I am reading a text file in python(500 rows) and it seems like:
File Input:
0082335401
0094446049
01008544409
01037792084
01040763890
I wanted to ask that is it possible to insert one space after 5th Character in each line:
Desired Output:
00823 35401
00944 46049
01008 544409
01037 792084
01040 763890
I have tried below code
st = " ".join(st[i:i + 5] for i in range(0, len(st), 5))
but the below output was returned on executing it:
00823 35401
0094 44604 9
010 08544 409
0 10377 92084
0104 07638 90
I am a novice in Python. Any help would make a difference.
There seems to be two issues here - By running your provided code, you seem to be reading the file into one single string. It would be much preferable (in your case) to read the file in as a list of strings, like the following (assuming your input file is input_data.txt):
# Initialize a list for the data to be stored
data = []
# Iterate through your file to read the data
with open("input_data.txt") as f:
for line in f.readlines():
# Use .rstrip() to get rid of the newline character at the end
data.append(line.rstrip("\r\n"))
Then, to operate on the data you obtained in a list, you could use a list comprehension similar to the one you have tried to use.
# Assumes that data is the result from the above code
data = [i[:5] + " " + i[5:] if len(i) > 5 else i for i in data]
Hope this helped!
If your only requirement is to insert a space after the fifth character than you could use the following simple version:
#!/usr/bin/env python
with open("input_data") as data:
for line in data.readlines():
line = line.rstrip()
if len(line) > 5:
print(line[0:5]+" "+line[5:])
else:
print(line)
If you don't mind if lines with less than five characters get a space at the end, you could even omit the if-else-statement and go with the print-function from the if-clause:
#!/usr/bin/env python
with open("input_data") as data:
for line in data.readlines():
line = line.rstrip()
print(line[0:5]+" "+line[5:])
I have a file with size 39MB, I set the block size as 36MB. When the file is uploaded to HDFS, it successfully stores the file in two blocks. Now when I run a Map-Reduce job(simple reading job) on this file, the job counters show :
"INFO mapreduce.JobSubmitter: number of splits:1"
That is , it is considering the 2 blocks as a single split, so I looked around and found the formula for calculating the split size which is as follows:
split size = max(minsize,min(maxsize,blocksize))
where minsize=mapreduce.input.fileinputformat.split.minsize and maxsize=minsize=mapreduce.input.fileinputformat.split.maxsize.
Now in my MR code I set the following properties:
Configuration conf = new Configuration()
conf.set("mapreduce.input.fileinputformat.split.minsize","1")
conf.set("mapreduce.input.fileinputformat.split.maxsize","134217728")
That is minsize=1 byte and maxsize=128 MB, so according to the formula the split size should be 36MB and hence two splits should be there, but still I am getting the same counter output as :
"INFO mapreduce.JobSubmitter: number of splits:1"
Can anyone explain why ?
The last split of a file can overflow by 10%.
This is called as SPLIT_SLOP and it is set at 1.1.
In this scenario,
39MB (Remaining Bytes) / 36MB (Input Split Size) = 1.08 is less than 1.1 (SPLIT_SLOP)
Thus the entire file is considered as one split.
Snippet on how splits are divided,
long bytesRemaining = FileSize;
while (((double) bytesRemaining)/splitSize > SPLIT_SLOP) {
String[][] splitHosts = getSplitHostsAndCachedHosts(blkLocations,length-bytesRemaining, splitSize, clusterMap);
splits.add(makeSplit(path, length-bytesRemaining, splitSize,splitHosts[0], splitHosts[1]));
bytesRemaining -= splitSize;
}
Refer getSplits() method to know how splits are divided for each file.
I have recently produced several thousand shapefile outputs and accompanying .dbf files from an atmospheric model (HYSPLIT) on a unix system. The converter txt2dbf is used to convert shapefile attribute tables (text file) to a .dbf.
Unfortunately, something has gone wrong (probably a separator/field length error) because there are 2 problems with the output .dbf files, as follows:
Some fields of the dbf contain data that should not be there. This data has "spilled over" from neighbouring fields.
