtype=types, ...)

### Creating a store chunk-by-chunk from a large csv file

store = pd.HDFStore('store.h5',mode='w')

for chunk in pd.read_csv('file.csv', chunksize=1000):

store.append('df', chunk)

store.close()

###

'''