An additional field has been added that should not be there (it actually comes from a section of the first record of the text file, "1000 201").
This is an example of the first record in the output dbf (retrieved using dbview unix package):
Trajnum : 1001 2
Yyyymmdd : 0111231 2
Time : 300
Level : 0.
1000 201:
Here's what I expected:
Trajnum : 1000
Yyyymmdd : 20111231
Time : 2300
Level : 0.
Separately, I'm looking at how to prevent this from happening again, but ideally I'd like to be able to repair the existing .dbf files. Unfortunately the text files are removed for each model run, so "fixing" the .dbf files is the only option.
My approaches to the above problems are:
Extract the information from the fields that do exist to a new variable using dbf.add_fields and dbf.write (python package dbf), then delete the old incorrect fields using dbf.delete_fields.
Delete the unwanted additional field.
This is what I've tried:
with dbf.Table(db) as db:
db.add_fields("TRAJNUMc C(4)") #create new fields
db.add_fields("YYYYMMDDc C(8)")
db.add_fields("TIMEc C(4)")
for record in db: #extract data from fields
dbf.write(TRAJNUMc=int(str(record.Trajnum)[:4]))
dbf.write(YYYYMMDDc=int(str(record.Trajnum)[-1:] + str(record.Yyyymmdd)[:7]))
dbf.write(TIMEc=record.Yyyymmdd[-1:] + record.Time[:])
db.delete_fields('Trajnum') # delete the incorrect fields
db.delete_fields('Yyyymmdd')
db.delete_fields('Time')
db.delete_fields('1000 201') #delete the unwanted field
db.pack()
But this produces the following error:
dbf.ver_2.BadDataError: record data is not the correct length (should be 31, not 30)
Given the apparent problem that there has been with the txt2dbf conversion, I'm not surprised to find an error in the record data length. However, does this mean that the file is completely corrupted and that I can't extract the information that I need (frustrating because I can see that it exists)?
EDIT:
Rather than attempting to edit the 'bad' .dbf files, it seems a better approach to 1. extract the required data to a text from the bad files and then 2. write to a new dbf. (See Ethan Furman's comments/answer below).
EDIT:
An example of a faulty .dbf file that I need to fix/recover data from can be found here:
https://www.dropbox.com/s/9y92f7m88a8g5y4/p0001120110.dbf?dl=0
An example .txt file from which the faulty dbf files were created can be found here:
https://www.dropbox.com/s/d0f2c0zehsyy8ab/attTEST.txt?dl=0
To fix the data and recreate the original text file, this snippet should help:
import dbf
table = dbf.Table('/path/to/scramble/table.dbf')
with table:
fixed_data = []
for record in table:
# convert to str/bytes while skipping delete flag
data = record._data[1:].tostring()
trajnum = data[:4]
ymd = data[4:12]
time = data [12:16]
level = data[16:].strip()
fixed_data.extend([trajnum, ymd, time, level])
new_file = open('repaired_data.txt', 'w')
for line in fixed_data:
new_file.write(','.join(line) + '\n')
Assuming all your data files look like your sample (the big IF being the data has no embedded commas), then this rough code should help translate your text files into dbfs:
raw_data = open('some_text_file.txt').read().split('\n')
final_table = dbf.Table(
'dest_table.dbf',
'trajnum C(4); yyyymmdd C(8); time C(4); level C(9)',
)
with final_table:
for line in raw_data:
fields = line.split(',')
final_table.append(tuple(fields))
# table has been populated and closed
Of course, you could get fancier and use actual date, and number fields if you want to:
# dbf string becomes
'trajnum N; yyyymmdd D; time C(4), level N'
#appending data loop becomes
for line in raw_data:
trajnum, ymd, time, level = line.split(',')
trajnum = int(trajnum)
ymd = dbf.Date(ymd[:4], ymd[4:6], ymd[6:])
level = int(level)
final_table.append((trajnum, ymd, time, level))
I want to count the total number of rows in a csv file/.txt, output/write it to a new csv file, then clean the file and write a 2nd column to the new file with total number of rows after cleaning. ( I currently have the code for cleaning, I only need help with accepting a file and writing the total rows to a new file before and after cleaning) I have attached the code below which writes only the column name to a new csv file and doesn't print the result.
import csv
data = open ('/anusha.csv','r')
#numline = len(file.readlines(data))
#print(numline)
before_clean = []
with open('out_anusha.csv', 'w') as f1:
for row in data:
f1 = len(file.readlines(data))
before_clean.append(f1)
writer = csv.writer(f1)
f1.write("Before_clean")
Any help is appreciated!
One way to count number of lines in file without going through whole reading process is to use wc utility if this program is supposed to run on *nix system.
You can refer Running "wc -l <filename>" within Python Code
I am putting my data into NASA's ICARTT format for archvival. This is a comma-separated file with multiple header lines, and has commas in the header lines. Something like:
46, 1001
lastname, firstname
location
instrument
field mission
1, 1
2011, 06, 21, 2012, 02, 29
0
Start_UTC, seconds, number_of_seconds_from_0000_UTC
14
1, 1
-999, -999
measurement name, units
measurement name, units
column1 label, column2 label, column3 label, column4 label, etc.
I have to make a separate file for each day that data were collected, so I will end up creating around thirty files in all. When I create a csv file via pandas.DataFrame.to_csv I cannot (as far as I know) simply write the header lines to the file before writing the data, so I have had to trick it to doing what I want via
# assuming <df> is a pandas dataframe
df.to_csv('dst.ict',na_rep='-999',header=True,index=True,index_label=header_lines)
where "header_lines" is the header string
What this give me is exactly what I want, except "header_lines" is bracketed by double-quotes. Is there any way to write text to the head of a csv file using to_csv or remove the double quotes? I have already tried setting quotechar='' and doublequote=False in to_csv(), but the double quotes still come up.
What I am doing now (and it works for now, but I would like to move to something better) is simply opening a file via open('dst.ict','w') and printing to that line by line, which is quite slow.
You can, indeed, just write the header lines before the data. pandas.DataFrame.to_csv takes a path_or_buf as its first argument, not just a pathname:
pandas.DataFrame.to_csv(path_or_buf, *args, **kwargs)
path_or_buf : string or file handle, default None
File path or object, if None is provided the result is returned as a string.
Here's an example:
#!/usr/bin/python2
import pandas as pd
import numpy as np
import sys
# Make an example data frame.
df = pd.DataFrame(np.random.randint(100, size=(5,5)),
columns=['a', 'b', 'c', 'd', 'e'])
header = '\n'.join(
# I like to make sure the header lines are at least utf8-encoded.
[unicode(line, 'utf8') for line in
[ '1001',
'Daedalus, Stephen',
'Dublin, Ireland',
'Keys',
'MINOS',
'1,1',
'1904,06,16,1922,02,02',
'time_since_8am', # Ends up being the header name for the index.
]
]
)
with open(sys.argv[1], 'w') as ict:
# Write the header lines, including the index variable for
# the last one if you're letting Pandas produce that for you.
# (see above).
for line in header:
ict.write(line)
# Just write the data frame to the file object instead of
# to a filename. Pandas will do the right thing and realize
# it's already been opened.
df.to_csv(ict)
The result is just what you wanted - to write the header lines, and then call .to_csv() and write that:
$ python example.py test && cat test
1001
Daedalus, Stephen
Dublin, Ireland
Keys to the tower
MINOS
1, 1
1904, 06, 16, 1922, 02, 02
time_since_8am,a,b,c,d,e
0,67,85,66,18,32
1,47,4,41,82,84
2,24,50,39,53,13
3,49,24,17,12,61
4,91,5,69,2,18
Sorry if this is too late to be useful. I work in archiving these files (and use Python), so feel free to drop me a line if you have future questions.
Even though it's still some years and ndt's answer is quite nice, another possibility would be to write the header first and then use to_csv() with mode='a' (append):
# write the header
header = '46, 1001\nlastname, firstname\n,...'
with open('test.csv', 'w') as fp
fp.write(header)
# write the rest
df.to_csv('test.csv', header=True, mode='a')
It's maybe less effective due to the two write operations, though...